SPATIAL BREAK-EVEN VARIABILITY FOR VARIABLE RATE TECHNOLOGY ADOPTION

 


INTRODUCTION

 

Farm fields are characterized by areas with varying potential to produce crop output. Across a given field, one could find variability with respect to soil type, nutrient status, landscape position, organic matter content, water holding capacity and so on. Variation in these factors leads to variation in the potential of different areas to utilize applied inputs and produce crop output (Carr et al., 1991; Hibbard et al., 1993; Malzer et al., 1996; Sawyer, 1994; Snyder, 1996; Wibawa et al., 1993).

Precision farming or site-specific farming uses site-specific information to identify homogeneous subareas within a field, make site-specific prescriptions, and apply inputs at spatially variable rates. Since precision technology helps in matching input application to crop and soil needs, under- and over-application of inputs like seeds, fertilizers and pesticides is reduced. As a result, the adoption of this technology is expected to result in greater yields and/or reduced inputs (Morgan and Ess, 1997; Sawyer, 1994; Snyder, 1996). Though management of within-field variability holds the promise of economic and environmental benefits, voluntary adoption of variable rate technology (VRT) is likely to be most dependent on profitability (Daberkow, 1997; Sawyer, 1994). Profitability is one favorable outcome desired by virtually all producers in a market economy for voluntary technology adoption (Lowenberg-DeBoer-J and Swinton, 1995).

Economic benefits from switching to VRT from uniform application methods depend on the economic value of assessing and treating within-field variability (Forcella, 1993; Hayes et al., 1994). This value in turn depends upon the extent of within-field variability. In nearly homogeneous fields, the cost incurred to assess variability and use the VRT might not be offset by additional returns generated. As a result, economic returns to VRT could be negative (Sawyer, 1994). From a purely economic perspective, therefore, there are to factors driving adoption of precision farming technology -- ‘spatial variability’ (Morgan and Ess, 1997) and the magnitude of spatial yield differences.

Forcella (1993) presented a nice hypothetical illustration of how within-field variability could influence the economic outcomes of VRT. He created 11 hypothetical fields, each field composed of 10 hectares with two types of soil in different proportions. Assuming that corn yield response to nitrogen (N) was characterized by a linear response and a plateau (LRP) function for both soil types, he calculated misapplication costs that would be incurred with average rate application. Misapplication costs were equivalent to money saved if soils were managed by prescription and they increased with increasing within-field variability. The analysis also showed changes in how the input commodity-price ratio would impact misapplication costs. The cost of technology, however, was not explicitly considered in the analysis.

Many VRT farmers currently use consulting firms to develop nutrient application maps and to custom apply nutrients (Morgan and Ess, 1997; Lowenberg-DeBoer and Swinton, 1995; Swinton and Ahmad, 1996). A farmer seeking to purchase VRT services on a custom-hire basis is primarily faced with the question, "Do additional returns generated at least cover custom charges?" Since the extent of economic returns from VRT is dependent on the extent of spatial variability within a field, the farmer ultimately needs to know the minimum spatial variability at which he/she can break even using the technology. A farmer with within-field variability that is higher than this minimum will employ VRT; one with lower variability will prefer the uniform application method. Knowledge of this minimum variability can help the farmer make appropriate economic decisions about the adoption of VRT.

We are not aware of any analytical illustrations that explicitly address the issue of minimum spatial variability at which a farmer can break even with the cost of using VRT. The objective of this analysis was to develop a hypothetical model and present a methodology for determining this minimum variability, which we refer to as spatial break-even variability, for VRT adoption. The role of input and crop prices and custom charges in determining spatial break-even variability will also be examined.

 

 
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Methods