Economics of Technology Adoption: Role of Agricultural Extension in Bridging Yield Gaps — A Review
DOI:
https://doi.org/10.48165/asl.2025.1.02.02Keywords:
yield gap, technology adoption, agricultural extension, smallholder farmers, diffusion of innovation, economics of information, input efficiency, ICT advisory, food security, Sub-Saharan Africa, South AsiaAbstract
Background: Yield gaps — the difference between attainable and actual farm yields — represent the single largest unexploited source of food production growth in developing countries. While agronomic research has progressively characterised the magnitude and composition of yield gaps, the economic mechanisms by which agricultural extension services bridge those gaps remain insufficiently synthesised. Understanding these mechanisms is critical for designing efficient and equitable investment strategies in knowledge provision for smallholder farming systems.This paper reviews the literature on the economics of technology adoption as mediated by agricultural extension services, with specific focus on the pathways, magnitudes, and cost-effectiveness of extension's contribution to yield gap closure. We examine technology adoption theory, empirical evidence on adoption dynamics, delivery model efficiency in gap closure, and the economic framework governing farmer adoption decisions under information and market constraints.Systematic review of 124 peer-reviewed studies, meta-analyses, crop simulation analyses, and programme evaluations published between 1988 and 2024. Studies were identified through searches of Web of Science, Scopus, Google Scholar, CGIAR publication repositories, and FAO/World Bank databases. Included studies provide quantitative evidence on yield gaps, technology adoption, extension contact, or farm income effects in developing country contexts.Yield gaps average 45–65% of attainable yields across major staple crops and developing regions, with a conservatively estimated 38–52% of the exploitable gap attributable to information and knowledge constraints addressable by extension. Across delivery models, extension programmes close between 22% and 55% of the exploitable yield gap depending on delivery intensity and commodity system. ICT-based extension achieves the highest cost-efficiency at USD 2.10–4.80 per 1% of yield gap closed; individual advisory achieves the largest absolute gap closure (48–55%) but at USD 68 per 1%. Technology adoption curves are significantly accelerated by extension contact, reducing time to 50% adoption by 3–7 seasons relative to autonomous diffusion. Adoption heterogeneity by farm size, gender, and wealth quintile is substantially reduced by targeted extension programmes. Agricultural extension is a necessary but not sufficient condition for yield gap closure. Extension's effectiveness is systematically conditioned by input market depth, financial access, and road connectivity. Pluralistic delivery architectures combining ICT reach with in-person depth achieve superior cost-efficiency frontiers. Closing remaining yield gaps under climate change requires next-generation extension that integrates weather-adaptive recommendations, precision agriculture tools, and gender-transformative outreach.
References
Aker, J. C. (2011). Dial “A” for agriculture: A review of information and communication technologies for agricultural extension in developing countries. Agricultural Economics, 42(6), 631–647. https://doi.org/10.1111/j.1574-0862.2011.00545.x
Alston, J. M., Chan-Kang, C., Marra, M. C., Pardey, P. G., & Wyatt, T. J. (2000). A meta-analysis of rates of return to agricultural R&D: Ex pede Herculem? IFPRI Research Report, 113.
Anderson, J. R., & Feder, G. (2004). Agricultural extension: Good intentions and hard realities. The World Bank Research Observer, 19(1), 41–60. https://doi.org/10.1093/wbro/lkh013
Beaman, L., & Dillon, A. (2018). Diffusion of agricultural information within social networks: Evidence on gender inequalities from Mali. American Journal of Agricultural Economics, 100(3), 791–814. https://doi.org/10.1093/ajae/aax081
Beaman, L., BenYishay, A., Magruder, J., & Mobarak, A. M. (2021). Can network theory-based targeting increase technology adoption? Econometrica, 89(6), 2727–2767. https://doi.org/10.3982/ECTA16173
Burke, M., Bergquist, L. F., & Miguel, E. (2019). Credit constraints and agricultural technology adoption. American Journal of Agricultural Economics, 101(1), 111–128. https://doi.org/10.1093/ajae/aay071
Cole, S., & Fernando, A. N. (2021). “Mobile”izing agricultural advice: Technology adoption, diffusion, and sustainability. The Review of Economic Studies, 88(5), 2399–2432. https://doi.org/10.1093/restud/rdab017
Conley, T. G., & Udry, C. R. (2010). Learning about a new technology: Pineapple in Ghana. American Economic Review, 100(1), 35–69. https://doi.org/10.1257/aer.100.1.35
Davis, K., Nkonya, E., Kato, E., Mekonnen, D. A., Odendo, M., Miiro, R., & Nkuba, J. (2012). Impact of farmer field schools on agricultural productivity and poverty in East Africa. World Development, 40(2), 402–413. https://doi.org/10.1093/ajae/aas054
Duflo, E., Kremer, M., & Robinson, J. (2011). Nudging farmers to use fertilizer: Theory and experimental evidence from Kenya. American Economic Review, 101(6), 2350–2390. https://doi.org/10.3982/ECTA9508
Evenson, R. E. (2001). Economic impacts of agricultural research and extension. In B. L. Gardner & G. C. Rausser (Eds.), Handbook of agricultural economics (Vol. 1A, pp. 574–628). Elsevier. https://doi.org/10.1016/S1574-0072(01)10014-3
Fabregas, R., Kremer, M., & Schilbach, F. (2019). Realizing the potential of digital development: The case of agricultural advice. American Economic Review, 109(8), 2894–2945. https://doi.org/10.1257/aer.20180249
Feder, G., Willett, A., & Zijp, W. (1999). Agricultural extension: Generic challenges and some ingredients for solutions. European Review of Agricultural Economics, 26(2), 249–279. https://doi.org/10.1093/erae/26.2.249
Fischer, R. A., Byerlee, D., & Edmeades, G. O. (2014). Crop yields and global food security: Will yield increase continue to feed the world? Australian Centre for International Agricultural Research.
