1932

Abstract

International agricultural research is often motivated by the potential benefits it could bring to smallholder farmers in developing countries. A recent experimental literature has emerged on why innovations resulting from such research, which often focuses on yield enhancement, fail to be adopted due to either external or internal constraints. This article reviews this literature, focusing on the traits of the different technologies and their complexity and distinguishing between yield-enhancing, variance-reducing, and water- or labor-reducing technologies. It also discusses how farmers’ reallocation of inputs and investments when external constraints are lifted suggests that they often do not seek to increase yield or input intensity. The article further reviews evidence indicating that a technology's potential as observed in agronomical trials is not necessarily a good predictor for smallholder farmers’ demands for the technology in real-life conditions. The last section derives conclusions for the research and policy agenda.

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2019-10-05
2024-03-28
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Literature Cited

  1. Abate GT, Bernard T, de Brauw A, Minot N 2018. The impact of the use of new technologies on farmers’ wheat yield in Ethiopia: evidence from a randomized control trial. Agric. Econ. 49:4409–21
    [Google Scholar]
  2. Affholder F, Poeydebat C, Corbeels M, Scopel E, Tittonell P 2013. The yield gap of major food crops in family agriculture in the tropics: assessment and analysis through field surveys and modelling. Field Crops Res 1432013:106–18
    [Google Scholar]
  3. Aggarwal S, Francis E, Robinson J 2018. Grain today, gain tomorrow: evidence from a storage experiment with savings clubs in Kenya. J. Dev. Econ. 134:1–15
    [Google Scholar]
  4. Aggarwal S, Giera B, Jeong D, Robinson J, Spearot A 2017. Market access, trade costs, and technology adoption: evidence from Northern Tanzania NBER Work. Pap. 25253
  5. Arthi V, Beegle K, De Weerdt J, Palacios-López A 2018. Not your average job: measuring farm labor in Tanzania. J. Dev. Econ. 130:160–72
    [Google Scholar]
  6. Ashour M, Billings L, Gilligan D, Hoel JB, Karachiwalla N 2017. Do beliefs about agricultural inputs counterfeiting correspond to actual rates of counterfeiting? Evidence from Uganda Discuss. Pap. 01552, Int. Food Policy Res. Inst Washington, DC: https://ideas.repec.org/p/fpr/ifprid/1552.html
  7. Assunção J, Bragança A, Hemsley P 2014. Geographic heterogeneity, social learning and technology adoption: evidence from the direct planting system in Brazil Work. Pap., Dep. Econ., Pontifícia Univ Católica do Rio de Janeiro: http://www.fucape.br/downloads/Geography%20and%20Technology%20Adoption%20April%202014.pdf
  8. Barnwal P, Dar A, von der Goltz J, Fishman R, McCord G, Mueller N 2017. The Green Revolution and infant mortality: evidence from 600,000 births Work. Pap., Univ. Calif San Diego: https://ispc.cgiar.org/sites/default/files/docs/Session%205_MV%20Releases_Fishman_McCord_slides.pdf
  9. Batista C, Vicente PC. 2017. Improving access to savings through mobile money: experimental evidence from smallholder farmers in Mozambique Work. Pap. 1705, NovAfrica Carcavelos, Port: https://novafrica.org/wp-content/uploads/2017/08/1705.pdf
  10. Beaman L, BenYishay A, Magruder J, Mobarak AM 2018. Can network theory-based targeting increase technology adoption? NBER Work. Pap. 24912
  11. Beaman L, Dillon A. 2018. The diffusion of agricultural technologies within social networks: evidence from composting in Mali. J. Dev. Econ. 133:C147–61
    [Google Scholar]
  12. Beaman L, Karlan D, Thuysbaert B, Udry C 2013. Profitability of fertilizer: experimental evidence from female rice farmers in Mali. Am. Econ. Rev. 103:3381–86
