The role of climate smart agricultural practices on household income and food security: A propensity score matching approach

References

• Ali, A., & Abdulai, A. (2010). The adoption of genetically modified cotton and poverty reduction in Pakistan. Journal of Agricultural Economics, 61(1), 175–19. https://doi.org/10.1111/j.1477-9552.2009.00227.x
   Web of Science ®Google Scholar
• Berry, W. D. (1993). Understanding regression assumptions, SAGE University Paper series on Quantitative Applications in the Social Sciences. Newbury Park.
   Google Scholar
• Bonilla-Findji, O., & Eitzinger, A. (2019). Training workshop report: Implementation of the CSA monitoring to assess adoption of climate smart agricultural options and related outcomes in Doyogena climate-smart landscape in Ethiopia.
   Google Scholar
• CCAFS. (2016). Climate-smart villages, an AR4D approach to scale up climate-smart agriculture. CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS).
   Google Scholar
• Diprete, T. A., & Gangl, M. (2004). 7. Assessing bias in the estimation of causal effects: Rosenbaum bounds on matching estimators and instrumental variables estimation with imperfect instruments. Sociological Methodology, 34(1), 271–310. https://doi.org/10.1111/j.0081-1750.2004.00154.x
   Web of Science ®Google Scholar
• Engel, S., & Muller, A. (2016). Payments for environmental services to promote “climate-smart agriculture”? Potential and challenges. Journal of Agricultural Economics, 47(S1), 173–184. https://doi.org/10.1111/agec.12307
   Google Scholar
• FAO. (2013a). Climate smart agriculture: Sourcebook. Food and Agriculture Organization of the United Nations.
   Google Scholar
• FAO. (2013b). Guidelines for measuring household and individual dietary diversity. Food and Agriculture Organization of the United Nations.
   Google Scholar
• FAO. (2015). Climate changes and food security: Risks and responses. Food and Agriculture Organization of the United Nations.
   Google Scholar
• FAO. (2017). Climate smart agriculture sourcebook. Summary (2nd ed.). Food and Agriculture Organization of the United Nations.
   Google Scholar
• Fentie, A., & Beyene, A. D. (2018). Climate smart agricultural practices and welfare of rural smallholders in Ethiopia: Does planning method matter? Environment for Development Discussion Paper Series, EFDDP. 18–08.
   Google Scholar
• Gertler, P. J., Martinez, S., Premand, P., Rawlings, L. B., & Vermeersch, C. M. J. (2011). Impact evaluation in practice (1st ed.). World Bank.
   Google Scholar
• Greenland, S., Pearl, J., & Robins, J. M. (1999). Causal diagrams for epidemiologic research. Epidemiology, 10(1), 37–48. https://doi.org/10.1097/00001648-199901000-00008
   PubMed Web of Science ®Google Scholar
• Hasan, M. K., Desiere, S., D’Haese, M., & Kumar, L. (2018). Impact of climate-smart agriculture adoption on the food security of coastal farmers in Bangladesh. Food Security, 10(4), 1073–1088. https://doi.org/10.1007/s12571-018-0824-1
   Web of Science ®Google Scholar
• Heckman, J., LaLonde, J. R., & Smith, J. (1999). The economics and econometrics of active labor market programs. In O. Ashenfelter & D. Card (Eds.), Handbook of labor economics (Vol. 3, pp. 1865–2097). Elsevier Science B.V.
   Google Scholar
• Heckman, J., & Navarro, S. (2004). Using matching, instrumental variables, and control functions to estimate economic choice models. The Review of Economics and Statistics, 86(1), 30–57. https://doi.org/10.1162/003465304323023660
   Web of Science ®Google Scholar
• Heinrich, C., Mafioli, A., & Vasquez, G. (2010). A primer for applying propensity score matching, impact evaluation guidelines, office strategic planning and development effectiveness. Inter-American Development Bank.
   Google Scholar
• Khandker, S. R., Koolwal, G. B., & Samad, H. A. (2010). Handbook on impact evaluation: Quantitative methods and practices. World Bank.
   Google Scholar
• Kifle, T. (2020). Climate-smart agricultural practices and its implications to food security in Siyadebrina Wayu district, Ethiopia. African Journal of Agricultural Research, 17(1), 92–103. https://doi.org/10.5897/AJAR2020.15100
   Google Scholar
• Lewis, K. (2017). Understanding climate as a driver of food insecurity in Ethiopia. Climate Change, 144(2), 317–328. https://doi.org/10.1007/s10584-017-2036-7
   Web of Science ®Google Scholar
• Mugabe, P. A. (2020). Assessment of information on successful climate-smart agricultural practices/innovations in Tanzania. In W. L. Filho (Ed.), Handbook of climate change resilience (pp. 1–21). Springer International Publisher.
   Google Scholar
• Mujeyi, A., Mudhara, M., & Mutenje, M. (2021). The impact of climate smart agriculture on household welfare in smallholder integrated crop–livestock farming systems: Evidence from Zimbabwe. Agriculture & Food Security, 10(1), 1–15. https://doi.org/10.1186/s40066-020-00277-3
   Google Scholar
• Ogada, M. J., Rao, E. J. O., Radeny, M., Recha, J. W., & Solomon, D. (2020). Climate-smart agriculture, household income and asset accumulation among smallholder farmers in the Nyando basin of Kenya. World Development Perspectives, 18, 100203. https://doi.org/10.1016/j.wdp.2020.100203
   Google Scholar
• Pal, B. D., & Kapoor, S. (2020). Intensification of climate-smart agriculture technology in semi-arid regions of India: Determinants and impact. CCAFS Working Paper no. 321, 2020.
