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Intensity of Precision Agriculture Technology Adoption by Cotton Producers

Published online by Cambridge University Press:  15 September 2016

Kenneth W. Paxton ,
Ashok K. Mishra ,
Sachin Chintawar ,
Roland K. Roberts ,
James A. Larson ,
Burton C. English ,
Dayton M. Lambert ,
Michele C. Marra ,
Sherry L. Larkin  and
Jeanne M. Reeves
...Show all authors
Kenneth W. Paxton
Affiliation:
Louisiana State University AgCenter in Baton Rouge, Louisiana
Ashok K. Mishra
Affiliation:
Louisiana State University AgCenter in Baton Rouge, Louisiana
Sachin Chintawar
Affiliation:
Louisiana State University AgCenter in Baton Rouge, Louisiana
Roland K. Roberts
Affiliation:
Department of Agricultural and Resource Economics at the University of Tennessee in Knoxville, Tennessee
James A. Larson
Affiliation:
Department of Agricultural and Resource Economics at the University of Tennessee in Knoxville, Tennessee
Burton C. English
Affiliation:
Department of Agricultural and Resource Economics at the University of Tennessee in Knoxville, Tennessee
Dayton M. Lambert
Affiliation:
Department of Agricultural and Resource Economics at the University of Tennessee in Knoxville, Tennessee
Michele C. Marra
Affiliation:
Department of Agricultural and Resource Economics at North Carolina State University in Raleigh, North Carolina
Sherry L. Larkin
Affiliation:
Food and Resource Economics Department at the University of Florida in Gainesville, Florida
Jeanne M. Reeves
Affiliation:
Agricultural Research at Cotton Incorporated in Cary, North Carolina
Steven W. Martin
Affiliation:
Delta Research and Extension Center at Mississippi State University in Stoneville, Mississippi
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Abstract

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.

Keywords

precision technologiesPoissonnegative binomial count data methodGPSeducationcotton
Type
Contributed Papers
Information
Agricultural and Resource Economics Review , Volume 40 , Issue 1 , April 2011 , pp. 133 - 144
Copyright
Copyright © 2011 Northeastern Agricultural and Resource Economics Association 

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