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Ouma - Technology adoption in banana-legume systems of Central Africa

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Presentation delivered at the CIALCA international conference 'Challenges and Opportunities to the agricultural intensification of the humid highland systems of sub-Saharan Africa'. Kigali, Rwanda, October 24-27 2011.

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Ouma - Technology adoption in banana-legume systems of Central Africa

  1. 1. Technology adoption in banana-legume systems of Central Africa
  2. 2. General framework Inputs/Activities Boundary partners: change agents (extension services, NGOs, farmer associations) Outcomes: Farmer awareness, adoption, productivity, profitability Impact: food security, incomes, nutrition Outputs Partnerships (NARS), capacity building Identification of best bet technologies (CIALCA products)
  3. 3. Categorisation of CIALCA products Category CIALCA product Productivity enhancing (ISFM) <ul><li>Improved germplasm. </li></ul><ul><li>Integrated crop components, </li></ul><ul><li>maize x legume, </li></ul><ul><li>cassava x legume, </li></ul><ul><li>banana x legume, </li></ul><ul><li>banana x coffee. </li></ul><ul><li>Management practices, </li></ul><ul><li>Banana zero-tillage mulch </li></ul><ul><li>Seed multiplication </li></ul><ul><li>Organic and inorganic fertilizer application </li></ul>Pest and disease risk mitigation (IPM) <ul><li>BXW control </li></ul><ul><li>BBTV control </li></ul>
  4. 4. Categorisation of CIALCA products Category CIALCA product Income enhancing <ul><li>Business plans </li></ul><ul><li>Strengthening farmer associations </li></ul><ul><li>Soybean transformation </li></ul>Nutrition improving <ul><li>Dietary diversification </li></ul><ul><li>Soybean enriched foods </li></ul>
  5. 5. Marketing framework for technology adoption Research Policy <ul><li>Private sector </li></ul><ul><li>Institutions of micro finances </li></ul><ul><li>Distributors, sellers of inputs </li></ul><ul><li>Bulk traders, buyers and processors </li></ul><ul><li>ICT/Information service providers </li></ul><ul><li>Infrastructure service providers (e.g. warehousing) </li></ul>Business plans Warrantage credit <ul><li>Input Kiosks </li></ul><ul><li>Fertilizers </li></ul><ul><li>Materials </li></ul><ul><li>Seeds </li></ul>MUSO Members’ guarantee Credit for input + Labour Credit for produce Financial capacity empowerment External credit External-Synergy Internal -Synergy
  6. 6. Approaches for improved marketing <ul><li>Business plan development </li></ul><ul><ul><li>Training of facilitators - CIALCA, NARS, NGO partners. </li></ul></ul><ul><ul><li>Total of 8 business plans prepared and implemented by farmer associations in Rwanda and Sud-Kivu (beans, maize, soybean, cassava and sorghum). </li></ul></ul><ul><ul><li>Several still under development in Burundi and Nord-Kivu. </li></ul></ul><ul><li>Outcomes </li></ul><ul><ul><li>Bulking, storage and collective sales, </li></ul></ul><ul><ul><li>Linkages with MFIs </li></ul></ul><ul><ul><li>Increase in sales revenue (50% for 1 association in Sud-Kivu) through strategic storage facilitated by warrantage credit schemes. </li></ul></ul>
  7. 7. <ul><li>Assessing level of farmer awareness of CIALCA products, adoption rates and outcomes </li></ul>
