evaluation of wetting front detector on irrigated
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Hydraulic engineering Thesis
2020-01
Evaluation of Wetting Front Detector on
Irrigated Conservation Agriculture under
Vegetable Production.
Kerie, Melkamu
http://hdl.handle.net/123456789/11036
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BAHIR DAR INSTITUTE OF TECHNOLOGY
SCHOOL OF RESEARCH AND POSTGRADUATE STUDIES
FACULITY OF CIVIL AND WATER RESOURCE ENGINEERING
EVALUATION OF WETTING FRONT DETECTOR ON IRRIGATED CONSERVATION
AGRICULTURE UNDER VEGETABLE PRODUCTION.
MSc. Thesis
By
Melkamu Kerie
Bahir Dar, Ethiopia
January, 2020
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EVALUATION OF WETTING FRONT DETECTOR ON IRRIGATED CONSERVATION
AGRICULTURE UNDER VEGETABLE PRODUCTION.
MELKAMU KERIE
THESIS
Submitted to the school of Research and Graduate Studies of Bahir Dar Institute of Technology,
BDU in partial fulfillment of the requirements for the degree of Master of Science in the
engineering hydrology in the faculty of civil and water resource engineering.
Supervised by Dr Seifu.A Tilahun
Co-Advisor Sisay Asres (PhD Candidate)
Bahir Dar, Ethiopia
January, 2020
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© 2020
Melkamu Kerie Tefera
ALL RIGHTS RESERVED
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ACKNOWLEDGMENTS
First of all, I must thank the Almighty God and his mother St. Marry helped me to bring this
work to the end.
I feel deeply indebted to express my special gratitude to my instructor and advisor Dr Seifu. A
Tilahun and my co-Advisor Sisay Asres, their own invaluable advice and professional guidance
from the start of proposal writing to the completion of my research.
This research was funded by the American people through support by the United States Agency
for International Development (USAID) Feed the Future Innovation Lab for Collaborative
Research on Sustainable Intensification (Cooperative Agreement No. AID-OAA-L-14-00006,
Kansas State University) through Texas A&M University‟s Sustainably Intensified Production
Systems and Nutritional Outcomes, and University of Illinois Urbana-Champaign‟s Appropriate
Scale Mechanization Consortium (ASMIC) projects.
Gratefully I would like to say thanks for Ethiopian road authority (ERA) for securing a full
sponsorship for the opportunity given to Carry out Master‟s Degree in Engineering Hydrology
and for the long mission of capacity building on master of science for numerous disciplines.
Acknowledgment is also expressed to all my families and research supporting friends; Tegegne
Debas, Enguday Bekele, Nigus Fentahun and data collectors in the duration.
Also I am especially grateful, to my beloved one Atsedemariam Girma, who has given me her
computer and moral support, strength and encouragement in completing my thesis.
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ABSTRACT
This research was conducted to evaluate water saving, yield performance and water productivity
of onion and pepper by using Wetting Front Detector (WFD) under irrigated conservation
agriculture in Dangeshita watershed experimental plots in Dangla, woreda during 2018/2019
irrigation season. The data collected included irrigation water applied, moisture content, plant
height and yields. This was done from four water management groups that were irrigation with
guidance of Wetting Front Detector with climate based irrigation scheduling (WFD-FAO), WFD
with farmer‟s irrigation practice (WFD-FIP), crop water requirement (CWR) and farmer
irrigation practice (FIP).
WFD-FAO and WFD-FIP treatments were found to be good for irrigation water management.
The overall findings of these experiments were that WFD-FAO irrigation water management
system can save water (32.6 % for onion field, 22.3 % for pepper field) and increased yield (29
% for onion, 50 % for pepper) as compared with farmer irrigation practice. Similarly, WFD-FIP
treatment irrigation water management system can save water (27.8 % for onion field, 32.4 % for
pepper field) and increased yield (32 % for onion, 39.3 % for pepper) with respect to farmer
irrigation practice. Water used by WFD-FAO and WFD-FIP treatments in onion field were
however reduced by 15.26 % and 8.5 % respectively and yield were also decreased by 15.6 %
and 12 % as compared to CWR treatment respectively. However, when WFD combined with
farmers‟ practices, it performed very well. WFD-FAO and WFD-FIP treatments water
productivity increased by 51 % and 50 % for onion production season and 60 % and 58 % for
pepper production season compared to FIP treatment respectively. Although many indicators
confirm that WFD-FAO and CWR treatments practicality at farmer‟s level is questioning as it is
more computers based. Thus, the use of WFD-FIP treatment appears to be an alternative for
water saving without negligible trade-off in yield.
Key words: Wetting Front Detector, Crop water requirement, crop and water productivity,
Conservation agriculture.
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Table of Contents
DECLARATION .......................................................................................................................................... ii
ACKNOWLEDGMENTS ........................................................................................................................... vi
ABSTRACT ................................................................................................................................................ vii
LIST OF ABBREVATIONS ....................................................................................................................... xi
LIST OF FIGURES .................................................................................................................................... xiii
LIST OF TABLES ...................................................................................................................................... xiv
1. INTRODUCTION .................................................................................................................................... 1
1.1 Background ......................................................................................................................................... 1
1.2 Problem statement ............................................................................................................................... 3
1.3. Objectives .......................................................................................................................................... 4
1.3.1 General objective ......................................................................................................................... 4
1.3.2 The specific objective of the study .............................................................................................. 4
1.4 Research questions .............................................................................................................................. 4
1.5. Significance of the study .................................................................................................................... 5
2. LITERATURE REVIEW ......................................................................................................................... 6
2.1 Irrigation water management .............................................................................................................. 6
2.2. Conservation agriculture (CA) ........................................................................................................... 6
2.2.1 Permanent or semi-permanent organic soil cover ........................................................................ 8
2.2.2 Minimal soil disturbance .............................................................................................................. 9
2.2.3 Rotations .................................................................................................................................... 10
2.3. Irrigation scheduling ........................................................................................................................ 10
2.3.1 Soil water balance ...................................................................................................................... 10
2.4. Practical Irrigation Scheduling Methods.......................................................................................... 12
2.4.1 Soil-based approaches ................................................................................................................ 12
2.4.2 Plant-based approaches .............................................................................................................. 13
2.4.3 Atmospheric-based approaches.................................................................................................. 14
2.5. Crop water requirement ................................................................................................................... 15
2.6. Monitoring soil water in irrigation scheduling ................................................................................ 16
2.6.1. The Wetting Front Detector (WFD) .......................................................................................... 16
2.7. Application depth ............................................................................................................................. 17
2.8 Computing water productivity and irrigation water use efficiency .................................................. 18
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2.9 Agronomy of crops ........................................................................................................................... 19
2.9.1. Onion (Allium cepa L.) ............................................................................................................. 19
2.9.2. Pepper (Capsicum annuum) ...................................................................................................... 20
3. MATERIALS AND METHODS ............................................................................................................ 21
3.1 Description of the study area ............................................................................................................ 21
3.2. Experimental Design and Treatment ................................................................................................ 22
3.3 Installation of wetting front detector (WFD) .................................................................................... 24
3.4 Determining irrigation water amount and application interval ......................................................... 25
3.4.1 Crop water requirement (climate) based irrigation scheduling .................................................. 25
3.4.2. Irrigation interval ...................................................................................................................... 27
3.5 Data Collection ................................................................................................................................. 28
3.5.1. Soil physico-chemical properties .............................................................................................. 28
3.5.2 Metrological data ....................................................................................................................... 28
3.5.3 Soil moisture .............................................................................................................................. 29
3.5.4. Crop data ................................................................................................................................... 30
3.5.5. Amount of irrigation water applied ........................................................................................... 31
3.6. Water productivity and irrigation water use efficiency.................................................................... 31
3.7. Data analysis .................................................................................................................................... 32
4. RESULT AND DISCUSSION ............................................................................................................... 33
4.1. Irrigation Water Applied .................................................................................................................. 33
4.1.1. Onion Irrigation Water Applied per onion growth stages ......................................................... 33
4.1. 2 Total irrigation water applied to onion ..................................................................................... 35
4.1. 3. Pepper Irrigation Water Applied per pepper growth stages ................................................... 37
4.1. 4. Total irrigation water applied to Pepper.................................................................................. 40
4.2 Agronomic Performance of onion and pepper .................................................................................. 41
4.2.1. Plant height for onion ................................................................................................................ 41
4.2.2 Plant height for Pepper ............................................................................................................... 42
4.2.3 Yield of onion ............................................................................................................................ 43
4.3 Water productivity (WP) ................................................................................................................... 45
4.4 Irrigation water use efficiency (IWUE) ............................................................................................ 47
5. CONCLUSION AND RECOMMENDATION ..................................................................................... 49
5.1. Conclusion ....................................................................................................................................... 49
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5.2. Recommendations ............................................................................................................................ 50
6. REFERENCE .......................................................................................................................................... 51
APPENDIXES ............................................................................................................................................ 55
Appendix A Irrigation data collection sheet for WFD.FAO, WFD.FIP, CWR and FIP ............................. 56
Appendix-B: ANOVA single factor for onion irrigation volume for each stages (m3/ha) between
treatments .................................................................................................................................................... 57
Appendix-C: ANOVA single factor for onion irrigation volume (m3/ha) between treatments ................. 58
Appendix-D: ANOVA single factor for Pepper irrigation volume (m3/ha) between treatments ............... 59
Appendix-E: ANOVA single factor for Onion plant height (cm) between treatments ............................... 60
Appendix-F: ANOVA single factor Analysis for Pepper plant height (cm) between treatments ............... 61
Appendix-G: ANOVA single factor Analysis for Onion yield (kg/ha) ...................................................... 62
Appendix-H: ANOVA single factor Analysis for pepper yield (kg/ha) ..................................................... 63
Appendix-I: ANOVA single factor Analysis for Onion water productivity (kg/m3) ................................. 64
Appendix-J: ANOVA single factor Analysis for Pepper water productivity (kg/m3) ................................ 65
Appendix-K: ANOVA single factor Analysis for Onion Irrigation Water use efficiency (kg*ha-1*mm-1)
.................................................................................................................................................................... 66
Appendix-L: ANOVA single factor Analysis for Pepper Water use efficiency (kg*ha-1*mm-1) ............. 67
Appendix-M: Normal Q-Q plot and frequency distribution curve for onion .............................................. 68
WFD-FAO onion irrigation (m3/ha) ........................................................................................................... 68
Appendix-N: Normal Q-Q plot and frequency distribution curve for pepper............................................. 79
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LIST OF ABBREVATIONS
AD Available Deficit
AMD Available Moisture Deficit
ANOVA Analysis of variance
ASMIC Appropriate scale mechanization consortium
AW Available Water
BEC Bulk Electrical Conductivity
CA Conservation agriculture
CV Coefficient of Variance
CWD Crop water demand
CWR Crop Water Requirements
CWUE Crop water use efficiency
DAP Di Ammonia Phosphate
DAT Day after transplanting
ET Crop Evapotranspiration
ETc Crop evapotranspiration
ETo Reference evapotranspiration
FAO Food and Agricultural Organization
FC Field capacity
FIP Farmers irrigation practice
FSWD Full Stop Wetting Detector
GPS Geographic Information System
Ha hectare
IWUE Irrigation Water use efficiency
IP Irrigation productivity
IR Irrigation requirements
IWMI International Water Management Institution
IWR Irrigation Water Requirements
ILRI International Livestock Research Institute
Kc Crop coefficient
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LSD List Significant Difference
MAD Maximum allowable depletion
m.a.s.l. Meter above Sea Level
MoA Ministry of Agriculture
MWR Ministry of Water Resource
NMM Neutron Moisture Meter
NT No-tillage
PH Power of hydrogen
PET Potential evapotranspiration
PVC polyvinylchloride
PWP Permanent wilting point
RCBD Randomized Complete Block Design
SMC Soil moisture content
SSI Small scale-irrigation
SOM Soil organic matter
TAW Total Available Moisture
TDR Time domain reflectometry
TT Traditional tillage
USAID United States Agency for International Development
WFD Wetting front detector
WFD-FAO Wetting front detector with Food and Agricultural Organization
WFD-FIP Wetting front detector with Farmers irrigation practice
WHC Water holding Capacity
WP Water productivity
Y Yield
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LIST OF FIGURES
Fig-1 Site description of the study area ...................................................................................................... 21
Fig- 2 Flow chart for the overall treatments and ways for the experiment design ...................................... 24
Fig- 3 wetting front detector use training and installation ......................................................................... 25
Fig-4 TDR calibration gragh ...................................................................................................................... 30
Fig- 5 Statistical quartile description of irrigation water applied for each crop stage ................................ 35
Fig- 6 Statistical quartile description of Total irrigation water applied to onion ........................................ 37
Fig- 7 Statistical quartile description of irrigation water applied for each crop stages ............................... 39
Fig- 8 onion tuber heights at each observation day (Day After Transplanting) ......................................... 42
Fig- 9 Statistical quartile description of Yield of onion and pepper for each water management .............. 45
Fig-10 Statistical quartile description of Onion and pepper water productivity (Kg/m3) ........................... 46
Fig- 11 Statistical quartile description of Onion and pepper irrigation water use efficiency (kg/m3) ....... 48
xiv
LIST OF TABLES
Table 1: Experimental plots historical and current information of Cropping pattern and rotations of CA. 22
Table 2 Calibrated reading for all plots .................................................................................................... 34
Table 3 onion Applied water (mm) to each growth stages of onion variations .......................................... 36
Table 4 Total irrigation water applied to onion .......................................................................................... 38
Table 5 Applied water (mm) to each growth stages of pepper variations ................................................. 40
Table 6 Total Water applied (mm) to Pepper fields .................................................................................... 41
Table 7 Descriptive statistical values of water applied to onion and pepper .............................................. 41
Table 8 summary of average plant height (cm) of onion for each treatment .............................................. 43
Table 9 pepper height for each treatment of onion and pepper for each water management ..................... 44
Table 10 Onion and pepper production season water productivity (Kg/m3) ............................................... 46
Table 11 IWUE of treatment under onion and Pepper irrigation production............................................. 47
1
1. INTRODUCTION
1.1 Background
Traditional intensive soil tillage systems, will generally lead to soil degradation and loss of crop
productivity, Thierfelder et al. (2013). For sustainable agriculture is to be achieved, the
paradigms of agricultural production and management must be changed and new farming
practices such as conservation agriculture must be implemented. Conservation Agriculture (CA)
is widely recognized as a viable concept for practicing sustainable agriculture. Its principles are
already widely adopted and there are opportunities for further collaboration, synergy and
complementarily(Le et al., 2017). Conservation agriculture (CA) refers to the simultaneous use
of three main principles namely; less disturbance of the soil i.e. reduced tillage zero-tillage and
direct seeding; soil cover i.e. crop residue, cover crops, relay crops or intercrops to mitigate soil
erosion and to improve soil fertility ; crop rotation to control weeds, pests and diseases (Gautam
et al., 2006, Busari et al., 2015). No-tillage experiencing a persistent and steady growth in the
world. Information in some parts of the world is very scarce or nonexistent, and in most
countries statistics on CA technologies are based on estimates. It is estimated that no-tillage is
practiced on about 95 million hectare around the world. Approximately 47% of the technology is
practiced in Latin America, 39% is practiced in the United States and Canada, 9% in Australia
and about 3.9% in the rest of the world, including Europe, Africa and Asia. Despite good quality
and lengthy research in these three continents, no-tillage has had only small rates of
adoption(Kassam et al., 2015).
