jas: vol. 4, no. 1, march 2016, published

ISSN: 2166-0379

Journal of Agricultural Studies


ISSN 2166-0379

2016, Vol. 4, No. 1

http://jas.macrothink.org

Editorial Team

Editor-in-Chief

Dr. Chenlin Hu, the Ohio State University, United States

Dr. Zhao Chen, Clemson University, United States

Associate Editors

Dr. Sahar Bahmani, University of Wisconsin at Parkside, United States

Dr. Hui Guo, University of Georgia, United States

Editor

Richard Williams, Macrothink Institute, United States

Editorial Board

Dr. Aftab Alam, Edenworks Inc. 234 Johnson Avenue, Brooklyn 11206 NY USA, United States

Mr. Ashit Kumar Paul, Patuakhali Science and Technology University, Babugonj, Barisal-8210, Bangladesh

Prof. Ph.D Carlos Alberto Zúniga González, Autonomous National University of Nicaragua, León, Nicaragua

Dr. Chenlin Hu, the Ohio State University, United States

Eliana Mariela Werbin, National University of Cordoba, Argentina

Ewa Moliszewska, Opole University, Poland

Dr. Ferdaous MANI, High Agronomic Institute Chott Mariem, Tunisia

Dr Gajanan T Behere, Indian Council of Agricultural Research (ICAR), India

Dr. Gerardo Ojeda, Universidad Nacional de Colombia, Palmira, Valle, Colombia

Dr. Gulzar Ahmad Nayik, SLIET Punjab, India

Dr. Hojjat Hasheminasab, College of Agriculture, Razi University, Iran, Islamic Republic of

Dr Hui Guo, University of Georgia, United States

Dr. Idress Hamad Attitalla, Molecular Evolution (Uppsala University, Sweden), Libya

Dr. Idin ZIBAEE, Department of Plant Protection College of Agriculture, University of Tehran

Dr. Luisa Pozzo, National Research Council, Italy

Dr. Martin Ernesto Quadro, Argentina

Dr. Muhammed Yuceer, Canakkale Onsekiz Mart University, Department of Food Processing, Turkey

Dr. Rasha Mousa, Assiut University, Egypt

Associate Professor Mohammad Reza Alizadeh, Rice Research Institute of Iran (RRII), Iran, Islamic Republic of

Dr. Mohamed Ahmed El-Esawi, Botany Department, Faculty of Science, Tanta University, Egypt

Dr. Pramod Kumar MishrA, GITAM School of International Business, GITAM University, India

Dr. Reham Ibrahim Abo-Shnaf, Plant Protection Research Institute, Agricultural research Center, Giza, Egypt

Dr. Richard R. E. Uwiera, University of Alberta, Canada

Dr. Sahar Bahmani, University of Wisconsin at Parkside, United States

Dr. Sait Engindeniz, Ege University Faculty of Agriculture, Turkey

Mr. Syed Rizwan Abbas, Research associate, Pakistan

Dr. Moses Iwatasia Olotu, Mkwawa University College of Education, Tanzania, United Republic of

Dr. Tran Dang Khanh, Agricultural Genetics Institute, Hanoi, Viet Nam

Dr. Zakaria Fouad Abdallah, National Research Centre, Egypt

Dr. Zhao Chen, Clemson University, United States

Dr. Zoubida Boumahdi, University Blida, Algeria

Dr. Zoi Parissi, Aristotle University of Thessaloniki, Greece


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2016, Vol. 4, No. 1

www.macrothink.org/jas 1

The Use of ICT for Tertiary Education in Agriculture

and Research in Swaziland: The Case of University of

Swaziland (UNISWA) Students

Raufu, M.O.

Department of Agricultural Economics and Management, P. O. Luyengo, Luyengo. M205,

University of Swaziland, Swaziland.

Masuku, M. B. (Corresponding author)



Tijani, A. A.



Received: August 11, 2015 Accepted: August 27, 2015 Published: September 19, 2015

doi:10.5296/jas.v4i1.8142 URL: http://dx.doi.org/10.5296/jas.v4i1.8142

Abstract

The primary goal of universities is to teach, provide community service, and conduct research.

Empirical evidence has shown that innovative research can best be conducted with the aid of

ICT. This study therefore, examines factors affecting the use of ICT for tertiary education and

research for development among UNISWA students in the Faculty of Agriculture. Stratified

random sampling technique was employed to select 113 UNISWA undergraduate and

postgraduate students from whom data were obtained using structured questionnaires.

Descriptive statistics and Tobit regression model were used to analyse the data.

The results revealed that the radio and television, audio-graphic, email, computer file transfer

and multimedia products were the main ICT facilities available. Accessing research and

relevant materials online and the use of ICT in improving efficiency of communication

among students and lecturers were ranked high by the respondents. Weak wireless services

and unemployment were the greatest challenges to the use of ICT facilities by students.

Estimated Tobit regression results revealed that availability, accessibility and necessity for

ICT facilities significantly influenced their use for learning and research among the

undergraduate students while family size, availability, necessity and proficiency were the


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main factors affecting the use of ICT facilities among the postgraduate students.

In order to encourage the use of ICT among UNISWA students, the study is therefore

recommending that (i) The university authorities should make ICT facilities available and

accessible to all categories of students, (ii) Departments should incorporate the use of ICT

facilities into their curriculum, and (iii) lecturers should give ICT based assignments and use

ICT-based teaching methods.

Keywords: Agriculture and Research, ICT, Uniswa, Tertiary Education.

1. Introduction

1.1 Background

Education is the key to a knowledge economy. To compete effectively in global context, we

need an educated and skilled labour force so as to create, share and use knowledge; vibrant

system of research and innovation to be able to tap into global knowledge, assimilate and

adapt it to the local need. One cannot rule out that knowledge and human capital are

increasingly important to successful economies. The skills to productively transform

knowledge and information into innovative products and services will largely define

successful knowledge economies.

Telecommunication and the Internet are now on the policy “radar screen” in every nation and

multinational organisations. Over the past two decades, there has been considerable debate

over the extent to which ICT is transforming the economies of the world. Central

governments, businesses and international organisations have invested heavily on ICT in

anticipation of greater productivity increase and economic transformations. Since knowledge

and information have become the most important currency for productivity, competitiveness,

an increased wealth and prosperity, nations have and continue to place greater emphasis and

priority on human capital development. Learners learn better when learning experience and

activities are illustrated with use of ICT materials (Etiubong, 2011). The multi-purpose

application of ICT is now regarded as a Utility such as water and electricity and hence has

become a major factor in socio-economic development of every nation. ICT now plays a

major role in education, learning and research in general, agriculture, health, commerce and

even in poverty alleviation by generating or creating new jobs and investment

opportunities. It must be stressed that a country without systematic, coherent, innovative and

coordinated research culture rarely develops scientifically and economically.

Universities are considered to be the nerve centre of knowledge, innovation and their

application to real life situation because of their continued existence is to solve the needs of

society. This responsibility could be better served when these institutions of higher learning

are current in terms of modern ICT infrastructure. In no doubt Universities are research

centres and innovative research can best be conducted by the use of ICT. A relevant education

is more important because today’s networked world demands a workforce that understands

how to use technology as a tool to increase productivity and creativity. If Universities are to

play their role and contribute to national development through research and providing

innovative education then much effort has to be put in to integrate ICT as pedagogical tool


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and as a component of subject contents.

It is a common practice that students find it difficult to get the right study materials for their

respective programmes. Most students complete their chosen programmes without even

having the chance to read some of the internationally acclaimed articles and journals. It is

also usual to see that most dissertations and thesis by students never see any practical

implementation. They are rather kept on the shelves only to collect dust. This can be

attributed to the fact that the writing of these thesis and dissertations are not thoroughly

researched into due to lack of access to relevant information. With ICT, students during their

learning process can access different information and even know the paradigm shift in most

literature as this will make them current and know the dynamics of industry. It also offers

them to be trained in a way that meets the job market requirements and their final project can

be carried out in an innovative manner so that it becomes more practicable than before. The

resources used as input in instruction in a systematic and goal directed manner guarantee

better qualitative output from school (Etim, 2008).

To keep pace with what is taking place globally, education should be the passport to develop

a vibrant economy and ICT is capable to enhance education especially University education

so that they will be able to perform their mandated functions to the benefit of mankind.

Scientific research in many fields has also been revolutionised by the new possibilities

offered by ICTs, from digitisation of information to new recording, simulation and data

processing possibilities (Atkins et al., 2003). The education sector has so far been

characterised by rather slow progress in terms of innovation development which impact on

teaching activities. This article therefore seeks to look at the use of ICT by students for

tertiary agricultural education and agricultural research for development in Swaziland.

1.2 Objective of the Study

The main objective of the study was to access the use of ICT by post graduate students for

tertiary agricultural education and agricultural research for development in Swaziland. The

specific objectives were to:

(i) Highlight the source of ICTs available for learning and research to the respondents;

(ii) Determine the level of use of ICT for learning and research by respondents;

(iii) Identify the factors affecting the use of ICT in enhancing education and research

productivity; and

(iv) Identify the key challenges and constraints in the use of ICT for learning and

research by the respondents.

2. Methodology

2.1 Study Area

The University of Swaziland (UNISWA) is the only Government University and the only

University offering agricultural sciences at the post graduate level in Swaziland. The

University of Swaziland, which comprised of three Campuses at Kwaluseni, Luyengo and


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Mbabane, was established in 1982 by an Act of Parliament of the Kingdom of Swaziland with

a mandate to teach, conduct research and carry out community service. The University offers

Certificate programmes, Diplomas, Bachelor's degrees, Master's degrees and a PhD degree.

2.1 Sampling Procedure

The Luyengo campus of the University was purposively selected for the study because it is

the only campus that offers degrees in Agricultural Sciences at both the undergraduate and

postgraduate levels. Stratified random sampling technique was use. The student population in

the faculty of agricultural sciences were divided into undergraduate and postgraduate strata.

Out of the 1172 and 86 students that registered between 2013-2014 academic years for

undergraduate and postgraduate studies respectively, 10% (125 respondents) of the total

population were sampled. Seventy of the undergraduate students and 55 of the postgraduate

students were randomly sampled. Out of the total 125 questionnaires administered, 103 of

them, 63 and 40 questionnaires form the undergraduate and postgraduate strata respectively

were useful for the analysis.

2.2 Instrument and Measurement of Variables

Primary data were collected through the use of questionnaire. The questionnaire composed of

5 sections concerning the demographic features of the students, sources of ICT available for

learning and research, which were sub-divided into synchronous and asynchronous media

subsections and section for level of use of ICT for learning and research were measured with

statements weighed by a 5 points Likert scale. Other two sections also measured by 5 points

Likert scales were sections for factors affecting ICT role in enhancing educational and

research productivity, comprising of statements on availability, accessibility, necessity and

proficiency in use of ICT facilities for learning and research while the last section was on

challenges and constraint to the use of ICT.

2.3 Data Analyses

2.3.1 Descriptive Statistics

Weighted mean score (WMS) was used to rank the available sources of ICT for learning and

research, the level of use of the ICT for academic purposes, factors affecting ICT role as well

as challenges and constraint to ICT use. The weighted means score was calculated by

summing up the responses divided by the number of respondents. Also, standard deviations

of the respective items were calculated to determine the overall acceptance of the rank result

by the respondents.

2.3.2 Tobit Regression

The Tobit regression model was used to analyse the data collected. The dependent variable,

the use of ICT was captured through a five point likert scale, and was regressed against some

socioeconomic characteristics, which include the gender of the respondents, age, marital

status, and family size, average monthly and monthly income/stipends of the students. Other

independent variables are availability, accessibility, necessity and proficiency in use of ICT

facilities for learning and research.

According to Long (1997), Sigelman (1999) and Green, (2003), the structural equation for the


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Tobit model is:

yi*=Xiβ+εi

Where: εi=N (0,σ2

). yi* is a latent variable that is observed for values greater than т and censored

otherwise. The observed y is defined by the following measurement equation:

yi= yi* if yi*> т

yi= т if yi*≤ т

It is assumed that т=0 i.e the data are censored at zero. Thus, we have:

yi= yi* if yi*> 0

yi= 0 if yi*≤ 0

3. Results and Discussion

Table 1 revealed that radio broadcast and television as well as audio graphic ranked highest

among the synchronous media of ICT sources available for both undergraduate and

postgraduate students in the University, while use of email, computer file transfer and use of

multimedia products ranked highest among the asynchronous media. This could be because

these are easily accessible ICT sources. In the case of emails, students also communicate with

their lecturers on academic assignments.

Table1. Sources of ICT Available for Learning and Research

Synchronous Media Asynchronous Media

Sources Undergraduate

WMS*

Postgraduate

WMS

Sources Undergraduate

WMS

Postgraduate

WMS

Audio-graphics 0.16(2nd

) 0.15(2nd

) Audio, video tapes and

CDs

0.14(4th

) 0.10(3rd

)

Audio

conferencing

0.06(4th

) 0.13(3rd

) Email 0.84(1st) 0.95(1

st)

Broadcast

radio & TV

0.62(1st) 0.18(1

st) Computer file transfer 0.32(2

nd) 0.38(2

nd)

Computer

conferencing

0.10(3rd

) 0.10(4th

) Virtual conferences 0.05(6th

) 0.05(5th

)

Multimedia products 0.21(3rd

) 0.35(4th

)

Web based learning

format

0.13(5th

) 0.05(5th

)

Note: Respondents were asked to select the sources of ICT available to them for learning and

research. The figures indicate the mean scores of the selected sources and their rating.

* denotes weighted means

Source: Field Survey 2014

As regard the level of use of ICT in Table 2, accessing research and relevant study materials

online was ranked highest among uses of ICT for the undergraduate students, as compared

with its use to improve efficiency of communication with colleagues and lecturers was


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ranked highest by the postgraduate students. The fact that response on net to course relevant

information and assignments, and communication with colleagues on tutorials and research

issues ranked least in level of use of ICT for both the undergraduate and postgraduate

students shows that the high ranking of ICT in improving efficiency of communication is not

for academic learning and research purposes. Likewise, it can be inferred from the results that

though respondents do access research and relevant materials on line but they rarely shared

such information among themselves in their online communications.

Table 2. Level of Use of ICT’ use for Learning and Research

Level of Use Undergraduate

WMS

SD Postgraduate

WMS

SD

Course relevant information online 3.21(4th

) 1.42 3.33(4th

) 1.25

Communicating with colleagues on tutorials and

research

2.92(5th

) 1.21 3.30(5th

) 1.47

Accessing research and relevant study materials online 4.19(1st) 0.88 4.03(2

nd) 1.19

Use of ICT for efficiency in communication with

colleagues and lecturers

3.84(2nd

) 0.94 4.18(1st) 0.93

Use of ICT effectively for learning and research

evolved

3.38(3rd

) 1.18 3.68(3rd

) 1.19

Note: Respondents were asked to rate the level of use of ICT for learning and research on a 5

point Likert scale. The figures indicate the mean scores and their rating.


In terms of availability and accessibility, Table 3 revealed that not having wireless loop in the

university to allow internet in the university buildings and classrooms as well as having no

access to computers in class were ranked highest respectively by all the respondents. As

regards necessity and proficiency in use, problem of not integrating ICT in the curriculum

and having no skills to facilitate use of available packages for learning and research

respectively were ranked highest by the postgraduate students while not being referred to net

for further study/research and lack of pre-requisite knowledge for effective use of ICT were

the highest factors in terms of necessity and proficiency among the undergraduate

respondents. Their standard deviation of less than 2 indicated that many of the students at

both level agreed to the identified factors as highly affecting the role of ICT in enhancing

education and research productivity in agriculture.

Table3. Factors Affecting ICT Role in Enhancing Education and Research Productivity

Factors Affecting ICT Role Undergraduate

WMS

SD Postgraduate

WMS

SD

Availability

*No wireless loop in the University to allow internet

in University buildings and classrooms

3.57(1st)

1.51

3.23(1st)

1.52


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*The University ICT centre do not have enough ICT

facilities to enhance tertiary agricultural education

and research for development

3.46(2nd

) 1.32 2.68(2nd

) 1.36

*Multimedia are not always available in classroom

for presentations

2.83(3rd

) 1.16 2.63(3rd

) 1.37

Accessibility

*No access to computers in class

3.27(1st)

1.54

3.70(1st)

1.47

Necessity

*Lecturers do not refer students to net for further

study/research

2.24(1st)

1.36

1.93(2nd

)

1.29

*ICT is not integrated in the curriculum 2.40(2nd

) 1.04 2.98(1st) 1.49

Proficiency

*No required skills to facilitate use of available

packages for learning and research

2.71(2nd

)

1.11

3.63(1st)

1.44

*Required knowledge is not gotten from the

educational administrators/lecturers

2.40(3rd

) 0.96 2.10(3rd

) 1.26

*Have no enough background or pre-requisite

knowledge for effective use of ICT for tertiary

agricultural education and research for development

2.97(1st) 1.28 2.35(2

nd) 1.44

Note: Respondents were asked to rate factors affecting ICT role in enhancing education and

research productivity vis-a-vis availability, accessibility, necessity and proficiency in use on a

5 point Likert scale. The figures indicate the mean scores and their overall rating.


Table 4. shows that students at both levels see weak wireless service in the library and the

classrooms as well as working and learning that reduce the chance of learning and research

through ICT as the greatest challenges and constraints to the use of ICT.

Table4. Challenges and Constraints to the Use of ICT

Challenges and Constraints Undergraduate

WMS

SD Postgraduate

WMS

SD

High charged fees for the use of ICT 2.67(4th

) 1.28 1.98(5th

) 1.17

Other office services are not available 2.40(5th

) 1.21 2.68(4th

) 1.69

Working and learning reduce the chance of learning

and research through ICT

3.40(2nd

) 1.14 3.30(2nd

) 1.47

Space constraint of face-to-face learning experience

affect learning and research for development

3.16(3rd

) 0.81 2.95(3rd

) 1.01

The University internet wireless service is weak in the

library and the classrooms

4.20(1st) 1.08 4.00(1

st) 1.36

Note: Respondents were asked to rate the challenges and constraint to the use of ICT on a 5

point Likert scale. The figures indicate the mean scores and their rating.


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Table 5 revealed that for the undergraduate students, availability, accessibility and necessity

significantly affect the level of use of ICT for learning and research in the institution. Both

accessibility and necessity for ICT facilities negatively influenced the probability of use of

ICT for learning and research at 5% and 1% significant level respectively. This implies that

with increase in access there is a probability of 14.1% decrease in use of ICT facilities by the

undergraduate students as well as 27.8% probability of decrease in use for learning and

research with increase necessity. However, the availability of these facilities has a potential of

increasing their use by 20% and it is significant at 10% level. This buttress the fact that

students tend to misuse the technology for leisure time activities and have less time to learn

and study. Yousef and Dahmani (2008) described online gaming, use of face book, chat

rooms, and other communication channels as perceived drawbacks of ICT use in learning and

research, because, students easily switch to these sites at the expense of their study. Internet

access at home, for instance, may be a distraction because of chat rooms and online games,

reducing the time spent in doing assignments and learning (Kulik,1994). Therefore, the

impact of availability of ICT on student learning strongly depends on its specific uses.

Table 5. Tobit Analysis of Factors Affecting Use of ICT for Learning and Research Among

the Undergraduate Students

Variable Coefficient Standard Error t-value p-value

Constant 0.9946 0.1833 5.425 0.0000

Sex 0.3636E-04 0.3848E-01 0.001 0.9992

Age -0.4612E-02 0.5820E-02 -0.792 0.4281

MS -0.4474E-01 0.7303E-01 -0.613 0.5401

Fmlsz -0.2628E-02 0.6650E-02 -0.395 0.6927

Amt-Spnt -0.4025E-03 0.1452E-02 -0.277 0.7817

Ravail 0.2004* 0.1034 1.938 0.0526

Raccess -0.1405** 0.6355E-01 -2.211 0.0270

Rproficn -0.5484E-01 0.1017 -0.539 0.5898

Rnecess -0.2780*** 0.9017E-01 -3.083 0.0021


*, **, and *** indicate significance at 10%, 5%, and 1%

From Table 6, the postgraduate students’ family size and the availability of ICT facilities

positively influenced their use at 5% and 1% significant level respectively, while proficiency

in use and necessity of use negatively affect their use and are both significant at 1% level.

This likewise implies that increase in the students’ proficiency and necessity to use ICT

facilities does not necessarily result to the use of these facilities for research purposes but

rather for other uses. This confirmed Ma, Andersson, and Streith (2005) that the student

teachers’ perceived ease of use had only an indirect significant effect on intention to use.


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Table 6. Tobit Analysis of Factors Affecting Use of ICT for Learning and Research Among

the Postgraduate Students


Constant 0.6834 0.2161 3.163 0.0016

Sex 0.2192E-01 0.3986E-01 0.550 0.5824

Age -0.6260E-02 0.5295E-02 -1.182 0.2371

Job 0.5967E-01 0.5016E-01 -1.189 0.2343

MS -0.2825E-02 0.4666E-01 -0.061 0.9517

Fmlsz 0.3291E-01** 0.1155E-01 2.117 0.0343

Yrdeg Compl 0.4734E-02 0.8278E-02 0.572 0.5674

Amt-Spnt 0.4755E-04 0.3443E-04 1.381 0.1672

Ravail 0.3384*** 0.1168 2.898 0.0038

Raccess -0.4571E-01 0.7596E-01 -0.602 0.5473

Rprofic -0.3086*** 0.1149 -2.686 0.0072

Rnecess -0.3867*** 0.1172 -3.299 0.0010



Analysis of the pooled data in Table 7 revealed that age, being employed, monthly expenses

on the programme, and necessity are the factors that affect use of ICT and they all have a

negative relationship with the use of ICT at 1% significant level except age that is significant

at 10%. They all reduce the probability of ICT use by about 2%, 48.6%, 1% and 42.2%

respectively among all the respondents.

Table 7. Tobit Analysis of Factors Affecting the Use of ICT for Learning and Research for the

Pooled Data


Constant 2.013 2.784 7.231 0.0000

Sex 0.4464E-02 0.5871E-01 0.076 0.9394

Age -0.1698E-01* 0.9022E-02 -1.882 0.0598

Job -0.4865*** 0.1148 -4.239 0.0000

MS -0.9006E-01 0.9355E-01 -0.963 0.3357

Fmlsz 0.1205E-01 0.1053E-01 1.144 0.2526

Amt-Spnt -0.9330E-03*** 0.1767E-03 -5.281 0.0000

Ravail 0.8032E-01 0.1581 0.508 0.6118

Raccess -0.1408 0.9593E-01 -1.468 0.1421

Rprofic 0.1941 0.1509 1.286 0.1983

Rnecess -0.4223*** 0.1435 -2.944 0.0032



4. Conclusion and Recommendations

4.1 Conclusion

The results revealed that the radio and television, audio-graphic, email, computer file transfer


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and multimedia products were the main ICT facilities available online and the use of ICT in

improving efficiency of communication among students and lecturers were ranked high by

the respondents. Weak wireless services and employment were greatest challenges to the use

of ICT facilities by the students. The estimated Tobit regression results revealed that

availability, accessibility and necessity for ICT facilities significantly influenced their use for

learning and research among the undergraduate students while family size, availability,

necessity and proficiency were main factors affecting the use of ICT facilities among the

postgraduate students.

4.2 Recommendation

The study therefore recommended that students should be taught to use ICT not only for

social interactions, but also for educational and research purposes by incorporating the use of

ICT facilities into their curriculum. They should be trained on the use of both synchronous

and asynchronous media as well as relevant packages for research and development. The

university should make ICT facilities available and accessible to all categories of students.

The like of MTN cyber zone provided for the institution by the communication service

provider, MTN, should be upgraded and updated so that a ratio one to one student-computer

ratio will be possible. Likewise, similar Non-governmental organizations (NGOs) should be

approached on the provision of ICT centre for postgraduate students to enhance educational

and research productivity.

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Yousef, A. B., & Dahamini, M. (2008). The economics of E- Learning: The impact of ICT on

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Copyright Disclaimer

Copyright for this article is retained by the author(s), with first publication rights granted to the journal.

This is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).

http://rusc.uoc.edu/


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Factors Influencing the Choice of an Agriculture

Specialisation by Primary teachers’ Diploma College

Students in Swaziland

Alfred F. Tsikati

Lecturer: William Pitcher Teacher Training College

Barnabas M. Dlamini

Professor: Department of Agricultural Education and Extension, University of Swaziland

Micah B. Masuku (Corresponding author)

Associate Professor: Department of Agricultural Economics and Management, University of

Swaziland. E-mail: [email protected]

Received: August 13, 2015 Accepted: August 28, 2015 Published: September 19, 2015.


Abstract

The choice of a specialisation is one of the lifetime career decisions students have to make

when entering college. The purpose of the study was to determine factors that influence the

choice of Agriculture specialisation by college student teachers in Swaziland. A desk review

and a Modified Delphi technique were used to generate items used in the survey

questionnaire for data collection. A census of 351 student teachers from three teacher training

colleges was used. Data were analyzed by means of descriptive statistics and multinomial

logistic regression. The findings of the study revealed that students’ interest, department’s

image, sex and influence by professionals were the predictors for the choice of Agriculture

specialisation in the teacher training colleges in Swaziland. The study recommends that the

Agriculture departments in the colleges must stage campaigns and craft policies to promote

the choice of the Agriculture specialisation. A study should be conducted to determine the

influence of the subject combinations that make an area of specialisation on the choice of the

Agriculture specialisation.

Keywords: Agriculture, multinomial regression, logistic regression, specialisation, teacher

training colleges.


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1. Introduction

1.1 Background

Career choice including that of choosing a college major is one of the most important

decisions to be made by a college student (Begs, Bentham & Tyler, 2008). In some

institutions information is provided to advise students in making correct decisions about the

choice of a specialisation (Begs, Bentham & Tyler, 2008; Schuster & Costantino, 1986). The

2002 Guidelines and Regulations for Colleges Affiliated to the University of Swaziland

indicate that there are three teacher training institutions offering Primary Teachers’ Diploma

(PTD) in Swaziland, these are Ngwane Teacher Training College, William Pitcher Teacher

Training College and Nazarene Teacher Training College (now known as Southern Africa

Nazarene University). The Guidelines and Regulations for colleges affiliated to the

University of Swaziland provide information that guides students on the entrance

requirements to each of the specialisation for the Primary Teachers Diploma.

The PTD is a three-year program offered to prospective primary school teachers. In the first

two years of study, the student teachers are trained in all subjects taught at the primary

schools in Swaziland and then choose a specialisation in third year. A specialisation is an

option with a group of subjects, which the student teachers take in the teacher training

colleges.

The PTD programme comprises of three groups of subjects (A, B, & C). Group A is teaching

practice which is the main professional component. It is of six weeks duration in second and

third year respectively. Group B comprises core subjects taught at primary school level,

which include English, Mathematics, siSwati, Science, Health, Agriculture, Home Economics,

Education and Social Studies. Group C subjects include Arts and Craft, Physical Education,

Numerical Skills and Academic Communication Skills (Passaic, Ben bow & Simiane, 1990).

The choice of a college major or specialisation is one of the most important decisions a

student has to make (Begs, Bentham & Tyler, 2008). This decision has lifetime implications

as students tend to have academic challenges if they happen to choose a subject specialisation

in which they have no interest.

In Swaziland, several studies on specialisations and factors affecting the choice of a

specialisation have been conducted at high school and university levels (Dlamini, 1993; Dube

& Habedi, 1989; Xaba, 2003) and none at the college level. Hence, factors influencing the

choice of specialisation at teacher training college are not known. Findings of this study will

guide PTD students in choosing Agriculture specialisation at the college level.

1.2 Purpose and objectives of the study

The purpose of the study was to determine factors influencing the choice of Agriculture

specialisation by Primary Teachers Diploma student teachers in Swaziland. The specific

objectives of the study were to:

i) Describe student teachers enrolled in a Primary Teachers’ Diploma by college subject

specialisation.


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ii) Describe student teachers enrolled in a Primary Teacher Diploma by their background and

demographic characteristics.

iii) Compare the respondents’ choices of subject specialisation by selected background and

demographic variables.

iv) Predictor variables influencing student teachers enrolled in a Primary Teacher Diploma to

choose Agriculture specialisation.

1.3 Hypotheses of the study

1.3.1 Research hypothesis

Student’s interest is not a distinguishing factor in choosing Agriculture specialisation by the

Primary Teacher Diploma student teachers in Swaziland.

1.3.2 Alternative (plausible or rival) hypothesis

The choice of Agriculture specialisation by student teachers enrolled in Primary Teachers’

Diploma in Swaziland is not based on: student grade, outside college experience; college

experience, department’s image, professionals, subject combination, significant others,

attitude and impressions; and background and demographic characteristics.

2. Literature Review

Exposure to a subject specialisation such as agriculture draws student teachers towards that

particular specialisation (Wildman & Torres, 2001). Interaction with professionals such as

head teachers, teachers, lecturers, counsellors and school auxiliary staff positively influences

the choice of a profession by student teachers. Similarly, families and friends of students also

influence the choice of a subject specialisation (Wildman & Torres, 2001).

Student interest was identified as one of the main factors influencing the choice of a

specialisation (Dube & Habedi, 1989; Esters, 2007; Mokalake, 2005; Samela, 2010; Wildman

&Torres, 2001). However, Jackman and Smith-Attisan (1992) argue that, family members

only influence students to enrol in college without guiding them on the choice of a subject

specialisation.

The course content, pedagogical strategies, reputation, friendliness of the department affect

subject specialisation (Donnermeyer & Kreps, 1994; Sutphin & Newsom-Stewart, 1995).

Consequently, the experience gained by student teachers at the college plays a critical role in

the student’s choice of agriculture (Jones & Larke, 2001).

Beliefs and attitudes are good predictors for participation in an agricultural programme

(Sutphin & Newsom-Stewart, 1995). Beliefs and attitudes were reported to have an influence

on the success of the Pre-vocational programme in Swaziland (Mndebele & Dlamini, 1999).

Certain subject specialisations are associated with sex, as some are dominated by males,

while others are dominated by females (Begs, Bentham & Tyler, 2008; Samela, 2010).

Student’s locations are also considered important factors when choosing a subject

specialisation (Whiteley & Porter, 2000).


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3. Conceptual Framework

Figure 1 shows the conceptual framework of the study. The dependent variable in this study

was PTD student teachers’ subject specialisation. The dependent variable had four categories:

Agriculture, Languages, Pure Sciences and Social Sciences. Respondents were asked to circle

their subject specialisation. The major independent variable was student interest in choosing

any area of specialisation. Students’ specialisation choices are influenced by student interests

(Dube & Habedi, 1989; Edwards & Quinter, 2011; Sutphin & Newsom-Stewart, 1995).

Interest was measured by requesting respondents to indicate their level of interest in choosing

an area of specialisation. The scale used to rate each item was: 1 = No interest, 2 = Very low

interest, 3 = Low interest, 4 = Moderate interest, 5 = High interest, 6 = Very high interest. The

rival independent variables were student grade; outside-college exposure related to the

specialisation; professionals, significant others; image of the department, experience in the

college, and the influence of background and demographic characteristics.

Exposure to the related subject refers to prior student exposure related to a subject

specialisation. Student grade relates to a grade used to admit student to the teacher training

institution having an influence on the choice of a specialisation (Edwards & Quinter, 2011;

Whiteley & Porter, 2000). Professionals are teachers, counsellors influencing students’ choice

of specialisation (Jackman & Smith-Attisano, 1992). Significant others are other individuals

such as relatives and friends influencing students’ choice of programme or a specialisation

(Wildman & Torres, 2001). Department’s image is how the students perceive

the department offering the subject specialisation (Donnermeyer & Kreps, 1994). Subject

combination means the combination of courses making up the specialisation. College

experience is the experience gained by students at the college before choosing a

specialisation. Beliefs and attitudes are attitudes or values held by students prior to their

choice of specialisation. Demographic characteristics such as sex and student location are

important factors when choosing a subject specialisation (Begs, Bentham & Tyler, 2008;

Samela, 2010; Whiteley & Porter, 2000).

The rival independent variables were measured by requesting respondents to rate each item

on a scale: 1 = No influence, 2 = Low influence, 3 = Slightly low influence, 4 = Slightly High

influence, 5 = High influence, 6 = Very high influence. Background and demographic

characteristics were obtained by requesting respondents to tick or fill in the requested

information.


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Figure 1. Conceptual Framework

4. Methodology

The study was a descriptive predictive research employing multinomial logistic regression

procedures. Multinomial logistic regression is used when the dependent variable is a nominal

with more than two categories. A triangulation of desk review, modified Delphi technique and

a survey questionnaire were used for data collection. The outcomes from the desk review and

modified Delphi technique were used to develop the survey questionnaire. The questionnaire

was used for data collection to determine factors predicting the choice of Agriculture by PTD

student teachers.

The target population was a census of the 2012/2013 third year PTD students (N= 351) from

Ngwane Teacher Training College; Nazarene Teacher Training College and William Pitcher

Teacher Training College. The instrument was validated through the Delphi process.

Reliability coefficients ranged between 0.61 and 0.90 for the domains.

Data were collected in May 2013 using self-administered questionnaires. There were nine

non-respondents and non-response error was controlled by comparing the means of early and

late respondents (Miller & Smith, 1983). There was no significant difference between the

early and late respondents (t=0.63, p=0.54). Thus, the findings were generalizable to the

Major Independent

Variable

Student interest

Dependent Variable

Subject specialisations

(Agriculture;

Languages; Pure

Sciences and Social

Studies)

Rival Independent Variables

Students’ grades

Outside college

experience

Image of department

College experience

Professionals

Significant others

Attitude and impression

Sex

Age

Home location

Short term teaching

contract

Subject combination


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target population. Using SPSS Version 20 data were analysed through descriptive statistics,

and multinomial logistic regression. An a priori probability level of .05 was established to

determine the level of statistical significance of factors that influenced the choice of

agriculture specialisation. The multinomial regression model used was:

In [p/ (1-p)] = α + β1 X1 + β2 X2 + …. βkXk

Where: ln [p/ (1-p)] = is the log odds ratio, or "logit"

P/ (1-p) = is the "odds ratio"

p = probability that the event Y occurs, p(Y=1)

α = the Y intercept

β = the regression coefficient,

X1.k = variables of the study


The results are discussed in terms of the following: college subject specialisation: level of

interest in a specialisation, rival independent variables, background and demographic

characteristics of respondents, comparing the respondents’ choices of subject specialisation,

and predictors for choosing Agriculture specialisation.

5.1 Objective one - Distribution of respondents by subject specialisation

Table 1 indicates that a majority (29.6%) of the respondents specialized in Social Studies;

followed by respondents who specialized in Agriculture (24.6%) and then Pure Science

(23.7%). Languages had the least number of student teachers (22.2%).

Table 1. Distribution of student teachers by college subject specialisation

Specialisation

NTTC

N=149

SANU

N=135

WPC

N=58

Total

N=342

f % f % f % f %

Agriculture 39 11.4 25 7.3 20 5.8 84 24.6

Languages 36 10.5 32 9.4 8 2.3 76 22.2

Pure Sciences 32 9.4 33 9.6 16 4.7 81 23.7

Social Studies 42 12.3 45 13.2 14 4.1 101 29.5

149 43.6 135 39.5 58 16.9 342 100.0

Note. DV = Area of specialisation: 1 = Agriculture, 2 = Languages, 3 = Pure Sciences, 4 =

Social studies.

5.2 Objective two – Description of respondents by background and demographic

characteristics

Table 2 presents the demographic variables of the respondents. About two thirds (219 or

64.0%) of the respondents were females. This finding is consistent with previous findings


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that females dominate jobs in social sciences (Lackland, 2001). A majority of the respondents

were in the aged between 20-25 years (n=166, 48.5%). About 85% of the respondents were

living in rural areas. A few of the respondents (17.5%) had prior short-term teaching contract

before enrolling for the PTD programme. A majority (66.7%) of the student teachers were

influenced by the subject combination in choosing a subject specialisation at the college.

Table 2. Description of respondents’ background and demographic characteristics by

specialisation

Variable Agriculture

(N=84)

Languages

(N=76)

Pure Science

(N=81)

Social Studies

(N=101)

Overall

(N=342)

f % f % f % f % f %

Sex

Female 70 83.3 54 71.1 37 45.7 58 57.4 219 64.0

Male 14 16.7 22 28.9 44 54.3 43 42.6 123 36.0

Age

20 – 25 46 54.8 43 56.6 38 46.9 39 38.6 166 48.5

26 – 30 29 34.5 26 34.2 34 42.0 39 38.6 128 37.4

31 -35 9 10.5 6 7.9 8 9.9 20 19.8 43 12.6

36 – 40 0 0 1 1.3 1 1.2 3 3.0 5 1.5

Home location

Urban 16 19.0 19 25.0 15 18.5 17 16.8 67 19.6

Rural 68 81.0 57 75.0 66 81.5 84 83.2 275 80.4

Short term teaching contract

No 76 90.5 58 76.3 66 81.5 82 81.2 282 82.5

Yes 8 9.5 18 23.7 15 18.5 19 18.8 60 17.5

Influenced by subject combination

No 27 32.1 21 27.6 30 37.0 36 35.6 114 33.3

Yes 57 67.9 55 72.4 51 63.0 65 64.4 228 66.7

5.3 Objective three - Comparing the respondents’ choices of subject specialisation by selected

background and demographic variables

A Chi-square test was conducted to compare the frequencies between the dependent variable

and selected demographic and background variables (Table 3). A statistically significant

difference existed between college specialisation and sex (chi-square = 31.03, p<.01). Several

studies revealed that the representation of women in scientific majors was low (Lackland,

2001).

Table 3. Comparison between the specialisation with demographic and background variables

Variables X2

p

Sex 31.03 .00*

Age 13.72 .13

Home location 2.08 .55

Subject combination 1.75 .63

Short-term teaching contract 5.70 .13

*=p≤.001

5.4 Objective four - Explanatory and predictor variables for the choice of a specialisation

A multinomial logistic regression analysis was conducted to predict the choice of subject


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specialisation by 342 student teachers from three colleges in Swaziland (Table 4). Findings

revealed that predictors of a choice of subject specialisation by college students were image

of the college department, student interest in the specialisation and teaching experience (short

term teaching contract). In order for the multinomial logistic regression model to be true or

acceptable to explain and predict a dependent variable, the following conditions must be

satisfied to assess the success of the model: 1) the overall relationship must be statistically

significant – using the Test full model; 2) there must be no evidence of multi-collinearity; 3)

and the stated individual relationship must be statistically significant and interpreted correctly

(Agresti, 1996; Agresti & Finlay, 1997).

A logistic model is said to provide a better fit to the data if it demonstrates an improvement

over the intercept only model (null model). The test of the full model against the constant

only model was statistically significant (Chi square = 126.937, p <.01). The

Hosmer-Lemeshow (H-L) goodness-of-fit test also confirmed that the model was significant

(Chi-square = 9843.816, p>.05). The Nagelkerke’s R2 was 0.34. This value indicates that the

fitted model could account for 34% of the variance.

There was no evidence of multicollinearity problems in the solution of the choice of subject

specialisation as the standard errors for the ‘b coefficients’ were all less than 2.0. The model

of fit indicated that the model was acceptable.

The Likelihood ratio test statistics (Model Chi-Square) determines if there is a statistical

relationship between the dependent variable and the combination of the independent variables.

The Likelihood ratio tests for this study demonstrated that generally, significant factors for a

choice of Agriculture at the college were interest (Chi-square = 21.163, p < .01), department

image (Chi-square = 18.839, p < .01), professionals (Chi-square = 8.944, p < .05), and sex

(Chi-square = 28.319, p < .01). Interpretation is done only for independent variables that

significantly distinguish between pairs of groups and having an overall relationship with the

dependent variable in the likelihood ratio test.