  8. 8. Data <ul><li>Farm level cross sectional surveys in 7 out of 10 CIALCA mandate areas in July-Aug 2011, </li></ul><ul><ul><li>Purposive selection of mandate areas based on intensity of dissemination of CIALCA technologies and crop types. </li></ul></ul><ul><ul><li>Stratification of villages per mandate area, 3 strata: </li></ul></ul><ul><ul><ul><li>Action site </li></ul></ul></ul><ul><ul><ul><li>Satellite site </li></ul></ul></ul><ul><ul><ul><li>Control site </li></ul></ul></ul><ul><ul><li>Random selection of 5 villages per mandate area per stratum. </li></ul></ul><ul><ul><li>Random sample of households per stratum from village level lists proportional to size yielding a total N = 945 hh. </li></ul></ul>
  9. 9. Methods <ul><li>ATE estimation framework proposed by Diagne and Demont (2007) </li></ul><ul><ul><li>accounting for selection and non-exposure biases. </li></ul></ul><ul><li>Adoption context, “treatment” -> “exposure to a technology” </li></ul><ul><li>Adoption - use of 2 or more of the CIALCA technologies. </li></ul><ul><li>Exposure - awareness of the CIALCA technology </li></ul>
  10. 10. Proportion of households exposed to and adopting CIALCA technologies Mandate area % of exposed farmers % of sample adopters Adoption intensity (#adopted/ #disseminated) n Rusizi 73 34 0.47 124 Gitega 70 56 0.48 100 Kigali-Kibungo 81 72 0.61 140 Umutara 68 50 0.74 127 Bas-Congo 64 42 0.56 133
  11. 11. Mode of technology acquisition n =143 163 44 63 107
  12. 12. Sources of CIALCA technology for adopters
  13. 13. Household characteristics Variable Adopters N = 303 (56%) Non-adopters N = 234 (44%) Difference t-values Farming experience 21.9 22.7 -2.8 ** -1.9 Secondary education of head of hh (dummy) 0.2 0.1 0.1 *** 3.3 Credit access (dummy) 0.2 0.3 -0.1 *** -2.8 Radio ownership (dummy) 0.8 0.6 0.2 *** 1.9 Off farm income (dummy) 0.3 0.5 -0.1 ** -2.7 Extension contact frequency 4.4 2.6 1.9 *** 3.2 Contact with CIALCA 0.5 0.2 0.2 *** 5.8 Membership to farmer group (dummy) 0.5 0.2 0.3 *** 7.5
  14. 14. Determinants of probability of exposure to CIALCA technologies Dependent variable Dummy variable 1=ever heard of CIALCA technology Explanatory variables Coefficient Gender of head of hh 0.40 Value of asset owned (US$) 0.01* Membership to farmer group 1.05*** Radio 0.30 Credit access -0.61** Awareness of CIALCA 0.68** Participate in CIALCA tech evaluation 0.33** Extension contact frequency 0.03 Gitega -0.69** Rusizi -0.45 Bas-Congo -0.26 Pseudo R 2 =0.297; n=413; LR Chi 2 =79.29; P>Chi 2 =0.000
  15. 15. Determinants of CIALCA technologies adoption Variables ATE adoption coefficients No formal education-hh head (dummy) -0.94* Primary education-hh head (dummy) 0.19 Secondary education-hh head (dummy) 1.09** Off-farm income (dummy) -0.39** Credit access -0.44* No. of extension visits -year 0.03** Member of farmer group 0.17* Participate in CIALCA tech evaluation 0.21 Rusizi -0.89*** Gitega -0.23 Bas-Congo -0.18* Pseudo-R2;LRChi2 0.34;66.6
  16. 16. Predicted adoption rates of CIALCA technologies Awareness exposure Estimate S. E. ATE-corrected popn estimates Predicted adoption rate-full popn – ATE 0.44*** 0.21 Predicted adoption rate-exposed sub-popn-ATT 0.46*** 0.02 Predicted adoption rate unexposed sub-popn-ATE0 0.29*** 0.04
  17. 17. Enabling agricultural policy environment Country Policy document Elements Rwanda (Vision 2020) <ul><li>Poverty Reduction Strategy Paper (PRSP), 2001. </li></ul><ul><li>Strategic Plan for Agricultural Transformation in Rwanda, 2004. </li></ul><ul><li>- Intensification of agriculture, </li></ul><ul><li>- Zero grazing policy and guardianage system, </li></ul><ul><li>- Promotion of soil fertility and protection, </li></ul><ul><li>Improved marketing initiatives. </li></ul>Burundi <ul><li>National Agricultural Policy (2006-2010). </li></ul>- Improving availability of fertilizer and CPPs - Capacity building of producer associations. DRC a) Lack of solid agricultural policy (post-colonial period) to guide agricultural production. <ul><li>- Governance centralized and concentrated in Kinshasa, </li></ul><ul><li>Poor enforcement at the local level. </li></ul>
