Exploring options for integrated nutrient management in semi-arid

doi:10.3763/ijas.2008.0267

Exploring options for integrated nutrient management in semi-arid tropics using farmer field schools: a case study in Mbeere District, eastern Kenya D.D. Onduru1*, C.C. du Preez2, F.N. Muchena1, L.N. Gachimbi3, A. de Jager4 and G.N. Gachini3 1 ETC-East Africa, 00508 Yaya, Nairobi, Kenya; 2Department of Soil, Crop and Climate, University of the Free State, Bloemfontein 9300, South Africa; 3Kenya Agricultural Research Institute, Nairobi, Kenya; and 4 LEI-DLO, 2502LS The Hague, The Netherlands

The farmer field school (FFS) approach was used in semi-arid eastern Kenya in the period 2002 –2003 to explore technology options for addressing declining soil fertility and to institute learning processes on integrated nutrient management (INM). Participatory diagnosis of soil fertility constraints and experimental design workshops led to the formulation of the INM-FFS learning curriculum and choice of INM technologies for testing in the central learning plot. INM technologies jointly chosen for testing included farmyard manure (FYM ¼ T1), diammonium phosphate (DAP ¼ T2), combined application of FYM and DAP (T3); and T3 combined with Tithonia diversifolia applied as green manure (T4). Maize was used as a test crop. The treatments were replicated twice using a pair-wise design and data collected, bi-weekly, using the agroecosystem analysis (AESA) framework to aid learning and data analysis with farmers. Farmers’ evaluation of the trials was conducted at the end of the study period using matrix scoring and ranking. Treatments with combined application of organic and inorganic materials had better agro-economic performance than sole application of either FYM or DAP (T4 . T3 . T2 and T1) and they had a high value cost ratio (VCR . 2). The technologies of T1 to T3 did not have a positive impact on either nitrogen or phosphorus balances. However, T4 resulted in a positive partial N balance. Farmers’ evaluation corresponded well with the majority of the quantitative agroeconomic analysis. The study showed that there is a potential to use FFS for INM technology development and testing by stimulating interactions, farmer learning and closer working relationships between farmers, research scientists, extension and the institutions that they represent. Keywords: farmer field schools, integrated nutrient management, participatory technology development

Introduction Soil fertility management under complex and dynamic farming systems in sub-Saharan Africa (SSA) is increasingly becoming a challenge to *Corresponding author. Email: [email protected] or [email protected]

farmers, extension workers and researchers alike. Diverse studies have contributed to highlighting the problem of declining soil fertility and land degradation in SSA, sometimes with conflicting perspectives on its causes, magnitude and impacts on food production (Hartemink & Van Keulen, 2005). In some studies in SSA, the high population growth rates and subsequent intensification of

INTERNATIONAL JOURNAL OF AGRICULTURAL SUSTAINABILITY 6(3) 2008, PAGES 208–228 # 2008 Earthscan. ISSN: 1473-5903 (print), 1747-762X (online). www.earthscanjournals.com

INTEGRATED NUTRIENT MANAGEMENT IN SEMI-ARID TROPICS

agriculture without proper land management and adequate nutrient replacements have been reported to be the primary causes of high nutrient depletion reported in the subcontinent (Stoorvogel & Smaling, 1990). During the last 30 years, this depletion has been estimated at an average of 660 kg N ha21, 75 kg P ha21 and 450 kg K ha21, from about 200 million hectares of cultivated land in 37 African countries (Smaling et al., 1997). However, other studies have challenged this view and have indicated that the soil nutrient depletion in SSA is not so much determined by the high population pressure-nutrient supply-fertilizer use debate, but by effective market demand for farmer products and opportunities for them to make money within the broader rural livelihood strategies (Mortimore & Harris, 2005). Despite the various perspectives on soil fertility management in SSA, soil fertility decline remains an intransigent challenge in the subcontinent and there is need to take a broader view of soil fertility to address it effectively (CGIAR, 2002). Nandwa (2003) has postulated that the two primary challenges in soil fertility management are to improve and maintain or sustain crop productivity to meet demands for food, fibre, fuel and materials required for agro-based industries; and to enhance the quality of the land, water and other natural resources. Strategies are thus required to return the depleted soils to their original production potential. Addressing the declining soil fertility using a high external input approach has not had remarkable impacts in the semi-arid areas of eastern Kenya and per capita food production has continued to decline among the smallholder farmers that account for 80% of Kenya’s farming community. This has been attributed to the biophysical, socioeconomic and institutional environment in which soil fertility management is practised (Onduru et al., 1999). At the research and development level, there is a need for priority setting and targeting of potential ‘best bet’ technology for smallholder farmers in terms of agronomic superiority, economic viability and culturally acceptable options. Meeting these goals requires a shift in research focus, approach and partnerships; and also creating and strengthening interactions between and among soil fertility subject matter specialists, ecological economists, agroenvironmentalists, extensionists and policy makers as well as establishing

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linkages with the associated market forces necessary for production growth. A broader perspective is thus needed to promote soil fertility management as, for example, advanced under integrated nutrient management (INM) (Nandwa & Bekunda, 1998). Furthermore, soil fertility management is no longer perceived to be just a technical issue but to have a strong socio-economic and policy dimension. Both domestic and macroeconomic policies have an influence in input and output markets and thus, directly and indirectly, influences farmer adoption of sound soil fertility management technologies (Scoones & Toulmin, 1999). Integrated nutrient management has been defined as the judicious manipulation of nutrient stocks and flows in order to arrive at a ‘satisfactory’ and sustainable level of agricultural production (Deugd et al., 1998). Other scientists argue that in addition to nutrient stocks and flows other agronomic practices of planting good seed, early planting, weed, pest and disease control should be included in the definition of INM (Okalebo & Woomer, 2003). Two main approaches have been used in the promotion of INM: ‘hard’ sciences that quantify and generate fundamental knowledge on nutrient flows and INM technologies; and ‘soft’ sciences (participatory approaches) that combine scientific, experiential and religious-cultural knowledge within a particular agro-ecological and socioeconomic setting. Combining these two knowledge bases in a learning process is essential to the success of INM strategy along similar lines as those of integrated pest management (IPM). Although there has been emphasis on technical aspects of INM, there is an increasing realization that agriculture cannot be developed without banking on the intelligence, creativity and competence of farmers. Instead of adoption, the emphasis now is on learning, as farmers become experts not by adopting science-based technologies, but by becoming better learners (Deugd et al., 1998). However, promotion of INM needs to be supported by well-structured research and extension services aimed at increasing the capacity of farmers to be better learners and to rise to new challenges and dynamism in the farming environment (Hagmann et al., 1998). One research and extension approach is the farmer field school (FFS), which builds farmers’ capacities to learn about the agroecology of their fields, cope with dynamic changes in their

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production environment, and make timely and informed farm management choices. It is based on the premise that participating farmers can test the various technological options available, during which they are able to decide the best alternatives for their particular circumstances according to their agro-ecological settings, farm size, available capital and access to markets (Matata & Okech, 1998). The present paper presents the findings of participatory technology development (PTD) INM trials implemented using the FFS approach in semi-arid Mbeere District, eastern Kenya, over two agricultural seasons. Emphasis is placed on exploring the potentials of organic and inorganic sources of nutrients for their additive and synergistic effects in enhancing performance of the staple food crop, maize; and in sharing learning experiences from using FFS as a research and extension approach.