Commercial home gardens under conservation agriculture (CA) combined with efficient water
application technology have potential to contribute towards a sustainable agriculture
development in sub-Sharan Africa (SSA) (Tewodros T, 2017). Conservation agriculture is a
model of sustainable agriculture as it leads to profitable food production, while protecting and
even restoring natural resources (Nigatu Dabi, 2017). Conservation agriculture benefits farmers
because it reduces production costs and increases yields, but it also has positive impacts on the
whole society: enhancement of food security to a better soil fertility, improvement of water
quality, reduction of erosion and mitigation of climate change by increasing carbon sequestration
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are mentioned among others. Conservation agriculture systems are also less sensitive to extreme
climatic events and therefore contribute to the adaptation to climate change and the resilience of
agricultural systems. Hence, conservation agriculture becomes a fundamental element of
sustainable production intensification, combining high production with the provision of
environmental services(Berger et al., 2010).
Efforts to ensure food self-sufficiency at house hold level require efficient use of irrigation water
and appropriate water application techniques. The farmers‟ irrigation method is aiming at
supplying sufficient water to crops to avoid water stress during the whole growing stage, and
achieve maximum yield (Megersa and Abdulahi, 2015). Effective use of irrigation water is a key
issue for agricultural development in regions where water is limiting factor for crop production.
The amount of water and land available for agriculture is limited in many developing countries.
Although efforts to increase crop production have been focused on the field of irrigation, the
world is continually challenged to increase production using an ever decreasing amount of water.
Therefore, the world wide decline in water resources requires further development of water-
saving irrigation strategies in order to improve irrigation water and water use efficiency. Thus,
increasing water use efficiency has been an urgent issue in a region where water demand has
been an increasing concern. One of the possible approaches is to increase the efficiency and
productivity of the existing irrigation systems to optimize water use i.e. less volume of applied
water with greater production (Mohammed-Salih and Quraishi, 2013).
There are many techniques through which optimization of irrigation water input to the crop can
be evaluated. Techniques like wetting front detector and crop water requirement under
conservation agriculture are very useful and popular these days. These techniques are required to
be evaluated for their efficiency as compared to farmer‟s practice on the basis of observation of
crop condition. The wetting front detector (WFD) was conceived and developed against the
background of poor adoption of commonly available technologies. Essentially the WFD
reframed the age-old irrigation scheduling question from „when to turn the water on‟ to „when to
turn it off‟( Fessehazion et al., 2011; Stirzaker et al., 2017).Scientists and extension workers
make irrigation scheduling sound easy. The soil holds water like a bucket. An irrigator should
not add too much water and overfill the bucket that would be a waste. The irrigator must also not
let the bucket get too empty that would stress the crop. There are excellent tools on the market
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for monitoring the soil water status, but the Full Stop Wetting Front Detector might be the
simplest of them all.
Irrigation scheduling is planning when and how much water to apply in order to maintain healthy
plant growth during the growing season. Proper irrigation scheduling is a means for optimizing
agricultural production and conserving water. The goal of irrigation scheduling is to control the
water status of the crop to achieve a targeted level of agronomic performance. The performance
level can vary from optimizing irrigation input to optimizing the output where crop yield is
maximized. The efficient use of water is also dependent upon the relationship of both
deficiencies and excesses of water to plant growth (Boutraa, 2010). Efficient water usage must
be based upon a thorough understanding of climatic, soil, crop and management factors. Climate
is uncontrolled but it is possible to modify its effects through good irrigation and crop
management. The practical questions are therefore: to answer what are the effects of over-
watering? How much water should be used? and what is the proper rate of watering?” (Wheater
and Evans, 2009). Excess irrigation can lead to permanent loss of land resources and leaching
out of nutrients through lateral flow and deep percolation. Water as well as nutrients, are lost
within the system leading to severe on-site (e.g. decreasing soil fertility, soil compaction) and
off-site effects.
Finally a well-designed technology such as wetting front detector and crop water requirement
under conservation agriculture allows sustainable management of the system, because it aids in
the suppression of weed and pest problems. Different crop species with different root systems
explore different soil horizons and hence increase the efficiency of the use of soil nutrients. A
permanent organic soil cover (from a crop, a cover crop or a vegetative mulch) ensures the
protection of the soil surface from wind, rain, sun and from drying out, and provides a regular
supply of organic matter, which is a key feature for soil fertility. Only the combination of these
techniques with their synergistic effects can lead to sustainable, resource-saving agriculture, and
at the same time be productive and profitable.
1.2 Problem statement
In common irrigation practices the irrigation water is applied over the irrigable area without
considering the amount needed and time of the requirement by plant. Within Ethiopia, irrigation
of farmland is often practiced through a flood system resulting in high water losses through
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runoff and leaching and therefore the removal of available plant nutrients. Improper input and
irrigation water application does not only lead to loss of those resources but also leads to
unsustainable production capacity of the land and water resources.
Sustainable development in irrigated agriculture can be achieved by a wise use and proper
utilization of scarce resources. Many parts of the region suffer from the lack of knowledge in
using adequate water application and poor irrigation scheduling. Most farmers think that the
more water used to irrigate a crop, the higher the crop productivity. In addition to more water
losses, over-irrigation washes away necessary plant nutrients and leads to deep percolation
beyond the root zone, resulting in soil fertility loss and productivity decreases. Therefore
irrigation water utilization is a serious problem in the study area.
1.3. Objectives
1.3.1 General objective
The general objective of this research was to test the combination of WFD with conservation
agriculture can improve crop and water productivity.
1.3.2 The specific objective of the study
To evaluate the effect of WFD under conservation agriculture on crop yield and water
productivity.
To evaluate the effect of WFD with farmer scheduling practice under conservation
agriculture on crop yield and water productivity.
To investigate the role of Crop water requirement scheduling for farm plots under
conservation agriculture.
To quantify the amount of water saving by using water management methods compared
to farmers irrigation practice.
1.4 Research questions
Does the use of wetting front detectors with crop water requirement scheduling under
conservation agriculture have a significant impact on crop yield and water productivity?
Does the use of wetting front detectors with farmer scheduling practice under
conservation agriculture have a significant impact on crop yield and water productivity?
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Does the use of crop water requirement scheduling under conservation agriculture have a
significant impact on crop yield and water productivity?
Which method of irrigation water management was best to save water and increase water
productivity?
1.5. Significance of the study
The availability of water use and crop management information on small scale irrigation in
farmer fields is not common. From this i observed that the data that required for properly
utilization of water and crop management information will be necessary. Because of
Traditionally irrigation water is applied in the field without considering the daily crop water
requirement as per the prevailing climatic conditions and the crop development as well as the
existing soil moisture content. As such, improper application of irrigation water on the field,
there is loss of scares resource like, water and land. Based on this the study would try to do the
proper irrigation system to the farmer, in order to save our scares resource and increase yield
productivity. To applied irrigation in one country it requires proper design and management of
water, and to design and manage proper water it use different data like crop coefficient, irrigation
productivity and water use efficiency. So this study was introduce wetting front detector under
conservation agriculture to advert the efficient irrigation system to the farmer, to evaluate the
effect of crop and water productivity value by conservation agriculture, as a result all can reduce
a problem like water lodging, runoff, salinity build-up, then it save our scares resource.
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2. LITERATURE REVIEW
2.1 Irrigation water management
The dependence of Ethiopian agriculture on rainfall makes the country vulnerable to drought and
famine caused by climatic variability. The increment in food demand can be met in one or a
combination of three ways: increasing agricultural yield per unit area, increasing the area of
arable land, and increasing cropping intensity (number of crops per year). So, increasing yield in
both rain-fed and irrigated agriculture and cropping intensity in irrigated areas through various
methods and technologies are the most viable options for achieving food security in the country
However the major limitation for surface irrigation in the Ethiopian highland is the availability
and suitability of water and land respectively (Worqlul et al., 2015).
Irrigation water management plays a great role in both water and crop productivity (Bjornlund et
al., 2017) and can be improved by irrigation scheduling techniques (Yazar). Irrigation scheduling
is the use of water management strategies to prevent over and under application of water while
minimizing yield loss due to water shortage or nutrient losses resulting in obtaining optimum
yield (Woldewahid et al., 2011). Irrigation scheduling helps to manage the water for maximum
yield production by creating the conducive environment for the nutrient movement within the
soil and good nutrient up take of the plant (Etissa et al., 2014).The quantity of water pumped can
often be reduced without reducing yield. Soil moisture and climatic data are crucial factors for
irrigation scheduling to calculate the appropriate water volume to be applied to the field
(Chakrabarti et al., 2014).
2.2. Conservation agriculture (CA)
The name CA has been used over the last seven years to distinguish this more sustainable
agriculture from the narrowly-defined „conservation tillage‟ (Wall, 2006). Conservation tillage is
a widely used terminology to characterize the development of new crop production technologies
that are normally associated with some degree of tillage reduction, for both pre-plant as well as
in-season mechanical weed control operations that may result in some level of crop residue
retention on the soil surface. The definition of conservation tillage does not specify any
7
particular, optimum level of tillage, but it does stipulate that the residue coverage on the soil
surface should be at least 30 % (Jarecki and Lal, 2003). CA, however, removes the emphasis
from the tillage component and addresses a more enhanced concept of the complete agricultural
system. CA is based on three principles: minimal soil disturbance, soil cover with crop residues,
and crop rotation. However, there is a considerable misunderstanding as to what actually
constitutes CA. There are those who advocate that “true CA” involves only the use of continuous
zero-till seeding in a narrow slit into untilled soils combined with permanent coverage of the soil
surface with crop residues. CA practiced in this manner has been implemented successfully,
particularly for rain-fed production systems.(Derpsch, 2005) estimated that in 2005, there was
over 96 million ha of zero-till CA worldwide with over 90 % of this area used primarily in rain-
fed production systems in five countries (USA, Brazil, Argentina, Canada and Australia). Thus,
less than 10 % of the zero-till CA area occurs in the rest of the world. Information in other parts
of the world is very scarce or nonexistent, and in most countries statistics on CA technologies are
based on estimates. It is estimated that no-tillage is practiced on about 95 million ha around the
world. Approximately 47 % of the technology is practiced in Latin America, 39 % is practiced in
the United States and Canada, 9 % in Australia and about 3.9 % in the rest of the world,
including Europe, Africa and Asia. Despite good quality and lengthy research in these three
continents, no-tillage has had only small rates of adoption (Kassam et al., 2015).
It is, therefore, very apparent that there are many crop production systems in the world at large
where the application of CA-based only on zero-till seeding with permanent soil residue cover is
not currently being used and may never be. However, there is a consensus developing that asserts
the best application or use of CA is defined by a set of principles(Kassam, 2009).Which can be
applied essentially to all crop production systems and that these CA-based principles can provide
the foundation to support most crop management/ improvement activities (Araya et al., 2012;
Corbeels et al., 2014). These CA principles are applicable to a wide range of crop production
systems from low yielding, dry, rain-fed conditions to high-yielding, irrigated conditions.
However, techniques to apply the principles of CA will be very different in different situations,
and will vary with biophysical and system management conditions and farmer circumstances.
Specific and compatible management components (pest and weed control tactics, nutrient
management strategies, rotation crops, appropriately-scaled implements) will need to be
identified through adaptive research with active farmer involvement (Corbeels et al., 2014)
8
Conservation agriculture is based on the healthy functioning of the whole agro-ecosystem with a
maximum attention and focuses on the soil. The soil is the entry point and it has to be considered
not only as a simple physical support for roots and plants, but as a living entity with its physical,
chemical and biological characteristics. The focus of CA embraces not only the nutrient contents
of the soil but also its structural and biological status, which are determinants of sustained
productivity. The paradigm of CA is that an undisturbed soil has the opportunity to develop and
produce healthier plants. Indeed, not disturbing the soil has a lot of positive outcomes: soil life
can develop in a stable habitat in quantity and quality better than on tilled soils; the structural
integrity of the soil is maintained, so continuous vertical macro-pores are not destroyed and
remain as drainage channels for rainwater into the soil; the weed seed bank in the soil does not
receive the stimulation for germination. Seeding under conditions of minimum soil disturbance is
achieved by direct seeding through the mulch cover without tillage. Zero-tillage is necessary, but
it is not sufficient to achieve a sustainable CA system. It has to be combined with at least two
complementary practices which are soil cover and diversified crop rotations (Berger et al., 2010).
Conservation agriculture (CA) aims to conserve, improve and make more efficient use of natural
resources through integrated management of available soil, water and biological resources
combined with external inputs. It contributes to environmental conservation as well as to
enhanced and sustained agricultural production. It can also be referred to as resource-efficient or
resource effective agriculture (Hobbs, 2007)
2.2.1 Permanent or semi-permanent organic soil cover
Surface mulch helps reduce water losses from the soil by evaporation and also helps moderate
soil temperature. This promotes biological activity and enhances nitrogen mineralization,
especially in the surface layers. We investigated the eff ect of conservation agriculture (grass
mulch cover and no-tillage) on water-saving on smallholder farms in the Ethiopian highlands
(Sisay A.2019). This is a very important factor in tropical and sub-tropical environments but has
been shown to be a hindrance in temperate climates due to delays in soil warming in the spring
and delayed germination showed that zero-till had lower soil temperatures in the spring in
Argentina, but traditional tillage (TT) had higher maximum temperatures in the summer, and that
average temperatures during the season were similar. A cover crop and the resulting mulch or
previous crop residue help reduce weed infestation through competition and not allowing weed
9
seeds the light often needed for germination. There is also evidence of allelopathic properties of
cereal residues in respect to inhibiting surface weed seed germination Weeds will be controlled
when the cover crop is cut, rolled flat or killed (Hossain, 2013). A farming practice that
maintains soil micro-organisms and microbial activity can also lead to weed suppression by the
biological agents(Chee-Sanford et al., 2006).Interactions between root systems and rhizo bacteria
affect crop health, yield and soil quality. Release of exudates by plants activate and sustain
specific rhizo bacterial communities that enhance nutrient cycling, nitrogen-fixing, bio control of
plant pathogens, plant disease resistance and plant growth stimulation. (Peters et al., 2003) gave
a review of this topic. The ground cover would be expected to increase biological diversity and
increase these beneficial effects.
