INTEGRATED NUTRIENT MANAGEMENT TO ATTAIN SUSTAINABLE PRODUCTIVITY INCREASES IN EAST AFRICAN FARMING SYSTEMS

EAST AFRICA

Evaluation of Organic, Inorganic Fertilizers AND Tithonia (Tithonia diversifolia) On Maize Performance In Nitisols Of

Central Kenya: A Farmer Field School Approach.

L.N.Gachimbi, Maina F., Obanyi S.N., Onduru D.D, Gachini, G.N., de Jager and Muchena F.N.

INMASP Report No. Ke-17 16th September 2004

ii

Table of Contents

1.0 INTRODUCTION .........................................................................................................1

1.1 Objectives of the Study..................................................................................................2

2.0 METHODOLOGY ........................................................................................................2

2.1. Study Site .......................................................................................................................2

2.2 Formation of the Farmer Field School...........................................................................3

2.3 Site Characterisation and Nutrient Monitoring..............................................................5

2.4 Learning Process and Participatory Technology Development.....................................6

2.4.1 Learning process and regular school meetings ..............................................................6

2.4.2. Participatory technology development ..........................................................................6

2.4.3 Data analysis ..................................................................................................................8

3.0 RESULTS ......................................................................................................................9

3.1 Soil Analysis Report for Ngaita Study Site ...................................................................9

3.2 Plant Height (Vigour) ..................................................................................................10

3.3 Plant Population...........................................................................................................11

3.4 Plant Colour .................................................................................................................12

3.5 Weed Population..........................................................................................................12

3.6 Farmer Evaluation of Crop Performance.....................................................................13

3.7 Grain and Stover Yield at Ngaita.................................................................................14

4.0 ECONOMIC ANALYSIS ...........................................................................................15

5.0 SOIL NUTRIENT BALANCES AND BUDGET......................................................17

6.0 PESTS AND DISEASE INCIDENCE ........................................................................17

7.0 CONCLUSIONS AND RECOMMENDATIONS ......................................................19

8.0 REFERENCES: ...........................................................................................................19

iii

List of Tables

Table 1: Average characteristics of FFS participants (Standard deviation (SD) in parenthesis)............................................................................................................................................5

Table 2: Selected chemical properties for Ngaita FFS farms. ...................................................9 Table 3: ANOVA for plant height at Ngaita village in Kiambu District.................................10 Table 4 Plant population ..........................................................................................................12 Table 5: Plant colour evaluation by farmers. ...........................................................................12 Table 6: Weed population evaluation by farmers. ...................................................................13 Table 7: Farmer general crop evaluation .................................................................................13 Table 8: Grain and stover yield (kg/ha) at Ngaita for seasons I and II 2002/03......................14 Table 9: Economic analysis and performance of the tested technologies. ..............................16 Table 10: Impacts of tested technologies on nutrient balances................................................17 Table 11: Mean score performance against tested technologies.......................................18

List of Figures

Figure 1: Location of the Ngaita FFS sites in Kiambu District. ................................................3 Figure 2: Process adopted for choosing technologies for experimentation ...............................7 Figure 3: The maize plant height for different treatments at Ngaita........................................10 Figure 4: Height of maize (H513) under different treatments over the growing season. ........11 Figure 5: Mean grain and stover yield (kg/ha) for Gachoka and Ngaita for 2002/03 Season 1.

..........................................................................................................................................14

1

1.0 INTRODUCTION Ngaita area of Kiambu District in Kenya has not been spared by the declining per capita food production common in sub-Saharan Africa. The main reason for this decline is soil fertility. Decline in soil fertility is as a result of complex interaction between biophysical and socio-economic factors governing the farmer. Farmers in this area are therefore faced with a dilemma of feeding an ever-increasing population while the land resources are declining and food production therefore inadequate. This challenge calls for a concerted effort from all stakeholders to tackle the soil fertility decline problem. Improving soil fertility has been identified as an essential micro-level strategy for increasing and sustaining food production in smallholder cropping systems (Sanchez et al; 1997). Further intensification and diversification of land use with high value crops is also advocated. The traditional approaches to soil fertility management range from recurring and occasional use of sub-optimal mineral fertilizer rates to applications of low external input agriculture based on organic sources of nutrients. The appropriateness and efficiency of these monolithic methods is a subject of an on-going debate. Many reports are now increasingly showing that a combined and judicious use of organic and inorganic sources of nutrients holds the key to further soil fertility interventions for increased farm productivity (Nandwa and Bekunda, 1998). The development of soil fertility initiatives needs to take farmers perspectives and their indigenous technical knowledge into account if farmers have to adopt the developed technologies. In the past many soil fertility farm interventions have tended to ignore farmer’s indigenous wisdom and to follow prescriptive methods of technology development and transfer on the assumption that farmers are ignorant and that they only needed to be told what to do. This has quite often led to selective adoption, modification, socially discriminatory uptake, early abandonment or plain rejection of technologies on offer and even management methods associated with such technologies (FURP, 1994). A divergence between farmers’ and scientists’ perceptions of what can be achieved with technologies quite often arises due to different prioritisation of needs, different methods of evaluating outputs and perception on labour availability. It also arises from unintended and unforeseen side effects of technologies and different time discounting rates (Mortimore et al, 2000). Farmers in general are not using fertilizers, manures, crop residues and/or available green manure in adequate quantities. Use of both mineral fertilizers and farmyard manure has been found to be a sustainable technology for crop production (Nandwa and Bekunda 1998, Argrowins-Kodhek 1997) and that integration of mineral fertilizers with Tithonia further increases maize crop yields (Jama, et al., 1997). It was felt important to evaluate and upscale this technology participatory where the farmers were not integrating FYM, Fertilizer and/or Tithonia as a source of nutrients for their crop production. Efforts in development and promotion of an Integrated Nutrient Management (INM) Strategy through the social learning process and adaptive research would be appropriate in development of agricultural technologies to alleviate identified farming constraints. This calls for a paradigm shift in the agricultural technology development to simultaneously address issues of social, economic and human resource management including better adoption of these technologies. Research and development partners have made great strides in utilizing farmer participatory research approaches and in generating appropriate technologies for smallholder farmers. The Farmer Field Schools (FFS) approach used in this study is one such technique. This involves researcher/farmer-designed trials that are fully farmer managed (Braun, 2000; Mureithi, 2002). In order to achieve the objectives of this project farmers have