Materials and methods Study site Mbeere District is located in eastern Kenya with an altitude range of 500 – 1200 m above sea level. Rainfall in the district has a bimodal pattern with annual averages of 500 – 1100 mm depending on location and altitude. However, most parts of the district receive less than 750 mm of rainfall annually. This rainfall is unreliable and unpredictable. The district has two growing periods with a total length of 90 – 119 days (Kassam et al., 1991). Soils in the district are variable and are generally low in fertility. Organic matter content is low and macronutrients such as nitrogen and phosphorus are limited in many places. Soil and water conservation (SWC) measures coupled with water harvesting techniques are practised in the study area to conserve the fragile soils. These include terraces (e.g. Fanya juu and cut-off drains), stone lines, trash lines and addition of farmyard manure and crop residue management. Mixed farming is practised at subsistence level with about 98% of the total cultivated land under smallholdings. Food crops grown include maize, millet, sorghum, beans, cowpeas, green grams, cassava and bananas. Livestock species kept in the district are mainly indigenous breeds and are kept under extensive systems of production (free range

system). They include cattle, sheep, goats and poultry. Land in the study site is held under a freehold tenure system.

Farmer field school approach Farmer field schools are typically organized around a season-long series of weekly meetings focusing on biological, agronomic and management issues, where farmers conduct agroecosystem analysis (AESA), identify problems and then design, carry out and interpret field experiments under the guidance of a facilitator (Stathers et al., 2005). The group-based learning process, in the weekly meetings, involves conducting AESA in a central learning plot (study plot or experimental plot), learning about new farming techniques and related topics (special topics) and carrying out group dynamic activities. Group dynamic activities enhance team building and group cohesion. Implementation of FFS involves the following iterative steps: ground working, training of facilitators, establishing and conducting regular FFS meetings, evaluating trials (participatory technology development) and conducting field days. The farmers graduate at the end of the learning period. Ground working activities is a collective term for activities conducted in a village with the objective of paving the way for the introduction of FFS activities. This study adopted this classical methodology, but made refinements on the same to suit farmer learning and experimentation on INM (Table 1). Details of the FFS methodological steps adopted are described hereafter.

Formation of farmer field schools and farm selection The school formation process began with sensitization of district agricultural extension stakeholders and selection of representative catchment where the FFS would be located. Community meetings were then held within the selected representative catchment. 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 based on their production resources and location of their farms in the catchment under consideration to ensure that they were representative of the



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Table 1 Adaptations made in the FFS methodology

Basis of comparison

Mainstream approach

Adaptations made in this study

1 Period of learning

One cropping cycle

Two cropping cycles for learning on the tested technologies. This was a part of a four-year FFS learning cycle. There was a longer period of commitment by both farmers and facilitating institutions to conduct the FFS process

2 FFS focus

† Integrated pest management

Integrated nutrient management aimed at maximising the use of local resources and optimising application of external inputs, where available

† Integrated production and pest

management 3 FFS orientation

FFS used mainly as an extension methodology to convey messages of known recommendations (scaling up of technologies).

FFS used as an extension and research methodology to test and develop technologies with farmers through discovery-based learning process

4 FFS meetings

† Weekly meetings (to capture pest

† Two times a month as changes in soil

dynamics); other meetings scheduled as and when necessary 5 Experimentation (participatory technology development)

characteristics are slow; other meetings scheduled as and when necessary

† Ad hoc and nonsystematic

† Systematic experimentation given emphasis

† Trials lack adequate replication in

† Trials replicated in space (using a pair-wise

time and in space

experimental design and in time (two cropping cycles) † Technologies tested decided upon with

farmers in an experimental design workshop after participatory diagnosis of farming constraints and opportunities 6 Documentation and analysis

† Data collected during

7 Building local institution/policy issues

† Formation of farmer organizations

agro-ecosystem analysis are qualitatively analysed by farmers during group meetings, without further analysis thereafter

(FFS networks) for marketing, input supply and for influencing on policies

† Qualitative and quantitative data collected by

farmers during the learning process using agroecosystem analysis framework are analysed by farmers during group meetings. They were also further analysed by facilitators for wider sharing with extension/research audience † Organization of FFS group to undertake

marketing, carry out group commercial activities and input supply as well as influence local policies as in the mainstream approach † Policy issues given emphasis during the FFS

learning cycle. A workshop organized for District stakeholders (government ministries, Civil Society Organisations, Church based groups, Input suppliers, etc.) to discuss implications of FFS process and results of INM trials, and to map out strategies for scaling up FFS


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population from which they were chosen. Participants who met the above criteria also had to be willing to participate in the FFS programme and to share information with others, as well as being full-time farmers. This was to ensure that participants had the potential to share information emanating from FFS with the wider community. Thirty farmers volunteered to participate in the learning process on behalf of the community. Farms were included after a follow-up visit to individual farmers in which it was assured that the volunteer farmers understood the objectives of the FFS process, met the selection criteria and were motivated enough to participate in the study. A great effort was made to guarantee that the farms selected were representative of the population from which they were selected from in terms of biophysical circumstances, production resources and socioeconomic factors.

Table 2 Farm production characteristics of FFS participants (standard deviation in parenthesis; for soil parameters minimum and maximum in parenthesis)

Characteristic

Mean values (n 5 30)

Land Total land area (ha)

1.4 (1.2)

Land cultivated (ha)

1.2 (0.8)

Average slope (%)

18.5 (5.1)

Labour Household size (persons)

6.5 (1.8)

Consumer units (aeu)

4.7 (1.5)

Labour units (aeu)

3.2 (1.3)

Education level of household head 1

Diagnosis of farming system constraints and opportunities Farming system constraints and opportunities were diagnosed through literature reviews on soil fertility constraints in the FFS site, baseline survey and soil sampling and analysis. A literature review was conducted by researchers while baseline survey and soil sampling were carried out in a participatory process involving researchers, extension staff and farmers. Through the baseline survey, production resources were identified (Table 2) and included farmers’ socio-economic circumstances such as ownership of productive assets, farming practices, broader livelihood strategies, current farming system opportunities, challenges of soil fertility management and farmers’ indicators of soil fertility. Soil sampling and analysis were done for every participating farm and from the central learning plot where experiments on INM were to be set. The central learning plot is FFS’s group plot for carrying out field studies and trials on various technologies. Soil analyses were carried out for particle size distribution, pH, organic carbon, total nitrogen, extractable phosphorus and exchangeable potassium. Particle size analysis was completed in accordance with a hydrometer method described by Hinga et al. (1980). Organic carbon was analysed using the Walkley – Black oxidation method (Black, 1965); total nitrogen using the Kjeldahl

