Chapter 29
ISFM Adaptation Trials: Farmer-to Farmer Facilitation, Farmer-Led Data Collection, Technology Learning and Uptake B.K. Paul, P. Pypers, J.M. Sanginga, F. Bafunyembaka, and B. Vanlauwe
Abstract Integrated Soil Fertility Management (ISFM) aims to increase crop yields while conserving natural resources. Participatory research approaches are designed to address challenges in uptake of these knowledge-intensive technologies. ISFM adaptation trials have been developed to evaluate technologies across a wide range of agroecological and socioeconomic environments, while enabling resource-extensive, large-scale participation through farmer-to-farmer facilitation. A study of 144 ISFM adaptation trials in South Kivu, DR Congo was conducted from June to July 2011 and consisted of questionnaire interviews, field evaluation, farmer-collected data analysis, and in-depth interviews. This study aimed to (a) document the farmer-to-farmer facilitation approach, (b) assess the success of farmer-led data collection, and (c) evaluate farmers’ learning and subsequent technology uptake. The farmer-to-farmer facilitation system ensured a high percentage of trained assistance to farmers: during trial installation, 87 % of farmers were helped either by farmer technical advisors, facilitators, or agronomists, whereas this percentage was 47–58 % during agronomic operations throughout the season. This facilitation system decreased project costs while increasing participant numbers, thus lifting participatory research above a small scale. The farmer-led data collection was successful in terms of uniform trial establishment
B.K. Paul (*) • P. Pypers CIAT (International Center for Tropical Agriculture), Tropical Soil Biology and Fertility Research Area, Nairobi, Kenya e-mail:
[email protected] J.M. Sanginga • F. Bafunyembaka CIAT (International Center for Tropical Agriculture), Tropical Soil Biology and Fertility Research Area, Bukavu, Democratic Republic of Congo B. Vanlauwe IITA-Kenya, Central Africa hub and Natural Resource Management Research, Nairobi, Kenya B. Vanlauwe et al. (eds.), Challenges and Opportunities for Agricultural 385 Intensification of the Humid Highland Systems of Sub-Saharan Africa, DOI 10.1007/978-3-319-07662-1_29, © Springer International Publishing Switzerland 2014
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and yield measurements: 76–90 % of adaptation trials were installed correctly in terms of manure and mineral fertilizer application and crop arrangement. A total of 82–85 % of farmer field books were returned after the growing season, and missing crop yield data was low in 91–93 % of all field books, although this percentage was less favorable for the participatory evaluation section. Farmers’ learning was medium to high among 79–89 % farmers with regard to sowing in line, mineral fertilizer application, improved seeds, and crop arrangement. However, technology uptake was more variable, with 53–85 % of farmers partially or fully taking up improved varieties, crop arrangements, and second legume planting, while only 27 % said they continued with mineral fertilizer application. Future research should develop a data quality assessment method of farmer-collected data, which would improve reliable statistical analysis. Further, the effect of intensity and quality of farmer-to-farmer facilitation on data collection and quality and farmers’ learning and technology uptake is not yet well understood. Keywords Integrated Soil Fertility Management (ISFM) • On-farm adaptation trials • DR Congo • Farmer-led data collection • Technology learning and uptake
Introduction Soil fertility depletion and soil degradation are major biophysical causes of low agricultural productivity levels in sub-Saharan Africa. An African Green Revolution is therefore urgently needed (Sanchez 2010). Integrated Soil Fertility Management (ISFM) aims to increase productivity while conserving natural resources through the use of improved germplasm, judicious mineral fertilizer application, organic matter management, and agronomy adapted to local conditions of smallholder farmers (Vanlauwe et al. 2010). On-farm demonstration trials have shown that ISFM can significantly increase economic benefits in legume cassava intercropping systems in the Central-African highlands as compared to farmers’ common practice (Pypers et al. 2011). However, knowledge-intensive practises such as ISFM tend to perform well on research stations but farmer adoption rates remain low, especially in sub-Saharan Africa (Giller et al. 2009). For simple technologies (e.g., high yielding varieties) and homogenous farming environments, conventional research with its top-down, linear technology transfer paradigm might have worked. For knowledge-intensive technologies and complex farming systems, it is increasingly recognized that we need more interactive, experiential approaches taking into account users’ experimentation (Ro¨ling 1996; Douthwaite et al. 2002). Participatory research has been proposed to bridge the gap between research and farmers’ reality. “Learning by doing” is likely to improve relevance and adoption of technologies (Chambers et al. 1989; Pretty 1995). Biggs (1989) differentiated between four modes of participation, which were further developed by Lilja and Ashby (1999) into a typology of participatory research: (a) Conventional: scientists take decisions alone without communicating with farmers; (b) Consultative: scientists take decisions alone, but engage in organized communication with