Table 4. Parameter estimate for individual variable contribution

Languages Pure Sciences Social Studies

B Wald Exp(B) B Wald Exp(B) B Wald Exp(B)

X0 3.23 2.31 - 1.96 .79 - 1.73 .72 -

X 1 -.03 12.44* .41 -.56 4.39* .57 -.97 15.33* .38

X2 .30 2.28 1.36 .27 1.91 1.32 .09 .22 1.10

X3 -.04 .05 .95 -.12 .38 .88 -.04 .05 .95

X4 .45 3.18* 1.57 .41 2.70 1.51 .51 4.38 1.67

X5 -.60 5.91* .54 -.67 7.33* .51 -.94 14.49* .39

X6 .08 .14 1.08 .38 4.18* 1.48 .54 8.34 1.72

X7 -.12 .45 .88 .08 .20 .92 .11 .41 1.12

X8 .24 2.82 1.27 -.11 .31* 4.55 .28 3.94 1.32

X9 -.72 3.03 .44 -1.84 21.70* .16 -1.35 11.85* .26

X10 .03 .34 1.03 .03 .21 1.03 .11 3.81* 1.11

X11 .47 1.28 1.61 .14 .09 1.15 -.03 .04 .98


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X12 -1.06 4.26* .35 -.88 2.81 .42 -.72 2.01 .49

X13 -.13 .11 .88 .36 .99 1.44 .16 .20 1.17

Coding: Variables: X0 = Intercept; X1 = Student interest; X2 = College grades; X3 = Outside;

college experience; X4 = College experience; X5 = Department image; X6 = Professionals; X7=

Significant others; X8 = Attitude and impression; X9 = Sex; X10 = Age; X11 = Home location;

X12 = Short-term teaching contract; X13 = Subject combination.

Reference category: an Agriculture specialisation, *P ≤ .05 (alpha level).

The Wald criterion indicates that the interest (Wald = 12.44, p< .01); image of department

(Wald = 5.91, p< .05); and short-term teaching contract (Wald = 4.26, p< .05) were the only

independent variables that were significant in distinguishing between the choice of

Languages specialisation and Agriculture specialisation. Even though college experience

(Wald = 3.18, p< .05); and attitude and impressions (Wald = 4.82, p< .05) were also statistical

significant, they were not considered because they were not statistically significant in the

overall model.

The Exponential Beta (Exp (B)) was used to explain the effect of the independent variable on

the "odds ratio”. The Exponential Beta value indicates that student teachers with less interest

were less likely to choose the Languages specialisation, rather than Agriculture specialisation

at college. This finding that interest is the main factor in the choice of a specialisation is

consistent with Wildman and Torres (2001) findings. The Exponential Beta value further

shows that when the level of interest was raised by one unit the odds ratio decreased by .41. It

implies that for every unit increase in interest there is a decrease of 59% (.41 - 1 = 0.59) in

the probability of choosing languages specialisation.

Student teachers who had a low perception about the image of department were less likely to

choose Languages specialisation, rather than Agriculture specialisation. Donnermeyer and

Kreps (1994) found that the image of the department could either sway away or toward itself

students when making decisions on subject specialisation. For each unit increase in the image

of department, the odds ratio decreased by .54. This literally meant that a unit increase in the

perceived level of influence by the department image decreased the probability of choosing

Languages by 46%.

The Exponential Beta value indicates that student teachers who had no prior exposure to

teaching were less likely to choose Languages rather than Agriculture specialisation at the

college. Sutphin and Newsom-Stewart (1995) postulated that experience in a related

specialization drew students towards that particular subject area of specialisation. When short

term teaching contract was raised by one unit the odds ratio decreased by 35. In other words,

an increase of one year in the number of years student teachers were exposed to teaching

reduced the probability of choosing for Languages specialisation by 65%.

The Wald criterion also demonstrated that interest (Wald = 4.39, p< .05); department image

(Wald = 7.33, p <.01); professionals (Wald = 4.18, p<.05) and sex (Wald = 21.70, p= .00)

were statistically significant in differentiating between choosing Pure Sciences and an

Agriculture specialisation. However, attitude and impressions (Wald = .31, p< .05) were not


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considered because they were not statistically significant in the Likelihood ratio test.

The Exponential Beta value indicates that student teachers with less interest were less likely

to choose a Pure Sciences specialisation, rather than Agriculture specialisation. Esters (2007)

noted that interest in agriculture was one of the major factors influencing the decisions of

students to enrol in the subject. The Exponential Beta value further shows that when interest

was raised by one unit the odds ratio declined by 0.57. It implied that for a unit increase in

the level of interest the chances of choosing Pure Sciences at college decreased by 43%.

The Exponential Beta value also indicated that student teachers who had low perception of

the department image were less likely to choose a Pure Sciences specialisation, rather than

Agriculture specialisation. Wildman and Torres (2001) concluded that if the department was

friendly, students would select specialisation from that particular department. One unit

increase in the perceived influence level of the department image resulted in the odds ratio

for choosing Pure Sciences at the college decreasing by 0.51. Thus, for each unit increase in

the level of the department’s image, the odds declined by 49%.

The Exponential Beta value indicates that student teachers receiving advice from

professionals were likely to choose a Pure Sciences specialisation, rather than Agriculture

specialisation. A study by Jones and Larke (2001) revealed statistically significant differences

between the choice of agriculture by students associated with individuals employed in

agriculture related fields and those who were not associated with such individuals. However,

Dlamini (1993) reported that individuals working for an institution did not influence student

choice. The Exponential Beta value further shows that when the level of professional advice

was raised by one unit the odds ratio multiplied by 1.48. Thus, for a unit increase in the level

of professional advice the chances of choosing Pure Sciences instead of Agriculture increased

by 48%.

The Exponential Beta value also indicated that female student teachers were less likely to

choose Pure Sciences, rather than an Agriculture specialisation. Generally, female students

shun hard sciences (Lackland, 2001). When the number of respondents increased by a female

student teacher the odds ratio decreased by .16. It implied that when the respondents were

raised by a female, the female student teachers were 84% less likely to choose Pure Sciences

rather than Agriculture specialisation.

Finally, the Wald criterion demonstrated that interest (Wald = 15.33, p<.01), department

image (Wald = 14.49, p<.01); and, sex (Wald = 11.85, p<.01) were statistically significant in

differentiating between the choice of Social Studies specialisation than Agriculture

specialisation. Even though respondents’ age (Wald = 2.012, p< .05) was statistically

significant, it was not considered because it was not statistically significant in the Likelihood

ratio test.

The Exponential Beta values indicated that student teachers with less interest were less likely

to choose Social Studies specialisation, rather than Agriculture specialisation. This is

consistent with the findings of the study by Dube and Habedi (1989) that concluded that

student interest is the main factor for the choice of a specialisation. The Exponential Beta


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value further revealed that when interest level was raised by one unit the odds ratio declined

by .38. It implied that for each unit increase in the level of interest, the likelihood of choosing

Social Studies decreased by 62%.

The Exponential Beta value indicated that a student teacher who had a low perception of the

department image was less likely to choose Social Studies specialisation, rather than

Agriculture specialisation. Naylor and Sanford (1980) concluded that the strength of the

department was the most frequent reason cited by students to enrol in department. One unit

increase in the perceived influence level of department’s image resulted in the odds of

choosing Social Studies decreasing by .39. Thus, for a unit increase in the image of

department, the likelihood declined by 61%.

Lastly, Exponential Beta values indicated that male student teachers were less likely to

choose Social Studies, rather than an Agriculture specialisation. Generally, more male

students enrol for science related disciplines than their female counterparts (Lackland, 2001).

When the number of respondents was raised by a female student teacher the odds ratio

decreased by 0.26. It implied that the probability of female student teachers choosing Social

Studies was 74% less likely.


6.1 Conclusion

The conclusion drawn is that student teachers at teacher training colleges in Swaziland are

more interested in Agriculture than the other specialisations. Interestingly, the colleges are

producing more PTD teachers with Social Studies than the other subject specialisation

because agriculture caters for a limited number of students. Another conclusion drawn was

that interest and departmental image were the important predictors for choosing the

Agriculture specialisation by college students compared with other specialisations. Beliefs

and attitudes were also competing with student interest when students choose agriculture

specialisation. Professionals were likely to advise students to choose other specialisation i.e.

Pure Science than Agriculture. Student teachers with prior teaching practice exposure in

Agriculture are more likely to major in agriculture at the college. Female student teachers are

more likely to specialise in social sciences than to either applied or pure sciences. The male

student teachers choose applied or pure science than social sciences. A majority of student

teachers were from rural areas, hence their interest in the Agriculture. This could be attributed

to their possible exposure to farm activities at their homes.

The research hypothesis was accepted because student interest was the main factor

influencing the choice of agriculture specialisation, and the alternative hypotheses were

rejected.

6.2 Recommendations

The study recommended that colleges must use students’ interest as their benchmark when

advising students on the choice of their specialisation. The agriculture department should

maintain the good image as it has been found to attract more learners. It is also recommended


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that the Agriculture department markets itself by staging campaigns, and craft policies that

would promote the subject and attract more student teachers.

Furthermore, it is also recommended that further research must be conducted to establish the

effects of specializing in agriculture on the performance of student teachers in their first year

of teaching. There is a need for a tracer study on the progression of the student teachers on

completion of their diploma with their specialisation in agriculture.

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Copyright for this article is retained by the author(s), with first publication rights granted to

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http://www.geography.org.uk/journals/journals.asp?articleID=328

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Vascular Hemostasis in Heifers on Rearing

Medvedev Ilya Nikolayevich (Corresponding author),

Kursk Institute of Social Education (branch) of Russian State Social Education,

Kursk, Russia

Zavalishina Svetlana Yur’evna

All-Russian Research Institute of Physiology, Biochemistry and Nutrition of Animals,

Borovsk, Russia

Received: August 11, 2015 Accepted: August 27, 2015 Available online: September 19, 2015.


Abstract

On investigation of 42 healthy black-and-white breed heifers on rearing the tendency for the

decrease of acylhydroperoxides and thiobarbituric acid content in their blood was stated as a

result of increase of plasma antioxidant protection activity in them. On the background of the

low level of endotheliocytemia in healthy heifers on rearing there was revealed a

considerable upward trend of the antiaggregational activity indices in the vascular wall with

all tested inductors and their combinations.

For endotheliocytes of heifers on rearing a tendency for gradual increase of antithrombin III

production, ensuring the necessary level of anticoagulant blood capacity is evident. In

animals from 12 to 15 months of life this was accompanied with increase of plasminogen

tissue activators secretion by the vascular wall.

Keywords: heifers; rearing; hemostasis; vascular wall; lipid peroxidation.

1. Introduction

Alongside with genetic potential (Amelina and Medvedev, 2008, 2009), vascular hemostasis,

provided with hemostatically significant substances produced in the vascular wall

(Zavalishina, 2012a; 2012b), is considered to be one of important elements for supporting the

homeostasis optimum in productive animals.

These substances are subdivided into compounds with antiaggregational, anticoagulant and

fibrinolytic activity, regulating liquid properties of blood and homeostasis functioning on the

whole, considerably determining the level of oxygen and nutrient substances supply to organs

and tissues (Medvedev and Zavalishina, 2014a; Zavalishina and Medvedev, 2012).

Having great physiological and biochemical significance, a vascular wall determines


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functional characteristics of an organism to a considerable state (Medvedev, Gromnatskij,

Volobuev, Osipova and Storozhenko, 2006; Medvedev, 2007; Simonenko, Medvedev,

Mezentseva and Tolmachev, 2007; Simonenko, Medvedev and Kumova, 2010).

There is no doubt, that on all stages of an organism growth and development vascular

hemostasis has an important role in providing the adaptation process (Medvedev, Zavalishina

and Krasnova, 2010; Medvedev and Zavalishina, 2015).

But, in spite of the significance of the vascular control in thrombocyte aggregation and

hemocoagulation (Krasnova and Medvedev, 2013a; Krasnova and Medvedev, 2013b;

Medvedev and Zavalishina, 2012), it is still not sufficiently investigated in heifers on rearing.

The capacity of the vascular wall to synthesize antiaggregants, antithrombin III (AT III) and

plasminogen tissue activators, which is significant for the future younger animals’

productivity, is still not elucidated.

The present investigation was planned and conducted due to the highlighted gaps in the

system of biological knowledge.

1.1 Purpose of the study

The aim is to determine functional capacities of the vascular wall hemostatic activity in

healthy heifers on rearing.

2. Methods

The investigation is conducted in the spring-summer period involving 42 healthy

black-and-white breed heifers on rearing, kept at the cattle breeding farm “Grand”, Kursk

region, Russia, which were inspected 4 times: at the age of about 12 months, about 13 months,

about 14 months and about 15 months.

The survey included the determination of plasma lipid peroxidation (PLP) activity according

to the acylhydroperoxides (AHP) (Gavrilov and Mishkorudnaja, 1983) and thiobarbituric acid

(TBA)-active products level using the “Agat-Med” set with the estimation of the antioxidant

activity (AOA) of the liquid part of blood (Volchegorskij, Dolgushin, Kolesnikov and

Cejlikman (2000).

Endotheliocytemia amount was recorded according to Zainulina M.S. method (Zainulina,

1999).

The state of the vascular wall antiaggregational ability was determined according to (Baluda,

Sokolov and Baluda, 1987) on the basis of visual micromethod of thrombocyte aggregation

(TA) recording (Medvedev, Zavalishina, Kutafina and Krasnova, 2015; Shitikova, 1999) with

ADF (0,5×10-4

М.), collagen (dilution 1:2 of the basic suspension), thrombin (0,125 u./ml),

ristomycin (0,8 mg/ml) and adrenalin (5,0×10-6

М), and also with their combinations: ADF +

adrenalin, ADF + collagen, collagen + adrenalin, ADF + thrombin, ADF + collagen +

adrenalin, ADF + thrombin + adrenalin and ADF + collagen + thrombin + adrenalin in

concentrations similar to standardized amount of thrombocytes (200×109thr.) in the

investigated plasma before and after temporary venous occlusion with determination of the

vascular wall antiaggregational index (VWAAI) by dividing TA time at temporary


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phlebostasis by TA development time without it.

Anticoagulational control of the vascular wall in animals was found out in accordance with

the value of the vascular wall anticoagulant activity index (VWACAI), which was counted by

dividing AT III activity (Barkagan and Momot, 1999) after venous occlusion by its value

before it (Baluda, Sokolov and Baluda, 1987). To find out the degree of the vascular wall

influence on the fibrinolytic blood activity the method of euglobulin lysis (Barkagan and

Momot, 1999) time determination before and after temporary venous occlusion, causing the

plasminogen tissue activator (Baluda, Sokolov and Baluda, 1987) discharge from vascular

wall into blood, was used with determination of the vascular wall fibrinolytic activity index

(VWFAI) by dividing euglobulin lysis time before the occlusion by the lysis time after it.

To determine the accounted biochemical data and TA, blood samples were taken from all

heifers in the morning before feeding without temporary venous occlusion through jugular

vein puncture.

In the sample with temporary venous occlusion, which allowed to estimate vascular wall

antiaggregational capacity, the blood was taken from animals’ popliteal vein 3 minutes

after fixing the tonometer cuff on the thigh with reaching the pressure 10 mm mc higher

than the systolic one.

The results of the investigation are processed using Student (td) criterion.

3. Results

The heifers under investigation demonstrated the downward trend of the content of primary

PLP – AHP products in the blood and of the secondary TBA-active compounds, which by

the 15th

month of life reached 1,29±0,11 Д233/1 ml and 3,05±0,21 mcmole/l, correspondingly,

(by the 12th

month of life1,36±0,16 Д233/1 ml and 3,20±0,13 mcmole/l, correspondingly).

The elucidated peroxidation intensity dynamics became possible as a result of the developing

tendency in animals to intensify the antioxidant protection of their organism during the

investigation period – their plasma antioxidant potential increased from 35,0±0,12% at the

age of 12 months up to 37,5±0,16% at the age of 15 months.

The investigated healthy heifers on rearing demonstrated high integrity of the endothelium

lining, which was estimated from the fact of keeping low endotheliocytemia level since 12

months (1,7±0,06 cells/ml) up to 15 months of age (1,4±0,04 cells/mcl) (see the table).

Table 1. Vascular indices in black-and-white breed heifers on rearing

(n=42, M±m, spring-summer period)

Value parameter Age, months Average values about 12 about 13 about 14 about 15

endotheliocytemia, cells/mcl

1,7±0,06 1,6±0,03 1,5±0,05 1,4±0,04 1,5±0,04

VWAAI with ADF 1,84±0,11 1,85±0,13 1,87±0,09 1,89±0,08 1,86±0,10 VWAAI with collagen

1,73±0,07 1,74±0,06 1,76±0,10 1,78±0,13 1,76±0,09

VWAAI with thrombin

1,62±0,03 1,62±0,02 1,63±0,06 1,64±0,07 1,63±0,04


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VWAAI with ristomycin

1,62±0,06 1,63±0,10 1,65±0,10 1,66±0,13 1,64±0,10

VWAAI with adrenalin

1,75±0,10 1,76±0,11 1,77±0,08 1,78±0,04 1,76±0,08

VWAAI with ADF +adrenalin

1,56±0,03 1,57±0,06 1,58±0,05 1,59±0,09 1,40±0,01

VWAAI with ADF + collagen

1,47±0,10 1,48±0,07 1,49±0,11 1,49±0,05 1,48±0,08

VWAAI with adrenalin + collagen

1,59±0,08 1,60±0,07 1,60±0,06 1,61±0,12 1,60±0,08

VWAAI with ADF +thrombin

1,47±0,04 1,48±0,08 1,49±0,05 1,50±0,07 1,48±0,06

VWAAI with ADF +collagen + adrenalin

1,41±0,06 1,42±0,11 1,43±0,09 1,44±0,08 1,42±0,08

VWAAI with ADF +thrombin + adrenalin

1,40±0,10 1,41±0,06 1,43±0,05 1,43±0,07 1,42±0,07

VWAAI with ADF +collagen + thrombin+ adrenalin

1,36±0,06 1,37±0,05 1,38±0,06 1,39±0,04 1,37±0,05

VWAAI 1,42±0,10 1,43±0,07 1,44±0,08 1,46±0,12 1,44±0,09 VWFAI 1,53±0,06 1,55±0,05 1,56±0,04 1,56±0,07 1,55±0,05

Note: valid dynamics of values is not obtained

In heifers on rearing the TA development time under collagen was 23,7±0,18 sec., in the

future having the tendency to decrease, reaching 23,0±0,20 sec. by the 15th month of life.

Similar TA dynamics in animals was found under ADF (32,7±0,12sec. and 32,0±0,19sec.,)

and under ristomycin (40,5±0,14 sec. and 39,7±0,18 sec., correspondingly), later thrombin

(45,6±0,22sec. and 44,8±0,13 sec., correspondingly) and adrenalin TA (87,8±0,26 sec. and

86,8±0,25sec., correspondingly) developed also having the tendency to acceleration during

the rearing period.

The downward trend of the TA development time in the investigated animals with the isolated

usage of inductors correlated with the decrease of the TA development time with the

application of their tested combinations, at the age of 12 and 15 months of life, which were:

for ADF+adrenalin – 30,2±0,07 sec. and 29,4±0,12 sec, for ADF+collagen – 21,3±0,08 sec.

and 20,6±0,11 sec., for adrenalin+collagen – 21,9±0,16 sec. and 21,1±0,14 sec., for

ADF+thrombin 21,6±0,08 sec. and 20,9±0,19 sec., for ADF+collagen+adrenalin 18,1±0,05

sec. and 17,3±0,08 sec., for ADF+thrombin+adrenalin 17,4±0,11 sec. and 16,8±0,09 sec., for

ADF+collagen+thrombin+adrenalin 15,1±0,06 sec. and 14,5±0,10 sec., correspondingly.

In investigated heifers at the age of about 12 months the TA development time on the

background of temporary venous occlusion was 41,0±0,17sec., remaining practically

unchangeable up to 15 months of age - 40,9±0,21sec.

The tendency for TA deceleration in the sample with temporary venous occlusion in the

heifers between 12 and 15 months of life was stated under the influence of ADF (60,2±0,19

sec. and 60,5±0,25 sec., correspondingly) and ristomycin (65,6±0,25 sec. and 65,9±0,28 sec.,

correspondingly), in the later period there developed thrombin (73,9±0,27 sec. and 73,5±0,23

sec., correspondingly) and adrenalin TA(153,6±0,23 sec. and 154,5±0,34 sec.,

correspondingly).


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The downward trend of the TA development time on the background of temporary venous

occlusion in the investigated animals with the isolated usage of inductors was accompanied

with a slight tendency for the acceleration of the TA development time in them with the

application of all tested combinations, at the age of 12 and 15 months of life, which were:

for ADF+adrenalin – 47,1±0,20 sec. and 46,7±0,22 sec., for ADF+collagen– 31,3±0,16 sec.

and 30,7±0,13 sec, for adrenalin+collagen – 34,8±0,16 sec. and 33,9±0,10 sec., for

ADF+thrombin 31,7±0,16 sec. and 31,3±0,19 sec., for ADF+collagen+adrenalin 25,5±0,11

sec. and 24,9±0,13 sec., for ADF+thrombin+adrenalin 24,4±0,13 sec. and 24,0±0,17 sec., for

ADF+collagen+thrombin+adrenalin 20,5±0,16 sec. and 20,1±0,10 sec., correspondingly.

In healthy investigated animals the upward trend for the VWAAI was recorded with all used

inductors and their combinations during all the period of observation (see the table). The

highest VWAAI was typical for ADF, as this inductor was characterized by maximum TA

impediment at venous occlusion. A somewhat lower VWAAI level was recorded with

adrenalin and collagen. VWAAI with thrombin (average 1,63±0,04) and ristomycin (average

1,64±0,10), also having the tendency for increase during all the period of observation,

yielded to them.

The values of vascular wall aggregational activity indices, obtained by application of all

tested combinations of inductors, though lower in absolute values, also demonstrated the

similar upward trend during all the period of observation.

During the study of the vascular wall anticoagulant activity in the blood of the heifers on

rearing the AT III level was estimated using the sample before temporary venous occlusion

and after it.

It was stated, that in the blood of healthy heifers between 12 and 15 months of life a slight AT

III increase from 128,6±0,08% up to 130,6±0,09% is evident. Besides, on the background of

temporary venous occlusion heifers demonstrate AT III activity increase in the blood from

182,6±0,16% up to 190,7±0,20%, accompanied with the upward trend for the VWACAI.

While studying the vascular wall fibrinolytic activity state in healthy heifers on rearing the

estimation of the plasminogen vascular activators intensity was conducted, this was recorded

in the euglobulin lysis test before and after the sample with dosaged venous occlusion. In

investigated animals a slight tendency for the reduction of the time of spontaneous euglobulin

lysis summing up to 4.3% was observed.

It was found out, that in investigated heifers on rearing the secretion of plasminogen tissue

activators, stimulated with the help of temporary venous wall ischemia creation, had the

general tendency for intensification – the time of euglobulin lysis after temporary venous

occlusion at 12 months was 223,4± 0,29 min., at 15 months – 214,5±0,22 min., providing

some VWFAI rise.

Thus, in healthy heifers on rearing on the background of some intensification of plasma

antioxidant protection and weakening of PLP in it, a slight rise of the vascular wall

antiaggregational, anticoagulant and fibrinolytic activity is stated, providing largely the

transition of hemostasis to the level, necessary for the conception and bearing of the posterity.


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4. Discussion

Being an anabolic stage in an organism development, the period of rearing of heifers is rather

significant for the completion of their development with most full preparation for the

pregnancy and realization of their productive qualities (Medvedev and Zavalishina, 2014b).

Vascular system (Medvedev and Zavalishina, 2012; 2014) is very important for bringing the

future cow organism together. It is polyfunctional and, through some mechanisms is closely

connected with all systems and organs, influencing in their turn the aggregational state of

blood (Medvedev, Savchenko and Kiperman, 2015).

Vascular wall activity, in younger productive animals also, determines the level of the factors

supporting optimal rheology of blood elements and, by that, the homeostasis of the growing

organism.

Low PLP level in heifers on rearing determines weak alteration of endothelium cells,

contributing to the optimal antiaggregational capacity of the vascular wall, having the

tendency to strengthen apparently due to the intensification of prostacyclin and NO synthesis

in it.

During the test with temporary venous wall ischemia in healthy heifers on rearing some

increase in the vessel control over adhesive ability of blood platelets, maintained through the

influence of desaggregants according to two mechanisms, was observed.

The first one – through the decrease of the density of Iа – IIа and VI collagen

receptors-glykoproteides on the thrombocyte membrane under their influence, which was

stated in the research indirectly from the tendency for intensification of inhibition in TA with

collagen in the sample with temporary venous ischemia.

The second mechanism of control over thrombocyte adhesive capacity in heifers on rearing

is connected with the decrease of the Villebrand factor production by the structures of the

vessels under the influence of antiaggregants with the decrease on this background of the

number of receptors to it (GPI) on the surface of blood platelets (Medvedev, Lapshina and

Zavalishina, 2010; Zavalishina, 2013).

In the conditions of intensification of discharge of physiological antiaggregants from blood

vessels of heifers on rearing the restriction of the fixation of strong aggregation agonists -

collagen and thrombin - to the receptors on the thrombocyte membrane is achieved,

restraining by that the phospholipase C activity, inhibiting the phosphoinositide way of

thrombocyte activation, weakening the phosphorylation of the contractive system proteins.

Under the influence of PGI2 and NO formed in the vessels the interaction of weak

aggregation inductors – ADF and adrenalin – with thrombocyte receptors is also considerably

restricted, including low expression of fibrinogen receptors (GPIIв-IIIа) and not high

phospholipase А2 activity, regulating the discharge of arachidonic acid from phospholipids

(Medvedev and Zavalishina, 2011).

It may be supposed, that the elucidated tendency for the TA deceleration with combinations

of aggregation inductors on the background of the temporary venous occlusion, observed in

the conditions of the real bloodstream, is connected with feebly marked vessel


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antiaggregational influences in relation to TA with combinations of inductors, rather than

with their individual application, which to a great extent creates conditions close to in vivo.

The upward trend of the vascular wall antiaggregational activity index values in relation to

combined application of aggregation inductors demonstrated functional sufficiency of the

vessel disaggregated substance production to retard the typical for animals TA intensification

in the conditions close to the real ones.

A significant role in the formation of adequate athrombogenic activity of the vascular wall in

heifers on rearing belongs to its high anticoagulant and fibrinolytic properties, which

demonstrated a tendency for growth during the investigation period. It is connected to a great

extent with unexpressed influence of their low PLP on the vessels, which preserves an

optimum functional state of endotheliocytes, including the sufficiency of the synthesis of the

substances controlling hemocoagulation in them.

Vessel anticoagulant capacities in the investigated heifers are determined by the elucidated

upward trend of the initially rather high production in their subendothelium of one of the

strongest physiological anticoagulants – AT III. In these animals the expressed vascular wall

control over blood fibrinolytic activity is ensured by the physiologically indispensable

intensity of the synthesis of plasminogen activators in it, also having a tendency for

intensification.

5. Conclusion

Thus, not high PLP activity of the liquid part of blood, recorded in heifers on rearing, largely

determines their physiologically significant upward trend of antiaggregational, anticoagulant

and fibrinolytic capacity of the vascular walls, providing an optimum level of vascular wall

control over the general hemostatic process.

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the journal.




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Water Use Dynamics of Peach Trees under

Postharvest Deficit Irrigation

Dong Wang (Corresponding author)

USDA, Agricultural Research Service, San Joaquin Valley Agricultural Sciences Center,

Water Management Research Unit, 9611 S. Riverbend Ave., Parlier, CA, 93648-9757, USA

Received: August 23, 2015 Accepted: September 19, 2015 Published: September 26, 2015


Abstract

Postharvest deficit irrigation is a potential strategy for conserving valuable fresh water for

production of early season tree fruit crops such as peaches. However, water use dynamics

under deficit irrigation conditions that can be described as seasonal changes in crop

evapotranspiration (ETc) and crop coefficient (Kc) are largely unknown. A three-year field

study was carried out in a 1.6 ha peach orchard to determine seasonal ETc and Kc

characteristics. The orchard was divided equally into 72 plots, in which 12 randomly selected

plots received deficit irrigation and the remaining 60 plots received full irrigation. A Bowen

ratio flux tower was installed in the orchard to make meteorological measurements for

estimating an integrated ETc for the orchard. The study showed that from July to August

75-85% of the daily net radiation was used by latent heat or partitioned into ETc. The average

monthly cumulative ETc was 151 mm in June, 162 mm in July, and 155 mm in August. Kc

values under deficit irrigation conditions or termed as Deficit_Kc was computed as ratios of

the ETc over potential evapotranspiration or ETo, and were compared with Kc derived from a

lysimeter study under non-water stressed conditions or termed as Lysimeter_Kc. The

maximum Deficit_Kc values were 0.90, 1.03, and 1.07 for the three field seasons but all were

smaller than 1.20, the maximum Lysimeter_Kc. The study demonstrated that water stress

under deficit irrigation can be characterized in Kc values. The approach may be used to detect

if portions of an orchard or the entire orchard are under water stress. Conversely, the method

may provide guidance on deploying deficit irrigation practices with pre-determined

Deficit_Kc.

Keywords: Bowen ratio, Potential evapotranspiration, Prunus persica L., Water stress


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1. Introduction

The recent episodic and wide-spread drought in the United States highlights the importance

of reliable fresh water supplies for agricultural production especially in the arid and semi-arid

regions such as the Central Valley of California. Although total farmed land area in California

decreased from 11.75 million ha in 1992 to 10.25 million ha in 2010, irrigated agricultural

land area increased from 3.08 million ha in 1992 to 3.24 million ha in 2010 (Klonsky, 2012).

Moreover, during the eight year time span farm lands cropped with orchards increased from

0.89 to 1.13 million ha because of the high cash values and consumer demands. Among the

significant land areas for orchard crops about 23,000 ha are peaches primarily grown in the

Central Valley of California. Like in all other orchard crops, growing peaches in the Valley

depends on irrigation to meet crop water demand by the peach trees.

For early ripening varieties of peaches, e.g. harvested in late May to early June, deficit

irrigation may be used to reduce water use during the postharvest non-fruit bearing periods,

e.g. June to August when the crop water demand is the highest. From 1992 to 2010, the

annual total amount of potential evapotranspiration required for the area ranges from 1200 to

1400 mm and the peak water use periods of June to August three month totals averaged 582

mm, which accounts for approximately 45% of the annual crop water use (CIMIS, 2013).

Therefore there is great potential for adopting water saving technologies such as deficit

irrigation during the non-fruit bearing summer months. Also, perennial crops such as fruit

trees are suitable candidates for applying deficit irrigation strategies because of deeper and

more extensive root systems than most annual crops (Costa et al., 2007; Fereres and Soriano,

2007; Girón et al., 2015). Various studies have been reported with respect to physiological

and yield responses of peach trees using deficit irrigation and indicated substantial water

savings without significantly impacting the yield and fruit quality (Chalmers et al., 1981;

Johnson et al., 1992; Goldhamer et al., 1999; Girona et al., 2005; Falagán et al., 2015).

To practice deficit irrigation, it is important to know the actual crop water needs, which can

be determined as crop evapotranspiration (ETc) or the amount of water needed to replenish

water lost by ETc. Determination of ETc can be done with direct field measurement using

in-situ weighing lysimeters (Dugas et al., 1985; Howell et al., 1985; Johnson et al., 1992);

micrometeorological energy balance approaches such as the Bowen ratio method (Fuchs and

Tanner, 1970; Angus and Watts, 1984; Heilman et al., 1994; Teixeira et al., 2007), the eddy

covariance method (Baldocchi, 1988; Testi et al., 2004; Paco et al., 2006), and the surface

renewal method (Paw et al., 1995; Castellvi and Snyder, 2009); or the FAO crop coefficient

(Kc) method, e.g. multiplying theoretical potential evapotranspiration or ETo with a plant-

dependent crop coefficient (Kc) value to determine ETc for a particular crop at a particular

growth stage (Doorenbos and Pruitt, 1977; Allen et al., 1998). Once a reasonable estimate is

made on ETc, the challenge is to determine how much deficit to use and methods of

monitoring plant water status without over stressing the plants. One approach for choosing

deficit is to pre-select a fraction of ETc as the irrigation target, such as replenishing only 50%

ETc using irrigation water. Other possibilities include taking a fraction of Kc, especially

during peak water use periods such as in the summer months (June – August) for peach trees.

This would be another way of implementing deficit irrigation and reducing irrigation amounts


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needed for essential physiological needs but not fully meeting the crop ETc requirement.

The objective of this study was to characterize water use or ETc and Kc dynamics of a peach

orchard in a three year field experiment when the orchard was managed under postharvest

deficit irrigation. Irrigation decisions were based on ETc estimates using real time ETo and

literature Kc values derived from an earlier lysimeter study. Because only portions of the

orchard received deficit irrigation and majority received full irrigation, the overall

orchard–wide ETc was estimated with an approximate Bowen ratio method with instrument

tower installed in the orchard downwind from the dominant wind direction. An orchard–wide

Kc was determined to reflect the effect of deficit irrigation.

2. Materials and Methods

2.1 Field Description and Deficit Irrigation Treatment

Field studies were carried out from 2008 to 2010 in a 1.6 ha mature peach orchard located

near Parlier, California, USA. The trees were early-ripening “Crimson Lady” (Prunus persica

(L.) Batsch) on “Nemaguard” rootstock planted in April 1999 (Bryla et al., 2005). Each year,

the peaches were harvested in late May to early June. The dimension of the orchard was 122

m in the east – west direction and 133 m in the north –south direction, with individual trees

spaced 1.8 m apart within rows (in the north-south direction) and 4.9 m between rows (in the

east-west direction). The orchard was laid out for irrigation studies using furrow, drip, and

micro-sprinkler irrigation systems and equally divided into 72 irrigation plots with each plot

consisted of 24 trees in three rows with eight trees per row per plot (Figure 1). A border row

and a border tree in each row were used on each side or end of the orchard. The soil at the

field site is a Hanford sandy loam soil (coarse-loamy, mixed, thermic Typic Xerorthents).

During the three year field experiment, 12 out of the 72 irrigation plots received postharvest

deficit irrigation and the remaining 60 plots received full irrigation (Figure 1). The deficit

irrigation treatment plots included 6 furrow irrigation plots and 6 drip irrigation plots to

replace a portion of the crop evapotranspiration (ETc). For the furrow deficit irrigation, a

watering event was initiated when stem water potential approached -2 MPa. For the drip

deficit irrigation, only one fourth of the full amount of ETc was applied during each irrigation

event. To guide irrigation decisions, the values of daily ETc were determined by multiplying

real-time values of potential evapotranspiration (ETo) with literature crop coefficients

(Lysimeter_Kc or Lys_Kc) developed for the same peach variety from an adjacent orchard

using a large underground weighing lysimeter (Johnson et al., 2005).

2.2 Meteorological and Energy Balance Measurements

To provide on-site ETc estimates, a modified Bowen ratio system was deployed in the orchard.

Because the predominant wind was from the northwest direction, the system tower was

installed near the southeast corner of the orchard approximately 138 m downwind from the

northwest corner of the orchard (Figure 1). Also because the tree heights can increase

significantly during each year, a telescoping pole of 5.1-cm diameter galvanized steel pipe

fitted in a 7.6-cm diameter steel pipe as the base was installed within a tree row to minimize

interferences with orchard management, e.g. annual pruning, chemical spray, etc.


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Tower

Figure 1. Schematic of peach orchard lay-out (Green = nondeficit irrigation; Orange = deficit

irrigation), tower location, and dominant wind direction. Orchard dimension = 122 m

(east-west) by 133 m (north-south)

Sensors were mounted on two 1.9-cm diameter aluminum cross beams on the telescoping

pole, placed at two heights separated by 2 m distance: the lower beam at canopy level, upper

beam at 2 m above the canopy. The pole was raised periodically during each growing season

to maintain the lower beam within ± 15 cm of the top of the average canopy height.

The tower site consisted of a set of meteorological and soil sensors for energy balance

measurements. Sensors mounted on the upper beam included a LI-COR silicon pyranometer

N


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for solar radiation (LI200X, Campbell Scientific, Inc., Logan, UT1), a net radiometer for net

radiation (Q*7, Radiation and Energy Balance Systems, Seattle, WA), an air temperature and

relative humidity sensor (Vaisala HMP 45C, Campbell Scientific, Inc., Logan, UT), and an

R.M. Young Wind speed and direction sensor (model 05103, Campbell Scientific, Inc., Logan,

UT). Sensors mounted on the lower beam included an air temperature and relative humidity

sensor (Vaisala HMP 45C, Campbell Scientific, Inc., Logan, UT) and a Met One wind speed

and direction set (model 034B, Campbell Scientific, Inc., Logan, UT).

To account for partial ground shading from the tree canopy, the soil heat flux was measured

with three heat flux plates (HFT3, Radiation and Energy Balance Systems, Seattle, WA), all

buried at 1 cm depth: the first one located in the tree row half way between two adjacent trees,

the second one located half way between two adjacent tree rows, the third one was at half

distance between the first and second plates. An arithmetic average from the three plates was

used to represent the soil heat flux in energy balance calculations. In addition to the heat flux

measurement, six type T copper - constantan thermocouples were installed at the tower site

for soil temperature measurements. They were located at the same relative distances to the

tree and tree rows as the heat flux plates (but 10 cm away from the plates), three were

installed at 1 cm depth and three at 10 cm depth, and an average temperature was used for

each soil depth. A thermocouple was also installed in the tree canopy at 1 m above ground to

monitor within canopy air temperature.

A datalogger (model CR23X, Campbell Scientific, Inc., Logan, UT) was used to record

sensor measurements at 1 Hz then averaged to 5-min readings in 2008 and 15-min average

readings in 2009 and 2010. Sensor readings were monitored daily to weekly for quality

control and repair for possible sensor malfunction. At the beginning of each season, all

sensors and their installation were rechecked for accuracy in readings and physical

installation.

2.3 Evapotranspiration and Crop Coefficient Calculations

Based on the net radiation (Rn), soil heat flux (G), and air temperature (Ta) and relative

humidity (hr) measurements, latent energy (LE or E ) available for evapotranspiration in

the peach orchard was estimated using the Bowen ratio method:

BR

GRE n

1 (1)

where BR is the Bowen ratio which was computed from:

u

a

l

a

u

a

l

a

ee

TTBR

(2)

1 Mention of trade names or commercial products in this publication is solely for the purpose of providing specific

information and does not imply recommendation or endorsement by the U.S. Department of Agriculture.


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where is apparent psychrometer constant (0.0662 kPa °C-1

, Monteith and Unsworth,

1990); l

aT , u

aT , l

ae and u

ae are respectively air temperature and apparent vapor pressure

measured at lower and upper beams above the canopy. As recommended by Foken et al.

(1997) for ensuring sufficient wind turbulence, instantaneous BR calculations for wind speed

differences < 0.3 m s-1

were excluded from BR calculations. Also the latent heat calculation

would be undefined when BR = -1, therefore all BR values between -0.75 and -1.25 were also

excluded, as suggested by Ohmura (1982). To fill the gaps from excluded BR values, 30-min

moving-windows averages were applied and used for latent heat calculations.

The vapor pressure ( l

ae and u

ae ) was calculated from relatively humidity and air temperature

using the Tetens formula (Buck, 1981):

cT

bTahe

ul

a

ul

aul

r

ul

a ,

,,, exp (3)

where coefficients used in the saturation vapor pressure function were a = 0.611 kPa, b =

17.502, and c = 240.97 °C (Campbell and Norman, 1998). Bowen ratio method ETc was

calculated by converting latent heat to depth of water.