Primary education (proportion)

0.87

Post primary vocational (proportion)

0.03

Secondary education (proportion)

0.03

Market and off-farm income Distance to the market (km) Off-farm income (% of family earnings)

10.6 (1.9) 51

Capital TLUa

1.1 (1.7)

Value of livestock (US$)b

206 (207)

Value of land (US$) Value of equipment (US$)

2302 (2203) 50 (59)

Ratios Land: Labour unit ratio (ha per labour unit)

0.5 (0.3)

Land: Consumer (ha per consumer unit)

0.3 (0.2)

Consumer: Labour unit

1.6 (0.3)


(Continued)


Table 2 Continued

Characteristic

Mean values (n 5 30)

Soil parameters pHwater (1 : 1.2.5 suspension)

5.57 (4.76 –6.32)

Organic C (%)

0.61 (0.29 –1.11)

Total N (%)

0.06 (0.03 –0.09)

Extractable P (mg/kg)

8.8 (2 –20)

Exchangeable K (cmol/kg)

0.4 (0.2 –0.98)

1

Education level of household head.

a

TLU ¼ Tropical livestock units (1 unit is equivalent to 250 kg live weight). b 1 US$ ¼ Ksh 75 at time of study.

digestion method (Black, 1965); extractable phosphorus using the Mehlich extraction followed by colorimetry (Mehlich et al., 1962); and exchangeable potassium using ammonium acetate followed by spectrophotometry (Okalebo et al., 2002).

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Experimental design workshop, learning process, data collection and analysis An experimental design workshop was organized in a two-stage process. The first stage comprised sharing the findings of the diagnostic exercises (baseline survey and soil analysis) and literature review with participating farmers and prioritizing production constraints. Baseline survey and literature findings were summarized into a list of constraints and opportunities before discussing them with farmers. Soil analysis results (pH, organic C, extractable P and exchangeable K) were presented as bar charts and evaluated with farmers in a group process where each farmer was given his or her own results. As an example the total N analyses are shown in Figure 1. The constraints emerging from soil analyses were correlated to general soil management constraints in the study site and to the findings of the baseline survey and literature review. This led to the development of a list of soil fertility management constraints. The list of constraints was further stratified in a group process with farmers into potential thematic constraints and opportunities that could be

Figure 1 Soil total nitrogen on individual holdings of the FFS


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experimented with, themes for conducting demonstrations and themes for learning to be included in the FFS curriculum. Themes for learning were delineated based on availability of literature and current knowledge. In addition, themes perceived to have high risks during experimentation were included as learning themes (special topics) in the FFS curriculum. Similarly, themes for demonstrations were delineated on the basis that information was already available on them and that they could be demonstrated through learning by doing, rather than experimentation during the FFS learning cycle. Themes that could be experimented with were further stratified using participatory processes and consensus building into priority theme for experimentation in the FFS central learning plot and into subsidiary themes for FFS members to try in their own farms according to their interests. The second stage of the experimental design workshop process involved exploring possible technologies (opportunities) for addressing the main thematic constraint through conducting experiments in the central learning plot. Technologies for experimentation were proposed by both farmers and researchers and discussed alongside each other, resulting in the choice of one technology for experimentation through a ranking method. Farmers proposed technologies for addressing declining soil fertility (low organic C, total nitrogen and extractable phosphorus) were the use of compost, farmyard manure, diammonium phosphate, zero tillage and combining farmyard manures and diammonium phosphate. Proposals from researchers and extension staff included combining farmyard manure and diammonium phosphate, use of Tithonia diversifolia (Asteracea) as a green manure, mulching, use of rock phosphate and minimum tillage. Tithonia

diversifolia is a green manuring shrub collected along the roadside and is not grown in situ. The proposed technologies from farmers and researchers/ extension staff (facilitators) were synthesized into one list for further discussions and prioritization. One technology was chosen from the list of combined proposals based on the criteria that such technology was of common interest to all farmers, extension staff and researchers, had a potential to address declining soil fertility, could show some impact in one experimental season, required little cash capital input for implementation and had the potential to fit into the current farming system with opportunities for wider adoption by resourcepoor farmers. In a similar process, one test crop was chosen for experimentation from the basket proposed by farmers and researchers. Farmers’ proposed test crops were maize (local variety), beans, pigeon peas and soybeans. Those of researchers and extension staff were a new maize variety, Cargill 4141, sorghum, cowpeas, greengram, soybeans and millet. One test crop was chosen through a priority setting based on the criteria that such a crop should give results in one season and could be completely harvested, was easy to manage and had readily available seeds at low cost. After choosing one technology for experimentation, factor and factor levels were agreed upon through consensus. Farmers’ current practice was included in the design as a control for comparing the effects of treatments (Mutsaers et al., 1997). Treatments agreed upon with farmers in a participatory process were kept simple and replicated twice using a pairwise design in the central learning plot. Four treatments were designed (Table 3). Plot sizes measured 5 m 10 m while maize was planted at 0.9 m 0.3 m. Farmers’ practice of using manure

Table 3 Treatments agreed upon

Treatment†

Description

T1

Farmyard manure (at 16 t ha21)

T2

DAP (at 216 kg ha21)

T3

Farmyard manure (at 16 t ha21) þ DAP (at 216 kg ha21)

T4

Farmyard manure (at 16 t ha21) þ DAP (at 216 kg ha21) þ Tithonia (at 3.6 t ha21 fresh weight)

†

DAP ¼ Diammonium phosphate (18-46-0); inputs measured in local units and later calibrated in metric equivalent units.



was used as control and a basis for comparing the other treatments. Inputs in the trial plots were measured in local units and later calibrated into metric units. The farmers’ criteria for monitoring the trials were inventoried during the workshop and this, together with the criteria of researchers and extension staff formed the basis for monitoring the trials and data collection during the trials. Farmers’ criteria included grain yields, colour of leaves, plant height, weed infestation, soil moisture retention, pest infestation and plant health, and soil colour. Researchers’ and extension staff’s 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 criteria from extension staff were crop output data such as grain yields and crop residues. During the experimental design meeting, an action plan was jointly devised specifying activities, persons involved and an estimate of when such activities would take place. The central learning plot (experimental field) where the experiments were conducted was provided by one of the members of the FFS. The trials (in the central learning plot) were used as a platform for learning during bi-weekly FFS sessions in which agroecosystem analysis (AESA) concepts and charts were used by farmers, in a learning process, to collect data in small subgroups followed by plenary presentations (Sones et al., 2003). Data collection was based on monitoring performance indicators as proposed by farmers and researchers during the experimental design stage. The monitoring indicators were included in the AESA chart. Agroecosystem analysis, ‘special topic’ sessions, group dynamic activities and team building exercises, and other topical issues of interest to farmers, were integrated into the FFS curriculum. Other activities included in the FFS curriculum were farmer study tours and field days. The FFS curriculum formed the basis for running the FFS activities. Special topics sessions were discussion fora for imparting new farming skills to farmers. Implementation of these activities was guided by a timetable (FFS schedule) drawn up jointly by farmers and facilitators (Table 4). In the curriculum, group dynamic activities were meant to develop FFS members into a cohesive