2

to be educated through demonstration trials in the field on the importance of soil fertility management in crop production. In the study farmers were exposed to locally available organic materials like Tithonia Diversifolia (wild sunflower) for use in soil fertility improvement. On-farm trials using organic nutrient sources (manure and Tithonia) as well as inorganic fertilizers (DAP and CAN) were compared to determine their effects on maize growth and yield.

1.1 Objectives of the Study The project has the following objectives: • To develop an institutional sustainable approach of identifying, testing, monitoring and

evaluation of farm or catchment-level technologies addressing soil nutrient management constraints using principles and institutional aspects of the Farmer Field School (FFS) approach;

• To develop and test a quick and efficient tool to diagnose productivity and sustainability of farming systems in East Africa focussing on soil nutrient management and combining quantitative and participative qualitative approaches;

• To generate appropriate and effective technologies to address problems of soil nutrient depletion aimed at a long-term increase of productivity and profitability of farming systems in East Africa; and

• To develop a participative policy formulation process involving researchers, extensionists and district policy makers aiming at formulating appropriate district policy recommendations and policy instruments to address soil nutrient depletion leading to a sustainable increase in productivity of farming systems in East Africa.

2.0 METHODOLOGY

2.1. Study Site The study was conducted in Kiambaa Division, Kiambu District of Central Kenya Highlands (Figure 1). The study site is agroecologically representative of Kenya's high agricultural potential areas (Jaetzold and Schmidt, 1983) and is densely populated. Kiambaa Division has a population density of 497 persons per km2 while Kiambu District has a population density of 562 persons per km2 and covers an area of 1323.9 km2 (MoPND, 1997; CBS, 2000). The altitude range within the district is 1200-2550 metres above sea level. The selected site, Kiambaa Division, has an altitude range of 1580-2000 metres above sea level. Rainfall is bimodal and the average rainfall received in the district varies from 600 to 2000 mm per year depending on location and altitude. The rainfall is reliable and favourable for agricultural activities. The average annual rainfall range for Githunguri, the nearest recording station is 1000-1600 mm (FURP, 1994). The district has two growing periods per year with a total length of 150-214 days (Kassam et al., 1991). The annual mean temperature ranges from 13.5oC in the Upper highlands to 21.9oC in the lowlands with a range of 18oC-19.5oC for the Kiambaa study site. The soils in the study site are inherently fertile, but continuous cropping with poor management of organic and inorganic sources of fertility has resulted in declining soil fertility, especially among small-scale farmers. The soils are well-drained, extremely deep, dusky red to dark reddish brown, friable and slightly smeary clay, with an acid humic top soil (humic Nitisols). The soils are low in available phosphorus while nitrogen availability is moderate and potassium levels are adequate (FURP, 1994; Sombroek et al., 1982).

3

Figure 1: Location of the Ngaita FFS sites in Kiambu District.

2.2 Formation of the Farmer Field School The school formation process began with sensitisation of the district agricultural extension officers and local leaders on the FFS programme and its focus on Integrated Nutrient Management. Although a growing number of organizations have been using models which might be both more effective in serving farmers’ needs and institutionally more sustainable, most of these “farmer-led extension” projects have been in the "voluntary sector" and few as yet have been mainstreamed within the existing extension services (Braun et al 2000). By invoking the participation of the extension staff at this early stage, the FFS programme sought to gain increased participation of the extension staff as the programme unfolds.