To estimate peach water use from the FAO crop coefficient method, potential ET or ETo was

calculated using the modified Penman-Monteith equation (Campbell and Norman, 1998):

s

pDGRsET an

o

/g )( vv (4)

where parameter s is slope of the saturation model fraction at apparent atmospheric pressure

(pa), λ is latent heat of vaporization of water (44 kJ mol-1

), gv is total vapor conductance of the

canopy, and Dv is vapor pressure deficit. Parameters s and Dv were determined using

measurements of Ta and hr and the Tetens formula for saturation vapor pressure:

a

a

aa Tc

bT

Tcp

abcs exp

)( 2 (5)

a

a

rvTc

bThaD exp)1( (6)

where Ta and hr from the upper beam were used in the calculations, and parameters a, b, c

were the same as in equation (3).

Total vapor conductance of the canopy (gv) was computed from stomatal conductance (gs)


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and boundary layer aerodynamic conductance (ga) using the following:

as

v

gg

g11

1

(7)

where gs was assumed constant at 300 mmol m-2

s-1

for non-water stressed peach trees

(Correia et al., 1997). The aerodynamic conductance depends on meteorological and

boundary layer properties including wind speed and temperature gradient at the crop surface.

The average June-August wind speed from 2008-2010 was 0.95 m s-1

(Table 1) and average

difference between the canopy temperature and air temperature was – 2 °C (Wang and

Gartung, 2010). This produced an average ga value of 250 mmol m-2

s-1

(Campbell and

Norman, 1998). Thus an estimated average gv of 136 mmol m-2

s-1

was used for ETo

calculations for the three year field experiment.

For energy partition assessment, the sensible heat (H) component was also estimated using

the Bowen ratio equation (Foken, 2008):

BR

BRGRH n

1)( (8)

Because a portion of the orchard was under post-harvest deficit irrigation, the composite

effect would be reductions in ETc from full irrigation ET. This effect could be reflected as a

stressed Kc or a deficit Kc, and it was calculated as Bowen ratio ETc divided by ETo.


3.1 Meteorological Conditions during the Experiment

For the postharvest months of June to August of 2009, as an example, daily air temperature in

the peach orchard was found in the range of 10 to 15 °C for daily minimum to approximately

30 to 40 °C for daily maximum (Figure 2). From late June to end of August, air temperature

inside the canopy at 1 m above ground was consistently lower than temperature at canopy top

or 2 m above the canopy. Daily maximum at 2 m above the canopy was 1-2 degrees higher

than temperature at the canopy top. The same trend was observed in 2008 and 2010, and as

expected the monthly average air temperature was consistently the highest at 2 m above the

canopy and the lowest at 1 m above ground inside the canopy (Table 1). Also air temperatures

in June and August of 2008 were generally higher than temperatures observed in respective

months in 2009 and 2010.


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10

20

30

40

6/1 6/16 7/1 7/16 7/31 8/15 8/30

Air te

mp

era

ture

, Ta (

oC

) Ta_2m Ta_canopy top Ta_canopy 1m

0

5

10

15

20

25

30

0

20

40

60

80

100

6/1 6/16 7/1 7/16 7/31 8/15 8/30

Va

po

r p

ressure

de

ficit, D

v (k

Pa

)

Re

lative

hum

idity,

hr

(%) hr_2m hr_canopy top Dv_2m

0

1

2

3

4

5

6/1 6/16 7/1 7/16 7/31 8/15 8/30

Win

d s

pe

ed

, u (

m s

-1) u_2m u_canopy top

-50

0

50

100

150

200

250

300

0

10

20

30

40

6/1 6/16 7/1 7/16 7/31 8/15 8/30

So

il he

at

flux, G

(W

m-2

)

So

il te

mp

era

ture

, Ts (

oC

)

Time

Ts_1cm Ts_10cm G_1cm

Figure 2. Real time meteorological variables measured in 2009 at the tower site in a peach

orchard,

Parlier, CA


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Table 1. Monthly average meteorological variables measured from 2008 to 2010 during

postharvest deficit irrigation treatment in a peach orchard, Parlier, CA ________________________________________________________________________________________________________________________

Month Parameter † 2008 2009 2010

________________________________________________________________________________________________________________________

June Ta (°C), 2 m above canopy 24.8 23.3 23.9

Ta (°C), at canopy top 24.0 22.9 23.6

Ta (°C), 1 m above ground 23.3 22.3 23.1

hr (%), 2 m above canopy 44.7 55.0 50.5

hr (%), at canopy top 45.6 51.7 45.7

Dv (kPa), 2 m above canopy 2.07 1.55 1.71

u (m s-1

), 2 m above canopy 1.30 1.31 1.70

u (m s-1

), at canopy top 0.71 0.58 1.32

Ts (°C), 1 cm depth 24.9 23.5 24.6

Ts (°C), 10 cm depth 24.5 23.4 24.1

G (W m-2

), 1 cm depth 8.93 5.49 6.75

July Ta (°C), 2 m above canopy 27.3 27.7 26.9

Ta (°C), at canopy top 26.5 27.0 26.5

Ta (°C), 1 m above ground 25.4 25.4 NA ‡

hr (%), 2 m above canopy 52.0 48.2 54.8

hr (%), at canopy top 51.3 45.6 49.7


u (m s-1

), 2 m above canopy 1.20 1.23 1.25

u (m s-1

), at canopy top 0.62 0.47 0.73

Ts (°C), 1 cm depth 27.7 25.6 26.9

Ts (°C), 10 cm depth 26.3 25.9 27.8

G (W m-2

), 1 cm depth 4.88 3.78 4.24

August Ta (°C), 2 m above canopy 27.0 25.7 24.8

Ta (°C), at canopy top 26.3 25.0 24.3

Ta (°C), 1 m above ground 24.5 22.8 21.6

hr (%), 2 m above canopy 50.4 53.7 56.9

hr (%), at canopy top 49.8 51.4 52.2


u (m s-1

), 2 m above canopy 1.07 1.10 1.16

u (m s-1

), at canopy top 0.46 0.32 0.60

Ts (°C), 1 cm depth 25.9 24.0 23.2

Ts (°C), 10 cm depth 25.1 24.6 24.9

G (W m-2

), 1 cm depth 2.75 2.60 3.96

________________________________________________________________________________________________________________________

† Ta = air temperature, hr = relative humidity, Dv = vapor pressure deficit, u = wind speed, Ts

= soil temperature, G = soil heat flux; ‡ NA = data not available.


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Responding to diurnal temperature fluctuations and vapor density changes, for the months of

June to August 2009 relative humidity ranged from approximately 20-30% for daily lows to

80-90% for daily highs (Figure 2). Monthly averages ranged from 45 to 55% for the three

year period during the study (Table 1). For potential evapotranspiration calculations, vapor

pressure deficit was computed at 2 m above the canopy and the values in 2009 generally

ranged from 0 to 5 kPa (Figure 2). Monthly averages of vapor pressure deficit were from 1.6

to 2.3 kPa during the three year experiment (Table 1). Similar to air temperature observations,

relatively higher vapor pressure deficit values were found for June and August of 2008 than

that in respective months of 2009 and 2010 (Table 1).

Measured wind speed during the three month period was relatively low where the daily

maximum was generally less than 3 m s-1

(Figure 2). As expected, wind speed at 2 m above

the canopy was consistently higher than that at the canopy level and the monthly average

wind speed from June to August of 2008 to 2010 was 1.1-1.7 m s-1

at 2 m above the canopy

and 0.3-1.3 m s-1

at the canopy level (Table 2).

Soil temperature at 1 cm depth fluctuated diurnally from upper teens to low 30 °C whereas

temperature at 10 cm depth was at low to mid 20 °C (Figure 2). Monthly average temperature

ranged from 23 to 28 °C for the three year experiment (Table 1). Higher soil temperature was

found in the month of July (26-28 °C) than in June or August (23-26 °C). Soil heat flux also

showed strong diurnal variations reaching a daily maximum of approximately 125 W m-2

(Figure 2). As expected, the monthly average heat flux decreased from 5.5-8.9 W m-2

in June

to 3.8-4.9 W m-2

in July to 2.6-4.0 W m-2

in August (Table 1).

Although 2008 appeared to be a warmer year than 2009 and 2010, in general, these

meteorological parameters found during the three year period were within the limit of long

term averages for the area (CIMIS, 2013).

3.2 Bowen Ratio Data Quality Control and Energy Partition

Figure 3 shows the 30-min moving windows averages of 5-min cumulative ETc in 2008, as

an example, for before (a) and after (b) data quality control by excluding conditions when

wind speed difference < 0.3 m s-1

(Foken et al., 1997) or -1.25 < < -0.75 (Ohmura, 1982).

Clearly, the data quality control procedures removed the unreasonably artificial high ETc

values caused by the inherent limitations of the Bowen ratio method. The same data quality

control procedure was applied to the entire dataset from the three year field study. The basis

of the Bowen ratio method assumes that the ratio of the gradients of temperature and

humidity between two heights behaves similarly to the ratio of the sensible to the latent heat

flux (Dugas et al., 1991). However, both the sensible and latent heat flux do not explicitly

consider wind speed differences at the two heights or differences between the measurement

heights. Larger differences between the measurement heights would most likely increase the

differences in measured air temperature and humidity, hence making the Bowen ratio method

more robust. Local or regional meteorological characteristics can also add needed

requirements for deployment of the Bowen ratio method. Places with more frequent low wind

speed or mixing conditions for turbulence likely require larger height separation than areas


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Figure 3. Real time 2008 crop evapotranspiration or ET before (a) and after (b) data quality

control by excluding conditions when wind speed difference < 0.3 m s-1

(Foken et al., 1997)

or -1.25 < < -0.75 (Ohmura, 1982) and taking 30-min moving windows averages of 5-min

cumulative ET measurements

frequently see strong wind turbulence. Logistic considerations, however, often limit the

height separation, especially for perennial tall plants such as trees and vines in that the lower

measurement height also needs to be at or above the canopy top. Teixeira et al. (2008) used

3-m height above a mango tree canopy for the upper beam measurement of a Bowen ratio

system and Heilman et al. (1994) placed the sensors at 1-m above a vineyard canopy. The

2-m height separation used in this study was a compromise between accuracy and logistical

feasibility. With this height and the 138 m distance upwind to the field edge, the

fetch-to-height ratio would be 69:1 which was well above the minimum requirement for

Bowen ratio measurements.

To illustrate energy partition, one day from July to August or the peak evaporative periods

was randomly selected for each of the three years of field measurements (Figure 4). As shown

in the figure, 75-85% of the daily Rn was used by latent heat (LE), 25-13% by sensible heat,

(a)

(b)


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Figure 4. Diurnal energy fluxes of net radiation Rn, latent heat LE, sensible heat H, and soil

heat G for (a) 29 July 2008, (b) 14 August 2009, and (c) 19 July 2010

and nearly a net zero usage for soil heat flux. Similar energy partitions were observed in a

mature mango orchard (LE = 80-85% Rn; Teixeira et al., 2008), in a young olive orchard

(approximately 70%; Testi et al., 2004), and in both wine and table grape vineyards (88%;

Teixeira et al., 2007). The relatively low sensible heat was unique to the area where surface

boundary layer is often under stable conditions with light winds especially in the mornings

(a)

(b)

(c)


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(Castellvi and Snyder, 2009; CIMIS, 2013). Convective conditions tend to start in the

afternoon after receiving solar heating in the morning. The dominant wind pattern will be

different if experiencing an incoming storm system but it is rare for the area during the

summer growing months. The small net soil heat flux was attributed to low exposure of bare

soil. Independent canopy cover measurements with a TetraCam camera showed

approximately 90% ground canopy cover during the months of June – August in this orchard.

3.3 Comparison of Crop Coefficients and Evapotranspiration

Actual crop coefficients (Kc) under postharvest deficit irrigation, thereafter termed Deficit_Kc,

were computed as ratios of ETc estimated from the Bowen ratio method over ETo (Figure 5).

For comparison purposes, the time-dependent linear relationship developed for the same

peach variety from a field site under fully irrigated conditions and in close proximity to this

study site (Johnson et al., 2005) was used to calculate the “potential” crop coefficients,

thereafter termed Lysimeter_Kc. It is worth to note that the Lysimeter_Kc increased linearly

until day of year (DOY) 187 or July 6 then remained constant at 1.20. As shown in the figures,

Deficit_Kc values were consistently lower than Lysimeter_Kc in 2008 starting in early June.

In 2009 and 2010, Deficit_Kc values were similar to Lysimeter_Kc until July when it became

slightly lower than Lysimeter_Kc. Discrepancies in Deficit_Kc and Lysimeter_Kc between

2008 and the following two years were likely attributed to the higher temperatures occurred

in 2008 than in 2009 or 2010. The generally lower values of Deficit_Kc in all three years,

compared to the Lysimeter_Kc values, were attributed to the deficit irrigation treatments in

the orchard. The average reduced (from 1.20) maximum crop coefficient, by the deficit

irrigation treatments, was 0.90, 1.03, and 1.07 for 2008, 2009, and 2010, respectively. Rather

than DOY 187, as in the full irrigation lysimeter study, the time for reaching the maximum

Deficit_Kc was DOY 158 in 2008 (06/08/2008), DOY 168 in 2009 (06/17/2009), and DOY

172 in 2010 (06/21/2010), respectively. These findings indicated that after the onset of deficit

irrigation treatment in early June, overall crop water use started to decrease and reached a

“stressed” equilibrium maximum value sooner than the typical DOY 187 date and at values

lower than 1.20 should all the trees be fully irrigated. Also, only 17% of the orchard was

deficit irrigated. Should the entire orchard be managed under deficit irrigation, smaller

Deficit_Kc values would be expected.

As shown in Figure 6, comparisons were made in estimated ETc using Lysimeter_Kc,

Deficit_Kc, and Bowen ratio values for each year. As expected, the ETc values estimated from

Lysimeter_Kc was higher or overly estimated than that using the Deficit Kc for most of the

postharvest periods: early June to end of August in 2008, late June to end of August in 2009,

and July to August in 2010. ETc values determined using the Deficit_Kc were virtually the

same as the direct Bowen ratio estimates. It is important to note the interdependence of

determination of Deficit_Kc on Bowen ratio estimates of ETc. The merit with using a deficit


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Figure 5. Daily crop coefficients of peach orchard under postharvest deficit irrigation in (a)

2008, (b) 2009, and (c) 2010. Lysimeter_Kc or Lys_Kc = crop coefficient from lysimeter

measurement under full irrigation (Johnson et al., 2005). Deficit_ Kc = crop coefficient

computed from potential ET (ETo) and actual ET (ETc) from Bowen ratio measurement

(b)

(a)

(c)


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Figure 6. Comparison of real time ETc using the Lysimeter_Kc, Deficit_Kc, and the Bowen

ratio (BR) methods for (a) 2008, (b) 2009, and (c) 2010

(b)

(c)

(a)


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Kc is the simple analogy to the FAO 56 method of using Kc to manage irrigation scheduling,

should a known or predetermined deficit is given. To further illustrate the improvement of

Deficit_Kc on ETc assessment, diurnal 5-min or 15-min ETc values were compared for the

dates when Kc reached the maximum Kc (i.e., 06/08/2008, 06/17/2009, 06/21/2010) with that

of July 31 of respective years (Figure 7). As can be seen in the figures, the three methods of

ETc estimates were similar up to reaching the respective maximum deficit Kc values. ETc

values on July 31 clearly showed over-prediction using the lysimeter Kc, especially during

middle part of the day. This is virtually caused the larger value for the multiplier (i.e. 1.20)

than the reduced maximum Deficit Kc (i.e., 0.90, 1.03, and 1.07 for 2008, 2009, and 2010

respectively). Using the lysimeter Kc values up to reaching the maximum Deficit Kc, then

using the constant Deficit Kc, monthly cumulative ETc was calculated and compared with

cumulative ETc estimated using the lysimeter_Kc and Bowen ratio methods for the three

years (Table 2). Cumulative ETc in June showed variable differences between the three

methods because the onset of deficit irrigation likely had different initial impact on crop

water use. In July and August, the Lysimeter Kc method consistently over-predicted

cumulative ETc compared to the Deficit_Kc and the Bowen ratio methods. The results also

indicated that if Deficit Kc can be determined or pre-selected, then the well-established FAO

56 method for ETc (Allen et al., 1998) may be used for deficit irrigation management. The

other way to describe water stress under deficit irrigation is to use a crop coefficient stress

factor, e.g. Ks, as proposed in Allen (2000) and adopted in Suleiman et al. (2007) for deficit

irrigation of cotton. For this study, the Ks factor would be the ratio of maximum deficit Kc

over 1.20 or 0.75, 0.86, and 0.89 for 2008, 2009, and 2010, respectively. For times before

reaching the maximum Kc, the Ks factor would be one.

The reason for a reduced maximum Kc in deficit irrigation management where crops are

under some degree of water stress is generally believed to be caused by stomatal regulation or

reduced stomatal conductance under these conditions. The challenge is how to estimate the

degree of stress or deviation of Kc from non-stressed conditions or basal Kc (Kcb) values.

Some recent approaches explored using thermal images from satellite (e.g., the METRIC

model by Allen et al., 2007) or unmanned aerial vehicles (UAVs, e.g., Zarco-Tejada et al.,

2012) to make water stress assessment. The merit with thermal images is the ability to detect

spatial variations in canopy temperature to infer water status or water stress caused by soil

variability or by variations in irrigation distribution uniformity. Under deficit irrigation, all

plants are under some water stress and spatial variability can make certain areas in an orchard

over-stressed to levels that might cause physiologically irreversible damages to the trees

(Fereres and Soriano, 2007). Therefore, the selection of levels of irrigation deficit in terms of

a deficit Kc value or a similar benchmark irrigation level with respect to ETo should consider

the potential spatial variability of water availability on a farm scale to minimize risks on crop

losses. In other words, some safety factor should be used in choosing a deficit Kc for deficit

irrigation. This study, in an inverse way, clearly demonstrated that water stress under deficit

irrigation treatment can be characterized in Kc or so defined as Deficit_Kc. If values of

Deficit_Kc can be pre-determined, the approach may be used to provide guidance on

deploying deficit irrigation practices.


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Figure 7. Comparison of real time ETc using lysimeter Kc, deficit Kc, and Bowen ratio (BR)

measurement for (a) 8 June 2008, (b) 31 July 2008, (c) 17 June 2009, (d) 31 July 2009, (e) 21

June 2010, and (f) 31 July 2010

(a) (b)

(c) (d)

(e) (f)


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Table 2. Monthly cumulative crop evapotranspiration (ETc) in 2008, 2009, and 2010 in a

peach orchard received postharvest deficit irrigation in 12 randomly distributed plots from a

total of 72 plots (Figure 1)

Cumulative ETc (mm)

Month Method † 2008 2009 2010

June Lysimeter Kc 177.7 165.8 113.2

Deficit Kc 155.7 160.0 111.3

Bowen ratio 136.0 186.6 129.7

July Lysimeter Kc 216.2 228.2 140.3

Deficit Kc 162.3 196.5 125.6

Bowen ratio 162.4 194.4 129.0

August Lysimeter Kc 201.7 197.2 142.0

Deficit Kc 151.3 169.3 126.6

Bowen ratio 151.5 175.6 136.5

† Lysimeter Kc method was product of potential evapotranspiration (ETo) and lysimeter crop

coefficient (Johnson et al., 2005). Deficit Kc method was product of ETo and deficit Kc, i.e.

correcting for deficit irrigation effect on maximum crop coefficient Kc ≤ 0.90 (2008), ≤ 1.03

(2009), ≤ 1.07 (2010). Bowen ratio method ETc was direct conversion of total monthly latent

heat to water depth.

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the journal.




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Informative Established Sequence Traits in Tepary Bean

(Phaseolus Acutifolius A. Gray) for Drought Stress

Satya S S Narina

Virginia State University-ARS, Petersburg, VA, USA

USDA-ARS, Stoneville, MS, USA

Brian E Scheffler


Linda A Ballard


Sheron E Simpson


Ramesh Buyyarapu

Dow-Agro Sciences, Indianapolis, Indiana, USA

Rao K Kottapalli

USDA-ARS, Texas Tech, Texas, USA

Brian L Sayre

Department of Biology, Virginia State University, Petersburg, VA, USA

Harbans L Bhardwaj

Virginia State University-ARS, Petersburg, VA, USA

Received: October 9, 2015 Accepted: October 21, 2015 Published: November 2, 2015


Abstract

Drought stress, a major abiotic stress adversely effects crop growth and yield. Tepary bean

was identified as one of the drought adapted legumes for arid and semi-arid regions. Drought

responsive functional genes in tepary bean were identified through current investigation. The

complementary DNAs (cDNAs) prepared from leaf messenger RNA (mRNA) of three


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genotypes ( #10, #15 and #20) were sequenced using 454, and 54,165 Expressed Sequence

Tags (ESTs) were generated and functionally annotated.

Results: A total of 18523, 16190 and 19452 unique ESTs respectively from cDNA libraries

from genotype #10, genotype #15 and genotype #20 had significant hits to NCBI’s non

redundant protein database (e value < 1e-05). All the ESTs were annotated using Gene

Ontology (GO) terms and drought related genes were identified. A total of 2922 microsatellite

markers were designed and 648 were selected for testing and 538 were successfully amplified

and were polymorphic.

Conclusion: The new EST resource provides more gene sequence information compared to

the previously developed 700 ESTs for drought stress. The polymorphic microsatellite

markers identified will be used to characterize the tepary bean germplasm for various traits of

importance and select superior lines.

Keywords: Drought, ESTs, Simple Sequence Repeats, SSRs, putative genes, Phaseolus

1. Background

Tepary bean (Phaseolus acutifolius, Fabaceae), native to the southwestern United States &

Mexico, was a major item of trade with the agrarian tribes of the pueblos of the southwest,

such as the Papago, Zuni, Hopi, Pima, and others. It is much more drought-resistant than

common beans and is grown in desert and semi-desert conditions from Arizona through

Mexico to Costa Rica. Tepary bean seed has excellent nutritional value (Bhardwaj and

Hamama, 2004, Blair et al., 2010, Narina et al., 2014, 2015) including anticancer and

antidiabetic properties.

The research on tepary bean is gaining momentum since 2002 in Virginia (Bhardwaj et al.,

2002 and Hamama and Bhardwaj, 2002) due to increase in human consumption (Scheerens et

al., 1983), its economical and nutritional values (Narina et al., 2014, & 2015). Drought

studies are in progress in locations like Nebraska and Virginia to screen drought tolerant

varieties (Narina et al., 2012) for food, feed and forage ( Bhardwaj, 2013) and utilize these

traits by introgression through breeding practices, to develop improved varieties with drought

tolerance in common bean as well as in tepary bean (Urea, 2009).

There are several mechanisms like drought avoidance, escape and tolerance observed in

tepary bean (Mohamed et al., 2002 and 2005), and in general used by plants for survival in

adverse environments such as drought, high temperature and salinity. Wild common bean can

be used as a source for exploitation of variation for drought tolerance (Cortes et al., 2013).

One of the inevitable consequences of drought stress is enhanced reactive oxygen species

(ROS) production in the different cellular compartments, namely in the chloroplasts, the

peroxisomes and the mitochondria. ROS signaling is linked to abscissic acid (ABA), Ca2+

fluxes and sugar sensing and is likely to be involved both upstream and downstream of the

ABA-dependent signaling pathways under drought stress (Maria, 2008). The rise of PvPIP2;

1 gene expression and PIP1 protein abundance in the leaves of P. vulgaris plants subjected to

drought was correlated with a decline in the transpiration rate (Ricardo, 2006).


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Herbaceous native shrub species have evolved mechanisms to withstand water deficit and

drought stress (Blum, 2011) through genes involved in anatomical, cellular and metabolic

components. Drought tolerant variety (Pinto Villa) was observed with well-developed root

vascular tissues and a number of upregulated functional genes for abiotic stress in common

bean (Montero-Tavera, et al., 2008). Plants adapt to drought stress through response

mechanisms like osmotic adjustment, reduction in DNA content, changes in metabolic

pathways with the accumulation of compounds like proline, appearance of new proteins,

synthesis of the hormone, and ABA(Caplan et al., 1989).

Tepary bean is an important crop in North America especially for farmers in Arizona and

Virginia. Identification of transcriptome regions associated with drought tolerance would

enable breeders to develop improved cultivars through marker-assisted selection. EST

analysis is an efficient approach for gene discovery and identification of polymorphic

markers (Gupta and Varshney, 2000, Varshney et al., 2013) such as microsatellites and single

nucleotide polymorphisms (SNPs). The lack of ESTs available for tepary bean in public

data bases (only 751 available at NCBI database on August 1, 2010 for tepary bean) created

interest to conduct research and develop crop specific genomic libraries for identification of

highly abundantly expressed genes related to various economically and nutritionally

important traits.

Therefore the objective of the current study is to construct and sequence the cDNA library for

three tepary bean genotypes showing differential response to drought, identify the genes

involved in drought stress tolerance by analyzing the ESTs developed and to generate

polymorphic EST - Simple Sequence Repeat (EST-SSR) markers for future germplasm

characterization, genetic linkage mapping and QTL analysis in tepary bean. Six tepary bean

lines were selected out of 200 Plant Introduction (PI) lines screened for drought stress at three

locations by Bhardwaj during 2008-2009 (unpublished). Three genotypes with the collection

numbers 10, 15 and 20 were selected for the current gene expression studies.

2. Methods

2.1 Tissue Source and RNA isolation

The drought susceptible (#15) and resistant (#10 and 20) genotypes of tepary bean were

selected for current study (Table1). These three lines were selected based on the field studies

at three locations giving treatments to water stress. The line 15 gave poor yields and 10 and

20 gave highest yield under severe drought condition. The seeds from these plants were

collected by PI, Dr. Harbans L Bhardwaj at VSU-ARS, Petersburg, VA. These seeds were

planted in pots in a greenhouse to collect fresh leaf tissue for RNA isolation. The plants at 4 -

6 trifoliate leaf stage were given water stress for seven complete days. The fully expanded

young leaves were collected on seventh day and total RNA was isolated using Qiazen’s

DNeasy plant Mini kit (Qiazen, Inc) on the same day. The RNA quality was tested using

UV-visible spectrophotometry and electrophoresing the samples on 1.1% formaldehyde

agarose gel to confirm the quality of RNA.


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2.2 The cDNA preparation and 454 sequencing

The cDNA was prepared and sequenced using the Roche 454 Life Sciences Genome

Sequencer FLX titanium by following the manufacturer's instructions (454 Life Science,

Roche). Libraries were prepared according to the 454 protocol: nebulization, purification, and

ligation of adaptors. The libraries were prepared with ~1 μg (cDNA fragments) using the

"Titanium General Library Preparation Kit". Sequencing on the Genome Sequencer FLX was

performed using the "GS FLX Sequencing Kit Titanium Reagents XLR70 (Research and

Testing laboratory, Lubback, Texas). NGEN was utilized by RT lab for assembly annotation

and functional assignments were performed using Kraken (www.krakenblast.com) and the

SwissProt/Tremble GOA curated database and cross references.

2.3 Gene Ontology (GO) annotation

After high quality sequence reads and aligned ESTs were obtained from Research and Testing

laboratory, Lubbock, Texas, the bioinformatic analysis was performed using Blast2Go tool

(Conesa, 2005). The Blast2Go tool was also used to assign Go ids, enzyme commission

numbers and mapping to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways.

The E value cut off was set to 1e-5 and P-value to </= 0.05. The distribution of genes in each

ontology categories was examined and the percentages of unique sequences in each of the

assigned Go terms were computed (Jonas, 2007) and assigned to three main categories of GO,

namely, biological process, molecular function, and cellular component.

2.4 Bioinformatic mining of microsatellites

The ESTs with more than 50 base pairs were searched for microsatellites using SSR finder

software (www.SSRIT.com). In this work, SSRs were considered for primer design that fitted

the following criteria: a minimum pattern length of 12 bp, excluding polyA and polyT repeat,

at least 7 repeat units in case of di-nucleotide and at least 5 repeat units for tri-, tetra-, penta-

and hexa-nucleotide SSRs. The default settings used for Primer3 input were optimum

temperature of 63oC and an optimum primer size of 24 bases. All the ESTs were also

analyzed for Blastx and Blastn similarities to Fabaceae database and results were compiled

with a cut off e value of 1e-5 because protein sequences are better conserved evolutionarily

than nucleotide sequences.

2.5 Identification of polymorphic SSR markers

A total of 648 primers were designed from 635 EST sequences and used for screening 6

genotypes 10, 15, 16, 18, 20 and 25 using Applied Biosystems (ABI) 3730xl DNA Analyzer.

The 5µl reaction mix has the following reagents in it

5µl reaction:

1.0 µl DNA (@ 10ng/ul)

0.8 µl FAM labeled primer (IDT 16854154)


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0.5 µl 10X Titanium Taq PCR Buffer (BD Biosciences, S1793)

0.04 µl Titanium Taq DNA Polymerase (BD Biosciences, S1792)

0.04 µl dNTP mix (25mM, Promega)

0.8 µl SSR specific primer

1.82 µl water

The PCR protocol has 10 steps and are Step1: 95oC for 3min, Step2: 95

oC for 1min, Step3:

60oC for 1min, Step 4: GOTO Step 2 for1 time, Step 5: 95

oC for 30 sec, Step 6: 60

oC for

30sec, Step 7: 68oC for 30sec, Step 8: GOTO Step 5 for 26 times, Step9: 68

oC for 4min, Step

10: 4oC forever. The resulting chromatograms were analysed using Genemapper (version 4.0)

software (ABI, Life Technologies Corporation, California) and the amplified fragments

displaying distinct sizes (peaks) were considered as different alleles. The allelic and

genotypic frequencies for each locus were calculated using the TFPGA software (Miller,

1997). The number of alleles for locus, expected heterozygosity and observed heterozygosity

were estimated by UPIC software (Renee et al., 2009). A measure of allelic diversity at a

given locus (PIC) was calculated for all the loci (Wang et al., 2008).


3.1 Analysis of ESTs and their putative identities

The cDNA libraries from three genotypes generated a total of 54, 165 (Table1) ESTs. There

are 44 EST sequences with less than 50 base pair in length not included for SSR as well as

Blast analysis from three libraries (LI_10, LI_15 and LI_20). The average sequence length of

the high quality ESTs range from 890-946 in three libraries. The length of the shortest

sequence is 53bp (LI_15) and the longest sequence is 7496 (LI_10). When the libraries

were blasted one on the other, there were 10,965 sequences from LI_15 hitting on sequences

in LI_10 and 12,818 sequences from LI_10 were hitting on sequences in LI_15. There were

5643 sequences unique to genotype #10 not showing hits to LI_15 and 5179 unique to

genotype #15 without any similar hits in LI_10.

The blast analysis against Fabaceae database revealed that there are 5260, 9882 and 16,714

sequences unique to tepary bean from libraries 10, 15 and 20 respectively showing no

functional similarities to the known genes in Fabaceae. About 99.85% of the hits were

showing significant similarities (eValue>= -05with Bit Score >50) to Fabaceae database. The

top hit species distribution and direct GO count for known genes were presented in figures 1

to 6. The crop species identified with majority of hits to tepary bean ESTs is peas, cowpea,

soyabean and alfalfa confirming the evolutionary relationship due to the conserved genes

among the Fabaceae members.


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It revealed highest homology with the sequences from Glycine max, Vitis vinifera, Ricinus

communis, Populus trichocarpa, Medicago truncatuala and other crop species (Figure 2, 4

and 6). The mapping and annotation steps resulted in GO IDs (Gene Ontology Identifiers)

to individual EST data sets of three libraries. A total of 13,263 (71.6%) of 18,523 ESTs were

annotated with non-redundant (nr) GO IDs in the resistant line 10 while 6308 (40%) of 16190

ESTs in susceptible line 15 and 2738 (14.1 %) of 19, 452 ESTs were annotated with nr GO

IDs. The remaining ESTs with unknown functions can be used in near future to identify the

homologies that were conserved in related species and to identify various seed traits that

could be used for grain quality improvement.

3.2 Functional classification of ESTs based on GO IDs and Fold enrichment scores

The ESTs were classified into subsets based on molecular function (level 2) and is

represented as a pie diagram for each library (Figure 7, 8, 9). A major subset of ESTs (83%)

across all libraries was linked to binding and catalytic activity, where the remaining groups

involved in transporter (5%), transcription regulator activity (4%), molecular transducer

activity (2%) and structural molecular activity (2%), electron career activity (1.8%), enzyme

regulator activity (1%) and antioxidant activities (0.4%). In the Biological Process category, a

major proportion of the genes were involved in cellular metabolic processes across all

libraries and were involved mainly in protein , energy and organic acid metabolisms. ESTs

were also grouped into different cellular components such as ribo-nucleoprotein complex,

organelle part, cytoplasm and intracellular part and macromolecular complex based on fold

enrichment (≥1). However, significant proportions of the expressed genes (≥10%) were

stress responsive and were conserved..

3.3 Genes involved in water stress tolerance

The putative genes related to various cellular and metabolic functions identified in tepary

bean transcriptome were listed in Table 2. The number of stress responsive genes identified

were abundant in genotype 10 compared to 15 and 20. The ESTs showing functional hits to

proteins that probably function in stress tolerance (functional proteins) are water channel,

transporters LEA proteins, chaperones and key enzymes for osmolyte biosynthesis (proline,

sugars) and the second group contains protein factors involved in further regulation of signal

transduction and gene expression that probably function in stress response (regulatory

proteins) like transcription factors, protein kinases, phospholipid metabolism and ABA

biosynthesis.

From the EST data sets,over expression of genes (115 GO terms) was clearly observed in

LI_10 compared to those in LI_15. Using the Blast2Go tool, statistical analysis related to

over expression of functional categories was performed based on Fisher Exact test. There

were 1442 stress responsive genes significantly over expressed in genotype 10 compared to

15 (GO: 0006950) while 2407 to response to stimulus (GO: 0050896). The over expression

of genes was also observed (286 Go terms) when combined files of LI_10 and LI_20

compared against LI_15.

The metabolic genes identified in the transcriptome are mostly involved in amino acid and


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fatty acid metabolism (Table 2). The Cytochrome P450, Anthranilate, lipoxynase were highly

expressed among the total number of metabolism related genes identified in line 10 compared

to the other lines. They were identified as drought inducible genes (Bray, 2002). The

cinnamoyl CoA reductase (CCR) activity, is over expressed in library 10 and 20 compared to

15 (GO:0016621), a key lignin gene, affect physical properties of the secondary cell wall

such as stiffness and strength (Bala et al., 2005). The stress tolerance in genotype 10 could be

due to pubescent leaf and stem surface, more leaves per plant and strong stem.

There were only few genes identified for energy metabolism in response to water stress

(Table 2) to recognize the decrease in photosynthetic carbon and energy metabolism under

water stress (Bray, 2002). The expression of oxygen evolving complex (GO: 0009654) was

almost same level in all the genotypes under water stress. The photosynthesis genes, (GO:

0009768; GO: 0019684; GO: 0031978) light harvesting Photosystem I (light reaction),

peroxidase (GO: 0004601) and oxido-reductase (GO: 0016491) activity were over expressed

in genotype 10 and 20 compared to 15.

Transcription factors induced in response drought stress in tepary bean were WRKY, ethylene

response, 14-3-3 like protein and MIP-PIP sub family (Table 2). A stress induced histone was

also identified which helps maintaining chromatin structure during water deficit (Bray, 2002,

Scippa et al., 2000). Aquaporins believed to play an important role in water homeostasis.

Apparently the aquaporins are well represented from genotype 10 over the other two (Table

2). In higher plants aquaporin-like proteins, also called major intrinsic proteins (MIPs), are

divided into five subfamilies. The MIP-PIP subfamily was over expressed in genotype 10

and was supported by previous reports in Arabidopsis (Alexandersson, 2010). The plasma

membrane intrinsic protein (PIP) subfamily generally upregulated in leaves upon drought

stress. The overall drought regulation of PIPs was a general and fundamental physiological

response and was well conserved in genotype 10. These are similar to stress inducible

transcription factors documented in Arabidopsis (Bray, 2002).

The genes participating in receptor signaling protein activity (GO: 0005057), receptor

signaling protein serine/threonine kinase activity (GO: 0004702), red or far-red light

signaling pathway (GO: 0010017) were over expressed in genotype 10 compared to 15. This

could be due to enhanced ROS production under water stress. The ROS signaling is likely to

be involved both upstream and downstream of the ABA-dependent signaling pathways under

drought stress (Maria, 2008).

Water stress may cause degradation or malformation of proteins which lead to new protein or

new genes induced. The mRNAs that code for ubiquitin, proline, leucine rich, zinc finger,

early response to drought (erd) and heat shock proteins (hsp) were abundant in genotype 10

compared to 15 and 20 under water stress (Table 2). It was supplemented with the previous

reports of rise in ubiquitin under stress (Caplan et al., 1989). Heat will denature the nascent

polypeptides and cause the increased production of hsps involved in the correct protein

folding (Caplan et al., 1989). It was revealed that drought tolerance of tepary bean genotype

10 could be due to biochemical processes related to proline metabolic enzymes beacausean

increase in proline oxidase activity was observed under drought stress (Camacho, 1998)


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compared to common bean. It was also supported by the electrophoretic patterns of matured

seeds showing more proteins in tepary beans than in common beans with limited irrigation

(Camacho, 1998). An abundant production of hydroxyl proline rich glycoproteins (HPRG's)

which strengthen the cell wall was observed in genotype 10 in response to drought and was

supported by previous reports (Showalter and Varner, 1989).

The gene expression to defense response is low (GO: 0050832) in genotype 15 during water

stress compared to 10 (124 ESTs). The genes coding for chitinase and thioredoxin were more

in genotype 10 compared to other two genotypes (Table 2)

Though 20 was a resistant line as per field observations, the gene expression data is not

comparable with the resistant line under water stress conditions. Therefore based on the

current gene expression studies, it was concluded that the genotype 10 is more tolerant to

water stress compared to 20. It was revealed that the deep root penetration and sensitive

stomata could cope with severe water stress in tepary bean genotype 10 and was supported

(Mohamed, 2002) in two drought tolerant tepary bean (Phaseolus acutifolius) lines (NE # 8A

and 19) for the responses to drought due to their deep root system.

3.4 Microsatellite containing ESTs

There were 1187 microsatellites found in 2329 ESTs containing SSRs out of 11,523 unique

ESTs of LI_10 . The major types were 990 tri-nucleotide followed by 125 di, 36 tetra, 14

penta and 21 hexa nucleotide repeats. A total of 1087 microsatellites were found out of

2012 ESTs containing SSRs found in 16190 Unique ESTs of LI_15. The majority were Tri

(901) followed by di (116), tetra (39), pent (11), hexa (14), hepta (4) and 24 (1) nucleotide

repeats. There are 635 EST sequences containing SSRs in LI_20 with 648 micorsatellites.

There are about a total of 3397 repeats containg di (93), tri (371), tetra (108), penta (10), hexa

(49), hepta (1), octa (1), nano (8), deca 91) and dodeca (2) nucleotide repeats which were

used for designing these 648 primers. A total of 2922 primer pairs were designed from three

libraries for future microsatellite based mapping studies.

3.5. Identification of polymorphic EST-SSR markers

Out of 648 primer pairs tested, 104 primer pairs were showed no amplification on six

genotypes selected for drought stress studies. All the amplified markers were polymorphic

and 3866 alleles were found.

4. Conclusion

Our results documented the informative transcriptome for functional genes involved in

drought stress tolerance and cellular metabolism. The information related to the effect of

water stress on enzymes involved in individual metabolic pathways is lacking, might be due

to lack of publicly available data for the genes involved, during the time of blast analysis.