    [Google Scholar]
  13. Beaman L, Karlan D, Thuysbaert B, Udry C 2015. Selection into credit markets: evidence from agriculture in Mali NBER Work. Pap. 20387
  14. BenYishay A, Mobarak MA. 2018. Social learning and incentives for experimentation and communication. Rev. Econ. Stud. 86:976–1009
    [Google Scholar]
  15. Bharadwaj P, Fenske J, Ali Mirza R, Kala N 2018. The Green Revolution and infant mortality in India Warwick Econ. Res. Pap. Ser. 1175, Univ Warwick, UK: https://ideas.repec.org/p/wrk/warwec/1175.html
  16. Binswanger HP, McIntire J. 1987. Behavioral and material determinants of production relations in land-abundant tropical agriculture. Econ. Dev. Cult. Change 36:173–100
    [Google Scholar]
  17. Blair R, Fortson K, Lee J, Rangarajan A 2013. Should foreign aid fund agricultural training? Evidence from Armenia Math. Policy Res. Rep. https://ideas.repec.org/p/mpr/mprres/74d16d1a54bd430eb39d64979cf38acc.html
  18. Bold T, Kaizzi KC, Svensson J, Yanagizawa-Drott D 2017. Lemon technologies and adoption: measurement, theory and evidence from agricultural markets in Uganda. Q. J. Econ. 132:31055–100
    [Google Scholar]
  19. Boserup E. 1965. Conditions of Agricultural Growth Chicago: Aldine
  20. Brune L, Giné X, Goldberg J, Yang D 2016. Facilitating savings for agriculture: field experimental evidence from Malawi. Econ. Dev. Cult. Change 64:2187–220
    [Google Scholar]
  21. Burke M, Bergquist LF, Miguel E 2018. Sell low and buy high: arbitrage and local price effects in Kenyan markets. Q. J. Econ. 134:785–84
    [Google Scholar]
  22. Carter M, de Janvry A, Sadoulet E, Sarris A 2017. Index insurance for developing country agriculture: a reassessment. Annu. Rev. Resour. Econ. 9:421–38
    [Google Scholar]
  23. Carter MR, Laajaj R, Yang D 2014. Subsidies and the persistence of technology adoption: field experimental evidence from Mozambique NBER Work. Pap. 20465
  24. Carter MR, Laajaj R, Yang D 2016. Subsidies, savings, and sustainable technology adoption: field experimental evidence from Mozambique https://www.poverty-action.org/study/savings-subsidies-and-sustainable-food-security-mozambique
  25. Carter MR, Lybbert T, Tjernström E 2015. The dirt on dirt: soil characteristics and variable fertilizer returns in Kenyan maize systems Work. Pap., Univ. Calif., Davis
    [Google Scholar]
  26. CIMMYT (Int. Maize Wheat Improv. Cent.) 1988. From Agronomic Data to Farmer Recommendations: An Economics Training Manual El Betan, Mex: CIMMYT. Revis. ed.
  27. Coe R, Njoloma J, Sinclair F 2019. To control or not to control: How do we learn more about how agronomic innovations perform on farms. Exp. Agric. 55:303–9
    [Google Scholar]
  28. Cole S, Fernando A. 2018. ‘Mobile'izing agricultural advice: technology adoption, diffusion and sustainability Work. Pap. 13-047, Harvard Bus. Sch https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2179008
  29. Cole S, Giné X, Tobacman J, Topalova P, Townsend R, Vickery J 2013. Barriers to household risk management: evidence from India. Am. Econ. J. Appl. Econ. 5:1104–35
    [Google Scholar]
  30. Cole S, Giné X, Vickery J 2017. How does risk management influence production decisions? Evidence from a field experiment. Rev. Financ. Stud. 30:61935–70
    [Google Scholar]
  31. Corral C, Giné X, Mahajan A, Seira E 2016. Improving yields with improved recommendations. Learning for Adopting: Technology Adoption in Developing Country Agriculture A de Janvry, K Macours, E Sadoulet 103–6 Clermont-Ferrand, Fr: FERDI
    [Google Scholar]
  32. Dar MH, de Janvry A, Emerick K, Raitzer D, Sadoulet E 2013. Flood-tolerant rice reduces yield variability and raises expected yield, differentially benefitting socially disadvantaged groups. Sci. Rep. 3:3315