   Google Scholar
• Radimer, K. L., Olson, C. M., & Campbell, C. C. (1990). Development of indicators to assess hunger. The Journal of Nutrition, 120, 1544–1548. https://doi.org/10.1093/jn/120.suppl_11.1544
   PubMed Web of Science ®Google Scholar
• Radimer, K. L., Olson, C. M., Greene, J. C., Campbell, C. C., & Habicht, J. P. (1992). Understanding hunger and developing indicators to assess it in women and children. Journal of Nutrition Education, 24(1), 36S–44S. https://doi.org/10.1016/S0022-3182(12)80137-3
   Google Scholar
• Ravallion, M. (2005). Evaluating anti-poverty programs. World Bank, Policy Research Working Paper Series 3625.
   Google Scholar
• Recha, J., Kimeli, P., Atakos, V., Radeny, M., & Mungai, C. (2017). Stories of success: Climate smart villages in East Africa.
   Google Scholar
• Rosenbaum, P. R. (1999). Choice as an alternative to control in observational studies. Statistical Science, 14(3), 258–304. https://doi.org/10.1214/ss/1009212410
   Web of Science ®Google Scholar
• Rosenbaum, P. R. (2002). Observational studies. Springer.
   Google Scholar
• Rosenbaum, P. R. (2010). Design of observational studies. Springer.
   Google Scholar
• Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55. https://doi.org/10.1093/biomet/70.1.41
   Web of Science ®Google Scholar
• Rubin, D. B. (2001). Using propensity scores to help design observational studies: Application to the tobacco litigation. Health Service Outcomes Research Methodology, 2(3/4), 169–188. https://doi.org/10.1023/A:1020363010465
   Google Scholar
• Rubin, D. B. (2007). The design versus the analysis of observational studies for causal effects: Parallels with the design of randomized trials. Statistics in Medicine, 26(1), 20–36. https://doi.org/10.1002/sim.2739
   PubMed Web of Science ®Google Scholar
• Siziba, S., Nyikahadzoi, K., Makate, C., & Mango, N. (2019). Impact of conservation agriculture on maize yield and food security: Evidence from smallholder farmers in Zimbabwe. African Journal of Agricultural and Resource Economics, 14, 89–105.
   Google Scholar
• Smith, M. D., Kassa, W., & Winters, P. (2017). Assessing food insecurity in Latin America and the Caribbean using FAO’s food insecurity experience scale. Food Policy, 71, 48–61. https://doi.org/10.1016/j.foodpol.2017.07.005
   Web of Science ®Google Scholar
• Swindale, A., & Bilinsky, P. (2006). Household dietary diversity score (HDDS) for measurement of household food access: Indicator guide (v.2). FHI 360/FANTA.( United Nations, Rome).
   Google Scholar
• Tamene, L., Adimassu, Z., Ellison, J., Yaekob, T., Woldearegay, K., Mekonnen, K., Thorne, P., & Le, Q. B. (2017). Mapping soil erosion hotspots and assessing the potential impacts of land management practices in the highlands of Ethiopia. Geomorphology, 292, 153–163. https://doi.org/10.1016/j.geomorph.2017.04.038
   Web of Science ®Google Scholar
• Tigist, B. (2016). Assessment of surface water resource and irrigation practices in gudo beret kebele, Amhara region. Thesis Presented to Addis Ababa University.
   Google Scholar
• Ville, A. S., Tsun Po, J. Y., Sen, A., Bui, A., & Melgar-Quiñonez, H. (2019). Food security and the food insecurity experience scale (FIES): Ensuring progress by 2030. Food Security, 11(3), 483–491. https://doi.org/10.1007/s12571-019-00936-9
   Web of Science ®Google Scholar
• Wekesa, B. M., Ayuya, O. I., & Lagat, J. K. (2018). Effect of climate‑smart agricultural practices on household food security in smallholder production systems: Micro‑level evidence from Kenya. Agriculture & Food Security, 7(1), 1–14. https://doi.org/10.1186/s40066-018-0230-0
   Google Scholar
• WFO. (2020). Climate smart agriculture supports resilience of Latin American farmers. Retrieved April 11, 2021. https://www.wfo-oma.org/frmletter-3_2020/climate-smart-agriculture-supports-resilience-of-Latin-American-farmers/
   Google Scholar
• Wordofa, M. G., Hassen, J. Y., Edris, G. S., Aweke, C. S., & Moges, D. K. (2021). Adoption on improved agricultural technology and its impact on household income: A propensity score matching estimation in eastern Ethiopia. Agriculture & Food Security, 10(1), 5. https://doi.org/10.1186/s40066-020-00278-2
   Google Scholar
• World Bank. (2019). Climate smart agriculture investment plan: Mali. The World Bank Group.
   Google Scholar
• World Bank. (2020). Climate risk profile: Ethiopia 2020. The World Bank Group.
   Google Scholar