Further, bioinformatics screening of the generated ESTs against current database would help

to find some more genes coding for enzymes involved in nutritional as well as structural,

functional and metabolic pathways, and could be a value to the future crop improvement

efforts at VSU’s Agricultural Research Station. Being a nutritionally valuable crop, a


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successful engineering of physiological and metabolic pathways for a number of genes

involved or induced under water stress will resolve the problems of farming community in

dryland areas. Therefore, future studies with continued funding could be directed towards 1)

in depth functional analysis of genomic data generated for the tepary bean to date, 2) testing

the genotypes selected for various agronomic, nutritional and anti-nutritional traits of interest

utilizing the crop specific and cross species SSR markers developed to identify nutritionally

valuable water stress tolerant genotypes for cultivation.

4.1 Accession numbers

The ESTs generated from this study have been deposited in Gen Bank and were assigned

with accession numbers from HO774968-HO793490 (Library 10) , HO793491-HO809680

(Library 15) with db EST _ Id 71386542 to 71421254 and from HO 224465 to HO 243917

with db EST _ Id 70648090 for library 20.

List of abbreviations

BLAST: Basic Local Alignment Search Tool; bp: base pairs; EST: expressed sequence tag;

GO: gene ontology; cDNA: Complementary DNA. LI: Library; SSR: Simple Sequence

Repeat

Contribution of authors

Harbans Bhardwaj selected the genotypes for drought stress based on field experimentation

and supplied all the materials required to complete the experiments. Brian E Scheffler

provided the facilities as needed. Satya Narina raised the plants in green house, isolated the

RNAs, performed the bioinformatic analysis of the ESTs, analyzed the results and drafted the

manuscript. Sheron Simpson technically assisted in genotyping analysis. Linda Ballard

performed the blast analysis and reviewed the manuscript. Ramesh Buyyarapu, Rao, K K.,

Harbans L Bhardwaj and Brian E Scheffler reviewed the manuscript. All the authors read and

accepted for final submission.

Acknowledgements

The project was supported by USDA-CBG funding awarded to Dr. Harbans L Bhardwaj.

Authors like to acknowledge Department of Biology for raising the teparybean plants in the

greenhouse facility, support of Mr. Bowen, and Mr. Bates while raising plants at Virginia

State University. Our team extends special acknowledgements to Dr. Shiva Kumpatla and Dr.

Ramesh Buyyarapu for training and assisting in the area of bioinformatics research and Dr.

Sayre for providing undergraduate students to give hands on training in plant genomics.

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SSR finder software (2003). Steven Schroeder: University of Missouri-Columbia, Maize

Mapping Project 2003-01-01

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Achievements and prospects of genomics-assisted breeding in three legume crops of the

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Urrea, C. A., & Porch, T. G. (2009). Phenotypic evaluation of a subset of the Phaseolus

vulgaris core collections and the P. acutifolius germplasm collection, and advanced common

bean lines for drought tolerance in Nebraska. Bean Improvement Cooperative Annual Report,

52, 104-105.

Figure1. Direct GO count for Putative ESTs from library TB10

Figure 2: Top blast hit homologies identified from library TB 10

http://www.maizemap.org/bioinformatics/SSRFINDER/README.ssrfinder


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Figure 3: Direct GO count for Putative ESTs from library TB15



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Figure 5: Direct GO count for Putative ESTs from library TB20



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Figure 7: GO distribution in TB10 based on molecular function.




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Table 1. Summary on genotypes, number of ESTs and SSR markers generated in tepary bean.

Genotype Special characters Total

number of

high quality

ESTs

(>50bp)

Number of

trinucleotide

repeats found

Number of ESTs

containing SSR

sequences

TB10 Resistant to Drought, profusely

branching type vine with more number

of broader trifoliate leaves at each node

with less intermodal space and early

germinating variety with green stem

and deep root system.

18523 990 2329

TB15 Susceptible to Drought, slow growing

type vine with few leaves and late

germinating variety with red stem

16190 901 2012

TB20 Resistant to Drought, slow growing

type vine with few narrow leaves with

greater internodal distance and late

germinating variety with read stem

19,452 371 635


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Table 2. List of putative genes identified from cDNA libraries generated in three tepary bean genotypes.

Putative water stress responsive genes

Drought resistant tepary bean

lines

Drought

susceptible tepary

bean line

Library10 Library 20 Library 15

Metabolism

Cytochrome P450

Hydroxynitrile lyase

Enolase

Amino acid biosynthesis and degradation

Anthranilate

Tryptophan synthase (beta-subunit)

Lactoylglutathione lyase

Chorismate mutase

Aromatic metabolism

4-Coumarate ligase

Cinnamyl alcohol dehydrogenase

Chalcone synthase

Cinnamoyl reductase

Phenylalanine ammonia lyase

0-methyltransferase protein

Fatty acid multifunctional protein

Hydroperoxide lyase

Epoxide Hydrolase

Omega-3 fatty acid desaturase

Lipoxygenase

Allene oxide synthase/cyclase

Energy

Oxygen evolving complex

Cell Growth

Nitrilase

131

1

3

11

3

5

0

5

11

1

5

2

8

2

4

7

52

6

4

1

8

0

4

4

0

1

2

1

0

2

4

0

3

0

1

0

2

0

5

1

40

0

2

6

0

2

0

5

1

2

4

1

2

0

3

1

1

1

3

0

Transcription

Ethylene response

14-3-3 protein

WRKY Transcription factor

mip pip subfamily

1

11

10

12

0

4

2

0

0

1

6

3

Protein synthesis/destination

Proline rich protein like/proline transporter

protein kinase (calcium dependent)

Ubiquitin

Homeobox -leucine rich/ zipper protein

18

207(12)

107

62

7

27(0)

27

7

7

130(5)

49

43


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Zink Finger Protein

Heat Shock Protein (hsp)

Early response to drought/dehydration (erd)

110

94

5

19

19

0

57

36

3

Transport

Aquaporin

Transporter (Sugar)

4

229(8)

3

29(1)

1

117(6)

Cell rescue/defence

Oxidative stress

Glutathione-S-transferase

Glutathione peroxidase

Catalase

Thioredoxin

Peroxiredoxin

Pathogenesis related

Chitinase

beta Glucanase

Metallothionein

Putative lectin

14

6

10

32

6

37

7

7

5

8

3

1

8

2

7

3

3

8

1

2

1

13

0

1

4

6

6



the journal.




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Soil Application of Cow Dung with Foliar Application

of Boost Extra, Effect on Growth and Yield of Okra in

an Ultisol, Nigeria

Ogundare, S. K.

Kabba College of Agriculture, Division of Agricultural Colleges, Ahmadu Bello University,

Zaria, Nigeria

Mohammed, S. A.


Zaria, Nigeria

Owolabi, J. F.


Zaria, Nigeria

Received: September 29, 2015 Accepted: October 10, 2015 Published: November 16, 2015


Abstract

Experiments were carried out at the student’s experimental field, Kabba College of

Agriculture, Horticulture section to examine the effect of soil application of cow dung

combined with foliar application of boost extra on growth and yield of okra in an ultisol,

Nigeria. The land was ploughed each year and harrowed with the aid of tractor mounted

implements. The experiment was laid out in a randomized complete block design (RCBD).

The treatments consisted of A = 3t/ha cow dung, B = 1.0 L/ha foliar + 2.5t/ha cow dung, C

=1.5L/ha foliar + 2t/ha cow dung, D = 2 L/ha foliar + 1.5 t/ha cow dung, E = 2.5L/ha

foliar + 1.0t/ha cow dung, F = 3t/ha foliar. Each year experiment was conducted using a

single field having dimension of 35 by 14m which was laid out into three blocks with 1m

guard row between blocks. Each block consists of six plots (5 by 4m) and 1m guard row

between plots. Cow dung manure was applied a week before planting. Okra variety Lady’s

finger was used. Three seeds per hole were planted on April 4th

in both years on the flat with a

spacing of 60cm x 25cm between and within the rows and later thinned to one plant per stand.

Data taken included plant height at 50 % flowering, number of branches per plant, leaf area,

pod length, pod diameter, number of pods per plant; and pod weight and yield (t/ha). The data

were subjected to Analysis of Variance (ANOVA) while the Least Significant Difference


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(LSD) was used to separate treatment means. The result shows that plot treated with 2.0L/ha

foliar + 1.5 t/ha cow dung had the best performance in yield and yield components in this

study. It is therefore recommended that okra farmers should integrated foliar fertilizer (boost

extra) at the rate of 2L per hectare with cow dung at rate of 1.5t per hectare be used for okra

production in the study area.

Keywords: Abelmochus esculentus, Lady finger, Malvaceae, Significant, Yield

1. Introduction

Okra (Abelmochus esculentus (L.) Moench is a member of the family Malvaceae (George

1999). It is usually grown in Nigeria primarily for its mucilaginous content. Pods and seeds

are rich in phenolic compounds (Arapitsas, 2008). Fresh pods are low in calories, no fat, high

in fiber, and have several valuable nutrients (NAP, 2006). Fresh okra pods are the most

important vegetable source of viscous fiber, an important dietary component to lower

cholesterol (Kendall and Jenkins, 2004). Seven-days-old fresh okra pods have the highest

concentration of nutrients (Agbo et al., 2008). Potential of mucilage for medicinal

applications includes uses as an extender of serum albumin (BeMiller et al., 1993), as tablet

binder (Ofoefule et al., 2001) and as suspending agent in formulations (Kumar et al., 2009).

Okra mucilage is used as a protective food additive against irritating and inflammatory

gastric diseases (Lengsfelf et al., 2004).

Use of organic manures as a means of maintaining and increasing soil fertility has been

advocated (Alasiri and Ogunkeye, 1999: Smil, 2000). Organic manures, when efficiently and

effectively used, ensure sustainable crop productivities by immobilizing nutrients that are

susceptible to leaching. Nutrients contained in manures are released more slowly and are

stored for a longer time in the soil, ensuring longer residual effects. Improved root

development and higher crop yields

(Sharma and Mittra, 1991: Abou El Magd et al., 2005). Manures are usually applied at higher

rates, relative to inorganic fertilizers. When applied at high rate, they give residual effects on

the growth and yield of succeeding crop (Makinde and Ayoola, 2008). Improvements of

environmental conditions as well as the need to reduce cost of fertilizing crops are reasons for

advocating use of organic materials (Bayu et al., 2006). Organic manures improve soil

fertility by activating soil microbial biomass (Ayuso et al., 1996). Applications of manures

sustain cropping system through better nutrient recycling (El-Shakweer et al., 1998). Manure

provides a source of macro and micro nutrient in available forms, thereby improving physical

and biological properties of the soil (Abou El-Magd et al., 2006). Long time use of cow dung

increased aggregate stability, pore space, bulk density and available water range (Vanlauwe et

al., 2001). Cow dung applied with inorganic nitrogen (N), increased soil pH and ameliorated

acidity (Olayinka and Ailenubhi, 2001). Continous application of cow dung increased soil

organic matter (Maestro et al., 2007), contents of available phosphorus and inorganic nitrogen

(Zhao et al., 2009), soil cation exchange capacity (Yadev and Prasad, 1992).

Crop plants require 17 nutrients to complete their life cycle. Macronutrients are required in

higher amounts compared to micronutrients. However, from the plant essentiality point of


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view, all the nutrients are equally important for plant growth. First three macronutrients (C, H

and O) are supplied to plants by air and water. Hence, their supply to plants is not a problem.

Hence, the remaining 14 nutrients should be present in the plant growth medium in adequate

amount and proportion for plant growth (Fageria, 2007). Research on foliar fertilization was

possibly started in the late 1940s and early 1950s (Fritz, 1978; Haq and Mallarino, 2000;

Girma et al., 2007). Unlike many technologies, its pace followed an unpredictable sequence

of events. In the early 1980s, studies on foliar application of fertilizers investigated for

selected crops, including cereals (Girma et al., 2007). The practice of applying liquid

fertilizer to plant leaves (foliar fertilization), is recently done in Nigeria, and it is gradually

gaining popularity among peasant farmers in many cultivated crops. This method of fertilizer

application has been reported to increase the growth, yield and quality of crops such as okra

(Selvi and Rani, 2000), soybean (Barge, 2001) and tomato (Alexander et al., 2004) among

others. Boost extra, is a foliar fertilizers that is commonly used by farmers in Nigeria. It is

manufactured by Candel Company and contains both the macro and micro nutrients in

various combinations (20% N, P and K, 0.075% Zn, Cu and Mg, 1.5% Fe, 0.35% Mn,

0.035% Bo and 0.012% Mo with pH range of 4.0-4.5).

The objective of this research is to examine the effect of soil application of cow dung

combined with foliar application of boost extra on growth and yield of okra in an ultisol,

Nigeria


Experiments were carried out at the student’s experimental field, Kabba College of

Agriculture, Horticulture section. It is located in the southern guinea savannah agro

ecological zone of Nigeria of 070 53

/N, 06

0 08E. Kabba has average rainfall of 1250mm per

year, temperature ranges from 180c - 32

0c. It also has the mean relative humidity (R.H) of

about 59% and four hundred and twenty seven meters (427m) above sea level, according to

Kabba College of Agriculture Metrological Station, field survey, (2011).

3. Field Work

The land was ploughed each year and harrowed with the aid of tractor mounted implements.

The experiment was laid out in a randomized complete block design (RCBD). The treatments

consisted of A = 3t/ha cow dung, B = 1 l/ha foliar + 2.5t/ha cow dung, C =1.5l/ha foliar +

2t/ha cow dung, D = 2 l/ha foliar + 1.5 t/ha cow dung, E = 2.5 l/ha foliar + 1t/ha cow

dung, F = 3t/ha foliar. Each year experiment was conducted using a single field having

dimension of 35 by 14m which was laid out into three blocks with 1m guard row between

blocks. Each block consists of six plots (5 by 4m) and 1m guard row between plots. Cow

dung manure was applied a week before planting. Okra variety Lady’s finger was used. The

seeds were treated with Peperomie pellucida leaf powder at 30g per 100 seeds as recommended

by Ibe et al. (1998) as quoted by Iyagba et al. (2012) to control disease causing organisms.

Three seeds per hole were planted on April 4th

in both years on the flat with a spacing of 60cm

x 25cm between and within the rows and later thinned to one plant/stand. The required quantity

of foliar spray of boost extra in formulated concentrations (20% N, P and K, 0.075% Zn, Cu

and Mg, 1.5% Fe, 0.35% Mn, 0.035% Bo and 0.012% Mo with pH range of 4.0-4.5) was


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applied to crop at 2, 4, 6 and 8 week after planting. The control treatment was sprayed with sole

water. Weeding was done manually at 3 and 8 weeks after sowing. Ten plants were

randomly selected at the centre of each plot for data collection.

4. Soil Sampling and Analysis

Before the commencement of the experiment in 2014, surface soil samples (O – 15cm depth)

were taken randomly from the experimental sites. The samples were bulked, air dried and

sieved using a 2mm sieve and analyzed for particle size, soil organic matter total N, P, K, Ca,

Mg and pH. The samples were taken, bulked and sub sampled as described by Carter (1993).

Particle size analysis was done using hydrometer method (Bouyoucos, 1962) while organic

matter was determined by the procedure of Walkley and Black using the di -chromate wet

oxidation method (Nelson and Sommers, 1982). Total N was determined by micro – Kjeldahl

digestion method (Bremner, 1965) and available P was by Bray – 1 extraction followed by

molybdenum blue colorimetry (Bray and Kurtz, 1945). Exchangeable K, Ca and Mg were

extracted by EDTA titration method (Jackson, 1962). Soil pH was determined in 1:2 soils –

water ratio using digital electronic pH meter. Cow dung was obtained form Obaba Farms,

Ponyan. Samples from these cow dung materials were taken for laboratory analysis in other

to determine nutrient composition. The samples were air dried, crushed, passed through a

2mm sieve before analysis. The samples were analysed for organic C, N, P, K, Ca and Mg

contents

Data taken included plant height at 50 % flowering, number of branches per plant, leaf area,

pod length, pod diameter, number of pods per plant, pod weight and yield (t/ha). The data

were subjected to Analysis of Variance (ANOVA) while the Least Significant Difference

(LSD) was used to separate treatment means following the procedure of Steel and Torrie

(1980)


5.1 Soil Analysis and Organic Material Used

The soil was 64.7% sand, 20.3% clay and 15.0% silt, slightly acidic (pH 5.8) and with a total

N content of 0.24%. Available phosphorus was 0.92 mg kg-1

and exchangeable potassium was

0.24 mg kg-1

, contents of Ca and Mg were 2.01 and 2.63 cmol/kg respectively (Table 1). The

chemical composition of cow dung revealed that it had a pH of 6.4 , total nitrogen content

of 3.6%,, 1.87% available P, 38.4% organic carbon, 3.14% available K and C/N ratio of

10.6% (Table 2). Fertilizer and manure are one of the most important inputs contributing to

crop production because it increases productivity and improve yield quality and quantity. The

general low ambient soil nutrient content made the soil suitable for study of response to

fertilizer.

5.2 Effect of Cow Dung and Foliar Fertilizer on Growth Component of Okra

Nutrients supplied in the form of cow dung and foliar fertilizer alone or in combination,

affect okra plant height, stem girth, number of leaves produced and leaf area (Table 3). Plants

treated with 1.5 l/ha foliar fertilizer plus 2t/ha cow dung had the tallest plants. Plots with

1l/ha foliar + 2.5 t/ha cow dung and 2l/ha foliar + 1.5 t/ha cow dung produced plants with

comparable heights. Sole application of both foliar fertilizer and cow dung had shorter plants

that were similar in height.


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Stem girth was affected by treatment (Table 3). Plants with combined application of cow

dung and foliar fertilizer had taller plants compared to plants with sole application of either

foliar fertilizer or cow dung. Plants with 1.5 l /ha foliar fertilizer + 2t/ha cow dung, 2l/ha

foliar + 1.5 t/ha cow dung had similar girth. The least stem girth was observed in plants

treated with 3t/ha cow dung alone.

Okra plant leaf area was highest in plant treated with 2l/ha foliar fertilizer + 1.5 t/ha cow

dung (Table 3). However, this was not significantly different from plots with 1 l/ha foliar +

2.5 t/ha cow dung, 1.5 l/ha foliar + 2 t/ha cow dung and plants with 2.5l/ha foliar + 1 t/ha cow

dung. Plants with foliar alone and cow dung residue alone were smaller and similar.

These results are in agreement with that of Alston (1979) who reported better vegetative

growth of crop with foliar application of N. Similar, Soyln et al., 2005; Kenbear and Sade

(2002) and Arif et al., (2006) reported significant increase in plant height, stem girth and leaf

area of crop with foliar application of different nutrients individually or in combination. The

result of this findings support the earlier results. The combination of cow dung materials with

foliar produced plants those were similar. This indicated that the high dose of organic

manures can be reduced by half and mix with reduced rate of inorganic fertilizer (foliar). The

nutrient use efficiency of crops is better with a mix of manure and inorganic fertilizer

(Murwira and Keirehmann, 1993). Nutrients seemed more available to Okra plants with the

mixes then either cow dung or foliar fertilizer alone.

5.3 Effect of Cow Dung and Foliar Fertilizer on Yield and Yield Components of Okra

Number of Okra fruit per plant, length of okra fruit and average weight of okra fruit were

lowest in plants treated with 3t of cow dung per ha. The highest fruit number was from

application of 2.5 l/ha foliar + 1 t/ha cow dung, this was not statically better than plants

with application of 2 l/ha foliar + 1.5 t/ha cow dung. Okra fruit yield was lowest for the

plants treated with cow dung alone (0.24 t/ha). Yield form plants treated with combined use

of cow dung and foliar fertilizer were similar and significantly higher than either plants with

cow dung alone or foliar application alone. Among the treatments, plants treated with 2 .0t of

foliar + 1.5t or cow dung recorded the greatest yield. However, the yield was not statistically

better than plot treated with 2.5l of foliar fertilizer + 1t or cow dung per ha = (Table 5).

Foliar fertilization gave significant higher fruits yields. Application of a mix or organic

manure (Cow dung) and inorganic fertilizer can be used to sustain okra in the tropics. A

similar trend of response had been earlier observed with other crops such as maize (Makinde

and Ayoola., 2008); Sorghum (Bayu et al., 2000); and wheat (Parvez et al., 2009).

6. Conclusion

Field experiment was carried out at Kabba on responses of okra yield to organic manure and

inorganic foliar fertilizer the experiment consist of six treatments which are 0 L/ha foliar +

3t/ha cow dung, 1.0L/ha foliar + 2.5 t/ha cow dung, 1.5 L/ha foliar + 2t/ha cow dung, 2 L/ha

foliar + 1.5 t/ha cow dung, 2.5 L/ha foliar + 1t/ha cow dung and 3L/ha foliar + 0t/ha cow dug.

The result shows that plot treated with 2.0L/ha foliar + 1.5 t/ha cow dung had the best

performance in yield and yield components in this study. It is therefore recommended that


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okra farmers should integrated foliar fertilizer (boost extra) at the rate of 2L per hectare with

cow dung at rate of 1.5t per hectare be used for okra production in the study area.

Table 1. Properties of soil before the experiment in 2014.

Particle Size Percentage (%)

Sand 64.7

Clay 20.3

Sit 15.0

Soil Texture Sandy Clay Loam

pH (H20) 5.8

Total N 0.24

Available p (mg/kg 0.36

Exchangeable Cation (Cmol/kg

K 0.24

Ca 2.01

Mg 2.63

Table 2. Chemical properties of cow dung used

Chemical properties (%)

Orgianic carbon 38.4

Total N 3.60

C/N 10.6

Phosphorus 1.87

Potassium 3.14

Calcium 1.22

Magnesium 0.31

Soil pH 6.4


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Table 3. Effects of cow dung and foliar fertilizer on growth component of okra (mean of two

years).

Treatment Average plant

height (cm)

Number of

leaves

Stem

diameter (cm)

Leaf area

(m2)

A 18.68c

16.02a

2.34a

90b

B 26.31ab

18.31a

2.63ab

105a

C 31.86a

18.42a

2.86a 108

a

D 29.41ab

16.94a

2.82a

112a

E 22.43bc

17.45a

2.61ab

94.6ab

F 18.64c

18.21a

2.53b

77c

In a column, figures bearing same letter(s) do not differ significantly at 5% level of

probability by DMRT. A = 3t/ha cow dung, B = 1 l/ha foliar + 2.5t/ha cow dung, C =1.5l/ha

foliar + 2t/ha cow dung, D = 2 l/ha foliar + 1.5 t/ha cow dung, E = 2.5 l/ha foliar +

1t/ha cow dung, F = 3t/ha foliar.

Table 4. Effect of application of cow dung and foliar fertilizer on yield and yield component

of okra (mean of two years).

Treatment Number of

fruit/plant

Length of

fruits (cm)

Fruit

diameter (cm)

Average

weight of

fruit (g)

A 6.2c

5.8a

3.2b

0.71b

B 7.4b

6.1bc

3.3ab

0.76b

C 7.6b

6.3b

3.l0b

0.91a

D 8.3a

7.4a

3.1b

0.94a

E 9.8a

6.2b

3.6a

0.85c

F 7.4b

6.5b

3.1b

0.78b






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Table 5. Effect of application of cow dung and foliar fertilizer on yield and yield component

of okra continued (mean of two years).

Fruit weight/plant (g) Yield t/ha

A 4.41c 0.24

c

B 5.40b 0.30

bc

C 6.92ab

0.38ab

D 7.80a 0.43a

E 7.48a 0.42

a

F 5.77b 0.32

b





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the journal.




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Evaluation of Fungicides Applied Via Drip Irrigation

for Control of Silver Scurf on Potato in Western

Washington McMoran, D. W.

Washington State University Skagit County Extension

11768 Westar Ln, Suite A

Burlington, WA 98233

(360) 428-4270 ext. 225

Buller, S. J.

Washington State University Skagit County Extension

11768 Westar Ln, Suite A

Burlington, WA 98233

(360) 428-4270 ext. 225

Received: November 3, 2015 Accepted: November 15, 2015 Published: November 30, 2015


Abstract

Silver scurf is an economically important disease on potato tubers caused by

Helminthosporium solani. Two studies were established near Mount Vernon, WA at

Washington State University NWREC on 20 May 2011 and 21 May 2012 in Skagit silt loam

soil. Five treatments included: penthiopyrad applied at 45 days after planting (dap),

penthiopyrad applied at 60 dap, azoxystrobin (Quadris; 9 oz/acre) applied at 45 dap, and

azoxystrobin applied at 60 dap, and a non-treated non-irrigated control. This study did not

control for the effect of irrigation, as azoxystrobin- and penthiopyrad-treated plots were

drip-irrigated while non-treated plots were not irrigated. The results of this study are therefore

limited but do suggest a reduction in silver scurf incidence and severity with no significant

impact on yield of potatoes when treated fungicide applied through drip irrigation systems.

Keywords: Potatoes, Silver Scurf, Helminthosporium solani, drip irrigation, chemigation

1. Introduction

Silver scurf is an economically important disease on potato tubers caused by

Helminthosporium solani (Figure 1). Silver scurf is a cosmetic skin blemish of the tuber


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that results in no internal damage (Powelson, 2008). The pathogen infects the tuber periderm

causing lesions that reduce marketability (Errampalli, 2001). Silver Scurf also overwinters in

soil, and it has been demonstrated that daughter tubers can become infected by soil borne

inoculum (Stevenson, 2004). Fungicides applied through drip irrigation were evaluated for

effectiveness in controlling silver scurf and as an alternative to preplant fungicide treatments

(maritime, Puget Sound region with mean temps of 58, 62, and 61°F and mean precipitation

of 1.84, 0.86, and 1.28 in. for Jun, Jul, and Aug respectively).

Figure 1. Silver Scurf (Helminthosporium solani) on Chieftian Potato

2. Methods

Two studies were established near Mount Vernon, WA at Washington State University

NWREC on 20 May 2011 and 21 May 2012 in Skagit silt loam soil. Treatments in both years

were arranged as a randomized complete block design with four replications. Treatments

were administered via drip irrigation tape. There were two treatments in 2011: penthiopyrad

(Vertisan; 24 oz/acre; emulsifiable concentrate) and a non-irrigated, non-treated control. In

2012, five treatments included: penthiopyrad applied at 45 days after planting (dap),

penthiopyrad applied at 60 dap, azoxystrobin (Quadris; 9 oz/acre) applied at 45 dap, and

azoxystrobin applied at 60 dap, and a non-treated non-irrigated control.

The seed lot had a mean of 100% incidence (percent of infected tubers per plot) and 79%

severity (percent of tuber surface infected) in 2011, and 70% incidence and 1.4% severity in

2012. Plots consisted of 10-ft rows on 38-inch centers with a 10-ft separation between plots.


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In 2011, non-irrigated non-treated and penthiopyrad-treated plots were randomized with other

treatments not evaluated for silver scurf and therefore not included in this study.

Azoxystrobin and penthiopyrad-treated plots were drip-irrigated (T-Tape US Model

506-12-220; low flow, 16 mm diam., 6 mil wall thickness, 9 in emitter spacing).

3. Management and Harvest

The trial was maintained both years with fertilizer and pesticide management practices

standard for commercial potato production in the area. Plants were topped (mowed) and then

sprayed with Diquat on 25 Aug 2011 and 31 Aug 2012. Harvest took place on 7 Oct 2011

and 10 Oct 2012 to ensure the maximum exposure to silver scurf inoculum in the soil. Each

tuber was weighed and graded. Twenty-five potatoes from each plot were evaluated

postharvest for 2 weeks, being careful to make sure the samples did not rot before evaluating

them for incidence and severity of silver scurf. Data were analyzed with one-way analysis of

variance (ANOVA) using PROC MIXED (SAS ver. 9.2). Homogeneity of variance was

assessed in all cases using Levene’s test in SAS. Treatment means were separated using

LSMeans (P = 0.05).

4. Results

In 2011, control plots had 38% silver scurf incidence, significantly higher than 4% for

penthiopyrad-treated drip-irrigated plots (P=0.004). Severity of silver scurf also was

significantly higher in control plots than penthiopyrad-treated drip-irrigated plots (6% vs.

0.8%, P = 0.0087). Total marketable yield of control plots was significantly less than

penthiopyrad-treated potatoes (6.24 vs. 6.83 tons/acre; P = 0.03). In 2012, incidence of silver

scurf again was higher for the control plots (96.92%) as compared to azoxystrobin-treated at

45 dap and 60 dap (83.00% and 81.45%, respectively) and penthiopyrad -treated plots at 45

and 60 dap (76% and 74%, respectively), although treatment differences were not significant

(Figure 2). Control plots also had higher silver scurf severity (11%), as compared to plots

treated with azoxystrobin at 45 and 60 dap (8% and 7.5%, respectively) and plots treated with

penthiopyrad at 45 and 60 dap (7% and 6%, respectively) (Table 1). However, mean severity

did not differ significantly among treatments. Although the control plots had lower yields

(7.56 tons/acre) than the plots treated with azoxystrobin at 45 and 60 dap (8.35 and 7.92

tons/acre, respectively) and plots treated with penthiopyrad at 45 and 60 dap (8.21 and 8.64

tons/acre, respectively), effect was not significant (Figure 3). This study did not control for

the effect of irrigation, as azoxystrobin- and penthiopyrad-treated plots were drip-irrigated

while non-treated plots were not irrigated. The results of this study are therefore limited but

do suggest a reduction in silver scurf incidence and severity with no significant impact on

yield of potatoes when treated fungicide applied through drip irrigation systems.


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Figure 2. Silver Scurf Severity 2012 Trial

Table 1. Average Silver Scurf Severity 2012 Trial

Row Labels Average of % Silver Scurf

Non-irrigated 11.32916667

Vertisan 45 DAP 6.666666667

Vertisan 60 DAP 6.061762422

Quadris 45 DAP 7.85

Quadris 60 DAP 7.4993083

Reel Big Gun 14.89347826

Average Total 9.050063719


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Figure 3. Average Yield 2012 Trial

Acknowledgement

Research was supported by the Washington State Potato Commission. Thanks to Dr. Debra

Inglis, WSU NWREC – Mount Vernon.

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(Helminthosporium solani) as an economically important disease of potato. Plant Pathology,

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Powelson, M. L., Randall, R.C. (2008). Managing Diseases Caused by Seedborn and

Soilborne Fungi and Fungus-Like Pathogens, Potato Health Management, second edition.

Stevenson, W. R., Loria, R., Franc, G. D., Weingartner, D. P. (2004). Compendium of Potato

Diseases, second edition.

Glossary

Dap: days after planting.


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Silver Scurf: disease affecting the skin of potatoes caused by the fungus Helminthosporium

solani.

Silver Scurf Severity: percent of tuber surface infected.



the journal.




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Possibilities of Using Semi-Transparent Photovoltaic

Modules on Rooftops of Greenhouses for Covering

Their Energy Needs

John Vourdoubas

Department of Natural resources and environmental engineering, Technological Educational

Institute of Crete, 3 Romanou str., 73133, Chania, Crete, Greece.

Tel: +30-28210-23070, Fax: +30-28210-23003.

Received: November 15, 2015 Accepted: November 22, 2015 Published: December 9, 2015


Abstract

Semi-transparent photovoltaic cells allow the transmittance of solar irradiance through them

and they have been used in building’s skylights and facades. Their use on rooftops of

greenhouses can result in electricity generation which can cover part or all of their energy

needs without affecting the growth of the plants. This also results in the decrease of cooling

requirements during the summer since less solar irradiance is entering the greenhouse and

lower CO2 emissions due to energy use in it. However, their current prices are high compared

with the prices of opaque PV cells. The purpose of the present work is to investigate the

possible use of semi-transparent PV modules placed on the roof of energy intensive

greenhouses in Crete-Greece in order to cover their energy requirements and sell the surplus

electricity into the grid. Two different cases have been studied where greenhouses of 1,000

m2 each cover their high heating needs using heat pumps and solid biomass. PV modules of

42.5 KWp can be placed on their roofs covering slightly less than 50 % of their surface

allowing enough solar irradiance to enter the greenhouse. In the first case the generated

electricity can cover more than 80 % of total energy needs and in the second all the energy

needs offering the possibility of selling the surplus electricity to the grid. However, the

current high prices of semi-transparent PVs do not favour their use by farmers since their

installation costs are high. Future financial support from the government could increase their

attractiveness for commercial applications in greenhouses.

Keywords: Semi-transparent photovoltaics, energy, electricity generation, greenhouses, cost,

environmental impacts


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1. Introduction

Greenhouses need energy to cover their requirements for heating, cooling, lighting and

operation of various electric devices. In Mediterranean region, greenhouse construction is

rather simple using light materials compared to central and northern Europe and their energy

requirements are relatively low. The majority of greenhouses use conventional energy sources

in order to cover their energy needs like heating oil, gas and electricity and the use of

renewable energy sources in them is rather limited. However, the necessity to mitigate

climate changes results in higher use of renewable energies in various sectors, including

greenhouses. The possibility of using agricultural greenhouses in order to grow various crops

and at the same time to generate energy, increasing farmer’s income has attracted little

attention until now. However, the advances of renewable energy technologies make it

possible since greenhouses can generate useful energy not only for covering their own needs,

but also for selling the surplus into the grid. This reduces the greenhouse gases emitted due to

fossil fuels use in them. The effect of installing opaque PV cells on the roofs of greenhouses

has been studied (Castellano, 2014). He found that, if they cover 25-50% of the horizontal

greenhouse roof, the growth of the plants was not affected. A feasibility study of

semi-transparent PV cells integrated on greenhouse covering has been presented (Gossu et.al.,

2010 ). PV moduli were connected to the grid, they had transparency in the range of 50-75%

and they were covering south oriented roof only. Shading level was 10-19%, nominal power

was 94-188 KWp and annual electricity production was 112,800- 260,200 KWh. PV cells

investment had a positive net present value and its payback time was 10-13 years. Limits and

prospects of PV cells on the rooftops of Mediterranean greenhouses has been studied

(Marucci et. al., 2013, Marucci et.al., 2012). They differentiated the light greenhouses with

cheap coverings used in Mediterranean region with the heavier greenhouses used in central

and northern Europe. They have investigated the possibility of using flexible semi-transparent

photovoltaics in order to generate an additional income to the farmers and to reduce the

cooling loads during the summer. An experimental evaluation and an energy modeling of a

greenhouse concept have been presented (Bambara et. al., 2015). With reference to the

vertical farm concept which requires less energy for heating and the same energy for cooling

compared with conventional greenhouses, semi-transparent photovoltaics generate electricity

and provide shading to them. A prototype semi-transparent PV cell for greenhouse roof

applications has been reported (Akira et. al., 2014). Two different types of cells have been

studied comparing electricity generation and greenhouse shading. The possibility of using

semi-transparent photovoltaics in Mediterranean greenhouses in order to generate electricity

and to shade them has been studied (Marucci et. al., 2013). The possibilities of using

renewable energies in order to cover all the energy needs of greenhouses have been

investigated (Vourdoubas, 2015). Two case studies have been analyzed where PV cells and

solid biomass were used in the first and PV cells and geothermal heat pumps in the second. A

performance analysis of greenhouses using integrated photovoltaic modulus supported by

computer simulation has been presented (Carlnini et. al.,2010 ). A study of the use of

semi-transparent PV films for Mediterranean greenhouses regarding the transmittance of the

PV films in the visible range and in the infrared range has been presented (Marucci et. al.,

2012 ). An assessment of a greenhouse cooling system using earth to air heat exchanger


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assisted with solar-PV cells has been made (Yildiz et. al., 2011). The Italian policy regarding

photovoltaic investments in greenhouses has been presented (Tudisca et.al., 2013).

Performance parameters of a heat pump used to meet the heating and the dehumidification

requirements in a greenhouse has been studied (Chou et.al., 2004). Trends and perspectives

for using renewable energy sources in the greenhouse industry have been discussed (Vox et.

al., 2008). Use of a ground source heat pump assisted with a solar energy system for heating a

greenhouse has been assessed (Ozgener et. al., 2007). Sustainable greenhouse horticulture in

Europe has been analyzed and suggestions for the use of various renewable energies like low

temperature geothermal energy, solid biomass, solar PV which should be supported have

been made (Campiotti et. al., 2012). An assessment of using various renewable energies like

geothermal energy, solar PV, solid biomass and geothermal heat pumps in greenhouses has

been reported (Vourdoubas, 2015). The author concluded that solid biomass and geothermal

energy are economically attractive, but solar-PV and geothermal heat pumps are not.

2. Semi-transparent photovoltaic cells

Semi-transparent photovoltaic cells allow solar irradiance to pass partly through them

presenting some benefits in various applications compared with opaque PV cells. Although

their prices are relatively high compared with opaque photovoltaic prices their use in

buildings (facades, skylights, etc) is increasing. Energy and cost parameters of crystalline

semi-transparent photovoltaics integrated in building’s skylights have been investigated (Li et.

al., 2009). Light transmittances of 20.1 % and 21.5 % have been found and power conversion

of 10.83%. Energy and cost parameters of applications of amorphous semi-transparent PV

cells integrated in office buildings have been also investigated (Li et. al., 2009). Solar

irradiance transmittance was estimated at 11.7 % and 11.4 % and the daily power conversion

efficiency at 6.3 %. Thermal and electrical performance of semi-transparent PV modules in

buildings has been analyzed (Park et. al., 2010). They found that a clear day temperature of

the PV modules placed on the building can reach 55o C which decreases the power output of

the photovoltaic cells. The behavior of semi-transparent photovoltaics in residential

applications has been studied (Wong et. al., 2008). They found that semi-transparent PV

modules placed on buildings result in power generation, increased heating during the winter,

increased indoor daylighting, but also in summer overheating due to solar irradiance

transmittance. Various characteristics of semi-transparent polycrystalline modules available

on the market are presented in Table 1.

Table 1. Characteristics of various semi-transparent polycrystalline modules

Nominal power per module 141-250 Wp

Area per module 0.71-2.22 m2

Weight 14.5-33.9 kg/m2

Power per m2 85-100 Wp/m

2

Efficiency 14-18 %

Transparency 16-37 %

Price 1-2.7 €/ Wp [142.4-282 €/m2]


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*Source: prices of various companies during 2015(BIC, ACROPOL, VIDURSOLAR,

ONYX). Prices depend on size and quantity ordered.

Various characteristics of semi-transparent thin film modules available on the market are

presented in Τable 2.

Table 2. Characteristics of various semi-transparent thin film modules

Nominal power per module 31.68-102 Wp

Area per module 0.72-2.30 m2

Weight 16.2-37.5 kg/m2

Power per m2 44-63 Wp/m

2

Efficiency 7-8 %

Transparency 10-14 %

Price 1-4.4 €/ Wp [45-193.6 €/m2]

*Source: prices of various companies during 2015 ( BIC, ACROPOL, ONYX), Prices depend

on size and quantity ordered.

3. Use of Semi-Transparent Photovoltaic Modules in Agricultural Greenhouses

Semi-transparent photovoltaics can be used in greenhouses in order to generate electricity for

covering their energy needs including electricity, heat and cooling and selling the surplus into

the grid. Since semi-transparent PV cells placed on the roof reduce the solar irradiance

incoming into the greenhouse, they also reduce the cooling load particularly in Mediterranean

greenhouses which have high cooling needs during the summer. For obtaining higher energy

efficiencies, it is preferable to place the PV modules on the rooftops in south orientation.

