Modelling Farmers' Behaviour Toward Risk in a Large Scale Positive Mathematical Programming (PMP) Model

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).