Kiambu District

Nairobi

4

Community meetings were held within the selected representative catchments in the target administrative locations. The purpose of the meetings was to introduce FFS principles and objectives to prospective participants and to gain rapport and collaboration, and to enlist volunteers to the programme as representatives of the community. Participants were selected on the basis of their willingness to participate in the FFS programme, their willingness to share information with others, location of their farms in the catchment under consideration, size of production resources and on the basis of being full-time farmers. The learning theme, Integrated Nutrient Management, and duration of learning were agreed upon by the interested farmers and the facilitators to be four years. The enlisting of farmers was followed by conducting a baseline survey during which soil fertility management problems, farmer's production characteristics (Table 1) and challenges, and sources of livelihood were identified. The baseline survey was conducted using a mix of participatory rural appraisal (PRA) tools. Information captured included farm inventory data, farm management practices, soil management constraints and opportunities and household's livelihood sources (Gachimbi et al 2004). Findings of the baseline survey were used as a basis for developing FFS curriculum and field trials. The survey was conducted concurrently with nutrient monitoring diagnostic study in order to further identify constraints to soil fertility management and opportunities for intervention. The nutrient monitoring study was conducted according to methods described by Vlaming et al (2001).

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Table 1: Average characteristics of FFS participants (Standard deviation (SD) in parenthesis)

Characteristic Mean values (n=29) Land Total land area (ha) 0.5 (0.4)Land cultivated (ha) 0.5 (0.4)Average slope (%) 14.7 (8.5)Labour Household size (persons) 5 Consumer units (aeu) 4 (2)Labour units (aeu) 4 (2)Primary education2 23 %Post primary vocational 22 % Secondary education 38 %Market Distance to the market (km) 5 (1.7)Capital TLUa 2.9 (3.9)Value of livestock (US$)b 811 (195)Value of land (US$) 6070 (16.1)Value of equipment (US $) 11 (128)Ratios Land: Labour unit ratio (ha per labour unit) 1 (1)Land: Consumer (ha per consumer unit) 1:1Consumer: Labour unit 4: 7

Education level of household head TLU = Tropical livestock units (1 unit is equivalent to 250 kg live weight). 1 US$ = Ksh 80 at time of study.

The FFS programme staff and farmers were trained on principles and concepts of FFS, its application and how to run FFS before the initiation of field activities in the first season of the programme. During the launching of the FFS programme, norms and rules to guide the school process were developed in a participatory way with the participants to guide the school operations while the central learning plot was donated by one of the school participants (Onduru et al 2002).

2.3 Site Characterisation and Nutrient Monitoring This was done for individual participating farmers and for the central learning plot. Composite soil samples were collected at a depth of 0-20 cm from individual participating farmers’ farms and analysed for total N, avail P exch. K and pH, after which the results were presented to the farmers at the FFS for discussions. A profile pit was opened at the central learning plot to characterise the soils of the FFS site. The profile was described according to FAO-UNESCO (1998). Each soil horizon was sampled for chemical and physical analysis. Analysis carried out were soil texture, pH, Organic carbon, CEC, exchangeable bases, total N, avail P and exchangeable K. Chemical analysis were done according to methods routinely used at National Agricultural Research Laboratories-KARI, Nairobi, Kenya (Hinga et al., 1980). The nutrient monitoring study was conducted according to the methods described by Vlaming et al (2001).

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2.4 Learning Process and Participatory Technology Development

2.4.1 Learning process and regular school meetings The trials (in the central learning plot) were used as a platform for learning during bi-weekly school sessions. During these sessions, learning took place in small sub-groups followed by plenary sessions where the findings of the sub-groups were shared, and treatment performance discussed and recommendations for future actions made. "Special topic" sessions on Integrated Nutrient Management, and other topical issues of interest to farmers were also included during the learning process. However, the cornerstone for the self-discovery learning was Agro-Ecosystem Analysis (AESA). AESA was carried out in sub-groups with each group being assigned a treatment in a rotating manner during each learning day. The objectives of the learning and conducting AESA in small sub-groups and plenary presentations were to analyze the treatment performance and to encourage leadership and team building processes among the group members. Besides stimulating learning, the AESA chart was also used to collect data on monitoring performance indicators as proposed by farmers during the experimental design stage. At the end of the cropping season, a participatory evaluation was conducted to draw farmers' opinions, preferences, criticisms and suggestions about the technologies tested. The end of season evaluation was one of the agreements jointly made with farmers during the experimental design workshop. The evaluation was conducted based on the same performance indicators as given by farmers during the experimental design workshop. These indicators included pest incidence, leaf colour, plant health, soil moisture, incidence of weeds, soil colour, plant height and grain yields. The evaluation was meant to give a whole season’s picture of the performance of treatments. It was conducted in four sub-groups using matrix ranking and scoring.

