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...

Precision Farming by Cotton Producers in Twelve Southern States: Results from the 2009 Southern Cotton Precision Farming Survey

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...

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...

A Heckman's two-stage method is used in conjunction with data from the 1998 Agricultural Resource Management Study to estimate the likelihood of adopting a variable rate application technology (VRT) and the impact of such adoption on the per-acre costs of fertilizers and lime in cash grain...

Replaced with revised version of paper 02/16/11.

The objective of this study is to test the widely‐held belief that the effect of education has a positive impact on technology adoption. Using 2006 Agricultural Resource Management Survey "ARMS" data, we estimate a simultaneous equations model to integrate farmers’ labor allocation decision...

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...

This study has used an empirical approach developed by Urga and Walters (2003) to examine the implications of the short-run specification of the standard translog cost specification along with the possible implications of non-stationarity. We have estimated a dynamic translog cost specification...