    [Google Scholar]
  33. de Janvry A, Dustan A, Sadoulet E 2011. Recent advances in impact analysis methods for ex-post impact assessments of agricultural technology: options for the CGIAR Rep., Indep. Sci. Partnersh. Counc., CGIAR Rome: https://ispc.cgiar.org/sites/default/files/docs/deJanvryetal2011.pdf
    [Google Scholar]
  34. de Janvry A, Sadoulet E, Dar MH, Emerick K 2016. The agricultural technology adoption puzzle: What can we learn from field experiments Work. Pap. P178, Fond. Étud. Rech. Dév. Int. Clermont-Ferrand, Fr: https://ideas.repec.org/p/fdi/wpaper/3400.html
  35. de Janvry A, Sadoulet E, Suri T 2017. Field experiments in developing country agriculture. Handb. Field Exp. 2:427–66
    [Google Scholar]
  36. de Roo N, Andersson JA, Krupnik TJ 2017. On-farm trials for development impact? The organisation of research and the scaling of agricultural technologies. Exp. Agric. 187:116–32
    [Google Scholar]
  37. Duflo E, Kremer M, Robinson J 2008. How high are rates of return to fertilizer. Am. Econ. Rev. 98:2482–88
    [Google Scholar]
  38. Duflo E, Kremer M, Robinson J 2011. Nudging farmers to use fertilizer: theory and experimental evidence from Kenya. Am. Econ. Rev. 101:62350–90
    [Google Scholar]
  39. Duflo E, Schillbach F, Kremer M, Robinson J 2015. Diffusion and appropriate use: evidence from Western Kenya Work. Pap MIT, Cambridge, MA:
  40. Emerick K. 2018. Trading frictions in Indian village economies. J. Dev. Econ. 132:32–56
    [Google Scholar]
  41. Emerick K, de Janvry A, Sadoulet E, Dar MH 2016a. Technological innovations, downside risk, and the modernization of agriculture. Am. Econ. Rev. 106:61537–61
    [Google Scholar]
  42. Emerick K, de Janvry A, Sadoulet E, Dar MH 2016b. BD56, an early-maturing and drought-tolerant variety for Bangladesh: results from a pilot evaluation SPIA Impact Brief 50, CGIAR Montpellier, Fr: https://ispc.cgiar.org/sites/default/files/pdf/SPIA_Impact-Brief-50_Sep2016.pdf
  43. Emerick K, de Janvry A, Sadoulet E, Dar MH 2017. Enhancing the diffusion of information about agricultural technology Work. Pap., Tufts Univ https://www.bu.edu/econ/files/2017/03/Emerick_Agricultural_Technology.pdf
  44. Evenson RE, Gollin D. 2003. Assessing the impact of the Green Revolution, 1960 to 2000. Science 300:5620758–62
    [Google Scholar]
  45. Fafchamps M, Minten B. 2012. Impact of SMS-based agricultural information on Indian farmers. World Bank Econ. Rev. 26:3383–414
    [Google Scholar]
  46. Feder G, Just RE, Zilberman D 1985. Adoption of agricultural innovations in developing countries: a survey. Econ. Dev. Cult. Change 33:2255–98
    [Google Scholar]
  47. Fink G, Kelsey Jack B, Masiye F 2014. Seasonal credit constraints and agricultural labor supply: evidence from Zambia NBER Work. Pap. 20218
  48. Floro VO4th, Labarta RA, Lopez-Lavalle LAB, Martinez JM, Ovalle TM 2018. Household determinants of the adoption of improved cassava varieties using DNA fingerprinting to identify varieties in farmer fields: a case study in Colombia. J. Agric. Econ. 69:2518–36
    [Google Scholar]
  49. Foster A, Rosenzweig M. 2010. Microeconomics of technology adoption. Annu. Rev. Econ. 2:395–424
    [Google Scholar]
  50. Franzel S, Coe R, Cooper P, Place F, Scherr SJ 2001. Assessing the adoption potential of agroforestry practices in sub-Saharan Africa. Agric. Syst. 69:37–62
    [Google Scholar]
  51. Freeman HA. 2001. Comparison of farmer-participatory research methodologies: case studies in Malawi and Zimbabwe. Work. Pap. Ser. 10, Socioecon. Policy Prog., Int. Crops Res. Inst. Semi-Arid Tropics, Nairobi. http://oar.icrisat.org/375/1/CO_0030.pdf