2.4.2. Participatory technology development An experimental design workshop was organized in order to choose technologies for testing in the central learning plot and the school learning process. Prior to designing experiments, the results of the ground working activities such as soil analyses were discussed with farmers in addition to constraints to soil fertility management. Based on these discussions on constraints, a list of possible technologies to be experimented on was proposed by the farmers. Similarly, facilitators also drew a list of possible technologies. The two lists of possible technologies for experimentation were discussed along side each other in plenary and one technology chosen through ranking method (Figure 2). The other technologies and issues not touching on soil fertility management were included in the curriculum as special topics.

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Figure 2: Process adopted for choosing technologies for experimentation Establishment of experimental objectives, formulation of hypothesis, selection of test crops and designing of treatments were carried out in a participatory process as described by Onduru et al (2002). The jointly formulated hypothesis was: "If we apply Tithonia as green manure and top dressing when planting maize variety H511, grain yields will increase because Tithonia improves soil conditions status provided that rains are adequate, good quality seeds are planted and that planting takes place early in the season." The objective of the trial was jointly formulated as follows: "To give insight into the opportunities and limitations of Tithonia in improving soil conditions provided that rains are adequate". Treatments tested.

1. Application of Tithonia as green manure alone; 2. Application of farm yard manure (FYM)+DAP+CAN (Framers practice); 3. Application of FYM +Top dressing using Tithonia extract; 4. Application of Tithonia (green manure) + Top dressing using Tithonia water extract

Design The design was pair –wise design replicated once. Plot sizes were 5m x 10m with spacing of maize H513:Inter-row, 0.75m; Intra-row, 0.30m. Seeding rate: Two seeds per hill were planted and thinned later to one seed per hole. Tillage was normal while fertilizer; DAP and CAN was applied at teaspoonful per planting hole. CAN applied when maize is knee high. Manure was applied at a rate of two-handfuls per planting hole (punje mbili); Top dressing using Tithonia water extract was at 250ml-cup per planting hole when maize is knee high.

Select one technology through ranking

Select technologies with impact within one year/season

Identify technologies addressing soil fertility only

List technologies of interest to all parties

List of farmer's proposals List of facilitator's proposals

Sharing results of soil and farm productivity constraints

8

During the field experimentation design workshop in the FFS at Ngaita farmers wanted to “see whether use of Tithonia (Maruru in Kikuyu) can improve soil fertility” and also “improve on manure use for better water retention in the soil”. The parameters considered in the trial were crop vigour (measured by taking plant height at two-week intervals), Maize stover yield, maize grain yield, plant colour, plant population and weed population. Farmers observed the general plant condition including pests (beneficial and harmful) and diseases. The treatments were kept simple and replicated twice using a pair wise design within the central learning plot. The farmer's criteria for monitoring the trials were inventoried during the workshop, and this together with facilitators criteria, formed the basis for monitoring the trials and data collection during the trial season. Farmers’ proposed criteria for monitoring the trials included grain yields, colour of leaves, plant height, weed infestation, soil moisture retention, pest infestation and plant health, and soil colour. Facilitators' criteria included monitoring of inputs (labour, fertilizers and related costs) in addition to crop development parameters (stand count, plant height, crop vigour, weed infestation, pest and disease incidence, nutrient deficiencies and crop losses). Other facilitators' criteria were also crop output data such as grain yields and crop residues. During the experimental design meeting, an action plan was jointly worked out specifying activities, persons involved and an estimate of when such activities were to take place.

2.4.3 Data analysis The data collected during AESA sessions were analyzed in sub-groups and shared in plenary during each meeting day. This qualitative data analysis was reinforced by quantitative data analysis at the end of the season in which the various data sets collected using AESA were further analyzed and shared with farmers in the FFS meeting session. Quantitative analysis included analysis of agronomic parameters, economic indicators of performance and nutrient budgets. To aid in quantitative analysis, farmers local units of measurements were translated into metric units. The non-cash inputs and outputs were valued at opportunity costs. In assessing economic impacts of the studied technologies, labour inputs were valued at opportunity costs needed to hire such labour in the site. Tithonia was valued at labour needed to collect it while manure and crop residues, which were not currently being traded in the site, were valued at opportunity costs for purchasing them. Maize grain yields were valued at prevailing market rates and thus assumed to have a cash value. The collected data were sorted, edited and triangulated before analysis. Data processing and analysis were done using Excel (from Microsoft Cooperation) and Statistical Programme for Social Scientists (SPSS) from SPSS Inc.

