Factors Affecting Farmers' Utilization of Agricultural Risk Management Tools: The Case of Crop Insurance, Forward Contracting, and Spreading Sales

By Margarita Velandia, R. Rejesus, T. Knight, B. Sherrick. Journal of Agricultural and Applied Economics, 2009

250 citations recorded by the source corpus.

Factors affecting the adoption of crop insurance, forward contracting, and spreading sales are analyzed using multivariate and multinomial probit approaches that account for simultaneous adoption and/or correlation among the three risk management adoption decisions. Our empirical results suggest that the decision to adopt crop insurance, forward contracting, and/or spreading sales are correlated. Richer insights can be drawn from our multivariate and multinomial probit analysis than from separate, single-equation probit estimation that assumes independence of adoption decisions. Some factors significantly affecting the adoption of the risk management tools analyzed are proportion of owned acres, off-farm income, education, age, and level of business risks.

Subfields: Economics and Econometrics, General Agricultural and Biological Sciences, Soil Science

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In Economics and Econometrics

In General Agricultural and Biological Sciences

In Soil Science

Citations in the corpus

Risk perceptions, preferences and management strategies: evidence from a case study using German livestock farmers (2017, background): “Thus, we can confirm findings by Velandia et al. (2009) and Mishra and El-Osta (2002) who claim that a higher proportion of rented land is associated with higher risk exposure, lower wealth, means, and incentives to build-up long-term capacities for risk bearing on the farm, resulting in a greater…”
Response to climate risks among smallholder farmers in Malawi: A multivariate probit assessment of the role of information, household demographics, and farm characteristics (2015, background): “In line with Velandia et al. (2009) but contrary to (Fernandez-Cornejo & Mishra, 2007),we find that households that have access to off-farm income are less likely to adapt through DT, EP and CD. Depending on the proportion of total household income emanating from non-farm income, and the prevailing…”