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group, create conducive learning environment and to enhance communication, and farmer problem solving and leadership skills. Farmer study tours to other FFS and to research and extension organizations were meant to expose FFS members to a wide basket of technology choices and to learn from activities of other farmers and institutions. Field days were conducted for the rest of the community members to learn from on-going FFS activities and to disseminate results of the experiments conducted by FFS members. At the end of each season, maize in the experimental plots was harvested jointly with FFS members. The FFS members were divided into four subgroups and each subgroup harvested one treatment (two plots). The harvest for each plot (unshelled maize) was put in separate, labelled bags and weighed. Similarly maize stover from each plot was weighed separately and records taken. The harvested unshelled maize was then sun dried in one of the FFS member’s (selected by the whole group) home, and later hand-shelled during the FFS meeting. Hand-shelling of maize was done for each plot separately before being weighed using a spring balance. Also at the end of each cropping season, a participatory evaluation was conducted to elicit farmers’ opinions, preferences, criticisms and suggestions about the technologies tested. The evaluation was conducted based on the same performance indicators as given by farmers during the experimental design workshop. It was conducted by farmers in four subgroups using matrix ranking and scoring followed by plenary discussions. The data collected by farmers during AESA sessions were analysed in subgroups and shared in a plenary session each meeting day. This qualitative data analysis was reinforced by quantitative data analysis at the end of each season in which the various data sets collected using AESA charts were further analysed and shared with farmers in the FFS meeting sessions. Quantitative analysis included calculations 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 FFS site.


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Table 4 Farmer field school schedule (timetable) for guiding the learning process

Time

Duration

9.00 –9.05

5 min

Activity † Arrival

Reason

Leader

† Know attendance

Host team

† Registration † Sitting

arrangements 9.05 –9.10

5 min

† Prayers

† Thanks giving to God

Host team

9.10 –9.15

5 min

† Programme for

† Keeping every participant informed of the

Facilitator/Host team

the day 9.15 –10.00

50 min

† AESA

tasks ahead † Making field observations on treatments/

central learning plot

Host team/ subgroups

† Improving decision making † Training farmers in data recording

10.00 –10.30

30 min

† Plenary

† Discussing AESA findings/observations † Sharing experiences

10.30 –11.30

1 hour

† Special topic

† Keeping participants abreast with new

Host team/ facilitator

Facilitator

farming ideas/skills and other matters of concern 11.30 –11.40

10 min

† Group

dynamics † Break

† Building communication skills

Host team

† Problem solving † Building leadership skills

11.40 –11.45

5 min

† Recap

† Exposing/recalling the day’s learning points Host team

11.45 –11.50

5 min

† Programme for

† Preparing participants for next meeting’s

next meeting

activities

† Announcements

Host team/ facilitator/group committee

11.50 –11.55

5 min

† Closing prayers

† Thanking God for the day

Host team

11.55 –12.00

5 min

† Registration

† Attendance and participation

Host team/ secretary

† Departure

Tithonia diversifolia was valued at labour needed to collect it while manure and maize stover were valued at opportunity costs for purchasing them from neighbouring areas. Maize grain yields were

valued at prevailing local market rates and thus assumed to have a cash value. Soil nutrient budgets were used in this study as one of the indicators of land quality. Nutrient budget is a



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net balance between incoming and outgoing nutrients in farm inputs and outputs and is affected by a 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, 2001). A nutrient balance was worked out by considering the differences between nutrient inputs and outputs for each of the technologies studied. Nutrient inputs were in the form of mineral fertilizers (In 1) and organic inputs (In 2) while nutrient outputs considered include harvested grains (Out 1) and crop residues (Out 2). The differences between these nutrient inputs and outputs are described as partial nutrient balance: (In 1 þ In 2) 2 (Out 1 þ Out 2).

subangular and blocky. Laboratory analysis of samples taken from individual farms showed that the soils were strongly to slightly acid. The reported mean values for organic C, total nitrogen and extractable phosphorus were low and should limit crop production (Table 2). Similar observations were also made from analysis of samples from a profile pit opened in the central learning plot (Table 5). Additionally, calcium was also limiting in the central learning plot while sodium was moderate. The clay content increased, while the calcium and magnesium contents decreased, with increasing soil depth.

Building community local institutions

The mean grain yields obtained in the majority of the treatments (Table 6) were higher than grain yields of 1– 1.5 tonnes ha21 reported at farm level but lower than the expected grain yields of 4– 6 tonnes ha21 for the maize variety CG 4141 used as a test crop (Gitari et al., 1996). Although the differences in grain yields between the treatments were masked by variability, it was observed that treatments with combined organic and inorganic nutrient sources had higher grain yields than single applications of organic or inorganic nutrient sources. The combined application of manure and diammonium phosphate (DAP) resulted in 48% and 28% higher grain yields than single manure and DAP applications, respectively. Jones et al. (1996) reported similar observations where combined application of inorganic and organic nutrient sources outperforms single applications of either organic or inorganic nutrient sources. Organic materials such as farmyard manure influence nutrient availability (1) by adding nutrients; (2) through mineralization– immobilization patterns; (3) as an energy source for microbial activities; (4) as precursors to soil organic matter; and (5) by reducing phosphorus sorption of the soil (Palm et al., 1997). The benefit of using inorganic fertilizers such as DAP is the rapid release of nutrients for crop growth. The judicious use of such inorganic fertilizers can play a critical role in preventing the soil resource degradation that stems from net depletion of nutrients (Quin˜ones et al., 1997).

Embedded within the FFS process were strategies to build FFS into a cohesive group and into a local institution capable of marketing their produce, influencing local agricultural policies, sustaining the FFS process as well as scaling it up. The FFS members were trained in leadership and team building and have formally registered with relevant Government authorities as legal entities. Group commercial activities have been initiated and include growing of high value crops to sell; upgrading of local goats (using a pedigree buck) at a fee; and constructing soil and water conservation structures at a fee. The FFS members have also been attending various field days in Mbeere District to convey messages on FFS. In addition, a district policy workshop organized at the end of this study was attended by selected members of the FFS to discuss the implications of FFS and how the process can be scaled up in the district.