9

3.0 RESULTS

3.1 Soil Analysis Report for Ngaita Study Site The soils indicate extreme to strong acidity in this area (Table 2). The situation is the same for central learning plot. Acidity problem could be solved through use of lime applied three weeks before planting, application of at least 7t/ha of farmyard manure. This is because low soil fertility may be as a result of nutrient fixation especially phosphorus due to the low pH. The recommended pH for optimal growth and production is 6.0 to 7 (Kanyanjua et al. 2000). The percentage nitrogen is adequate on all the farms while the percent carbon is moderate. This is despite the high levels of nitrogen. This indicates that the rates of mineralization are low and uptake of N is higher than can be made available. Therefore there is a need to supply nitrogen in a readily available form and from non-acidifying sources of fertilizer. The levels of available phosphorus are low and 70% of the farms having less than 20ppm. This shows the need to apply P especially from manure or inorganic fertilizer to above critical level of 20ppm. Potassium levels, range from adequate to high. However, K levels should be monitored for decline especially those that are close to the critical level of 0.2meq for maize production. Technologies to solve some of the above identified constraints include use of manure and compost, deep digging, terracing, use of mineral fertilizers, crop rotation, early planting, planting in holes as opposed to broadcasting, use of agro forestry technologies, use of animal slurry, Tithonia and rock phosphate as nutrient sources.

Table 2: Selected chemical properties for Ngaita FFS farms.

N (%) Organic carbon (%) P (ppm) K (meq) Soil pH

Average for all FFS members’ (n=30) farms

0.26 1.86 15.17 1.10 4.66

Central learning plot 0.25 1.67 14 0.26 4.2

N/B: Critical value N=0.2%; K=0.2 meq; P (Mehlic)= 20 ppm; Organic carbon=0.2%

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3.2 Plant Height (Vigour) There was no significant difference in plant height between the treatments although FYM and plant tea gave the highest height. The trend however, shows that FYM + top dressing using Tithonia extract (tea) gave the tallest plants (Figure 3 and 4). The lowest plant height was obtained from using Tithonia alone. The results show that the nitrogen obtained from plant tea increased plant vigour as well as the CAN that was used in topdressing. Timely application of N is therefore critical.

Table 3: ANOVA for plant height at Ngaita village in Kiambu District.

Source Type III Sum of Squares

df

Mean Square

F

Sign.

Model 242723.887a 5 48544.777 14.942 .000Replications 946.459 1 946.459 .592 .592Treatment 9398.529 3 3132.843 .419 .419Error 133200.711 41 3248.798 Total 375924.597 46

a. R Squared= .646 (Adjusted R Squared =. 602)

0

10

20

30

40

50

60

70

80

90

100

Tithonia alone Manure + DAP + CAN FYM + tea Tithonia + tea

Treatment

Plan

t hei

ght (

cm)

Figure 3: The maize plant height for different treatments at Ngaita

11

0

20

40

60

80

100

120

140

160

180

200

2 4 6 8 10

Time (weeks)

Hei

ght (

cm) Tithonia only

FYM + DAP + CANTithonia+ plant teaManure + plant tea

Figure 4: Height of maize (H513) under different treatments over the growing season.

The growth rate was fastest with manure + plant tea at the 10th week after planting. However, at four weeks after planting FYM + DAP + CAN had the fastest growing plants. Between four and six weeks, manure + plant tea had very slow growth while Tithonia + tea had fast growth.

3.3 Plant Population There was no significant (P = 0.05) difference in plant population (Table 4). However, Tithonia alone and manure + DAP + CAN had the highest number of plants. Tithonia + tea had the least number of plants possibly due to poor germination in these plots. This may explain why they were the tallest since there was decreased competition for nutrients among the plants unlike Tithonia alone that had more plants by 21% therefore having a higher nutrient demand.

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Table 4 Plant population

Treatment Plant Population Tithonia alone 282 ns Manure +DAP+CAN 282 Manure + tea 276 Tithonia+ tea 234

3.4 Plant Colour There were 6 categories of colour description given by farmers. The highest number of farmers indicated that FYM and plant tea had the greenest plants. The poorest plants were obtained using Tithonia alone (Table 5) followed by manure + DAP + CAN and Tithonia + plant tea. Plant colour is not a very good indicator for predicting crop performance, as colours can be misleading unless farmers are well trained on colour identification.

Table 5: Plant colour evaluation by farmers.

Count. Green Purple Yellow Purple/

Yellow Purple/Green

Green/Yellow

Treatment

1 2 3 4 5 6 TotalTithonia alone 1 5 2 1 1 2 1.2

Manure + DAP+ CAN 2 6 3 1 1.2FYM + Tea 12 1.2

Tithonia + Tea 2 2 3 4 1 1.2Total 17 13 5 4 6 3 4.8

% Observed 35.4 27.1 10.4 8.3 12.5 6.3 1.00

3.5 Weed Population At every observation rating of the weed population using low, medium and high descriptions were used. The results show that most farmers (47%) rate the treatments as having low weed population (Table 6). High weed incidences were recorded in Tithonia alone and FYM + DAP + CAN.

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Table 6: Weed population evaluation by farmers.

Weed population Treatment 1 (high) 2 (medium) 3 (low)

Total

Tithonia alone 5 2 5 12 Manure + DAP+ CAN 7 5 12 FYM + Tea 1 2 9 12 Tithonia + Tea 1 7 3 11

Total 14 11 22 47 % Observation 29 23 47

3.6 Farmer Evaluation of Crop Performance Observations made by farmers rated the crop performance as poor (53%), however using FYM + Tea was rated as good in all the 12 observations made (Table 7). The poorest treatment was Tithonia alone while FYM + Tea was rated best. This visual observation does not, however, agree with grain and stover yield which had manure + DAP + CAN as the best performer. This underscores the fact that the greenest plants do not always produce the highest grain yield. The plant nutrient sources are more for vegetative growth than grain formation hence N: P ratio imbalance.