Since the use of the abovementioned semi-transparent PV modules on the top of greenhouses

will reduce the incoming solar radiation, it is better to cover only part of their upper surface

allowing enough irradiance to reach to the crop. Therefore, the use of semi-transparent

photovoltaic modules for covering rooftops of greenhouses may result in many benefits

including:

1. Generation of electricity in order to meet their energy needs.

2. Generation of surplus electricity which can be sold to the grid offering an additional

income to the farmers. In order to do so, the legal framework for selling solar-PV

electricity to the grid must exist.

3. Reduction of the cooling load during the summer since semi-transparent photovoltaics

reduce the incoming solar irradiance into the greenhouse

4. Decrease or zeroing CO2 emissions from the greenhouse due to energy use in it.


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The use of semi-transparent photovoltaics is of particular interest for modern highly

automated Mediterranean greenhouses (like hydroponics) growing crops which require

heating and cooling, therefore consuming a lot of energy. High annual solar irradiance in

Mediterranean region allows the generation of large electricity amounts from PVs which can

cover the greenhouse energy requirements selling at the same time the excess electricity to

the grid.

4. Energy Needs of Greenhouses

Agricultural greenhouses consume energy for heating, cooling, lighting, and operation of

various electric devices. Energy consumption depends mainly on the type of construction,

local climate and the cultivated crop. In Mediterranean region greenhouses construction is

light, climate is mild compared with central and northern Europe and their energy

requirements are relatively low. In general, in Mediterranean region heating requirements are

low, but cooling needs are higher compared with northern countries. Most of the energy used

in them is consumed for heating while only a small percentage of it for lighting and operation

of electric devices including cooling devices. Renewable energy sources including solar

energy, solid biomass and geothermal energy are not often used in greenhouses, which are

using mainly fossil fuels including heating oil and natural gas for heat generation and grid

electricity for other operations. There are currently not many commercial greenhouses using

solar thermal energy, geothermal energy and solid biomass for heat generation or solar PV

and wind mills for power generation, although renewable energies can cover all the energy

requirements of modern greenhouses (Vourdoubas, 2015). Energy requirements of a

greenhouse with flowers cultivation have been estimated at 14 KWh/m2 year for electricity

and 220 KWh/ m2 year for heating (Vourdoubas, 2015). Although typical light Mediterranean

greenhouses used for vegetables production do not require a lot of energy, there are other

types of greenhouses like hydroponics or those used for flowers cultivation which consume a

lot of energy. Usually maintenance of indoor temperatures at desired levels with proper

heating and cooling increases crops productivity, but also increases energy requirements and

energy cost in them.

5. Use of semi-transparent photovoltaic cells in greenhouses

High energy consumption agricultural greenhouses can meet their energy requirements with

semi-transparent photovoltaic cells covering partly their roofs as is shown in the following

examples. Two cases of energy intensive greenhouses located in Crete-Greece will be

examined. In the first, the greenhouse can cover its needs for lighting and operation of

various electric devices with electricity and its heating and cooling needs with a high

efficiency heat pump (which also consumes electricity). In the second case, the greenhouse

can cover its heating requirements with solid biomass and its needs for lighting, operation of

various electric devices and cooling with electricity. In the first case, the energy consumption

of the abovementioned greenhouse located in Crete-Greece in an area of 1,000 m2 is

presented in Table 3.


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Table 3. Energy consumption of an agricultural greenhouse using a heat pump for covering its

heating and cooling requirements

Surface of the greenhouse 1 000 m2

Electricity requirements excluding heat pump 14 000 KWh/year

Heating and cooling needs 220 000 KWh/year

Total energy consumption 234 000 KWh/year

Peak heating load 160 000 kcal/h

Efficiency of the heat pump 350 %

Power of the heat pump 53.1 KW

Electricity consumption of the heat pump 62 857 KWh/year

Total electricity consumption of the greenhouse 76 857 KWh/year

In the case that the semi-transparent photovoltaic cells will be placed on the roof of the

greenhouse, they can cover 500 m2 of its inclined surface (less than 50% of its horizontal

surface) in order to allow enough solar radiation to be transmitted inside the greenhouse.

Since crystalline photovoltaic cells have higher transmittance than the corresponding thin

film cells, they will be preferred. Assuming that the nominal power of the semi-transparent

PVs will be 85 Wp/m2, it is concluded that the overall power of the modules covering partly

the greenhouse will be 42.5 KWp. Taking into account that in Crete-Greece annual electricity

generation from PVs is approximately 1,500 KWh/KWp, the electricity generated from the

photovoltaic modules in the greenhouse will be 63,750 KWh/year. Therefore, the generated

electricity can cover a large percentage of their annual energy requirements. Characteristics

of the semi-transparent crystalline modules located on the roof of the greenhouse are

presented in table 4.

Table 4. Characteristics of semi-transparent crystalline photovoltaic modules which can be

placed on the roof of a greenhouse in Crete-Greece using heat pumps for heating and cooling


Surface of PV modules 500 m2

Power of PV modules 42.5 KWp

Annual electricity generation from the PV modules 63 750 KWh


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Annual electricity consumption of the greenhouse 76 857 KWh

Annual electricity consumption from the grid 13 107 KWh

Percentage of total electricity consumption which is

covered from the electricity generated from the PV

modules

82.95 %

The proposed semi-transparent PV modules placed on the top of the greenhouse covers less

than 50% of its horizontal surface and it can generate enough electricity in order to cover

more than 80% of total electricity needs of the abovementioned greenhouse which uses heat

pumps for its space heating and cooling. Therefore, a large part of its energy needs will be

covered with solar-PV energy reducing significantly its CO2 emissions due to energy use. In

the case that a higher percentage of its roof is covered with the PV modules, all its energy

requirements can be met with the generated solar electricity, but in this case less solar

irradiance will be transmitted inside the greenhouse. Also, in the case that the greenhouse is

less energy intensive with lower energy requirements, these can be fully covered with the

abovementioned semi-transparent photovoltaic modules.

In the second case where solid biomass is used for heating, the energy consumption of the

greenhouse is presented in table 5.

Table 5. Energy consumption of the greenhouse using solid biomass for heating and

electricity for lighting, cooling and operation of various electric devices.


Electricity requirements 18 000 KWh/year

Heating requirements 216 000 KWh/year

Total energy consumption 234 000 KWh/year

Characteristics of the semi-transparent PV modules placed on the roof of the greenhouse are

presented in table 6.

Table 6. Characteristics of semi-transparent crystalline photovoltaic modules which can be

placed on the roof of a greenhouse in Crete-Greece using solid biomass for heating.


Surface of the PV modules 500 m2

Nominal power of PV modules 42.5 KWp


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Annual electricity generation from the PV modules

63 750 KWh

Annual electricity consumption of the greenhouse

18 000 KWh/year

Electricity surplus which can be sold to the grid

45 750 KWh/year

Percentage of total electricity generation

which can be sold to the grid

71.76 %

Therefore, in the case that the abovementioned energy intensive greenhouse will cover all its

high heating requirements with solid biomass, the semi-transparent PV modules covering

partly its roof will generate more electricity than it needs and the surplus can be sold to the

grid, generating an extra income to the farmer. In this case the greenhouse will cover all its

energy requirements with renewable energies, solar energy and solid biomass and it will

generate also surplus electricity for the grid. Therefore, it will have negative CO2 emissions

to the atmosphere due to energy use in it.

6. Economic and environmental considerations

Current costs of crystalline and thin film semi-transparent photovoltaics are high and the total

cost of their installation including cells, inverters, cabling, controllers, labour and metal

infrastructure varies between 4-5 € per Wp. Therefore, the overall cost of installing

semi-transparent PV modules with nominal power 42.5 KWp on the roof of the greenhouse

will be 170,000-212,500 Euros. Due to their high investment costs, the price of solar

generated electricity is higher than the price of grid electricity. Therefore, future use of

semi-transparent PV cells in greenhouses needs a financial support from the governments in

the framework of renewable energies promotion in agriculture. On the medium and long term

their prices are expected to decrease due to higher production and technological

improvements. Two other factors should be assessed for the use of semi-transparent

photovoltaics in greenhouses together with the cost of generated electricity. : The first is

related with reduction of the cooling loads during the summer, which results in energy saving

in the greenhouse; the second is related with the reduction of CO2 emissions in the

greenhouse due to energy use in it. Use of semi-transparent PV cells in greenhouses will

result in the reduction of CO2 emissions due to energy use in them and, depending on the

specific case; it could result in zero or negative CO2 emissions. In the two abovementioned

cases the use of crystalline PV modules will result in the reduction of 63,049 kg of CO2

emissions provided that 1 KWh of grid electricity corresponds to 0.989 kg CO2.

7. Conclusions

Crystalline semi-transparent photovoltaic cells with high transmittance can be placed on the

rooftops of agricultural greenhouses in a way that they will not affect the growth of the

cultivated crops. Since they generate electricity, they can cover their energy requirements and

the surplus can be sold to the grid. They can also contribute in energy saving since they


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reduce the cooling load during the summer. Therefore, the farmers can have an income

additional to the cultivated crop decreasing also CO2 emissions due to energy use in the

greenhouses. Crystalline PV cells have higher transmittance than thin films and their use is

preferable on the rooftop of greenhouses. In Mediterranean region greenhouses do not require

a lot of energy due to their light construction and the mild climate. However, high solar

irradiance in the region allows the generation of high amounts of electricity from the PV cells.

The possibility of installing semi-transparent modules in two energy intensive greenhouses

located in Crete-Greece has been investigated and various estimations have been made. In the

first which uses heat pumps for its heating and cooling, PV modules of a surface of 500 m2

can be placed on the rooftop of the greenhouse with an area of 1,000 m2.The nominal power

of the PV modules is estimated at 42.5 KWp and their annual generated electricity can cover

more than 80 % of its total energy needs. In the second greenhouse which uses solid biomass

for its heating, the same PV modules can cover all its energy needs, generating a surplus

which corresponds to 71.76 % of its annual electricity generation and it can be sold to the

grid. The overall investment cost of the crystalline PV modules is estimated at

170,000-212,500 €. Therefore, current cost of semi-transparent PVs is still high and without

subsidies it is not expected to generate profits to the farmers. Further work is needed in order

to estimate the rooftop area in the greenhouses which can be covered with either crystalline

or thin film semi-transparent PV modules without affecting the growth of the crops and the

maximum temperatures reached in the cells which adversely affects energy generation. More

experimental data are also needed in order to estimate better the annual generated electricity,

as well as the effect of the semi-transparent PVs in greenhouse cooling.

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the journal.




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Sensory and Organoleptic Cup Attributes of Robusta

Coffee (Coffea canephora Pierre ex A. Froehner)

Ngugi K.

Department of Plant Sciences and Crop Protection, Faculty of Agriculture, College of

Agriculture and Veterinary Sciences, University of Nairobi, P.O Box 30197-00100, Nairobi,

Kenya

Aluka P.

National Agricultural Research Organization (NARO), National Coffee Resources Research

Institute (NaCORRI), P.O. Box 185 Mukono, Uganda

Bakomeza F., Neumbe B., Kyamuhangire R., & Ngabirano H.

Uganda Coffee Development Authority (UCDA), P.O. Box 7267 Kampala, Uganda

Kahiu Ngugi (Corresponding Author)

Email;[email protected]

Received: August 31, 2015 Accepted: September 16, 2015 Published: December 28, 2015


Abstract

Coffea canephora organoleptic cup attributes are the most important factors that define its

price in world markets. Determining the components that contribute to the diversity of

organoleptic characters will help in the improvement of these qualities in order to obtain

favourable markets. Two hundred and six genotypes from twenty one districts and two

research institutes were analyzed by a three member expert panel from Uganda Coffee

Development Authority using a 10 point descriptive scale and protocols from, The Coffee

Quality Institute of America (CQIA). The results revealed that the evaluators‟ organoleptic

cup trait ratings were significantly different (p< 0.05) for all attributes, reflecting a diversity

of cup interests. Four multivariate groups that were significantly different for fragrance,

aroma and flavour were formed offering diverse cup tests to different markets. A variety of

fine and commercial flavours were detected in ripe cherry and green roasted beans. Cup

balance contributed the highest regression coefficient (R2=0.90) to overall assessment while

fragrance/aroma had the least (R2=0.22). The above average rating of 75% for cup balance,

flavour, mouth feel, aftertaste, fragrance and aroma revealed that Ugandan Robusta coffees

were of high quality with a mild taste. The higher cup acidity among land races,„nganda‟ and


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„erecta‟ genotypes revealed that genotypes with high sugars and cup acidity could be selected

for from local germ-plasm. Coffee types and environmental factors such as soil texture,

altitude and location influenced the content and level of organoleptic cup attributes. A

diversity of flavours that exist among Uganda Robusta coffee and has so far remained

unexploited, would provide new marketing channels, enhance quality and earn the country

the much desired foreign exchange capital.

Keywords, Acidity, Aroma, Bitterness, Cup balance, Cup profiles

1. Introduction

Coffee being the most traded agricultural crop in the world has several quality classification

systems aimed at facilitating the market and value addition (Leroy et al., 2006a). Coffee

quality may be defined by widely varying characteristics such as physical appearance,

moisture content and organoleptic measurements. Organoleptic qualities are the most difficult

to define because they are based on consumer perception of subjective and sensory factors of

fragrance, aroma, taste and flavours. Two types of analyses have been used to describe

sensory evaluation; the hedonic organoleptic cup evaluation which is done by 60 assessors

representing a population sample where the preference is sought (Leroy et al., 2006a) and

the „ descriptive analysis‟ where trained assessors use a triangular test. In „descriptive

organoleptic cup analysis‟, trained assessors sample three types of coffee, whereby two cups

belong to the same coffee type and they have to discriminate the unique coffee type using

standard descriptors and define a cup profile (Leroy et al., 2006a). The market value of C.

canephora has persistently remained lower than that of C. arabica, because the Arabica

coffees are regarded as having more acidity and less caffeine than the neutral Robusta coffees.

Neither, the traditional dry and occasional wet processing methods nor the blending with other

coffees, has improved the Robusta coffee quality (UCTF, 2008; 2009). It is most likely that the

diploidy nature of C. canephora (2n=2x=22) compared to the tetraploidy level of C. arabica

(2n=4x=44) is responsible for the weak, neutral and pronounced bitterness in Robusta coffee

(Bertrand et al., 2003). Beverage quality, which is under the influence of the level of

biochemical compounds, is the factor that determines the price of coffee in the export markets.

Higher levels of chlorogenic acid and caffeine found in Robusta than in the Arabica coffee are

reported to impart unfavourable effects on the beverage quality whereas the lower trigonelline

and sucrose levels might be responsible for the neutral taste in the Robusta coffee (Clifford,

1985; Ky et al., 2001a, b).

Robusta grows naturally in Uganda and constitutes 80% of the total area under coffee. Most

of the Robusta genotypes are the local landrace cultivars, namely the „erecta‟ and „nganda‟

with improved varieties being grown on a limited scale. These coffee types offer a wide

genetic diversity for an array of many agronomic characteristics, including biochemical

compounds (Aluka et al., 2006). Nevertheless, cup profiles and organoleptic factors that are

critical to market prices have so far not been described (UCTF 2008; 2009).

For many years in Uganda, the organoleptic qualities of Robusta coffee were assessed

through the protocols developed for Arabica coffee. These protocols have been misleading

since the biochemical levels in the two species are different. The development of an


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independent sensory assessment for Robusta using a standardized protocol might help to unveil

cup traits that are specific to Robusta. A preliminary organoleptic analyses was conducted by

Aluka et al,. (2006) on 40 accessions of Robusta coffee. Earlier, Moschetto et al., (1996) had

established that there were significant differences in aroma, acidity, body and bitterness, between

the Guinean and Congolese Robusta coffee groups. There have been efforts in the past to

improve the genetic factors that influence cup quality in Robusta coffee. Needless to say, the

genotypic constitution of the biochemical compounds that determine quality are greatly

influenced by environmental factors such as altitude, rainfall and temperature (Cannell, 1985;

Clifford et al., 1985; Decazy et al., 2003). Montagnon et al., (1998) reported that biochemical

compounds and organoleptic cup traits could be improved without affecting yield. Positively

significant correlation coefficients between preference and factors such as acidity and aroma

have been reported in hybrids and commercial clones (Moschetto et al., 1996). In other

efforts, C. canephora quality improvement has been attempted through interspecific crosses

with C. congensis and C. liberica , the latter two species having larger bean size and a

better organoleptic quality (Moschetto et al., 1996 ; Yapo et al., 2003). In Uganda,

introduction of Arabusta hybrids between C. arabica and C. canephora with improved cup

qualities have not been adopted because of farmers continued preference for C. canephora

agronomic traits (Ky et al., 1999). Recently, the opportunity to introgress farmer and

consumer preferences from Arabica coffees into Robusta, appears greater than before, since

genetic maps for coffee quality have been developed and even Quantitative trait loci (QTL)

for biochemical compounds such as trigollenine and chlorogenic acid have been mapped

(Leroy et al., 2011;Campa et al., 2003).

But because, up to now, there has been no considerable research in Uganda to improve

Robusta organoleptic quality, this work set to analyze the structure of organoleptic cup

variability and to develop cup profiles that would be useful in demarcating potential market

niches.


2.1 Samples and Green bean preparation prior to cupping

A representative sample size of 206 Robusta coffee genotypes was selected from an initial

germ-plasm based on the Mahalanobis distances of major bean biochemical compounds that

influence cup quality derived from Near Infra Red Spectroscopy (NIRS) fingerprint (Aluka et

al., 2006). The 206 selected samples comprised of on-farm collections from 21 districts and

also from Kawanda and Kituza Robusta germ-plasm collections. Three other samples with

proven cup test characteristics from Uganda Coffee Development Authority (UCDA) were

included as controls. 300 grams of green beans were measured out and randomly selected for

evaluation.

A trained Robusta roaster from Uganda Coffee Development Authority (UCDA) roasted 10

clean green bean samples a day comprising of 55-120 grams each, at temperatures that

exceeded 200oC. The roasted coffee was left to cool until room temperature and stored

overnight in a cool dry place free from other odours and air flow to minimize contamination.

The silver skin was removed by rubbing softly and by use of a motorized blowing machine.


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Three separate measurements of 14.0 grams each from the roasted sample were ground to

medium size using a motorized grinding machine and the powder was kept in three different

cupping glasses (“rocks” glass with thick walls) and covered with a paper lid. The grinding

machine was constantly cleaned with a ground powder of the same sample in advance. The

three glasses with coffee powder were arranged in a triangular manner (1, 2 per row) for

evaluation. Roasted and green coffee of each sample were placed beside a triangle tip and

covered until after the cupping session was over to provide additional comments about the

cup based on bean appearance.

2.2 Data scoring

Data collection was scored by three professional Robusta coffee organoleptic cup testers from

Uganda Coffee Development Authority (UCDA). The cupping exercise used protocols

developed by ICO (1991), The Coffee Quality Institute of America (CQIA) and Specialty

Coffee Association of America (SCAA). Biochemical flavours that influence Robusta cup

quality were subjectively detected quantified and described using the cupping vocabulary.

Ground coffee fragrance was rated on a numeric scale of 1-10 (1=least perceived and

10=strongly perceived) based on the cupper‟s previous experience. To avoid staling and

oxidation, a ratio of 8.25 ± 0.25 grams of roasted ground powder per 150 ml of boiled water

was infused (brewed) within 15 minutes of grinding using boiled mineral water (94°C) from

Rwenzori Beverage Company Limited, Uganda, an International Standards Organization

(ISO) 9001-2000 certified Company. The water mineral composition in parts per million

(ppm) comprised of Sodium (9.2), Potassium (2.5), Fluoride (0.6), Chloride (4.0), Copper

(0.005), Magnesium (3.7), Iron (0.04) and Calcium (10.5) with a pH of 6.9. Organoleptic

cup attributes scored included aroma, flavour, aftertaste, salt/acid, bitter sweet, mouth feel,

aftertaste and balance. Defective unpleasant smell sensations were also recorded. Cupping

spoons and cuppers mouths were rinsed with boiled water between each coffee taste test.

Coffee aroma that pre-evaluates flavour and coffee brightness was perceived by sniffing

brewed coffee volatile compounds nasally, holding brewed coffee in the mouth and then

swallowing (http://www.coffeeresearch.org/science/aromamain.htm). After 5 minutes of

coffee brewing and sufficient cooling, brew flavour and aftertaste was assessed by passing the

liqour in the mouth. Mouth-feel or liquor body determined by fiber and fat content in the brew

was assessed by comparing the viscosity (weight) and slipperiness (texture) of coffee liquor

with that of pure water. The cup brew was perceived as either sweet or bitter and remarks were

scored. A brew with equal intensities of flavour, aftertaste, mouth feel and bitter/sweet was

quantified as a balanced cup. Aftertaste was the lingering remnant sensation experienced at the

back of the throat after swallowing and often changed over time. All flavour attributes in the

coffee brew were put together in a single personal judgment of one score guided by past

experience. Total scores were obtained by summing scores for the different primary attributes.

Each cup attribute was evaluated three times as the liquor brew cooled. The scores ranged from

1-100%. Cupping was kept to a maximum of 10 samples per session to minimize accumulation

of caffeine in the mouth that adversely alters cupping ability. After the cupping session, coffee

samples were uncovered and additional comments were scored based on appearance.

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2.3 Data analysis

Analysis of Variance (ANOVA) using XLSTAT version 2011.2.05 statistical program was

used to compare to organoleptic cup attributes. Means were separated with Tukey Honestly

Significant Difference (HSD) at 95% confidence. Shapiro-Wilk test in XLSTAT version

2011.2.05 statistical program (Addinsoft, Paris, France) was used to establish the

non-parametric distribution of the data. Spearman correlation coefficients were used to

quantify the strength between a pair of organoleptic cup attributes relationship measured on

ordinal scale at 5% significance level.

Since the sampled cup bean biochemical compounds were assumed not to be normally

distributed, the t-test was used to identify any significant differences between means.

Bartlett's specificity test in XLSTAT version 2011.2.05 statistical program established the

significant differences among correlated phenotypic parameters before performing Principal

Component Analysis (PCA). The PCA aggregated genotypes into high internal homogeneity

and high external heterogeneity using genetic distances estimated from the Euclidean straight

line method (Mohammadi and Prasanna, 2003). Varimax rotation in XLSTAT version

2011.2.05 statistical program was used to improve the principal component plot reliability

(Mohammadi and Prasanna, 2003).

Four organoleptic cup diversity groups derived from the PCA were clustered using the K

means analysis that categorized genotypes with related cup biochemical compounds beyond

PCA analysis (Mohammadi and Prasanna, 2003). A similarity index calculated the distance of

each organoleptic character from the average and determined whether the accessions were

from the same or different populations. Group means in K clustering were created based on

un-weighted paired group mean algorithms. The factorial step discriminant analysis was used

to distribute the K means spatially. The Mahalanobis and Fisher inter-group distances at 95%

probability were calculated using factorial step discriminant analyses to ascertain how the

populations were related. The confusion matrix was used to estimate the efficiency of

genotype placement among groups. The percentage similarity variance contributed by each of

the organoleptic cup attributes to the K means analysis group formation and mean group

abundance was estimated using Bray-Curtis distance measure.

3. Results

There were significant differences among evaluators for all organoleptic cup attributes as

shown in Table 1. Evaluators 1 and 2, rated accessions as significantly different for

fragrance/aroma (p<0.05) and poor cup balance (p<0.0001). Evaluators 2 and 3 detected

significant differences in low salt/acidity (p< 0.0001) in bitterness/sweetness (p< 0.0001) and

gave a total low score.

Evaluator 1 scored low for flavour, mouth feel, and overall cup taste whereas, Evaluator 2

detected significant differences in aftertaste and cup balance.


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Table 1. ANOVA of Robusta coffee organoleptic cup attributes by three evaluators

Cup attributes R2 v.r F pv Evaluators Coefficient F pv

Fragrance/Aroma 0.03 9.89 *** EV1,EV2 0.13,0.4 **,**

Flavour 0.03 10.66 *** EV1 -0.24 **

Aftertaste 0.05 15.85 *** EV2 0.23 **

Salt/acidity 0.20 76.90 *** EV2,EV3 -0.55,-0.54 ***,***

Bitterness/sweetness 0.19 70.39 *** EV2,EV3 -0.62,-0.59 ***,***

Mouth-feel 0.06 18.38 *** EV1 -0.30 **

Cup balance 0.14 50.73 *** EV1,EV2 -0.26,-0.22 ***,***

Overall 0.05 14.57 *** EV1 -0.25 **

Total score 0.09 31.73 *** EV2,EV3 -0.64,-0.67 **,***

Fstat;F2, 621

Key to Table 1 ; **, *** significant at p< 0.05, 0.0001 levels of probability

R2

=coefficient of multiple determination; v.r = variance ratio; F pv = Fisher‟s probability

value, p; coefficient=model coefficient; EV=evaluator; EV1=Fidel; EV2=Beatrice; EV3=Rita;

F Stat; F2, 621 = Fisher statistics, 2 degrees of freedom for factor (evaluators) =F2 and 621

degrees of freedom for cup attribute variables

The least rated organoleptic cup attribute was salt/acid with a minimum score of 6.0,

maximum value of 7.67 and mean rating of 6.80 (Table 2). The highest was for cup balance

with a minimum score of 6.5 and mean rating of 7.43 was for cup balance. Aftertaste and

fragrance/aroma had the most organoleptic cup variance range of 2.34 and 2.0 respectively

while bitter/sweetness and cup balance with variance ranges of 1.50 and 1.67 were the least

(Table 2) an indication that these two latter attributes were the most difficult to measure.

61-70% of all the accessions were of average grade, whereas 71-80% and 81-90% were of

high quality and fine grades respectively (Table 3).

Table 2. Variability of organoleptic cup attributes derived from 206 genotypes

Faroma Flavour Aftaste Salt/acid B/sweet Mth feel Cup-Bal Overall

Minimum 6.33 6.33 6.33 6.0 6.33 6.25 6.5 6.17

Maximum 8.33 8.33 8.67 7.67 7.83 8.17 8.17 8.33

Mean 7.32 7.42 7.36 6.80 7.09 7.30 7.43 7.41

V range 2.0 2.0 2.34 1.67 1.50 1.92 1.67 2.16

S.E 0.03 0.02 0.03 0.02 0.02 0.02 0.02 0.02

Key to Table 2: S.E-standard error ; Faroma, fragrance-Aroma; Aftaste-aftertaste;

Salt/acid-saltiness and acidity; B/sweet-bitter sweet ; Mth feel-mouth feel; Bal-cup balance

Only 50% of the accessions were of fair grade. There were no accessions that could be

described as belonging to the fine grade in terms of being high in salt/acidity and

bitterness/sweetness (Table 3) and even in the very good category, only 39 accessions could

be classified as being high salt/acidity, the majority being in the average category. In terms of


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bitterness/sweetness, about half of the genotypes could be categorized as average or very

good grades (Table 3). Most of the accessions belonged to the very high grade and scored

high for fragrance/aroma, flavour, aftertaste, mouth feel, balance and overall cup attributes

(Table 3).

Table 3. Organoleptic cup attribute score rating for 206 coffee genotypes

Organoleptic cup

attributes

Accession score rating

Fair Average Very good Fine

50-60% 61-70% 71-80% 81-90%

Fragrance/aroma 0 50 (32.7%) 153 (64.9%) 5 (2.4%)

Flavour 0 29 (14%) 173 (83.1%) 6 (2.9%)

Aftertaste 0 43 (20.7%) 155 (74.5%) 10 (4.8%)

Salt/acid 0 169 (81.3%) 39 (18.7%) 0

Bitter /sweet 0 107 (51.4%) 101 (48.6%) 0

Mouth feel 0 48 (23.1%) 156 (75%) 4 (1.9%)

Balance 0 19 (9.1%) 186 (89.5%) 3 (1.4%)

Overall assessment 0 27 (13%) 175 (84.1%) 6 (2.9%)

All organoleptic cup attributes had a significant and positive correlation with overall cup

assessment as indicated in Table 4. Cup balance (r=0.75), mouth feel (r=0.73), flavour,

(r=0.72) and aftertaste (r=0.62), were all highly positively significantly correlated with the

overall score but aroma (r=0.21) salt/acid (r=0.41) and bitterness/sweetness(r=0.54) though

positively correlated with overall score had lower coefficients. Fragrance/aroma was not

correlated with salt/acid and with bitterness/sweetness taste.

Table 4. Relationships among coffee organoleptic cup attributes in 206 genotypes

Faroma Flavour Aftaste Salt/acid Bsweet Mthfeel Balance

Faroma -

Flavour 0.32** -

Aftaste 0.27** 0.75*** -

Salt/acid 0.10 0.34*** 0.25** -

B/sweet 0.13 0.43*** 0.33*** 0.31*** -

Mthfeel 0.21* 0.60*** 0.54*** 0.31*** 0.37*** -

Balance 0.24** 0.61*** 0.57*** 0.51*** 0.42*** 0.64*** -

Overall 0.25** 0.72*** 0.62*** 0.41*** 0.54*** 0.73*** 0.75***


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Key to Table 4; *, **, *** = p> 0.05, 0.003, 0.0001 levels ;

Faroma-Fragrance/Aroma ;Aftaste-Aftertaste ; Mthfeel-mouth feel ;salt/acid-salt/acidity ;

Bitter/sweet-bitterness/sweetness

As shown in Figure 1, cup balance was regarded the most important organoleptic attribute in

overall cup assessment (R2=0.90). Flavour and mouth feel were rated second in overall cup

assessment. Fragrance/ aroma and salt/acidity were considered the least important attributes

in overall cup test respectively (R2=0.22, 0.42).

Figure 1. Contribution of organoleptic cup attributes to overall organoleptic cup assessment

using regression coefficients

„Erecta‟ and „nganda‟ landraces had a more salty/acidic organoleptic cup as compared to the

commercial and hybrid types shown by Figure 2 a. The „erecta‟ types had a more bitter/sweet

cup than hybrids, and it appears that salt/acidity and bitter/sweet were closely associated with

each other in all cultivars, a fact also confirmed by the positive significant correlation

coefficient in Table 3 (r=0.31). Altitude range of 1301-1400 metres above sea level (m a s l)

produced better aftertaste while elevation of 1401-1500 metres above sea level had reduced

aftertaste (Figure 2 b). Robusta coffee acidity mean values were highest at 1201-1300 metres

above sea level and lowest at 1501-1600 metres above sea level. Organoleptic cup balance

was highest at elevation 1301-1400 metres above sea level and lowest at 1501-1600 metres

above sea level.

(a) (b)

Figure 2. Comparison of mean organoleptic cup attributes for (a) coffee types, salty/acidity

and bitterness/sweetness (b) altitude ranges for aftertaste, balance and salty/acidity

Figure 3 a, b, shows PCA when considering only the variance of the organoleptic attributes in

the accessions. Factor 1 contributed the most variance (85.75%) while factor 2, contributed

only 15.40%. All the seven organoleptic attributes measured were on the positive side of the

PCA and none on the negative side. The accessions were separated into four diversity groups,

two on either side of the X axis. One group comprised of genotypes that were superior in

fragrance/aroma, flavour and aftertaste while directly opposite the second group genotypes


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were devoid of these attributes. A third group comprised of genotypes that had better cup

balance, salt/acidity and bitter/sweet attributes while opposite to this group, the genotypes, in

group 4 were limited in those attributes.

Figure 3(a) Principal Component Analysis of Robusta seven coffee organoleptic cup diversity

in 206 accessions (b) Factorial Discriminant Analysis of the groups

Differences in organoleptic cup attribute diversity in the four groups is shown in Figures 4a-g.

Except for fragrance/aroma where group 3 had the highest mean, the rest of the attribute

means were highest in group 1 and lowest in group 4 (Figure 4 a-g). Attribute group means

were placed either above or below the median. All attribute sub-groups variance and inter

quartile range values varied pointing out the different levels of variation present among in the

groups as shown in Figures 4a-g.


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a b c d

e f g

e f g

Figures 4 (a-g). Description of organoleptic cup attribute diversity groups

All Mahalanobis distances were greater than 3, implying that the groups were different

populations (Table 5). Equally, all the Fisher distances were significantly different, implying

that the formed groups had different organoleptic cup attributes. The Fisher as well as the

Mahalanobis data indicated that the longest distances was between group 1 and 4 while the

shortest was between group 2 and 3. The distance between group 1 and 2 was greater than

that between group 1 and 3.

Table 5. Mahalanobis and Fisher distances for organoleptic cup attribute groups

Fisher distances

Gp 1 2 3 4 1 2 3 4

1 0 0

2 11.81 0 46.18* 0

3 9.80 5.76 0 36.35* 22.31* 0

4 35.73 9.10 11.14 0 117.04* 30.97* 36.20* 0

Fisher‟s distances marked * were significant at p<(0.0001)

Figures 5 (a and b), further confirms the results of Figures 3 (a and b) and those in Table 6. In

Table 5 (a), factor 1 of the PCA, contributed 38.85% variance while factor 2 contributed

12.52% variance when altitude and age were also considered alongside accessions. Age and

altitude accounted for the organoleptic attributes variance in all accessions. Group 1

comprised of genotypes that were superior in all organoleptic cup attributes (Figure 5 a, b).

Group 2 had more acidity, sweet mouth feel while group 3 genotypes had better fragrance and

aroma, aftertaste, balance and flavour (Figure 5 a, b). Genotypes that were inferior in all


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organoleptic cup attributes were placed in group 4 (Figure 5 a, b). Although fragrance and

aroma increased with altitude, very high elevations and old trees had reduced organoleptic

quality. The longest distance was between group 1 to 4 while group 2 and 3 distances were in

between 1 and 4 groups.

Figure 5 (a). Organoleptic cup attribute relationships with altitude and tree age as shown by

the Principal Component Analyses (b) Organoleptic cup attribute groups from a factorial step

discriminant analysis

Table 6 also confirms that all the groups were correctly placed. Correct group placement

ranged from 88.10-96.23% with an average of 92.79% (Table 6).

Table 6. Estimated correct genotype group placement and pair wise distance comparison for

farm Robusta organoleptic cup attributes using confusion matrix

from \ to 1 2 3 4 Total % correct

1 50 2 2 0 54 92.59 %

2 1 54 4 0 59 93.22 %

3 0 1 52 0 53 96.23 %

4 0 1 0 41 42 88.10 %

Total 51 58 58 41 208 92.79 %

All the organoleptic attributes measured had more or less the same mean abundance that

ranged from 0.35 for cup balance to 0.47 for aftertaste (Table 7). But cup balance had a

cumulative percentage variance of 100 % meaning that the groups were distinctly different

for this trait and less so for aftertaste (Table 7).


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Table 7. Pooled contribution of variance for Robusta coffee organoleptic cup attributes and

mean group content derived from Bray-Curtis distance measure

Variance Mean group abundance

Variable Contribution Cumulative % 1 2 3 4

Aftaste 0.47 16.49 7.76 7.16 7.47 6.97

Mthfeel 0.43 31.43 7.66 7.22 7.36 6.91

Flavour 0.43 46.33 7.77 7.31 7.49 7.02

Faroma 0.42 60.95 7.45 7.18 7.48 7.16

Salt/acid 0.39 74.53 7.05 6.93 6.6 6.55

B/sweet 0.37 87.63 7.34 7.14 7.01 6.79

Cup Balance 0.35 100 7.72 7.45 7.37 7.09

4. Discussion

The protocol used to evaluate farm Robusta coffee organoleptic cup attributes was able to

detect, differentiate and segment organoleptic cup differences using their technical experience

(Tables 1; 2; 3). The significant differences found in evaluator organoleptic cup scores reflect

the subjective individual preference enriched by past experience. Individual cup taste

perception and preference for varietal specific characters such as acidity, body, aroma, flavour

and taste that constituted the nature and scoring of the brew implied that markets too vary and

are specific. Coffee drink preference is personal and consumers have a specific taste

according to their nationality, which makes it further difficult to define organoleptic cup

quality (Leroy et al., 2006). Furthermore, cup flavour in roasted coffee is reported to

constitute of over 800 multiple aromatic compounds and individuals perceive them

differently (Wintgens, 2004). Other factors that might also have contributed to liquor

differences among the accessions but were not measured in this study include, date of

harvesting, processing and storage.

Organoleptic cup characteristics were variable as shown in Table 2. Overall salt/acidity had

the least cup grading, an attribute that confers a low grade rating for Robusta coffee (Prakash

et al., 2005), despite some cultivars being achieving premium grade in all other attributes. Of

the 206 genotypes, 81% had average liquor salt/acidity of the usual good quality while 19%

were of very good premium grade.

Most of the assessed attributes were positively significantly correlated to each other as

shown by Table 4, meaning that there is opportunity to improve most of the desirable

organoleptic characters simultaneously. The overall cup quality was highly positively

significantly correlated with cup balance (r=0.75), mouth feel (r=0.73) and flavour (r=0.72)

than with aroma (r=0.32) and salt/acidity (r=0.41). However, cup acidity had positive

significant correlation coefficients with a sweet cup (r=0.31), good mouth feel (r=0.31), cup

balance (r=0.51) and overall cup (r=0.41 (Table 4) implying that acidity is an important

determining factor by consumers. Cup balance accounted for most to the total variance


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(Figure 1) making it, a major organoleptic trait in Robusta coffee and a priority in quality

improvement. This fact is further supported by the results in the PCA analysis (Figures 3a, b;

4 a-g and 5 a, b) and by the non-Euclidean dissimilarity cumulative variance shown in Table

7. Cup balance stands out as one trait that could be targeted for improvement as it

consistently demonstrated larger variance and diversity than the rest of the attributes.

Aroma/fragrance on the other hand was a lesser variable organoleptic trait. Aroma was not at

all correlated with acidity or sweetness in this study, though Moschetto et al., (1996) reported

linear correlation coefficients between preference and acidity and aroma in Robusta coffee

hybrids and in commercial clones. In this study, cup balance and aftertaste were at the highest

levels at altitudes between 1301-1400 metres above sea level whereas acidity was on the

decline (Figure 2 b). At higher altitudes of 1500 metres above sea level and above, cup

balance and acidity levels decreased but aftertaste levels were on the increase (Figure 2 b).

Decazy et al., (2003) also supported the idea that high sensory quality is associated with

altitude, which can be a criterion for selecting genotypes with high levels of salt/acidity

(Leroy et al., 2006b).

The „nganda‟ and „erecta‟ landraces had significantly more acidity and were more

bitter/sweeter than the research elite commercial and hybrid types (Figure 2). As suggested by

(Bertrand et al., 2006; Dessalegn et al., 2008), selection for vigour and larger seeds in the

hybrids and in the commercial types may have led to reduced variability for acidity and

sweetness.

The four categories of organoleptic cup attributes obtained from the PCA in Figures 3 and 5 a,

b) reflected the combined effect of genotype and genotype x environment variances. Group 1

(Figures 5, a, b) regarded as having the best brew had the highest ratings for fragrance/aroma,

flavour, aftertaste, acidity, sweetness, mouth-feel and cup balance. Group 1 was also

influenced by altitude whereas group 2 which was defined by mouthfeel, acidity and

bitter/sweetness was also mostly influenced by age. All the variances in groups 3 and 4 were

entirely due to altitude and age. Table 7 confirmed that indeed group 1 was the most superior

in organoleptic qualities and group 4, the least. The Malanobis and Fisher distances in Table 5,

show that the differences between group 1 and 4 that translate to genetic distances, again

implied that these two groups of genotypes are distantly related. The fact that four groups

comprised of genotypes from different locations that were far apart with varying ecologies

and crop husbandry practices, suggest that organoleptic variability was not restricted to any

site or location (Figures, 3,4 5; Table 4) but was influenced by both genotype and the

environment (Leroy et al., 2006b). The diverse organoleptic cup characteristics (Table 2)

coupled with a wide geographical distribution (Table 4; Figures 2; 3) provide immense

variability among Ugandan grown landraces of coffee that can not only be traded to diverse

markets but that can be selected for quality and other desirable agronomic traits.

