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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #167299


item MASSEY, R
item Kitchen, Newell
item MYERS, D
item Sudduth, Kenneth - Ken
item Drummond, Scott

Submitted to: International Conference on Precision Agriculture Abstracts & Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 7/26/2004
Publication Date: 7/1/2005
Citation: Massey, R.E., Kitchen, N.R., Myers, D.B., Sudduth, K.A., Drummond, S.T. 2005. Site-specific profitability methods, anaylsis, and decisions. In: Mulla, D.J., editor. Proceedings of the 7th International Conference on Precision Agriculture, July 25-28, 2004. Precision Agriculture Center, University of Minnesota, St. Paul, MN. [unpaginated CDROM]

Interpretive Summary: Many farmers installed crop yield monitoring systems on their combines in the mid-1990s. Since then they have been collecting yield data and creating yield maps. Collecting quality data (i.e., making sure all the sensors are functioning correctly) and then making representative yield maps (with erroneous numbers removed) takes considerable time and effort. These farmers are now eager to use these maps to help them be more profitable in their farming practices. In this study we transformed a decade of yield maps from a Missouri field into profit maps and explored how these maps could help identify alternative management options for the future. These profitability maps allowed direct comparison between crops and years, a comparison not easily made using yield maps. We classified profitability into four classes, based on how well different areas within the field covered operation and land costs. We called these profitability decision zones, and used them to show where alternative management options should be considered. For example, in several relatively large portions of the study field the revenue generated was only sufficient to pay the rent on the land, but not to cover other costs of farming, such as machinery, seed, fertilizer, and herbicide. Clearly, these areas are a liability to the farmer, and he may want to consider changes to improve production, decrease expenses, or entirely remove them from cropping. In many cases, these profitability decision zones were aligned with observable features of the field, such as a surface water drainage way, tree lines near field edges, and eroded soils. Knowing the associations between profitability and soil and field characteristics, the farmer would be able to develop reasonable ideas for how to manage them. Therefore, the primary value of the profit maps was the visual representation of how soil and field traits affect the bottom line. This research shows that farmers will benefit when they transform yield maps into profitability maps, helping them make better decisions that can improve efficiency of their operations.

Technical Abstract: Farmers have been mapping crop yields since the early- to mid-1990s, but for many the value of these maps is still unclear. Procedural outlines and case-study illustrations are needed to show farmers and land managers how they can use yield maps for future decisions. The objective of this analysis was to explore the methods of creation and interpretation of profit maps derived from yield maps. This would allow for the identification of a list of potential decisions that might improve management planning. Profit maps were created from corn (4 yrs), soybean (5 yrs), and grain sorghum (1yr) yield mapped data and the underlying accounting information obtained over a decade (1993-2002) for a conventionally managed (no site-specific management) central Missouri field. Profit was defined as the return to management. Profit maps were created by subtracting input costs/expenses (fixed, variable, and land) from gross revenue. The average profitability over this 10-yr period was about $12/ha, with soybean generally more profitable than corn. This crop difference was largely explained by one highly profitable soybean year (high yield and grain prices in 1996), and droughty growing conditions for the majority of corn years. Profit maps allowed direct comparison between crops and years. Profitability decision zones were superimposed on the maps to highlight four areas within the field where alternative management should be considered in order to improve profitability. In many cases, these zones were aligned with observable features of the field, such as a surface water drainage way, tree lines, and eroded back slope soils. The primary value of the profit maps was in the cognitive perceptions they gave, evoking questions about management choices. As a visual representation of an underlying database of site-specific profitability information, the profit map rapidly communicates the spatial contiguity and extent of profitability problems.