Table 7: Farmer general crop evaluation

Farmer Evaluation Treatment

1 (good) 2 (fair) 3 (poor) Total

Position

Tithonia alone 4 8 12 4 Manure + DAP+ CAN 4 8 12 1 FYM + Tea 12 12 2 Tithonia + Tea 1 1 9 11 3

Total 13 9 25 47 % Observation 28 19 53 100

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3.7 Grain and Stover Yield at Ngaita The highest maize grain yield was obtained using manure + DAP + CAN similar to stover yield while the lowest for both stover and grain were obtained from use of Tithonia alone (Table 8). Similar results were obtained in season II. When comparing the two seasons, season II had a general better performance than season I. However, in both seasons FYM + DAP + CAN had the best performance both in terms of grain and stover yield.

Table 8: Grain and stover yield (kg/ha) at Ngaita for seasons I and II 2002/03

Treatment Maize grain Mean Maize stover Mean S 1 S 2 S 1 S 2 FYM + DAP + CAN 2,800 6,300 4,550 21,280 24,100 22,690 Tithonia + Tea 1,160 4,500 2,830 9,700 14,800 12,250 FYM + Tea 580 3,100 1,840 8,850 13,500 11,175 Tithonia only 510 4,700 2,605 8,550 18,900 13,725 When comparing the Gachoka (an ASAL) and Ngaita sites it is clear that Ngaita performed better than Gachoka. The main reason among others being that Ngaita had better rains (>300 mm) than Gachoka with only (150 mm) during that season. With little rain topdressing with Tithonia tea gives better results because of the added water at Gachoka (Figure 5). Better rains at Ngaita means more conservation of the moisture when manure is used and the deficient nutrients added e.g. phosphorous at Ngaita giving higher yields for both stover and grain.

0

5000

10000

15000

20000

25000

30000

Tith

onia

+Pla

ntte

a

Tith

onia

+CAN

FYM

Tith

onia

Tith

onia

+Pla

ntte

a

FYM

+DA

P+C

AN

FYM

+Pla

nt te

a

Tith

onia

Treatments

Yiel

d (k

g/ha

)

Grain Stover

Gachoka Ngaita

Figure 5: Mean grain and stover yield (kg/ha) for Gachoka and Ngaita for 2002/03 Season 1.

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4.0 ECONOMIC ANALYSIS In season I the treatment involving a combination of FYM + DAP + CAN had the highest return with a VCR of 3.76. In season II, however, Tithonia + plant tea was the best economic performer followed by Tithonia only (Table 9). The short rains, however, had the higher grain yields than the long rains. The gross margin from use of manure + tea was negative in season I. The cost of manure was high compared to fertilizer, however, some of the manure input is obtained on-farm and only bought to supplement what is available. In this case it was assumed that all manure was bought. On risk evaluation of the technologies, the value cost ratio is frequently used (Muriuki and Qureshi, 2001). Using a value cost ratio of 2 as a risk factor, all technologies can be practiced without much risk except manure + plant tea in season I. In season II Tithonia + plant tea is least risky.

16

Tab

le 9

: Eco

nom

ic a

naly

sis a

nd p

erfo

rman

ce o

f the

test

ed te

chno

logi

es.

Seas

on 1

(Lon

g ra

ins)

Nga

ita

Tre

atm

ent

Gra

in

yiel

d (M

T/h

a)

Stov

er

yiel

d (M

T/h

a)

Gro

ss

inco

me

(Ksh

/ha)

Lab

our

cost

(K

sh/h

a)

Non

ca

sh

vari

able

co

st

(Ksh

/ha)

Cas

h va

riab

le

(Ksh

/ha)

Tot

al

vari

able

co

st

(Ksh

/ha)

Gro

ss

mar

gin

(Ksh

/ha)

VC

R

Tith

onia

+ p

lant

tea

0.58

8.

8515

294

2520

1750

50

0092

7060

243.

06M

anur

e +

DA

P+ C

AN

2.

8 21

.28

5239

190

0049

32

1393

227

864

2452

73.

76FY

M +

Pla

nt te

a 1.

16

9.7

2258

992

8049

32

1393

228

144

-555

51.

62Ti

thon

ia o

nly

0.51

8.

