Economic Determinants of IR2793-80-1 Rice Yield in Bunyala Irrigation Scheme, Kenya: Evidence from Comparative Production Function Modelling
Authors
Department of Economics, School of Business and Economics, Maseno University (Kenya)
Department of Economics, School of Business and Economics, Maseno University (Kenya)
Department of Economics, School of Business and Economics, Maseno University (Kenya)
Article Information
DOI: 10.47772/IJRISS.2026.100800158
Subject Category: Economics
Volume/Issue: 10/8 | Page No: 2290-2303
Publication Timeline
Submitted: 2026-08-16
Accepted: 2026-08-21
Published: 2026-08-29
Abstract
Rice productivity in Kenya remains below domestic demand despite continued investment in irrigation and improved varieties. This study analysed the economic determinants of IR2793-80-1 paddy yield among smallholder farmers in Bunyala Irrigation Scheme, Kenya, and examined whether the commonly used Cobb-Douglas production function adequately represented the observed input-output relationships. An explanatory cross-sectional survey design was adopted. A target sample of 333 farmers was determined, 266 questionnaires were administered, and 258 complete responses were retained for analysis. Paddy yield was measured in kilograms per acre; inorganic fertiliser was converted into formulation-adjusted nutrient quantity per acre; farm size was measured as acreage planted with IR2793-80-1; labour as person-days per acre; and productive credit as borrowed funds used directly for rice production per acre. Descriptive statistics and Spearman correlation preceded Ordinary Least Squares estimation of Linear, Cobb-Douglas and centred Translog production functions. The Cobb-Douglas benchmark explained 35.8% of variation in logged yield, whereas the centred Translog model explained 50.9%. The flexible terms significantly improved explanatory power (ΔR² = .151; F-change = 7.484, p < .001), while AIC and BIC also favoured the Translog specification. In the preferred model, land (B = .217, p < .001), labour (B = .151, p < .001) and productive credit (B = .030, p < .001) had positive first-order effects at the mean input combination. Fertiliser and credit exhibited significant positive curvature, land showed diminishing curvature, and the labour-credit interaction was negative and significant. The findings demonstrate that IR2793-80-1 yield is characterised by nonlinear and interdependent input relationships. The study recommends coordinated nutrient management, manageable acreage, timely labour organisation and production-linked credit rather than isolated increases in individual inputs.
Keywords
IR2793-80-1 rice; rice productivity; Bunyala Irrigation Scheme; productive credit; Translog production function
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References
1. Abong'o, O. M. (2016). Factors influencing rice production among smallholder farmers in the Ahero Irrigation Scheme, Kenya [Master's thesis, Maseno University]. Maseno University Repository. [Google Scholar] [Crossref]
2. Adjognon, S. G., Liverpool-Tasie, L. S. O., & Reardon, T. A. (2017). Agricultural input credit in Sub-Saharan Africa: Telling myth from facts. Food Policy, 67, 93–105. https://doi.org/10.1016/j.foodpol.2016.09.014 [Google Scholar] [Crossref]
3. Africa Rice Centre. (2021). Competitiveness analysis of rice production in Kenya. AfricaRice. https://riceforafrica.net/wp-content/uploads/2023/12/Kenya_competitiveness-analysis_20210808.pdf [Google Scholar] [Crossref]
4. Awotide, B. A., Abdoulaye, T., Alene, A., & Manyong, V. M. (2015). Impact of access to credit on agricultural productivity: Evidence from smallholder cassava farmers in Nigeria. Agriculture & Food Security, 4, Article 7. https://doi.org/10.1186/s40066-015-0024-3 [Google Scholar] [Crossref]