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Fig. 29.1 Typology of participatory research (Adapted from Lilja and Ashby (1999) and Franzel and Coe (2002))
farmers. Scientists choose whether or not their decisions are affected by farmers’ priorities; (c) Collaborative: decision-making is shared between scientists and farmers through organized two-way communication; (d) Collegial: farmers take decisions alone, but are in organized communication with scientists. Farmers choose whether or not their decisions are affected by scientists’ priorities; (e) Farmer experimentation: farmers take decisions individually or in a group, without being involved in organized communication with scientists. If considerable decision-making power is transferred to the farmers (collaborative, collegial, farmer experimentation), the research becomes empowering. These forms of participation could occur in three distinct project phases: Design, testing, and diffusion. Franzel and Coe (2002) add that different participatory trials are suited for different scientific objectives. If researchers’ control over the trials is high (conventional, consultative), trials are likely to fulfil conditions of scientific rigor and are therefore well suited for biophysical (statistical) evaluations. Assessments of farmers’ preferences and constraints and profitability are more realistic if farmers have high control over trials (collaborative, collegial, farmer experimentation) (Franzel and Coe 2002). Figure 29.1 summarizes these concepts. The Consortium for Improvement of Agriculture-Based Livelihoods in Central Africa (CIALCA) has been operating in 10 mandate areas in DR Congo, Rwanda, and Burundi since 2005 (CIALCA 2009). CIALCA adapted the Mother and Baby trials (Snapp 2002; Snapp et al. 2002) to develop new participatory on-farm trials, called ISFM demonstration and adaptation trials. The adaptation trials aim to tackle two fundamental criticisms of participatory approaches: Firstly, it is claimed that biophysical evaluation is difficult under farmer management due to uncontrolled factors (Franzel and Coe 2002; Snapp 2002). Secondly, critics argue that participatory research is time and resource intensive so that it can only be conducted on a small scale (Johnson et al. 2003). Table 29.1 summarizes similarities and differences between Mother and Baby and ISFM demonstration and adaptation trials. Both approaches are similar with respect to modes and objectives of the Mother or ISFM demonstration trials. Contrary to baby trials, ISFM adaptation trials aim to collect biophysical performance data, covering a wide range of agroecological
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Table 29.1 Comparison between Mother and Baby trials and ISFM demonstration and adaptation trials. Typology is based on the classification of Lilja and Ashby (1999) and scientific objectives on Franzel and Coe (2002). Facilitation refers to assistance during trial setup, management and data collection (Data for Mother-Baby trials is retrieved from Snapp et al. 2002) Mother and Baby trials
ISFM demonstration and adaptation trials
Mother Baby Consultative Consultative Consultative Collegial
Demonstration Adaptation Consultative Collaborative Collaborative Collegial
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Design Testing
Scientific objectives
Biophysical Yes performance Profitability No Farmers’ No preferences
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Yes
Yes
Yes Yes
Yes No
Yes Yes
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Researchers
Farmers and Researchers enumerators
Management Data collection
Researchers Researchers
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Researchers Researchers
Farmers (assisted by technical advisors) Farmers Farmers (checked by technical advisors)
and socioeconomic conditions. Participating farmers are trained, install and manage the trial, and collect biophysical data, with assistance from farmer technical advisors, who are elected on the level of farmer associations. This farmer-to-farmer facilitation allows for large-scale participation and data collection, while costs are kept low. Despite of the considerable interest in participatory research, there are only few studies that document and evaluate the success of participatory on-farm approaches (Snapp 2002). Therefore, this study aims to document the adaptation trials, more specifically: • Document the farmer-to-farmer facilitation approach. • Assess farmer-led biophysical data collection. • Examine farmers’ learning and technology uptake.