  52. Gignoux J, Macours K, Stein D, Wright K 2017. Agricultural input subsidies, credit constraints and aid expectations: evidence from Haïti Paper presented at Annu. Meet. Lat. Am. Carib. Econ. Assoc., Paris, Novemb. 9–11
  53. Giné X, Yang D. 2009. Insurance, credit, and technology adoption: field experimental evidence from Malawi. J. Dev. Econ. 89:11–11
    [Google Scholar]
  54. Gisselquist D, Pray CE, Nagarajan L, Spielman DJ 2013. An obstacle to Africa's Green Revolution: too few new varieties Work. Pap., Rutgers Univ https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2263042
  55. Glennester R, Suri T. 2017. Agricultural technology and nutrition: the impacts of NERICA rice in Sierra Leone Work. Pap MIT, Cambridge, MA:
  56. Gollin D, Hansen CW, Wingender AM 2016. Two blades of grass: the impact of the Green Revolution NBER Work. Pap. 24744
  57. Goyal A, Nash J. 2017. Reaping Richer Returns: Public Spending Priorities for African Agriculture Productivity Growth Washington, DC: World Bank
  58. Haggblade S, Minten B, Pray C, Reardon T, Zilberman D 2017. The herbicide revolution in developing countries: patterns, causes, and implications. Eur. J. Dev. Res. 29:3533–59
    [Google Scholar]
  59. Hanna R, Mullainathan S, Schwartzstein J 2014. Learning through noticing: theory and evidence from a field experiment. Q. J. Econ. 129:31311–53
    [Google Scholar]
  60. Hayami Y, Ruttan VM. 1985. Agricultural Development: An International Perspective Baltimore: Johns Hopkins Univ. Press
  61. Hotz C, Loechl C, de Brauw A, Eozenou P, Gilligan D et al. 2012a. A large-scale intervention to introduce orange sweet potato in rural Mozambique increases vitamin A intakes among children and women. Br. J. Nutr. 108:1163–76
    [Google Scholar]
  62. Hotz C, Loechl A, Lubowa JK, Tumwine G, Ndeezi A et al. 2012b. Introduction of β-carotene–rich orange sweet potato in rural Uganda resulted in increased vitamin A intakes among children and women and improved vitamin A status among children. J. Nutr. 142:101871–80
    [Google Scholar]
  63. Huffman WE, Just RE. 2000. Setting efficient incentives for agricultural research: lessons from principal-agent theory. Am. J. Agric. Econ. 82:4828–41
    [Google Scholar]
  64. Ilukor J, Kilic T, Stevenson J, Gourlay S, Kosmowski F et al. 2017. Blowing in the wind: the quest for accurate crop variety identification in field research, with an application to maize in Uganda Paper presented at CGIAR SPIA Conference on the Impact of Agricultural Research: Rigorous Evidence for Policy Nairobi:
  65. Islam M. 2014. Can a rule-of-thumb tool improve fertilizer management? Experimental evidence from Bangladesh Work. Pap., Harvard Univ https://scholar.harvard.edu/files/mahnazislam/files/jobmarketpaper_mahnazislam_dec31.pdf
  66. Jack BK. 2013. Market inefficiencies and the adoption of agricultural technologies in developing countries White Pap., Agric. Technol. Adopt. Init Cambridge, MA:
  67. Jack BK, Oliva P, Severen C, Walker E, Bell S 2015. Technology adoption under uncertainty: take-up and subsequent investment in Zambia NBER Work. Pap. 21414
  68. Jack W, Kremer M, De Laat J, Suri T 2019. Borrowing requirements, credit access, and adverse selection: evidence from Kenya NBER Work. Pap. 22686
  69. Jones K, de Brauw A 2015. Using agriculture to improve child health: results from a randomized controlled trial on vitamin A intake. World Dev 74:15–24
    [Google Scholar]
  70. Karlan D, Osei RD, Osei-Akoto I, Udry C 2014. Agricultural decisions after relaxing credit and risk constraints. Q. J. Econ. 129:2597–652
    [Google Scholar]
  71. Kijima Y, Otsuka K, Sserunkuuma D 2011. An inquiry into constraints on a Green Revolution in Sub-Saharan Africa: the case of NERICA rice in Uganda. World Dev 39:177–86
    [Google Scholar]
  72. Kondylis F, Mueller V, Zhu SJ 2017. Seeing is believing? Evidence from an extension network experiment. J. Dev. Econ. 125:1–20