5. Bandumula, N. (2018). Rice production in Asia: Key to global food security. Proceedings of the National Academy of Sciences, India Section B: Biological Sciences, 88, 1323–1328. https://doi.org/10.1007/s40011-017-0867-7 [Google Scholar] [Crossref]
6. Bevis, L. E. M., & Barrett, C. B. (2020). Close to the edge: High productivity at plot peripheries and the inverse size-productivity relationship. Journal of Development Economics, 143, Article 102377. https://doi.org/10.1016/j.jdeveco.2019.102377 [Google Scholar] [Crossref]
7. Binswanger, H. P., & Khandker, S. R. (1995). The impact of formal finance on the rural economy of India. The Journal of Development Studies, 32(2), 234-262. https://doi.org/10.1080/00220389508422413 [Google Scholar] [Crossref]
8. Cobb, C. W., & Douglas, P. H. (1928). A theory of production. The American Economic Review, 18(1), 139–165. https://www.jstor.org/stable/1811556 [Google Scholar] [Crossref]
9. Christensen, L. R., Jorgenson, D. W., & Lau, L. J. (1973). Transcendental logarithmic production frontiers. The Review of Economics and Statistics, 55(1), 28–45. https://doi.org/10.2307/1927992 [Google Scholar] [Crossref]
10. Feder, G., Lau, L. J., Lin, J. Y., & Luo, X. (1990). The relationship between credit and productivity in Chinese agriculture: A microeconomic model of disequilibrium. American Journal of Agricultural Economics, 72(5), 1151–1157. https://doi.org/10.2307/1242524 [Google Scholar] [Crossref]
11. Greene, W. H. (2018). Econometric analysis (8th ed.). Pearson. [Google Scholar] [Crossref]
12. Gujarati, D. N., & Porter, D. C. (2009). Basic econometrics (5th ed.). McGraw-Hill/Irwin. [Google Scholar] [Crossref]
13. Houngue, V., & Nonvide, G. M. A. (2020). Estimation and determinants of efficiency among rice farmers in Benin. Cogent Food & Agriculture, 6(1), Article 1819004. https://doi.org/10.1080/23311932.2020.1819004 [Google Scholar] [Crossref]
14. Islam, M. S., Bell, R. W., Miah, M. M., & Alam, M. J. (2024). Determinants of farmers' fertiliser use gaps under rice-based cropping systems: Empirical evidence from Eastern Gangetic Plain. Journal of Agriculture and Food Research, 17, Article 101228. https://doi.org/10.1016/j.jafr.2024.101228 [Google Scholar] [Crossref]
15. Johnson, J. M., Ibrahim, A., Dossou-Yovo, E. R., Senthilkumar, K., Tsujimoto, Y., Asai, H., & Saito, K. (2023). Inorganic fertiliser use and its association with rice yield gaps in sub-Saharan Africa. Global Food Security, 38, Article 100708. https://doi.org/10.1016/j.gfs.2023.100708 [Google Scholar] [Crossref]
16. 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 Development, 39(1), 77-86. https://doi.org/10.1016/j.worlddev.2010.06.010 [Google Scholar] [Crossref]
17. Kuso, Y., & Gachunga Muhia, J. (2019a). Agricultural exports and food insecurity in Sub-Saharan Africa: A qualitative configurational analysis. International Journal of Management and Economics. [Google Scholar] [Crossref]
18. Mabe, F. N. (2023). Impacts of farmer innovation systems and improved agricultural technologies on rice yield in Ghana. Ghana Journal of Science, Technology and Development, 9(1), 25–41. https://doi.org/10.47881/343.967x [Google Scholar] [Crossref]
19. Mahajan, G., Kumar, V., & Chauhan, B. S. (2017). Rice production in India. In B. S. Chauhan, K. Jabran, & G. Mahajan (Eds.), Rice production worldwide (pp. 53–91). Springer. https://doi.org/10.1007/978-3-319-47516-5_3 [Google Scholar] [Crossref]
20. Mati, B. M., Nyangau, W. W., Ndiiri, J. A., & Wanjogu, R. (2021). Enhancing production while saving water through the system of rice intensification in Kenya's irrigation schemes. Journal of Agriculture, Science and Technology, 20(1), 24-40. [Google Scholar] [Crossref]
21. Ministry of Agriculture, Livestock, Fisheries and Irrigation. (2019). National Rice Development Strategy II, 2019–2030. Government of Kenya. [Google Scholar] [Crossref]
