Using count data models to determine the factors affecting farmers’ quantity decisions of precision farming technology adoption

The following study investigates the adoption of various precision farming technologies in terms of both the probability and the use intensity of technology components implemented. Zero-inflated Poisson and Negative Binomial count data model regressions were used to determine factors influencing farmers’ decision to adopt greater number of precision technologies. Results from the count data analysis of a random sample of Ohio farm operators demonstrate that several factors were significantly associated with the adoption intensity and probability of precision farming technologies, including farm size, farmer demographics, soil quality, urban influences, farmer status of indebtedness, and location of the farm within the state.