5. Conclusion

Ugandan Robusta coffee was characterized into four distinct organoleptic cup groups using

the principle component and factorial step discriminant analyses. Organoleptic cup

differences based on Robusta coffee types were detected. About 84% of Robusta coffee

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produced in Uganda was of the premium grade while 13% was of the average grade and 3%

of the fine grade. Genotypes with low caffeine, high acidity and superior organoleptic cup

attributes exist among „nganda‟ and „erecta‟ land races and can be identified. Cup balance

was the most important organoleptic trait in determining overall cup.

Acknowledgement

We thank NARO for granting Pauline Aluka study leave to do this work and also

acknowledge financial support from USDA. We thank the Ugandan Robusta coffee farmers

for providing the ripe beans used in this study, field extension officers for identifying sample

farms and the evaluators, for their expertise on coffee cupping. We also thank Mr. Brian

Isabirye for his help in data analysis.

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2356-2361.

Dessalegn, Y., Labuschagne, M. T., Osthoff, G., & Herselman, L. (2008). Genetic diversity

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Resource-Use Efficiency of Honey Production in

Kachia Local Government Area, Kaduna-State, Nigeria

Ahmad,* Olohungbebe, S (Corresponding Author)

Department of Agricultural Economics and Extension, Faculty of Agriculture,

University of Abuja, PMB 117, Gwagwalada, Abuja, [email protected]

Alabi, Olugbenga. O (PhD)


University of Abuja, PMB 117, Gwagwalada, Abuja, Nigeria.

Daniel, P. O.


University of Abuja, PMB 117, Gwagwalada, Abuja, Nigeria.

Received: December 4, 2015 Accepted: December 24, 2015 Published: December 28, 2015


Abstract

This study examined resource-use efficiency of honey production in Kachia Local

Government Area, Kaduna, Nigeria. The primary data used for the investigation were

obtained using structured questionnaires administered to 50 producers. The data were

analysed using descriptive statistics, farm budget techniques, multiple regression analysis and

resource-use efficiency. Multiple regression analysis used to examine factors influencing

output of honey in the study area revealed that the coefficient of multiple determinations (R2)

of 0.59 which implies that 59% of the dependent variable in the model was explained by the

independent variables included in the model. Number of bee hives was significant at (p<

0.01).Estimated resource use efficiency revealed that number of bee hives and family labours

were underutilized. This study concluded that the bee farmers in the study area should be

given adequate training on rudiments of beekeeping.

Keywords: Resource-Use Efficiency, Honey Production, Kaduna State, Nigeria.

1. Introduction

In recent years, the production of honey through beekeeping is becoming popular among the

small scale farmers. This is due to the fact that the farmers have resorted to the need for


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income in diverse ways; thereby reducing the risk involved in depending on conventional

crop and animal production as the only source of income (Olarinde et al, 2008). Apiculture is

an aspect of the agricultural sector that has not been given much attention particularly at the

commercial level in the country (ICTA, 2004). Modern beekeeping is undertaken because it

serves as a source of food, employment and income (Olagunju and Adejumobi, 2003).

Beekeeping for honey production has been identified as one of the most lucrative enterprise

in many parts of the World. In United States of America, about 109,799,366.60 Kg of honey

worth $24, 200, 00.00 is produced each year; the same goes to the former USSR. Australia

produces 18,375,00051 Kg of honey and exports 5,898,313.08 Kg of it worth 900,000 pounds,

and Tanzania about 750,000 pounds worth of honey produce annually (Canadian Statistics,

2003). Presently in Nigeria, honey production is still at its developmental stage, though its

awareness was created for back early 1950s. This could be attributed to inefficient and

inadequate information on the enterprise and the belief that swarms of bees are a taboo and

signifies that a terrible mayhem is about to befall the individual whom it visits (Oyekuru,

2004). Generally, honey production enterprise attracts the attention of a greater percentage of

the populace these days because of its profitability and it is a visible complementary activity

for rural people and requires very little investments and in addition, produces quick returns

(Onyekuru, 2004).The demand for bee honey in Nigeria is on the increase, but organized

beekeeping as an enterprise is low (Eluagu and Nwani, 1999). ). In most parts of Africa it is

used for brewing honey beer and to a much lesser degree, as medicine; honey provides a

valuable food when it is consumed in its unprocessed state, i.e. liquid, crystallized or in the

comb. In these forms it is taken as medicine, eaten as food or incorporated as an ingredient in

various food recipes (Olstrom, 1983). Also, in addition to the thousands of "home-made"

recipes in each cultural tradition, honey is largely used on a small scale, as well as, at an

industrial level in baked products, confectionary, candy, marmalades, jams, spreads, breakfast

cereals, beverages, milk products and many preserved products (Olstrom, 1983). The broad

objective of the study is to evaluate the resource-use efficiencies of honey production in

Kachia Local Government Area, Kaduna State, Nigeria. The specific objectives are to:(i)

examine the socio-economic characteristics of honey producers in the study area, (ii) estimate

the costs and returns of honey production in the study area, (iii) evaluate resource-use

efficiency of honey production in the study area.

2. Methodology

2.1 The Study Area

The study was undertaken in Kaduna metropolis. Kaduna State is located in the mid-central

portion of the Northern parts of Nigeria, approximately between Latitudes 10020 N and

Longitudes 70 45’ E and covers an area of 45, 71.2 Square Kilometres. It has a population of

6,113503 (2006 census figures) and a population density of 130 people per Square Kilometre.

It accounts for 4.3% of Nigeria’s total population. The mean annual rainfall in the southern

part (in places like Kafanchan and Kagoro) is 1,1016mm. The State experiences a tropical

continental climate with two distinct seasonal climates, dry and rainy seasons. The wet

season (May to October) is heavier in the southern part of the state than the northern part.

Agriculture constitutes the largest occupation of the people with many citizens participating


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in small scale farming. The State is a major region of animal husbandry. Major foods and

cash crops produced in the State includes, cotton, groundnut, guinea corn, millet, ginger,

tobacco, beans etc. (www.google.com).

2.2 Sampling Technique and Sample Size

For the purpose of this study, two communities were visited in Kachia Local Government Area

in Kaduna State, specifically Katari and Azara Areas of the State. Simple random sampling

technique was used for this study. Twenty-five (25) respondents were randomly selected from

each of the villages giving a total sample size of fifty (50) respondents.

2.3 Method of Data Collection

The data used for this study consist mainly of primary data. Data were obtained through the use

of questionnaire. The questionnaire was used to collect information on the socio-economic

variable of the beekeepers, their management practices, the cost incurred, their income,

revenue, efficiency and production data. The interview was carried out during their group

meeting. The survey was carried out in 2014 production season. The production cycle as stated

by the beekeepers take place within a year (9-12 months).

2.4 Methods of Data Analysis

The analytical tools that were used in this study to achieve stated objectives include the

following:

(i) Descriptive Statistics

(ii) Farm Budgeting Technique

(iii) Multiple Regression Analysis

(iv) Resource-Use Efficiency

2.4.1 Descriptive Statistics

This analytical tool was used to examine the socio-economic characteristics of honey

producers which include; their gender, marital status, household size, age, level of education

etc. Statistical package for Social Science (SPSS) was used. Descriptive statistics involve the

use of mean, frequency distribution tables, percentages etc. This was used to achieve objective

one (1).

2.4.2 Farm Budgeting Technique

Costs incurred and returns in honey and beeswax production were estimated based on

prevailing market price. Costs of production include; raw materials such as bucket, container;

expenses on land (rent or lease); labour (wages) etc.

The Net Farm Income (NFI) is calculated thus:

NFI=TR-TC

Where, NFI = Net Farm Income (N)

http://www.google.com/


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TR = Total Revenue from Honey and other Hive Products

TC = TFC + TVC

TR = P.Q

Where, P = Price of honey produced in Naira per Litre.

Q = Output of honey produced in Litre

This was be used to achieve specific objective two.

2.4.3 Multiple Regression Analysis

The model is implicitly stated as:

Y = f(X1, X2, X3, X4, Ui)

Where,

Y =Output of Honey Produced (Litres)

X1 = Number of Hives set by the Beekeeper (Units)

X2 = Hired Labor (Mandays)

X3 =Family Labour (Mandays)

X4 = Cost of Bating Materials (Naira)

Ui = Random Error Term/Disturbance Error Term.

Explicitly, the functions are stated as:

Y = a + bX1 + CX2 + dX3 + eX4 + Ui (Linear)

Log Y = a + b LogX1 + cLogX2 + dLogX3 + eLogX4 + Ui (Double-Log)

Y = a + bLog X1 + cLogX2 + dLogX3 + eLogX4 + Ui (Semi-Log).

This was used to achieve objective two (2).

2.4.4 Resource-Use Efficiency

Resource-Use Efficiency of honey production was estimated using the formula,

MVP = r

MFC

Where,

MVP = Marginal Value Product

MFC = Marginal Factor Costs (N)

r= Resource-Use Efficiency of Honey Production.

If r = 1, it indicates that the resource-use efficiency of honey production is utilized.


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If r > 1, it indicates that the resource-use honey production is underutilized.

If r < 1, it indicates that the resource-use efficiency of honey production is over

utilized.

MVP = MPP x Pq

Where,

MPP = Marginal Physical Product

Pq = Price of Unit Output (N)

This was used to achieve objective three (3).


Table 1 show that ninety (90) percent of honey producers in the study area were male, thus

confirming the notion that bee keeping is a hazardous occupation, and about ten (10) percent of

the beekeepers were women (Babatunde et al, 2007). Furthermore, seventy-eight (78) percent

of honey producers were married. Most of the respondents were educated (Lawal, 2002).

Sixteen (16) percent of honey producers were single, four (4) percent were widow and only two

(2) percent were widower. About ninety (90) percent of honey producers had less than10

members as household. This is in line with finding of Mbah (2012) who opined that beekeepers

rely so much on household labour for beekeeping activities. However, under subsistence

agriculture, much reliance is often placed on the strength of household to supply the much need

farm labour in the absence of mechanized equipment. Thus, the larger the household size, the

higher the supply of household labour. Also, about ten (10) percent had between 11-20

members of household size who worked on the farm of honey. Honey producers in the study

area are middle-aged. About eighty-eight (88) percent of honey producers were less than fifty

(50) years of age, which means they are still in their active productive age, which signifies

increase in the output of honey as also observed by Mbah (2012). The study also revealed that

twelve (12) percent of honey producers are between fifty-one (51) and seventy (70) years of

age. The results further showed that majority (42 percent) of honey producers in the study area

are into farming, and sixteen (16) percent are civil servants. This corroborates with the views of

Folayan et al (2013) that the both (farmers and civil servants) diversify into honey production

to ensure optimum and continuous flow of income. This study further shows that students (10

percent) are among honey producers in the study area. Furthermore almost all the beekeepers in

the study area are literate. About ninety –eight (98) percent of honey producers had formal

education. This implies that beekeeping is practiced mostly by educated people and therefore,

adoption of modern beekeeping techniques would not be a problem. Studies have shown that

education is positively related to adoption of innovation (Balogun, 2000). Nevertheless, only

two (2) percent of the honey producers had non-formal education. Also, about ninety (90)

percent of honey producers had less than 20 years of experience in honey production. This is in

line with finding of Tijani et al (2011) who observed that the higher the numbers of years spend

in farming by a farmer, the more he becomes aware of new production techniques, thereby

increasing the level of productivity. The results of the farm budgeting analysis are presented in


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Table 2. Costs incurred on various resources used and profits obtained from the sale of the

produce were estimated based on the market price at the period under consideration 2014

family season. The gross income was calculated by multiplying the total quantity of honey

produced by the price of output sold. The gross income of the honey producers was N363, 672.

The total cost was put into consideration by adding the total variable costs which was

N50071.49 and the total fixed cost was N153259.73 which gave the total cost of N6, 953,820.

The total variable costs include: cost of land cleaning, cost of bating materials, cost of chemical,

cost of hired labour, and cost of family labour to mention a few. Also, total fixed cost include:

cost of uniform, cost of boot, cost of container etc. The net income was estimated to be N

17,980,268.78. The factors influencing output of honey produced were expressed in the

econometric multiple regression analysis Table 3. The variables examined in the model include;

number of hives (colony) X1; hired labour (mandays) X2; family labour (mandays) X3; and cost

of bating materials (N) X4. Double-log functional form was selected as the lead equation. In the

lead equation, numbers of hives X1 were significant at 1% probability level. The coefficient of

multiple determinations (R2) is 0.529. This implies that 52.95 of variations in the dependent

variable were explained by variations in the explanatory variables included in the model. An

estimate obtained from the double-log functional form is direct elastics. For instance, the

estimated coefficient for number of hives was 0.895. This implies that if numbers of hives are

increased by 1% holding other variable constant, the output of honey produced will increase by

0.895. Estimated resource use efficiency shows that number of bee hives(X1) and family labour

(X4) are underutilized (Table 4).

Table 1. Socio-Economic Characteristics of Honey Producers in Kachia Local Government

Area, Kaduna State.

Variable Frequency Percentage (%)

Sex

Male 45 90.00

Female 05 10.00

Marital Status

Married 39 78.00

Single 08 16.00

Widow 02 04.00

Widower 01 02.00

Household Size

1-10 45 90.00

11-20 05 10.00

Age (Years)

21-30 09 18.00

31-40 15 30.00

41-50 20 40.00

51-60 04 08.00


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61-70 02 04.00

Major Occupation

Farming 21 42.00

Civil servant 08 16.00

Business 03 06.00

Caterpillar mechanic 01 02.00

Honey producers & Business 04 08.00

Farming and business 01 02.00

Honey producers & Farming 03 06.00

Clergy 01 02.00

Mason 01 02.00

Student 05 10.00

Student and Business 01 02.00

Honey producer 01 02.00

Mode of Honey Production

Full - Time 33 66.00

Part - Time 17 34.00

Education Level

Primary 09 18.00

Secondary 19 38.00

Tertiary 21 42.00

Non- Formal 01 02.00

Experience of Honey Production (Years)

1-10 25 50.00

11-20 20 40.00

21-30 03 06.00

31-40 02 04.00

Total 50 100.00

Source:-Field Survey, 2014.

Farm Budget Technique

Table 2. Costs and Returns Analysis of Honey Production in the Study Area.

Items Mean value (N)

A Variable Cost

(i) Cost of Land Clearing 15,636,36.00

(ii) Cost of Bating Material 2,654.00

(iii) Cost of Chemical (Additive) 410.00

(iv) Cost of Labour (Hired) 3,525.00


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(v) Labour Cost of Children 2,519.05

(vi) Labour Cost of Women 2,645.83

(vii) Rentage Cost of Land 22,681.25

(B) Total Variable Cost 50,071.49

(C) Fixed Cost

(i) Cost of Hive 30,491.53

(ii) Cost of Uniform 14,157.14

(iii) Cost of Boot 7,418.75

(iv) Cost of Machete 4,296.88

(v) Cost of Basin 13,903.45

(vi) Cost of Wheelbarrow/Motorbike 36,903.45

(vii) Cost of Container 27,501.00

(viii) Cost of Bucket 8,503.19

(ix) Cost of Plastic Bowl 20,060.71

(D) Total Fixed Cost 153, 259.73

Total Cost (B + D) 203, 333. 22

Gross Income 363,672.00

Net Income 17, 980, 268. 78

Source: - Field Survey, 2014.

Multiple Regressions Analysis

Table 3. Multiple Regressions Analysis (Double Equation as Lead Equation)

Variables Regression Coefficient

Standard Error

t-value

Constant 3.279 0.928 3.532

X1(Number of Bee Hives)

0.895 0.130 6.867***

X2(Hired Labour) -0.257 0.201 -1.277 X3(Family Labour) 0.053 0.229 0.232 X4 (Cost of Bating Material)

-0.014 0.120 -1.116

R2

Value 0.529 Adjusted R

2 Value 0.487

F – Value 12.615***

Source- Field Survey, 2014.


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Table 4. Estimated Resource- Use Efficiency of Honey Production in the Study Area.

Resource MPP MVP MFC Allocative

Efficiency (r)

Number of Bee Hives (X1) 15.59 11310.55 725.5 15.59

Hired Labour (X2) -118.9

8

-86319.04 725.5 -118.98

Family Labour (X3) 135.27 98138.39 725.5 135.27

Cost of Bating Material (X4) -0.009 -6.66 725.5 -0.009

Source: Field Survey, 2014


The study revealed that the honey production in the study area is a viable and profitable

enterprise. Based on the results, the following recommendations are made: the farmers in the

study area should be given adequate training on rudiments of beekeeping. This will ensure

proper understanding of modern equipment and adopt technology capable of increasing not

only the profitability of the enterprise but also make efficient use of bee resources.

Establishment of bee farmers’ cooperation association for annexing financial aids, marketing

information and inputs from government and non-government organisations .Creating a

market channel that will take care of commensurate price for product of new beekeeping

enterprise .Government at all levels should endeavour to stimulate farmers to boost honey

production by providing and subsidise if need be, necessary infrastructures and enabling

environment which provide impetus that will ease people transition from traditional to

modern beekeeping easy.

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the journal.



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Improving Seed Potato Leaf Area Index, Stomatal

Conductance and Chlorophyll Accumulation Efficiency

through Irrigation Water, Nitrogen and Phosphorus

Nutrient Management

Gathungu Geofrey Kingori

Department of Plant Sciences, Chuka University, P. O. Box 109 – 60400, Chuka, Kenya.

Aguyoh Joseph Nyamori

School of Agriculture, Natural Resources & Environmental Studies, Rongo University

College, P.O. Box 103-40404, Rongo, Kenya.

Isutsa Dorcas Khasungu

Chuka University, P. O. Box 109 – 60400, Chuka, Kenya and Department of Crops,

Horticulture and Soils, Egerton University, P. O. Box 536-20115, EGERTON, Njoro, Kenya.

Received: December 17, 2015 Accepted: January 4, 2016 Published: January 19, 2016

doi:10.5296/jas.v4i1. 8908 URL: http://dx.doi.org/10.5296/jas.v4i1.8908

Abstract

A study was conducted in a Rainshelter (RTrial) at Horticultural Research and Teaching Farm,

Egerton University to determine the effect of integration of irrigation water, nitrogen (N) and

phosphorus (P) application on seed potato leaf area index (LAI), stomatal conductance and

chlorophyll content. The treatments arranged in a split-split plot layout in a completely

randomised block design, consisted of three irrigation water rates (40%, 65% and 100% field

capacity), four N rates (0, 75, 112.5 and 150 kg N/ha) supplied as urea (46% N), and four P

rates (0, 50.6, 75.9, 101.2 kg P/ha) supplied as triple superphosphate, replicated three times

and repeated once. During the growth leaf area, stomatal conductance, and chlorophyll

content were measured. Data collected were subjected to analysis of variance and

significantly different means separated using Tukey’s Studentized Range Test at P≤0.05. Leaf

area index was greater with high irrigation water at 100%, N at 150 kg N/ha and P at 101.2 kg

P/ha, which was 2.6 and 1.3 at 51 days after planting (DAP) and 3.5 and 3.1 at 64 DAP.

Furthermore, low irrigation water rate at 40% together with low N and P rates of 0 kg N/ha

and 0 kg P/ha had the least LAI, which was 0.28 and 0.19 at 51 DAP and 0.28 and 0.24 at 64

DAP both in RTrials I and II, respectively. Subjecting potato to 100% compared to 40%


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irrigation rate increased stomatal conductance at 87 days after planting (DAP) by 32.82 and

31.99 mmolm⁻²s⁻¹, leaf chlorophyll content index by 16.2 and 16.5, 19.8 and 19.6, and 15

and 20.3, when integrated with high compared with low N and P application rates at 59, 73

and 87 DAP, in RTrials I and II respectively. Irrespective of N and P rates LAI, stomatal

conductance and chlorophyll content were significantly greater with high irrigation water at

100% followed by 65% and was lowest with 40% irrigation water rate.

Keywords: Potato, Irrigation, Nitrogen, Phosphorus, Leaf, Conductance, Chlorophyll

1. Introduction

Plant growth and development and consequently yields are influenced by many stress factors,

such as low or high temperatures, water deficit, excessive radiation within the

photosynthetically active range, mechanical injuries, gaseous contamination, phytotoxins,

herbicides applied, or intensive fertilization (Sawicka et al., 2015). Although potato is the

most widely distributed crop in tropical and subtropical zones of the world (Burhan et al.,

2007), its productivity and quality are inadequate due to disjointed investigation of the many

factors that hinder them. These factors include poor seed potato tuber quality, irrigation

management, mineral fertilization, insect pest and disease forecasting, as well as poor

planting dates and storage conditions (Walingo et al., 2004). Potato growth depends on a

supply of plant nutrients, such as nitrogen (N), phosphorus (P) and potassium (K), each with

a specific function for plant growth and lack of them results in retarded growth processes and

reduced yields (van der Zaag, 1981).

In Kenya, low application of N and P under continuous cultivation is a major constraint that

leads to poor potato growth and productivity. Mineral nutrients are essential for healthy plant

growth, optimum yield, and better economic returns. Potato plants have a high demand on

soil nutrients and their proper management is one of the most important factors required to

obtain maximum tuber yield (Braun et al., 2015).Therefore, it is important to maintain high

soil fertility through balanced nutrient supply (ICIPE, 2006). Optimum use of mineral

fertilisers by crops is essential for sustainable agriculture and nutrient use efficiency

comprises both uptake efficiency and utilisation efficiency (Hawkesford, 2012). The aim of

fertiliser application is to feed the soil, which in return feeds the plant. Another factor that has

limited seed potato production in many parts of Kenya is unreliable rainfall. Potato is

sensitive to soil water deficit (Bowen, 2003; Kiziloglu et al., 2006) and is often considered as

a drought sensitive crop and its sustainable production is threatened due to frequent drought

episodes (Obidiegwu et al., 2015). Plant needs for water and nutrients are interdependent, as

a good water supply improves the nutritional status of crops, and adequate nutrient supply

saves water (Roy et al., 2006).

Effective management and proper coordination of N, P and irrigation water can increase

potato productivity through their efficient use. Most work on seed potato tuber quality has

focused on effect of diseases and little attention has been given to the effect of nutrient and

water management in different genotypes. Irrigation has been increasingly employed to

curtail effects of drought (Thompson et al., 2007) in other countries, but in Kenya potato

farmers rarely use this practice due to cost and lack of knowledge, among other factors.

Farmers in the informal seed production sector are inconsistently and inappropriately


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applying N and P fertilisers due to lack of information on their combined effects on potato

growth and development for high quantity and quality seed potato tuber production. In the

long-term misapplication influences potato leaf area expansion, stomatal conductance,

chlorophyll content, seed potato yield and quality, as well as market and consumer values.

Conductance of the diffusion and the transport of CO2 play a major role in determining

CO2:O2 ratio (Biman et al., 2014) which is crucial in determining photosynthetic rates. The

lower stomatal conductance inhibits water losses, thus helping the plants to cope with the

drought in better way (Naveed et al., 2012) although it inhibits the CO2 fixation, resulting in

lower productivity (Cornic 2000; Chaves et al., 2002). Nitrogen is an important nutrient since

it has a positive effect on chlorophyll concentration, photosynthetic rate, plant height and dry

matter accumulation and higher potato tuber yields (Sinfield et al., 2010; Tremblay et al.,

2011). Chlorophyll is one of the most photochemically active compounds in photosynthesis

and its determination provides useful information concerning the photosynthetic status

(Yadav et al., 2010)

Where fertilization is done, farmers do not supply irrigation water to the crop to enable it

utilize the nutrients efficiently and realize better returns. As the need for food production

increases with increasing population growth, it is important that strategies are developed to

enhance the nutrient uptake and utilization efficiencies (Liu et al., 2012). This can be

achieved through combined investigation of N, P and irrigation water effects. Furthermore, in

the face of increased fertilizer and irrigation water cost and stringent environmental

regulation, there is a critical need to improve N, P and water use efficiency to ensure seed

potato production remains sustainable. Excess water can be a cause of nutrient losses, and

insufficient water at a critical stage can limit growth and yield, and timing of water

application influences nutrient use efficiency (Roy et al., 2006). Therefore, water

management and/or rainfall are among the most important factors determining yield and

quality of potatoes (DAFF, 2013).

There is therefore need to evaluate, document and disseminate comprehensive management

packages and knowledge on leaf expansion capacities, stomatal conductance and chlorophyll

content in environments characterized by varied rainfall and nutrient amounts. The ability

of leaves to expand, stomatal conductance and the chlorophyll concentration determined by

water and nutrient supply forms critical determinants of leaf photosynthetic capacities which

influences the photoassimilates available for plant growth and consequent yield. Study of

tolerance of seed potato to varying irrigation water and mineral nutrient supply rates will

assist producers in predicting expected potato growth and tuber yields under their prevailing

agro-ecological conditions.


2.1 Potato Growth in the Field

Potatoes were planted in a rainshelter at the Horticultural Research and Teaching Farm of

Egerton University, Njoro between 19th

August and 19th

December 2011 (RTrial I) and the

trial was repeated between 5th

April and 6th

August 2012 (RTrial II). Potatoes were planted to

determine the effect of irrigation water, nitrogen (N) and phosphorus (P) application rates on

leaf area index, stomatal conductance, and chlorophyll content of seed potato. The three


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factors were tested in a split-split plot design with the irrigation water rate assigned to main

plots, N to subplots and P to sub-subplots. The treatments were replicated three times. The

treatments consisted of three irrigation water (W) rates (40%, 65% and 100% field capacity

[FC]), applied throughout the potato growth period through drip tube lines. Water was

supplied through irrigating only the root zone, leaving the inter-row spaces dry. A

WaterScout (Model SM 100 Sensor) connected to 2475 Plant Growth Station (Watch Dog

Model, Spectrum Technologies, Plainfield, IL 60585, USA), which is applicable between 0%

to saturation was used to indicate the need for irrigation.

Nitrogen (N) was supplied as urea (46% N) at four rates (0, 75, 112.5 and 150 kg N/ha), each

in two splits, with the first half at planting and the second at 5 weeks after planting.

Phosphorus (P) was supplied at planting time as triple superphosphate (46% P2O5) at four

rates (0, 50.6, 75.9, 101.2 kg P/ha). Each plot measured 1.8 m x 2.25 m. Each

experimental unit consisted of seven rows each with seven tubers planted. Routine field

maintenance practices such as weeding and spraying against diseases and insect pests using

appropriate fungicides and insecticides was done when necessary. Weeding or physical

uprooting of weeds was done any time weeds were visible. Recommended fungicides for

control of early and late blight such as Ridomil® were used. Insect pests mainly aphids,

thrips, and white flies were controlled using Metasystox® and mites using miticides. Earthing

up was done during weeding. The haulm was not cut off before harvesting for purposes of

shoot growth determination at harvest.

2.2 Leaf area index (LAI)

Three plants per treatment were pegged and the leaf area measured using the graphical

method in both RTrials I and II. The total leaf area per plant was estimated using a graph

paper whereby leaves within a plant were randomly selected and divided into four growth

categories namely smallest, small, medium and large. The leaves within these categories were

removed from a potato plant and placed on a graph paper and their approximate area

determined by counting the number of 1 cm2 grids on the graph paper occupied by the

individual leaf. The individual leaf area for the smallest, small, medium and large was 7 cm2,

18 cm2, 34 cm

2, and 42.5 cm

2, respectively. When the individual leaf area of these four

categories of leaves within the potato was determined, leaves within the plant similar to the

smallest, small, medium and large were counted separately. The total leaf area per category

was obtained by multiplying the number of leaves counted per category by the respective

individual leaf area i.e. multiplying the leaf area per active haulm by the number of active

haulms per plant. The total leaf area of the plant was obtained by adding the total leaf area of

smallest, small, medium and large leaf categories. The total leaf area was determined at 51

and 64 DAP a period characterised by tuber set and initiation of tuber bulking within the

potato plant. The resulting total leaf area was used to calculate LAI using the formula: LAI

= Total leaf area (cm2)/ground area (cm

2) (Beedle, 1987).

2.3 Leaf stomatal conductance

The stomatal conductance was measured on fresh tissues of one randomly selected leaf of

medium growth on three middle randomly pegged plants per treatment at 59, 73 and 87 days


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after planting (DAP) in both RTrials I and II using a leaf porometer (SC-1; Decagon

Devices, Pullman, WA). Stomatal regulation of gas exchange by leaves is of great importance

to photosynthesis and stomatal movements can be affected by various environmental factors,

including plant water status, CO2 concentration and light (Raschke, 1975; Kim et al., 2004).

2.4 Leaf chlorophyll content index

Leaf chlorophyll content was measured at 59, 73 and 87 DAP using chlorophyll content

meter (CCM-200 plus; Opti-Sciences, Tyngsboro, MA) on fresh tissues of lower, middle and

uppermost fully expanded leaves on the three randomly pegged plants per plot. The

measurements were taken halfway from the leaf base to the tip and halfway from the midrib

to the leaf margin. Chlorophyll content meter assists in rapid, non-destructive, determination

of chlorophyll content in intact leaf samples. A non-destructive estimation of leaf Chlorophyll

and Chlorophyll Concentration Index (CCI) value that is proportional to the amount of

chlorophyll in the sample is the units of measurements. Leaf chlorophyll content provides

valuable information about physiological status of plants (Gitelson et al., 2003).

2.5. Data Analysis

Data collected was subjected to analysis of variance using the SAS system for windows V8

1999-2001 by SAS Institute Inc., Cary, NC, USA (SAS, 2011) and significantly different

means separated using Tukey’s Studentized Range Test at P 0.05.

3. Results

3.1. Leaf area index (LAI)

Leaf area index significantly differed among the treatments at 51 and 64 DAP. Potatoes that

received high irrigation water, N and P rates had significantly higher LAI than those that

received lower rates. Leaf area index significantly increased between 51 and 64 DAP with

integrated application of high irrigation water, N and P rates in both RTrials. Leaf area index

was greater with high irrigation water at 100%, N at 150 kg N/ha and P at 101.2 kg P/ha,

which was 2.6 and 1.3 at 51 DAP and 3.5 and 3.1 at 64 DAP. Furthermore, low irrigation

water rate at 40 % together with low N and P rates of 0 kg N/ha and 0 kg P/ha had the least

LAI, which was 0.28 and 0.19 at 51 DAP and 0.28 and 0.24 at 64 DAP both in RTrials I and

II, respectively.

Irrespective of N and P rates LAI was significantly greater with high irrigation water at 100%

followed by 65% and was lowest with 40% irrigation water rate. High compared to low

irrigation water together with high N and P application rates increased the LAI by 1.54 and

0.61 at 51 DAP and by 2.06 and 1.78 at 64 DAP both in RTrials I and II, respectively.

Similarly LAI significantly increased from low to high rates of N and P at all irrigation water

rates. However, slight but significant differences were observed when 40% and 65%

irrigation water rates was supplied together with high N and P rates of 150 kg N/ha and either

75.9 kg P/ha or 101.2 kg P/ha (Table 1).

3.2. Leaf stomatal conductance (mmolm⁻²s⁻¹)

Leaf stomatal conductance was significantly affected by all the treatments at the various


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stages of potato growth. Integration of irrigation water, N and P application rates did not

affect leaf stomatal conductance. However, effects of integration of N and P on leaf stomatal

conductance were observed at all growth stages. Leaf stomatal conductance increased with

irrigation water, N and P application rates. While high irrigation water rate increased the leaf

stomatal conductance, low irrigation water rate reduced the leaf stomatal conductance (Table

2). Therefore water stress resulted in decrease in the net leaf stomatal conductance

Furthermore, leaf stomatal conductance increased from 59 DAP and was highest at 73 DAP

after which it reduced regardless of irrigation water application rate later in the growth season.

Average leaf stomatal conductance at 87 DAP decreased by 24.1 and 35 mmolm⁻²s⁻¹ with

high compared to 21 and 33.9 mmolm⁻²s⁻¹ observed with low irrigation water rate in RTrials

I and II, respectively. Therefore although decreases in leaf stomatal conductance were also

observed with high irrigation water rate greater reduction resulted from low irrigation water

application rate at the later growth stages. Therefore, higher irrigation water application

rates maintained higher leaf conductance compared to lower application rates in both RTrials

(Table 2).

Similarly leaf stomatal conductance increased with N and P application rate. High rates of N

and P application increased leaf stomatal conductance from 59 to 79 DAP after which there

were declines regardless of their application rate (Table 3).

Table 1. Effect of irrigation water, N and P application rate treatments on potato LAI

RTrial I LAI at 56 DAP LAI at 64 DAP

P rate (kg P/ha) P rate (kg P/ha)

kg N/ha 0 50.6 75.9 101.2 0 50.6 75.9 101.2

Irri

gat

ion

wat

er r

ate

(% F

C)

100%

0 0.78d* 0.85d 1.46c 1.58d 1.05d 1.14d 1.95c 2.12d

75 0.92c 1.02c 1.45c 1.76c 1.23c 1.37c 1.95c 2.37c

112.5 1.34b 1.22b 1.76b 2.14b 1.63b 1.8b 2.35b 2.87b

150 1.37a 1.8a 2.36a 2.64a 1.84a 2.46a 3.17a 3.54a

65%

0 0.44d 0.65d 1.02d 1.11d 0.59d 0.87d 1.37d 1.48d

75 0.65c 0.73c 1.1c 1.32c 0.87c 0.98c 1.48c 1.75c

112.5 0.93b 1.05b 1.3b 1.4b 1.24b 1.4b 1.74b 1.88b

150 0.97a 1.13a 1.34a 1.51a 1.29a 1.54a 1.79a 2.03a

40

%

0 0.21d 0.35d 0.53d 0.69d 0.28d 0.47d 0.71d 0.93d

75 0.37c 0.48c 0.82c 0.91c 0.49c 0.64c 1.1c 1.22c

112.5 0.5b 0.60b 0.88b 1.07b 0.67b 0.8b 1.18b 1.44b

150 0.62a 0.64a 0.93a 1.1a 0.83a 0.85a 1.25a 1.48a

MSD 0.02(N) 0.02 (P) 0.02 (W) 0.03 (P) 0.03 (N) 0.02 (W)

CV (%) 5.80 6.05

RTrial II

Irri

gat

ion

wat

er

rate

(%

FC

) 100 %

0 0.59d* 0.64d 0.76d 0.82d 0.91d 0.98d 1.63d 1.83d

75 0.69c 0.75c 0.78c 0.91c 1.06c 1.18c 1.67c 2.04c

112.5 0.76b 0.81b 0.88b 1.03b 1.41b 1.55b 2.03b 2.45b

150 0.85a 1.11a 1.16a 1.29a 1.59a 2.08a 2.73a 3.05a

65

%

0 0.38d 0.55d 0.59d 0.64c 0.51d 0.75d 1.18d 1.28d


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75 0.55c 0.62c 0.65c 0.76b 0.75c 0.84c 1.27c 1.53c

112.5 0.6b 0.68b 0.71b 0.78b 1.07b 1.21b 1.49b 1.62b

150 0.63a 0.73a 0.75a 0.84a 1.12a 1.31a 1.55a 1.75a

40

%

0 0.2d 0.31d 0.39d 0.45d 0.24d 0.41d 0.61d 0.8c

75 0.35c 0.45c 0.54c 0.59c 0.42c 0.55c 0.95c 1.05b

112.5 0.42b 0.48b 0.56b 0.66b 0.58b 0.69b 1.01b 1.24a

150 0.51a 0.52a 0.59a 0.68a 0.71a 0.73a 1.08a 1.27a

MSD 0.02 (N) 0.02 (P) 0.02 (W) 0.04 (N) 0.04 (P) 0.03 (W)

CV

(%)

8.23 8.83

Means followed by the same letter(s) along the column for different irrigation water rate with

N by P rates are not significantly different at P≤0.05 according to Tukey’s Studentized Range

Test. FC = Field Capacity, DAP = Days after Planting, MSD = Minimum Significant

Difference. Mean separation was done within each season.

High compared to low P application rate increased the leaf stomatal conductance by 22.8 and

27.2 mmol m⁻²s⁻¹ while high N application increased the same by 24.6 and 24.2 mmol m⁻²

s⁻¹ at 87 DAP in RTrials I and II, respectively (Table 3).

Generally potato leaf stomatal conductance significantly increased with irrigation, N and P

application rates. Significant difference in the leaf stomatal conductance was observed among

the treatments throughout the growth period in both RTrials I and II (P ≤ 0.05).

Table 2. Effect of irrigation water rates on potato leaf stomatal conductance

59 DAP 73 DAP 87 DAP

Irrigation rate (% FC) Irrigation rate (% FC) Irrigation rate (% FC)

RTrial I 100 65 40 100 65 40 100 65 40

Mean 131.7a* 112.7b 98.7c 148.2a 125.7b 112.2c 124.1a 102.3b 91.2c

MSD 4.8 (N) 3.8 (W) 5.7 (N) 4.5 (W) 5.2 (N) 4.1 (W)

CV (%) 11.9 12.6 14

RTrial II

Mean 138.9a 123.7b 104c 150a 132.3b 117.3c 115.4a 108.9b 83.4c

MSD 5.3 (N) 4.2 (W) 5.8 (N) 4.6 (W) 4.3 (N) 3.4 (W)

CV (%) 12.4 12.3 12.0

*Means followed by the same letter (s) along the row at the same DAP are not significantly

different at P≤0.05 according to Tukey’s Studentized Range Test. FC = Field Capacity, DAP

= Days after Planting, MSD = Minimum Significant Difference. Mean separation was done

within each season.

3.2 Leaf chlorophyll content index (CCI)

The average leaf chlorophyll content index of potato increased significantly over the growth

period with irrigation water, N and P application rates (Table 4). Interactions between

irrigation water, N and P rates resulted to significant differences in leaf chlorophyll content


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index at all growth stages both in RTrials I and II. Significant differences were also observed

between irrigation water and P rates at all growth stages, except at 59 DAP in RTrial I.

However, interactions between N and P were not significant at 59 and 79 DAP in RTrial II.

Water stress due to low irrigation water rate resulted in decrease in the leaf chlorophyll

concentration. High irrigation water rate resulted to a higher amount of chlorophyll compared

to low irrigation water rate. Similarly application of higher rates of N led to high chlorophyll

concentration in both RTrials. However, application of high N rates with 40% and 65%

irrigation water rates reduced the leaf chlorophyll concentration at 73 and 87 DAP both in

RTrials I and II. The leaf CCI increased with integrated irrigation water, N and P from 59

DAP and was highest 73 DAP after which it decreased 87 DAP (Table 4).

Integration of high compared with low N and P application rates together with 100%

irrigation water rate increased the leaf chlorophyll concentration by 16.2 and 16.5, 19.8 and

19.6, and 15 and 20.3 CCI at 59, 73 and 87 DAP both in RTrials I and II respectively. When

low irrigation water rate was integrated with high compared with low N and P application

rates the leaf chlorophyll concentration increased by 10.1 and 7.2, 18.8 and 14.9, and 17.8

and 9.1 CCI at the same growth stages both in RTrials I and II respectively. The highest leaf

chlorophyll concentration was 53.7 and 53.6 CCI that resulted from combined application of

100% irrigation water, 112.5 kg N/ha and 101.2 kg P/ha 73 DAP while the lowest was 20.9

and 22.2 CCI recorded with 40% irrigation water, 0 kg N/ha and 0 kg P/ha both in RTrials I

and II respectively. Therefore integration of high irrigation water, N and P application

compared to low irrigation water greatly increased the leaf chlorophyll concentration in

potato. Integration of low irrigation water and higher N and P rates beyond 112.5 kg N/ha and

75.9 kg P/ha reduced the leaf chlorophyll concentration at all growth stages in both RTrials

(Table 4).

