Economic viability of rangeland based ranching enterprises

Abstract

Ranch management's ability to cope with climate variability, especially drought, critically impacts the economic viability of rangeland based ranching enterprise alternatives. In rangeland ecosystems, drought is not uncommon and has become expected, but ranchers' management practices tend to be reactive to weather conditions rather than proactive. With increased availability and technological advancements of seasonal forecasts, this study investigates the potential for ranchers to increase the profitability of their enterprises by becoming more proactive in their management practices. An annual economic model is used to analyze the effects of using seasonal climate forecasts in cattle ranching enterprises in Sutton County, Texas. Unique to this study, is the use of stocking rate decision rules elicited from a focus group of ranchers, rather than decision rules derived from a modeling exercise. Decision rules from a previous focus group are used as the prior information scenario. A reconvened focus group was presented forecasts of forage deviations from a long-term average. Their input provided decision rules for the "with forecast" information scenario. Using an economic model and PHYGROW, a forage simulation model, the "with" and "without" forecast information scenarios are compared to evaluate the use of climate forecasts on net returns of a ranching enterprise. Results were then presented to the panel for their response. The focus group responded positively to participating in the study and to the study results. Results suggest in a market in which stocker cows are bought or sold at the same price, overall expected net returns from using seasonal climate forecasts are negative. A decrease in net returns does not necessarily imply the value of climate forecasts are negative. The single year model fails to capture improved long-term ecological conditions associated with the use of climate forecasts. If cattle prices differ for buying and selling cows (by 7-43% lower selling price depending on the scenario), the seasonal climate forecasts show a positive value. Generally, variability in expected net returns increases with the use of seasonal climate forecasts.