How Does the Farmer Strike a Balance between Income and Risk across Inputs? An Application in Italian Field Crop Farms

Abstract

The European Union’s Farm-to-Fork strategy, aiming at increasing the environmental sustainability of farming, is oriented to limit farm inputs that could harm the environment. This restrictive policy affects farmers’ productive choices and economic well-being. However, limited attention has been paid to how this could affect the economic risk farmers face. To do so, we examine how risk is affected by fertilisers, crop protection, irrigation water, and labour choices. This study relied on Antle’s method of moments applied to the irrigated field crop farms of the Italian Farm Accountancy Data Network from 2008 to 2019. This paper fills the literature gap jointly using three aspects usually adopted separately. First, consider the three moments and the semi-variance to investigate the risks of farmers’ strategies. Second, it accounts also for government payments to consider the relationship between these and risk. Finally, it adopts an estimation strategy that relies on the Generalised Method of Moments (GMM) for the first step and the Fixed Effects-Generalized Least Squared (FE-GLS) estimator for the second, considering time and individual fixed effects and considering interaction terms effects. According to our research, constraining fertilisers, crop protection, and irrigation water increases income variability, causing farming to be potentially riskier. However, restricting fertilisers and crop protection use decreases the downside risk. These results indicate that policy measures constraining input use, such as those foreseen in the EU, strongly influence the extent and type of risks farmers face. Therefore, policymakers should consider this evidence when designing environmental policies.

Towards a holistic approach to sustainable risk management in agriculture in the EU: a literature review

“…Indeed, it is likely that farmer decisions are more affected by variations of crop yield and farm revenue below the average than by variations above the average. Specific econometrics approaches exist to address higher moments of the distribution such as the moment-based approach (Antle, 1983b) recently applied to Italian farm data by Biagini et al (2022b) and its updated version that uses partial moments (Antle, 2010).…”