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...

Most of the empirical literature in this area tends to analyze labor allocation decisions of economic agents using cross-sectional data. But such methods implicitly assume that model parameters are stable (constant) across firms and over time. The use of cross-sectional methods is therefore...

In the past three decades, farm families have relied on government payments and off-farm income to reduce income risk and increase total household income. Studies have shown that, as income effect dominates, government payments tend to reduce off-farm labor of farm operators and spouses. But...

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 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...

Using average treatment effect and data from 2012 the Central Luzon Loop Survey, this study investigates the role of gender in rice production. Results indicate that female-headed farm households, despite having limited access to land, have a higher value of rice production than their male...

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...

The Internet is becoming an increasingly important management tool in production agriculture. Using data from the 2004 Agricultural Resource Management Survey (ARMS) and a double-hurdle estimation approach, we explore the adoption of computers with Internet access by and Internet purchasing...

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