  18. 18. Agricultural advisory services Country Attributes Rwanda <ul><li>Effective government extension system. </li></ul>Burundi <ul><li>Weak government extension system (human, technical and financial capacity lacking). </li></ul><ul><li>NGOs involved in extension </li></ul>DRC <ul><li>Agricultural extension – NGOs – uncoordinated, operating under emergency framework. </li></ul>
  19. 19. Adoption constraints for exposed non-adopters
  20. 20. Fertilizer retail prices in selected countries MONO-PHOSPHATE International price 08/2011
  21. 21. Human nutrition and health <ul><li>Trainings on processing of soybeans and other legumes for improved nutrition and diet diversification. </li></ul><ul><ul><li>Soy milk </li></ul></ul><ul><ul><li>Soy bean curd (Tofu) </li></ul></ul><ul><ul><li>Soy bean flour </li></ul></ul><ul><li>Soybean product acceptability studies. </li></ul>Nutritional composition of soybean products Malnutrition prevalence among 2-5 year olds Calories (kcal) Protein (g) Soybean, dry roasted, ½ cup 386 32.0 Tofu, firm, raw, 120g 116 11.8 Soymilk,½ cup 162 3.2
  22. 22. Summary-adoption drivers <ul><li>Awareness of CIALCA products mainly influenced by information access variables: </li></ul><ul><ul><li>social networks </li></ul></ul><ul><ul><li>participation in technology evaluation </li></ul></ul><ul><li>Adoption is influenced by a number of factors; </li></ul><ul><ul><li>binding capital constraints </li></ul></ul><ul><ul><li>institutional and location factors </li></ul></ul><ul><ul><li>farmer perceived attributes of the technology. </li></ul></ul>
  23. 23. Outlook <ul><li>Resource constraints </li></ul><ul><ul><li>Financial - implications on affordability of inputs (fertilizer and seeds) </li></ul></ul><ul><ul><li>Institutional – supportive policy environment </li></ul></ul><ul><li>Long term measures to accelerate productivity growth and achieve impact at scale. </li></ul><ul><ul><li>Support to farmers through subsidies? Credit policy? </li></ul></ul><ul><li>How to unlock poverty traps for small scale farmers </li></ul><ul><ul><li>Mix of underlying challenges and a mix of interventions for different categories of farmers – not “one size fits all” </li></ul></ul>
  24. 24. Thank You
  25. 25. Adoption and Exposure <ul><li>Determinants of adoption conditional on exposure – corresponds to the conditional ATE( x ). </li></ul><ul><li>Parametric estimation procedure of ATE (x): </li></ul><ul><li>w = binary exposure variable, w=f(z) </li></ul><ul><li>y = adoption outcome variable, y=f(x) </li></ul><ul><li>g= linear or non-linear function of the vector covariates and β unknown estimated parameter vector. </li></ul><ul><li>Conditional independence assumption </li></ul><ul><li>ATE, ATT an ATE0 are estimated. </li></ul>
  26. 26. Proportion of households (%) adopting CIALCA technologies, per mandate area Mandate area Banana germplasm Banana systems Banana IPM Legume germplasm Legume systems Market Rusizi 10 16 13 Gitega 15 30 44 53 7 Kig-Kib 17 56 8 43 72 23 Umutara 6 38 59 11 Bas-Congo 63 54 29

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