Impacts of climate-resilient push–pull technology on farmers’ income in selected counties in Kenya and Tanzania: propensity score matching approach
Tóm tắt
Agricultural research and technology adoption play a key role in improving productivity and therefore generate impact on household livelihoods. The push–pull technology developed by the International Centre of Insect Physiology and Ecology and collaborators/partners has been recognized for its multiple roles in productivity improvement and income generation. However, the subsequent impacts after its adaptation to drier agro-ecologies have not been ascertained. An ex-post study was conducted to evaluate the impact of the climate-resilient push–pull technology on farmers’ income. This study was conducted in eight counties in Kenya and Mara region in Tanzania, involving 486 farmers; half were climate-resilient push–pull technology adopters. The study adopted the propensity score matching (PSM) technique in order to correct the self-selection bias in adoption. From the results, education of the farmer, household size, Tropical Livestock Unit and group membership positively and significantly influenced adoption. The average treatment effect on the treated was positive for all the matching methods; USD 455.8 for Nearest Neighbor Matching, USD 474.2 for the Kernel Matching and USD 439.1 for the Radius/Caliper Matching. The balancing test for self-selection bias showed that none of the observed covariates was significant after matching. The results demonstrate that adopting climate-resilient push–pull technology has a positive impact on the adopter farmers’ income. Adopter farmers were able to earn much more in terms of gross margin. The positive change in income for adopters was attributable to the technology. With increased incomes, farmers were able to access alternative foodstuff, hence had more food security and diversity than those without. Efforts to expand dissemination and adoption of climate-resilient push–pull technology will have positive impacts on adopting families and hence to the economy.
Tài liệu tham khảo
Khan ZR, Midega CAO, Hassanali A, Pickett JA, Wadhams LJ, Wanjoya A. Management of witchweed, Striga hermonthica, and stemborers in sorghum, Sorghum bicolor, through intercropping with greenleaf desmodium, Desmodium intortum. Int J Pest Manage Sci. 2006;52:297–302.
Midega CA, Pittchar JO, Pickett JA, Hailu GW, Khan ZR. A climate-adapted Push-Pull system effectively controls Fall armyworm, Spodoptera frugiperda (J E Smith), in maize in East Africa. Crop protect. 2018;105:10–5.
Khan ZR, Pittchar JO, Midega CA, Pickett JA. Push-Pull farming system controls fall armyworm: lessons from Africa. Outlooks on Pest Manage. 2018;29(5):220–4.
Khan ZR, Midega CAO, Wadhams LJ, Pickett JA, Mumuni A. Evaluation of Napier grass (Pennisetum purpureum ) varieties for use as trap plants for the management of African stemborer (Busseola fusca) in a Push-Pull strategy. Entomol Exp et Appl. 2007;124:201–11.
Khan ZR, Midega CAO, Bruce TJA, Hooper AM, Pickett JA. Exploiting phytochemicals for developing a ‘push–pull’ crop protection strategy for cereal farmers in Africa. J Exp Bot. 2010;61:4185–96.
Khan ZR, Pickett JA, Wadhams L, Muyekho F. Habitat management for the control of cereal stem borers in maize in Kenya. Insect Sci Appl. 2001;21:375–80.
Khan ZR, Hassanali A, Khamis TM, Overholt WA, Hooper AM, Pickett JA, Wadhams LJ, Woodcock CM. Control of the witchweed, Striga hermonthica, by intercropping with Desmodium spp., and the mechanism defined as allelopathic. J Chem Ecol. 2002;28:1871–85.
Khan ZR, Hassanali A, Pickett JA, Wadhams LJ, Muyekho F. Strategies for control of cereal stemborers and striga weed in maize-based farming systems in eastern Africa involving ‘Push-Pull’ and allelopathic tactics, respectively. Afr Crop Sci Soc. 2003;6:602–8.
Tsanuo MK, Hassanali A, Hooper AM, Khan ZR, Kaberia F, Pickett JA, Wadhams LJ. Isoflavanones from the allelopathic aqueous root exudates of Desmodium uncinatum. Phytochemistry. 2003;64:265–73.
Midega C, Khan ZR. Habitat management system and its impact on diversity and abundance of maize stemborer predators in western Kenya. Insect Sci Appl. 2003;23:301–8.
Cook SM, Khan ZR, Pickett JA. The use of ‘Push-Pull’ strategies in integrated pest management. Ann Rev Entomol. 2007;52:375–400.
