Interpretable Digital Soil Organic Matter Mapping Based on Geographical Gaussian Process-Generalized Additive Model (GGP-GAM)

Liangwei Cheng, Mingzhi Yan, Wenhui Zhang, Weiyan Guan, Lang Zhong and Jianbo Xu ()
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Liangwei Cheng: College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
Mingzhi Yan: College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
Wenhui Zhang: College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
Weiyan Guan: College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
Lang Zhong: College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
Jianbo Xu: College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China

Agriculture, 2024, vol. 14, issue 9, 1-18

Abstract: Soil organic matter (SOM) is a key soil component. Determining its spatial distribution is necessary for precision agriculture and to understand the ecosystem services that soil provides. However, field SOM studies are severely limited by time and costs. To obtain a spatially continuous distribution map of SOM content, it is necessary to conduct digital soil mapping (DSM). In addition, there is a vital need for both accuracy and interpretability in SOM mapping, which is difficult to achieve with conventional DSM models. To address the above issues, particularly mapping SOM content, a spatial coefficient of variation (SVC) regression model, the Geographic Gaussian Process Generalized Additive Model (GGP-GAM), was used. The root mean squared error (RMSE), mean average error (MAE), and adjusted coefficient of determination (adjusted R 2 ) of this model for SOM mapping in Leizhou area are 7.79, 6.01, and 0.33 g kg −1 , respectively. GGP-GAM is more accurate compared to the other three models (i.e., Geographical Random Forest, Geographically Weighted Regression, and Regression Kriging). Moreover, the patterns of covariates affecting SOM are interpreted by mapping coefficients of each predictor individually. The results show that GGP-GAM can be used for the high-precision mapping of SOM content with good interpretability. This DSM technique will in turn contribute to agricultural sustainability and decision making.

Keywords: digital soil mapping; soil organic matter; Generalized Additive Model; SOM mapping interpretability; SOM mapping models comparison (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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