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Factors Influencing the Selection of Precision Farming Information Sources by Cotton Producers

Published online by Cambridge University Press:  15 September 2016

Amanda Jenkins
Affiliation:
Cooperative Extension Service of the University of Kentucky in Elizabethtown, Kentucky
Margarita Velandia
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
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
Steven W. Martin
Affiliation:
Delta Research and Extension Center at Mississippi State University in Stoneville, Mississippi
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Abstract

Precision farming information demanded by cotton producers is provided by various suppliers, including consultants, farm input dealerships, University Extension systems, and media sources. Factors associated with the decisions to select among information sources to search for precision farming information are analyzed using a multivariate probit regression accounting for correlation among the different selection decisions. Factors influencing these decisions are age, education, and income. These findings should be valuable to precision farming information providers who may be able to better meet their target clientele needs.

Type
Contributed Papers
Copyright
Copyright © 2011 Northeastern Agricultural and Resource Economics Association 

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References

Belsley, D.A., Kuh, E., and Welsch, R.E. 1980. Regression Diagnostics: Identifying Influential Data and Sources of Collinearity. New York, NY: John Wiley & Sons.Google Scholar
Binder, D.A., and Théberge, A. 1988. “Estimating the Variance of Raking Ratio Estimators.The Canadian Journal of Statistics 16(Supplement): 4755.Google Scholar
Daberkow, S.G., and McBride, W.D. 2003. “Farm and Operator Characteristics Affecting the Awareness and Adoption of Precision Agriculture Technologies in the U.S.Precision Agriculture 4(2): 163177.CrossRefGoogle Scholar
Ford, S.A., and Babb, E.M. 1989. “Farmers' Sources and Uses of Information.‘ Agribusiness 5(5): 465476.Google Scholar
Geweke, J. 1989. “Bayesian Inference in Econometric Models Using Monte Carlo Integration.Econometrica 57(6): 13171339.Google Scholar
Greene, W.H. 2003. Econometric Analysis. 5th Edition. Upper Saddle, NJ: Prentice Hall. Google Scholar
Greene, W.H. 2007. LIMDEP Version 9.0: Econometric Modeling Guide, Vol 1. Plainview, NY: Econometric Software, Inc. Google Scholar
Griffin, T.W., and Lambert, D.M. 2005. “Teaching Interpretation of Yield Monitor Data Analysis: Lessons Learned from Purdue's Top Farmer Crop Workshop.Journal of Extension 43(3): 14.Google Scholar
Hajivassiliou, V. 1993. “Simulation Estimation Methods for Limited Dependent Variables Models.” In Maddala, G.S., Rao, C.R., and Vinod, H.D., eds., Handbook of Statistics (Vol. 11). Amsterdam: Elsevier Sciences.Google Scholar
Judge, G.G., Hill, R.C., Griffiths, W.E., Lutkepohl, H., and Lee, T. 1988. Introduction to the Theory and Practice of Econometrics. New York, NY: John Wiley & Sons.Google Scholar
Just, D., Wolf, S.A., Wu, S., and Zilberman, D. 2002. “Consumption of Economic Information in Agriculture.American Journal of Agricultural Economics 84(1): 3952.Google Scholar
Just, D., Wolf, S.A., Wu, S., and Zilberman, D. 2006. “Effect of Information Formats on Information Services: Analysis of Four Selected Agricultural Commodities in the U.S.Agricultural Economics 35(3): 289301.Google Scholar
Khanna, M. 2001. “Sequential Adoption of Site Specific Technologies and Its Implications for Nitrogen Productivity: A Double Selectivity Model.American Journal of Agricultural Economics 83(1): 3551.Google Scholar
