Evaluating the Profitability of Site‐Specific Farming

Abstract

Site‐specific farming (SSF) practices are being adopted at an accelerating rate, but evidence of their profitability has been mixed or missing. This contribution evaluates the profitability of SSF practices by synthesizing quantitative and qualitative research results within the context of the economics of information technology. The profitability results from nine published field research studies on variable rate (VR) fertilizer application are reviewed using partial budgets adjusted to include minimum costs and grid cell areas for each study. Profitability results correlated closely with the gross revenue earning potential of the crop, so VR fertilizer application was unprofitable on wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.), sometimes profitable on corn (Zea mays L.), and profitable on sugarbeet (Beta vulgaris subsp. L. vulgaris). Although the formal published literature has ignored the profitability of yield mapping, production economics and farmer interviews suggest that yield mapping is profitable when it reveals yield patterns that can be managed at acceptable cost and when the information has compensating off‐field value. Manageable yield variability includes not only VR input management, but also whole‐field improvements such as field drainage, land leveling, windbreaks, and fencing. Off‐field value can come from cheaper on‐farm experimentation and greater negotiating power with landlords. Farmers and agribusinesses committed to field crop production for the long term should develop SSF capabilities. But because SSF practices are site‐specific, their profitability potential too is site‐specific. This site specificity extends beyond the farm field to the crop rotation, and the capabilities and opportunities available to the farm or agribusiness manager. Research Question Site‐specific farming (SSF) practices are being adopted at an accelerating rate, especially for midwestern U.S. field crop production. It is widely claimed that SSF will increase profitability by reducing unneeded input use and increasing crop yields where there exists unmet yield potential. Yet evidence of profitability is often based on anecdotes rather than more generalizable quantitative research. The formal, published research studies that exist are difficult to compare, because they use different budgeting assumptions. For practices such as yield mapping, there are no empirical research analyses of profitability. This study evaluates the profitability of SSF practices (i) by standardizing budgeting assumptions, where appropriate, and (ii) by drawing on economic insights about information system value where budgeting is not appropriate. Literature Summary The profitability of variable rate (VR) application of macronutrient fertilizers was found to range from quite profitable to very unprofitable, according to crop, size of field sub‐areas sampled, and nutrient applied. This was true both among research studies conducted in the field and among those conducted as computer simulations. However, partial budgeting methods vary widely across studies, including assumptions on density of soil sampling, useful life of a soil sample, and costs of sampling, data analysis, VR application, and record‐keeping. VR technology appeared profitable in higher‐value crops (e.g., sugarbeet) more frequently than in low‐value crops (e.g., wheat, barley, corn). There was no peer‐reviewed research published on the profitability of yield mapping or other sensor technologies. Study Description The first part of this study standardizes the partial budget profitability results from research studies on VR input application, using a common set of minimum costs and grid cell areas across all studies. The second part of this study evaluates the profitability of yield mapping, discussing appropriate criteria, and why no general research results are available. This study uses economic theory and personal interviews to recommend whether and how farm and agribusinesses should adopt SSF practices, and suggests research needed to enhance the future profitability of SSF technology. Applied Questions When was VR fertilizer application profitable? After adjusting the cost assumptions of published field research studies, VR fertilizer application was not profitable in wheat and barley, sometimes profitable in corn, and profitable in sugarbeet. Profitability correlated closely with peracre gross revenue earning potential of the crops grown. But three caveats are important. First, only one of these studies controlled more than two low‐cost fertilizer inputs. VR control of more inputs can be expected to increase profitability by spreading the information costs of VR technology across more beneficial inputs (assuming no major increases in information and application costs). Second, emerging evidence suggests VR input application may stabilize net income per acre from crop production. Reducing income risk has value to many producers, even if average incomes are no different. Third, all published results are based on manual soil sampling to collect data on soil nutrient needs. Emerging sensor technologies may sharply alter the profitability of VR nutrient and weed management by reducing data collection costs and slashing the delay between sampling and treatment. When is yield mapping profitable? Yield mapping is profitable when it reveals yield patterns that can be managed at acceptable cost and when the information has other off‐field value. Manageable yield variability includes more than VR technology for seeding density, variety, fertilizer rates, and herbicides; it also includes whole‐field improvements such as field drainage, land leveling, windbreaks, and fencing. Beyond managing variability in the field, yield maps can profitably lower the costs of on‐farm experimentation and enhance negotiating power with landlords. Because yield maps and other SSF practices are site‐specific, their profitability potential too is site‐specific. It depends not only on the field, but also on capabilities and opportunities available to the farm manager. How should a farm or agribusiness decide whether to adopt SSF practices? Field crop farmers who plan to stay in business for the long term should learn about SSF and begin to develop historic, spatial data bases of their fields. As SSF technologies continue to evolve rapidly, this strategy will put producers in position to jump in when the evidence of profitability potential for their specific situation becomes sufficiently clear. Yield monitoring is a relatively low‐cost way to begin, and combines equipped with yield monitors can be leased. Farmers who would benefit most from adopting SSF in some form include those who (i) are in a strong financial position, (ii) are willing to take risks, (iii) live where SSF services are available at modest cost, (iv) have the skills to implement SSF practices cheaply themselves, or (v) are obliged to reduce the environmental impacts of their input use. Most field crop input suppliers and crop consultants will have to build SSF capability if they intend to remain competitive. The challenge is to find or develop suitably skilled employees. For dealerships who provide custom applicator services, a second challenge is to find low‐cost one‐ or two‐product VR application methods or to build rapidly an acreage base large enough to justify costly multiproduct VR spreader equipment. Strategic alliances with national agribusiness companies and cooperatives may be the most practical route to meeting these training and capital investment challenges.

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