Materials and Methods Study Area The study was conducted in South Kivu, DR Congo, and included all four Action Sites—Burhale, Lurhala, Kabamba, and Luhihi. In the highlands of South Kivu (1,600–2,000 m above sea level), rainfall is bimodal and allows for two growing
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Table 29.2 Treatments of adaptation trial packages. Farmers could choose between three different trial packages. Each package comprised three treatments, which illustrated the additive benefits of ISFM technologies. Treatments were laid out in three adjunct plots (6 m 6 m) Package 1 Crop arrangement T1 Free T2 1 m 1 m T3 1 m 1 m
Package 2 Mineral fertilizer No No Yes
Crop arrangement Free 0.5 m 2 m 0.5 m 2 m
Package 3 Mineral fertilizer No No Yes
Crop arrangement Free 1 m1 m 0.5 m 2 m
Mineral fertilizer Yes Yes Yes
seasons. The short rains last from February to June (season B), while the long rains stretch from September to January (season A). The area receives a total of 1,500–1,800 mm rainfall per year. Main food crops include cassava, maize, sweetpotatoes, sorghum, bananas, common bean, groundnut, and soybean. Farmers commonly intercrop cassava with legumes without any specific arrangement. Average yields range from 400 to 800 kg/ha for grain crops and 10 to 15 t/ha for cassava fresh tubers. Until recently, the area has mainly been isolated from new research and development projects. Most farmers have very limited access to improved varieties, manure, and mineral fertilizer. Population density is high (300–350 inhabitants per km2), and average agricultural land size therefore low (0.3–0.4 ha). Soils in Burhale and Lurhala are highly weathered and rather infertile, characterized by a heavy clay texture, low soil pH, and nutrient deficiency. In Kabamba and Luhihi, soils benefited from recent volcanic ash or mudflow deposits, resulting in higher pH and more nutrient content and therefore higher soil fertility (Farrow et al. 2007; CIALCA 2011; Pypers et al. 2011).
Trial Establishment and Management ISFM adaptation trials commenced in the 2008B/2009A and 2009A/2009B growing seasons. From discussion and evaluation of the ISFM demonstration trials with farmers and NGO partners, best-bet ISFM technologies for cassava intercropping had been identified. Cassava needs two seasons to mature, which allows for two legume intercrops. Farmer organizations within the Action Sites informed their members about the trials and collected names of interested farmers. Participating farmers committed to collect all required data in a field book, and received a trial package with all necessary inputs in return. Farmers could choose among three different packages with three treatments each, which demonstrated the additive effect of ISFM technologies (Table 29.2). Improved germplasm for cassava, soybean, and common bean was used throughout. If available, farmers were asked to apply an equal amount of organic inputs on all three plots. The trials were supposed to be installed on homogenous land (similar land use history, no gradient) as three adjunct 6 m 6 m plots. The farmers executed all field operations from trial installation to weeding and harvesting.
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Fig. 29.2 Schematic presentation of farmer-to-farmer facilitation system of the ISFM adaptation trials. The central column specifies different actors and assigned roles during key periods. A ¼ before growing season, B ¼ trial installation, C ¼ trial management, D ¼ data collection. The left column refers to the level at which the respective actor is operating, and the right column specifies the approximate participants per mandate area and season at each level
Farmers were supported in trial installation, management, and data collection by a farmer-to-farmer facilitation system (Fig. 29.2) comprising: • Farmer technical advisors: the members of participating farmer associations elected three technical advisors, who were supposed to provide close followup of 3–10 adaptation trials, and assist in data collection. • Facilitators: all association members elected one facilitator per action site, who coordinated all activities and constituted the contact point for researchers. • CIALCA agronomists: composed the packages, trained the facilitator and technical advisor, and reminded them of agronomic activities.
