Tackling key issues for smallholder farmers: the Farmer Research Network approach. Bettina I.G. Haussmann

Transcription

1 Tackling key issues for smallholder farmers: the Farmer Research Network approach Bettina I.G. Haussmann University of Hohenheim, Stuttgart, Germany Previously:

2 The Collaborative Crop Research Program (CCRP) Vision: CCRP seeks to contribute to a world where all have access to nutritious food that is sustainably produced by local people. Mission: We do this through collaborative agro-ecological systems research and knowledge-sharing that strengthen the capacities of smallholder farmers, research institutions and development organizations. Presently: 4 Communities of Practice (CoPs), 12 countries West Africa CoP since 2006 on sorghum- & pearl millet-based systems Target countries & N of CCRP projects: Andes Eastern Africa Southern Africa Western Africa

3 PY West Africa Regional Context Target countries: Mali, Burkina Faso, Niger, ranking 175, 185 and 187, for 2016 HDI, out of 188 C O 51-66% living below 1.25 US$ /d Human population growth rates up to 3.8% (Niger) Projection: N of inhabitants in Niger will more than triple between now and 2050 (to 55m) O T 64-83% dependency on agriculture smallholder farmers! D O N Farming systems based on sorghum & pearl millet (and partially maize), associated with legumes (cowpea, groundnut, bambara nut), combined with minor crops (fonio, sesame, hibiscus ), animal production and trees or shrubs.

4 Major Constraints High inter-annual rainfall variability (for example mm/year at same site) Low soil fertility (low P availability, low N, low organic matter) Increasing pressure on land, land degradation Biotic production constraints (Downy mildew, headminer, Midge, Striga ) Repeated food insecurity, high vulnerability Severe malnutrition and hidden hunger Fragmented markets Total annual rainfall at Ouahigouya, Burkina Faso (years )

5 CCRP Strategic priorities for West Africa Agro-ecological intensification (AEI) which comprises legume intensification, crop-treelivestock integration, systems-oriented breeding (eg for intercropping, fodder or dual use), IPM, local seed systems, local value chain development, gender- and nutrition-informed research, risk management (don t do any harm!) Diverse, context-specific options to serve heterogeneity of farmers, including vulnerable and poor Strong farmer participation, farmer-led research to increase research relevance and change power relations, experimenting with farmer research network approaches Diversified partnerships Integration of students & young scientists

6 Farmer Research Network (FRN) approach - Rationale Limited success of present R&D system Heterogeneity of socio-ecological contexts of target farmers understanding requires large scale of operation Need a representative data base on agroecological intensification (AEI) options also requires large scale Farmer organizations, NGOs and development projects (and private sector) form collective infrastructure and social capital which could support research at scale Need to link technical and social innovations as well as local and global knowledge Level of farmer participation high low none Participatory Research (1985+) (Nelson, R., R. Coe, B.I.G. Haussmann, 2016, Exp. Agr.) Farmer Research Networks for AEI (2015+) Conventional ag research (1920+) Small moderate large Scale (N) A large scale is needed in complex and heterogeneous situations (R. Coe)

7 Farmer Research Network approach Vision: To transform the way that much of agricultural R+D is done, to engage (rural, but also peri-urban and urban) people in prioritization, observation, experimentation and utilization of research results. Transforming agriculture and food systems Context-specific agro-ecological intensification Care for culture, production ecology, equity Improved yields, nutrition, and sustainability Transforming the way we do research and development Research as part of development, not as basis Extension : from blanket recommendations to support for understanding of principles and better decision making Support farmers to bundle agro-ecological options (Source: Rebecca Nelson, Scientific Director of CCRP) Farmer-researcher co-creation (Photo Credit: KvBrocke)

8 Scientific Knowledge Scientific Innovations, modeling, trade-off analysis, literature Farmer Research Network approach Scientific knowledge Scientists, private sector Facilitation, negotiation, Basket of AEI Options & Principles Large-scale farmer experimentation Farmer Organisations, NGOs, Development projects Joint Data base, participatory data analysis and interpretation Local knowledge (Source: CCRP Leadership Team) Local Knowledge & social capital Local innovation, evidence, experience, collective action, local adaptation of options

9 Principles for Farmer Research Networks About Farmers Research Networking Principles Farmers who represent the social and biophysical diversity of their communities participate in the whole research process Research is rigorous, democratized, and useful, providing practical benefits to farmers as well as scientific evidence and insights on biophysical and social variation Networks foster collaboration and opportunities for learning and knowledge sharing (Source: CCRP Leadership Team)

10 Farmer Research Network related to variety testing and seed production around the farmer organization ULPC (Mali) Private sector (Source: E. Weltzien, S. Guindo, M. Sidbe, CCRP Seed systems project) Sustainable business models? Network functions: Production and decentralized commercialization of sorghum (OPVs & hybrids) & legume seed Digital data collection tools Establishment of databases on varietal performance and seed production/marketing Joint interpretation of results and planning of seed production Development of farmer-friendly information and communication tools Eva Weltzien-Rattunde with sorghum farmer in Mali Farmer-managed seed cooperatives to increase smallholder farmers access to quality seed in West Africa!

