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Farm’s Sequence of Adoption of Information-intensive Precision Agricultural Technology

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  Applied Engineering in Agriculture. 33(4): 521-527 . (doi: 10.13031/aea.12228) @2017
Authors:   Terry W. Griffin, Noah J. Miller, Jason Bergtold, Aleksan Shanoyan, Ajay Sharda, Ignacio A. Ciampitti
Keywords:   Adoption, Information-intensive, Markov chain, Precision agriculture, Sequential, Site specific, Soil sampling, Transition probability, Variable rate, Yield monitor.

Abstract. Precision agriculture (PA) has been commercially available for decades, however only specific technologies have been readily adopted. The overall goal of this study was to provide information of the historical changes (from 2000 to 2016), current status of PA utilization, and sales expectations in the next time period. Within this overarching objective, specific goals included 1) determining the specific technologies that farmers adopt and 2) estimating the probability of transitioning from one bundle of PA technologies to another. The three information-intensive technologies included: 1) yield monitor (YM) with or without GNSS 2) variable rate (VR) application of inputs, and 3) precision soil sampling (PSS). Combinations of these three technologies in addition to a possible “no technology adopted” response resulted in eight categories of PA technology bundles. Each year, farms were classified as having one of these eight possible bundles of PA technology. Adoption of PA technologies has increased over time, with the use of only YMs and the bundle of all three PA technologies (YM, PSS, and VR) as the two primary bundles being adopted. When only VR was adopted, there was a 47% probability that the farm would add a YM by next year. When a farm used YM, VR, and PSS, there was a 99% probability that a farm would continue using the bundle in the following year. The results are useful for farmers, extension professionals, and policymakers to understand prior adoption paths for bundles of PA technology. Future steps can connect this database on adoption of PA technology with farm meta-descriptors such as acreage, type of crop, rotation, other relevant management practices, and financial variables so to better understand how farmers are integrating technologies into their farming operations.

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