Academic journal article Journal of Agricultural and Applied Economics

Changes in Producers' Perceptions of Within-Field Yield Variability after Adoption of Cotton Yield Monitors

Academic journal article Journal of Agricultural and Applied Economics

Changes in Producers' Perceptions of Within-Field Yield Variability after Adoption of Cotton Yield Monitors

Article excerpt

This article investigates how information from cotton yield monitors influences the perceptions of within-field yield variability of cotton producers. Using yield distribution modeling techniques and survey data from cotton producers in 1 1 southeastern states, we find that cotton farmers who responded to the survey tend to underestimate within-field yield variability (by approximately 5-18%) when not using site-specific yield monitor information. Results further indicate that surveyed cotton farmers who responded to a specific question about yield monitors place a value of approximately $20/acre/year (on average) on the additional information about within-field yield variability that the yield monitor technology provides.

Key Words: precision farming, risk, spatial yield distributions, within-field variability yield monitor, yield perceptions, yield variability

JEL Classifications: Q12, Q16

The widespread availability of satellite signals in 1995, together with the availability of Global Positioning System (GPS) technology, made it possible for farmers to locate yield data spatially using yield monitors (Lechner and Baumann, 2000). Moreover, these geo-referenced data from yield monitors enabled farmers to create field maps to facilitate variable-rate (VR) application of inputs. Spatial information from yield monitors have implications for how farmers perceive yield variability in their fields and, consequently, for their management of inputs. Therefore, a detailed analysis of this issue is valuable.

With advances in yield monitor technology in the 1990s, the adoption of yield monitors in the United States spread rapidly over the next decade, especially for grain and oilseed crops (i.e., corn and soybeans). In 2000, for example, 30% of total corn area and 25% of total soybean area in the United States were already being harvested by machines with yield monitors (Daberkow, Fernandez-Cornejo, and Padgitt, 2002). In 2001, the total corn area harvested in the United States by such machines increased to 37%, whereas for soybean, it increased to 29% in 2002 (Griffin et al., 2004a). By 2006, yield monitor adoption was estimated to be 42% of total corn acreage and 45% of total soybean acreage (Schimmelpfennig and Ebel, 2011). In comparison, less than 3% of die total cotton area of the United States was harvested by machines with yield monitors between 2000 and 2002. By 2005, that area had increased to only approximately 8%. More recent data for cotton farmers in the South indicate approximately a 10% adoption rate (Mooney et al., 2010).

The slower rate of adoption of yield monitors in cotton farming was initially constrained primarily by ineffective equipment (Durrence et al., 1999; Sassenrath-Cole et al., 1998; Searcy and Roades, 1998; Valco, Nichols, and Lalor, 1998). Early cotton yield monitors, first introduced in 1997, had many problems including poor accuracy, failure to maintain calibration, and sensors that became blocked by dust and other materials (Durrence et al., 1999; Roades, Beck, and Searcy, 2000; Wolak et al., 1999). Progress was made when cotton yield monitoring technologies became more reliable and, consequently, cotton growers became more receptive to adopting and using this technology (Perry et al., 200 1).1

Given the more effective cotton yield monitors available today, it is important to determine how this technology influences producers' yield variability perceptions of their fields. This issue is important because how producers perceive within-field yield variability fundamentally affects their decision-making behavior, as explained further subsequently. (See Delavande, Gine, and McKenzie, 2009; Manski, 2004, for a summary of the literature on how subjective expectations or perceptions could affect economic decision-making in other contexts.)

In a precision farming context, for example, a farmer without yield monitoring technology may believe that the spatial yield variability in his or her field is low (i. …

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