Proximal soil sensing for Precision Agriculture: Simultaneous use of electromagnetic induction and gamma radiometrics in contrasting soils

The use of high spatial resolution, on-the-go proximal soil sensing of apparent electrical conductivity (ECa) through electromagnetic induction (EMI) is increasingly common, in concert with yield mapping, to assist in the delineation of management zones for Precision Agriculture (PA). Less common, but gaining in popularity, is the use of gamma-radiometric (gamma) soil sensing. Using contrasting sites in South Australia and Queensland, the specific objectives of the study were to assess for each site, region or all sites together, how well soil cation exchange capacity (CEC) and clay content may be predicted by EMI and gamma sensing; to see whether the predictions were improved when both sensors were used, compared to a single sensor; and to evaluate the potential utility of the multi-sensor data in terms of understanding the variation in observed crop yield within sites. Of particular interest was evaluating a generic, as opposed to site-specific, approach to the simultaneous use and calibration of EMI and gamma sensing at contrasting sites chosen across a dispersed geography and pedology. EMI and gamma soil surveys were carried out at five sites across three cereal growing regions in South Australia, and at three sites in Queensland used for sugarcane production. Soil samples were also collected from each site for laboratory analysis. Data analysis comprised simple correlation analysis between soil sensor data and soil properties; fusion of sensor data by region and across all sites using weighted principal component analysis (PCA), with the data weighted on the basis of the two source sensors (weight of 0.5 assigned to ECa and the remaining weight divided equally amongst U-238, Th-232, K-40 and 'total count' (CPS); weights of 0.125 to each). The output from the PCA was used to predict maps of CEC and clay using multiple regression. Simple correlation analysis showed the expected potential utility of both sensors for predicting soil properties by site and by region. The first three principal components (PCs) explained 98% of the data variation across regions and all sites. Models for the prediction of CEC and clay content, derived from the all sites PCs, were significant (p < 0.05) at five of the eight study sites. Overall, the results show that PCA may be used as a generic approach to the fusion of EMI and gamma sensor across dispersed geography and contrasting pedology and farming systems and that maps of predicted CEC and clay content were potentially helpful in understanding within-paddock yield variation. (C) 2015 Elsevier B.V. All rights reserved.