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Timing of precision agriculture technology adoption in US cotton production

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Abstract

The timing of technology adoption is influenced by profitability and farmer ability to bear risk. Innovators are typically more risk tolerant than laggards. Understanding the factors influencing early adoption of precision agriculture (PA) technologies by cotton farmers is important for anticipating technology diffusion over time. The factors influencing the timing of grid soil sampling (GSS), yield monitoring (YMR) and remote sensing (RMS) adoption by cotton producers was evaluated using multivariate censored regression. Data for cotton farmers in 12 states were obtained from a survey conducted in 2009. The factors hypothesized to influence the timing of adoption included farm characteristics, operator characteristics, PA information sources, adoption of other PA technologies, and farm location. The results suggest that different factors influenced when cotton farmers adopted GSS, YMR and RMS after these technologies became commercially available. For example, land ownership was associated with the timing of GSS adoption, but not YMR or RMS adoption; farmer age was correlated with the timing of GSS and YMR adoption, but not RMS adoption; and obtaining PA information from consultants affected the timing of GSS and RMS adoption, but not YMR adoption. The only factors correlated with the early adoption of all three technologies were beliefs that PA would improve environmental quality and the adoption of at least one other PA technology. Thus, the potential for improved environmental quality appears to be a strong adoption motivator across PA technologies, as is the earlier adoption of other PA technologies. This research may be useful for farmers, researchers, Extension personnel, machinery manufacturers, PA information providers and agricultural retailers to anticipate the future adoption of new and emerging PA technologies.

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Notes

  1. To conserve space, hereafter the words “used ‘technology k’” replace the words “adopted ‘technology k’ before or at the same time as ‘technology j’”, where k = GSS, YMR, RMS, and OTHERS and j = GSS, YMR and RMS (k ≠ j) (see Table 1 for definitions).

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Acknowledgments

This research was funded by Cotton Incorporated and the agricultural research institutions at the University of Florida, Louisiana State University, Mississippi State University, North Carolina State University, University of Tennessee, and Texas Tech University. The authors thank the anonymous reviewers for providing useful comments and suggestions.

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Authors and Affiliations

  1. Department of Agricultural and Resource Economics, University of Tennessee, 302 Morgan Hall, Knoxville, TN, 37996-4518, USA

    Pattarawan Watcharaanantapong, Roland K. Roberts, Dayton M. Lambert, James A. Larson, Margarita Velandia & Burton C. English

  2. Department of Agricultural and Resource Economics, North Carolina State University, Raleigh, NC, USA

    Roderick M. Rejesus

  3. Department of Agricultural and Applied Economics, Texas Tech University, Lubbock, TX, USA

    Chenggang Wang

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  1. Pattarawan Watcharaanantapong
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  2. Roland K. Roberts
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Correspondence to Roland K. Roberts.

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Watcharaanantapong, P., Roberts, R.K., Lambert, D.M. et al. Timing of precision agriculture technology adoption in US cotton production. Precision Agric 15, 427–446 (2014). https://doi.org/10.1007/s11119-013-9338-1

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