Yield Map Interpretation

Abstract

Yield monitoring and mapping are becoming commonplace with many crops throughout North America. Some fields now have a 3- to 5-yr yield map history. A yield map, however, only documents the spatial distribution of crop yield and does not explain what factor(s) caused the variation. The goal of yield map interpretation is enhanced profitability through better understanding and control of natural and management-induced sources of yield variation. Numerous causes of crop yield variation have been documented, including climate, soil-water relationships, soil physical and chemical properties, site attributes, crop pest infestations, crop inputs and condition, field history, and cultural practices. Proper visual presentation of yield monitor data in yield map form and the accurate identification of characteristic patterns of yield variation are essential for meaningful interpretation of the yield map. Unfortunately, all yield monitor data sets and maps contain inherent error, some of which cannot be easily corrected. Error-induced patterns must be separated from real yield variation in order to make correct interpretations. In general, irregular areas, blotches or, speckles, and elliptical patterns are the result of naturally occuring yield-limiting factors. Conversely, rectangles, abrupt boundaries, circles, arcs, and streaks or lines reflect management-induced patterns of yield variation. In addition, divergence of parallel swaths, missing data points, and a repeating sawtooth pattern along field margins usually result from errors associated with Global Positioning System (GPS) signal reception and yield monitor data collection. Yield map interpretation is greatly enhanced by ongoing grower involvement and the insightful use of auxiliary agronomic, spatial, and historical site information. Geographic Information System (GIS) tools are virtually essential to evaluate multiple layers of spatial data. GIS software can be used to systematically and quantitatively evaluate the relationships between yield data and other spatial features. In the future, tools such as data-mining software, and other sophisticated mathematical and spatial models may provide additional power to interpret single-year as well as multi-year yield map information.