    [Google Scholar]
  73. Kosmowski F, Aragaw A, Kilian A, Ambel A, Ilukor J et al. 2019. Varietal identification in household surveys: results from three household-based methods against the benchmark of DNA fingerprinting in Southern Ethiopia. Exp. Agric. 55:371–85
    [Google Scholar]
  74. Kremer M, Zwane AP. 2005. Encouraging private sector research for tropical agriculture. World Dev 33:187–105
    [Google Scholar]
  75. Laajaj R, Macours K. 2016. Learning-by-doing and learning-from-others: evidence from agronomical trials in Kenya. Learning for Adopting: Technology Adoption in Developing Country Agriculture A de Janvry, K Macours, E Sadoulet 97–102 Clermont-Ferrand, Fr: FERDI
    [Google Scholar]
  76. Laajaj R, Macours K, Masso C, Thuita M, Vanlauwe B 2018. Yield gap or field gap? Reconciling yield gains in agronomic trials and African smallholder conditions Work. Pap., Paris Sch. Econ .
  77. Langyintuo AS, Mwangi W, Diallo AO, MacRobert J, Dixon J, Banziger M 2010. Challenges of the maize seed industry in eastern and southern Africa: a compelling case for private-public intervention to promote growth. Food Policy 35:4323–31
    [Google Scholar]
  78. Ligon E, Sadoulet E. 2018. Estimating the relative benefits of agricultural growth on the distribution of expenditures. World Dev 109:417–28
    [Google Scholar]
  79. Lobell DB, Cassman KG, Field CB 2009. Crop yield gaps: their importance, magnitudes, and causes. Annu. Rev. Environ. Resour. 34:179–204
    [Google Scholar]
  80. Lybbert T, Magnan N, Spielman DJ, Bhargava AK, Gulati K 2018. Targeting technology to increase smallholder profits and conserve resources: experimental provision of laser land-leveling services to Indian farmers. Econ. Dev. Cult. Change 66:2265–306
    [Google Scholar]
  81. Macours K. 2018. Comment on “Estimating the productivity impacts of technology adoption in the presence of misclassification.”. Am. J. Agric. Econ. 101:117–18
    [Google Scholar]
  82. Magruder J. 2018. An assessment of experimental evidence on agricultural technology adoption in developing countries. Annu. Rev. Resour. Econ. 10:299–316
    [Google Scholar]
  83. Maredia MK, Reyes BA, Manu-Aduening J, Dankyi A, Hamazakaza P et al. 2016. Testing alternative methods of varietal identification using DNA fingerprinting: results of pilot studies in Ghana and Zambia MSU Int. Dev. Work. Pap. 149, Mich. State Univ East Lansing:
  84. Marenya PP, Barrett CB. 2009. State-conditional fertilizer yield response on western Kenyan farms. Am. J. Agric. Econ. 91:4991–1006
    [Google Scholar]
  85. Matsumoto T, Yamano T, Sserunkuuma D 2013. Technology adoption and dissemination in agriculture: evidence from sequential intervention in maize production in Uganda GRIPS Discuss. Pap. 13-14, Natl. Grad. Inst. Policy Stud Tokyo:
  86. McNiven S. 2014. The role of social interactions in the adoption of an agricultural technology PhD Thesis, Univ. Calif Davis:
  87. Michler JD, Tjernström E, Verkaart S, Mausch K 2019. Money matters: the role of yields and profits in agricultural technology adoption. Am. J. Agric. Econ. 101:710–31
    [Google Scholar]
  88. Mobarak AM, Rosenzweig M. 2012. Selling formal insurance to the informally insured Work. Pap. 97, Econ. Growth Cent., Yale Univ http://www.econ.yale.edu/growth_pdf/cdp1007.pdf
  89. Munshi K. 2004. Social learning in a heterogeneous population: technology diffusion in the Indian Green Revolution. J. Dev. Econ. 73:185–213
    [Google Scholar]
  90. Pingali PL. 2012. Green Revolution: impacts, limits, and the path ahead. PNAS 109:3112302–8
    [Google Scholar]
  91. Qaim M. 2009. The economics of genetically modified crops. Annu. Rev. Resour. Econ. 1:665–94
    [Google Scholar]
