Early adoption,experience, and farm performance of GM corn seeds

The current article explores the characteristics that distinguish early from late adopters of GM corn and measures the productivity impacts of early adoption, for a sample of farmers in Minnesota and Wisconsin. The results of the adoption analysis confirm that size, education as well as specialization are positively correlated with early adoption. In addition, these results also show that farms that are mostly worked by family labor but hire some off‐farm labor are more likely to adopt GM seeds earlier in the diffusion process. The productivity analysis demonstrates the superiority of stacked varieties. At the same time, we find no evidence of a direct impact of experience on yields. Given the previously documented impact of early adoption on the use of stacked varieties, we conclude that experience plays a role through the adoption of these new technologies but does not play a role in allowing the producer to use the technology more efficiently, once it has been adopted.     

Integrating Technology Traits and Producer Heterogeneity: A Mixed-Multinomial Model of Genetically Modified Corn Adoption

This article proposes a model of technology adoption that integrates demand for individual traits of new technologies with the potential for heterogeneity based on farm and farmer characteristics. The model is applied to recent genetically modified corn adoption data from Minnesota and Wisconsin farmers, using a mixed-multinomial logit (MMNL) model to estimate the effects of traits and farm and farmer characteristics on adoption outcomes. This approach allows explicit recovery of estimates of farmers' shadow prices for individual technology traits. Results show the importance of producer and regional heterogeneity in preferences for seed traits.