11 Farmer Research Network to determine Pathways to AEI (WUR, IER, AMEDD, ICRISAT, farmers in the Koutiala region, Mali) Modeling at field, farm and landscape levels (Source: Descheemaeker et al. 2014; CCRP «Pathways to AEI» project) Scaling Innovation platform: Interactions with other actors along the value chain Link to global knowledge Exploration and ex-ante assessment of productivity, profitability & risks of options (trade-off analysis) using 4 modeling tools 5 Diagnosis, identification of global constraints and opportunities in target areas (PRA) Farm typology, identification of specific constraints and opportunities Farmer appraisal : feedback sessions - multi-criteria assessment of options d Offer a basket of AEI options, Large-N on-farm trials a Describe, Explain, Explore, Design (DEED) cycle 3 Annual research cycle Farmer appraisal : farmer field day 1 2 Local knowledge, context-specific options Farmers choose options and test b c

12 Farmer Research Network around the Farmer Federation FUMA Gaskiya (Niger) Network of 2 farmer federations, INRAN, 4 universities, 2 CGIAR centers, several projects Focus on pearl millet-based production systems in the Sahel Evolved since 2002 (long-term engagement!) Basket of options tested, large-n trials, use of ODK for digital data collection Ongoing developments: FRN App Farmer typology Data base Large-N trials (N=2655 tests in 2017) Communication tools Scaling is embedded in the process! (Source: A.M. Aminou, H. Moussa, B. Haussmann, 2017)

13 Selected learnings: 1. Performance means more than grain yield on a plot basis! Depending on the farmers individual context and production objective, performance of a genotype (or any ag option) can mean, e.g.: Final food yield (not grain yield per se) Nutritional quality and taste acceptability Early maturity to provide grain in hungry period Suitability as dual purpose or fodder crop Yield & effect on associated crop in an intercrop Profitability Risk reduction potential (stable yield) Provision of ecosystem services Fit in the overall farming operations (costs, labor) Importance of grain processing and culinary tests

14 2. Need to extend GxExM to GxExMxS (or to genotype x context interaction, GxC) Farmers performance indicators and specific preferences are influenced by individual farmers social context, e.g.: Gender Ethnic group Production objective (food security versus marketing) Access to markets Farm type (with/without animals) Farm resource endowment Risk aversion Need to understand smallholder farmers heterogeneity! Need to get away from Best-bet to best fit! There are no one-size-fits-all options for smallholder farmers!

15 3. Include the genetic correlation between performance in selection versus target environment (r G ) in the response to selection formula! Heterogeneity of smallholder farmers selection is not always done in target context! Relative efficiency of indirect selection (R i ) versus direct selection (R d ), assuming same sel. intensities: R i /R d = r G x h i /h d In case of cross-over GxC interaction, the correlation coefficient r G is close to zero or even negative; resulting in zero or negative gains from selection! Factors that may cause cross-over GxC interaction for various performance indicators: High-input in-fields versus low-input outfields, low-p versus high-p conditions Early planting versus late planting Healthy location versus biotic stress hotspot Pure stand versus intercrop Food security versus marketing objective Food, beer, or fodder production objective Women s versus men s preferences Farmer with / without animals Need to understand GxC interactions to be abe to develop appropriate varietal diversity for the diversity of farmers! Context including social factors

16 4. Baskets of options instead of fixed recommendations Smallholder farmers individual contexts are heterogeneous and dynamic, changing Access to a Basket of options that can be applied in flexible ways and adapted to local contexts and individual situations may serve smallholder farmers needs better than fixed recommendations and socalled best-bet packages Promoting farmer experimentation at scale and joint exploration of data can lead to a better understanding of principles that explain performance variation in heterogeneous environments and can support farmers (and breeders ) decision making processes

17 Further selected learnings related to sorghum research targeting smallholder farmers in West Africa Learning Photoperiod sensitivity is an important trait to cope with rainfall variability in West Africa Guinea-race sorghum hybrids are a game-changing option for smallholders in Mali (30% yield increase over wide range of E) Sorghum adaption to low-p conditions requires direct selection under low-p conditions Farmer participatory, on-farm early-generation yield testing of sorghum is possible and increases gains from selection in farmers fields Consumption of whole grain sorghum has the potential to reduce malnutrition Strengthening local village-based value chains contributes to women empowerment, income generation and better nutrition in rural areas References Haussmann et al. 2012; Rattunde et al Rami O307, Teme O79 Rattunde et al. 2013; Kante et al. 2017; Kante O76; Nebie O355 Leiser et al. 2012; 2017; Kante O76 Rattunde et al Bauchspies et al. 2017; CCRP AnBeJigi project Moussa O345; Hamaker O130

18 Paradigm change: Final plea Consider smallholder farmers as equal research partners and no longer as beneficiaries or passive adopters of best-bet technologies developed by researchers! 39 Highlights;

19 Acknowledgements West Africa Community of Practice Willmar Leiser Moctar Kante Baloua Nebie C. Tom Hash Ali M Aminou Katrien Descheemeaker Kirsten vom Brocke Anja Christinck All CCRP colleagues and grantees Thank you for your attention! Eva Weltzien & Fred Rattunde, Justus von Liebig Award for World Nutrition 2015 Farmer Federation FUMA Gaskiya: UNDP Equator Prize 2014 Partner Farmer Oganizations in Niger, Burkina Faso and Mali