showing 2 related works from this author
Modelling agricultural risk in a large scale positive mathematical programming model
2020
International audience; Mathematical programming has been extensively used to account for risk in farmers' decision making. The recent development of the positive mathematical programming (PMP) has renewed the need to incorporate risk in a more robust and flexible way. Most of the existing PMP-risk models have been tested at farm-type level and for a very limited sample of farms. This paper presents and tests a novel methodology for modelling risk at individual farm level in a large scale model, called individual farm model for common agricultural policy analysis (IFM-CAP). Results show a clear trade-off between including and excluding the risk specification. Albeit both alternatives provid…
Modélisation du comportement des agriculteurs face au risque dans un modèle de programmation mathématique positive (PMP) à grande échelle
2017
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 methodol…