22. Nakano, Y., Kajisa, K., & Otsuka, K. (2016). On the possibility of rice Green Revolution in irrigated and rainfed areas in Tanzania: An assessment of management training and credit programs. In K. Otsuka & D. F. Larson (Eds.), In pursuit of an African Green Revolution (pp. 39–64). Springer. [Google Scholar] [Crossref]
23. National Irrigation Authority. (2022). Bunyala Irrigation Scheme. https://www.irrigationauthority.go.ke/projects/bunyala-irrigation-scheme/ [Google Scholar] [Crossref]
24. Ndiiri, J. A., Uphoff, N., Mati, B. M., Home, P. G., & Odongo, B. (2017). Comparison of yields of paddy rice under System of Rice Intensification in Mwea, Kenya. American Journal of Plant Biology, 2(2), 49-60. https://doi.org/10.11648/j.ajpb.20170202.12 [Google Scholar] [Crossref]
25. Nguyen, T. T., Nguyen, T. T., & Grote, U. (2023). Credit, shocks, and production efficiency of rice farmers in Vietnam. Economic Analysis and Policy, 77, 780–791. https://doi.org/10.1016/j.eap.2022.12.023 [Google Scholar] [Crossref]
26. Ochieng, G. J., & Kuso Ghabon, Y. (2026). Role of government subsidies on agricultural productivity in Migori County, Kenya. International Journal of Research in Management, 16(1), 140–157. https://doi.org/10.5281/zenodo.18419469 [Google Scholar] [Crossref]
27. Omondi, S. O., & Shikuku, K. M. (2013). An analysis of technical efficiency of rice farmers in Ahero Irrigation Scheme, Kenya. Journal of Economics and Sustainable Development, 4(10), 9-16. [Google Scholar] [Crossref]
28. Ouma, M. A., Ouma, L. O., Ombati, J. M., & Onyango, C. A. (2024). A cost-benefit analysis of the adoption of a system of rice intensification: Evidence from smallholder rice farmers within an innovation platform in Oluch Irrigation Scheme, Kenya. PLOS ONE, 19(1), Article e0290759. https://doi.org/10.1371/journal.pone.0290759 [Google Scholar] [Crossref]
29. Qiu, H., Yang, S., Jiang, Z., Xu, Y., Han, J., & Liu, Y. (2022). Effect of irrigation and fertilizer management on rice yield and nitrogen loss: A meta-analysis. Plants, 11(13), Article 1690. https://doi.org/10.3390/plants11131690 [Google Scholar] [Crossref]
30. Sadiq, M. S., Singh, I. P., & Ahmad, M. M. (2022). Labour-use efficiency of rice farmers in Nigeria's north-central region. Siembra, 9(2), Article e3969. https://doi.org/10.29166/siembra.v9i2.3969 [Google Scholar] [Crossref]
31. Samal, P., Babu, S. C., Mondal, B., & Mishra, S. N. (2022). The global rice agriculture towards 2050: An inter-continental perspective. Outlook on Agriculture, 51(2), 164–172. https://doi.org/10.1177/00307270221088338 [Google Scholar] [Crossref]
32. Snapp, S., Sapkota, T. B., Chamberlin, J., Cox, C. M., Gameda, S., Jat, M. L., & Govaerts, B. (2023). Spatially differentiated nitrogen supply is key in a global food-fertiliser price crisis. Nature Sustainability, 6(10), 1268–1278. https://doi.org/10.1038/s41893-023-01164-2 [Google Scholar] [Crossref]
33. Ssengonzi, J., Ghabon, Y., & Moni, A. (2025). Contribution of social capital and microcredit accessibility on economic welfare of small-scale farmers in Mityana District, Uganda. East African Journal of Business and Economics, 8(1), 393–408. https://doi.org/10.37284/eajbe.8.1.2968 [Google Scholar] [Crossref]
34. Taremwa, N. K., Macharia, I., Bett, E., & Majiwa, E. (2021). Impact of agricultural credit access on agricultural productivity among maize and rice smallholder farmers in Rwanda. Journal of Agribusiness and Rural Development, 59(1), 39-58. https://doi.org/10.17306/J.JARD.2021.01341 [Google Scholar] [Crossref]
35. Wijetunga, C. S. (2016). Rice production structures in Sri Lanka: The normalized translog profit function approach. Asian Journal of Agriculture and Rural Development, 6(2), 21–35. https://doi.org/10.18488/journal.1005/2016.6.2/1005.2.21.35 [Google Scholar] [Crossref]
36. Wooldridge, J. M. (2016). Introductory econometrics: A modern approach (6th ed.). Cengage Learning. [Google Scholar] [Crossref]
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