Using count data models to determine the factors affecting farmers' quantity decisions of precision farming technology adoption

Computers and Electronics in Agriculture

Volumn 62, Issue 2, 2008, Pages 231-242

  Isgin, Tamer ^a   Bilgic, Abdulbaki ^a   Forster, D Lynn ^b   Batte, Marvin T ^b  

^a HARRAN UNIVERSITY   (Turkey)

^b OHIO STATE UNIVERSITY   (United States)

Author keywords

Adoption intensity; Precision farming technologies; Zero inflated Poisson and Negative Binomial models

Indexed keywords

DATA STRUCTURES; PROBABILITY; REGRESSION ANALYSIS;

ADOPTION INTENSITY; NEGATIVE BINOMIAL MODELS; PRECISION FARMING TECHNOLOGIES; ZERO-INFLATED POISSON MODELS;

AGRICULTURAL ENGINEERING;

AGRICULTURAL TECHNOLOGY; DATA ACQUISITION; DECISION MAKING; SOIL QUALITY;

NORTH AMERICA; OHIO; UNITED STATES;

EID: 40849092618     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2008.01.004     Document Type: Article

References (48)

1. Akinola A.A. An application of the probit analysis to the adoption of tractor hiring service scheme in Nigeria. Oxford Agrarian Stud. 16 (1987) 70-82
2. Alvarez B., and Delgado M.A. Goodness-of-fit techniques for Count Data Models: an application to demand for dental care in Spain. Empirical Econ. 27 (2002) 543-567
3. Arnholt, M., Batte, M.T., Prochaska, S., 2001. Adoption and use of precision farming technologies: a survey of Central Ohio precision farmers. Report of a Survey of precision farming patrons of a central Ohio cooperative. The Ohio State University report series: AEDE-RP-0011-01.
4. Atherton B.C., Morgan M.T., Shearer S.A., Stombaugh T.S., and Ward A.D. Site-specific farming: a perspective on information need, benefits and limitations. J. Soil Water Conserv. 54 2 (1999) 455-461
5. Batte M.T., and Arnholt M.W. Precision farming adoption and use in Ohio: case studies of six leading-edge adopters. Comput. Electron. Agric. 38 (2003) 125-139
6. Batte M.T., Jones E., and Schnitkey G.D. Computer use by Ohio commercial farmers. Am. J. Agric. Econ. 72 4 (1990) 935-945
7. Cameron A.C., and Trivedi P. Regression Analysis of Count Data (1998), Cambridge University Press, Cambridge
8. Daberkow S.G., and McBride W.D. Socio-economic profiles of early adopters of precision technologies. J. Agribusiness 16 2 (1998) 151-168
9. Daberkow S.G., and McBride W.D. Farm and operator characteristics affecting the awareness and adoption of precision agriculture technologies in the US. Precision Agric. 4 (2003) 163-177
10. Dorfman J.H. Modeling multiple adoption decisions in a joint framework. Am. J. Agric. Econ. 78 (1996) 547-557
11. Faria A., Fenn P., and Bruce A. A count Data Model of technology adoption. J. Technol. Transfers 28 (2003) 63-79
12. Fernandez-Cornejo J., Daberkow S., and McBride W.D. Decomposing the size effect on the adoption of innovations: agrobiotechnology and precision agriculture. AgBioForum 4 2 (2001) 124-136
13. Fernandez-Cornejo J., Hendricks C., and Mishra A. Technology adoption and off-farm household income: the case of herbicide-tolerant soybeans. J. Agric. Appl. Econ. 37 7 (2005) 549-563
14. Fernandez-Cornejo J., Beach E.D., and Huang W.Y. The adoption of IPM techniques by vegetable growers in Florida, Michigan and Texas. J. Agric. Appl. Econ. 1 (1994) 158-172
15. Florkowski W.J., Bilgic A., Paraskova P., Beuchat L.R., Chinnan M.S., Resurreccion A.V.A., and Jordanov J. Beverage consumption patterns in a new market economy. Poznan Univ. Econ. Rev. 6 (2006) 25-53
16. Forster, D.L., Pohlman, C.A., Batte, M.T., Sohngen, B.L., 2003. Ohio Farm Real Estate Markets. Report on the 2003 Farm Real Estate and Farming Practices Survey, AEDE-RP-0035-03. The Ohio State University.
17. Greene W. LIMDEP Version 8.0 User's Manual (2002), Econometric Software, Inc., New York
18. Greene W. Econometric Analysis. fifth ed. (2003), Prentice Hall, New Jersey
19. Gurmu S., and Trivedi P.K. Excess zeros in count models for recreational trips. J. Business Econ. Stat. 14 (1996) 469-477
20. Gurmu S., and Trivedi P.K. Semi-parametric estimation of hurdle regression models with an application to medical utilization. J. Appl. Economet. 12 (1997) 225-242
21. Gurmu S., and Trivedi P.K. Generalized hurdle count data regression models. Econ. Lett. 58 (1998) 263-268