2.2.2 Minimal soil disturbance
Tillage and current agricultural practices result in the decline of soil organic matter due to
increased oxidation over time, leading to soil degradation, loss of soil biological fertility and
resilience (Araya et al. (2012). Although this soil organic matter (SOM) mineralization liberates
nitrogen and can lead to improved yields over the short term, there is always some mineralization
of nutrients and loss by leaching into deeper soil layers. This is particularly significant in the
tropics where organic matter reduction is processed more quickly, with low soil carbon levels
resulting only after one or two decades of intensive soil tillage. Zero-tillage, on the other hand,
combined with permanent soil cover, has been shown to result in a build-up of organic carbon in
the surface layers (Thierfelder et al. (2015). Zero-tillage minimizes SOM losses and is a
promising strategy to maintain or even increase soil C and N stocks (Majanen and Scherr (2011).
Tillage takes valuable time that could be used for other useful farming activities or employment.
Zero-tillage minimizes time for establishing a crop. The time required for tillage can also delay
timely planting of crops, with subsequent reductions in yield potential (Hobbs & Gupta
2003).reducing turn-around time to a minimum, zero-tillage can get crops planted on time, and
thus increase yields without greater input cost. Turn-around time in this rice-wheat system from
rice to wheat varies from 2 to 45 days, since 2–12 passes of a plough are used by farmers to get a
good seedbed (Hobbs & Gupta 2003). With zero-till wheat this time is reduced to just 1 day.
Tractors consume large quantities of fossil fuels that add to costs while also emitting greenhouse
10
gases (mostly CO2) and contributing to global warming when used for ploughing(Dyer and
Desjardins, 2003). Animal-based tillage systems are also expensive since farmers have to
maintain and feed a pair of animals for a year for this purpose. Animals also emit methane, a
greenhouse gas 21 times more potent for global warming than carbon dioxide (Jungkunst and
Fiedler, 2007). Zero-tillage reduces these costs and emissions. Farmer surveys in Pakistan and
India show that zero-till of wheat after rice reduces costs of production by US$60 per hectare
mostly due to less fuel (60–80 L ha−1
) and labor (Hobbs and Gupta, 2004).
2.2.3 Rotations
Crop rotation is an agricultural management tool with ancient origins.(Peters et al., 2003)
reviewed the cultural control of plant diseases from a historical view and included examples of
disease control through rotation. The rotation of different crops with different rooting patterns
combined with minimal soil disturbance in zero-till systems promotes a more extensive network
of root channels and macro pores in the soil. This helps in water infiltration to deeper depths.
Because rotations increase microbial diversity, the risk of pests and disease outbreaks from
pathogenic organisms is reduced, since the biological diversity helps keep pathogenic organisms
in check (Hossain, 2013). Integrated pest management (IPM) should also be added to the CA set
of recommendations, since if one of the requirements is to promote soil biological activity,
minimal use of toxic pesticides and use of alternative pest control methods that do not affect
these critical soil organisms are needed.
2.3. Irrigation scheduling
2.3.1 Soil water balance
Food and Agricultural Organization explained that when surface irrigation methods are used, it is
not very practical to vary the irrigation depth and frequency too much. In surface irrigation,
variations in irrigation depth are only possible within limits. It is also very confusing for the
farmers to change the schedule all the time. Therefore, it is often important to estimate the
irrigation schedule and to fix the most suitable depth and interval to keep the irrigation depth and
the interval constant over the growing season (FAO, 1989).
11
Irrigation scheduling is about knowing when to apply water and how much, for there are well
developed scientific tools needed to schedule irrigation. Implementation of irrigation scheduling
technologies could play a big role in improving water use efficiency and reducing production
costs (Aladenola and Madramootoo, 2014). Accurate irrigation scheduling requires that water be
applied not on the day the soil simply appears to be dry or on the day that happens to be most
convenient, but that the correct amount is applied when the crop requires it (Bonachela et al.,
2006).The correct amount should be applied at the correct time, based on the understanding of
each individual crop‟s requirement, soil type and the practicalities of application Thus, irrigation
scheduling aims at applying water before the soil becomes dry enough to affect the crop, and
thereby providing an environment to maximize plant growth. This purpose, quite often, is
constrained by the need to use a limited amount of water to stretch water supplies, and to reduce
drainage and minimize pollution. To accomplish this it is necessary to quantitatively consider the
soil water balance, as it is impossible to do so by visual examination of the crop or soil (Pereira
et al., 2007).
Numerous irrigation scheduling aids have been developed in the past. Every method follows the
basic question of when and how much water to apply, and focuses on the understanding of the
soil water balance. It is evident that the goal of scientific irrigation scheduling is achievable, but
there is a need for simple basic approaches that can be adaptable to practical situations at farm
level. The understanding of the soil water balance in irrigation planning is fundamental. All
aspects of irrigation management require an understanding of the soil water balance, which
necessitates simulation or measurement of the amount of water in the root zone at any given time
(Maeko, 2003).
I + P = Es + T + R + Dr+ ∆S (2 .1)
Where:- I = irrigation P = precipitation Es=soil evaporation T = transpiration
R =run-off Dr= drainage below root zone ∆S = change in soil water storage
Climatic conditions indicate the timing and amount of precipitation, and it has a direct influence
on potential evapotranspiration (PET), and hence actual evapotranspiration (ETa), through
evaporative demand. The rate of ETa increases with an increase in net radiation and a decrease in
relative humidity provided the soil water status can provide for water lost due to E and T, and if
precipitation occurs in quantities greater than the soil water holding capacity, drainage will
occur. Some of the water will be lost as run-off if the rate of water Infiltration into the soil is low.
12
Surface runoff, R, is also difficult to estimate in many instances. There are few measurements
made of R in irrigation, it is difficult to estimate R, so additional uncertainty is introduced.
However, there are many conditions where R is zero and can be predicted as such. Technologies
know-how exist for determining soil water depletion. Such tools involve the use of devices like
the neutron probe for measuring soil water status at the beginning and end of a certain time
period (Greacen, 1981).
2.4. Practical Irrigation Scheduling Methods
There are three main approaches to scheduling soil-based plant-based and atmospheric driven.
Each has strong and weak points, and there is a history associated with each. For example,
atmospheric based methods were the most common (because with Epan it was reasonably easy to
measure evaporation from a pan) but then the advent of the neutron probe and tensiometer made
soil-based measurements more popular(Greacen, 1981). while, (mostly in the 70‟s and 80‟s),
plant-based methods looked promising scientifically, but never took off in the marketplace
(Seyfried and Murdock, 2001).
Among the methods used for determining when and how much to irrigate to the field are wetting
front detector (WFD), tensiometer, FAO and TDR are some irrigation scheduling techniques
Frequently a minimum of 10 years climatic data is used representing the average conditions on-
site. The method is easier and less costly compared to the soil moisture based TDR as it does not
require equipment and frequent measurements, however depending on the climatic variability the
method might over or underestimate the irrigation requirement. In some cases a combination of
both methods is used to correct for climatic variations or real-time climatic data is used.
2.4.1 Soil-based approaches
Soil water content and soil water potential relate to the state (amount/availability) of water in the
soil, and soil water conductivity relates to the movement of water in the soil. Water content is
generally described in terms of the mass of water per unit mass of soil, or on a volume basis.
This measurement describes the amount of water stored in the soil. Direct measurement of water
content is possible by sampling the soil and weighing, drying, and reweighing the samples
gravimetric water content (gram of water per gram of soil). However, most literature cites water
content on a volumetric basis because irrigation amount is commonly expressed as a depth of
13
water. Soil water potential is the amount of work that must be done per unit quantity of pure
water in order to transport reversibly and isothermally an infinitesimal quantity of water from a
pool of pure water at a specified elevation at atmospheric pressure to the soil water at a specified
point (energy per unit quantity of soil water relative to that of pure free water at atmospheric
pressure), and is useful for describing the availability of water to plants and the driving forces
that cause water to move in soil (Lampurlanés et al., 2001).
Soil-based scheduling methods rely on sensors that monitor the moisture level in the soil at
appropriate locations and depths. As a plant uses water, the root zone soil moisture reservoir is
depleted. The wetting front detector is used to detect the soil moisture. When sensors indicate
that the remaining soil moisture level reaches a critically low value, irrigation is applied. Sandy
soils have a low water storage capacity and a high infiltration rate. They therefore, need frequent
but small irrigation applications. None of the surface irrigation methods can be used if the
infiltration rate is more than 30 mm/hour (Brouwer et al., 1985).
2.4.2 Plant-based approaches
Visual examination of plant responses to soil and environmental conditions can serve as a logical
indicator for irrigation scheduling (Hsiao and Bradford, 1983). These methods are based on the
delicate balance between crop water uptake from the soil and water loss (Hsiao and Bradford,
1983) through ET. Water stress occurs when atmospheric demand exceeds water supply from the
soil. Plants draw quantities of water in excess of their essential metabolic needs; this water is
transmitted to an unquenchably thirsty atmosphere through the stomata as transpiration (T)
(Hillel, 1998).
There are numerous methods, destructive and non-destructive, for determining plant water status,
like thermocouple psychrometry, which measures leaf water potential(Lampurlanés and Cantero-
Martinez, 2003).These methods are labor intensive and require many samples. Measurements
must be normalized with well-irrigated fields for accurate estimations (Maeko, 2003). Plants can
also be used to schedule irrigation through visual observation. However, visual indicators of
plant stress are often an after the fact method of scheduling, and thus considerable dry matter
loss may occur before being noticed. Plants can indicate when to irrigate, but we still need to
14
know how much irrigation water to apply. Most of the techniques mentioned above are not
practical as they are too difficult for use in the field on a routine basis.
2.4.3 Atmospheric-based approaches
The effect of climate on crop water use has long been recognized. As such, the atmospheric-
based approach follows a meteorological imposed evapotranspiration demand that varies over
time. The irrigation requirements are determined by the rate of evapotranspiration (ET) (Wanjura
et al., 1990). Thelevel of ET is related to the evaporative demand of the atmosphere and the
supply rate of water from the soil/root system.ET is directly inferred from the residual of the soil
water balance after all other components have been measured in equation 2.1 and are given as:
ET =I + P – R –Dr - ∆S (2.2)
Reference evapotranspiration (ETo) is defined as the rate at which readily available soil water is
vaporized from specified vegetated surfaces without restrictions other than the atmospheric
demand (Gwate et al., 2018). The concept of the reference evapotranspiration was introduced to
study the evaporative demand of the atmosphere independently of crop type, crop development
and management practices. As water is abundantly available at the reference evapotranspiring
surface, soil factors do not affect ET, the only factors affecting ETo are climate parameters
(Meshram et al., 2011). Crop ETo is commonly estimated from climatic data using
meteorological equations that relate a reference ETo value with a crop coefficient (Kc).The
reference ETo is based on the Evaporation and Transpiration losses from a uniform, well-
watered, actively growing, and well covered vegetative surface, either a cool season grass (ETo)
or alfalfa (Skaggs and Irmak, 2011).
ETo represents the rate of evapotranspiration of an extended surface of an 8 to 15cm tall green
grass cover, actively growing, completely shading the ground and not deficient of water.
Methods to calculate the reference evapotranspiration include the Penman-Monteith grass cover
equation, and the Pan evaporation of water is also used as a reference method of
estimating(Hargreaves and Merkley, 1998).ETa is related to maximum crop evaporation (ETm)
by an empirically determined crop coefficient (Kc) when water supply fully meets the water
requirements of the crop.
ETm= Kc.Eta (2 .3)
15
The value of Kc varies with crop, development stage of the crop, to some extent with wind-speed
and humidity, and management (irrigation frequency). For most crops, the Kc value increases
from a low value at the time of crop emergence to a maximum value during the period when it
reaches full development, and declines as the crop matures. Kc values of different crops have
been developed (Lazzara and Rana, 2010).
ETa is a dynamic process driven by the available energy, and can be limited by the ability of the
plant to conduct water from the soil to the leaf. To compute ETa one requires; minimum and
maximum temperature, solar radiation, minimum and maximum relative humidity, and average
wind speed.
2.5. Crop water requirement
In irrigated agriculture, Crop water requirement (CWR) is important scheduling tools to optimize
water applications during the growing season (Schütze, de Paly et al. 2012). Improvement in
water use efficiency can be achieved through accurate estimations of CWR and proper irrigation
scheduling practices. Different crops have different water requirement at different development
stage so that crop type and its development stage identification is critical to quantify the volume
and time of irrigation (Xiangxiang et al., 2013).
Standard information on crop coefficient, rooting depth, depletion level and yield response
factors, and length of individual growth stages are also needed. Crop water requirement is the
total amount of water required to sustain the normal plant growth. The water requirement of
crops is the amount of water that is needed to meet the evapotranspiration rate so that crops may
thrive. It is the quantity of water required by a crop in a given period of time for normal growth
under field conditions and includes evaporation and other unavoidable wastes. Crops need a
continuously and right amount of water from the time of sowing to maturity. However, the rate
of use of water varies with the kind of crop grown, time taken by the crop to mature, and the
weather conditions like: temperature, wind, solar radiation and relative humidity. Other
important factors need to be considered in the computation of the crop water requirement on
daily basis are rooting depth, growth stage as affected by soil moisture deficit, and the allowable
soil water depletion.
16
2.6. Monitoring soil water in irrigation scheduling
2.6.1. The Wetting Front Detector (WFD)
The Wetting Front Detector (WFD) is a simple user friendly device designed to help farmer‟s
better management irrigation. It is a funnel shaped device that is buried open end up in the soil.
The WFD is buried in the root zone and gives a signal to farmers when water reaches a specific
depth in the soil. Farmers can use the detector to know whether they are applying too little or too
much (Jenny et al., 2008).
In response to low adoption of existing irrigation tools, a Wetting Front Detector (WFD) was
developed in an attempt to attain maximum simplicity (Stirzaker, 2005) for an irrigator,
especially those farmers who cannot read and write. The wetting front detectors are the
mechanical version having a float visible at the surface to provide the signal that a wetting front
had reached the prescribed depth. The WFD comprises a specially shaped funnel, a filter and a
mechanical float mechanism. The funnel is buried in the soil within the root zone of the crop.
When the soil is irrigated, water moves downwards through the root zone. The infiltrating water
converges inside the funnel and the soil at the base becomes so wet that water seeps out of it,
passes through a filter and is collected in a reservoir. This water activates a float, which in turn
operates an indicator flag above the tool.