Khan ZR, Midega CAO, Amudavi DM, Hassanali A, Pickett JA. On-farm evaluation of the “Push-Pull” technology for the control of stemborers and striga weed on maize in western Kenya. Field Crops Res. 2008;106:224–33.
Khan ZR, Midega CAO, Amudavi DM, Njuguna EM, Wanyama JW, Pickett JA. Economic performance of the “Push-Pull” technology for stemborer and striga control in smallholder farming systems in western Kenya. Crop Protect. 2008;27:1084–97.
Chepchirchir R, Macharia I, Murage AW, Midega CAO, Khan ZR. Impact assessment of push–pull technology on incomes, productivity and poverty among smallholder households in Eastern Uganda. Food Secur. 2017;9(6):1359–72.
Chepchirchir R, Macharia I, Murage AW, Midega CAO, Khan ZR. Ex-post economic analysis of Push-Pull technology in Eastern Uganda. Crop Protect. 2018;112:356–62.
Kassie M, Stage J, Diiro G, Muriithi B, Muricho G, Ledermann ST, Pittchar JO, Midega C, Khan Z. Push-Pull farming system in Kenya: implications for economic and social welfare. Land Use Policy. 2018;77:186–98.
Khan ZR, Midega CAO, Pittchar J, Murage AW, Birkett MA, Bruce TJ, Pickett JA. Achieving food security for one million sub-Saharan African poor through push–pull innovation by 2020. Phil Trans R Soc B. 2014;369(1639):20120284.
Midega CAO, Bruce TJA, Pickett JA, Pittchar JO, Murage A, Khan ZR. Climate-adapted companion cropping increases agricultural productivity in East Africa. Field Crops Res. 2015;180:118–25.
Midega CAO, Wasonga CJ, Hooper AM, Pickett JA, Khan ZR. Drought-tolerant Desmodium species effectively suppress parasitic striga weed and improve cereal grain yields in western Kenya. Crop Protect. 2017;98:94–101.
Cheruiyot D, Midega CAO, Van den Berg J, Pickett JA, Khan ZR. Genotypic responses of Brachiaria grass (Brachiaria spp.) accessions to drought stress. J Agron. 2018;17:136–46.
Cheruiyot D, Midega CAO, Van den Berg J, Pickett JA, Khan ZR. Genotypic response of brachiaria (Urochloa spp.) to spider mite (Oligonychus trichardti) (Acari: Tetranychidae) and adaptability to different environments. Field Crops Res. 2018;225:163–9.
Cheruiyot D, Midega CAO, Van den Berg J, Pickett JA, Khan ZR. Suitability of brachiaria grass (Brachiaria spp.) as a trap crop for management of Chilo partellus. Entomol Exp et Appl. 2018;166:139–48.
Mendola M. Agricultural technology adoption and poverty reduction: a propensity-score matching analysis for rural Bangladesh. Food Policy. 2007;32:372–93.
Winship C, Morgan SL. The estimation of causal effects from observational data. Annu Rev Sociol. 1999;25:659–706.
Kassie M, Fisher M, Muricho G, Diiro G. Women’s empowerment boosts the gains in dietary diversity from agricultural technology adoption in rural Kenya. Food Policy. 2020;95: 101957.
Shoko AP, Limbu SM, Mrosso HDJ, Mkenda AF, Mgaya YD. Effect of stocking density on growth, production and economic benefits of mixed sex N ile tilapia (Oreochromis niloticus) and African sharptooth catfish (Clarias gariepinus) in polyculture and monoculture. Aquac Res. 2016;47(1):36–50.
NBS. Population and housing census. Gaithersburg: Government of Tanzania Dar es Salaam, National Bureau of Standards; 2012.
Food and Agricultural Organisation of the United Nations (FAO) (1997). Chapter 7: Sampling In Marketing Research. In Marketing research and information systems. (Marketing and Agribusiness Texts - 4). Agriculture and Consumer Protection, FAO. http://www.fao.org/docrep/W3241E/w3241e08.htm#cluster%20and%20multistage%20samplin.
Agresti A, Finlay B. Statistical options for the social sciences 4th edition. Upper Saddle River: Prentice Hall; 2008.
Yamane T. Statistics: an introductory analysis. 2nd ed. New York: Harper and Row; 1967.
