Abstract
The objective of this study was to estimate multiple soil property local regression models, confirm the accuracy of the predicted values using visible near-infrared subsurface diffuse reflectance spectra collected by a mobile proximal soil sensor, and show that digital soil maps predicted by multiple soil property local regression models are able to visualize empirical knowledge of the grower. The parent materials in the experimental fields were light clay, clay loam, and sandy clay loam. The study was conducted in Saitama Prefecture, Japan. To develop local regression models for the 30 chemical and 4 physical properties, a total of 231 samples were collected; to evaluate accuracy of prediction, 65 samples were collected. The local regression models were developed using 2nd derivative pretreatment by the Savitzky–Golay algorithm and partial least squares regression. The local regression models were evaluated using the coefficient of determination (R2), residual prediction deviation (RPD), range error ratio (RER), and the ratio of prediction error to interquartile range (RPIQ). The R2 accuracy of the 34 local regression models was 0.81 or higher. In the predicted values for 65 unknown samples, the local regression models could ‘distinguish between high and low’ for 3 of the 34 soil properties, but were ‘not useful’ as absolute quantitative values for the other 31 soil properties. However, it was confirmed that the predicted values followed the transition in measured values, and thus that the developed 34 regression models could be used for generating digital soil maps based on relative quantitative values. The grower changed the ridge direction in the field from east–west to north–south just looking at the digital soil maps.
Highlights
Proximal soil sensing (PSS), coupled with GNSS and visible and near-infrared (VNIR) spectroscopy, is a promising approach for detailed characterization of spatial soil heterogeneity
4the physical properties, including soil moisture content smoothed by thefocused soil flattener and packed field-moist soil samples in sealable plastic bags, In this study, we focused on chemical and physical properties, including soil moisture (MC, weight ratio); soil organic matter
With the exception of Mn, it was confirmed that local regression model estimation was possible for both the 10 smoothing points (SP) and the 18 SPs
Summary
Proximal soil sensing (PSS), coupled with GNSS (global navigation satellite system) and visible and near-infrared (VNIR) spectroscopy, is a promising approach for detailed characterization of spatial soil heterogeneity. Using PSS in agricultural fields can dramatically enhance this technique by enabling innovative approaches based on appropriate field understanding which characterize local soil and environmental conditions in space and time, improving the efficiency of production to minimize environmental side effects and maximize farm incomes by increasing crop quality and/or yield [2]. Sensing by PSS can help us to better articulate the potential of agricultural soil to meet the world’s needs with regard to soil regeneration, soil contamination monitoring, food security, global climate change, and environmental sustainability, enabling the design and implementation of innovative management practices and the efficient pursuit of sustainable development goals (SDGs)
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