Project Summaries

08-372  Project Manager: J. M. Reeves


Steven W. Martin, Mississippi State University

Workgroup members: Roland K. Roberts, Burton C. English, James A. Larson, Dayton M. Lambert, Margarita Velandia, Daniel F. Mooney, David Harper, Nathanael M. Thompson, and
Pattarawan Watcharaanantapong, University of Tennessee
Sherry L. Larkin, University of Florida
Roderick Rejesus, Michele C. Marra, and Sofia Kotsiri, North Carolina State University
Ashok Mishra, Krishna Paudel, Kenneth W. Paxton, Jeremy M. D'Antoni, and Hyunjeong Joo, Louisiana State University
Chenggang Wang, Eduardo Segarra, Jeff Johnson, Shyam S. Nair, and Eric Belasco, Texas Tech University
Steven W. Martin, Mississippi State University

The overall objective of the Working Group is to determine the conditions under which specific PF technologies are profitable for cotton farmers to adopt. Precision farming (PF) information obtained from a 2009 mail survey of cotton producers in 12 southern states was evaluated, and a new survey of cotton producers in 14 states was developed for mailing in 2013. Eight separate research activities were performed to determine cotton farmers' use of precision farming (PF) technologies. Findings from these activities can be used by cotton farmers to better understand the use of PF, by agribusiness firms to better target their promotional efforts toward farmers who can benefit the most from PF adoption, and by universities and government to provide better PF educational programs to meet farmers' needs. Key findings from the eight research activities include: 1) Cotton growers are more likely to adopt GPS-guidance technology if they are younger, think PF will be profitable and important in the future, use a computer for farm management, use newer and larger cotton pickers, and rank input-cost savings higher than other reasons for adopting PF technology; 2) Cotton producers placed an average value of $20 per acre on the additional information obtained from their yield monitors, and the farmers who did not use a cotton yield monitor underestimated the variation in yields within their fields by between 5% and 30%; 3) The adoption of variable-rate input application technologies (VRT) within the State of Texas varies significantly across regions, with the greatest likelihood of adoption in the Coastal Region, where higher yield variability exists. Younger farmers, farmers with larger farms, and farmers who use computers for farming operations are more likely to adopt VRT. Results suggest that adoption of VRT does not lead to significant yield improvements for cotton in Texas, implying that the economic advantage for VRT adoption may come from input cost savings; 4) Farm size, ownership of the land, and exposure to Extension activities are important factors affecting cotton farmers' choices of soil-based (for example, grid soil sampling) and plant-based (for example, yield monitoring) variability detection technologies (VDTs). Also, the farmers adopting both soil-based and plant-based VDTs are more likely to adopt VRT. The probability VRT adoption is lower for Texas cotton farmers than for farmers in other states, regardless of the type of VDT adopted; 5) Farming experience, farm size, land ownership, variable rate fertilizer management plans and use of soil electrical conductivity devices are important characteristics in determining cotton farmers' perceptions of the useful life of the information obtained from precision soil sampling (grid soil sampling or zone soil sampling). Understanding how producers perceive the useful life of soil-test information is important for monitoring the effectiveness of best nutrient management practices; 6) Farmers can increase fertilizer efficiency by using PF technologies. Changes in the perceived amount of fertilizer used by cotton farmers from using PF technologies are associated with the use of different VDTs (for example, yield monitoring or grid soil sampling), sources of PF information (for example, Extension services), harvest technologies (picker versus stripper), and cotton area farmed. Results are useful to farmers and policy makers interested in reducing fertilizer use in the face of rising fertilizer prices and growing concerns about the environmental risks of farming; 7) Different factors affect whether cotton farmers adopted yield monitoring (YMR), remote sensing (RMS), grid soil sampling (GSS) and management zone soil sampling (MSS) sooner after these technologies became commercially available. For example, farmers with larger farms and better land quality adopted YMR earlier than other farmers, but these factors did not affect the timing of the adoption of RMS, GSS or MSS. Also, use of a computer for farm management and use of a laptop/PDA in the field affected the timing of GSS and MSS adoption, but not the timing of YMR or RMS adoption. Results can be used to develop education programs targeting technology-specific information to meet the needs of specific groups of farmers, and agribusiness firms can use the results to aim promotional efforts toward farmers who are likely to benefits the most from early adoption of similar new technologies; 8) The 2013 cotton precision farming survey questionnaire was developed by participants from the six universities involved in the Cotton Incorporated Economics of Precision Farming Working Group, in consultation with Cotton Incorporated personnel. Input from farmers was obtained at the University of Tennessee Milan Field Day (Milan, TN) and incorporated into the questionnaire. The questionnaire will be mailed in January and February, 2013, to more than 13,500 cotton farmers in 14 states.


Project Year: 2012

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