Results Soils of the study site The soils were classified as Luvic Arenosols (Muya, 2003; Sombroek et al., 1982). They were well-drained, deep, yellowish brown (10YR 4/4), loamy sand to sandy loam. Its consistency was loose when dry, very friable when moist, non-sticky and non-plastic when wet. Their structure was very weak, fine

Agronomic performance of tested technologies


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Table 5 Soil profile characteristics in the central learning plot

Depth (cm)

0– 16

Sand (%)

16–46 46– 55 55– 941

82.0

78.0

74

72.0

Silt (%)

8.0

12.0

14

14.0

Clay (%)

0.10

0.10

LS

SL

Texture class

0.12

0.14

SL

SL

pH (H2O)

5.9

5.4

5.39

5.85

EC (mmhos/cm)

0.02

0.02

0.01

0.01

Organic C (g kg21)

4.0

2.0

1.9

3.3

Cation exchange capacity (cmol kg21)

5.46

7.5

5.2

5.5

Exchangeable Mg (cmol kg21)

0.21

0.19

0.16

0.14

Exchangeable K (cmol kg21)

0.12

0.06

0.06

0.14

Exchangeable Na (cmol kg21)

0.35

0.20

0.15

0.45

Sum (cmol kg21)

8.33

1.4

7.1

0.73

Exchangeable Ca (cmol kg21)

7.65

0.95

Trace

Base saturation (%)

.100*

18.0

100

Trace 13

*High base saturation could be due to analytical error in exchangeable Ca.

The grain yields with the Tithonia treatment (T4) were 72% and 47% higher, respectively, than treatments with manure (T1) and DAP (T2) alone. Similar studies in the semi-arid areas of eastern Kenya, employing Tithonia with half the recommended rate of inorganic fertilizers, recorded maize grain yields of 4.8 tonnes ha21 compared with zero-control of maize grain yields of 1.5 tonnes ha21 (Mucheru et al., 2003). The effectiveness of fresh Tithonia leafy biomass for soil fertility improvement can partly be attributed to the fact that Tithonia is a high quality biomass that rapidly decomposes releasing plant available nutrients such as nitrogen, phosphorus and potassium (Mafongoya et al., 2003). Tithonia also reduces phosphorus sorption by producing organic acids during decomposition that compete with phosphorus for soil sorption sites (Nziguheba et al., 1998). Other studies have also reported that the

integrated use of Tithonia with commercial inorganic phosphorus fertilizers results in greater soil biological activity as determined by higher microbial biomass phosphorus than either sole Tithonia or sole inorganic fertilizers (Jama, 1999 cited in Mafongoya et al., 2003).

Economic performance of tested technologies The treatment involving a combination of farmyard manure, DAP and Tithonia (T4) had 62%, 75% and 23% higher net cash income than treatments involving FYM (T1), DAP (T2) and combined application of farmyard manure and DAP (T3). The application of organic inputs and the associated opportunity costs, included in the calculations, depressed the gross margins for T3 and T1 compared with treatment with DAP alone (T2). The returns to labour



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Table 6 Agro-economic performance of the tested technologies (standard deviation in parentheses)

T1 manure (at 16 t ha21)

T2 DAP (at 216 kg ha21)

T3 manure 1 DAP

T4 manure 1 DAP1 Tithonia

2530 (1017)

2969 (1479)

3741 (918)

4350 (772)

30,661 (9700)

33,718 (16965)

42,679 (9293)

51,437 (9313)

Manure

13,932 (0)

0 (0)

13,932 (0)

13,932 (0)

Tithonia

0 (0)

0 (0)

0 (0)

979 (0)

Labour

9280 (554)

8680 (139)

9000 (231)

10,120 (1524)

0 (0)

5832 (0)

5832 (0)

5832 (0)

5346 (0)

5346 (0)

5346 (0)

5346 (0)

Total variable costs (Ksh ha21)

28,558 (554)

19,858 (139)

34,110 (231)

36,209 (1524)

Gross margins (Ksh ha21)

2104 (9148)

13,860 (17102)

8569 (9110)

15,228 (8528)

Net cash income (Ksh ha21)

19,954 (10172)

18,510 (14794)

26,232 (9182)

32,322 (7724)

Return to labour (Ksh day21)

95 (78)

210 (160)

155 (79)

199 (59)

Benefit –cost ratio

11 (0.3)

1.7 (0.9)

1.3 (0.3)

1.4 (0.5)

20.4

2.2

2.7

Description Maize grain yields (kg ha21) Gross income (Ksh ha21)† Non cash variable costs

Cash variable costs DAP Seeds

Value –cost ratio (V/C)‡ †

‡

1 US$ ¼ Ksh 75 at time of study; DF associated with each mean ¼ 3.

V/C ¼ P(OT1-OTC) / P(IT1-ITC) where P ¼ price; OT1 ¼ output treatment 1; OTC ¼ output treatment control; IT1 ¼ input treatment 1; ITC ¼ input treatment control.

were twice the opportunity costs for labour for treatments T2 to T4 while for the treatment involving FYM (T1), it was 20% higher. This implies that the studied technologies (ceteris paribus), give farmers better returns from their labour than when such farmers would have hired out their labour for agricultural purposes. The cost–benefit ratios for all technologies studied were positive, demonstrating that they were economically viable. However, it is not known whether similar impacts could have been attained by local maize varieties, since they were not included in the trial design. In evaluation of risks to agricultural production, value–cost ratio has been frequently used (Muriuki & Qureshi, 2001). Using a

value–cost ratio of 2 as a risk factor, the technologies that can be practised without much risk are FYM þ DAP (T3); and FYM þ DAP þ Tithonia (T4). These results agree with farmers preference of using FYM þ DAP þ Tithonia as the most feasible option at farm level. This was because the option requires the use of few quantities of DAP while the rest of the inputs (FYM and Tithonia) do not require cash outlay and are locally available.

Impacts of tested technologies on soil nutrient balances In this study, partial nutrient balances were calculated to gauge the impacts of the tested technologies


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D.D. ONDURU ET AL.

Table 7 Impacts of tested technologies on nutrient balances (standard deviation in parentheses)

T1 manure (at 16 t/ha21)

T2 DAP (at 216 Kg ha21)

T3 manure 1 DAP

T4 manure 1 DAP 1 Tithonia

In 1 (Mineral fertiliers)

0.0 (0.0)

38.9 (0.0)

38.9 (0.0)

38.9 (0.0)

In 2 (organic inputs)

36.3 (0.0)

0.0 (0.0)

36.3 (0.0)

55.5 (0.0)

Out 1 (harvested grains)

37.0 (14.5)

43.4 (21.6)

54.7 (13.4)

63.6 (11.3)

Out 2 (crop residues)

20.9 (2.0)

17.4 (8.9)

22.8 (4.7)

29.8 (5.1)

221.6 (14.4)

221.9 (30.5)

22.3 (17.8)

1.0 (16.3)


0.0 (0.0)

43.4 (0.0)

43.4 (0.0)

43.4 (0.0)


13.2 (0.0)

0.0 (0.0)

13.2 (0.0)

15.1 (0.0)


