Many studies on the adoption of precision technologies have generally used logit models to explain the adoption behavior of individuals. This study investigates factors affecting the intensity of precision agriculture technologies adopted by cotton farmers. Particular attention is given to the...

We estimate a bivariate probit model with sample selection to identify factors affecting adoption and abandonment of precision farming technologies for cotton farmers, using the 2009 Southern Cotton Precision Farming Survey conducted in 12 Southern states in the United States. Farmers for whom...

We used survey data collected from cotton producers in eleven U.S. states to address the issues of correlated events and individual heterogeneity in multiple precision technologies adoption. Results from a conditional frailty model indicated that younger, better educated cotton producer adopted...

We used the 2009 Southern Cotton Precision Farming Survey data collected from farmers in twelve U.S. states (Alabama, Arkansas, Florida, Georgia, Louisiana, Missouri, Mississippi, North Carolina, South Carolina, Tennessee, Texas, and Virginia) to understand why farmers do not adopt seemingly...

We analyzed data obtained from the 2009 Southern Cotton Precision Farming Survey of farmers in twelve states (Alabama, Arkansas, Florida, Georgia, Louisiana, Missouri, Mississippi, North Carolina, South Carolina, Tennessee, Texas, and Virginia) to identify reasons for adoption/nonadoption of...