Identifying risk in production agriculture: an application of best-worst scaling

• Published in: The international food and agribusiness management review Vol. 23; no. 2; pp. 283 - 300
• Main Authors: Atta, Cosmos, Micheels, Eric T
• Format: Journal Article
• Language: English
• Published: The Netherlands Brill | Wageningen Academic 01-01-2020; Wageningen Academic Publishers
• Subjects: RESEARCH ARTICLE; risk management; risk; latent class cluster; best-worst scaling
• ISSN: 1559-2448; 1559-2448
• DOI: 10.22434/IFAMR2019.0133

The identification and management of risk plays a significant role in reducing variability in farm income. The choice of risk management tools and strategies may depend on several factors, including the perceived importance of the risk and the perceived level of control that producers have in managing the risk. This study uses data from a 2017 survey of grain and oilseed farmers in Saskatchewan and employs a count-based approach of best-worst scaling and latent class cluster analysis to examine their perception of the most important sources of risk and the factors that influence these perceptions. The results suggest production and marketing risks, such as variation in output prices, rainfall variability, and changes in input prices, are the most important risks to farmers. However, results also reveal heterogeneity in responses to these identified risks, suggesting that a multifaceted approach is needed by farmers to address risk.