5.1 (2.1)

6.0 (3.0)

7.6 (1.9)

8.8 (1.6)


4.4 (0.5)

3.7 (1.9)

4.9 (1.2)

6.2 (1.0)

Partial P balance (Kg ha21)

3.7 (2.0)

33.7 (4.9)

44.1 (3.0)

43.5 (3.0)


0.0 (0.0)

0.0 (0.0)

0.0 (0.0)

0.0 (0.0)


0.04 (0.0)

0.0 (0.0)

0.04 (0.0)

0.06 (0.0)


10.3 (4.2)

12.1 (6.0)

15.3 (3.8)

17.8 (3.2)


5.6 (0.6)

4.7 (2.4)

6.1 (1.3)

7.9 (1.3)

215.9 (4.1)

216.8 (8.4)

221.4 (5.0)

225.6 (4.5)

Nutrient balances N partial flows (Kg ha21)

Partial N balance (Kg ha21) P partial flows (Kg ha21)

K partial flows (Kg ha21)

Partial K balance (Kg ha21)

on soil quality (Table 7). There was net partial nitrogen and potassium mining while phosphorus was balanced. The combined application of FYM, DAP and Tithonia turned the negative partial nitrogen balances into positive. Crop removals in the form of grains accounted for most of nitrogen and potassium losses while the positive values for partial phosphorus balances can be attributed to phosphorus inputs through DAP and limited phosphorus removals in the maize grains and residues. In general, many organic materials do not have sufficient phosphorus to meet maize crop requirements and need to be supplemented by inorganic

phosphorus in areas where phosphorus is deficient (Palm, 1995).

Learning through evaluation of the treatments An end of season evaluation was conducted based on indicators of performance as defined by both farmers and facilitators at the beginning of the trials. Farmers’ evaluation was done in four subgroups. Each subgroup comprised seven to eight FFS members (both men and women mixed together) and carried out evaluation of the



1

Overall rank of the treatments with 1 showing the most preferred technology and 4 the least preferred treatment.

1 7.25 (1.8) 6.75 (2.1) 8.13 (0.8) 8.80 (0.5) 7.1 (0.8) 8.50 (1.5) 8.25 (0.9) 2.13 (1.2) FYM þ DAP þ tithonia

8.1 (1.0)

2 5.25 (0.9) 4.90 (2.0) 3.63 (1.6) 5.00 (0.8) 5.0 (1.2) 4.90 (1.0) 5.00 (1.4) 6.00 (0.0) FYM þ DAP

4.8 (1.5)

3 3.13 (1.2) 5.38 (1.7) 5.13 (1.6) 3.75 (1.0) 2.9 (0.8) 2.50 (1.1) 4.63 (0.7) 3.25 (0.9) DAP

4.8 (1.0)

4 4.38 (1.7) 3.00 (0.9) 3.13 (2.0) 2.50 (0.6) 5.0 (1.2) 4.12 (1.1) 2.12 (0.8) 8.63 (1.3)

2.4 (0.9)

Labour demand Yields Plant height Soil colour Soil moisture Incidence of retention weeds Plant health Maize leaf colour

FYM

The higher maize yields in this study than yields at farm level shows that there is a potential to raise maize yields through integrated nutrient management practices. The low maize yields at farm level in eastern Kenya is explained by low soil fertility,

Incidence of pests

Agro-economic performance of studied technologies

Treatment

Discussion

Table 8 Mean parameter scores for the tested technologies (mean of scores assigned by four subgroups, standard deviation in parentheses)

treatments as ‘a subgroup’ separately from the other FFS subgroups. The evaluation was conducted by each of the subgroups using matrix scoring and ranking based on 20 scores per criterion, that is, for each criterion 20 points were distributed according to the perceived performance of the four treatments. The scores assigned by the four subgroups to each of the treatments were averaged per criterion to reveal farmers’ preferences (Table 8). The exercise on evaluation resulted in a number of learning points with regard to characteristics of the treatments/technologies tested. The treatments differed with regard to pest incidences, maize leaf colour, plant health, soil moisture retention, weed incidences and grain yields. The treatment FYM þ DAP þ Tithonia was rated the best by farmers and the most feasible under smallholdings. It had low incidence of pests, ‘best’ maize leaf colour, plant health, soil moisture retention and high yields. Tithonia was perceived by farmers to be locally available and its use did not require cash costs. However, it was perceived to require more labour in fetching it and incorporating it in the soil than the use of inorganic fertilizers. During the evaluation it was clear that farmers consider technologies that require cash input to be more expensive than those that do not. Thus, farmers considered adoption and diffusion of the use of Tithonia þ FYM þ DAP possible and practical with relatively little cash outlay for purchasing DAP (compared to when nutrients were to be applied from DAP alone). The use of manure was perceived to be favoured by the fact that it did not require cash inputs for purchase and therefore many farmers could easily use it to address declining soil fertility. However, its use was perceived to be constrained by limited availability at farm level due to low livestock densities.

Overall rank1


221

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D.D. ONDURU ET AL.

low adoption of recommended maize varieties and low plant populations as well as socio-economic factors such as low cash availability that limits the use of inorganic fertilizers; low returns to investments; poor market infrastructure and consumption preferences (Micheni & Gathama, 1999). The effects of integrated nutrient management (INM) rest on the fact that nutrients supplied or removed (immobilized) by the addition of organic nutrient sources are additive to those supplied by inorganic nutrient sources. Palm et al. (1997) have postulated that the added benefits, or disadvantages, of combined additions are probably related to the carbon substrate of the organic material and its effects on nutrient availability. Similarly, long-term studies have shown that INM practices result in high yields that cannot be expected from mere additive effects of sole applications of either organic or inorganic sources of soil fertility (Bekunda et al., 1997; Vanlauwe et al., 2002). Economic performance of treatments involving a combination of organic (farmyard manure and Tithonia) and inorganic (DAP) material inputs was higher than single applications of either organic or inorganic inputs. The gross margins, net cash income and value cost ratio were high. However, it has to be noted that the use of organic inputs such as farmyard manure and Tithonia depend on their availability and the labour required to apply them. These constraints not withstanding, farmers in the FFS site regard inputs requiring cash payments as costly and inputs such as farmyard manure and Tithonia as ‘costly’ for their use did not require cash outlay. However, the ‘freely available inputs’ (Tithonia and farmyard manure) are likely to become increasingly scarce and valuable as use expands at the farm and village levels and eventually a market might emerge for them. Assuming that farmers adopt their most preferred treatment option of planting maize with a combined application of FYM, DAP and Tithonia (T4), then farmers could earn an average net cash income of Ksh 32 322 ha (US$431/ha) from maize or Ksh 38 786 (US$517) from 1.2 hectares of cultivated land every year when all maize is sold. The cash earnings could allow farmers to meet their obligations of purchasing DAP and Cargill seeds, (Ksh 11 178 ha or Ksh 13 414 for 1.2 ha). However, in reality smallholder farmers do not sell all their

