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This study explored the potential of the Innovation Platform approach, in improving the participation of rural female farmers in Maize value chain. It intends to identify the peculiarities, in terms of challenges and opportunities related to its application to the rural women realities. The study collected data from 120 small scale maize producers in South Kivu province of the Democratic Republic of Congo (DRC) from 2015 to 2017, using individual interview and focus group discussion (FGD) for data confirmation. Data was analyzed using the Average Effect of Treatment of treated (ATT) and the propensity matching score to assess the effect of IP approach on rural women, who were randomly selected to participate in an innovation platform composed solely of women (100%) against those participating in a mixed innovation platform, made of 70% of men. The results show that the Innovation platform approach allowed women to address their basic challenges and improve their participation in the maize value chain.  Average individual income from participation to the Innovation Platform increased from $ 100 to $ 300 per cropping season and the average earning of a women in a platform made of women solely was $552.6 higher than that of women participating in mixed platform $432.4. We hypothesized that the main benefits from the female IP would be increased maize yields. However, the analysis shows that although yield increased, the main effect was due to improved market access provided by the IP." The implementation of the innovation platform process encountered several challenges, in particular: building a consensus when the interests of the groups in place have proven to be divergent, the barrier of social consideration (social stereotype), inability of smallholder’s farmers to learn quickly and fully play expected role, the traditional culture of learning, visioning the process. Despite these challenges, IPs offered small-scale maize producers many technical, organizational and material opportunities, including income generation, access to inputs and to lucrative markets, acquisition of diversified knowledge and skills, ability to work in a commercial environment, benefiting from the services of experts, accessing new sources of financing, they could not benefit otherwise. These findings imply that to be effective for rural women, an innovation platform should include individuals with no wide social disparity and diversify the sources of income, including livestock and others off farm activities

References

  1. C. Man-Kwun, “Improving Opportunities for Women in Smallholder-based Supply Chains: Business Case and Practical Guidance for International Food Companies,” Prepared for the Bill & Melinda Gates Foundation. 2010.
     Google Scholar
  2. C. B. Barrett, “Smallholder market participation: concepts and evidence from eastern and southern Africa,” Elsevier (Ed.). Science Direct, Food Policy 33:99-317. 2008.
     Google Scholar
  3. E. Bardasi, C. M. Blackden, and J. C. Guzman, “Gender, Entrepreneurship, and Competitiveness in Africa,” World Bank. 2007.
     Google Scholar
  4. A. Ellis, C. Manuel, and M. Blackden, “Gender and Economic Growth in Uganda: The Power of Women, Directions in Development,” World Bank. 2006.
     Google Scholar
  5. WORLD BANK [a], “Global Monitoring Report 2007: Millennium Development Goals: Confronting the Challenges of Gender Equality and Fragile States,” Washington, DC: World Bank. 2007.
     Google Scholar
  6. WORLD BANK [b], “Gender and Economic Growth in Tanzania,” Washington, DC: Directions in Development, 2007
     Google Scholar
  7. Lastarria-Cornhiel, “Impact of privatization on gender and property rights Africa,” Elsevier, Volume 25, Issue 8, Pages 1317-1333. 1997.
     Google Scholar
  8. K. S. Oueraogo, C. Y. Kabore-Zoungrana, I. Ledin, (2008), “Important characteristics of some browse species in an agrosilvopastoral system in West Africa,” Available at https://www.feedipedia.org/node/4552
     Google Scholar
  9. H. Pamuk, E. Bulte, A. A. Adekunle, “Do decentralized innovation systems promote agricultural technology adoption? Experimental from Africa,” Elsevier, Food Policy 44 (2014) 227–236. 2014.
     Google Scholar
  10. E. Bulte, G. Beekman, S. Di Falco, J. Hella, “Behavioral Responses and the Impact of New Agricultural Technologies: Evidence from a Double-blind Field Experiment in Tanzania”, American Journal of Agricultural Economics, Pp 813-830 , 2017
     Google Scholar
  11. R. Hawkins, W. Heemskerk, R. Booth, J. Daane, A. Maatman and A. A. Adekunle. “Integrated Agricultural Research for Development (IAR4D),” FARA, Accra, Ghana, pp. 2009.