Survey Design To evaluate the ISFM adaptation trials in the 2008B/2009A and 2009A/2009B seasons, several instruments were combined. A questionnaire survey was conducted in all action sites between 5 and 22 July 2011 among 144 farmers and
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36 technical advisors. Using a stratified sampling strategy, we randomly selected 4–6 farmer associations at each action site, 2–3 technical advisors of each selected farmer association, and 2–6 farmers of each selected technical advisor. The questionnaire took around 1 h, included closed and open questions, and addressed farmer learning, technology uptake, and facilitation. A technical evaluation survey verified trial installation and management among all participating farmers. CIALCA students and agronomists visited the farmers’ fields between 9 April and 28 April 2008 (2008B/2009A) and 19 November and 28 December 2008 (2009A/2009B). For this study, only data from the same 144 surveyed farmers was analyzed. Field books were collected after the 2009A and 2009B growing seasons, and the total return rate calculated. Different sections of the field books of the 144 surveyed farmers were analyzed. In-depth, semi-structured interviews were undertaken with seven key informants in July 2011, including CIALCA agronomists and facilitators. Questions addressed the level and quality of facilitation, constraints, and ideas for improvement.
Results and Discussion Farmer-to-Farmer Facilitation System A survey among ISFM adaptation trial participants revealed assistance by different actors during key field operations (Fig. 29.3). During installation, only 1 % of the participants did not receive any assistance, and in 11 % of the cases only untrained assistance (family members, neighbors). The farmer technical advisors helped 78 % of farmers, whereas the facilitators assisted 8 % and the CIALCA agronomist could only help 1 % of the farmers. The proportion of trained assistance (CIALCA agronomist, facilitator, technical advisor) decreased with subsequent planting and harvest operations. Technical advisors still assisted approximately half of the participating farmers (45–54 %), whereas the facilitator helped 2–4 %. These results show that the farmer-to-farmer system ensures high assistance rates, especially at the time of trial installation. Although farmers are trained in trial setup and management, it can be assumed that a large proportion still needed assistance with understanding and implementing the trial protocol. Further, these results underline that farmer technical advisors are responsible for the major fieldwork. This saves project resources while reaching a maximum number of farmers, although technical advisors should be compensated for their opportunity costs (Fig. 29.3).
Farmer-Led Data Collection The prerequisite for biophysical data collection is a uniform trial installation. The 144 trials were assessed in terms of homogeneity of the plot, manure
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Fig. 29.3 Farmer-to-farmer facilitation during field operations. Farmers were asked if somebody assisted them in key field operations (trial installation, harvest of first legume, planting of second legume, harvest of second legume, harvest of cassava), and if yes, who assisted them (CIALCA > facilitator > technical advisor > neighbor/family member). If several actors assisted, the highest level was counted
application, fertilizer application, and crop arrangement (Fig. 29.4a). For the latter three criteria, 76–90 % of the farmers installed the trials correctly. However, only 56 % of participating farmers chose a homogenous plot for their trials. This is the result of high population pressure in the area, which corresponds with scarcity of land. The overall high correct trial installations might be the consequence of the high assistance rates of the technical advisors during trial setup (Fig. 29.4a). Of the 276 (2008B/2009A) and 387 (2209A/2009B) field books that were distributed in all Action Sites in South Kivu, 82 % and 86 %, respectively, were returned after the growing seasons (data not presented). The assessment of a subsample of the 144 field books revealed that missing data differed between field book sections (Fig. 29.4b). Missing data for first legume and cassava yields was low in 91 % and 93 %, respectively, of the assessed field books, whereas the same proportion was
ä Fig. 29.4 (continued) choice, manure and fertilizer application, and cassava and legume arrangement. Installation was coded as incorrect if plots were situated on a strong slope, if manure was not applied at equal quantities to all plots, if fertilizer was applied to incorrect plots, and if crops were planted without arrangement. (b) Missing field book data: values are calculated for different field book sections (household information, field information, first legume management/yield/farmer evaluation, second legume management/yield/farmer evaluation, cassava management/yield/ farmer evaluation). Low refers to 50 % missing data
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participatory evalution
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Fig. 29.4 Farmer-led data collection of adaptation trials. (a) Correct trial installation: proportion (%) of farmers who have respected the trial protocol concerning homogenous plot
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only 54 % in case of the second legume. Many farmers did not plant or harvest the second legume because they perceived shading from the growing cassava plants too excessive for second legume growth. The percentage of missing data was higher for the participatory evaluations (15–19 %) than for yield data (7–9 % when not considering the second legume). The participatory scoring of technologies according to pre- and self-defined criteria appeared to be more difficult to understand for both participating farmers and technical advisors than biophysical data collection.
