Economic Analysis of Summer Fallow Management to Reduce Take-All Disease and N Leaching in a Wheat Crop Rotation |
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Authors: | Stéphane De Cara Florence Jacquet Arnaud Reynaud Gaël Goulevant Marie-Hélène Jeuffroy Françoise Montfort Philippe Lucas |
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Affiliation: | 1.INRA,UMR 210 Economie Publique INRA-AgroParisTech,Thiverval-Grignon,France;2.TSE (LERNA-INRA),Université Toulouse 1,Toulouse,France;3.INRA,UMR BIO3P,Le Rheu Cedex,France;4.INRA,UMR 211 Agronomie INRA-AgroParisTech,Thiverval-Grignon,France |
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Abstract: | This paper addresses the question of summer cover-crop adoption by farmers in presence of a risk of yield loss due to take-all disease and climate variability. To analyze the public incentives needed to encourage farmers to adopt summer cover crops as a means of reducing N leaching, we combine outputs from an economic, an epidemiological and an agronomic model. The economic model is a simple model of choice under risk. The farmer is assumed to choose among a range of summer fallow managements and input uses on the basis of the expected utility criterion (HARA assumption) in presence of both climate and take-all risks. The epidemiological model proposed by Ennaïfar et al. (Eur J Plant Pathol 118:127–143, 2007) is used to determine the impact of take all on yields and N uptake. The crop-soil model (STICS) is used to compute yield developments and N leaching under various management options and climatic conditions. The input parameters are chosen to match the conditions prevailing in Grignon, located in the main wheat-growing area in France. Eight management systems are examined: four summer fallow managements: ‘wheat volunteers’ (WV), ‘bare soil’ (BS), ‘early mustard’, ‘late mustard’, and two input intensities. We show that the optimal systems are BS (WV) when the take-all risk is (not) taken into account by agents. We then compute the minimum payment to each system such that it emerges in the optimum. We thus derive the required amounts of transfer needed to trigger catch-crop adoption. The results of the Monte Carlo sensitivity analysis show that the ranking of management systems is robust over a wide range of input parameters. |
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