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This paper proposes an integrated approach towards rapid decision-making in the agricultural sector aimed at improvement of its resilience. Methodologically, we seek to devise a framework that is able to take the uncertainty regarding policy preferences into account. Empirically, we focus on the effects of COVID-19 on agriculture. First, we propose a multi-criteria decision-making framework following the Pugh matrix approach for group decision-making. The Monte Carlo simulation is used to check the effects of the perturbations in the criteria weights. Then, we identify the factors behind agricultural resilience and organize them into the three groups (food security, agricultural viability, decent jobs). The expert survey is carried out to elicit the ratings in regard to the expected effects of the policy measures with respect to dimensions of agricultural resilience. The case of Lithuania is considered in the empirical analysis. The existing and newly proposed agricultural policy measures are taken into account. The measures related to alleviation of the financial burden (e.g., credit payment deferral) appear to be the most effective in accordance with the expert ratings.

sustainability

Policies for Rapid Mitigation of the Crisis’ Effects on Agricultural Supply Chains: A Multi-Criteria Decision Support System with Monte Carlo Simulation