     Google Scholar
  12. P. Njingulula, P. Wimba, M. Musakamba, K. F. Masuki, M. Katafiire, M. Ugen, E. Birachi, “Strengthening local seed systems within the bean value chain: Experience of agricultural platforms in the Democratic Republic of Congo,” African Crop Science Journal, Volume, 22, pp 1003-1012, 2014.
     Google Scholar
  13. MINAGRI, “Monographie de la province du Sud - Kivu, Unité de Pilotage du Processus DSRP, Kinshasa/Gombe”, 2014
     Google Scholar
  14. IITA, “The International Institute of Tropical Agriculture, IITA-Kalambo’s annual report of crop-livestock integration project. Unpublished report. 2015
     Google Scholar
  15. S. Bacigale, L. Nabahungu, C. Okafor, G. Manyawu and A. Duncan, “Assessment of feed resources and potential feed options in the farming systems of Eastern DR Congo and Burundi”, CLiP working paper no 3. 2018
     Google Scholar
  16. P. M. Dontsop-Nguezet, “Productivity Impact Differential of Improved Rice Technology Adoption Among Rice Farming Households in Nigeria,” Journal of Crop Improvement 26(1): 1-21. 2012.
     Google Scholar
  17. A. Diagne, and A. Arouna, “Impact de la production de semence riz sur le rendement et le revenue des ménages agricoles: une étude de cas du Bénin,” Paper presented at the 4th International Conference of the African Association of Agricultural Economists, Hammamet, Tunisia, September 22-25. 2013.
     Google Scholar
  18. P R. Rosenbaum and D. B. Rubin, “The Central Role of the Propensity Score in Observational Studies for Causal Effects”, Biometrika, Vol. 70, No. 1, pp. 41-55. April 1983.
     Google Scholar
  19. P. R. Rosenbaum and D. B. Rubin. Constructing a control group using multivariate matched sampling, in The American Statistician, vol 39, no 133.1985
     Google Scholar
  20. G. Esquivel and A. Huerta-Pineda, “The impact of migrant workers' remittances on living standards of families in Morocco: A propensity score matching approach,” Transnational Press London. 2006
     Google Scholar
  21. M. Ravallion, “The contribution of demographic change to aggregate poverty measures for the developing world,” Policy Research Working Paper Series 3580, The World Bank. 2005
     Google Scholar
  22. J. J. Heckman, R. LaLonde, and J. Smith, “The economics and econometrics for active labor market programs,” Handbook of Labor Economics; Vol 3 (1) 1865-2097. 1999.
     Google Scholar
  23. J. J. Heckman, H. Ichimura, and P. Todd, “Characterizing selection Bias using experimental data,” Econometrica, vol 66. no 5, pp. 1017-1098. 1998
     Google Scholar
  24. D. B. Rubin, “Estimating the causal effects of treatments in randomized and nonrandomized studies,” The Journal of Educational Psychology, vol 66 (5), pp. 688-701. 1974
     Google Scholar
  25. J. Sampson, R. Jeffrey, D. Morenoff and Felton Earls, “Beyond Social Capital: Spatial Dynamics of Collective Efficacy for Children,” American Sociological Review, vol. 64, No. 5 (Oct., 1999), pp. 633-660. 1999
     Google Scholar
  26. P. M. Dontsop-Nguezet “Impact of Nerica Rice on Rice farming households’ welfare in Nigeria,” Lambert Academic published, staarbruken, Deutchland. April, 2011
     Google Scholar
  27. M. Schut, (2018). “Innovation platforms in agricultural research for the development,” Available at www.kit.nl..../2018/....5b1a850a93008_innovation_platforms_in_agricultural_research_for_Development-1.pdf
     Google Scholar