Economic modelling of climate change scenarios and adaptation of Mediterranean agriculture

This paper discusses some of the most relevant economic findings of two research projects, Agroscenari ( and MACSUR (, which analyze the impact of climate change (CC) on production levels and profitability of Italian and European agriculture for defining effective adaptation actions. Both projects provide research lines on climatology, agronomy, animal breeding and economics for building integrated models that simulate farmers’ decision making in the context of CC. The integration sought in these projects aims at determining how climate variability (CV) normally influences production and management of farms, then, at assessing the impact of CC based on the change of this variability (CCV). The influence of CV on crop production and livestock farming is considered in this study. The relationships among CV, agricultural and livestock production are expressed by means of Probability Distribution Functions (PDFs) estimated for the main agricultural variables in both climate scenarios, present (Ps) and future (Fs). These PDFs, appropriately discretized, are then used to represent the expectations on productive variability in an economic Discrete Stochastic Programming model that simulates farm management decision making process under Ps and Fs. Comparing the model results in the two scenarios indicates the effects of CCV, given the possibility to adapt the use of resources and the cultivation techniques. These possibilities of adjustment are modelled based on the current technologies, production structures and markets. So, even if Fs is not far away in time, they may appreciably increase during the transition period. For this reason, the comparison of the economic results achievable in Ps and Fs is not intended to provide an estimate of the final economic impact, but to indicate the farm types and cropping systems that will suffer the greatest stresses from CC.

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Conference Paper/ Presentation
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JEL Codes:
C61; Q12; Q54

 Record created 2017-04-01, last modified 2018-01-23

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