Modélisation du comportement des agriculteurs face au risque dans un modèle de programmation mathématique positive (PMP) à grande échelle
| Autor: | Angel Perni, Iván Arribas, Kamel Louhichi, Sergio Gomez-y-Paloma, José Vila |
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| Přispěvatelé: | Universitat de València (UV), Economie Publique (ECO-PUB), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, JRC Institute for Prospective Technological Studies (IPTS), European Commission - Joint Research Centre [Seville] (JRC), Department of Economic Analysis, Universitat de València (UV)-Universitat de Valeencia |
| Rok vydání: | 2017 |
| Předmět: |
2. Zero hunger
Estimation Mathematical optimization business.industry 05 social sciences 0211 other engineering and technologies Climate change 021107 urban & regional planning Sample (statistics) 02 engineering and technology modèle d'offre agricole européen [SHS]Humanities and Social Sciences Task (project management) Scale (social sciences) 0502 economics and business Econometrics 050207 economics Agricultural productivity Psychology business Common Agricultural Policy Risk management |
| Zdroj: | Advances in Applied Economic Research ISBN: 9783319484532 Advances in Applied Economic Research, Proceedings of the 2016 International Conference on Applied Economics (ICOAE) Advances in Applied Economic Research, Proceedings of the 2016 International Conference on Applied Economics (ICOAE), Springer International Publishing, 888 p., 2017, Springer Proceedings in Business and Economics, 978-3-319-48453-2. ⟨10.1007/978-3-319-48454-9⟩ |
| Popis: | Agricultural production is characterized for being a risky business due to weather variability, market instability, plant diseases as well as climate change and political economy uncertainty. The modelling of risk at farm level is not new, however, the inclusion of risk in Positive Mathematical Programming (PMP) models is particularly challenging. Most of the few existing PMP-risk approaches have been conducted at farm-type level and for a very limited and specific sample of farms. This implies that the modelling of risk and uncertainty at individual farm level and in a large scale system is still a challenging task. The aim of this paper is to formulate, estimate and test a robust methodology for explicitly modelling risk to be incorporated in an EU-wide individual farm model for Common Agricultural Policy (CAP) analysis, named IFM-CAP. Results show that there is a clear trade-off between the behavioural model (BM) and the behavioural risk model (BRM). Albeit the results show that both alternatives provide very close estimates, the latter increases three times the computation time required for estimation. Despite this, we are convinced that the modelling of risk is crucial to better understand farmer behaviour and to accurately evaluate the impacts of risk management related policies (i.e. insurance schemes). |
| Databáze: | OpenAIRE |
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