22. Haab T.C., and McConnell K.E. Count data models and the problem of zeros in recreation demand analysis. Am. J. Agric. Econ. 78 (1996) 89-102
23. Harper J.K., Rister M.E., Mjelde J.W., Drees B.M., and Way M.O. Factors influencing the adoption of insect management technology. Am. J. Agric. Econ. 72 (1990) 997-1005
24. Hellerstein D. Using count data models in travel cost analysis with aggregate data. Am. J. Agric. Econ. 73 (1991) 860-866
25. Hellerstein D. The treatment of nonparticipants in travel cost analysis and other demand models. Water Resour. Res. 28 (1992) 1999-2004
26. Hellerstein D. Estimating consumer surplus in the censored linear model. Land Econ. 68 (1992) 83-92
27. Hellerstein D., and Mendolsohn R. A theoretical foundation for count data models. Am. J. Agric. Econ. 75 (1993) 604-611
28. Kara, E., Ribaudo, M., Johansson, R.C. On how environmental stringency influences adoption of best management practices in agriculture. J. Environ. Manage., in press, doi:10.1016/j.jenvman.2007.07.029.
29. Khanna M. Sequential adoption of site-specific technologies and its implications for nitrogen productivity: a double selectivity model. Am. J. Agric. Econ. 83 1 (2001) 35-51
30. Kim S., Gillespie J.M., and Paudel K.P. Count data analysis of the adoption of best management practices in beef cattle production. Selected Paper Prepared for Presentation at the Southern Agricultural Economics Association Annual Meetings. Little Rock, Arkansas, February 5-9 (2005)
31. Lambert D. Zero-inflated Poisson regression with an application to defects in manufacturing. Technometrics 34 (1992) 1-14
32. Lawless J.F. Negative binomial mixed Poisson regressions. Can. J. Stat. 15 (1987) 209-225
33. McCullagh P., and Nelder J.A. Generalized Linear Models. Monographs on Statistics and Applied Probability vol. 37 (1989), Chapman & Hall, London
34. Mullahy J. Specification and testing of some modified count data models. J. Economet. 33 (1986) 341-365
35. Napier T.L., Robinson J., and Tucker M. Adoption of precision farming within three Midwest watersheds. J. Soil Water Conserv. 55 2 (2000) 135-141
36. Norris P.E., and Batie S.S. Virginia farmers' soil conservation decisions: an application of Tobit analysis. South J. Agric. Econ. 19 (1987) 79-89
37. Pitt M.M., and Sumodiningrat G. Risk, schooling and the choice of seed technology in developing countries: a meta-profit function approach. Int. Econ. Rev. 32 (1991) 457-473
38. Plant R.E. Site-specific management: the application of information technology to crop production. Comput. Electron. Agric. 30 (2001) 9-29
39. Polson R.A., and Spencer D.S.C. The technology adoption process in subsistence agriculture: the case of cassava in South Western Nigeria. Agric. Syst. 36 (1991) 65-77
40. Rahelizatovo N.C., and Gillespie J.M. The adoption of best-management practices by Louisiana dairy producers. J. Agric. Appl. Econ. 36 1 (2004) 229-240
41. Roberts R.K., English B.C., and Larson J.A. Factors affecting the location of precision farming technology adoption in Tennessee. J. Extension 40 1 (2002) (pages not available)
42. Roberts R.K., English B.C., Larson J.A., Cochran R.L., Goodman W.R., Larkin S.L., Marra M.C., Martin S.W., Shurley W.D., and Reeves J.M. Adoption of site-specific information and variable rate technologies in cotton precision farming. J. Agric. Appl. Econ. 36 (2004) 143-158
43. Smale M., and Heisey P.W. Simultaneous estimation of seed-fertilizer adoption decisions: an application to hybrid maize in Malawi. Technol. Forecast. Soc. Change 43 (1993) 353-368
44. Spanos A. Probability Theory and Statistical Inference: Econometric Modeling with Observational Data (1999), Cambridge University Press, Cambridge, United Kingdom
45. Su S.-J.B., and Yen S. A censored system of cigarette and alcohol consumption. Appl. Econ. 32 (2000) 729-737
46. Traore N., Landry R., and Amara N. On-farm adoption of conservation practices: the role of farm and farmer characteristics, perceptions, and health hazards. Land Econ. 74 1 (1998) 114-127
47. Welsh A.H., Cunningham R.B., Donnelly C.F., and Lindenmayer D.B. Modelling the abundance of rare species: statistical models for counts with extra zeros. Ecol. Model. 88 (1996) 297-308
48. Welsh A.H., Cunningham R.B., and Chambers R. Methodology for estimating the abundance of rare animals: seabird nesting on North East Herald Cay. Biometrics 56 (2000) 22-30