Economic risk analysis of agricultural tillage systems using the SMART stochastic efficiency software package

At the national level, one of the major challenges to United States agriculture during the coming decades will be to produce sufficient food and fiber for a growing world population while maintaining environmentally acceptable farming practices. At the farm level, farmers face various decision-making challenges to reach these national goals. Farmers invest heavily in inputs (e.g., management and labor, equipment purchase and maintenance, fuel, seed, fertilizers, pesticides, etc.) every single farming season, but face uncertain natural and market conditions at harvest. One of the major decision-making processes farmers face is tillage system selection, either across the whole farm or for a specific crop. This decision has significant implications for the farm enterprise, both economically and environmentally. Reduced tillage or no-tillage (hereafter referred to as no-till) are considered to be conservation tillage practices that assist in maintaining acceptable environmental goals at potentially lower economic costs; however, the decision to invest in conservation tillage systems also involves risk. Despite incontrovertible benefits, farmers in the United States are still reluctant to adopt reduced tillage or no-till systems due to a lack of information about the consequences involved, including a lack of understanding concerning potential economic (e.g., purchase of new equipment) and environmental (e.g., increased herbicide use under no-till) impacts. More specifically, farmers lack knowledge about risks related to tradeoffs between the upfront (or short-term) costs of implementing conservation management practices compared to long-term economic benefits that might be expected in the future. Recently, a variant of stochastic dominance called stochastic efficiency with respect to a function (SERF) has been developed and applied. Unlike traditional stochastic dominance approaches, SERF uses the concept of certainty equivalents (CEs) to rank a set of risk-efficient alternatives instead of finding a subset of dominated alternatives. The Screening and Multivariate Analysis for Risk and Tradeoffs (SMART) software package (both web-based and MS Excel spreadsheet applications) has been developed for integrated economic and environmental risk analysis through ranking of risky alternatives using the CE and SERF concepts. The SMART software also functions as a risk visualization tool for graphically displaying the CEs at various levels of decision maker attitude towards risk (e.g., risk neutral, moderately risk averse, or extremely risk averse). This paper provides a brief overview of the SMART risk analysis framework, and then describes use of the web-based tool to evaluate the efficacy of the SERF methodology for analyzing conventional and conservation tillage systems using 14 years (1990-2003) of economic budget data (collected from 36 experimental plots at the Iowa State University Northeast Research Station near Nashua, Iowa, USA). Specifically, the SERF approach implemented in SMART is used to examine which of three different tillage systems (chisel plow, no-till, and ridge-till) on continuous corn and corn-soybean rotation cropping systems are the most risk-efficient in terms of maximizing economic profitability (net return) across a range of risk aversion preferences. In addition to the SERF analysis, an economic analysis of the tillage system alternatives is also performed using decision criteria and simple statistical measures. Finally, we demonstrate the use of a complementary method, the probability of target value or Stop Light approach, for analyzing and visually displaying the probabilistic information contained in the tillage system cumulative density functions (CDFs). Decision criteria analysis of the economic measures alone provided somewhat contradictive and non-conclusive rankings, e.g., examination of the decision criteria results for corn net return showed that different tillage system alternatives were the highest ranked depending on the decision criterion. SERF analysis results for corn net return showed that the no-till tillage system was preferred across the entire range of risk aversion (risk neutral to strongly risk averse). For the Stop Light analysis, the no-till tillage system was also preferred, regardless of whether the objective of the decision maker is minimizing risk or maximizing net return.