Relationship Between Secondary Variables and Soybean Oil and Protein Concentration

Abstract

The spatial variation of soybean seed oil and protein concentration has been demonstrated at a field scale. We hypothesize that aerial images of crops during the late growing season, in combination with site property maps, can be used to delineate areas of similar seed oil and protein concentration within a given field. If these areas can be identified, then they can be harvested differentially. The main objective of this is study was to predict soybean yield and seed composition using site properties (elevation, slope, soil electrical conductivity (ECdeep and ECshallow), soil organic matter (SOM), and soil reflectance) as well as indices derived from hyperspectral remote sensing images (NNDVI, PRI, NDVI, GNDVI, and the ratio between NDVI and GNDVI). The study was conducted on two adjacent 16 ha subsections during the 2000 and 2001 growing seasons. Since the predictor variables presented multicollinearity, three groups of principal components (PC) were defined: a group corresponding to the site properties, a group corresponding to vegetation indices, and a group corresponding soybean performance (seed yield, and seed oil and protein concentrations). Then a spatial autoregressive error model predicted the soybean performance PC using site properties and vegetation indices while accounting for spatial the autocorrelation. Relationship between site properties, hyperspectral vegetation indices, and soybean seed protein and oil concentration were found for both environments. The regression analysis indicated that PCs of vegetation indices predicted soybean seed protein and oil concentration more consistently than site properties.