Using publicly available high-resolution imagery products and terrain maps can provide information field conditions and help growers identify vulnerable areas of fields. This research explores in-field variability and uses online databases to collect information on vegetation and topography.

A 110-acre production field (Wagstaff silty clay loam soil) in southeast Kansas was selected in cooperation with a grower. This field has been in a long-term corn-wheat-soybean rotation. Waterways drain the field to the south and north (Figure 1).

Figure 1. USDA National Agricultural Imagery Program remote image from early summer 2012 of a crop production field in southeast Kansas. Arrows indicate waterways draining the field.

Yield and plant growth information were collected at harvest. High-resolution imagery was downloaded through the USDA National Agricultural Imagery Program (NAIP) and elevation data and orthoimagery were downloaded from the U.S. Geological Survey (USGS). Plant canopy coverage was analyzed using ArcGIS with Spatial Analyst (ESRI, Redlands, CA). NAIP 4-band imagery for the production field was collected from June 8-July 24, 2012 and was used to calculate the normalized difference vegetation index (NDVI) (Figure 2). The NDVI indicates the uneven crop growth within the field, with areas of sparse (pink-orange) and dense (green) crop canopy growth (Figure 2).

Figure 2. Calculation of normalized difference vegetation index (NDVI) from USDA National Agricultural Imagery Program imagery for the crop production field.

Digital elevation maps (DEMs) were used to calculate surface curvature of the field and perform terrain analysis using ArcGIS and TauDEM (Utah State University). Although the field had only a moderate slope (1–3%), calculation of surface curvature indicated a ridge through the center of the field (Figure 3) that coincided with areas of thin vegetation identified on the NDVI map.

Figure 3. Curvature of the field surface derived from USGS digital elevation map data.

Soybean yield was reduced in areas of low vegetation (Figure 4). Analysis of the DEM allowed determination of areas of the field that held water and areas of high potential runoff where soil loss was likely (Figure 5). These areas could benefit by altered management practices to slow water runoff from the field and keep topsoil and nutrients on the field.

Figure 4. Soybean yield in 2013 from hand-harvested subplots within the field.

Figure 5. Terrain analysis of the crop field showing areas of high runoff potential.

This kind of information can be used to develop protocols for alternative management to protect vulnerable areas and reduce topsoil loss. We are also developing a simple hydrological model use the Hydrologic Simulation Program – Fortran (HSPF) and the Soil Water Assessment Tool (SWAT) to further delineate erosion and impact on crop performance.