  92. Rogers EM. 1962. The Diffusion of Innovations New York: Free Press. , 1st ed..
  93. Rosenzweig M, Udry C. 2017. External validity in a stochastic world: evidence from low-income countries Work. Pap., Yale Univ
  94. Setimela PS, Badu-Apraku B, Mwangi W 2009. Variety Testing and Release Approaches in DTMA Project Countries in Sub-Saharan Africa Harare, Zimb: CIMMYT
  95. Sheahan M, Barrett C. 2017. The use of modern inputs viewed from the field. Agriculture in Africa: Telling Myths from Facts L Christiansen, L Demery 85–94 Washington, DC: World Bank
    [Google Scholar]
  96. Stevenson JR, Macours K, Gollin D 2018. The rigor revolution in impact assessment: implications for the CGIAR Rep., Indep. Sci. Partnersh. Counc Rome:
  97. Stevenson JR, Vanlauwe B, Macours K, Johnson N, Krishnan L et al. 2019. Farmer adoption of plot and farm-level natural resource management practices: between rhetoric and reality. Glob. Food Secur. 20:101–4
    [Google Scholar]
  98. Stevenson JR, Vlek P. 2018. Assessing the adoption and diffusion of sustainable agricultural practices: synthesis of a new set of empirical studies Synth. Rep., SPIA, Indep. Sci. Partnersh. Counc Rome:
  99. Sunding D, Zilberman D. 2001. The agricultural innovation process: research and technology adoption in a changing agricultural sector. Handb. Agric. Econ. 1:207–61
    [Google Scholar]
  100. Suri T. 2011. Selection and comparative advantage in technology adoption. Econometrica 79:1150–209
    [Google Scholar]
  101. Takahashi K, Barrett CB. 2004. The system of rice intensification and its impacts on household income and child schooling: evidence from rural Indonesia. Am. J. Agric. Econ. 96:1269–89
    [Google Scholar]
  102. Thaler R. 1985. Mental accounting and consumer choice. Marketing Sci 4:3199–214
    [Google Scholar]
  103. Thiele G, van de Fliert E, Campilan D 2001. What happened to participatory research at the International Potato Center. Agric. Hum. Values 18:429–46
    [Google Scholar]
  104. Tjernström E. 2015. Signals, similarity and seeds: social learning in the presence of imperfect information and heterogeneity Work. Pap., Univ. Wis., Madison
  105. Van Ittersum MK, Cassman KG, Grassini P, Wolf J, Tittonell P, Hochman Z 2013. Yield gap analysis with local to global relevance—a review. Field Crops Res 143:4–17
    [Google Scholar]
  106. Vanlauwe B, Coe, Giller KE 2019. Beyond averages: new approaches to understand heterogeneity and risk of technology success or failure in smallholder farming. Exp. Agric. 55:84–106
    [Google Scholar]
  107. Walker TS, Alwang J 2015. Crop Improvement, Adoption and Impact of Improved Varieties in Food Crops in Sub-Saharan Africa Wallingford, UK: CABI
  108. World Bank 2007. Agriculture for development World Dev. Rep., World Bank Washington, DC:
  109. Wossen T, Abdoulaye T, Alene A, Nguimkeu P, Feleke S et al. 2019. Estimating the productivity impacts of technology adoption in the presence of misclassification. Am. J. Agric. Econ. 101:1–16
    [Google Scholar]
  110. Young A. 2013. Inequality, the urban-rural gap and migration. Q. J. Econ. 128:41727–85
    [Google Scholar]
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