The essential role of soil maps to support precision agriculture

Abstract

Soil mapping is an essential tool for precision agriculture users. Although many studies emphasize modeling the spatial variation of different soil properties, the temporal variation is usually underestimated. Therefore, this study focuses on the spatial and temporal variation of soil fertility properties at the field scale and evaluates different methods to improve soil maps and their implementation in precision agriculture. Soil samples were collected monthly from a field in Central Iowa over two consecutive growing seasons following a maize (Zea mays) – soybean (Glycine max) rotation. Significant temporal changes were observed for soil nitrate, phosphorus, potassium, organic matter, and pH. Spatial variability was described across hillslope positions and the data’s spatial autocorrelation was evaluated with geostatistics. Soil maps were then created with ordinary kriging, and a time sequence was constructed for each soil property. Alternatively, machine learning was used to create soil fertility maps using remote sensing spectral data and topographic attributes as predictor variables (covariates). Finally, a method to delineate and validate management zones for precision agriculture was evaluated utilizing unsupervised classification algorithms, soil maps, and spectral data. The results showed that spectral data can describe sources of temporal variability that topography alone cannot explain, such as the impact of management practices. However, there was not an absolute best combination of covariates to construct spatial models to create soil fertility maps or to delineate management zones.