Abstract
On-farm adoption of individual and groups of precision agriculture technologies has grown in the past 15 years. Based on a sample of 545 farm observations collected by the Kansas Farm Management Association, farm adoption of bundles of embodied knowledge and information intensive technologies was analyzed using a Markov transition approach. Three separate analyses estimated transition probabilities to show the adoption of bundles of embodied knowledge technologies, the adoption of bundles of information intensive technologies, and the adoption of variable rate technologies contingent on prior adoption of embodied knowledge and/or information intensive technologies. Each analysis was estimated for two separate time periods (2009–2012) and (2013–2016). The probability that farms retain the same bundle or transition to a different bundle by the next time period are reported. The results indicate that persistence with the same technology bundle is the predominant behavior and that this behavior has strengthened in the study’s most recent time period.
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Notes
Erickson et al. (2017) began reporting lightbar and automated guidance services since 1999 and 2004, respectively, by agricultural service providers. Their results show relatively fast adoption rates for embodied knowledge technologies. Schimmelpfennig (2016) report increased acreage covered by harvesters equipped with yield monitors for various crops and that guidance technologies have been adopted at a faster rate than variable rate technologies.
Note that there are only seven bundles specified in Table 3. The bundle, “VR” (i.e., variable rate alone) was dropped during the transition probability estimation because there were no observations for this bundle for the time period of interest.
Convergence of the Markov transition probabilities for the embodied knowledge, information intensive, and variable rate bundles occurred (respectively) after 53 steps (Time Period 1) and 376 steps (Time Period 2); after 48 steps (Time Period 1) and 660 steps (Time Period 2); and after 292 steps (Time Period 1) and 881 steps (Time Period 2).
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Acknowledgements
The authors appreciate the technical assistance provided by Robert Roper. The authors are grateful to those affiliated with the Kansas Farm Management Association (KFMA) - especially Kevin Herbel, KFMA economists, and KFMA farmers for continued data collection and their support of this project.
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Miller, N.J., Griffin, T.W., Ciampitti, I.A. et al. Farm adoption of embodied knowledge and information intensive precision agriculture technology bundles. Precision Agric 20, 348–361 (2019). https://doi.org/10.1007/s11119-018-9611-4
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DOI: https://doi.org/10.1007/s11119-018-9611-4