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Farmers’ Adoption Path of Precision Agriculture Technology

Published online by Cambridge University Press:  01 June 2017

N. J. Miller ,
T. W. Griffin ,
J. Bergtold ,
I. A. Ciampitti  and
A. Sharda
N. J. Miller*
Affiliation:
Kansas State University, 342 Waters Hall, Manhattan, Kansas, USA
T. W. Griffin
Affiliation:
Kansas State University, 342 Waters Hall, Manhattan, Kansas, USA
J. Bergtold
Affiliation:
Kansas State University, 342 Waters Hall, Manhattan, Kansas, USA
I. A. Ciampitti
Affiliation:
Kansas State University, 342 Waters Hall, Manhattan, Kansas, USA
A. Sharda
Affiliation:
Kansas State University, 342 Waters Hall, Manhattan, Kansas, USA
*
†E-mail: njmill@ksu.edu
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Abstract

Precision agriculture technologies have been adopted individually and in bundles. A sample of 348 Kansas Farm Management Association farm-level observations provides insight into technology adoption patterns of precision agriculture technologies. Estimated transition probabilities shed light on how adoption paths lead to bundling of technologies. Three information intensive technologies were assigned to one of eight possible bundles, and the sequence of adoption was examined using Markov transition processes. The probability that farms remain with the same bundle or transition to a different bundle by the next time period are reported. Farms with the complete bundle of all three technologies were likely to persist with their current technology.

Keywords

AdoptionInformation intensiveMarkov chainSequentialTransition probability
Type
PA in practice
Information
Advances in Animal Biosciences , Volume 8 , Special Issue 2: Papers presented at the 11th European Conference on Precision Agriculture (ECPA 2017), John McIntyre Centre, Edinburgh, UK, July 16–20 2017 , July 2017 , pp. 708 - 712
Copyright
© The Animal Consortium 2017 

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References

Daberkow, SG, Fernandex-Cornejo, J and Padgitt, M 2003. Precision Agriculture Technology Diffusion: Current Status and Future Prospects. In: Proceedings of the 6th International Conference on Precision Agriculture and Other Precision Resources Management, edited by PC Robert et al. ASA/SSA/CSSA, Madison, WI, USA.Google Scholar
Eddy, SR 1998. Profile hidden Markov models. Bioinformatics Review 14 (9), 755–763.Google Scholar
Erickson, B and Widmar, DA 2015. Precision Agricultural Services Dealership Survey Results. Purdue University. August 2015, USA.Google Scholar
Fountas, S, Blackmore, S, Ess, D, Hawkins, S, Blumhoff, G, Lowenberg-DeBoer, J and Sorensen, CG 2005. Farmer Experience with Precision Agriculture in Denmark and the US Eastern Corn Belt. Precision Agriculture 6, 121–141.CrossRefGoogle Scholar
Gillespie, JM and Fulton, JR 2001. A Markov Chain Analysis of the Size of Hog Production Firms in the United States. Agribusiness 17 (4), 557–557.CrossRefGoogle Scholar
Griffin, TW 2016. Adoption of Precision Agricultural Technology in Kansas. Kansas State University Department of Agricultural Economics Extension Publication. KFMA Research Article KSU-AgEcon-TG-2016. https://www.agmanager.info/adoption-precision-agricultural-technology-kansas (Retrieved January 21, 2017).Google Scholar
Griffin, TW, Lowenberg-DeBoer, J, Lambert, DM, Peone, J, Payne, T and Daberkow, SG 2004. Adoption, Profitability, and Making Better Use of Precision Farming Data. Staff Paper #04-06 Department of Agricultural Economics, Purdue University, USA. http://ageconsearch.umn.edu/bitstream/28615/1/sp04-06.pdf (Retrieved January 21, 2017).Google Scholar
Griffin, TW, Miller, NJ, Torrez, C, Ciampitti, I and Sharda, A 2016. Precision Agriculture Technology Adoption and Obsolescence. Kansas State University Department of Agricultural Economics Extension Publication. KFMA Research Article. KSU-AgEcon-TG-2016.1 https://www.agmanager.info/kfma/research-articles/precision-agriculture-technology-adoption-and-obsolescence (Retrieved January 21, 2017).Google Scholar
Hallberg, MC 1969. Projecting the Size Distribution of Agricultural Firms - An Application of a Markov Process with Non-Stationary Transition Probabilities. American Journal of Agricultural Economics 51 (2), 289–302.CrossRefGoogle Scholar
Jung, J. 2006. Estimating Markov Transition Probabilities between Health States in the HRS Dataset. http://pages.towson.edu/jjung/papers/markovtransitions.pdf.Google Scholar
Kitchen, NR, Snyder, CJ, Franzen, DW and Wiebold, WJ 2002. Educational Needs of Precision Agriculture. Precision Agriculture 3 (4), 341–351.CrossRefGoogle Scholar
Lambert, DM, Paudel, KP and Larson, JA 2015. Bundled Adoption of Precision Agriculture Technologies by Cotton Producers. Journal of Agricultural and Resource Economics 40 (2), 325–345.Google Scholar
Miller, NJ, Griffin, TW and Bergtold, J 2016. Kansas Farms’ Sequence of Information-intensive Precision Agriculture Technology Adoption in Bundles. Kansas State University Department of Agricultural Economics Extension Publication. KFMA Research Article September 2016 KSU-AgEcon-NM-TG-JB_ 2016.1.Google Scholar
Muller, MR and Middleton, J 1994. A Markov model of land-use change dynamics in the Niagara Region, Ontario, Canada. Landscape Ecology 9 (2), 151–157.CrossRefGoogle Scholar
Olson, K and Elisabeth, P 2003. An Economics Assessment of the Whole-farm Impact of Precision Agriculture. Annual Meeting of the American Agricultural Economics Association. Montreal, Canada, July 27-30, 2003.Google Scholar
Popp, J, Griffin, TW and Pendergrass, E 2002. How Cooperation May Lead to Consensus Assessing the Realities and Perceptions of Precision Farming in Your State. Journal of the American Society of Farm Managers and Rural Appraisers 65 (1), 26–31.Google Scholar
Schimmelpfennig, D and Ebel, R 2016. Sequential Adoption and Cost Savings from Precision Agriculture. Journal of Agricultural and Resource Economics 41 (1), 97–115.Google Scholar
Skaggs, R and Ghosh, S 1999. Assessing Changes in Soil Erosion Rates: A Markov Chain Analysis. Journal of Agricultural and Applied Economics 31 (3), 611–622.Google Scholar
Stabel, J, Griffin, TW and Ibendahl, G 2016. Likelihood of Kansas Farm Financial Persistence. Kansas State University Department of Agricultural Economics Extension Publication. KFMA Research Article. December 2016. https://www.agmanager.info/likelihood-kansas-farm-financial-persistence (Retrieved January 21, 2017).Google Scholar
Torrez, C, Miller, NJ, Ramsey, S and Griffin, TW 2016. Factors Influencing the Adoption of Precision Agricultural Technologies by Kansas Farmers. Kansas State University Department of Agricultural Economics Extension Publication. KFMA Research Article December 2016. KSU-AgEcon-CT-NM-SR-TG-2016.1. https://www.agmanager.info/kfma/research-articles/factors-influencing-adoption-precision-agricultural-technologies-kansas (Retrieved January 21, 2017).Google Scholar
USDA-NRSC Natural Resource Conservation Service, U.S. Department of Agriculture 2016. Environmental Quality Incentives Program. Available at: https://www.nrcs.usda.gov/wps/portal/nrcs/main/national/programs/financial/eqip/ (Retrieved January 21, 2017).Google Scholar