Profitability Maps as an Input for Site-Specific Management Decision Making

Authors: MASSEY, R - UNIVERSITY OF MISSOURI; MYERS, D - UNIVERSITY OF MISSOURI; Kitchen, Newell; Sudduth, Kenneth - Ken

Submitted to: Agronomy Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/10/2007
Publication Date: 1/21/2008
Citation: Massey, R.E., Myers, D.B., Kitchen, N.R., Sudduth, K.A. 2008. Profitability Maps as an Input for Site-Specific Management Decision Making. Agronomy Journal. 100:52-59.

Interpretive Summary: In the mid-1990's farmers began installing yield monitoring systems on their combines so they could collect yield data on-the-go and create yield maps. Because collecting quality yield data and then making representative yield maps takes considerable time and effort, farmers are eager to know how these multiple years of yield maps might be used to improve future management decisions. In this case study, we converted a decade of yield maps from a Missouri field into profit maps and explored how these maps could help identify alternative, more profitable management options for the future. The profit maps were created by subtracting all input, equipment, and land costs from the gross revenue. We learned that creating a common legend representing the range of profit or loss encountered over all years examined is crucial for map comparisons. Using a common legend, these maps allowed direct comparisons among crops and years, comparisons not easily made using yield maps. Composites of multiple-year maps visually displayed those field areas generating much profit, low profit, and those that lost money. Clearly, these low-profit or loss areas represent a liability to the farmer, who might want to consider changes to improve production, decrease expenses, or entirely remove areas from cropping. In many cases, areas of low profit or loss were aligned with observable features of the field, such as surface drainage, 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 managing these areas. Therefore, the primary value of the profit maps was the visual representation of how soil and field traits affect the “bottom line.” This analysis explores how profitability mapping supports multiple aspects of farmers’ decisions, including identification of problems, development of solutions, and final selection of new management. This research shows that farmers will benefit when they transform yield maps into profitability maps, making better decisions that can improve sustainability of their operations.

Technical Abstract: For over a decade, farmers have been collecting site-specific yield data. Many have formed doubts about this investment because of their inability to directly apply this information as feedback for improving management. The objective of this case-study analysis was to investigate how site-specific decisions can be improved by transforming a long-term multiple-crop yield-map dataset into profit maps that contain economic thresholds representing profitability zones. Ten years (1993-2002) of cleaned yield map data [four, five, and 1 year(s) for corn (Zea mays L.), soybean (Glycine max (L.) Merr.) and grain sorghum (Sorghum bicolor L.), respectively] were collected for a 35.6 ha claypan-soil field in Missouri. Actual input costs and crop prices, published custom rates for field operations, and region-specific land rental prices were used to transform yield maps into profitability maps by year, by crop, and over all ten years. Profit maps revealed large field areas where net profit had been negative, largely due to negative profit from corn production on areas where topsoil was eroded. The areal extent and degree to which other unique field features affected profitability, such as a tree line and a drainage way, were discussed. This analysis demonstrated how changing yield into profitability metrics could help a producer consider and then decide on different management options. We explore how assessment and exploratory analysis with profitability mapping supports multiple aspects of the decision process, including identification, development, and selection. This decision process supports a producer’s need to manage fields with incomplete information and where satisficing rather than optimizing behavior often occurs. This analysis demonstrates how profit mapping can be of value for a producer and provides impetus for the precision agriculture community to consider profit mapping protocols and standards.