Yield map interpretation

Yield monitoring and mapping are becoming commonplace with many crops throu ghout 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. N umerous causes of crop yield variation have been documented, including clim ate, soil-water relationships, soil physical and chemical properties, site attributes, crop pest infestations, crop inputs and condition, field histor y, and cultural practices. Proper visual presentation of yield monitor data in yield map form and the accurate identification of characteristic patter ns of yield variation are essential for meaningful interpretation of the yi eld map. Unfortunately, all yield monitor data sets and maps contain inhere nt error, some of which cannot be easily corrected. Error-induced patterns must be separated from real yield variation in order to make correct interp retations. In general, irregular areas, blotches or speckles, and elliptica l patterns are the result of naturally occuring yield-limiting factors. Con versely, rectangles, abrupt boundaries, circles, arcs, and streaks or lines reflect management-induced patterns of yield variation. In addition, diver gence of parallel swaths, missing data points, and a repeating sawtooth pat tern 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 involvemen t and the insightful use of auxiliary agronomic, spatial, and historical si te information. Geographic Information System (GIS) tools are virtually ess ential to evaluate multiple layers of spatial data. GIS software can be use d to systematically and quantitatively evaluate the relationships between y ield data and other spatial features. In the future, tools such as data-min ing software, and other sophisticated mathematical and spatial models may p rovide additional power to interpret single-year as well as multi-year yiel d map information.