Wetting front detectors are simple devices that are buried at points of interest and provide
information to growers as to when the water has reached that point in the soil profile. When soil
moisture increases above a set point the detector switches on, or is activated, indicating that
water has reached a given depth. When the soil moisture dries to below a set point the detector
switches off but practically it is not perfect. Wetting front detectors are often placed near the
bottom of the root zone so that they can be used to warn against over-irrigation. WFDs are based
around the tipping bucket analogy, where soil layers are viewed as a sequence of buckets that
store water. As the upper bucket is filled by irrigation, it tips and spills excess water into the
bucket below and so on down the profile. The WFD was designed to show when water moved
from one layer to the next. It is comprised of a specially shaped funnel, a filter, and a float plus
indicator mechanism. The funnel shape was designed so that the soil at its base reaches
saturation when matrices potential of the soil outside the funnel is around 2 k Pa to 3 k Pa which
corresponds to a relatively „strong‟ wetting front. Once saturation occurs at the base of the
17
funnel, free water flows through a filter into a small reservoir and activates a float. The float trips
a magnetically latched indicator, visible to the irrigator. These detectors are often installed at
different depths and used in a similar way to that for tensiometre, namely a shallow detector
indicating water entering the root zone and a deeper detector possibly warning of over-irrigation
(Stirzaker and Hutchinson, 2006).There are two versions of the newly patented wetting front
detector; one is called a full stop, which derives its name from a logical combination of the
words „full‟‟ and „stop‟‟. A full stop can stop an irrigation event by breaking the circuit to a
solenoid valve when the soil is „full‟‟ of water to a required depth, hence the name „‟Full Stop‟‟.
Once saturation occurs at the base of the funnel, free water flows through a filter into a small
reservoir and activates the float. The float trips a magnetically latched indicator, visible to the
irrigator. These detectors are often installed at different depths and used in a similar way to that
for tensiometr, namely a shallow detector indicating water entering the root zone and a deeper
detector possibly warning of over-irrigation (Stirzaker and Hutchinson, 2006).
2.7. Application depth
Critical to any irrigation management approach is an accurate estimate of the amount of water
applied to a field. Too little water causes unnecessary water stress and can result in yield
reductions. Too much water can cause water logging, leaching, and may also result in loss of
yield. The efficiency of irrigation water is partially affected by the irrigation application method
i.e. how water is applied. There are different methods that have been attempted to conserve water
and use it efficiently. Common irrigation methods in vegetable production are overhead,
sprinkler, furrow irrigation or, micro (drip) irrigation. Overhead irrigation with its ability to
provide controlled and frequent water applications directly in the vicinity of the crop root zone
can be relatively efficiently compared to furrow irrigation, decreasing water losses. The depth of
water application usually is the amount of water in millimeters that needs to be supplied to the
soil in order to bring it back to field capacity. In another words, it is the amount of water diverted
to the irrigated land by the irrigator up to plant needs met, or the amount of water required to be
detected by the wetting front detector. Mathematically, it is calculated as follows (Quraishi et al.,
2016).
dnet= (FC-PWP)*Drz*P (2.4)
Where:-dnet= depth of water to apply (mm) FC = soil moisture at field capacity (%)
18
PWP = soil moisture at permanent wilting point (%)
Drz= the depth of soil that the roots exploit water effectively (m)
ρ= the allowable portion of available moisture permitted for depletion by the crop
before the next irrigation
2.8 Computing water productivity and irrigation water use efficiency
“In its broadest sense, water productivity (WP) is the net return for a unit of water used”(Molden
et al., 2010), reported. Improvement of water productivity aims at producing more food, income,
better livelihoods and ecosystem services with less water. Water productivity, in its broader
sense, defines the ratio of the net benefits from crop, forestry, fishery, livestock and mixed
agricultural systems to the amount of water consumed to produce those benefits. There is a need
to find new ways to increase water productivity by improving biological, economic and
environmental output per unit of water used in both irrigated and rain fed agricultural systems.
An increase in agricultural water productivity is the key approach to mitigate water shortage and
to reduce environmental problems in arid and semiarid regions. In its broadest sense, water
productivity (WP) is the net return for a unit of water used. Improvement of water productivity
aims at producing more food and/or income with less water. Improving agricultural water
productivity is about increasing the production of irrigated crops per unit of water use. Precision
irrigation technique is among the practices used to achieve improved water productivity.
Application of irrigation water closing the right amount is the major limiting factor for crop
production. According to the report of Andreas and Karen (2002), managing the time and
amount of applied irrigation is critical to achieve optimum yield. Crop water productivity is
already quite high in highly productive regions. Consider complex biophysical and
socioeconomic factors (Hsiao et al., 2007).
The low water use efficiency in farmer‟s fields compared with well-managed experimental sites
indicates that more efforts are needed to transfer water saving technologies to the farmers. Under
such scenarios, water-saving agriculture and water saving irrigation technologies, including
deficit irrigation, low pressure irrigation, subsurface drips, drip irrigation under plastic covers,
furrow irrigation, rainfall harvesting and conservation agriculture shall be quite helpful. Water
saving agriculture includes farming practices that are able to take full advantage of the natural
19
rainfall and irrigation facilities. Where water is more limiting than land, it is better to maximize
yield per unit of water and not yield per unit of land.
2.9 Agronomy of crops
2.9.1. Onion (Allium cepa L.)
Onion (Allumcepa) crop can be grown under a wide range of climates from temperate to tropical.
Onion is one of the most important vegetable crops produced in Ethiopia. Among different
varieties, Bombay Redis the most widely used as a cash crop by the farmers in the rift valley
areas (Desalegne and Aklilu, 2003). Onion is a cool-season biennial monocot with a prominent
bulb, hollow cylindrical leaves and a strong odor when bruised. The optimum temperature for
plant development varies between 130C and 24
0C, while, for raising seedlings, it requires up to
20-250C and generally require high temperatures for bulbing and curing (Shanmugasundaram
and Kalb, 2001). Onions grow on a variety of soils ranging from sand to clay loams. However,
they prefer loamy soil that is fertile, well-drained and high in organic matter, with a preferred pH
range of between 6.0 and 8.0 (Olani and Fikre, 2010). Onions do not thrive in soils below pH 6.0
because of trace element deficiency, or occasionally, aluminum or manganese toxicity. Onions
could be produced on slightly alkaline soils, but are sensitive to soil salinity.
The crop coefficient (Kc) relating reference evapotranspiration to water requirements for
different development stages after transplanting is, for the initial stage 0.4 to 0.6 (15 to 20 days),
development stage 0.7 to 0.8 (25 to 35 days), mid-season stage 0.95 to 1.1 (25 to 45 days) and
late- season stage 0.75 to 0.95 (35 to 45 days). Managing the time and amount of applied
irrigation is critical to achieving optimum yield and quality. Light and frequent irrigations are
required through irrigation systems throughout the growing season for several reasons: root
system is shallow; therefore, very little water is extracted from a soil depth deeper than 0.6 m,
and most are from the top 0.3 m. This indicates that upper soil areas must be kept moist to
stimulate root growth. Rates of transpiration, photosynthesis and growth are lowered by even
mild water stress (Balasubrahmanyam et al., 2004). Onions usually harvested from 100 to 140
days after transplanting when 80% of the bulbs become completely mature, which is evident by
the collapse of 20 to 50% of the neck tissue and falling of the tops.
20
2.9.2. Pepper (Capsicum annuum)
Pepper (Capsicumannum) is a seasonal plant of the family solanaceae (Tong and Bosland, 2003)
which is thought to originate from tropical America. Pepper thrives in climates with growing
season temperatures in the range of 180C to 27
0C during day and 15
0C to 18
0C during the night.
Temperature range of 4-60C will stop the plants thriving. Fruit set is also prevented by
temperature below 160C and above 32
0C. Higher yield results when daily air temperature ranges
from 180C to 32
0C, and below 18
0C provide neglible growth in pepper plants (Lownds et al.,
1994). The altitude range of pepper production is 1000 to 1800 m.a.s.l. Light-textured soils with
adequate water holding capacity and drainage are preferred. Optimum pH is 5.5 to 7 and acid
soils require liming. Water logging, even for short periods, causes leaf shedding. The crop is
moderately sensitive to soil salinity, except in seedling stage when it is more sensitive.
The spacing of 30 cm between plants and 80-100 cm between rows has been recommended. As
with most crops, the ideal soil for producing pepper is one described as deep, well-drained,
medium textured sandy loam or loam soil that holds moisture and has some organic matter.
Transplanting pepper in row at recommended space (80 x 30 cm for irrigation, to a depth of 9 cm
holes) increase productivity and has many advantages. It avoids plant competition for resources,
allows root growth and development, enhances plant growth and lateral branch production and
thereby increases flowering. Besides, it facilitates to carry out field operation: cultivation,
weeding, hoeing, fertilizer application, drain land and conduct crop protection activities (Carter,
1994). Total Water Requirements are 600 to 900 mm and up to 1250 mm for long growing
periods and several pickings. The crop coefficient (Kc) is 0.4 following transplanting, 0.95 to 1.1
during full cover and for fresh peppers 0.8 to 0.9 at time of harvest. When maximum
evapotranspiration is from 5 to 6 mm/day, the total available soil water depletion from 0.25
to0.40 can be allowed(Allen et al., 1996).
21
3. MATERIALS AND METHODS
3.1 Description of the study area
The study was conducted in the Dangishita Watershed which is situated adjacent to the town of
Dangla in the Dangla Woreda, Awi Zone in Amhara Regional State. The watershed is located
about 80 km south west from Bahir Dar,36.83° N and 11.25° E and an average 2000 m above sea
level. It has a sub-tropical (“Woina Dega”) climate with average annual rainfall of 1800mm, with
minimum temperature of 25oC and maximum temperature of 33
oC. Crop production mainly
includes cereals (mainly maize, teff, millet, barley); and high value irrigated crop production like
tomato, onion, potato, pepper and garlic. Groundwater experience in smallholder irrigation is
relatively high. Shallow groundwater is the main water sources used for irrigation. The area
suffers from a dry period, which begins in December and lasts until the end of May. From
June/July to September/October there is a rainy period. In the woreda, there are 27 rural Kebeles
among which one of them supported by ASMIC (Appropriate scale mechanization consortium)
project that selected for the study.
Fig-1 Site description of the study area
AWI ZONE
22
3.2. Experimental Design and Treatment
This research was conducted over on-farm research experimental plots which have been
selected in 2014 year using full participation of farmers. Among 50 plots, forty smallholder
farmers were selected who fulfilled the criteria of having hand dug wells and plots for
vegetable production at their home garden. Water availability in the wells was checked
throughout the season from the mid of November to end of May. Conservation Agriculture
Experimental practice have been started since 2016 and farmers have been experienced in
different water management challenges over cropping different vegetables in this
experimental area. All the Experimental plots were coded and the Codes explained that
irrigation water applied moisture content, plant height and crop data are collected by using
these codes. Conservation agriculture in this study includes a combination of little soil
disturbance, crop rotation and cover the soil (mulching). Average of 2 ton/ha local grass soil
mulch cover was used in each irrigation season and Cropping pattern and rotations in a
continuous wet and dry vegetable production of CA before this experiment and the current
experiment was in the order of Onion-Pepper-Garlic-pepper-onion-pepper-onion. This
research design includes only the fifth (onion in the dry period) and the sixth (pepper in
partial irrigation period).
Table 1: Experimental plots historical and current information of Cropping pattern and rotations
in a continuous wet and dry vegetable production of CA.
Irrigation season year Irrigation period Irrigated crop Soil cover per season
First irrigation season 2016 Selection of farmers and training the users about the project
Soil sampling and land preparation was conducted
Second irrigation season 2017 Dry irrigation period Onion 2 ton/ha
Third irrigation season 2017/2018 Wet rainy period Green Pepper 2 ton/ha
Forth irrigation season 2018 Dry irrigation period Garlic 2 ton/ha
Fifth irrigation season 2018/2019 Dry irrigation period Onion 2 ton/ha
Sixth irrigation season 2019 Wet rainy period Green pepper 2 ton/ha
Seventh irrigation season 2019/2020 Dry irrigation period Onion 2 ton/ha
23
The experimental design was a one factor experiment arranged in Randomized Complete Block
Design (RCBD). The case covered two irrigation seasons, first onion experiment conducted in
the year of 2018/2019 and second pepper experiment conducted in the year of 2019 dry season
irrigation experiment with overhead irrigation application. Both onion and pepper experiment
were used to investigate the effect of different scheduling practices (WFD-FAO, WFD-FIP,
CWR and FIP) to changes in onion and pepper water productivity (WP). This experiment on-
farm set up was designed using size of farms plots 100 m2 and 20 cm spacing between rows and
30 cm spacing between plants for onion and spacing of 30 cm between plants and 80-100 cm
between rows has been recommended for pepper. Accordingly, 10 plots (replicates) were
selected for each treatment (WFD-FAO, WFD-FIP, CWR and FIP) under over head irrigation
application method (fig.2). All farmers grew the same variety of Adama Red Onion (Allium cepa
L.) and pepper (Capsicum annuum).
The Four treatments organized in to each plot were as follows.
WFD-FAO: Irrigation amount was determined by wetting front detector (WFD) and
irrigation interval determined by climate based scheduling (FAO).
WFD-FIP: Irrigation amount determined by wetting front detector and irrigation interval
managed by farmer‟s irrigation practice.
CWR: Both irrigation amount and interval managed by Crop water requirement
scheduling.
FIP: Both irrigation amount and interval managed by Farmers irrigation scheduling.
24
Fig-2 Flow chart for the overall treatments and ways for the experiment design
3.3 Installation of wetting front detector (WFD)
Wetting front detector (WFD) instrument was given to 20 farmers and data collectors were
trained to assist in data collection. During installation of WFD we selected places where
irrigation started first within the plot. Twenty WFDs were installed in pairs with the first at one
third root depths, and the second at two thirds depth. The depth for the shallow detector was 20
cm and for the deep detector was 40 cm (from the base). The shallow detector gives
information‟s to guide the irrigation amount and the time to stop irrigation while the deep
detector informs farmers over irrigation (Fig- 3).
The procedure for installing the WFDs was as follows:
Make a hole with the shovel and trowel that can accommodate the wide part of the
funnel. When the hole is deep enough, to make a hole for the bottom of the Full Stop
If the soil texture changes with depth, keep the different soil layers separate
Add the filter sand (supplied with the WFD) to the detector until it covers the locking
ring
Place the detector in the hole and measure the distance to the rim of the funnel to check
that it reaches the required depth
Make sure the extension tubes are vertical
25
Fill the funnel with soil from the same layer, and lightly pat it down
Fig-3 wetting front detector use training and installation
3.4 Determining irrigation water amount and application interval
3.4.1 Crop water requirement (climate) based irrigation scheduling
The calculation of crop evapotranspiration (ETc) was done by the determination of the potential
evapotranspiration(ETo) by the method of penman-monteith equation using excel spread sheet
and multiplying with the crop coefficient (Kc) for a specific crop stage. Afterward the gross
irrigation water requirement is calculated using the application efficiency whilst the irrigation
interval is calculated taking into account the maximum allowable moisture depletion. The
fraction which 25 % for onion and 30 % for pepper. Other important factors need to be
26
considered in the computation of the crop water requirement on daily basis are rooting depth,
growth stage as affected by soil moisture deficit, and the allowable soil water depletion.