Table 3. Effect of N and P rates on potato leaf stomatal conductance

*Means followed by the same letter(s) along the row for N main effects and the column for P


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rates are not significantly different at P≤0.05 according to Tukey’s Studentized Range Test.

Some interactions were not significant. DAP = Days after Planting, MSD = Minimum

Significant Difference. Mean separation was done within each season.

Table 4. Effect of irrigation water, N and P rates on potato leaf chlorophyll content index

RTrial I CCI at 59 DAP CCI at 73 DAP CCI at 87 DAP

P rate (kg P/ha) P rate (kg P/ha) P rate (kg P/ha)

kg N/ha 0 50.6 75.9 101.2 0 50.6 75.9 101.2 0 50.6 75.9 101.2

Irri

gat

ion

wat

er r

ate

(%

FC

)

10

0%

0 26.6d* 33.6c 35.9b 38.2a 33d 39.8c 42.2b 46.8a 22.6c 28.6b 29.7b 32.7a

75 30.3c 35.2b 36.2b 38.9a 37.1d 42.1c 45.2b 49.3a 26.5c 29.6b 29.8b 33.8a

112.5 33.1d 36.6c 39.2b 43.2a 40.5d 46.6c 51.4b 53.7a 29.5c 30.3c 32.2b 36.4a

150 35.3d 38.6c 40.5b 42.8a 41.4d 44.5c 49.2b 52.8a 27.5d 30.6c 35.2b 37.6a

65%

0 27.4d 29.5c 32.2b 34.8a 30.1c 35.3b 36.2b 41.9a 23.1c 23.6c 26.1b 30.1a

75 29.4d 31.7c 33.8b 36.4a 34c 38.9b 40.2b 45.4a 23.2c 27.4b 28.6b 31.4a

112.5 31d 33.7c 39.4b 42.7a 35.5c 42.4b 48.9a 48.8a 26.1c 31b 34.6a 34.4a

150 31.8d 35.9c 41.4a 38.6b 37d 42.2c 48.3a 45.7b 26.1d 29.3c 35.6a 32.9b

40%

0 23.6d 28.8c 30.4b 33.6a 29.2c 30.9b 35.7a 36.8a 20.9c 22.7b 26.3a 26.3a

75 27.3c 30.4b 33.4a 33.9a 30.9c 36.6b 40.2a 39.7a 22.7c 24.3b 27.9a 28.3a

112.5 28.9c 30.4b 33.2a 33.2a 36.1c 39.9b 47.1a 48a 25.5c 26.5c 38.7a 36.7b

150 27.8c 32.5b 36.9a 33.7a 37.2d 41.6c 47.7a 40.3b 25.5c 27.3b 31a 27.5b

MSD 1.1 (N,P) 0.9 (W) 1.5 (N,P) 1.2 (W) 1.1(N,P) 0.8 (W)

CV (%) 16.2 18.1 18.2

RTrial II

Irri

gat

ion

wat

er r

ate

(% F

C)

10

0%

0 29.4d 35.1c 37.5b 39.5a 32.9c 41.7b 42.7b 47.8a 25.7c 28.8b 29.7b 34.1a

75 32d 36.6c 38.1b 39.9a 36.9d 44.6c 47.4b 51.4a 29.2c 31.3b 32.1b 35.7a

112.5 32.6d 36.9c 39.7b 42.5a 42.8c 49.2b 49.9b 53.6a 31c 32.6b 33.5b 35a

150 36.5d 39c 41.6b 45.9a 43.9d 48c 51.1a 52.5a 29.5d 32.6c 39.7b 46a

65%

0 28.6d 31.2c 32.5b 36.2a 31.8d 37.6c 44b 45.9a 23.7c 25.8b 28.8a 29.5a

75 29.9d 33.6c 34.7b 38.7a 35.4d 38.5c 46.2b 47.9a 24.9d 28.1c 29.9b 31.5a

112.5 33.6d 36.1c 37.6b 39.6a 41.8c 44.4b 45.7b 48.5a 27.9d 31.4c 32.6a 33.3a

150 28.8d 32c 38.2b 40.3a 37.9d 45.8c 47.6b 50.6a 25.3d 28.8c 36.7a 32.9b

40%

0 27.9c 30.9b 31.7b 33.7a 30b 31.5b 38.2a 37.70 22.2d 23.9c 29.1a 27.8b

75 28.7c 31.5b 35.6a 35.7a 31.7c 37b 40.3a 41.6a 24.6d 26.7c 30.7a 28.1b

112.5 29.9d 31.7c 35.7a 34.1b 35.7d 38.9c 41.8b 44.9a 24.5c 26.1b 31.3a 30.5a

150 28.9d 32.6c 38.3a 35.1b 39.3b 43.8a 43.7a 42.7a 26.5c 28.8b 30.2a 28.2b

MSD 1.1 (N,P) 0.9 (W) 1.6 (N,P) 1.3(W) 1.1(N,P) 0.8(W)

CV (%) 15.5 18.9 17.5

*Means followed by the same letter(s) along the row at the same DAP and irrigation water

and N rate are not significantly different at P≤0.05 according to Tukey’s Studentized Range

Test. FC = Field Capacity, DAP = Days after Planting, MSD = Minimum Significant

Difference. Mean separation was done within each season.


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4. Discussion

In Kenya, farmers grow seed potato during the rainy season using fertilizer rates of

commercial potato production. In this study, 100% irrigation water rate represented a normal

rainy season, and fertilizer rates were varied from zero to recommended commercial potato

production rates. In the present study, potato plants supplied with high irrigation water, N and

P rates had larger LAI, higher leaf stomatal conductance and chlorophyll content index.

Potatoes supplied with 100% irrigation water had better growth and development compared

to those supplied with 65%, which had intermediate and those supplied with 40% rate had the

least.

It is possible that low irrigation water led to droughty conditions and water stress within the

potato plant, which possibly resulted in low LAI, stomatal conductance and chlorophyll

content index and consequently reduced photosynthetic activity. Plants under water stress

have been reported to show a decrease in leaf conductance (Obidiegwu et al., 2015), total

area of leaves (Albiski et al., 2012), reduced plant chlorophyll content (Anithakumari et al.,

2012), and the photosynthesis rate (Li et al., 2015).

Loggini et al. (1999), and Apel and Hirt (2004) reported that drought inhibits or slows down

photosynthetic carbon fixation mainly through limiting the entry of CO2 into the leaf or

directly inhibiting metabolism. Probably potato supplied with high compared to low irrigation

water experienced higher rates of leaf stomatal conductance, which lead to high metabolism

and consequently greater chlorophyll content index. Chlorophyll is the key pigment involved

in the primary reactions of photosynthesis which is the global biological process that provides

primary biomass and energy for almost all living beings (Shpilyov et al., 2013). High

chlorophyll content index might have led to higher photosynthetic activity within the potato

supplied with high irrigation water. Van der Zaag (1992) reported that insufficient water

supply reduces foliage growth and efficiency in use of intercepted light by reducing the rate

of photosynthesis, and consequently stimulating maturity through death of the leaves. This

possibly explains why potato plants supplied with low irrigation water, N and P mineral

nutrients attained lower stomatal conductance, chlorophyll content and consequently reduced

total dry matter (biomass) accumulation as indicated by the least LAI.

Nitrogen and phosphorus are crucial elements required for different roles in potato plant

growth and development. Low N and P probably impaired potato plant growth and

development, leading to low leaf stomatal conductance and chlorophyll accumulation.

Chlorophyll traps light and transfers energy for driving photochemical reactions (Yadav et al.,

2010) and photosynthetic activity is related to the content of the photosynthetic pigment,

chlorophyll (Maclntyre et al., 2002; Huang et al., 2014). Therefore, amount of irrigation

water, N and P applied was an important factor in determining the rate of growth and

development of potato plant. It was observed that the key to potato plant growth and

development depended on establishment of a large LAI that is durable through the

reproductive phase. This was achieved through high irrigation water, N and P rates. Early

foliage development due to high irrigation water, N and P rates indicated by high LAI

possibly lead to a high interception of solar radiation and radiation use efficiency (RUE),


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mainly due to the greater photosynthetic surface area of the resultant potato crop.

Stalham and Allison (2012) reported that RUE was increased by irrigating and that this was

also associated with significant increase in total dry matter and tuber yield compared with

non-irrigated plots. Therefore, LAI could be a significant feature in determining

photosynthetic activity. Kara and Mujdeci (2010) reported that LAI is a key structural

characteristic of plants due to the role green leaves play in controlling many biological and

physical processes in plant canopies. The increased LAI due to high irrigation water, N and P

rates could have resulted in increased photosynthetic capacity and supply of assimilates

necessary for high growth and development. Elsewhere, N has also been reported to increase

the total chlorophyll content, meristematic cells and growth, leading to the formation of

branches in addition to leaf expansion (Tabassum et al., 2013). The low stomatal

conductance and chlorophyll content observed in potato plants that received low irrigation

water, N and P rates could have lead to low LAI and consequently to low interception of solar

radiation and hence low photosynthetic capacity to support potato plant growth.

Photosynthesis in plants has been reported to be as a result of interaction among different

factors like carbon dioxide concentration, ambient temperature, chlorophyll content, and

water and nutrient supply, which influence LAI (Tabassum et al., 2013).

Overall greater LAI was observed with high irrigation water, N and P rates. However,

treatments which received high irrigation water together with low N and P rates and

vice-versa did not record greater LAI. This suggests that the effect of irrigation water, N or P

was closely related to the ability of potato plant to utilize them from the soil. Waraich et al.

(2011) reported that when water inside the plant declines below a threshold level, stomata

close and decrease transpiration rate resulting in reduction in water transport through the

plant, consequently affecting roots ability to absorb water and nutrients as effectively as

supposed to be done under normal transpiration. Therefore, it is possible that normal

transpiration required certain amounts of irrigation water below which the high N or P rates

cannot lead to greater potato growth and development. It therefore seems there is a

synergistic relationship between the irrigation water, N and P rates towards potato growth and

development. Probably, availability of N and P to the potato crop depends on the amount of

irrigation water supplied. Furthermore, the utilization of the applied irrigation water by the

potato crop depends on the amount of N or P applied.

Segal et al. (2000) reported that high irrigation amounts and frequency provide desirable

conditions for water movement in soil and uptake by roots. However, it is possible that under

moisture stress conditions resulting from low irrigation water rate, mobility of N and P was

interfered with and therefore curtailing the benefits of these mineral nutrients. Najm et al.

(2010) reported that increased N fertilizer can increase N uptake for a positive effect on

chlorophyll content, photosynthetic rates, leaf expansion, total number of leaves and dry

matter accumulation. Similarly, in this study, high irrigation water, N and P rates could have

increased water, N and P uptake by the potato plant which led to a positive effect on leaf

stomatal conductance, chlorophyll content, LAI, and total biomass accumulation. Kumar et al.

(2013) reported that the increased dry matter production when inorganic and organic minerals

are applied is attributable to higher photosynthetic activity and translocation of


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photosynthates. This probably explains why low potato growth and development was

observed where low irrigation water together with high N or P rates were applied.


The overall combination of irrigation water, N and P rates affects soil moisture and nutrient

content during the potato growing period. This result influences the physiological status of

the potato plants, including leaf expansion capacities, stomatal conductance and chlorophyll

content. Integration of high irrigation water at 100%, N and P rates at 150 kg N/ha and

101.2 kg P/ha increases potato physiological activities that enhances greater growth and

development rates. It is recommended to avoid low irrigation water rates at 40% FC and low

N and P rates at 0 kg N/ha and 0 kg P/ha due to their potential negative effects on seed potato

growth and development.

Acknowledgements

The authors acknowledge the support given by National Commission for Science,

Technology and Innovation in funding this study, Egerton University for logistical support,

Joseph Muya Githaiga for maintaining the trial and Julia Muthoni Ndegwa of USA for

unconditionally assisting in procurement of the Plant Growth Station.

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the journal.




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Analyzing the Linkage between Agricultural Exports

and Agriculture’s Share

of Gross Domestic Products in South Africa

Mushoni B. Bulagi (Corresponding author)

Senior Researcher, Centre for Science, Technology and Innovation Indicators, Human

Science Research Council

Jan J. Hlongwane

Senior Lecturer, Department of Agricultural Economics and Animal Production, University

of Limpopo

Abenet Belete

Professor, Senior Lecturer: Department of Agricultural Economics and Animal Production,

University of Limpopo

Received: December 22, 2015 Accepted: January 6, 2016 Published: January 23, 2016


Abstract

The paper analyses the link between avocado, apple, mango and orange exports and

agriculture’s share of Gross Domestic Product in South Africa. The study used secondary

time series data that covered a sample size of 20 years (1994 - 2014) of avocado, apple,

mango and orange exports in South Africa. Two Stages Least Square models were used for

data analysis. Empirical results for agricultural exports equation revealed that agricultural

economic growth in South Africa was significant with a positive coefficient. Also a negative

relationship between the Net Factor Income (NFI) and the agricultural exports in South

Africa was noticed. Real Capital Investments had a significant positive coefficient.

Consequently, results from agricultural economic growth equation revealed that agricultural

exports were significant with a positive correlation. A relationship between NFI and

agricultural GDP was also witnessed. Like other variables, Real Capital Investment was

significant but negatively correlated.

Keywords: agricultural exports, agricultural GDP, exports policy, agricultural trade, South

African agriculture


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1. Introduction

1.1 Background

South Africa’s agricultural sector is dualistic: a developed commercial farming sector

co-exists with a large number of small-scale farms. South African agriculture is increasingly

export-oriented with approximately one third of total production exported. Although reforms

of agricultural policies has also been initiated, changes in South African agriculture policies

in the past decade have been shaped by substantial macroeconomic and social reforms

implemented during the mid-1900s to date. According to (Organisation for Economic

Co-operation and Development, 2006) such policies included the deregulation of the

marketing of agricultural products, abolishing certain tax concessions favoring the sector,

reductions in budgetary expenditure on the sector, land reform, and trade policy reform. The

opening of the agricultural sector after 1996 placed South Africa among the world’s exporters

of agro-food products such as wine, fresh fruit and sugar. South Africa is also an important

trader of agricultural exports in Africa. In the global market, Europe is by far the largest

destination, absorbing almost one half of the South African agricultural exports. Imports were

also growing but less rapidly than exports (DAFF, 2012).

The South African Gross Domestic Product (GDP) increased from 2.9% to 3.1% while

agriculture contributes less than 3% to the share of GDP (DAFF, 2012). Wine and fruit

production has seen the most dynamic development in the past ten years with a large share of

total output exported, mainly to Europe. Agricultural products, particularly those with export

potential, have been viewed by many underdeveloped nations around the globe as playing a

vital role in economic development. The debate on the relationship between agricultural

export and agricultural GDP has exhibited considerable interest in the field of development

economics. Several empirical studies (Xu, 1996; Tyler, 1981; Shirazi and Manap, 2004;

Faridi, 2012) were conducted to assess the role of exports towards the economic growth of

developing countries from various aspects. While the true measure of these nations’

development needs to be expressed through improvements in the standard of living of the

people, their economic growth plays a significant part in this process by providing increased

per capita income, increased revenue for government sponsored social services and leading to

export led-growth. Relatively recent studies [Tiffin and Irz (2006); Memon et al., (2008);

Shombe (2008); Sanjuan-Lopez and Dawson (2010); Raza et al., (2012); Faridi (2012)], have

their main emphasis on causality between export growth and economic growth. This has been

adopted in a number of recent studies designed to assess whether or not individual countries

exhibit evidence for export-led growth hypothesis using time series data. The major

disadvantage of these causality test results is that the Granger or Sims tests used in these

studies are only valid if the original time series are co-integrated.

The opportunity to expand exports is a key determinant of the prospects for economic growth

in developing countries. Regardless of whether or not exports drive economic growth, one of

the primary aims of any country’s economic policy includes trade, industry policy and

internationally competitive sectors which contribute to job creation. It is within this

framework that the linkage between avocado, apple, mango and orange exports and

agriculture’s share of GDP needs to be studied and also its roles in economic development in

South Africa need to be assessed. Literature [Katircioglu (2006); Shombe (2008), Khalafalla


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and Webb (2001)] on economic development and growth discusses the relationship between

exports and economic growth. This group of studies found correlation between these two

variables and better support an export led-growth hypothesis.

The role of agricultural exports to agriculture’s share of Gross Domestic Product (GDP) in

South Africa is of extreme importance and exhibit strong interest from all parts of the

economy. Many believe that agriculture can salvage the declining economic growth under

such global economic conditions. The decision to diversify and expand exports of these

produce will improve the unstable South African economy. This is a result of the effect of

agricultural export diversity and it’s expectation to provide economic development and

sustainable finances needed. It is necessary for the study to focus on an individual in order to

account for all the factors that are truly unique to a nation’s economy. Therefore, the study

will help to shift the focus of avocado, apple, mango and orange growers to export more due

to the international market demand for such produce.

1.2 Purpose and objectives of the study

The purpose of the study was to analyse the link between agricultural exports and

agriculture’s share of gross domestic products in South Africa. The specific objectives of the

study were to :

(i) determine the correlation between avocado, apple, mango and orange exports and the

agriculture’s share of Gross Domestic Product in South Africa and

(ii) investigate the contribution of avocado, apple, mango and orange exports to agriculture’s

share of Gross Domestic Product in South Africa.

1.3 Hypotheses of the study

(i) there is no correlation between avocado, apple, mango and orange exports and the

agriculture’s share of Gross Domestic Product in South Africa and

(ii) there is no contribution of avocado, apple, mango and orange exports to agriculture’s

share of Gross Domestic Product in South Africa.

2. Avocado, apple, mango and orange exports in South Africa

In the past five years tonnes of avocado, apple, mango and orange have been exported to

different destinations, and these exports face competition in the markets.


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Figure 1. Avocado, apple, mango and orange exports in tonnes from 2007 to 2011

Source : DAFF (2012)

Figure 1 shows flactuating exported tonnes over the past five years and does not have room

for mango exports due to low export volumes, with the highest tonnes reached in 2007. It is

worthwhile to note that should the share of other exports increasing with lower volumes,

avocado exports rise with very big volume due to favourable climatimacal conditions and its

market share.

3. Data and Methodology

3.1 Study area and sampling technique

The study of the impact analysis of the linkage between avocado, apple, mango and orange

exports to agriculture’s share of Gross Domestic Products was conducted in South Africa.

The study used the secondary time series data that were obtained from Department of

Agriculture, Fishery and Forestry Statistical Directorate and Statistics South Africa. The

study used a sample size of 20 years (1994 – 2014) of avocado, apple, mango and orange

exports in South Africa. Data was broken into four sub sectors i.e. Agricultural exports,

Agricultural Economic Growth, Net Factor Income and Real Capital Income. Agricultural

exports covered avocado, apple, mango and orange exports. These exports were expressed

physically in monetary value and tonnes. Agriculture GDP, NFI and INV were expressed in

percentage. The data were lagged at two, to avoid stationary using the Augmented

Dickey-Fuller test. This test was run independently from the models used to analyse data in

order to show the ability of secondary time series data to address objectives of the study.

3.2 Analytical technique

The study used the Two-stage Least Square (2SLS) model to determine the relationship

between avocado, apple, mango and orange exports and agriculture’s share of Gross


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Domestic Products in South Africa from 1994 to 2014. The Statistical Package for Social

Sciences (SPSS) 22.0 was used to analyse. The 2SLS model assumes that agricultural

economic growth is simultaneously determined along with avocado, apple, mango and orange

exports. As proposed by Davidson and McKinnon (1981), the J-test was used in the process

of model selection, and only statistically significant variables were included in the two stage

least square system equation. This assures that the model is specifically tailored to suit the

South African agricultural economic growth. The two stage least squares systems approach

was used to estimate the equations in this study in order to avoid simultaneity bias. The

selected model for this study has a system of two simultaneous equations determining

agricultural economic growth and avocado, apple, mango and orange exports of South Africa.

General models

Avocado, apple, mango and orange exports equation:

log (Xa) t = β

1 log (β

1)

t + β

2 log (β

2)t-1

+ β3 log (β

3)t-2

+ U ……………………..1

Agricultural economic growth equation:

log (AGE) t = β

1 log (β

1)

t + β

2 log (β

2)t-1

+ β3

log (β3)t-2

+ U ………………...............2

Specific models

Avocado, apple, mango and orange exports equation:

log (Xa) t-1

= 0 + AGE log(AGE)t +NFI log(NFI)

t-1 +INV log(INV)

t-2 + U…................3

Agricultural economic growth equation:

log (AGE) t-2

= 0 + Xa log(Xa)t-2

+NFI log(NFI)t-2

+ INV log(INV)t-2

+ U ……………4

Xa represents avocado, apple, mango and orange exports; AGE represents agricultural

economic growth; NFI and INV represents Net Factor Income and Real Capital Investments

which are all coefficients; t is the time factor and U is the disturbance term.

4. Empirical Results and Discussion

Table 1. Correlation matrixes of agricultural GDP and agricultural exports

Variables Correlation Co-efficient Significance(2-tailed) Avocado - 0.558** 0.001 GDP 1.000 1.000 Apple - 0.399** 0.023 GDP 1.000 1.000 Mango 0.452* 0.010 GDP 1.000 1.000 Orange - 0.739*** 0.000

*, **, *** Correlation is significant at 1%, 5% and 10% levels (2-tailed)

In table 1 above the direction of association that exists between avocado and agriculture’s


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share of GDP was significant at 10%. This implies a linear correlation between these

variables such that when avocado exports increase the agricultural GDP will also increase or

decrease thus supporting the export-led growth hypothesis. Avocado exports have a negative

coefficient which implies that the avocado exports of the above produce have a negative

influence on the agricultural GDP of South Africa. Due to higher volumes of apple exports

across the borders, the share of apple exports towards agricultural GDP differs with the

avocado’s contribution. This is because of a significant correlation between apple exports and

the agriculture’s share of GDP; hence the export-led growth notion still holds true. These

variables were significant at 5%, which implied that when apple exports increase a decrease

in agriculture’s share of GDP is witnessed.

A very low quantity of mango exports goes over South African borders as a result mango

exports make a limited contribution to the GDP. Table 1 above shows the direction of

association that exists between mango exports and agriculture’s share of GDP which was

significant at 10%. An increase in the mango exports will trigger an increase in agricultural

GDP and this goes to support the export-led growth hypothesis. Orange exports attract most

markets in international trade. In a season, higher volumes of oranges are exported across the

borders; the share of orange exports towards agricultural GDP contribution was positive. This

is because of a significant correlation between orange exports and the agriculture’s share of

GDP like other three produce. This supports the export-led growth hypothesis. Orange

exports were significant at 10%, which implies that when orange exports increase the

agriculture’s share of GDP will decrease.

Table 2. Empirical results of agricultural GDP equation

Variables Co-efficient Standard Error

Wald statistics

Significance

Xa 0.463** 0.422 1.095 0.040 NFI 0.742 0.633 1.172 0.266 INV -0.032*** 0.745 -0.043 0.007 Constant -0.038 0.190 0.199 0.846 - 2 Log likelihood 12.39 Pseudo R square 35% % cases correctly predicted 95.0% Chi – square 20.50

*, **, *** represent significance at 1%, 5% and 10% respectively

Table 2 above shows that agricultural exports are significant at 5% level, this denotes that

when the exports increase the agricultural GDP will also increase hence supporting the

export-led growth hypothesis. Agricultural exports have a positive coefficient which implies

that the exports of the above produce have a positive influence on the agricultural GDP of

South Africa. In most recent studies agricultural export was found to be a factor that matters

most to the agricultural GDP all over the world. There is a positive relationship between the

net factor income and the agricultural GDP of South Africa. Table 1 shows that this variable

does not appear to be significant at any level. The implication of an increase in net factor

income is that there will be an increase in the exports of mangoes from South Africa which

also leads to an increase in the agricultural GDP.


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Apple, avocado, mango and orange farmers desire results that earn them much needed

foreign currency and to the economist, on the other hand, it would be the law of demand.

Conversely; when the farmer is not exposed to international trade, which heightens exports,

the possibilities of farming for exports and international trade are very slim. Real Captical

Investment as a variable was significant at 10% and has a negative coefficient which means

that money invested by farmers for exporting apple, avocado, mango and orange in South

Africa has a negative influence on the agricultural GDP. This is a result of lack of other funds

which can be used to export more products in the future and also a result of different

investment decisions that may be taken.

Table 3. Empirical results of agricultural exports equation

Variables Co-efficient Standard Error

Wald statistics

Significance

AGE 0.215** 0.191 1.124 0.030 NFI -0.002 0.276 -0.006 0.995 INV 0.237*** 0.266 0.890 0.000 Constant 0.771 0.787 0.980 0.346 - 2 Log likelihood 18.46 Pseudo R square 41.6% % cases correctly predicted 51.4% Chi – square 6.62

*, **, *** represent significance at 1%, 5% and 10% respectively

Increasing GDP is one of the major targets of almost every economy (Shombe, 2008). There

are many ways which can be used to achieve economic growth, and to mention one, exports

have received a notable attention in recent studies. Table 3 shows that the agricultural GDP

of South Africa is significant at five percent level. This implies that an increase in the

agricultural GDP results from an increase of agricultural exports in South Africa hence

leading to the export-led growth hypothesis. The agricultural GDP of South Africa has a

positive coefficient and it means that the country’s agricultural GDP can somewhat benefit

from the exports of avocados, apples, mangoes and oranges.

There is a negative relationship between the net factor income and the apple, avocado, mango

and orange exports in South Africa. Table 3 above shows that this variable does not appear to

be significant at any level. This implies that if the net factor income increases there will be a

decrease in the exports of mangoes in South Africa hence a decline in the agricultural GDP.

This will be a result of excessive spending of farmers that export apple, avocado, mangoes

and orange in respect to other international markets which leads to a negative balance of

payments hence a negative GDP. Most authors who found a positive relationship between

exports instability and economic growth argue that if we assume risk-averse behaviour,

uncertainty about export earnings can lead to a reduction in consumption and in turn, an

increase in saving and investment and thus economic growth (Sinha, 1999). Furthermore,

table 3 above shows that this variable is significant at ten percent and has a positive

coefficient which means that money invested by farmers exporting mangoes in South Africa

have a positive influence on the agricultural GDP. This will be a result of much needed

interest and other benefits that are earned from other investments (physical and monetary


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from these exports).

The relationship between agricultural GDP and apple, avocado, mango and orange exports in

South Africa was demonstrated by the estimated parameter for the independent agricultural

exports variable in the GDP equation and by the estimated parameter for GDP growth in the

agricultural exports. In the Two-Staged Least Square System Approach which estimated the

agricultural GDP, it was found that agricultural export was positive and statistically

significant at a level of five percent. According to this model, a five percent increase in

agricultural exports would ultimately result in examined in a variety of ways. In this quest the

agricultural exports equation was drawn into the study. This equation helped to analyse the

link between agricultural exports and agriculture’s share of GDP in South Africa. The

estimation of two among all three variables was positive and significant at one and five

percent while the other variable was negative and not significant at any level. The positive

estimates for the agricultural GDP and the Real Capital Investments which were significant at

ten percent and one percent and positive coefficients respectively.

The consistent positive relation suggests that the positive externalities generated by avocado,

apple, mango and orange exports on the agricultural GDP increase when the share of avocado,

apple, mango and orange exports increases also helped in not rejecting these two hypotheses.

The NFI was negative and not consistent with prior expectations; however the coefficients

were very small, implying that an increase in income remittance on agricultural GDP as a

result of agricultural exports being negative will result in a direct role that was determined in

the agricultural GDP equation. These outcomes draw serious conclusion. In responding to the

last two hypotheses, it was found that agricultural exports in South Africa were influenced by

other factors over the past 20 years. With the possible factor been higher rate of interest

which makes it difficult for farmers to loan money to improve their exports.

The information of this nature is important for the analysis of agricultural economic

development strategies in South Africa. The timing of this information is critical as South

African policy makers now face major economic reforms in a quest for a more export

oriented and stable agricultural economic development. As the South African government is

under pressure to alter the UK Citrus ban, historical land claims and agricultural policies,

understanding the impact of agricultural export for few quantities or exports on GDP growth

will provide important information for policy analysis.

5. Conclusions and Recommendation

The results of this study show that the amount of avocado, apple, mango and orange exports

was positively related to agricultural economic growth. Another point of interest in this study

was that while avocado, apple, mango and orange exports are positive and significantly

related, the magnitude of its coefficient is smaller than the coefficients of Real Capital

Investments. This implies that the agricultural economic growth benefits more from an

exports structure which is rich due to direct investments. It is in this framework that a

positive correlation exists between agriculture economic growth and avocado, apple, mango

and orange exports. It is recommended that investments opportunity in the agricultural sector

need to be investigated because knowledge on such issue is very limited. There are reasons


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related to this notion which this study did not investigate, but this could be one of the

important issues to investigate. The department of Agriculture, Forestry and Fishery and the

private sector need to join hands and forge a mutual relationship to aid in developing an

agricultural economy atmosphere which can be able to encourage and open doors for more

exports than imports. This can also be done by subsidising farmers with capital to endure

other cost.

References

Davidson, R., & Mackinnon, J. G. (1981). Several Tests for Model Specification in the

Presence of Alternative Hypotheses, Econometrica, Econometric Society, 49, 781 – 793.

DEPARTMENT OF AGRICULTURE, FISHERY AND FORESTRY (DAFF), (2012).

Economic Review of the South African Agriculture. South Africa. Available at

http://www.daff.gov.za/docs/statsinfo/EconReview2012.pdf [accessed :27/10/2015]

Faridi, M. Z. (2012). Contribution of Agricultural Exports to Economic Growth in Pakistan,

Pakistan Journal of Commercial and Social Sciences, 6, 133 – 146.

Katircioglu, S. T. (2006). Causality between Agriculture and Economic Growth in a small

nation under Political isolation: A case from North Cyprus. International Journal of Social

Economics, 33, 331 – 343.

Khalafalla, K.Y., & Webb, A. J. (2001). Export-led Growth and Structural Change: Evidence

from Malaysia, Applied Economics, 33, 1703 – 1715.

Memon, M.H.,Baig, W. S., & Ali, M. (2008). Causal Relationship between Exports and

Agricultural GDP in Pakistan: Applied Economic Research Center, University of Karachi,

paper No 11845.

ORGANISATION FOR ECONOMIC CO-OPERATIVE AND DEVELOPMENT (OECD),

(2006). OECD Review of Agricultural Policies: South Africa, ISNB 92-64-03679-2.

http://www.fanrpan.org/documents/d00456/OECD_review_agricultural_policies_SA.pdf

[accessed: 27/10/2015]

Sanjuan-Lopez, A. I., & Dawson, P. J. (2010). Agricultural Exports and Economic Growth in

Developing Countries: A Panel Cointegration Approach. Journal of Agricultural Economics,

61, 565 – 583.

Shirazi, N.S., & Manap, T. A. A. (2004). Export and Economic Growth Nexus: The case of

Pakistan, Pakistan Development Review, 43, 563 – 581.

Shombe, N. H. (2008). Causality Relationships between Total Exports with Agricultural and

Manufacturing GDP in Tanzania, Institute of Developing Economies, Discussion Paper

No.136.

Sinha, D. (1999). Exports Instability, Investment and Economic growth in Asian Countries: A

Time Series Analysis, Economic Growth Center, Yale University. Center Discussion Paper

No. 799.

Tiffin, R., & Irz, X. (2006). Is Agriculture the engine of Growth, Agricultural Economics,

International Association of Agricultural Economists, 35, 79-89.

Tyler, W.G. (1981). Growth and Export Expansion in Developing Countries: some Empirical

http://ideas.repec.org/a/ecm/emetrp/v49y1981i3p781-93.html

http://ideas.repec.org/a/ecm/emetrp/v49y1981i3p781-93.html

http://ideas.repec.org/s/ecm/emetrp.html

http://www.daff.gov.za/docs/statsinfo/EconReview2012.pdf

http://www.fanrpan.org/documents/d00456/OECD_review_agricultural_policies_SA.pdf


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evidence. Journal of Development Economics, 9, 121 – 130.

Raza, S. A., Ali, Y., & Mehboob, F. (2012). Role of Agriculture in Economic Growth of

Pakistan. International Research Journal of Finance and economics, 83, 1450 - 2887.

Xu, Z. (1996). On the causality between export growth and GDP growth: An empirical

investigation. The Review of International Economics, 4, 172-184.



the journal.




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Effects of Plant Pattern and Nitrogen Fertilizer on Yield

and Yield Components of Silage Corn Cultivars

Mohammad Hossein Haddadi

Scientific member of Agronomic and Horticulture Crops Research Department, Mazandaran

Agricultural and Natural Resources Research and Education Center, AREEO, Sari,

Iran.Box:48175-556

Masoud Mohseni

Scientific member of Agronomic and Horticulture Crops Research Department, Mazandaran

Agricultural and Natural Resources Research and Education Center, AREEO, Sari, Iran

Received: January 18, 2016 Accepted: January 23, 2016 Published: February 14, 2016


Abstract

To study effect of sowing pattern and nitrogen on maize silage yield and its dependents

components a trial was done at split-split plot design in completely randomized block at four

replications in Qrakheil (Qaemshahr) agricultural research station in 2015 in Iran. Row

spacing was chosen as main plots including: 65cm, 75cm and 85cm.Two cultivars (Sc704 and

Sc770) in sub plots and three levels of N (250, 350 and 450 kg ha1) in sub-sub plots were laid

out. Silage forage yield, ear and dry ear yield, leaf and dry leaf yield, plant dry weight and

stem and dry stem weight, plant height, ear height, kernel number in ear row and row number

in ear were measured. The results indicated that row spacing, were not affected on

investigated traits. While nitrogen effected on silage yield, ear and dry ear yield, leaf and dry

leaf yield, plant dry weight and stem and dry stem weight, plant height and ear height and

showed significant difference. Sc704 had significant difference on plant silage weight, dry

and wet stem weight with Sc770. Amount of 350kg/ha nitrogen was caused the most silage

yield (36.74t/ha) and plant dry weight (16.64 t/ha) that had not significant difference with

usage of 450kg/ha nitrogen(16.46 t/ha). Amount of 450kg/ha nitrogen was caused the most

silage yield (37.30t/ha) that had not significant difference with usage of 350kg/ha nitrogen

(36.74 t/ha). The most silage yield (38.21t/ha) obtained from Sc704 that was better than

Sc770 (34.22 t/ha). The most plant dry weight (16.54) obtained from Sc704 that had not

significant difference with Sc770 (15.92 t/ha).

Keywords: Corn, nitrogen and potassium fertilizer, row spacing.


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1. Introduction

Maize (Zea maize L. ) is importance grain-forage plant in Iran (Hashemi, et al 2005).

The mean kernel yield of maize is greater than eight t/ha and produce increase each years. New

hybrids for each location can increase maize yield (Allard, 1960 ). maize have importance role

in the live of the people of Americas (Kiniry et al., 1992).

Maize requires three main element nitrogen ( N ) potassium ( K ) and phosphorus (P and on

most soils, the amount of nitrogen is very much needed and provide sufficient nitrogen in

various stages of growth for having a good performance(Tad,2004). according to the study

conducted by subedi et al (2006 ), nitrogen consumption increased corn silage yield. Amount

of corn nitrogen necessary depends on the silage yield when no restrictions have on the other

elements( Pampolino et al 2007 ).nitrogen application have significant effect on qualitative and

quantitative maize yield( sanjeev and Bangawa ,1997).Application of nitrogen is appropriate

method for increasing the yield of corn (Norwood, 2000).Study have shown that the best

response of maize to nitrogen was 250 and 300 kg per hectare net nitrogen (Di paolo and

Rinaldi, 2008). Nitrogen is a importance factor for corn growth (Adediran and Banjoko,

1995,.Tollenaar and Aguilera. 1992).Protein and nucleic acids have nitrogen and when

Nitrogen is no sufficient; produce is reduced (Haque et al., 2001).For suitable corn growth it is

necessary that nitrogen must be enough,throut the growing period. Nitrogen is a necessary

element for many other essential compounds for plant growth processes such as chlorophyll

and many enzymes. It is also necessary for using of phosphorus, potassium and other elements

in plants (Brady, 1984). Therefore, deficiency or excess of nitrogen were caused reduces of

corn produce. Sowing pattern is a main factor that determines the produce of corn (Cardwell,

1982). Sowing method affects germination, water requirements of crop, growth and

development of roots and exploitation of moisture from soil layers. If plants have a suitable

pattern, water and nutrients is better utilized (Ali et al ,1998). Efficiency of radiation use is

influenced by Sowing pattern. Sowing of corn in narrow rows results increasing light using for

each plant. Therefore, narrow rows increase photosynthetic activity and yield (Tollenaar and

Aguilera, 1992).Suitable plant density was caused increasing. Maize have low tolerance to

high plant density. Maize yield was negatively correlated to radiation interception at time of

pollination with the wider spacing (Andrade et al., 2002 ). Higher yields were obtained for

maize in narrow rows vs. wide rows regardless of hybrids and plant density in Indiana and

Michigan (Widdcombe and Thelen, 2002).

Narrower row spacing with higher plant density results in a more equidistant planting pattern,

is expected to delay initiation of intra specific competition (Duncan , 1984) .Early crop growth

is increased with narrow row spacing(Bullock et al 1988). Optimum row spacing differs among

plant genotype; yields will generally be maximized by sowing in rows an equidistant spacing

among plants (Saseendranet al, 2005). Narrow-row has been advocated in recent years to

increase grain yield (Kucharik. 2008). These differences in yield associated with row spacing

appear to be emphasize for corn grown at more northerly location within the U.S. Corn Belt

(Saseendran et al, 2005).Positive response was showed in yield of corn in narrower rows

( Ottman and Welch,1989). An 11% lower yield for corn grown in 0.19- m rows vs. 0.38- and

0.76-m rows in Wisconsin was shoewd (Pedersen and Lauer, 2003). Farnham (2001) showed a


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2% decreasing yield for maize grown in 0.38-m rows vs. 0.76-m rows in Iowa.

2. Material and Methods

The study was done at the Agricultural Research station of Mazandran in Qaemshahr (31°28' N,

52°35' E, and an altitude of 14. 7 meters above of sea level) in Iran. The soil type was clay loam.

To determine the effect of plant pattern, nitrogen on forage yield and its dependents

components on corn (Zea mays L. SC704 and SC770), an experiment was done as split-split

plot with a randomized complete block design in Qrakheil (Qaemshahr) agricultural research

station in 2015in Iran. Main plot is row space (P1=65cm, P2=75cm,P3=85cm),sub plot is

cultivar V1=SC704andV2=SC770,sub-sub plot is nitrogen (N1=250kg/ha, N2=350kg/ha ,

N3=450 kg/ha Urea manure).Plant at each treatment were planted at four rows an four

replications.

Weeds control was done with hand. Irrigation was done with a sprinkler system. Plants in each

treatment harvested separately. Plants were cut done at the two middle rows in the plots (Area

of 9 m2). In harvest time, plants in each plot were weighted then dry and wet ear, stem, leaves

and silage yield were measured. Number of kernel in ear row, number of row in ear, ear height

(cm) and plant height (cm) also were measured. Data was analyzed by MSTAT-C

procedure .The DMRT procedure was used to make tests of simple and interaction effects. All

differences are significant at P< 0.05 unless otherwise stated.

3. Result and Discussion

3.1 Plant height

The highest plant height was in related to SC770 with 251.7 cm. There was 235.9 cm and

162.8 cm plant height for SC704.

Row spacing had not significant effect on plant height. Lowest plant height was in related to

row spacing of 65cm (241.3cm). Row spacing of 85cm (245.7 plant/ha) and 75cm (244.4

plant/ha) have highest plant height (Table 2).Nitrogen had significant effect on dry silage

yield. The highest plant height was obtained from 450 kg/ha N (246.8cm). The lowest plant

height was obtained from 250 kg/ha N (240.0cm) (Table 2).