Farmers’ Learning and Local Technology Dissemination Farmers assessed their own learning experiences with ISFM technologies (Fig. 29.5a). Regarding sowing in line, mineral fertilizer application, utilization of improved seeds, and crop arrangement, 79–89 % of the farmers rated their learning medium to high. This percentage is lower for second legume planting (50 %) due to reasons discussed in the previous paragraph. High learning is especially prevalent for improved seeds (46 %) (Fig. 29.5a). When looking at uptake of the same ISFM technologies, the variance between technologies was higher than within the learning experiences (Fig. 29.5b). Between 53 and 85 % of all respondents said that they (partially) took up sowing in line, utilization of improved seeds, crop arrangement, and second legume planting. This proportion was considerably lower for mineral fertilizer application (27 %). Most farmers expressed resource constraints and lack of market access as major hurdles to fertilizer use. In general, uptake was lower than learning about the technologies. Farmer experimentation (farmers take research decisions only under consultation with researchers) could further empower farmers, and researchers could better learn more about farmers’ constraints in using mineral fertilizer (Fig. 29.5b).
Conclusions The ISFM self-test trials seemed successful in terms of data collection (correct trial installations, return of field books, completeness of data) and farmers’ learning, although technology uptake seemed to be low. Farmer experimentation would shift more decision-making power from researchers to farmers, which could enable researchers to better understand farmers’ constraints and preferences. The farmer-to-farmer facilitation is an integral part of the self-test trials, ensuring participating farmers receive assistance in correct trial installation and harvest. This facilitation decreases project costs while increasing participant numbers, thus lifting participatory research above the small scale. Socially just compensation schemes for technical advisors are crucial to justify their high workload. Future research should further examine the quality of the data collected in field books.
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Fig. 29.5 ISFM technology learning and uptake. (a) ISFM technology learning: respondents classified their knowledge on ISFM technologies (fertilizer application, improved germplasm, crop arrangement, second legume planting) before and after their adaptation trial on a scale from 1 to 4. The differences between both scores (before and after) were classified as no learning (0), low learning (1), medium learning (2), and high learning (3). (b) ISFM technology uptake: respondents were asked if they adopted ISFM technologies (sowing in line, fertilizer application, improved germplasm, crop arrangement, second legume planting), and if yes on parts or all of their field(s)
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A verification method needs to be developed that would improve reliable data collection and statistical analysis of farmer-collected data. Further, the effect of intensity and quality of farmer-to-farmer facilitation on data collection and quality and farmers’ learning and technology uptake is not yet well understood. Acknowledgements This study was funded by the Directorate General for Development Cooperation in Belgium (DGCD) through CIALCA. Without all participating farmers and technical advisors, this study would not have been possible. Thanks to the CIALCA staff, all enumerators, and action site facilitators in Bukavu, DRC for their diligent work. We also acknowledge the logistical support of the administrative and financial staff members of CIATTSBF in Nairobi, Kenya. We are grateful for comments by Mirjam Pulleman and Lijbert Brussaard of Wageningen University on earlier versions of this manuscript.
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Snapp SS (2002) Quantifying farmer evaluation of technologies: the mother and baby trial design. In: Bellon MR, Reeves J (eds) Quantitative analysis of data from participatory methods in plant breeding. CIMMYT/PRGA/IRRI, Mexico Snapp SS, Kanyama-Phiri GY, Kamanga B, Gilbert RA, Wellard K (2002) Farmer and researcher partnerships in Malawi: developing soil fertility technologies for the near-term and far-term. Exp Agric 38:411–431 Vanlauwe B, Bationo A, Chianu J, Giller KE, Merckx R, Mokwunye U, Ohiokpehai O, Pypers P, Tabo R, Shepherd KD, Smaling EMA, Woomer PL, Sanginga N (2010) Integrated soil fertility management: operational definition and consequences for implementation and dissemination. Outlook Agric 39:17–24
