Sustainable Agricultural Mechanization and Farmers’ Performance: A case study of smallholder farmers in Kenya

Abstract Agricultural ecosystems have evolved under human management. Human selection has favored readily harvestable crops with higher net production and penalized biomass production and accumulation on the landscape. Since the green revolution, mainstream agriculture has mainly involved soil fertility with the application of chemical fertilizers, controlling crop variety and their genetics, and management of pests with chemical pesticides (De Groote et al, 2018). However, the impact of agricultural intensification on the environment has been severe leading to climate change. Rural livelihoods more so women smallholder farmers are highly vulnerable to such variability. Questions then arise as to what extent agriculture and food systems can contribute to mitigate the effects of climate change while improving production and productivity. Climate smart agriculture is one way through which smallholder farmers can adapt to and mitigate from the adverse effects of climate change. It integrates agricultural development and sustainable environmental management practices to improve productivity, increase adaptability, and mitigate emissions from greenhouse gases (Lipper et al, 2014). The main aim is to increase food security and preserve the environment. Sustainable agricultural mechanization enables farmers to expand the range of their activities and diversify their livelihoods in ways that can reduce their vulnerability to climate change (Diao et al, 2014) and is the first stage in the broad spectrum of climate-smart agricultural practices. Significant efforts are necessary to develop, deploy, and scale up climate smart farming technologies and practices. In this study, we employ the treatment effect logic to investigate the effects of agricultural mechanization hiring on farmers’ productivity, profitability, and adoption of climate-smart agriculture using detailed household data from a sample of farm households in western and eastern Kenya. Propensity Score Matching is combined with difference in difference (PSM-DID) to control for potential endogeneity biases. Results shows that approximately 37% of sampled households hire agricultural machinery and the agricultural mechanization service provision increases maize productivity and profitability by 720kg/Ha and 90 USD/Ha (Approximately 11,915 Kenya Shillings per Hectare) respectively but decreases the adoption of conservation agriculture by 12.4%. These results suggest that agricultural interventions aimed at the diffusion of agricultural machinery through mechanization service provision in Kenya should sensitize farmers on the need to adapt tractors and farm implements to local conditions so to minimize soil disturbance, reduces degradation, soil erosion and carbon dioxide emissions, relative to tillage-based systems. Acknowledgment This study is part of my Doctoral Dissertation at Marche Polytechnic University in Ancona Italy under to supervision of Professor Roberto Esposti. References De Groote, H., C. Marangu, and Z. M. Gitonga. 2018. “Trends in Agricultural Mechanization in Kenya’s Maize Production Areas from 1992–2012.” Agricultural Mechanization in Asia, Africa and Latin America 49 (4): 20–32. Diao, X., F. Cossar, N. Houssou, and S. Kolavalli. 2014. “Mechanization in Ghana: Emerging Demand, and the Search for Alternative Supply Models.” Food Policy 48: 168–181 Lipper, L., Thornton, P., Campbell, B.M., Baedeker, T., Braimoh, A., Bwalya, M., Caron, P., Cattaneo, A., Garrity, D., Henry, K., Hottle, R., Jackson, L., Jarvis, A., Kossam, F., Mann, W., McCarthy, N., Meybeck, A., Neufeldt, H., Remington, T., Sen, P.T., Sessa, R., Shula, R., Tibu, A., Torquebiau, E.F., 2014. Climate-smart agriculture for food security. Nat. Clim. Chang. 4, 1068–1072. https://doi.org/ 10.1038/nclimate2437