The amount of water needed to compensate the amount of water lost through
evapotranspiration (ETc) requires reference evapotranspiration (ETo) and crop coefficient (Kc).
Periodic reference crop evapotranspiration (ETo) for each day of climatic record is
calculated based on the modified FAO Penman-Monteith equation using water balance excel
spreadsheet. The FAO Penman-Monteith method uses standard climatic records of solar
radiation (sunshine), air temperature, humidity and wind speed for daily, weekly and monthly
calculations and calculates ETo as (Allen et al., 1998).
( )
( )
( ) ( )
Where: Rn= net radiation at the crop surface (MJ m2 day
-1),
G= soil heat flux density (MJ m-2 day-1),T= mean daily temperature at 2 m height (0 C),
U2= wind speed at 2 m height (ms-1),es = saturation vapour pressure(kpa),
ea = actual vapour pressure(kpa),(es-ea) = saturation vapour pressure deficit (kpa),
∆ = slop vapour pressure curve (kpa0C
-1), γ = pyschrometric constant (kpa
0C
-1).
3.4.1.1 Calculating the actual crop evapotranspiration
The monthly averaged daily ETC (mm day-1
) is calculated by multiplying the average monthly
potential evapotranspiration (ETo, mm day-1) with the crop coefficient at different development
stages. The crop coefficient (Kc) relating reference evapotranspiration (ETo) to water
requirements (ETc) for different development stages after transplanting to harvest (Báldi and
Faragó, 2007). Research conducted by (Bekele and Tilahun, 2007) showed that to obtain
maximum yield it is necessary to avoid water deficit, especially during the bulb development.
( )
The equation can be re-organized as follows:
( )
27
(
)
( )
(
) ( )
Where TAW is the total available water content of the soil in (mm) in the root zone, FC field
capacity (%), PWP permanent welting point (%) and D is effective root depth (m) which is taken
as 0.25 for onion and 0.3 for pepper, SMC is soil moisture content of the succeeding date (mm)
whereas SMCt-1 is the previous soil moisture content (mm), P is the effective rainfall (mm) and
I is the irrigation (mm) at time step. At the onset of the irrigation season SMCt-1 is the initial soil
moisture measured or estimated in the field to calculate the initial amount of water irrigated. Soil
water near the permanent welting point is not readily available resulting in crop stress. Hence,
TAW cannot be fully used by the plant and hence irrigation frequency cannot be determined
from TAW. The factor at which crop water stress occurs is multiplied with the TAW to derive
the manageable allowable depletion or sometimes called maximum allowable depletion
(MAD)(Chakrabarti et al., 2014). This is different for each crop. Hence irrigation is needed
before the SMC reaches the MAD level.
The calculated irrigation depth (mm) is finally converted to irrigation volume through
multiplication of irrigation depth by the area:
( )
Irr vol= irrigation volume (m³), Irr = the irrigation depth (mm) and A the plot area (m²)
3.4.2. Irrigation interval
It is the interval between successive irrigation applications in days which is guided by the
susceptibility of the crop to water shortage, and was conducted for WFD-FAO and CWR
treatments from CWR prediction result, as well as the for WFD-FIP and FIP treatments from
their own traditional knowledge of scheduling in this study. It was computed from the required
depth and crop water use rate as (Brouwer et al., 1989):
( )
28
Where: f = irrigation interval in days,
Zreq = required depth of water applied (mm) and
ETc = crop water requirement (mm/day)
3.5 Data Collection
3.5.1. Soil physico-chemical properties
The soil characteristics of the study area have been determined in the studies of 2019, (Sisay.et
al., 2019), so this data was collected from that document. The collected soil characteristics data
would tell us physical and chemical characteristics such as initial soil moisture content, total
moisture content, and bulk density, and CEC, EC, N and soil texture, field capacity, permanent
welting point. As determined in the previous study, the average soil physical and chemical data
are for onion experiment soil pH analysis result values within 20 cm depth was ranging for
WFD treatment‟s within 6.18 - 5.56 values. The pH of experimental site was approximately 6 for
both treatment groups, which is suitable for onion requirement in terms of PH. The soil texture of
all of the experimental plots was clay and clay loam and which is medium textured soil and
suitable for onion growing (FAOSTAT, 2001). In this study the value of EC within 20cm
ranging for WFD treatment‟s within 0.71 – 0.122 dS/m. Onion crop is sensitive to soil salinity
and yield decrease at varying levels of EC is: 0% at EC 1.2dS/m, 10% at EC 1.8dS/m, 25% at
EC 2.8dS/m, 50% at EC 4.3dS/m and 100% at EC 7.5dS/m. For this as the soil salinity values in
the study area was below 1.2dS/m it will not affect crop production.
3.5.2 Metrological data
Weather data was collected from Dangla Meteorological Agency. The collected data contained
maximum and minimum temperature (°C), wind speed (m s-1
), solar radiation (MJ m-2
day-1
) and
relative humidity (%). The daily rainfall during the cropping season was calculated by
summarizing the 10 minute recordings from the automatic rain gauge located in the watershed.
Gimenez et al., (1996) offered various starting points for irrigation scheduling and ETo
calculation, depending on the extent that a country may be developed. Climate and soil data sets
were identified as the first step in optimizing irrigation scheduling under the constraints of water
supply.
29
The rainfall data used during the growing season of onion and pepper were taken from a manual
gauge which was installed in the study area. The effective rainfall is used based on FAO (1986a)
which is calculated by the following formulas.
( ) ( )
( ) ( )
Where Pe is effective rain fall and P is monthly rainfall in millimeter.
3.5.3 Soil moisture
The Time Domain Reflector meter was used at each crop stage to obtain moisture readings in
each plot. The TDR has 20 cm rods giving average soil moisture content in the first 20cm of the
soil profile. Soil moisture readings were taken six readings per plot in every crop stage for each
plot and the average was calculated. Based on the readings the calculation of irrigation quantity
to be applied in the field was calculated for each farmer. To know the total available water in the
root zone of onion information on field capacity, permanent wilting point and root depth is
required. The total available water is the water holding capacity of the root zone.
Table 2 Calibrated TDR reading for all plots
TDR
reading
weight of
container
weight of
soil
moisture
weight
of dry
soil
weight of
moisture
gravimetric
water
constant(Ө)
in %
dry
bulk
density
wet
bulk
density
Volumetr
ic water
content
in (%)
18.3 136 100 75.5 24.5 32.45 1 1.32 32.45
30 136 108 80.5 27.5 34.16 1.06 1.43 36.42
21.2 139.5 116 89 27 30.33 1.17 1.53 35.76
31.6 142.5 106 77.5 28.5 36.77 1.02 1.40 37.74
23.7 136.5 73.5 59 14.5 24.57 0.78 0.97 19.20
29.5 136 100.5 50.5 50 99.00 0.66 1.33 66.22
19.4 140 77 55 22 40 0.72 1.01 29.13
30 139.5 74 48.5 25.5 52.57 0.64 0.98 33.77
31.4 136.5 99 71 28 39.43 03 1.31 37.08
49.8 140 117 77.5 39.5 50.96 1.02 1.54 52.31
20 137.5 90.5 65 25.5 39.23 0.86 1.19 33.77
41 139.5 105 73 32 43.83 0.96 1.39 42.38
23.9 139.5 108 82 26 31.70 1.08 1.43 34.43
The TDR calibration result was described in the graph below.
30
Fig-4 TDR calibration result
3.5.4. Crop data
The crop is shallow rooted and sensitive to water stress which requires light and frequent
irrigation. For this reason, Overhead irrigation was selected for onion experiment in 2018/2019
production year. Red Bombay Onion (AlliumcepaL.) variety with recommended root depth was
transplanted on October 2018/2019 at a spacing of 20 cm between rows and 30 cm between
plants (Sara, 2015). The lengths of the four growth stages were 20 days for initial, 30 days for
development, 40 days for mid, and 30 days for the harvest stage without considering seedling
ages. The lengths for the four growth stages were adjusted according to previous records of
onion growth in the area.
The plant agronomic data like plant heights, number of plants, and number of leaves were
collected during the irrigation season every 10 days. Onion was harvested on the end of February
2019 and yield was collected from the total plot area. Similarly, Spacing of 30 cm between plants
and 80-100 cm between rows on the first week of January 2019 after onion harvest pepper was
transplanted. Transplanting pepper in row at recommended space (80 x 30 cm for irrigation, to a
depth of 9cm holes) increase productivity and has many advantages. It avoids plant competition
for resources, allows root growth and development, enhances plant growth and lateral branch
Production and thereby increases flowering. The plant agronomic data like plant heights, number
of plants, and number of leaves were collected during the irrigation season.
y = 0.6303x + 17.781 R² = 0.8136
0
10
20
30
40
50
60
0 20 40 60Aver
age
TD
R s
oil
mo
iture
rea
din
g i
n %
Average volumetric content in %
TDR measurement
TDR measurement
Linear (TDRmeasurement)
31
3.5.5. Amount of irrigation water applied
All actual water applied to crops‟ fields with respect to treatments was recorded during the
irrigation events. During the irrigation season, irrigation water was quantified based on different
treatments: In the case of WFD-FAO and CWR treatments Irrigation water was applied at
allowable constant soil moisture depletion (p=0.25) of the total available soil moisture
throughout onion growth stage (Enchalew et al., 2016). Similarly for pepper Irrigation water was
applied at allowable constant soil moisture depletion (p=0.3) of the total available soil moisture
throughout pepper growth stage. In the case of WFD-FAO treatment scheduling was fixed at a 1
to 2-days interval whereas the amount of water was determined by when the yellow flag popup
and was recorded in the field book. Thus the amount of irrigation applied on any day was
directly relates to the initial water content. This means when the initial soil is wet before
irrigation, the response of flag is quickly and requires small amount of water. When the soil is
dry before irrigation, then the response of flag is slowly and requires high amount of water. For
the case of WFD-FIP the amount of water applied is determined by the yellow flag popup and
the irrigation interval is applied by the farmer‟s traditional practice. For CWR treatment, it was
the crop water requirement to meet the evapotranspiration rate and includes evaporation and
other unavoidable wastes. In the WFD-FAO treatment and in the CWR treatment amount of
water applied and irrigation interval were determined and given to the farmers. Those values
were compared against the amount of water actually applied by the different farmer‟s irrigation
practice in the water management treatments. These technologies and other water managements
can be evaluated based on the basis of water saving and water use efficiency. Irrigation was
ceased 2 weeks before harvest for best yield of onion and to prevent rotting and sprouting.
3.6. Water productivity and irrigation water use efficiency
Water productivity is the measure of the physical or economic value generated from a given
quantity of water. Water productivity of a crop for a particular plot could be quantified by the
ratio expression of yield produced within that particular area to the total sum of irrigation water
applied and rainwater received by that area.
WP = ( )
( ) ( )
Where: WP = Water productivity (Kg/m3) and Yield is (Kg/ha).
I=irrigation water applied (m3/ha)
32
( )
Where: IWUE = irrigation water use efficiency (kg/m3).
3.7. Data analysis
At the end of cropping season, onion and pepper yield and its parameters for all irrigation
treatments are determined. Firstly, the collected data such as irrigation amount, crop yield
and water use efficiency was checked by Q-Q plot normality test. Afterwards a to two-way
analysis of variance (ANOVA) using the Least Significant Differences (LSD) test at the 5%
probability level (P < 0.05) will performed. All statistical procedures involved in this study
were done using SPSS16.0 version software.
33
4. RESULT AND DISCUSSION
4.1. Irrigation Water Applied
4.1.1. Onion Irrigation Water Applied per onion growth stages
The irrigation amount in WFD-FAO treatment onion field was 40.8, 62.5, 67.9 and 46.8 mm, in
WFD-FIP treatment onion field was 48.5, 72.0, 80.7 and 54.9 mm, in CWR treatment was 50.3,
74.9, 87.8 and 43.4 mm and in FIP treatment onion field it was 66.4, 78.9, 62.9 and 78.5 mm for
early, development, mid and late stage respectively (Table 3). Under onion experiment, the
average irrigation water applied to each stage of onion was compared among each treatment. At
the initial stage of onion, the lowest (40.8mm) applied water was achieved in the WFD-FAO
treatment, and the highest (66.4 mm) applied water was attained in FIP treatment. In other
words, a 38.5 % irrigation water reduction was achieved under WFD-FAO treatment compared
to the FIP treatment (Table 3). Similarly, at development stage of onion, WFD-FAO treatment
used the lowest (62.5 mm) irrigation water while the FIP treatment used the highest (78.9mm)
water. This showed that a 20.7 % irrigation water reduction was attained under the WFD-FAO
treatment compared to the FIP treatment. At mid-season stage, the WFD-FAO received the
lowest (67.9mm) irrigation water, and the CWR treatment received the highest (87.8mm).
Similarly, a 22.5 % reduction in irrigation water was attained under WFD-FAO compared to the
CWR treatment (Table 3). At late season stage the lowest (43.4mm) applied water was achieved
in the CWR and the highest (78.5mm) water applied was achieved in the FIP showing a 44%
reduction in irrigation water under the CWR compared to FIP treatment (Fig-5).
However, statistically there was insignificant difference (P<0.05) among all the treatments for
each growing stage except the mid-stage as computed by using T-test with assuming equal
variance at α=0.05 significant level (Table 3). This study conservation agriculture with WFD-
FAO experimental onion irrigation season water applied with compared to previous studies
conservation agriculture without technology installation conducted by Sisay A. (2019), a 70, 71,
and 44 % reduction water was applied in initial, development, mid-season stage respectively
and 11.4 % higher water was applied in the late-season stage. The reason of the water applied
reduction is due to the WFD-FAO technology and the conservation year increment.
34
Table 3 Applied water (mm) to each growth stages of onion variations using analysis of variance
(α = 0.05) *
Crop
stage Treatments
WFD-FAO WFD-FIP CWR FIP* P-value LSD(0.05)
Initial 40.8 48.5 50.3 66.4 0.067ns
12.4
Dev.t 62.5 72.2 74.9 78.9 0.75ns
19.6
Mid 67.9 80.7 87.8 62.9 0.014s 13.1
Late 46.8 54.9 43.4 78.5 0.053ns
21.5
*WFD-FAO=Wetting Front Detect with FAO, WFD-FIP= Wetting front detector with farmers irrigation schedule,
CWR= Crop water requirement, FIP = Farmer Irrigation Practice, s = significance, ns = non significance.
As shown below in the box plot graph water attained at the mid-stage was relatively high in the
WFD-FIP and CWR compared to FIP (fig-5). Because of flowering stage of the crop
development, the crop needs more water and exhibits its peak physiological response (Fig 5).