5514

217

5040

2625

50

0012

665

1552

2.84

Seas

on 2

(Sho

rt r

ains

) Nga

ita

Tre

atm

ent

Gra

in

yiel

d (M

T/h

a)

Stov

er

yiel

d (M

T/h

a)

Gro

ss

inco

me

(Ksh

/ha)

Lab

our

cost

(K

sh/h

a)

Non

cas

h va

riab

le

cost

(K

sh/h

a)

Cas

h va

riab

le

(Ksh

/ha)

Tot

al

vari

able

co

st

(Ksh

/ha)

Gro

ss

mar

gin

(Ksh

/ha)

VC

R

Tith

onia

onl

y 3.

1 13

.547

944

2520

1750

50

0092

7038

674

5.17

Man

ure

+ D

AP

6.3

24.1

9410

090

0049

32

1393

227

864

6623

63.

38FY

M +

Pla

nt te

a 4.

5 14

.864

800

9280

4932

13

932

2814

436

656

2.30

Tith

onia

+ P

lant

tea

4.7

18.9

7112

250

4026

25

5000

1266

558

457

5.26

17

5.0 SOIL NUTRIENT BALANCES AND BUDGET Soil nutrient budgets (Table 10) were used as one of the indicators of soil quality. Nutrient budget is a net balance between incoming and outgoing nutrients in farm inputs and outputs. It is affected by complex interaction of factors such as nutrient management practices, regeneration and protection, livestock integration, soil and water conservation, agricultural policies and marketing structures (De jager et al., 1998)

Table 10: Impacts of tested technologies on nutrient balances.

N partial flows (kg ha-1)

Tithonia only

Manure + DAP + CAN

FYM + Plant tea

Tithonia + plant tea

IN 1 0 0 0 0 IN 2 112 112 112 112 Out 1 9.976 48.16 19.952 87.72 Out 2 66.375 159.6 72.75 64.125 N Balance 35.649 -95.76 19.298 -39.845 IN 1 0 43.4 0 0 IN 2 1.3 13.2 13.2 1.3 Out 1 2.146 10.36 4.292 18.87 Out 2 8.85 21.28 9.7 8.55 P Balance -9.696 -18.44 -0.792 -26.12 IN 1 0 0 0 0 IN 2 292 332.8 624.8 292 Out 1 1.74 8.4 3.48 15.3 Out 2 177 425.6 194 171 K Balance 113.26 -101.2 427.32 105.7 Partial nutrient balances were calculated in this study to gauge the impacts of tested technologies on soil quality (Table 11). Negative N balances were obtained with manure + DAP + CAN and Tithonia + tea probably due to the higher yields obtained in these two treatments in terms of stover removed. Phosphorus balances were all negative implying that more P needs to be added. Potassium balances were positive except for manure + DAP + CAN where negative values were obtained due to the high stover yields. Stover contains 2% K that is removed from the soil. Higher levels of nutrients inputs are required to maintain positive balances.

6.0 PESTS AND DISEASE INCIDENCE Farmer evaluation (Table 11) shows that manure + DAP + CAN had the best crop followed by FYM + tea. The worst performer was Tithonia alone. Four groups of farmers were asked to evaluate the treatments by giving scores to various performance indicators such as pest incidence, plant colour, plant health, soil moisture, weed infestation, soil colour, plant height and yield. The results of the mean score performance are given in Table 19. Analysis showed no significant difference in pest incidence among the treatments. Tithonia + plant tea treatment had the least green plant while manure + DAP + CAN had the greenest plants. A similar trend was observed for plant health. Soil moisture was also highest in manure + DAP + CAN treatments while Tithonia only had the driest soil. Plant height showed no significant

18

differences between treatments. Yields obtained were highest in manure + DAP + CAN in both seasons. The farmers’ choice was therefore manure + DAP + CAN followed by FYM + plant tea >Tithonia + plant tea > Tithonia only. Table 11: Mean score performance against tested technologies

Treatment Pests

Plant colour

Plant health

Soil m

oist

Weed

infest

Soil colour

Plant height

Yield

Choice

Manure + DAP + CAN 6 ns 2b 3b 4b 3b 2b 2 ns 2b 2bFYM + Plant tea 6 5ab 5ab 5ab 4b 5b 5 4b 4bTithonia only 2 8a 8a 7a 3b 9a 9 8a 11aTithonia + plant tea 6 5ab 5ab 4 9a 5b 14 5a 5b

19

7.0 CONCLUSIONS AND RECOMMENDATIONS At Ngaita FFS, the treatment that should be adopted is use of manure + DAP + CAN which is the farmers practice. However, the use of lime to correct the soil acidity is of immediate action and that explains the favourable response by manure. Use of Tithonia at planting and topdressing with Tithonia tea offers another option to use of mineral fertilizers. This is because it may be cheaper than mineral fertilizers. If rains are not adequate then use of water harvesting techniques should be adopted together with use of manure to retain the water in the soil for longer periods. Alternatives such as use of rock phosphate should be explored to provide phosphorous nutrient, which is low or use 250kg/ha-1 of triple super phosphate during planting time and then top dress with CAN at knee high at 80kg/ha for maize crop. INM approach to soil fertility management and maintenance should go along way in increasing crop yields as well as improving nutrient use efficiency.

