Unveiling the effects of crop rotation on soil pH mapping: a soil sample grouping strategy
Description
We explored the effects of incorporating crop rotation on digital mapping of soil pH. Facing the challenge of quantifying the extent to which crop rotation affects soil pH mapping accuracy, we introduced the soil sample grouping strategy. We hypothesized that grouping soil samples by crop rotation could built optimized sub-models for different rotations and achieve a higher accuracy than simply incorporating crop rotation. To test this hypothesis, we selected a typical acidic soil region in Southern China where the agricultural landscape is highly heterogeneous due to intensive and diversified cropping systems. Specifically, the objectives of this study are (1) to explore the effectiveness of simply incorporating crop rotation in mapping soil pH and (2) to verify whether and to what extent the soil sample grouping strategy and separate modeling for different crop rotations could further improve digital mapping of soil pH.
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To train and validate soil pH prediction models, we collected a total of 150 topsoil samples (0–20 cm) across the study area in October 2021 by the grid sampling scheme. The measured soil pH varies from 3.62 to 9.44 with a mean of 5.46 and a standard deviation of 0.83. First, we created a 2×2 km grid and overlayed it with the GLC_FCS30 cropland layer to screen out the grids outside of cropland. Then, we collected soil samples within five meters of each sampling point by multi-point mixed sampling. Finally, we air-dried, crushed the collected soil samples, filtered them with a 1.0 mm sieve, and determined soil pH (in water, 1:2) using a pH detector. According to the soil spatial prediction function with spatially autocorrelated errors and reference (Scorpan-SSPFe) and the characteristics of soil pH, we selected a suite of factors to represent soil-forming environments. All the variables in raster format were resampled to 10 m using the bilinear method and were used as the candidate independent variables in predicting soil pH. The soil samples were overlaid with all the environmental variables.