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Decision-making processes for crop management on African farms. Modelling from a case study of cotton crops in northern Cameroon
Institution:1. INA, Département AGER, 16 rue Claude Bernard, 75005 Paris, France;2. INRA-SAD, 78850 Thiverval-Grignon, France;1. Council for Agricultural Research and Economics, CREA-AA, Via di Corticella 133, 40128 Bologna, Italy;2. University of Florence, Department of Agri-food Production and Environmental Sciences (DISPAA), Piazzale delle Cascine, 18, 50144 Firenze, Italy;3. Fondazione per il Clima e la Sostenibilità, Via G. Caproni 8, 50145 Firenze, Italy;4. University of Udine, Department of Agricultural and Environmental Sciences (DISA), Via delle Scienze 206, 33100 Udine, Italy;1. Centre for the Study of Governance Innovation, Department of Political Sciences, University of Pretoria, Pretoria, South Africa;2. Tyndall Centre for Climate Change Research, School of Environmental Sciences, University of East Anglia, Norwich, UK;1. Jiangsu Key Laboratory of Wireless Communications, Nanjing University of Posts and Telecommunications, Nanjing 210003, China;2. Key Laboratory on Wideband Wireless Communications and Sensor Network Technology, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Abstract:Recent work on decision processes on French farms and irrigated systems in Africa has shown that farmers plan their cyclical (recurrent) technical operations, and that one can model this planning process. Taking the case of cotton crop management in North Cameroon, we show that with certain adjustments, modelling of this kind can also be done for rainfed crop farming in Africa. The adjustments are needed to take account of the differences in social status between different fields on one farm and the implications of the fact that farm work is primarily manual. This produces decision models with a similar structure to that described for technical management of an annual crop break in a temperate climate using mechanised implements. Not only do these models give us a detailed understanding of the variability of farming practices, we can also classify them into categories according to weather scenarios yield level as a function of weather scenario. We show that one can attribute farms to these types of model using simple indicators concerning work organisation. By analysing North Cameroon farmers' decision processes for managing cotton crops we can thus produce an effective tool for organising technical supervision of farmers at the regional level: advisers can work with these decision model types by measuring some simple indicators at farm level to predict which types of model are applicable, without the onerous work of constructing individual decision models.
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