Important sources of risk in agriculture are yield and price fluctuations caused by unpredictable and uncontrollable events, inducing income volatility and adding considerable complexity to farmers' decisions. The literature suggests that these events could affect farmers' risk aversion in decision making and justify their preferences for risk minimizing and safety-first survival, rather than a profit maximization strategy. The aim of this study is to test this hypothesis by using a quadratic programming in linearized version and the sumex utility function, which is representable as sum of products of polynomials and exponential (or "polynex") functions to simulate risk aversion for specific traits of the E-V frontier (Nakamura, Mathematical Soc Sci 31:39-47, 1996). The linear approximation of the utility function is obtained with the MOTAD approach, consisting in the minimization of errors generated by total absolute deviations of gross income from the expected value (Hardaker et al. Rev Mark Agric Econ 59:9-22, 1991). This method allows different portfolio simulations to be run of selected cereal and oilseed crops as risky prospects, by varying the risk parametrically. The results obtained confirm the hypothesis that risk affects farmers' decisions and that crop diversification is a viable strategy as a hedge against risk.
The increasing demand for energy and expected shortage in the medium term, solicit innovative energy strategies to fulfill the increasing gap between demand-supply. For this purpose it is important to evaluate the potential supply of the energy crops and finding the areas of EU where it is most convenient. This paper proposes an agro-energy supply chain approach to planning the biofuel supply chain at a regional level. The proposed methodology is the result of an interdisciplinary team work and is aimed to evaluate the potential supply of land for the energy production and the efficiency of the processing plants considering simultaneously economic, energy and environmental targets. The crop simulation, on the basis of this approach, takes into account environmental and agricultural variables (soil, climate, crop, agronomic technique) that affect yields, energy and economic costs of the agricultural phase. The use of the Dijkstra's algorithm allows minimizing the biomass transport path from farm to collecting points and the processing plant, to reduce both the transport cost and the energy consumption. Finally, a global sustainability index (ACSI, Agro-energy Chain Sustainability Index) is computed combining economic, energy and environmental aspects to evaluate the sustainability of the Agroenergy supply chain (AESC) on the territory. The empirical part consists in a pilot study applied to the whole plain of Friuli Venezia Giulia (FVG) a region situated in the North-Eastern part of Italy covering about 161,300 ha. The simulation has been applied to the maize cultivation using three different technologies (different levels of irrigation and nitrogen fertilization: low, medium and high input). The higher input technologies allow to achieve higher crop yields, but affect negatively both the economic and energy balances. Low input levels provides, on the average, the most favourable energy and economic balances. ACSI indicates that low inputs levels ensure a more widespread sustainability of the agro-energy chain in the region. High ACSI values for high input levels are observed only for areas with very high yields or near the processing plant.
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