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Planting strategies of maize farmers in Kenya: a simultaneous equations analysis in the presence of discrete dependent variables

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  • Rashid M. Hassan

Abstract

A fairly comprehensive range of planting choices made by maize farmers in Kenya (including discrete endogenous variables creating self‐selectivity) is modelled and estimated as one system of interrelated decisions. Two‐stage and three‐stage probit procedures are used to handle the simultaneity and self‐selectivity problems. Results showed that population pressure and agroclimatic diversity are important determinants of crop intensification and planting regimes among maize farmers and further supported the importance of focusing maize research in terms of agroclimate and socio‐economic domains. Shorter maturity and efficient double and multiple cropping methods are needed to increase land productivity and intensity of labour use in areas of high population pressure and bimodal rainfall, i.e. mid‐altitude zones. On the other hand, technologies that would lead to increased productivity of capital and higher response to external inputs are desired for the highlands of Kenya. Access to extension and machine services, distance to the maize plot, and time of onset of the rains were also found to significantly influence the planting strategies of maize farmers.

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  • Rashid M. Hassan, 1996. "Planting strategies of maize farmers in Kenya: a simultaneous equations analysis in the presence of discrete dependent variables," Agricultural Economics, International Association of Agricultural Economists, vol. 15(2), pages 137-149, November.
    • Handle: RePEc:bla:agecon:v:15:y:1996:i:2:p:137-149
    DOI: 10.1111/j.1574-0862.1996.tb00427.x
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    Cited by:

    1. Birungi, Patrick & Hassan, Rashid M., 2010. "Poverty, property rights and land management in Uganda," Journal of Cooperatives, NCERA-210, vol. 4(1), March.
    2. Kimhi, Ayal, 2003. "Plot Size And Maize Productivity In Zambia: The Inverse Relationship Re-Examined," Discussion Papers 14980, Hebrew University of Jerusalem, Department of Agricultural Economics and Management.
    3. Srivastava, Amit Kumar & Mboh, Cho Miltin & Gaiser, Thomas & Webber, Heidi & Ewert, Frank, 2016. "Effect of sowing date distributions on simulation of maize yields at regional scale – A case study in Central Ghana, West Africa," Agricultural Systems, Elsevier, vol. 147(C), pages 10-23.
    4. Obare, G. A. & Omamo, S. W. & Williams, J. C., 2003. "Smallholder production structure and rural roads in Africa: the case of Nakuru District, Kenya," Agricultural Economics, Blackwell, vol. 28(3), pages 245-254, May.
    5. Daniel Lederman, 2005. "Income Wealth, and Socialization in Argentina," Latin American Journal of Economics-formerly Cuadernos de Economía, Instituto de Economía. Pontificia Universidad Católica de Chile., vol. 42(125), pages 3-30.
    6. Moranga, Lawrence Ongwae & Otieno, David Jakinda & Oluoch-Kosura, Willis, 2016. "Analysis Of Factors Influencing Tomato Farmers’ Willingness To Adopt Innovative Timing Approaches For Management Of Climate Change Effects In Taita Taveta County, Kenya," Dissertations and Theses 269270, University of Nairobi, Department of Agricultural Economics.
    7. Lederman, Daniel, 2002. "Income, wealth, and socialization in Argentina : provocative responses from individuals," Policy Research Working Paper Series 2821, The World Bank.
    8. Birungi, Patrick & Hassan, Rashid M., 2010. "Poverty, property rights and land management in Uganda," African Journal of Agricultural and Resource Economics, African Association of Agricultural Economists, vol. 4(1), pages 1-22, March.

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