A Multinomial Logit Analysis of the Adoption of Cotton Precision Farming Technologies (original) (raw)
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Agricultural and Resource Economics Review, 2011
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 role of spatial yield variability on the number of precision farming technologies adopted, using a count data estimation procedure and farm-level data. Results indicate that farmers with more within-field yield variability adopted a higher number of precision agriculture technologies. Younger and better educated producers and the number of precision agriculture technologies used were significantly correlated. Finally, farmers using computers for management decisions also adopted a higher number of precision agriculture technologies.
Adoption of Precision Agriculture for Cotton in the Southern United States
Journal of Agribusiness, 2011
A nested logit model was applied to the 2009 Southern Cotton Precision Farming Survey to study the influence of farmer and farm characteristics on the adoption of Variability Detection Technologies (VDTs) and Variable Rate Application Technology (VRT). The results reveal that farm size, ownership of the land, and exposure to Extension activities are important factors affecting the choice of VDTs. Also, the farmers adopting both soilbased and plant-based VDTs were found to be more likely to adopt VRT. The probability of adoption of VRT was found to be lower for Texas cotton farmers compared to those in other surveyed states, regardless of the type of VDT adopted.
Adoption of Precision Agricultural Practices for Cotton in Southern United States
2011
A nested logit model was applied to the 2009 Southern Cotton Precision Farming Survey to study the influence of farmer and farm characteristics on the adoption of Variability Detection Technologies (VDTs) and Variable Rate Application Technology (VRT). The results reveal that farm size, ownership of the land, and exposure to Extension activities are important factors affecting the choice of VDTs. Also, the farmers adopting both soilbased and plant-based VDTs were found to be more likely to adopt VRT. The probability of adoption of VRT was found to be lower for Texas cotton farmers compared to those in other surveyed states, regardless of the type of VDT adopted.
Adoption and Nonadoption of Precision Farming Technologies by Cotton Farmers
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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 precision farming technologies. Farmers provided cost, time constraint, satisfaction with the current practice and other as reasons for not adopting precision farming technology. Profit, environmental benefit and to be at the forefront of agricultural technology are main reasons for adopting precision farming technology. Results from a nested logit model indicated that formal education, farm size, and number of precision farming meeting attend by farmers have positive effect on adoption of PF technologies. Moreover, spatial yield variability increases probability of adopting precision farming technologies for profit reasons.
Precision Agriculture Technology Adoption for Cotton Production
2010
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 number of specific types of precision agriculture technologies adopted by cotton farmers. Particular attention is given to the influence of spatial yield variability on the number of precision farming technologies adopted, using a Count data estimation procedure and farm-level data. Results indicate that farmers with more within-field yield variability adopted a larger number of precision agriculture technologies. Younger and better educated producers and the number of precision agriculture technologies were significantly correlated. Finally, farmers using computers for management decisions also adopted a larger number of precision agriculture technologies.
2011
This paper examines how cotton farmers' perceptions about their spatial yield variability influence their decision to adopt precision farming technologies. Utilizing cross-section survey data from 12 Southeastern states and a two-step econometric modeling approach, we find that farmers who perceive their yields as more spatially heterogeneous will more likely use site specific information gathering technologies and apply their inputs at a variable rate. In addition, our empirical analysis shows that perceptions about future profitability and importance of precision farming, along with socio-economic factors, also drive the technology adoption decision. These results have implications for producers contemplating the variable rate management decisions, as well as dealers selling these precision farming technologies.
Reasons for Adopting Precision Farming: A Case Study of U.S. Cotton Farmers
2011
We used survey data collected from cotton farmers in 12 southern U.S. states to identify factors influencing cotton farmers' decisions to adopt precision farming. Using a seemingly unrelated ordered probit model, we found that younger, educated and computer literate farmers chose precision farming for profit reason. Farmers who perceived precision farming to be profitable adopt it to be at the forefront of agricultural technology. We also found that farmers who were concerned with environment emphasize precision farming adoption as a reason to improve environmental quality. Our results also indicate that farmers in coastal states such as Alabama, Mississippi, and North Carolina chose environmental benefits as a reason for precision farming technology adoption.
Why Don't Farmers Adopt Precision Farming Technologies in Cotton Production?
2011
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 profitable precision farming technology. Farmers provided cost, time constraint, satisfaction with the current practice and other as reasons for not adopting precision farming technology. Results from a multinomial logit regression model indicated that manure application on field, more formal education, larger farm size, participation in conservation easement or agricultural easement generally decreases the probability of nonadoption of precision agriculture in cotton production.
RePEc: Research Papers in Economics, 2011
The effectiveness of sources of information (SI) on adoption of precision farming technologies (PFT) by US cotton producers is evaluated with data from the 2009 Southern Cotton Precision Farming Survey. The conceptual framework considers information flows as production inputs with a derived demand from the demand for PFT's and farm output. Coefficients of the chosen multivariate probit model are estimated with simulated maximum likelihood. The results indicate that information from the internet significantly affected the adoption of yield monitors with GPS and soil survey map technologies. Information from farm dealers impacted significantly the adoption of zone soil sampling technologies and soil survey maps. The use of university extension per se was not a statistically significant SI. Nevertheless, the use of university publications and attendance to events organized by universities had more consistent and significant positive effects on adoption of PFT's. Income, farmer's education and use of computer for management and field operations had positive effects as well. In conclusion, SI's have positive and asymmetric effects on adoption of PFT's. The paper ends providing recommendations for creation and delivery of outreach materials in the context of strategic communication plans executed by organizations serving this clientele.