This study compared to previous Experimental results found by Aynadis, (2018) showed that at
initial stage 26 % higher amount of water was applied but in development, mid-season stage and
late season stage water applied were reduced by 26 %, 57 %, 53 % in WFD-FAO treatment.
Similarly, this study conservation agriculture with WFD-FAO experimental onion irrigation
season water applied with compared to previous studies conducted by Bante et. (2015), 74, 52,
56 and 55 % reduction water was applied in initial, development, mid-season stage and late
season stage respectively. The reason of high water applied reduction is due to the conservation
agriculture and he explored that high irrigation water was attained at the initial crop stage though
in his work because, he did not use conservation agriculture practices like this study, CA water
was conserved and evaporation was minimized giving optimum soil moisture.
Detail irrigation water used by each plot with corresponding treatment groups and other
parameters showed in (Appendix B) for each stage of treatment. Similarly, statistical quartile
descriptions of irrigation amount for each growth stage were displayed by the following box plot.
35
Fig-5 Statistical quartile description of irrigation water applied for each crop stage
*WFD-FAO=Wetting Front Detect with FAO, WFD-FIP= Wetting front detector with farmers irrigation schedule,
CWR= Crop water requirement, FIP = Farmer Irrigation Practice.
4.1. 2 Total irrigation water applied to onion
Total irrigation water applied to onion was 211, 228.2, 249.3 and 313.3 mm, respectively, for
WFD-FAO, WFD-FIP, CWR and FIP treatments. The lowest (211mm) irrigation water was
applied in the WFD-FAO treatment, and the highest (313 mm) irrigation water applied was in the
FIP treatment. The total volume of irrigation water saved by using the WFD-FAO was
36
considerably higher which was about 32.6 % followed by the WFD-FIP (27.83 %) compared to
the farmer practice (FIP) (Table 3). These findings correspond with an earlier study conducted
with the same crop (Melaku.et.2015). The water applied to the onion field was reduced by 43 %
when using WFD-FAO under conservation agriculture compared to Melaku‟s (WFD with non-
conservation agriculture) experiment. Similarly, the water applied to the field was reduced by 41
% when using WFD-FAO under conservation agriculture compared to WFD with non-
conservation agriculture experiment (Talake‟s (2017). Corespondgly earlier conservation
agriculture without any technology with the same crop experimental results found by Sisay,
(2019) showed that total water applied were reduced by 59.5,56 and 52 % compared to WFD-
FAO,WFD-FIP and CWR treatments. This implies that the water saved by the WFD-FAO,
WFD-FIP and CWR treatment with conservation agriculture could irrigate more area compared
with WFD-FIP, WFD-FIP and CWR with non-conservation agriculture.
There was no significant difference among the irrigation treatments WFD-FAO, WFD-FIP and
CWR under onions for the amount of irrigation water applied but a significant difference
between WFD-FAO and FIP, between WFD-FIP and FIP and between CWR and FIP. The detail
of each treatment is shown in (Table-4).
Table 4 Total irrigation water applied to onion
Treatments
% reduction of onion
applied water (m3/ha)
Replication
WFD-FAO WFD-FIP CWR FIP WFD-FAO WFD-FIP CWR
(mm) (mm) (mm) (mm) (%) (%) (%)
1 235.8 220.3 271 382.9 38.4 42.46 29.23
2 215 229.6 284.1 344 37.51 33.25 17.4
3 2310 2325 2405.4 366 36.88 36.48 34.28
4 172 246.1 257.8 221.2 22.21 -11.27 16.56
5 201.8 218.2 251.7 296.4 31.9 26.39 15.09
6 217.1 224.1 244.3 291.2 25.43 23.06 16.11
7 203.5 227 196 291.2 30.1 22.01 32.7
Avg. 211 228.2 249.3 313 32.6 27.83 20.4
St.dev 213 93 280 552
37
* WFD.FAO=Wetting Front Detect with FAO, WFD.FIP= Wetting front detector with farmers irrigation schedule,
CWR= Crop water requirement, FIP = Farmer Irrigation Practice.
For onion cultivation under farmer‟s irrigation practice the FAO recommended average irrigation
amount is 350-550 mm (Allen, 1998a) whilst when irrigated using WFD-FAO only 210 mm
water was applied (Table 4). This shows that an average irrigation amount was reduced by 40 to
62 % under WFD-FAO with conservation agriculture compared to the FAO recommendation for
onion water requirement.
Detail irrigation water used by each plot with corresponding treatment groups and other
parameters showed in (Appendix Table-C) for WFD-FAO, WFD-FIP, CWR and FIP treatments
respectively. Similarly, other statistical quartile descriptions of irrigation amount for the total
values were displayed by the following box plot. From the graph below farmer‟s irrigation
practice was higher value than WFD-FAO, WFD-FIP and CWR by all maximum, minimum and
average value of total irrigation water applied (Fig-6).
Fig-6 Statistical quartile description of Total irrigation water applied to onion
* WFD.FAO=Wetting Front Detect with FAO, WFD.FIP= Wetting front detector with farmers irrigation schedule,
CWR= Crop water requirement, FIP = Farmer Irrigation Practice.
4.1. 3. Pepper Irrigation Water Applied per pepper growth stages
The irrigation amount in the WFD-FAO treatment pepper field was 123.7; 116.4; 125.8 and 61
mm, in WFD-FIP treatment pepper field was, 103.9, 96.8, 108.3 and 61 mm, in the CWR
treatment pepper field was 112.6, 107.7, 124.4 and 61 mm and in the FIP treatment pepper field
0.0
50.0
100.0
150.0
200.0
250.0
300.0
350.0
400.0
450.0
WFD-FAO WFD-FIP CWR FIPTota
l onio
n i
rrig
atio
n
wat
er a
ppli
ed
(mm
)
Q1 Q2-Q1 Q3-Q2 Min Outlier Max Outlier
38
it was 165.4; 159; 163 and 61 mm for early, development, mid and late-season stage respectively
(Table 4). Under pepper experiment, the average irrigation water applied to each stage of pepper
was compared among each treatment. At the initial stage of pepper field, the lowest (103.9 mm)
applied water was achieved in the WFD-FIP treatment, and the highest (165 mm) applied water
was attained in the FIP treatment. In other words, a 37 % irrigation water reduction was achieved
under WFD-FIP treatment compared to the FIP treatment and 8.5 % irrigation water reduction
was achieved compared to CWR treatment (Table 5). Similarly, at development stage of pepper,
WFD-FIP treatment used the lowest (96.8 mm) irrigation water while the FIP treatment used the
highest (159mm) water. This showed that a 38 % irrigation water reduction was attained under
the WFD-FIP treatment compared to the FIP treatment and 9.5 % irrigation water reduction was
received compared to CWR treatment. At mid-season stage, the WFD-FIP received the lowest
(108 mm) irrigation water, and the FIP treatment received the highest (163 mm). Similarly, a
33.7 % reduction in irrigation water was attained under WFD-FIP compared to the FIP treatment
(Table 5).
However, statistically there was significant difference (P<0.05) among all the treatments for
each growing stage computed by using T-test with assuming equal variance at α=0.05 significant
level (Table 5).
Table 5 Applied water (mm) to each growth stages of pepper variations using analysis of
variance (α = 0.05) *
Crop
stage
Treatments
WFD-FAO WFD-FIP CWR FIP* P-value LSD(0.05)
Initial 123.7 103.9 112.6 165.4 0.55
ns 0.96
Dev.t 116.4 96.8 107.7 159.1 0.52
ns 1.35
Mid 125.8 108.3 124.0 163.2 0.32
ns 1.55
Late 61.0 61.0 61.0 61 - -
Total 426.8 370.0 405.3 548.6
39
*WFD-FAO=Wetting Front Detect with FAO, WFD-FIP= Wetting front detector with farmers irrigation schedule,
CWR= Crop water requirement, FIP = Farmer Irrigation Practice, s = significance, ns = non significance.
Detail irrigation water used by each plot with corresponding treatment groups and other
parameters, statistical quartile descriptions of irrigation amount for each growth stage were
displayed by the following box plot.
Fig-7 Statistical quartile description of irrigation water applied for crop stages and total pepper
field
0
2
4
6
8
10
12
WFD-FAO
WFD-FIP
CWR FIP
Irri
gat
ion
wat
er a
pp
lied
(m
m)
Initial crop stage
Q1 Q2-Q1 Q3-Q2
Min Outlier Max Outlier
0
1
2
3
4
5
6
7
8
9
WFD-FAO
WFD-FIP
CWR FIP
Irri
gat
ion
wat
er a
pp
lied
(m
m)
Development stage
Q1 Q2-Q1 Q3-Q2
Min Outlier Max Outlier
0
1
2
3
4
5
6
7
8
WFD-FAO
WFD-FIP
CWR FIP
Irri
gat
ion w
ater
appli
ed (
mm
)
Mid-season stage
Q1 Q2-Q1
Q3-Q2 Min Outlier
Max Outlier
0
100
200
300
400
500
600
700
WFD-FAO
WFD-FIP
CWR FIP
Tota
l ir
rig
atio
n w
ater
appli
oed
(m
m)
Q1 Q2-Q1 Q3-Q2
Min Outlier Max Outlier
40
4.1. 4. Total irrigation water applied to Pepper
Total irrigation water applied to pepper field was 426, 370, 404 and 548 mm in the WFD-FAO,
WFD-FIP, CWR and FIP treatments, respectively (Table 6). The lowest (370mm) irrigation
water was applied in the WFD-FIP treatment, and the highest (548mm) irrigation water applied
was in the FIP treatment. The mean volume of irrigation water saved by using the WFD-FAO
and WFD-FIP, was respectively, 22.3 % and 32.4 % compared to FIP treatment and WFD-FAO
treatment was 5.2 % increment and WFD-FIP treatment was 13.2 % reduction compared to
CWR treatment (Table 6).
For Pepper cultivation under farmer‟s irrigation practice the FAO recommended average
irrigation amount is 600-900 mm (Allen, 1998a) whilst when irrigated using WFD-FAO only
426 mm water was applied (Table 6). This shows that an average irrigation amount was reduced
by 29 to 52% under WFD-FAO with conservation agriculture compared to the FAO
recommendation for pepper water requirement.
There was no significant difference among the irrigation treatments WFD-FAO, WFD-FIP and
CWR under pepper. However, the amount of irrigation water applied was significantly different
between WFD-FAO and FIP, between WFD-FIP and FIP, and between CWR and FIP. The detail
of each treatment was shown in (Appendix Table-D).
Table 6 Total Water applied (mm) to Pepper fields
Treatments
% Reduction of treatments
Replication
WFD-FAO
(mm)
WFD-FIP
(mm)
CWR
(mm)
FIP*
(mm) WFD-FAO WFD-FIP CWR*
1 443 409 317 512 13.4 20.1 38.1
2 375 382 505 586 36.0 34.9 13.8
3 458 342 411 526 12.9 35.0 21.9
4 498 380 365 516 3.5 26.3 29.2
5 385 352 423 587 34.4 40.0 28.0
6 395 356 - 551 28.3 35.3 -
7 - - - 555 - - -
Avg. 426.0 370.6 404.5 548.0 22.3 32.4 26.2
Stdev 422 224 629 312
41
* WFD.FAO=Wetting Front Detect with FAO, WFD.FIP= Wetting front detector with farmers irrigation schedule,
CWR= Crop water requirement, FIP = Farmers Irrigation Practice.
Table 7 Descriptive statistical values of water applied to onion and pepper
Onion Pepper
WFD-FAO
(mm)
WFD-FIP
(mm)
CWR
(mm)
FIP
(mm)
WFD-FAO
(mm)
WFD-FIP
(mm)
CWR
(mm)
FIP
(mm)
average 211 228 249 313 426 370 404.5 548
max 249.39 324.1 285.3 366 595.4 356.9 505.7 802.5
min 172.15 218.22 218.2 221.25 380.35 244.4 317.2 516.1
St.dev 22.13 36.29 32.38 53.36 71.2 40.6 57.4 90.5
* WFD.FAO=Wetting Front Detect with FAO, WFD.FIP= Wetting front detector with farmers irrigation schedule,
CWR= Crop water requirement, FIP = Farmer Irrigation Practice
4.2 Agronomic Performance of onion and pepper
4.2.1. Plant height for onion
Onion tuber height was higher (39.9 cm) in the WFD-FIP irrigation treatment, and lower (30.2
cm) in the farmer‟s treatment (FIP) (Table-8). However, there was no significant difference in
Plant height among experimental treatment at 5% level (Appendix E). The higher onion height in
the development stage which might result in larger biomass accumulation and potentially lead to
higher yields.
Table 8 summary of average plant height (cm) of onion for each treatment
Dayof
WFD.FAO WFD.FIP CWR FIP planting
20 17.68 20.11 15.19 14.22
40 30.38 32.76 27.58 24.84
60 41.88 39.76 38.89 30.08
80 68.55 67.09 64.83 51.78
Avg. 39.6 39.9 36.6 30.2
42
* WFD.FAO=Wetting Front Detect with FAO, WFD.FIP= Wetting front detector with farmers irrigation schedule,
CWR= Crop water requirement.
As shown below in the graph even if statistically no difference was there in biomass between the groups,
there was observed difference in biomass. It shows that from development to maturity stage the maximum
biomass was found in WFD-FAO, WFD-FIP and CWR treatments (Fig-8). The higher tube height in the
development stage might result in larger biomass accumulation and potentially lead to higher yields.
Fig-8 onion tuber heights at each observation day (Day After Transplanting)
* WFD.FAO=Wetting Front Detect with FAO, WFD.FIP= Wetting front detector with farmers irrigation
schedule, CWR= Crop water requirement, FIP = Farmer Irrigation Practice.
4.2.2 Plant height for Pepper
Similarly, pepper height was higher (38.25 cm) in the WFD-FAO irrigation treatment, and lower
(28.7cm) in the farmer‟s treatment (FIP).Generally WFD-FAO, WFD-FIP and CWR under
conservation agriculture was better growth parameters than FIP treatment by crop height.
However, there was no significant difference in plant height among experimental treatment at
5% level (Appendix F).
0
10
20
30
40
50
60
70
80
20 40 60 80
On
ion
pla
nt
hei
gt(
cm)
Day After Transplanting
WFD.FAO(cm)
WFD.FIP (cm)
CWR (cm)
FIP (cm)
43
Table 9 pepper height for each treatment
Days
of
Transplanting
WFD.FAO WFD.FIP CWR FIP
(cm) (cm) (cm) (cm)
20 20.1 19.7 20.3 15.5
40 31.1 29.7 29.4 23.7
60 45.8 42.5 42.1 34.3
80 56 53.7 53.7 41.4
Arg. 38.2 36.4 36.3 28.7
* WFD.FAO=Wetting Front Detect with FAO, WFD.FIP= Wetting front detector with farmers irrigation schedule,
CWR= Crop water requirement, FIP = Farmer Irrigation Practice.