maize produce and part or all of the produce is used for household consumption. Other studies among smallholder farmers in Kenya have reported that smallholder and large-scale farmers sell up to 21% and 80% of their maize produce respectively (Jayne et al., 2001). Other studies have further indicated that, on average, about 40% of maize produced in Kenya is sold (Wangia et al., 2004). When 21% of the maize produce is sold, then the smallholder farmers would earn net cash income of Ksh 6788/ha (US$91/ha) or KSh 8145 (US$109) from 1.2 hectares of cultivated land every year. This cash earning would be less than what is required by the farmer to purchase DAP and Cargill seeds and other inputs (see Table 6) and would not enable the farmer to meet other household expenditures such as paying school fees. For farmers in this study to meet the costs of DAP and Cargill seeds, at least they need to sell a minimum of 35– 40% of their maize produce. However, even if they do so, the net cash earnings would still be insufficient to purchase all farm inputs, meeting other household expenditures as well as paying school fees. In practice, smallholder farmers do not rely on maize as the only source of income and clearly, the idea of small rural farms relying mostly on grain crops for their incomes is an outdated perception. Among smallholder farmers, maize is grown both for home consumption (staple food for households) and for sale. Previous studies in rural Kenya have indicated that maize accounts for only 14–25% of total household income (including consumption) and that small-scale farm households derive between 25% and 70% of their income (depending on location) from nonfarm sources (Jayne et al., 2001). Part of this nonfarm source of income is used to buy maize to bridge household deficits where needed. In this study, about 51% of family earnings were from off-farm income (Table 2). Labour demand is one of the factors that influence adoption of INM technologies, especially technologies based on organic resources (Sanchez et al., 1997). Nyathi (1997) has reported that the application of manure to arable areas will depend on availability of labour and transport (access to carts and draught animals). The present study has indicated that all the technologies tested resulted in high returns to labour compared with the opportunity cost of labour and that the integrated use of



organic and inorganic inputs had the highest labour returns. Thus, despite the high labour required for transporting and applying organic inputs (farmyard manure and Tithonia), their integrated use with inorganic fertilizers was still profitable.

Studied INM technologies and soil nutrient balances Farmers’ practice of applying farmyard manure at current rates (used in this trial) did not impact positively on soil nitrogen and potassium balances. Crop response to manure is due more to the contribution of phosphorus and cations such as calcium and magnesium than the addition of nitrogen (Grant, 1967) or due to physical effects of organic matter on water infiltration and retention (Mugwira & Murwira, 1997). Increasing manure application to arrest nutrient mining is still a formidable task at farm level where farmyard manure availability is low (Probert et al., 1995). It was the combination of manure with other nutrient sources (organic and inorganic) such as inorganic fertilizers and Tithonia that resulted in positive nitrogen, phosphorus and potassium balances. However, the challenge remaining in using this strategy is how to integrate the use of organic and inorganic nutrient sources for increased nutrient availability and use efficiency (Goma, 2003; Palm et al., 1997).

Farmers’ qualitative evaluation and the results of quantitative assessments There has been an increasing emphasis on integrating farmers’ opinions, reactions and evaluation criteria in the assessment of new technologies, particularly with the realization that agro-economic analysis is incomplete without a full understanding of the criteria farmers use to evaluate new technologies. Increasing evidence also points to the fact that the decision to adopt a new technology is more than just purely a technical option. It is a holistic and complex trade-off among various household needs and objectives. According to Ashby (1990), farmers’ evaluation plays a crucial role in stimulating free expression of farmers’ opinions, preferences, criticisms and suggestions about new technologies; sharing information among different stakeholders (researchers and farmers) on the viability of the technology under testing; eliciting

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farmers’ priorities and preferences among several alternatives; and in involving future users in making decisions which eventually may lead to the recommendation of a particular technology. While farmers use mostly qualitative measures and researchers and facilitators use mostly quantitative measures to evaluate technologies, the present study showed that there was a considerable degree of correlation between the two knowledge systems. Farmers’ evaluation using matrix ranking and scoring showed that the combined application of FYM, DAP and Tithonia (T4), had the highest maize grain yields and was the best in performance based on different evaluation criteria. The results of farmers’ evaluation were corroborated with quantitative analysis which showed that combined application of FYM, DAP and Tithonia had the highest agro-economic performance as well as contributing positively to addressing nitrogen balances.

Methodological learning experiences Experiences from this study show that AESA can effectively be used to capture both qualitative and quantitative data. Qualitative farmers’ perceptions and quantitative data (agronomics and socioeconomics) were correlated and analysed under one platform to explore different dimensions of INM technologies. Past experiences with FFS in developing countries have focused on qualitative and learning aspects of FFS methodology without marrying qualitative – quantitative data for wider sharing and scaling up of results to the development, scientific and policy worlds (Onduru et al., 2002). Currently, one of the practical dilemmas in Kenya is how to improve the performance of agricultural extension services, which is facing constraints in resources, logistics and methodology. The use of FFS in the present study showed a positive contribution in bridging the gap between agricultural extensionists and the farmers as it provided a forum and the means by which agricultural extension agents interacted more closely with farmers than before, on a demand-driven basis based on regular fortnightly meetings. In the site where the FFS was situated, extension agents were perceived to be inaccessible and ‘hard to see’ before the inception of the FFS in the area. During each FFS sessions, farmers made observations on INM technologies under testing and


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agreed on issues in subgroups before presenting them in plenary sessions. This improved the personality of the various subgroup members as presentations were made in a rotating manner. Gender relations among the FFS members also improved as the women members of the groups became more and more expressive and aired their views freely, a situation that was not observed at the inception of the FFS process. These dynamics in the FFS process fostered group cohesiveness and a sense of belonging that further led to FFS members starting to contribute their own microfunds through merry-go-round activities. Further lessons learned from the FFS process showed that although FFS on INM was highly appreciated by both sexes, women seem to especially value the approach due to the practical, field-based, learning focus and the social value of the FFS group. Although FFS has been implemented in Asia and other countries over a period spanning one cropping cycle and with emphasis on pest management, experiences from this study showed the necessity of adapting this approach when dealing with integrated nutrient management. There was need to stretch the learning period over several seasons for farmers to appraise the full range of costs and benefits associated with INM technologies. Changes in productivity and on soil fertility brought about by implementation of INM technologies take long to be observed compared with those of IPM. Despite the efforts invested in facilitating dialogue, reflection analysis and experimentation using the FFS approach, the challenge for the partnership developed with extension agents was how to keep up with the regular FFS meetings especially during peak periods when regular FFS activities coincide with other extension activities in the same locality or in nearby localities. Similar challenges were also met by researchers. Thus, for the FFS process to work, it requires commitment and dedication on the part of all stakeholders (researchers, extension staff/facilitators and farmers). Further lessons learnt from this study showed that the FFS process placed demands on researchers and extension staff that were not consistent with the transfer of the technology model used in extension and research approaches. In the study, indigenous farmers knowledge had to be recognized, communication gaps between farmers and research scientists

had to be bridged through observational aids, skills building had to be done through learning by discovery exercises and strategies for strengthening FFS group dynamics (through team building and leadership, and group exercises) and fostering of local group or community structures and action had to be integrated to make the process work. Thus, the FFS approach demanded a new way of working, breaking the professional identity of scientists with regards to relationships and working with farmers, and a move away from telling farmers what is technically sound and best practice to interactive learning and INM technology testing and development based on appreciation of scienceindigenous technical knowledge linkages.

