Remote Soil-Sensed Management Zones Help Increase Crop Input Efficiency

Accurate prescription maps are essential for effective variable rate technology (VRT) fertilizer application. Grid soil sampling is frequently used to develop these prescription maps. However, research indicates technical and economic limitations associated with this approach. For example, samples need to be kept to a minimum, while still allowing a reasonable level of map quality. Yet the optimum grid density may depend on the coefficient of variation. In many cases, where the spatial distribution is rather complex, much finer grid densities than those currently used commercially are required to produce accurate prescription maps.


Due to these limitations, management zones for VRT fertilizer application has received considerable attention. A management zone is a sub-region of a field that expresses a combination of yield limiting factors for which a single rate of a specific crop input is appropriate. Researchers and farmers understand the value of dividing whole fields into smaller, similar regions, or management zones, for fertility management. Delineating management zones that characterize the spatial variability within a field will provide effective prescription maps for VRT.

Earlier studies proposed dividing fields by soil type. However, most studies conclude National Cooperative Soil Survey is not adequate for variable rate application. Landscape position also has been used to divide fields. Studies also find that landscape position alone is not effective in dividing fields into units for variable rate management. Management zones using various combinations of yield maps, topography, soil conductivity, and remote sensing have been proposed by researchers. Fleming et al., report that management zones based on soil electrical conductivity and bare soil remote sensing have an economic advantage over grid sampled and uniform applications.

Persistence Data Mining’s management zone system utilizes remote soil sensed hyper-spectral imaging to map soil nutrients. Developing these maps from imagery has been shown to be a more accurate and cost effective alternative to ground sampled maps. The program also develops a spatial organic matter map from the soil imagery to estimate mineralized N. From these data layers a spatial N, P, and K recommendation is determined across the field for variable rate application. Fertilizer savings — and significant yield increases — are achieved using this system. The platform is also environmentally effective in reducing fertilizer over-application, which leads to runoff and leaching into ground water.


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