4.2.3 Yield of onion
Average yield of onion was 10.8, 11.3, 12.8 and 7.6 ton/ha in the WFD-FAO, WFD-FIP, CWR
and FIP treatments, respectively, (Table 10). This showed that there was about 29.2 %, 32 %
yield reduction in the FIP compared to WFD-FAO and WFD-FIP treatments, respectively and
WFD-FAO, WFD-FIP treatments were reduced by 15.6 % and 11.7 % compared to CWR
treatment. The highest (18.7 ton/ha) onion yield was observed in the CWR treatment which was
higher compared to the WFD-FAO, WFD-FIP and FIP treatments and the lowest (13.7ton/ha)
onion yield was obtained in the FIP treatment. However, There was no significant difference in
onion yield among WFD-FAO, WFD-FIP, and CWR but significant at 5% significance level
between WFD-FAO and FIP, WFD-FIP and FIP and CWR and FIP (Appendix-G). This might be
related to the differences in irrigation water management, time of irrigation & amount of water
applied as well as differences in labors experiences. The experimental onion yield with compared
to previous studies WFD technology without conservation agriculture conducted by Melaku
(2015), 63 % and 64.5% larger yield variation was obtained in the WFD-FAO and WFD-FIP
treatments respectively. Similarly, the experimental onion yield with compared to previous
studies WFD technology with conservation agriculture conducted by Aynadis (2018), 10 % and
44
15 % larger yield variation was obtained in the WFD-FAO and WFD-FIP treatments
respectively. The increment in yield is due to the conservation year increment.
Average yield of pepper was 18.5, 15, 18 and 9.1 ton/ha for WFD-FAO, WFD-FIP, CWR and
(FIP) treatments, respectively (Table 10). The highest (18.5ton/ha) pepper yield was achieved in
the WFD-FAO treatment. The lowest (9.1ton/ha) yield was obtained in FIP treatment. There was
50.8 %, 39.3 %, yield reduction in the FIP compared to WFD-FAO, WFD-FIP treatments,
respectively. However, the average yield among WFD-FAO, WFD-FIP and the CWR treatment
did not differ significantly but significantly different compared to FIP treatment. Both the
standard deviation and the CV showed a medium variability within each of the WFD-FAO,
WFD-FIP and CWR treatment groups. This might be related to the differences in irrigation water
management, time of irrigation & amount of water applied as well as differences in labors
experiences. The detail is shown in the table below.
Table 10 Yield of onion and pepper for each water management
Onion
Pepper
Repn. WFD.FAO
(ton/ha)
WFD.FIP
(ton/ha)
CWR
(ton/ha)
FIP
(ton/ha)
WFD.FAO
(ton/ha)
WFD.FIP
(ton/ha)
CWR
(ton/ha)
FIP
(ton/ha)
1 9.3 4.6 17.5 12.5 45.5 21.7 22.5 13.7
2 15.9 14.2 9.5 6.9 14.3 5.0 19.2 15.2
3 12.0 14.0 4.5 13.7 19.6 10.3 18.7 6.2
4 8.0 14.7 14.6 4.3 10.3 16.3 19.5 10.0
5 14.2 7.2 18.7 6.2 11.5 13.7 10.0 7.1
6 7.6 13.7 11.4 5.4 9.6 23.1 - 6.4
7 8.5 10.3 13.4 4.3 - - - 5.0
Average 10.8 11.3 12.8 7.6 18.5 15.0 18.0 9.1
* WFD.FAO=Wetting Front Detect with FAO, WFD.FIP= Wetting front detector with farmers irrigation schedule,
CWR= Crop water requirement, FIP = Farmer Irrigation Practice.
Detail onion and pepper yield in each plot with corresponding treatment groups and other
parameters showed in (Appendix Table-H) for WFD-FAO, WFD-FIP, CWR and FIP treatments
respectively. Similarly, other statistical quartile descriptions of the total yield values were
displayed by the following box plot. From the graph below CWR was higher value than WFD-
FAO, WFD-FIP and FIP by average value of total yield (Fig-9).
45
Fig-9 Statistical quartile description of Yield of onion and pepper for each water management
* WFD.FAO=Wetting Front Detect with FAO, WFD.FIP= Wetting front detector with farmers irrigation schedule,
CWR= Crop water requirement, FIP = Farmer Irrigation Practice.
4.3 Water productivity (WP)
In onion production season water productivity were 4.12, 4.01, 4.28 and 2.02 kg/m3, respectively
for WFD-FAO, WFD-FIP, CWR and FIP treatments. This showed that 51 % and 49.6 % WP
lower in FIP compared to in the WFD-FAO, and WFD-FIP treatments. A maximum (4.28 kg/m3)
result of the average water productivity was observed in the CWR treatment. The least average
water productivity (2.02 kg/m3) was observed in the FIP treatment (Table 11). The difference
was also highly significant among the above variables compared to FIP treatment at 5 %
significance level (Appendix-J).
Similarly, pepper production season water productivity of the treatments were 4.3, 4.0, 4.4 and
1.7 kg/m3 was obtained in the WFD-FAO, WFD-FIP, CWR and FIP, respectively. A maximum
(4.4 kg/m3) result of the average water productivity was observed in the CWR treatment. The
least average water productivity (1.7 kg/m3) in pepper production season was observed in the FIP
treatment. This showed that 60.5%, 57.5% lower water productivity was attained in the FIP
compared to WFD-FAO and WFD-FIP treatments in pepper production (Table 11). The
difference was also highly significant among the above variables compared to FIP treatment at
5% significance level (Appendix-J).
0
2
4
6
8
10
12
14
16
18
20
WFD-
FAO
WFD-
FIP
CWR FIP
On
ion
yie
ld (
ton
/ha)
Q1 Q2-Q1Q3-Q2 Min OutlierMax Outlier
0
5
10
15
20
25
30
35
40
45
50
WFD-
FAO
WFD-
FIP
CWR FIP
Pep
per
yie
ld (
ton
/ha)
Q1 Q2-Q1
Q3-Q2 Min Outlier
Max Outlier
46
Table 3 Onion and pepper production season water productivity (Kg/m3)
Onion Water productivity(kg/m3) Pepper water productivity(kg/m
3)
Rep. WFD.FAO WFD.FIP CWR FIP WFD.FAO WFD.FIP CWR FIP*
1 3.2 1.7 5.4 2.9 10.3 5.3 6.6 2.7
2 6.0 5.1 2.9 1.8 3.8 1.3 3.8 2.6
3 4.3 4.9 1.6 3.3 4.3 3.0 4.3 1.2
4 3.6 4.9 4.7 1.6 2.1 4.3 5.0 1.9
5 5.6 2.7 6.2 1.8 3.0 3.9 2.2 1.2
6 2.8 5.0 3.9 1.6 2.5 6.5 - 1.2
7 3.4 3.7 5.4 1.3 - - - 0.9
Average 4.12 4.01 4.28 2.02 4.3 4.0 4.4 1.7
Max 5.97 5.07 6.17 3.29 10.3 6.5 6.6 2.7
Min 2.84 1.72 1.55 1.27 2.1 1.3 2.2 1.2
St.dev 1.21 1.34 1.63 0.75 2.8 1.6 1.4 0.7
* WFD.FAO=Wetting Front Detect with FAO, WFD.FIP= Wetting front detector with farmers irrigation schedule,
CWR= Crop water requirement, FIP = Farmer Irrigation Practice.
Detail water productivity of each plot with corresponding treatment groups and other parameters
is shown in (Appendix; J).For WFD-FAO, WFD-FIP, CWR and FIP, the statistical quartile
description of values displayed in (fig- 10).
Fig-10 Statistical quartile description of Onion and pepper water productivity (Kg/m3)
* WFD.FAO=Wetting Front Detect with FAO, WFD.FIP= Wetting front detector with farmers irrigation schedule,
CWR= Crop water requirement, FIP = Farmer Irrigation Practice.
0
2
4
6
8
10
12
WFD-FAO
WFD-FIP
CWR FIPPe
pp
er
pro
du
ctio
n s
ea
so
n W
P
(Kg
/m3
)
Pepper production season water productivity
Q1 Q2-Q1 Q3-Q2 Min Outlier Max Outlier
0
1
2
3
4
5
6
7
WFD-
FAO
WFD-
FIP
CWR FIPOnio
n pro
duct
ion s
easo
n W
P
(Kg/m
3)
Onion production season Water
productivity
Q1 Q2-Q1 Q3-Q2 Min Outlier Max Outlier
47
4.4 Irrigation water use efficiency (IWUE)
Irrigation water use efficiency (IWUE) for onion and pepper vegetables was increased in WFD-
FAO, WFD-FIP, and CWR treatments compared to FIP treatment. IWUE of onion were 5.1, 4.9,
5.19 and 2.3 kg/m3
respectively, in WFD-FAO, WFD-FIP, CWR and FIP treatments. This shows
that IWUE in WFD-FAO and WFD-FIP treatments was 54.3 %, and 52 % higher than FIP
treatment (Table 12). The maximum (5.19 kg/m3) irrigation efficiency was found in the CWR
followed by the WFD-FAO and the lowest (2.3 kg/m3) irrigation efficiency was in the FIP
treatment under onion production. However, there was no significant difference among the
treatments in WFD-FAO, WFD-FIP and CWR. Moreover, there exists a significant difference in
irrigation water use efficiency (IWUE) between the treatment WFD-FAO and FIP, WFD-FIP
and FIP, and CWR and FIP treatments (Appendix Tables K).
Similarly, IWUE of pepper was 4.3, 4.0, 4.6 and 1.6 kg/m3), respectively, in WFD-FAO, WFD-
FIP, CWR and FIP treatments. It implies that IWUE in WFD-FAO and WFD-FIP treatment was
61.3 % and 58.5 % higher than FIP treatment. The maximum (4.6 kg/m3) irrigation efficiency
was found in the CWR and the lowest (1.6 kg/m3) irrigation efficiency was in the FIP treatment
(Table 12). However, there was no significant difference among the treatments in WFD-FAO,
WFD-FIP and CWR. Moreover, there exists a significant difference in irrigation water use
efficiency (IWUE) between the treatment WFD-FAO and FIP, WFD-FIP and FIP, and CWR and
FIP treatments (Appendix Tables L).
Table 4 IWUE of treatment under onion and Pepper irrigation production
Onion irrigation Water use efficiency
(kg/m3)
Pepper irrigation Water use efficiency
(kg/m3)
WFD-FAO WFD-FIP CWR FIP WFD-FAO WFD-FIP CWR FIP*
3.9 2.1 6.4 3.2 10.2 5.3 7.0 2.6
7.4 6.2 3.3 2.0 3.8 1.3 3.8 2.6
5.2 6.0 1.8 3.7 4.2 3.0 4.5 1.1
4.6 5.9 5.1 1.9 2.0 4.2 5.3 1.9
7.2 3.3 7.4 2.1 3.0 3.8 2.3 1.2
3.5 6.1 4.6 1.8 2.4 6.4 - 1.1
4.2 4.5 6.8 1.5 - - - 0.9
Avg. 5.1 4.9 5.1 2.3 4.3 4.0 4.6 1.6
* WFD.FAO=Wetting Front Detect with FAO, WFD.FIP= Wetting front detector with farmers irrigation schedule,
CWR= Crop water requirement, FIP = Farmer Irrigation Practice
48
Results in (Table-12) showed that the WFD-FAO treatment had less water input yet was still
able to generate onion and pepper yield comparable to those WFD-FIP and CWR treatments. In
case of onion, WFD-FAO and WFD-FIP produced higher yield than the FIP treatment but lower
than the CWR. In case of pepper, Yields of pepper under the WFD-FAO treatment higher but
statistically similar to those under the WFD-FIP and CWR treatments. The maximum efficiency
of irrigation water was shown by CWR followed by the WFD-FAO treatment than the FIP under
onion field, whereas all WFD-FAO, WFD-FIP and CWR treatments were statistically (P=0.05)
have no significant difference, showing desirable results than the FIP treatment under pepper
production. A significant difference also recognized among the treatments that the desirable
(maximum) result of the water productivity (Kg/m3) is observed under the CWR followed by the
WFD-FAO than the FIP particularly under onion. The WFD was equally important as CWR for
water productivity under pepper. The least water productivity 1.05 kg/m3 for onion and 0.15
kg/m3 for pepper were observed from the FP. The water productivity results of onion also were
consistent with the significant improvements in the water productivity that has been associated
with CWR (Doorenbos and Kassam, 1996) and the WFD is also within the range of the
productivity.
Detail irrigation Water use efficiency of each plot with corresponding treatment groups and the
statistical quartile description of values displayed in (fig- 11).
Fig-11 Statistical quartile description of Onion and pepper irrigation water use efficiency (kg/m3)
* WFD.FAO=Wetting Front Detect with FAO, WFD.FIP= Wetting front detector with farmers irrigation schedule,
CWR= Crop water requirement, FIP = Farmer Irrigation Practice
0
10
20
30
40
50
60
70
80
WFD-
FAO
WFD-
FIP
CWR FIP
Onio
n w
ater
use
eff
icie
ncy
(kg/m
3)
Q1 Q2-Q1 Q3-Q2 Min Outlier Max Outlier
0
20
40
60
80
100
120
WFD-
FAO
WFD-
FIP
CWR FIP
Pep
per
wat
er u
se e
ffic
iency
(kg/m
3)
Q1 Q2-Q1 Q3-Q2 Min Outlier Max Outlier
49
5. CONCLUSION AND RECOMMENDATION
5.1. Conclusion
Using wetting front detector combined with conservation agriculture has reduced irrigation water
use, increased yield of onion and pepper, increased water productivity and irrigation water use
efficiency compared to any of the technologies so far used in this experiment. The results of this
study indicated that farmers scheduling practices has increased the total irrigation water use for
onion by about 33 % and 27 % compared to wetting front detector with FAO and wetting front
detector with farmers irrigation practices, respectively and also water applied in wetting front
detector with FAO and wetting front detector with farmers irrigation practices were reduced by
15.26 % and 8.5 % compared to onion field CWR treatment respectively. But statistically there
was no significance difference. Similarly, the results showed that farmers scheduling practices
has increased the total irrigation water use for pepper by about 22 % and 32 % compared to
wetting front detector with FAO and wetting front detector with farmers irrigation practices
scheduling, respectively.
On the other hand, the yield of onion in farmers scheduling practices was reduced by about 29 %
and 32 % compared to wetting front detector with FAO and wetting front detector with farmer‟s
irrigation practices, respectively. Correspondingly, the yield of pepper in farmers scheduling
practices was reduced by about 50 % and 39.3 % compared to wetting front detector with FAO
and wetting front detector with farmer‟s irrigation practices, respectively.
