Exploring producer perceptions for cattle price and animal performance in the stocker industry

Abstract

Stocker cattle economic research is very limited in scope. A focus of this research is to deepen our understanding of how cattle price and animal performance variability is viewed and approached by stocker cattle producers in the United States. Another part of this research focuses on what characteristics may be drivers of whether producers choose to practice different risk management strategies. To analyze how cattle price and animal performance variability is viewed and approached by stocker cattle producers, a stated preference valuation method was used to find willingness-to-pay (WTP) estimates. Two different approaches were used to provide outcome probability information where one approach had probabilities for expected ADG change across scenarios and ADG ranges were held constant (Treatment Group A) and the second approach had ADG ranges change across scenarios and the probabilities were held constant (Treatment B). The results of our study suggest that survey respondents process scenarios differently when presented in formats Treatment Group A versus Treatment Group B. The underlying reason for this is beyond identification in this study as respondent certainty and comfort as assessed in follow-up questions was similar across the treatments. Results indicate that producers value buying cattle versus opting out of purchasing cattle and they value higher performing cattle; however, each additional pound is not valued the same. To determine the characteristics of producers and their operations that use different risk management practices, we estimated multiple probit models with the dependent variables being use of the different risk management practices. Results from the probit models suggest how producers source cattle for their operation, whether it is the region or the different markets they source from, are key determinants on whether producers practice different management strategies for market and price risk. The results suggest the model were not a good fit. Of the 30 explanatory variables included in the model, on average five explanatory variables were significant throughout the seven different dependent variables. This could be attributed to factors our study does not explicitly observe; therefore it remains a knowledge gap for the industry.