Japanese Journal of Soil Science and Plant Nutrition

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To adapt to precision agriculture, the current agricultural soil map’s soil information must be updated. Digital Soil Mapping (DSM) updated soil maps using field data and environmental covariates, as well as data mining and classification techniques, and then generated spatially high-resolution maps. The goal of this study was to create a high-resolution, high accuracy agricultural soil map in paddy fields where land improvement had previously occurred, and to predicted soil group distribution using machine learning with the Random Forest.

The model accuracy and Kappa coefficient of test data obtained from machine learning were 62.5％ and 0.46, respectively. These values were nearly identical to previous studies that used relatively small point data, as did this study. While the Gley Lowland soil group predicted with high accuracy, the Lowland Paddy soil group and the Gray Lowland soil group were not clearly separated. The Confusion Index indicated that the predicted soil map was uncertain at 0.64, and four out of the five soil groups had high values. This high level uncertainty was attributed to a small sample size of minor classes and the difficulty in distinguishing between the Lowland Paddy soil group and the Gley Lowland soil group. In predicted soil group map, the distribution area of the Gley Lowland soil group was reduced while that of Gray Lowland soil group was increased. Despite differences in accuracy between soil groups, the predicted agricultural soil map for paddy fields could reflect the current situation and had a high resolution by using DSM.

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To predict nitrogen (N) mineralization of various organic amendments in soil, we developed a statistical (hierarchical Bayesian) model with the main input variables being soil temperature, soil moisture, and acid detergent-soluble organic N (ADSON) content in organic amendments. First, we ran a soil incubation experiment in four soils to see how different combinations of soil temperature and moisture affected N mineralization in 32 different organic amendments (e.g., composted animal manure, plant oil cake, fish meal, green manure crops, and crop residues). Second, we predicted model parameters ( k_ij, a ₁, Q₁₀, and b) using the incubation experiment results and a modified simple-type model. The model demonstrated that N mineralization of organic amendments in soil increased with increasing ADSON content in the amendments, and it responded clearly to soil temperature in amendments containing more than 30 mg N g⁻¹ of ADSON. Soil temperature influenced N mineralization more than soil moisture. The ratio of N mineralization at 10°C to that at 30°C was 0.46. Potential (maximum) N mineralization was found to be positively correlated with ADSON content in organic amendments, indicating chemical property of organic amendments that accounts for N mineralization in soil.

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