3.2 Ear height

SC704 had highest ear hight(116. 4cm).SC770 (115.56 cm) had less ear hight than SC704but

no significant difference had between cultivars. There is not significant difference between

row spacing .Row spacing of 85cm and 65cm had higher ear height with 117.9 cm and 116.5

cm respectively. Nitrogen had significant effect on ear hight . The highest ear hight was

obtained from 450 kg/ha N (118.7cm). The lowest ear height was obtained from 250 kg/ha N

(113.4cm) (Table 2).

3.3 Kernel Row Number

Cultivars were nearly similar in point of kernel row number and had not significant difference.

SC770 and SC704 had 15.44and 15.61 kernel row number respectively.Row spacing had not

significant difference at this trait. Row spacing of 65cm had the highest kernel row

number(15.58n) (Table 2).Nitrogen had not significant difference on this trait.450 kg/haN had

the highest kernel row number(36.54n) (Table 2).

3.4 Kernel number in ear row


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There was no difference effect between cultivars on kernel number in ear row .SC704 had the

highest kernel number in ear row with 36.14n.SC770 had 35.94n) .The highest kernel row in

ear row with 30.33n was optained from 75000p/ha.Row spacing had not significant different

effect on this trait.Row spacing of 65cm, 75cm and 85cm, had 36.50, 35.79 and 35.83 n

kernel row in ear row respectively (Table 2).

Table 1. Variance analysis of experimental traits

Plant

height(cm)

Plant height(cm) Row

number

Kernel number in

row

df Source of variation

1155.61* 41.57ns 0.65ns 67.09ns 3 Replication

120.68ns 115.26ns 0.06ns 3.79ns 2 Row spacing

159.18 61.26 5.32 22.31 6 Error(a)

4449.39* 15.13ns 0.500ns 0.67ns 1 Cultivar(b)

270.01ns 51.54ns 1.17ns 1.68ns 2 Row spacingx

Cultivar

250.73 175.99 1.09 44.16 9 Error( b)

284.26ns 168.43ns 1.72ns 4.54ns 2 N(c)

69.41ns 73.18ns 2.72ns 53.08* 4 Row spacingxN

78.43ns 41.63ns 1.50ns 30.85ns 2 CultivarxN

233.37ns 76.79ns 0.17ns 86.97** 4 Row

spacingxCultivarxN

206.93 75.39 1.43 18.66 36 Error( c)

5.90 7.49 7.69 11.98 3 %CV

*,** and ns significant at the 5% , 1% and non-significant respectively.

Table 2. Mean comparison of different treatments for the studied agronomic characteristics in corn

Plant height(cm) Ear height(cm) Row number Kernel number in row Treatments

Row spacing

241.3a 116.5a 15.58a 36.50a 65cm

244.4a 113.6a 15.50a 35.79a 75cm

245.7a 117.9a 15.50a 35.83a 85cm

Cultivar

235.9b 116.4a 15.44a 36.14a Sc704

251.7a 115.5a 15.61a 35.94a Sc770

N

240.0c 113.4c 35.75a 15.42a Kg 250

244.6b 115.8b 35.83a 15.83a Kg 350

246.8a 118.7a 36.54a 15.33a Kg 450

Means followed by the same letters in each column and factor are not significantly different

by Duncan’s test at 5% probability level.


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3.5 Silage yield

Variance analysis and means comparision of traites was shown in tables 3 and 4. Row spacing

had not a significant effect on silage yield (Table 4). The highest silage yield was produced in

65cm row spacing with 66.54and 36.30 t/ha.The lowest silage yield(34.21) was produced in

250kg/ha nitrogen(Urea manure) with 34.21t/ha. Amount of 250kg/ha and 350 kg/ha nitrogen

produced highest silage yield with 36.74 and 37.30t/ha respectively. There is no significant

difference between 350kg/ha and 450kg/ha nitrogen on silage yield.SC704 produced the

highest silage yield with 38.21t/ha .The lowest silage yield(34.22 t/ha) were produced in

related to SC770(Table4). Light interception was not affected by corn row spacing. No yield

advantage was showed in narrow (spacing of 0.38 m) rows vs. conventional (spacing of 0.76 m)

rows in maize at two growing seasons in Minnesota (Westgate et al.,1997).

Nitrogen is the main element in increasing productivity and the increase of agricultural food

production worldwide over the past four decades (Rahimizadeh, 2010), but large amount of

fertilizer N was caused a serious environmental problem such as groundwater ontamination. In

investigation of comparing liquid swine manure with chemical N and P fertilizer sources,

maize yield and N and P uptake was similar for both N sources (Asghar et al , 2010).

3.6 Dry and wet leaf weight

Cultivars were nearly same in point of dry and wet leaf weight,SC770 had the higher and

lower dry and wet leaf weight with 2.13t/ha and 6.56 t/ha than SC704 with 2.11t/ha and 6.60

t/ha(Table 4and Table 6).The lowest wet leaf weight was in related to 250 kg/ha with

5.87t/ha.350kg/haN (6.99 t/ha)and 450kg/ha N (6.89t/ha) effects had not significant

difference on this trait . There was no difference effect between row spacings on dry and wet

leaf weight(Table 4and Table 6).The highest and lowest wet leaf weight was obtained from

75cm (7.09t/ha)and 65cm (6.02t/ha)respectively. Nitrogen had not significant effect on dry

leaf weight. The lowest dry leaf weight was in related to 250 kg/ha with 2.10 t/ha(Table 6).

3.7 Dry and wet stem weight

SC704 had the highest dry and wet stem weight with 7.77t/ha and 18.47t/ha in compare of

SC770 with 6.53 t/ha and 14.82t/ha respectively. Row spacings had not significant difference

on dry and wet stem weight (Table 4and Table 6).Nitrogen had significant effect on dry and

wet stem weight. The lowest wet stem weight was obtained from 250kg/ha N with 15.8t/ha .

350kg/ha N with16.65t/ha and 450kg/ha Nwith17.07t/ha wet stem weight had not significant

difference on wet stem weight. The lowest dry stem weight was obtained from 250kg/ha N

with 6.82t/ha. 350kg/ha N with7.48 t/ha and 450kg/ha Nwith7.17t/ha dry stem weight had not

significant difference on dry stem weight (Table 6).

3.9 Dry and wet ear weight

Row spacings had not significant difference on dry and wet ear weight .65cm row spacing

had highest dry and wet stem weight with 13.10 t/ha and 7.52 t/ha respectively.SC704 had

higher wet ear weight (13.14t/ha) than SC770(12.84t/ha). SC770 had higher dry ear weight

(7.26t/ha) than SC704(6.66t/ha).The lowest dry and wet ear weight were obtained from


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250kg/ha N with 6.60t/ha and 15.80 t/ha respectively.There is no significant difference

between 350kg/ha N and 450 kg/ha N on dry and wet ear weight (Table 4and Table 6).


Ear weight (t/ha) stem weight(t/ha) Leaf weight (t/ha) Silage yeild(t/ha) df

Source of variation

6.62* 6.55ns 9.44ns 36.35ns 3 Replication


4.05 6.53 2.71 17.66 6 Error(a)

1.60* 239.88* 0.03ns 286.88* 1 Cultivar(b)

2.09ns 5.52ns 3.38ns 29.92ns 2 Row spacingx Cultivar

9.84 6.97 4.72 25.59 9 Error( b)

5.71ns 7.69ns 9.12ns 4.18ns 2 N(c)



3.57ns 3.54ns 5.29ns 17.99** 4 Row spacingxCultivarxN

3.88 4.03 2.69 15.37 36 Error( c)

15.17 12.06 19.93 10.83 %CV

*,** and ns significant at the 5% , 1% and non significant respectively.

Table 4.-Mean comparison of different treatments for the studied agronomic characteristics in corn

Ear weight (t/ha) stem weight(t/ha) Leaf weight (t/ha) Silage yield(t/ha) Treatments

Row spacing

13.10a 17.18a 6.02a 36.30a 65cm

12.83a 16.27a 7.09a 36.19a 75cm

13.04a 16.47a 6.64a 36.15a 85cm

Cultivar

13.14a 18.47a 6.60a 38.21a Sc704

12.84b 14.82b 6.56a 34.22b Sc770

N

12.54b 15.80b 5.87b 34.21b Kg 250

13.10a 16.65a 6.99a 36.74a Kg 350

13.34a 17.07a 6.89a 37.30a Kg 450



3.8 Dry silage yield

SC704 (16.54 t/ha) and SC770 (15.92t/ha) were nearly similar for dry silage yield and had


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not significant effect on dry silage yield at 0.05 probability levels (Tables 6). The highest dry

silage yield were produced in 350 kg/haN (16.64 t/ha) and 450kg/haN (16.46 t/ha) plant/ha

and the lowest dry silage yield (15.52 t/ha) were produced in 250kg/haN. Row spacing had

not significant difference on dry silage yield. 65cm row spacing with 16.92t/ha had highest

dry silage yield, 75cm row spacing had 15.83t/ha and 85 cm row spacing had 15.91t/ha dry

silage yield (Table 6).


Dry stem

weight(t/ha)

Dry ear weight

(t/ha)

Dry leaf weight

(t/ha)

Dry silage

yield(t/ha)

df

Source of variation

5.61ns 2.89* 0.28ns 19.63ns 3 Replication


4.35 10.84 0.29 29.63 6 Error(a)

27.44* 6.51* 0.01ns 6.89ns 1 Cultivar(b)

2.29ns 0.47ns 0.04ns 4.79ns 2 Row spacingx

Cultivar

3.87 7.16 0.34 19.37 9 Error( b)

1.27ns 0.70ns 0.07ns 0.55ns 2 N(c)



5.04ns 10.64ns 0.60ns 37.35** 4 Row

spacingxCultivarxN

3.12 5.19 0.21 13.53 36 Error( c)

16.69 19.74 15.55 18.66 %CV

*,** and ns significant at the 5% , 1% and non significant respectively.

Table 6. Mean comparison of different treatments for the studied agronomic characteristics in corn

Dry stem weight(t/ha) Dry ear yield(t/ha)

Dry leaf yield(t/ha)

Plant dry weight(t/ha)

Treatments

Row spacing

7.52a 7.29a 2.11a 16.92a 65cm

7.07a 6.75a 2.01a 15.83a 75cm

6.86a 6.84a 2.21a 15.91a 85cm

Cultivar

7.77a 6.66b 2.11a 16.54a Sc704

6.53b 7.26a 2.13a 15.92a Sc770

N

6.82b 6.60b 2.10a 15.52b Kg 250

7.48a 7.03a 2.13a 16.64a Kg 350

7.17a 7.24a 2.15a 16.46a Kg 450




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Knowledgement

This project was supported financially by the Agricultural and Natural Resources Research

Center of Mazandaran, highly appreciated.

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the journal.




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Managing Data Acquisition, Cleansing and

Transformation in an Agriculture Data Warehouse

Ahsan Abdullah (Corresponding author)

Department of Information Technology, King Abdulaziz University

Jeddah, Kingdom of Saudi Arabia

Fuad Bajaber

Department of Information Technology, King Abdulaziz University

Jeddah, Kingdom of Saudi Arabia

Received: November 19, 2015 Accepted: December 3, 2015 Published: February 17, 2016


Abstract

Pakistan is the world‟s fourth largest cotton producer (Anonymous, 2015). The country relies

heavily on cotton yield to sustain and enhance its export and economic growth. Several state

run organizations have been monitoring the cotton crop for decades through pest-scouting,

agriculture and meteorological data-gathering processes. This non-digitized and

non-standardized dirty data is of little use for strategic analysis and decision support. This

paper is based on the data collection and cleansing issues of that cotton pest-scouting data

consisting of approximately 15,000 sheets from 20 cotton-growing districts of Punjab

province. Various real-life agriculture data management and data quality problems are

discussed and explained in this paper using several real examples.

Keywords: Agriculture, Pest Scouting, Pesticides, Data warehouse, Data Quality, Data

Cleansing

1. Introduction

For decades, different government departments have been monitoring dynamic agricultural

conditions all over the Punjab province i.e. the breadbasket of Pakistan (Davidson et al. 2000).

Subsequently, hundreds and thousands of digital and non-digital i.e. conventional files are

created from hundreds of pest-scouting, yield surveys, meteorological data recordings and

other similar undertakings. The multivariate data collected from different sources is

heterogeneous and dirty, which is difficult to integrate. The lack of data standardization,


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cleansing and integration contributes to an under-utilization of this valuable and expensive

asset. This results in a limited capability and utility of this data to provide decision support,

analysis, research and development (Abdullah et al. 2006). Data quality in agriculture has

different facets too, such as field sensor data which is typically noisy [ifti15]. For such data, the

popular data cleansing methods based on prediction, moving averages or classification are not

suitable.

ADSS (Agriculture Decision Support System) aims at using agro-met data for analysis,

decision support, research, development, education and ultimately, to solve agro-related

problems. Conventionally agricultural analysis and decision making process is based on expert

opinion. ADSS employs in-house cutting-edge IT tools and techniques and offers decision

makers an advantage over traditional way of analyzing data. To achieve the objectives, data

inscribed on pest-scouting sheets has been collected, digitized, cleansed and used in the

agriculture Data Warehouse. This wealth of agricultural data is then presented to, and analyzed

by ADSS users for their strategic analysis and decision support. ADSS has the analysis

capability of using decades old pest-scouting data, but in this paper we will discuss the data for

six years i.e. from 2001 to 2006 covering approximately 15,000 sheets with 244,000 records.

This paper is divided into five sections. Section 2 gives the background information and

definitions to familiarize readers with relevant terminologies used in this paper. Related work

is in Section 3. Section 4 discusses the data extraction and transformation along with data

cleansing. The paper concludes with conclusions in section-5.

2. Background

2.1 Data Warehouse and Decision Support Systems (DSS)

A decision is a choice among alternatives which are centered on estimates of alternative values.

Decisions are based on qualitative or quantitative approaches or their combination. This may

also require using past experience and knowledge of the current situation. Figure-1 shows the

decision making process (Heinemann, 2010). Decision Support Systems (DSS) are meant to

make the corporate historical data available for decision makers in the course of strategic

planning and analysis. DSS relies on a Data Warehouse for keeping summarized records and

making the data available as a single source of truth. Supporting a subsequent decision means

people work alone or in a group and gather necessary intelligence, generate alternatives and

finally make choice(s). Supporting the choice-making process involves supporting the

approximation, the evaluation and/or the assessment of alternatives (Daniel, 2001). However,

agriculture decision making is a complex problem, as this requires extensive and hard number

crunching.


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Figure-1: The decision making process (Heinemann, 2010)

To address the complexities of large volume of historical data with low selectivity, a data

warehouse is used. A Data Warehouse is the main repository, in fact, a corporate memory, of

an organization‟s historical data. Data Warehouse contains the processed, integrated and clean

data used by management for decision support. A data analyst can run complex queries and

elaborate analysis using the Data. A Data Warehouse can be viewed as a data-driven approach

for running complex queries with low selectivity. Prompt availability of information is vital for

strategic analysis as well as for decision making. Online Analytical Processing (OLAP) is used

as a supporting application to obtain high performance analysis running ad-hoc “queries” using

the Data Warehouse.

2.2 Data Quality

Data is the raw material on which a data warehouse runs. If we consider the analogy of water,

tap water is suitable for bathing and washing clothes, but is not suitable for drinking or cooking.

Similarly, the bottled water is suitable for drinking, but not for intravenous injection. Similarly,

data of right quality and quantity is required for desirable results that are motivated by suitable

decision support. Figure-2 shows the six main dimensions of data quality (Hichhorn, 2015).

Very briefly, validity deals with compliance with requirements such as, application of

definitions, consistency over time and consistency with others. Accuracy means is the data

accurate enough for intend purposes and use, such as, is there balance between use, cost and

effort and how closely the data is to the point of activity and that the accuracy compromises are

clear. Timeliness influences decision i.e. how quickly the data was captured after the event and

how quickly it is available and how frequently enough. Completeness is matching quality to

meet data needs which includes missing data, invalid data and incomplete data.


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Figure-2: Data Quality Dimensions (Hichhorn, 2015)

2.3 The Need for ADSS

The agriculture decision-making environment is complex. As per Tarrant (1974), there are

three possible methods to the theoretic approaches to agriculture: the first supposes that the

physical environment controls agricultural decision-making; the second assumes that uniform

producers react in a uniform and rational manner in response to economic circumstances this is

called as economic determinism; and the third assumption recognizes that there are additional

set of stimuli and effects on agriculture which are neither based on economic nor on

physical-environmental factors.

Information Technology (IT) facilitates the smooth running of systems and processes. It is not

merely a theoretical discipline but a system for solving problems by using tried and tested

techniques. Agro-Informatics i.e. Agriculture + IT is a hybrid of different knowledge-based

systems and disciplines that include (but not limited to), agri-sciences, computer sciences,

statistics, remote sensing and GIS. IT can enable not only experts and policy makers but also

the farmers to utilize the intellectual resources and find the solution of agro-related problems.

With the ever-increasing complexity of farming operations both in nature and scope resulting

in data generation, the need for prompt and swift flow of information has become important.

3. Related Work

The initial concept of ADSS was presented in (Abdullah et al., 2006). The paper presents a

comprehensive discussion of a complete life-cycle implementation of a Pilot Agriculture

Extension Data Warehouse. This was followed by query-based data analysis. Some interesting

conclusions have been drawn through data mining, using an indigenous clustering technique.

Actual cotton pest-scouting data of only 1,500 farmers consisting of about 4,000 data records

for 2001-02 of District Multan was processed and used in the pilot project with few forecasted

values of weather parameters. The ADSS discussed in the current paper is a full-scale system

unlike the pilot project.


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NASS (National Agricultural Statistics Service), USDA (U.S. Department of Agriculture) has

developed a user-friendly Data Warehouse System that integrates prior survey and census data,

and makes the data readily accessible to all NASS employees (Nealon, 2008). Users of the

integrated and generalized Data Warehouse are required to navigate only seven tables in a star

scheme. The schema consists of the central fact table containing all the survey and census data,

and six dimension tables that provide all the necessary metadata to readily access the data. The

ADSS developed is different from the NASS Data Warehouse because it uses pest-scouting

and Metrological data. In addition to this, ADSS not only provides useful information to the

researchers but has also been designed for decision makers and farmers, so that they can get

timely information and make suitable decisions based on analysis of historical data.

A NATP (National Agriculture Technology Project) called Integrated National Agricultural

Resources Information System (INARIS) was undertaken at IASRI (Indian Agricultural

Statistical Research Institute) India. In this project, Central Data Warehouse (CDW) was

developed. CDW provided systematic and periodic information to research scientists, planners,

decision makers and development agencies in the form of an Online Analytical Processing

(OLAP) decision support system (Rai 2007). The ADSS is different from INARIS as the

system provides detailed analysis to decision makers and farmers using OLAP as well as

query-based applications that provide analysis at District, Tehsil and Markaz Level based on

monthly and weekly grain of data.

The National Electronics and Computer Technology Center (NECTEC) in collaboration with

the Ministry of Agriculture in Thailand launched an "Agriculture Information Network (AIN)"

in response to the information requirements of the agricultural sector (Paiboonrat, 2002).

Farmers can get access to the contents through the Internet by themselves or from groups of

professionals called "Information Brokers". Although the ultimate beneficiaries of both ADSS

and AIN are farmers, but the scope of ADSS is comparatively smaller.

4. Data Extraction and Transformation

ADSS provides decision support by virtue of performance reporting, chart generation, farmer

demographic analysis, pest and pesticide analysis, predator and cotton-yield analysis etc.

Analysis is performed using historical and latest-recorded data from which projected and

derived outcomes are ascertained. Using ADSS processes, the utility of the extracted data is

increased by transforming the outcomes into derived attributes and projected results. The major

steps of the ADSS workflow are shown in Figure-3.

1. Data Collection: The scouts of the Directorate General of Pest Warning

(http://www.pestwarning.agripunjab.gov.pk/) visit the target area i.e. South Punjab

including District Multan. After interviewing the local farmers, the scouts fill the

corresponding information in pest-scouting sheets. The summaries generated from these

sheets serve the purpose of informing policy makers where pest hot spots for a particular

pest have developed.


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Figure-3. ADSS Work Flow

2. Archiving of Data Files: Manually-filled pest scouting sheets are put in files and stored in

rooms in different cities of Punjab. This has been the practice for several decades.

3. Folder Preparation Process: The sheets at the Directorate of Pest warning are copied and

shifted to ADSS premises where these sheets are assigned unique IDs, sorted and separated

according to their IDs and subsequently organized into properly labeled folders.

4. Folder Maintenance: The folders are maintained in the folder bank for easy retrieval by

following a spatial indexing procedure for locating the folder of interest. The folder bank is

accessible to data entry personnel who need the pest-scouting sheets for the purpose of

scanning, data-entry and error reconciliation.

5. Scanning of Sheets: Every pest scouting sheet is scanned and saved as an image in the

database. A scanning plan and storage is hierarchy is created prior to the actual scanning.

The objective is to ensure that all scanned sheets are just as accessible (actually more) as

compared to the original pest-scouting sheets in hard-copy format.

6. Data Digitization: A team of Data-entry Operators transform the manually filled data

sheets into digitized form i.e. transcribed in a staging database as text/numbers. The

process is completed using an indigenous data-entry tool.


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7. Data Cleansing and Transformation: Digitized data is then analyzed using data profiling

tools and necessary transformation performed and then loaded in the data warehouse.

4.1 Data Collection

This section describes in detail the data collection procedure. The process includes: data

collection, folder preparation, scanning, data-entry process, data cleansing and data validation.

Pest-Scouting data is collected periodically in the Province of Punjab by the Directorate

General of Pest Warning and Quality Control of Pesticides (DGPWQCP) and is recorded on

pest-scouting sheets. The scouts from the Directorate General of Pest Warning and Quality

Control of Pesticides weekly sample 50 points in each Tehsil of the cotton- growing districts of

Punjab. Historically, 3,000 sample points in 60 tehsils of Punjab province are sampled with

roughly 150 of these points are from District Multan. It is estimated that until now cotton

pest-scouting has resulted in about 3 million records.

The pest-scouting data is recorded on hand-held pads in the field by field scouts, which is

subsequently typed or handwritten on pest scouting sheets. Photocopied pest-scouting sheets

were acquired from DGPWQCP over a period of several weeks. The data sheet has 27

attributes, such as farmer‟s name, date of scout visit, variety of cotton sown, land owned (in

acres), date of sowing, plant population, bollworm infestation, incidence of sucking pests,

incidence of Cotton Leaf Curl Virus (CLCV), pesticide spray date, etc.

Other than statistical tests of data quality, such as p-value, t-test and ANOVA, a rigorous

field-based procedure was adopted for validation of the data recorded on the pest scouting

sheets. ADSS team conducted field visits of District Multan. This was meant to learn and

observe the data acquisition techniques in the field conditions and to meet the randomly

selected farmers in person whose data is used in this study. The ADSS team verified the

pest-scouting information after interviewing farmers at different cotton farms. For this purpose,

modified pest-scouting sheets similar to the DGPWQCP sheets were used, but with some

additional information recorded (farmer demographics etc.). The ADSS team recorded

additional data about farmers, such as farmer education, area owned, mobility/transport (car or

motor bike), accessibility to TV, radio, computer, internet etc. A total of 36 farmers were

visited by the ADSS team in four Tehsils. Furthermore, separate meetings were held with

different officers of the Directorate General of Pest Warning and Quality Control of Pesticides

(DGPWQCP) in which data collection and data quality processes were discussed.

4.1.1 Process of Folder Preparation

The pest-scouting sheets obtained from DGPWQCP from Multan and Lahore cities were

photocopied, labeled with lead pencil and sent back to ADSS premises in the form of packets.

This procedure was adopted to ensure that pest-scouting sheets obtained by the ADSS team

were in the same order as those of DGPWQCP. The packets were opened and the pest-scouting

sheets were punched and organized in folders. The average number of sheets per folder was

350. The folders were prepared methodically so as to make the sheets uniquely identifiable. By


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way of this process duplicate sheets were eliminated and redundant data was not entered into

the database.

Figure-4 illustrates the whole process of the preparation of folder and their storage in the

folder/data bank for easy retrieval in the future data-entry purposes. One folder requires

approximately 22 man-hours of work to make the folder useable for data-entry into the

database.

Figure-4: Folder Preparation Process

Description of the components of the ADSS sheet ID used is given in Table-1. After folder

coding, the folder is cross-checked for any possible mistake made in the code-allocation

process.

Table-1: ADSS Scouting Sheet ID Coding

4.1.2 Data Sheet Archiving

Following folder preparation, the pest-scouting sheets are scanned and saved in the computer.

The purpose of this exercise is to keep a digital record of all the data sheets at the ADSS

premises to ensure safety against loss due to moisture, termites, fire etc. ADSS used multiple

scanners with vertical document feeder allowing fast scanning.


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4.1.3 Sheet Scanning

Each ADSS data collection agent scans the sheets from one folder at a time. The sheets from

one folder are neither assigned to more than one person, nor are the folders split. This

restriction is to ensure that sheets from various folders do not become mixed and that they

remain in their original order in the folder undisturbed. Each scanned sheet is then saved as

a .jpg file.

4.1.4 Saving the File

The soft folder is named according to the name of the folder in hard form. Tags or separator

dates (from the hard copy) are used to create more folders in the parent folder along with one

for the summary, which is compiled during the folder preparation. Each of these has a folder

name according to the districts scouted during that date (separator). The scanned pest-scouting

sheets are saved in the district files. Each image is saved according to the unique ID which has

been stamped on the sheet. The division of sheets according to each district ensures that the

risks of image mixing in a file are eliminated. Once saved on the hard drive, they are

additionally copied on a writable DVD as a backup.

4.1.5 Image Quality

The scanned images are in .jpg format. Pictures in this format take less space as compared to

bitmaps of the similar quality. The images are scanned using resolution settings of „200dpi‟ and

paper size is set to „Legal‟. This translates into size of 1.5 to 1.7 Mb per sheet image.

4.2 Process of Data-entry

ADSS uses SET-C (Scouting Entry Tool for Cotton) for digitizing data recorded in the

pest-scouting sheets. Benefits of this tool are as follows:

a. SET-C contains a First, Second and Third time data-entry process which assures the

quality of data by identifying mismatches found in one record after a second-time entry.

The highlighted mismatches are resolved in the reconciliation (third-time entry) stage.

b. Built-in checks prohibit the entry of duplicate records and sheets.

c. Performance, mismatch, reconciliation and summary reports can be generated on daily,

weekly and monthly basis.

d. Data is directly stored in the database server.

e. New varieties and pesticides can be added easily.

f. The search option is available in SET-C which makes it possible to search any sheet or

record promptly.

After scanning, sheets from different folders are issued to Data-Entry Operators (DEO‟s)

according to the targeted districts. The following Data-Entry process is followed:

1. Determining which district‟s data is to be entered and for which year. Information related to

the target district is copied from summaries prepared at folder preparation time (see section

4.1.1) and initially kept in an „Excel‟ file as shown in Figure-5. This also includes the initial

plan of assigning sheets to different data-entry operators.


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Figure-5: Multan sheets selected from folder summaries to be entered into the database

2. A list is prepared in „Excel‟ file (which is later loaded into an SQL database) with the

information containing District, Tehsil, Markaz, Union Council, Mouza and Farmer‟s

Name as shown in Figure-6:

Figure-6: Demographics list of Multan sheets that are uploaded into the database

3. The purpose of this list is to maintain consistency regarding farmers‟ names and their

demographics. Once the list is prepared, it is entered into the SET-Cotton system tables by

the DBA. The Data-Entry Operators can now select the appropriate entry from a

drop-down menu. One person prepares the list by looking at the pest-scouting sheets. This

method is time consuming to implement, but the cost is effectively amortized across all

entries made.

4. The DEO Team Lead adds pest-scouting sheets one by one in SET-C for Data-entry. The

„Excel‟ file prepared in Step 1 greatly reduces the time required for this process as all

required sheets and their relevant information is available at one location.

5. After adding sheets in SET-C, the DEO Lead assigns these sheets to Data-entry Operators

for first-time data-entry. The assignment of sheets is done according to the list prepared

during Step 1.

6. Data-Entry Operators use the form containing pest-scouting sheet details for data-entry.

The demographics data is entered from a drop down menu using the list created in Step 3.

7. After the first-time data-entry, the DEO Lead receives that particular sheet and assigns the

sheet to another DEO for second-time data-entry. The same process is repeated when the

second-time data-entry is performed and is ready for reconciliation. Only the records of a

sheet are visible to the third DEO (to whom the sheet was assigned) which do not match for


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first and second time data-entry, these mismatches are checked and corrected at the time of

reconciliation. The reconciliation process is the final step in data-entry. The DEO lead

finalizes the reconciled data sheets in SET-C. After reconciliation the data is ready to be

cleansed.

4.3 Data Quality Management

The process of data quality management adopted in ADSS is as per Figure-7. Here SME is the

subject matter expert. Note that the self-explanatory process is iterative and the more the

number of iterations, the higher will be the data quality. But one must keep in mind the

cost-quality trade-off i.e. unless one is careful, in the extreme case the cost of achieving high

data quality will increase exponentially with little increase in actual data quality.

Figure-: Data Quality Methodology (DeLua, 2009)

The pest-scouting sheets contain non-standardized as well as erroneous data recorded by pest

scouts during surveys. Moreover, errors could have been made during the data-entry process

due to the poor quality of photocopied pest-scouting sheets. The end result is a dataset which is

dirty and not ready for scientific analysis. Data standardization and cleansing procedures are

performed to address these problems.

Problems encountered during the discovery of errors and their resolutions are of many types.

To avoid confusion and maintain timely delivery of quality data, the entire process is divided

into well-defined parts which are performed sequentially (For data transformation details see

section 4.4). The procedures used to cleanse and standardize the data are described in the

following sections.

The indigenous prototype Data Profiling Tool identifies the business rule violations, trends in

data, null values and duplication. These features are utilized to assess data quality and help

cleanse the pest-scouting data. The most commonly used features of this tool are „detailed

profiling‟ and „summarized profiling‟ which show unique value distribution, null values and

prevalent trends.


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Other features of this tool include „House Holding‟ (to identify duplication), Cleansing

(loading standard values against non-standard data) and Data Quality Assessment (shows a

graph to describe quality of data).

The selected columns are checked by the profiling tool for unique value distributions (for

standardization as well as quality assessment purposes) and trends. A unique number of

instances, listed in alphabetical order are especially useful in determining misspellings in virus

incidence, pesticides, units and location columns. The trends and errors are highlighted and

recorded in the quality assessment report. This report contains quality assessment graphs for

selected columns, along with general comments and trends in data.

4.3.1 Data Standardization

Erroneous names and misspellings are particular problems for the Pesticide Names‟, Tehsil and

Markaz names, Variety names and Units‟ columns. The profiling tool and SQL queries are

used to find errors in these columns. These problems and their solutions are documented after

consultation with agriculture expert.

Example-1

This example shows data standardization for pesticide names. A sample of standard pesticide

names is given in Figure-7. It can be seen that the pesticide „Abamechin‟ contains six

abbreviations and is misspelled on the pest-scouting sheets. These values were converted to

one standard name with the help of the data profiling tool by creating a column with the correct

value.

Figure-7: Standard names for pesticides

The same procedure is used for other columns where multiple values of the same name may

exist. The same procedure is followed for Variety column. For example the variety NIAB 111

is also commonly written as N-111in the pest scouting sheets.

4.3.2 Data Cleansing

Validation checks on SET-C restrain certain types of data to be entered during the data-entry

process. This is necessary to decrease errors at the time of data entry. These errors are recorded

in the Data quality document (excluding location columns). Once standardization has been

performed, further errors (if any) are removed using the data profiling tool. All these errors

(and their resolution in later stages) are recorded in the „Quality Assessment Report‟ for future

reference and resolution of errors.


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4.3.2.1 Dose Unit Errors

No unit has been assigned to the dose of pesticide used i.e. ml or gm. This problem arises when

the unit is not given with the pesticide name on the pest-scouting sheet. The problem is solved

by using an update query which assigns a unit value where the dose column is not null. Entries

in the Data quality document also point out unusual figures, for example numerical values like

1 or 2 are occasionally found in pesticide dose columns due to errors made during data-entry.

The entries are shown to the agriculture specialist who provides the solution. Here, entries must

be inscribed and corrected manually in the database.

Example-2

This example shows a query that is used for mass updation in instances where units are

missing.

Update combined set spray1unit = 'gm'

where spray1pesticide IN ('Actra', 'Buprofizen', 'Crown', 'Getred', 'Imidacloprid',

'Lanate', 'Larvan', 'Pride', 'Thiodicarb', 'Imicon', 'Pestidor')

AND spray1dosage is not null

4.3.2.2 Wrong Location Name

This situation occurs when one markaz has erroneously been assigned to more than one district

(Markaz is a sub-region in a District). These mistakes are due to ambiguous entries on the

pest-scouting sheets. This data quality problem is handled during standardization.

Example-3

This example explains how incorrect location names are corrected in the database. Figure-8

shows a problem in the pest-scouting data of the year 2001 for Tehsil Shujabad. Hafizwala is

shown as one of the Markaz in this Tehsil, where in reality only one Markaz (Shujabad) is

located. If the errors are minor, they are corrected manually; otherwise, SQL queries are used.

Figure-8: Incorrect geographic entry in the database

4.3.2.3 Variety Names

Improper variety names are sometimes seen on the pest-scouting sheets. This is usually due to

hasty entries made by scouts or illegible handwriting.

Example-4

This example shows how incorrect crop variety names are corrected during the cleansing phase.

These entries are almost always corrected at the time of data-entry with the consultation of the

agriculture specialist. Unresolved errors at the time of data-entry are resolved in the cleansing

phase. SET-C uses a drop-down list for the variety column and any variety name not found in

that list is dealt with as per the method described. Figure-9 shows an example of this problem

and its solution in the Data quality document. An incorrect entry of IRFH-901 was entered

correctly as FH-901.


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Figure-9: Solution for incorrect variety name entry

4.3.2.4 Issues of wrong dates

This is one of the most common problems encountered in pest-scouting data. Cross checking is

required with the original pest-scouting sheets in order to fix these problems. Some of these

problems have to be solved at entry time and recorded in the Data quality document.

Example-5

This example explains issues related to date columns and their resolution. Figure-10 shows an

entry that was confusing as two dates were used for one pesticide. As the later date was not

possible (due to the given visit date), only the first date was used. This issue was solved at the

time of data-entry.

Figure-10: Solution for wrong date issues

4.3.3 Data Cleansing Using Business Rules

Business rules (total 22 business rules) have been developed as part of ADSS for all attributes

of the pest-scouting sheets to find errors or unusual entries. Some of these rules are described

as follows:

Rule-1: Plant Population: Cannot be zero. If so then these might have been left out by scouts.

Values can be from 20,000 to 75,000.

Rule-2: Whitefly Adult: 1 to 10 (ETL = 5)

Rule -3: Pesticide Spray Date: Must be later then sowing date and earlier the visit date.

Rule-4: Area: Cannot be zero

Rule-5: Variety: Cannot be zero. Must have a prefix (variety type) before number

Rule-6: CLCV Incidence: Cannot be greater then 100.

Rule-7: Predators: Can be up to 50.

Note that here ETL (Economic Threshold Level) is the pest population beyond which it is

economical to use pesticides. The errors found by implementing these rules must be cleansed

by manually cross-checking with the original pest-scouting sheets. The record of any changes

made is written in the Quality Assessment Report.


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4.4 Data Transformation

The data is received by the Data Quality Assurance Manager in an „MS Access‟ database in a

denormalized, flat-file form. The main reasons for this is that the prototype profiler works only

on MS Access and cleansing process becomes much easier when dealing with data in flat file

form. During the data transformation phase, different non-standard column values are

converted into a standardized format. Some important conversions which take place are as

follows:

4.4.1 Transforming Farmer Demographics Column

Data-entry for farmer demographics is done through a list prepared before actual data-entry

begins. The farmer demographics details are entered in the SET-C database to ensure that

spelling mistakes are kept to a minimum and to decrease data-entry time. Data in this form has

to be divided into separate columns. Transformation of the farmer demographics list, as entered

by DEO‟s takes place, and one column is converted into 6 distinct columns (Figure-11 and

Figure-12) i.e. 1:M transformation.

Figure-11: Actual form of data as entered by DEOs in the SQL server

Figure-12: Transformed form of the data as corrected by the DBA

4.4.2 Plant Height Problems

Cm and Inch values are used for recording plant height on the pest-scouting sheets. All inch

values are converted to cm by multiplying the value by 2.5 and thereby replacing inches with

cm. This is done by an update SQL query. (The use of a distinct query or data profiling tool is

necessary to find which kinds of units are being used for one column) i.e. 1:1 transformation.

update Combined set plantheight = plantheight*2.5 , plantheightunit = 'cm'

where plantheightunit = 'inch';


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4.4.3 Separation of other pests

The pest-scouting sheets contain only one column for „Other Pests‟ and might contain more

than one pest. Original entries are divided into separate columns, with standard pest names.

Figure-13 shows an example of such a step in the database.

Figure-13: Transforming ambiguous entries to clearly readable names

5. Conclusions

Although DSS have been around for a while, and financial or telecommunication data

warehouses are not something new, however, an agriculture data warehouse is relatively a new

entrant in the domain of DSS. In the absence of an MIS system for agriculture data, managing

the corresponding data is a challenge. Therefore, processes and procedures are presented in this

paper demonstrating how to manage the pest scouting data. Data quality or the lack of it is an

important and time consuming issue in traditional data warehouses, and for number of reasons,

this being of higher complexity for an agriculture data warehouse. For agriculture pest scouting

data, quality is a hard problem for a number of reasons, such as absence of an online transaction

processing system, variability and diversity in the pesticide names, pests, plant viruses and

crop varieties. In this paper, we have discussed the data management and data quality problems

associated with agriculture pest scouting data, and also given the processes and techniques to

fix or reduce the impact of these problems.

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the journal.



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https://tdwi.org/Articles/2009/01/28/Data-Quality-Management-Getting-More-from-Your-BI-Investment.aspx?Page=1


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http://jas.macrothink.org 179

Reviewer Acknowledgements

Journal of Agricultural Studies would like to acknowledge the following reviewers for their assistance with peer

review of manuscripts for this issue. Many authors, regardless of whether JAS publishes their work, appreciate

the helpful feedback provided by the reviewers. Their comments and suggestions were of great help to the

authors in improving the quality of their papers. Each of the reviewers listed below returned at least one review

for this issue

Reviewers for Volume 4, Number 1

Ashit Kumar Paul

BOUMAHDI MERAD

ZOUBEIDA

Carlos Alberto Zúniga González

Chenlin Hu

Eliana Mariela Werbin

Ewa Moliszewska

Ferdaous Mani

Gajanan T Behere

Gerardo Ojeda

Gulzar Ahmad Nayik

Hojjat Hasheminasab

Hui Guo

Idin Zibaee

Idress Hamad Attitalla

Ivo Vaz Oliveira

Luisa Pozzo

Martin Ernesto Quadro

Ming-Chi Wei

Mohamed EL Sayed Megahed

Mohammad Reza Alizadeh

Moses Olotu

Muhammed Yuceer

Pramod Kumar Mishra

Rasha Mousa Ahmed

Reham Ibrahim Abo-Shnaf

Richard Uwiera

Sahar Bahmani

Sait Engindeniz

Syed Rizwan Abbas

Tran Dang Khanh

Zakaria Fouad Abdallah

Zhao Chen

Zoi M. Parissi

Richard Williams

Editor


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