Broader perspectives and socio-economic and policy dimensions The study has shown that the growing of Cargill maize using FYM, DAP and Tithonia combinations results in high yields and thus, tangible benefits to the farmer. However, success in the adoption of these technologies may either be facilitated or limited by domestic and macroeconomic policies, prevailing biophysical factors and availability of labour among other factors. The domestic policy pursues food self sufficiency and security as well as affordability of maize by consumers. The use of low cost resources (manure and Tithonia) for maize production contributes to meeting the objectives of the domestic food policy. However, inorganic fertilizer use is perceived costly by smallholder farmers. Achieving sustained use of inorganic fertilizers among smallholder farmers will thus require domestic policy to build farmers’ effective demand for fertilizer, by making its use profitable as well as building durable output markets that can absorb the increased output without gluts that depress producer prices. Another factor that can limit the uptake of the tested technologies is the ever-increasing gap between maize production and supply, and demand in the domestic scene. The maize deficits have necessitated maize imports from other countries (e.g. Western countries) to meet the national shortages. These countries, however, produce maize efficiently at cheaper costs or have input subsidies not found in the domestic scene. Because these imports are cheaper, the smallholder farmers cannot compete against them and thus the



adoption of the tested technologies may be slower than expected. Currently, maize marketing and trade policy (terms of trade in global market) are in the public debate especially with regard to the food price dilemma, the necessity for efficient and low cost production, input prices and liberalization, the appropriateness of trade barriers (import duties and bans) and the role of government in ensuring adequate returns to maize production (GabreMahdin; Haggblade, 2004 & Jayne et al., 2001). In the biophysical environment, the high rainfall variability in the study site may depress maize yields or result in crop failure in some seasons. It is a common experience in semi-arid areas that poor rainfall distribution during the growing season may result in damaging drought at critical periods of crop growth (Biamah, 2005). Also when rains fail, farmers’ investments in Cargill seeds and fertilizers may be risky and thus, the growing of Cargill maize should appropriately be done using organic – inorganic fertilizers and not inorganic fertilizers alone. Organics conserve soil moisture. In addition, the use of water harvesting practices, as an accompanying measure, may increase the chances of successful maize production in the semi-arid environments. Although returns to labour for the technologies tested in this study were positive, the use of labourintensive technologies in agriculture especially in SSA is a subject of debate. In the study site, most of the labour used for farming is family labour. Family labour is perceived by rural households as ‘inexpensive’ as it does not require cash outlays. However, it is increasingly becoming clear that the extent to which households decide to use their own family labour in food production, in commercial farming activities or in practising labour-intensive agricultural technologies could be influenced by what labour might earn elsewhere. Previous studies in the densely populated Kisii district of Kenya (800 persons km22) have shown that labour for farming is becoming scarce because of attractive off-farm incomes, which tempt labour away from farming (Scoones & Toulmin, 1999). This scenario is, however, different from the semi-arid areas of Kenya where off-farm income sources are limited and family labour is better deployed in farming activities, even if labour intensive. Thus, the adoption of the technologies tested in this study is expected to be influenced by household labour dynamics.

225

Conclusions The results from this study have demonstrated how agricultural research and extension can work together in developing and testing integrated nutrient management technologies with farmers for increased agricultural production by adapting promising solutions to actual conditions faced by smallholders based on farmer field school platforms. Agro-economic assessment shows that treatments with combined organic and inorganic nutrient sources had higher yields than single applications of organic or inorganic nutrient sources and that the maize grain yields increased with increased nitrogen and phosphorus applications. Thus, nitrogen and phosphorus were limiting maize production in the study site. The returns to labour were higher than the opportunity costs of labour for all technologies studied. This demonstrates that farmers could be better off using family labour in their own farms than hiring it out (for farm activities) in other farmers’ fields. However, the extent to which households decide to use their own family labour in practising labour-intensive agricultural technologies is expected to be influenced by what labour might earn elsewhere, as high labour demand could be a constraint to adoption of agricultural technologies. The limited opportunities for off-farm income in the semi-arid areas of Kenya implies that family labour is still better off deployed in farming activities (even if labour-intensive), than otherwise. Taking risk factors into account, (value–cost ratio of 2), it is the combined application of farmyard manure (FYM) and diammonium phosphate (DAP) and or FYM, DAP and Tithonia diversifolia that offers the potential to be practiced under smallholder farmer conditions. Implementation of the latter technology (combined applications of FYM, DAP and Tithonia diversifolia.) also results in a positive impact on the partial nitrogen balance. Farmers’ evaluation of the treatments, through matrix scoring and ranking, tended to correlate well with quantitative analysis. Farmers’ high rating of combined applications of farmyard manure, DAP and Tithonia diversifolia, was congruent with quantitative analysis. However, while farmers use qualitative measures and researchers’ quantitative data to evaluate performance of treatments, the considerable degree of correlation between the two


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systems suggests that researchers and other development facilitators could have confidence in farmers’ qualitative assessments and incorporate them in the technology development process. When done, it is envisaged that research would develop technologies that become widely adopted, resulting in more productive, stable and equitable sustainable agricultural systems. Lessons learned from this study show that for INM-FFS to work there is need for mutual and gainful interaction between farmers, research scientists and extension workers in a process that captures indigenous technical knowledge and science-based knowledge; for no matter how scientifically sound an INM technology is, it is the completeness with which it meets the sociocultural and economic needs of the day and institutions that determines its merits and therefore adoption and diffusion. Thus, the future development of INM technologies will probably depend on a social process of constant experimentation and innovation in which farmers’ knowledge interacts with scientific knowledge to identify the most pressing problems, test solutions and share results with others and to start the process all over again as new areas of concern emerge. However, the sustainability of the INM-FFS process will not depend on the fact that farmers have learned and are able to apply INM technologies soundly, but on farmers’ own motivation to continue experimenting in the search for new INM technologies and their willingness to take risks where little information is available.

Acknowledgements The European Union (EU-INCODEV) is greatly acknowledged for financing this study, done under INMASP project. The authors also wish to thank the participating Kenyan farmers (Munyaka FFS) and Ministry of Agriculture staff in Siakago Division of Kenya, especially Mr Kariuki Kiigi, for their participation in the FFS process.

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