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A latent class analysis of improved agro-technology use behavior in Uganda: Implications for optimal targeting:

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  • Bizimungu, Emmanuel
  • Kabunga, Nassul Ssentamu

Abstract

This study uses a large dataset that covers a wide geographical and agricultural scope to describe the use patterns of improved agro-technology in Uganda. Using latent class analysis with data on more than 12,500 households across the four regions of Uganda, we classify farmers based on the package of improved agro-technologies they use. We find that the majority of farmers (61 percent) do not use any improved agricultural practices (the “nonusers†), whereas only 5 percent of farmers belong to the class of “intensified diversifiers,†those using most of the commonly available agro-technologies across crop and livestock enterprises. Using multinomial regression analysis, we show that education of the household head, access to extension messages, and affiliation with social groups are the key factors that drive switching from the nonuser (reference) class to the other three (preferred) classes that use improved agrotechnologies to varying degrees. Results reveal the existence of heterogeneous farmer categories, certainly with different agrotechnology needs, that may have implications for optimal targeting.

Suggested Citation

  • Bizimungu, Emmanuel & Kabunga, Nassul Ssentamu, 2018. "A latent class analysis of improved agro-technology use behavior in Uganda: Implications for optimal targeting:," IFPRI discussion papers 1704, International Food Policy Research Institute (IFPRI).
    • Handle: RePEc:fpr:ifprid:1704
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    1. Madhu Khanna, 2001. "Sequential Adoption of Site-Specific Technologies and its Implications for Nitrogen Productivity: A Double Selectivity Model," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 83(1), pages 35-51.
    2. Esther Duflo & Michael Kremer & Jonathan Robinson, 2011. "Nudging Farmers to Use Fertilizer: Theory and Experimental Evidence from Kenya," American Economic Review, American Economic Association, vol. 101(6), pages 2350-2390, October.
    3. Bold, Tessa & Kaizzi, Kayuki C. & Svensson, Jakob & Yanagizawa-Drott, David, 2015. "Low Quality, Low Returns, Low Adoption: Evidence from the Market for Fertilizer and Hybrid Seed in Uganda," Working Paper Series rwp15-033, Harvard University, John F. Kennedy School of Government.
    4. Abay, Kibrom A. & Berhane, Guush & Taffesse, Alemayehu Seyoum & Koru, Bethlehem & Abay, Kibrewossen, 2016. "Understanding farmers’ technology adoption decisions: Input complementarity and heterogeneity:," ESSP working papers 82, International Food Policy Research Institute (IFPRI).
    5. Nassul Ssentamu Kabunga & Thomas Dubois & Matin Qaim, 2012. "Heterogeneous information exposure and technology adoption: the case of tissue culture bananas in Kenya," Agricultural Economics, International Association of Agricultural Economists, vol. 43(5), pages 473-486, September.
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    Cited by:

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    innovation adoption; technology; agriculture; agricultural productivity;
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