In similar way, water productivity in farmers scheduling practices during onion production
period was reduced by about 51 % and 50 % compared to wetting front detector with FAO and
wetting front detector with farmer‟s irrigation practices, respectively. Consistently, water
productivity in farmers scheduling practices during pepper production period was reduced by
about 60 % and 58 % compared to wetting front detector with FAO and wetting front detector
with farmer‟s irrigation practices, respectively.
50
In conclusion, wetting front detector with FAO, wetting front detector with farmers irrigation
practice and crop water requirement under conservation agriculture were save a substantial
amount of water without yield reduction. Although many indicators confirm that WFD-FAO and
CWR treatments practicality at farmer‟s level is questioning as it is more computers based. Thus
WFD-FIP treatment would be an important tool to be considered to improve the current on farm
water optimization by smallholder irrigators.
5.2. Recommendations
Especially recommended that the results of the experiment showed that scheduling tool
wetting front detector combining with conservation agriculture management system was
good technique to improve irrigation productivity and water use efficiency as found in
this study.
For developed countries which have skilled man farmers CWR treatment scheduling is
recommended.
Integrated pest management (IPM) should also be added to the conservation agriculture
(CA) set of recommendations.
Irrigation water managers should follow the irrigation water optimization tools in order to
Improve the water use efficiency.
51
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55
APPENDIXES
56
Appendix Tables
Appendix A Irrigation data collection sheet sample for WFD.FAO, WFD.FIP,
CWR and FIP and Event based onion irrigation data analyzing process
AREA 64 CA
date Irri.litre Irri. Mm
Actual
area in
ha
volume in
lit/ha m3
initial 2/16/11
260.00
4.06 0.0064
40,625.00
40.63
2/18/11
260.00
4.06 0.0064
40,625.00
40.63
2/20/11
260.00
4.06 0.0064
40,625.00
40.63
2/22/11
260.00
4.06 0.0064
40,625.00
40.63
2/24/11
260.00
4.06 0.0064
40,625.00
40.63
57
Appendix-B: ANOVA single factor for onion irrigation volume for each
stages (m3/ha) between treatments
Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance
WFD.FAO 4 2181.55 545.38 16317.2
WFD.FIP 4 2562.65 640.663 22238.2
ANOVA
Source of Variation SS df MS F P-value F crit
Between Groups 18154 1 18154.1 0.94171 0.3693 5.9873
Within Groups 115666 6 19277.7
Total 133820 7
Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance
WFD.FIP 4 2562.65 640.663 22238.2
FIP 4 2565.69 641.423 43230.8
ANOVA
Source of Variation SS df MS F P-value F crit
Between Groups 1.1552 1 1.1552 3.53E-05 0.9954 5.9873
Within Groups 196407 6 32734.5
Total 196408 7
Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance
CWR 4 2565.69 641.423 43230.84
FIP 4 3069.8 767.45 5076.73
ANOVA
Source of Variation SS df MS F P-value F crit
Between Groups 31765 1 31765.5 1.315137 0.2951 5.9873
Within Groups 144922 6 24153.7
Total 176688 7
58
Appendix-C: ANOVA single factor for onion irrigation volume (m3/ha)
between treatments
Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance
WFD.FAO 7 14763 2109 45371.33
WFD.FIP 7 15980.91 2282.987 8724.222
ANOVA
Source of
Variation SS df MS F P-value F crit
Between
Groups 105950.3 1 105950.3 3.917155 0.071206 4.747225
Within Groups 324573.3 12 27047.78
Total 430523.7 13
Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance
WFD.FIP 7 15980.91 2282.987 8724.222
CWR 7 17456.68 2493.811 78474.57
ANOVA
Source of
Variation SS df MS F P-value F crit
Between
Groups 155564.1 1 155564.1 3.568033 0.083304 4.747225
Within Groups 523192.8 12 43599.4
Total 678756.9 13
Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance
CWR 7 17456.68 2493.811 78474.57
FIP 7 21931.37 3133.053 304959.3
ANOVA
Source of
Variation SS df MS F P-value F crit
Between
Groups 1430204 1 1430204 7.459975 0.018223 4.747225
Within Groups 2300603 12 191716.9
Total 3730807 13
59
Appendix-D: ANOVA single factor for Pepper irrigation volume (m3/ha)
between treatments
Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance
WFD.FAO 5 21610 4322 265186.8
WFD.FIP 5 18667.55 3733.51 69933.39
ANOVA
Source of
Variation SS df MS F P-value F crit
Between Groups 865801.2 1 865801.2 5.167108 0.052633 5.317655
Within Groups 1340481 8 167560.1
Total 2206282 9
Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance
WFD.FIP 5 18667.55 3733.51 69933.39
CWR 5 20226.86 4045.372 494585.9
ANOVA
Source of
Variation SS df MS F P-value F crit
Between Groups 243144.8 1 243144.8 0.861422 0.380492 5.317655
Within Groups 2258077 8 282259.6
Total 2501222 9
Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance
CWR 5 20226.86 4045.372 494585.9
FIP 5 27292 5458.4 145370.8
ANOVA
Source of
Variation SS df MS F P-value F crit
Between Groups 4991620 1 4991620 15.59987 0.004238 5.317655
Within Groups 2559827 8 319978.3
Total 7551447 9
60
Appendix-E: ANOVA single factor for Onion plant height (cm) between
treatments
Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance
WFD.FAO 4 158.49 39.6225 469.5979
WFD.FIP 4 159.72 39.93 393.9786
ANOVA
Source of
Variation SS df MS F P-value F crit
Between Groups 0.189113 1 0.189113 0.000438 0.983982 5.987378
Within Groups 2590.729 6 431.7882
Total 2590.919 7
Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance
WFD.FIP 4 159.72 39.93 393.9786
CWR 4 146.49 36.6225 447.3078
ANOVA
Source of
Variation SS df MS F P-value F crit
Between Groups 21.87911 1 21.87911 0.052013 0.82717 5.987378
Within Groups 2523.859 6 420.6432
Total 2545.738 7
Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance
CWR 4 146.49 36.6225 447.3078
FIP 4 120.92 30.23 249.9324
ANOVA
Source of
Variation SS df MS F P-value F crit
Between Groups 81.72811 1 81.72811 0.234433 0.645422 5.987378
Within Groups 2091.721 6 348.6201
Total 2173.449 7
61
Appendix-F: ANOVA single factor Analysis for Pepper plant height (cm)
between treatments
Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance
WFD.FAO 4 153 38.25 250.87
WFD.FIP 4 145.6 36.4 220.0933
ANOVA
Source of
Variation SS df MS F P-value F crit
Between
Groups 6.845 1 6.845 0.029068 0.870226 5.987378
Within Groups 1412.89 6 235.4817
Total 1419.735 7
Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance
WFD.FIP 4 145.6 36.4 220.0933
CWR 4 145.5 36.375 213.3292
ANOVA
Source of
Variation SS df MS F P-value F crit
Between
Groups 0.00125 1 0.00125 5.77E-06 0.998162 5.987378
Within Groups 1300.268 6 216.7113
Total 1300.269 7
Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance
CWR 4 145.5 36.375 213.3292
FIP 4 114.9 28.725 130.6292
ANOVA
Source of
Variation SS df MS F P-value F crit
Between
Groups 117.045 1 117.045 0.680577 0.440946 5.987378
Within Groups 1031.875 6 171.9792
Total 1148.92 7
62
Appendix-G: ANOVA single factor Analysis for Onion yield (kg/ha)
Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance
WFD.FAO 7 75822.8 10831.83 10712969
WFD.FIP 7 79200.6 11314.37 15943623
ANOVA
Source of
Variation SS df MS F P-value F crit
Between
Groups 814966.6 1 814966.6 0.061146 0.808874 4.747225
Within Groups 1.6E+08 12 13328296
Total 1.61E+08 13
Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance
WFD.FIP 7 79200.6 11314.37 15943623
CWR 7 89869.8 12838.54 23593757
ANOVA
Source of
Variation SS df MS F P-value F crit
Between
Groups 8130845 1 8130845 0.411299 0.533369 4.747225
Within Groups 2.37E+08 12 19768690
Total 2.45E+08 13
Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance
CWR 7 89869.8 12838.54 23593757
FIP 7 53634.4 7662.057 14913485
ANOVA
Source of
Variation SS df MS F P-value F crit
Between
Groups 93786015 1 93786015 4.871084 0.047531 4.747225
Within Groups 2.31E+08 12 19253621
Total 3.25E+08 13
63
Appendix-H: ANOVA single factor Analysis for pepper yield (kg/ha)
Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance
WFD.FAO 6 111109.9 18518.32 1.88E+08
WFD.FIP 6 90163.8 15027.3 47236248
ANOVA
Source of
Variation SS df MS F P-value F crit
Between
Groups 36561592 1 36561592 0.310559 0.589592 4.964603
Within Groups 1.18E+09 10 1.18E+08
Total 1.21E+09 11
Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance
WFD.FIP 5 67060.4 13412.08 39478293
CWR 5 90003 18000.6 22161232
ANOVA
Source of
Variation SS df MS F P-value F crit
Between
Groups 52636289 1 52636289 1.707875 0.227574 5.317655
Within Groups 2.47E+08 8 30819762
Total 2.99E+08 9
Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance
CWR 5 90003 18000.6 22161232
FIP 5 52361.1 10472.22 15729571
ANOVA
Source of
Variation SS df MS F P-value F crit
Between
Groups 1.42E+08 1 1.42E+08 7.478926 0.025656 5.317655
Within Groups 1.52E+08 8 18945401
64
Total 2.93E+08 9
Appendix-I: ANOVA single factor Analysis for Onion water productivity
(kg/m3)
Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance
WFD.FAO 7 28.9 4.128571 1.522381
WFD.FIP 7 28 4 1.816667
ANOVA
Source of
Variation SS df MS F P-value F crit
Between Groups 0.057857 1 0.057857 0.034655 0.855431 4.747225
Within Groups 20.03429 12 1.669524
Total 20.09214 13
Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance
WFD.FIP 7 28 4 1.816667
CWR 7 30.1 4.3 2.6
ANOVA
Source of
Variation SS df MS F P-value F crit
Between Groups 0.315 1 0.315 0.142642 0.712264 4.747225
Within Groups 26.5 12 2.208333
Total 26.815 13
Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance
CWR 7 30.1 4.3 2.6
FIP 7 14.3 2.042857 0.562857
ANOVA
Source of
Variation SS df MS F P-value F crit
Between Groups 17.83143 1 17.83143 11.27552 0.005696 4.747225
Within Groups 18.97714 12 1.581429
Total 36.80857 13
65
Appendix-J: ANOVA single factor Analysis for Pepper water productivity
(kg/m3)
Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance
WFD.FAO 6 26 4.333333 9.202667
WFD.FIP 6 24.3 4.05 3.263
ANOVA
Source of
Variation SS df MS F P-value F crit
Between
Groups 0.240833 1 0.240833 0.038639 0.848102 4.964603
Within Groups 62.32833 10 6.232833
Total 62.56917 11
Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance
WFD.FIP 5 17.8 3.56 2.278
CWR 5 21.9 4.38 2.602
ANOVA
Source of
Variation SS df MS F P-value F crit
Between
Groups 1.681 1 1.681 0.688934 0.430598 5.317655
Within Groups 19.52 8 2.44
Total 21.201 9
Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance
Column 1 5 21.9 4.38 2.602
Column 2 5 9.6 1.92 0.527
ANOVA
Source of
Variation SS df MS F P-value F crit
Between
Groups 15.129 1 15.129 9.670182 0.014452 5.317655
66
Within Groups 12.516 8 1.5645
Total 27.645 9
Appendix-K: ANOVA single factor Analysis for Onion Irrigation Water use
efficiency (kg/m3)
Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance
WFD.FAO 7 361.9932 51.71331 241.7073
WFD.FIP 7 344.3205 49.18864 268.5401
ANOVA
Source of
Variation SS df MS F P-value F crit
Between Groups 22.30895 1 22.30895 0.087444 0.772507 4.747225
Within Groups 3061.484 12 255.1237
Total 3083.793 13
Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance
WFD.FIP 7 344.3205 49.18864 268.5401
CWR 7 358.9678 51.28111 398.8934
ANOVA
Source of
Variation SS df MS F P-value F crit
Between Groups 15.32452 1 15.32452 0.045921 0.833918 4.747225
Within Groups 4004.601 12 333.7168
Total 4019.926 13
Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance
CWR 7 358.9678 51.28111 398.8934
FIP 7 164.96 23.56571 67.81119
ANOVA
Source of
Variation SS df MS F P-value F crit
Between Groups 2688.501 1 2688.501 11.52121 0.005326 4.747225
Within Groups 2800.227 12 233.3523
67
Total 5488.728 13
Appendix-L: ANOVA single factor Analysis for Pepper Water use efficiency
(kg/m3)
Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance
WFD.FAO 6 259.0033 43.16722 917.5718
WFD.FIP 6 242.8032 40.46721 322.7574
ANOVA
Source of
Variation SS df MS F P-value F crit
Between Groups 21.8703 1 21.8703 0.035265 0.854797 4.964603
Within Groups 6201.646 10 620.1646
Total 6223.516 11
Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance
WFD.FIP 6 242.8032 40.46721 322.7574
CWR 5 231.6144 46.32287 310.0103
ANOVA
Source of
Variation SS df MS F P-value F crit
Between Groups 93.51495 1 93.51495 0.294914 0.600278 5.117355
Within Groups 2853.828 9 317.092
Total 2947.343 10
Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance
CWR 5 231.6144 46.32287 310.0103
FIP 7 116.8062 16.6866 54.37633
ANOVA
Source of
Variation SS df MS F P-value F crit
Between Groups 2561.733 1 2561.733 16.35532 0.002345 4.964603
Within Groups 1566.299 10 156.6299
Total 4128.032 11
68
Appendix-M: Normal Q-Q plot and frequency distribution curve for onion
WFD-CWR onion irrigation (m3/ha)
WFD-FIP onion irrigation (m3/ha)
69
Crop water requirement onion irrigation (m3/ha)
Farmers irrigation practice (m3/ha)
70
WFD-FAO onion yield (kg/ha)
WFD-FIP onion yield (kg/ha)
71
CWR onion yield (kg/ha)
FIP onion yield (kg/ha)
72
Water productivity of WFD-FAO treatment of onion (kg/m3)
Water productivity of WFD-FIP treatment of onion (kg/m3)
73
Water productivity of CWR onion treatment (kg/m3)
Water productivity of FIP onion treatment (kg/m3)
74
75
76
77
78
79
Appendix-N: Normal Q-Q plot and frequency distribution curve for pepper
80
81
82
83
84
85
86
87
88
89
90
Photos during irrigation season
91
Wetting front installation for each field
92
Field Visiting and discussion
93