Keane, M. 1994. “A Computationally Practical Simulation Estimator for Panel Data.Econometrica 62(1): 95116.Google Scholar
Lambert, D.M., Wojan, T.R., and Sullivan, P. 2009. “Farm Business and Household Expenditure Patterns and Local Communities: Evidence from a National Farm Survey.Review of Agricultural Economics 31(3): 604626.Google Scholar
Lohr, S. 1999. Sampling: Design and Analysis. Pacific Grove, CA.: Brooks/Cole.Google Scholar
McFadden, D. 1984. “Econometric Analysis of Qualitative Response Models.” In Griliches, Z. and Intrilligator, M., eds., Handbook of Econometrics (Vol. 2). Amsterdam: Elsevier Science.Google Scholar
McBride, W.D., and Daberkow, S.G. 2003. “Information and the Adoption of Precision Farming Technologies.Journal of Agribusiness 21(1): 2128.Google Scholar
Ortmann, G.F., Patrick, G.F., Musser, W.N., and Doster, D.H. 1993. “Use of Private Consultants and Other Sources of Information by Large Cornbelt Farmers.Agribusiness 9(4): 391402.Google Scholar
Rogers, E.M. 1983. Diffusion of Innovations. New York, NY: The Free Press.Google Scholar
Schnitkey, G., Batte, M., Jones, E., and Botomogno, J. 1992. “Information Preferences of Ohio Commercial Farmers: Implications for Extension.American Journal of Agricultural Economics 74(2): 486496.Google Scholar
Soule, M.J., Tegene, A., and Wiebe, K.D. 2000. “Land Tenure and the Adoption of Conservation Practices.American Journal of Agricultural Economics 82(4): 9931005.Google Scholar
Torbett, C.J., Roberts, R.K., Larson, J.A., and English, B.C. 2007. “Perceived Importance of Precision Farming Technologies in Improving Phosphorous and Potassium Efficiency in Cotton Production.Precision Agriculture 8(3): 127137.Google Scholar
U.S. Census Bureau. 2000. U.S. Census 2000. Available at http://www.census.gov/main/www/cen2000.html (accessed March 2009).Google Scholar
U.S. Census Bureau. 2002. County Business Patterns 2002. Available at http://www.census.gov/epcd/cbp/index.html (accessed March 2009).Google Scholar
U.S. Census Bureau. 2000. U.S. Office of Management and Budget. Standards for Defining Metropolitan and Micropolitan Statistical Areas 2000. Available at http://www.census.gov/population/www/cen2000/briefs/phc-t29/index.html (accessed June 2009).Google Scholar
U.S. Department of Agriculture, Economic Research Service (USDA/ERS). 2004a. County Typology Codes. Available at http://www.ers.usda.gov/Data/TypologyCodes/ (accessed June 2009).Google Scholar
U.S. Department of Agriculture, Economic Research Service (USDA/ERS). 2004b. Natural Amenities Scale. Available at http://www.ers.usda.gov/Data/NaturalAmenities/ (accessed September 2008).Google Scholar
U.S. Department of Agriculture, National Agricultural Statistics Service (USDA/NASS). 2004. 2002 Census of Agriculture. Available at http://www.agcensus.usda.gov/Publications/2002/index.asp (accessed March 2009).Google Scholar
Walton, J.C., Larson, J.A., Roberts, R.K., Lambert, D.M., English, B.C., Larkin, S.L., Marra, M.M., Martin, S.W., Paxton, K.W., and Reeves, J.M. 2010. “Factors Influencing Framer Adoption of Portable Computers for Site-Specific Management: A Case Study for Cotton Production.Journal of Agricultural and Applied Economics 42(2): 193209.Google Scholar
Walton, J.C., Lambert, D.M., Roberts, R.K., Larson, J.A., English, B.C., Larkin, S.L., Martin, S.W., Marra, M.C., Paxton, K.W., and Reeves, J.M. 2008. “Adoption and Abandonment of Precision Soil Sampling in Cotton Production.Journal of Agricultural and Resource Economics 33(3): 428448.Google Scholar
Wooldridge, J. 2002. Econometric Analysis of Cross-Section and Panel Data. London, UK: MIT Press.Google Scholar