Shi D, Tong X. Mitigating selection bias: a Bayesian approach to two-stage causal modeling with instrumental variables for nonnormal missing data. Sociol Methods Res. 2022;51(3):1052–99.
Kiwanuka-Lubinda RN, Ng’ombe JN, Machethe C. Impacts of interlocked contractual arrangements on dairy farmers’ welfare in Zambia: a robust Bayesian instrumental variable analysis. Agrekon. 2021;60(1):10–30.
Xu X. Contemporaneous causal orderings of US corn cash prices through directed acyclic graphs. Empirical Econ. 2017;52(2):731–58.
Acheampong P.P, and Owusu V. Impact of improved cassava varieties’ adoption on farmers’ incomes in Rural Ghana. Presented at the 29th International Conference of Agricultural Economists (ICAE), Universita Degli, 8th to 14th August 2015.
Dibaba R. and Goshu D. Impact of high yielding wheat varieties on farm income of smallholder farmers in Ethiopia. Review of Agricultural and Applied Economics Acta Oeconomica et Informatica ISSN 1336–9261, XXI (Number 2, 2018): 103–112 doi: https://doi.org/10.15414/raae.2018.21.02.103-112.
Asar Y, Kılınç K. A Jack-knifed ridge estimator in probit regression model. J Theor Appl Statist. 2020;54(4):667–85.
Caliendo, M. and Kopeinig, S. (2008). Some practical guidance for the implementation of propensity score matching, IZA Discussion Paper No. 1588. University of Cologne. http://ftp.iza.org/dp1588.pdf.
Kassie M, Shiferaw B, Muricho G. Agricultural technology, crop income, and poverty alleviation in Uganda. World Dev. 2011;39(10):1784–95.
Khandker SR, Koolwal GB, Samad H. Handbook on impact evaluation: quantitative methods and practices. Washington: World Bank Publications; 2010.
Linden A, Adams JL. Combining the regression discontinuity design and propensity score-based weighting to improve causal inference in program evaluation. J Eval Clin Pract. 2012;18:317–25.
Nabasirye M, Kiiza B, Omiat G. Evaluating the impact of adoption of improved maize varieties on yield in Uganda: a propensity score matching approach. J Agric Sci Technol. 2012;2(3):368.
Heckman J, Ichimura H, Todd P. Matching as an econometric evaluation estimator: evidence from evaluating a job training program. Rev Econ Stud. 1997;64:605–54.
Rosembaum PR, Rubin DB. Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. Am Stat. 1985;39(1):33–8.
Adjin, K. C., Goundan, A., Henning, C. H. and Sarr, S. Estimating the impact of agricultural cooperatives in Senegal: Propensity score matching and endogenous switching regression analysis (No. WP2020–10). Working Papers of Agricultural Policy, 2020, 1–30.
Ghimire R, Wen-chi H, Shrestha RB. Factors affecting adoption of improved rice varieties among rural farm households in Central Nepal. Rice Sci. 2015;22(1):35–43.
Rosembaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika. 1983;701:41–55.
Doss CR, Morris ML. How does gender affect the adoption of agricultural innovations? The case of improved maize technology in Ghana. Agric Econ. 2001;25:27–39.
Murage AW, Obare G, Chianu J, Amudavi DM, Midega CAO, Pickett JA, Khan ZR. The Effectiveness of dissemination pathways on adoption of “push-pull” technology in Western Kenya. Q J Int Agric. 2012;51(1):51–71.
Amudavi DM, Khan ZR, Wanyama JM, Midega CAO, Pittchar J, Hassanali A, Pickett JA. Evaluation of farmers’ field days as a dissemination tool for push–pull technology in Western Kenya. Crop Protect. 2009;28:225–35.
Murage AW, Amudavi DM, Obare G, Chianu J, Midega CAO, Pickett JA, Khan ZR. Determining smallholder farmers’ preferences for technology dissemination pathways: the case of “Push-Pull” technology in the control of stemborer and striga weeds in Kenya. Int J Pest Manage. 2011;57:133–45.
Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information technology: toward a unified view. MIS Q. 2003;27(3):425–78.
Ogutu SO, Okello JJ, Otieno DJ. Impact of Information and Communication Technology-based Market Information Services on Smallholder Farm Input Use and Productivity: The Case of Kenya. Selected paper prepared for presentation at the 4th International Conference of the African Association of Agricultural Economists (ICAAAE), Hammamet, Tunisia, 22–25 September, 2013.