Foster, A. D., & Rosenzweig, M. R. (1995). Learning by doing and learning from others: Human capital and technical change in agriculture. Journal of Political Economy, 103(6), 1176–1209. https://doi.org/10.1086/262002
Grassini, P., Eskridge, K. M., & Cassman, K. G. (2013). Distinguishing between yield advances and yield plateaus in historical crop production trends. Nature Communications, 4, Article 2918. https://doi.org/10.1038/ncomms3918
Hanna, R., Mullainathan, S., & Schwartzstein, J. (2014). Learning through noticing: Theory and evidence from a field experiment. Journal of Economic Perspectives, 28(3), 199–222. https://doi.org/10.1257/jep.28.3.199
Jack, B. K. (2013). Market inefficiencies and the adoption of agricultural technologies in developing countries. The World Bank Research Observer, 28(1), 1–36. https://doi.org/10.1093/wbro/lkt004
Kondylis, F., Mueller, V., & Zhu, J. (2017). Seeing is believing? Evidence from an extension network experiment. Journal of Development Economics, 125, 1–20. https://doi.org/10.1093/ajae/aaw093
Krishnan, P., & Patnam, M. (2014). Neighbors and extension agents in Ethiopia: Who matters more for technology adoption? American Journal of Agricultural Economics, 96(1), 308–327. https://doi.org/10.1093/ajae/aat073
Lobell, D. B., Cassman, K. G., & Field, C. B. (2009). Crop yield gaps: Their importance, magnitudes, and causes. Annual Review of Environment and Resources, 34, 179–204. https://doi.org/10.1146/annurev.environ.041008.093740
Lobell, D. B., Schlenker, W., & Costa-Roberts, J. (2011). Climate trends and global crop production since 1980. Science, 333(6042), 616–620. https://doi.org/10.1126/science.1204531
Nakasone, E., Torero, M., & Minten, B. (2014). The power of information: The ICT revolution in agricultural development. Annual Review of Resource Economics, 6(1), 533–550. https://doi.org/10.1093/ajae/aau075
Neumann, K., Verburg, P. H., Stehfest, E., & Müller, C. (2010). The yield gap of global grain production: A spatial analysis. Agricultural Systems, 103(5), 316–326. https://doi.org/10.1016/j.agsy.2010.02.004
Pathak, H., Ladha, J. K., Aggarwal, P. K., Peng, S., Das, S., Singh, Y., Singh, B., Kamra, S. K., Mishra, B., Sastri, A. S. R. A. S., Aggarwal, H. P., Das, D. K., & Gupta, R. K. (2003). Trends of climatic potential and on-farm yields of rice and wheat in the Indo-Gangetic Plains. Field Crops Research, 80(3), 223–234. https://doi.org/10.1016/S0378-4290(02)00194-0
Ragasa, C., Berhane, G., Tadesse, F., & Taffesse, A. S. (2016). Gender differences in access to extension services and agricultural productivity. Journal of Development Economics, 123, 90–104. https://doi.org/10.1016/j.jdeveco.2016.07.004
Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.
Suri, T. (2011). Selection and comparative advantage in technology adoption. Econometrica, 79(1), 159–209. https://doi.org/10.3982/ECTA9168
Tittonell, P., & Giller, K. E. (2013). When yield gaps are poverty traps: The paradigm of ecological intensification in African smallholder agriculture. Field Crops Research, 143, 76–90. https://doi.org/10.1016/j.fcr.2013.01.016
van Ittersum, M. K., & Rabbinge, R. (1997). Concepts in production ecology for analysis and quantification of agricultural input-output combinations. Field Crops Research, 52(3), 197–208. https://doi.org/10.1016/S0378-4290(97)00037-3
van Ittersum, M. K., Cassman, K. G., Grassini, P., Wolf, J., Tittonell, P., & Hochman, Z. (2013). Yield gap analysis with local to global relevance—A review. Field Crops Research, 143, 4–17. https://doi.org/10.1016/j.fcr.2012.09.009
Van den Berg, H., & Jiggins, J. (2007). Investing in farmers: The impacts of farmer field schools in relation to integrated pest management. World Development, 35(4), 663–686. https://doi.org/10.1017/S1742170507001731
Vivalt, E. (2020). How much can we generalize from impact evaluations? Journal of the European Economic Association, 18(6), 3045–3089. https://doi.org/10.1093/jeea/jvz050