8.0 REFERENCES: Argwings-Kodhek, Gem. (1997). Factors affecting fertilizer use in Kenya. An Interim report

on on-going work. Tegemeo Institute of Agricultural Policy and Development, Nairobi: Egerton University.

Braun, A.R, Thiele, G and Fernandez, M. (2000) Farmer Field Schools and Local Agricultural Research Committees: Complementary platforms for integrated decision making in sustainable agriculture. Agricultural Research and Extension Network-Network paper No. 105. Overseas Development Institute (ODI).

Central Bureau of Statistics (CBS), (2000). The 1999 Population and Housing Census. Vol. 1. Ministry of Finance and Planning, Republic of Kenya.

De Jager, A., Nandwa, S.M. and Okoth, P.F., (1998). Monitoring nutrient flows and economic performance in African farming systems (NUTMON) I. Concepts and methodologies. Agriculture Ecosystems. And Environment. 71: 37-48.

De Jager, A., Onduru, D., van Wijk, M.S., Vlaming, J. and Gachini, G.N., (2001). Assessing sustainability of low-external-input farm management systems with the nutrient monitoring approach: A case study in Kenya. Agricultural systems 69: 99-118.

FURP, (1994). Fertilizer use recommendations, vol. 1-23. KARI, Nairobi. Gachimbi, L.N., Gachini, G.N., Onduru, D.D., Maina, F.W., Muchena, F.N. and A. de Jager.

(2004). Samllholder Farming and Rural livelihoods in Ngaita Village, Kiambu Divison Kiambu District, Kenya. A Baseline Survey Report. INMASP Report no. Ke-07, ETC-East Africa and KARI (NARL) Nairobi and LIE-DLO- The Netherlands.

Hinga, G., Muchena F.N. and C. Njihia (1980). Physical and chemical methods of analysis National Agricultural Laboratories, Ministry of Agriculture. 1980.

Jaetzold, R., and Schmidt, H., (1983). Farm management Handbook of Kenya. Vol. II/C., East Kenya. Natural Conditions and Farm Management Information. Ministry of Agriculture and German Agricultural Team (GTZ), (1983).

Jama B., Swinkes R.A and Buresh, R.J. (1997). Agronomic and economic evaluation of organic and inorganic sources of phosphorous in Western Kenya. J. Agro. 89: 597- 604.

Kanyanjua S.M; Ireri L.; Wambua, S and Nandwa S.M. (200). Acid Soils in Kenya: Constraints and Remedial Options. KARI Technical Note Series. No. 11. Kenya Agricultural Research Institute.

Kassam, A.H., Velthuizen, H.T., Fischer, G.W. and Shah, M.M., (1991). Agroecological land resources assessment for agricultural development planning. A case study of Kenya.

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Resources database and land productivity, Technical Annex 1, land resources, land and Water development Division, FAO and IIASA.

MoPND, (1997) Kiambu District Development plan, 1997-2001. Office of the Vice President and Ministry of Planning and National Development, Government of Kenya, Nairobi.

Mortimore, M., Adams, B., Haris F.2000. Poverty and Systems Research in the Dry lands. Gate Keepers Series No. 94. International Institute for Environment and Development (IIED). London, UK.

Murithi, F.M. (2002). Adaptive Research needs and the role of farmer participatory Research in: KARI, 2002 participatory technology development for soil management by small holders in Kenya. Proceedings of the 2nd Scientific Conference of the soil management and legume Research Network projects. June, 2000, Mombassa, Kenya.

Muriuki, A.W and Qureshi, J.N (2001). Fertilizer Use Manual. Kenya Agricultural Research Institute, Nairobi.

Nandwa, S.M. and Bekunda, M.A., (1998). Research on nutrient flows and balances in East and Southern Africa: State-of-the art. Agriculture, Ecosystems and Environment 71:5-18.

Onduru, D.D., Gachimbi, L.N., De Jager A and Muchena F.N. (2002). Experimental Design Workshops held for INMASP Farmer field schools in Kiambu and Mbeere Districts of Kenya. INMASP Report No. Ke-14. ETC-East Africa and KARI (NARL), Nairobi and LIE-DLO, The Netherlands.

Sanchez, P.A., Shephered, K.D., Soule, M.J., Place, F.M., Mokwunye, A.U., Buresh, R.J., Kwesiga, F.R., Izac, A-M, N., Ndiritu, C.G. and Woomer, P.L., (1997). Soil fertility replenishment in Africa: an investment in natural resource capital. In: Buresh, R.J. Sanchez, P.A. and Calhoun, F. (Eds.) Replenishing soil fertility in Africa. Soil Science Society of America and ICRAF, Special publication 51, Madison.

Vlaming, J., H. van den Bosch, M.S. van Wijk, A. de Jager, A. Bannink, H. van Keulen. (2001). Monitoring nutrient flows and economic performance in tropical farming systems (NUTMON). Part 1: Manual for the NUTMON-toolbox.