Spatial Prediction Method of Farmland Soil Organic Matter in Weibei Dryland of Shaanxi Province

被引：1
|
作者：
Wei F. [1 ,2 ]
Liu J. [1 ,2 ]
Xia L.-H. [1 ,2 ]
Xu Z.-W. [1 ,2 ]
Long X.-C. [1 ,2 ]
机构：
[1] College of Natural Resources and Environment, Northwest A&F University, Yangling
[2] Key Laboratory of Plant Nutrition and the Agri-environment in Northwest China, Ministry of Agriculture, Yangling
来源：
Huanjing Kexue/Environmental Science | 2022年 / 43卷 / 02期
关键词：
Geographic detector; Geographic weighted regression(GWR); Random forest(RF); Soil organic matter(SOM); Spatial prediction;
D O I：
10.13227/j.hjkx.202106114
中图分类号：
学科分类号：
摘要：
Accurately predicting the spatial distribution of soil organic matter (SOM) content is of great significance for improving soil quality and improving the level of regional soil management. In order to explore the optimal model for predicting the SOM content of farmland in the Weibei Dryland of Shaanxi Province, the influence factors closely related to SOM content were selected as the modeling covariables, and a geographic detector, the ordinary kriging method (OK), geographic weighted regression model (GWR), partial least squares regression model (PLS), geographically weighted regression extended model (GWRPLS), and random forest model (RF) were used to predict the spatial distribution of SOM content in training samples. Additionally, the validation set samples were used to compare and analyze the prediction accuracy of the five methods. The results showed: ① the main factors affecting the spatial variability of soil SOM were total nitrogen, fertilizer application, available potassium, available phosphorus, and altitude, and the interaction between any two factors was more explanatory for SOM than any single factor. ②ω(SOM) in farmland was between 2.25 and 30.23 g•kg-1, with an average value of 15.14 g•kg-1 and a coefficient of variation of 30.00. Although there were local differences in the prediction results of SOM by the five methods, the overall spatial distribution trend was basically the same. In the study area, the content of organic matter was low in the north and northeast and high in the west and southeast. ③ From the perspective of the prediction accuracy of the five methods, the root mean square error (RMSE) and mean absolute error (MAE) of RF were the smallest, and the prediction deviation (RPD) of GWRPLS was the largest. Compared with the OK method, the correlation coefficients (r) of GWR, PLS, RF, and GWRPLS increased to 0.907, 0.836, 0.968, and 0.972, respectively. Comprehensive analysis results showed that the random forest model had the highest prediction accuracy. © 2022, Science Press. All right reserved.
引用
收藏
页码：1097 / 1107
页数：10
相关论文
共 49 条
  • [11] Wang Y Q, Zhang X, Zhao Y P, Et al., Interpolation of soil CEC of sandy fields using GIS and geographically weighted regression-kriging, Soils, 52, 2, pp. 421-426, (2020)
  • [12] Alsamamra H, Ruiz-Arias J A, Pozo-Vazquez D, Et al., A comparative study of ordinary and residual kriging techniques for mapping global solar radiation over southern Spain, Agricultural and Forest Meteorology, 149, 8, pp. 1343-1357, (2009)
  • [13] Dai F Q, Zhou Q G, Lv Z Q, Et al., Spatial prediction of soil organic matter content integrating artificial neural network and ordinary kriging in Tibetan Plateau, Ecological Indicators, 45, pp. 184-194, (2014)
  • [14] Moura-Bueno J M, Dalmolin R S D, Horst-Heinen T Z, Et al., Environmental covariates improve the spectral predictions of organic carbon in subtropical soils in southern Brazil, Geoderma, 393, (2021)
  • [15] Xia Z S, Bai Y R, Wang Y Q, Et al., Distribution and prediction of soil saturated hydraulic conductivity in Ningdong based on GIS and random forest algorithm, Journal of Soil and Water Conservation, 35, 1, pp. 285-293, (2021)
  • [16] Zhu F B, Ding S W, Gan X Y, Et al., Prediction of spatial distribution of available potassium in cultivated soil of Anqing city based on three spatial prediction methods, Soils and Fertilizers Sciences in China, 1, pp. 1-8, (2021)
  • [17] Wang C S, Yao L W, Liu Y H, Et al., Prediction of soil nickel spatial distribution in agricultural soil under GWR model, Remote Sensing Information, 36, 1, pp. 43-49, (2021)
  • [18] Wang H Z, Yilihamu Q, Yuan M N, Et al., Prediction models of soil heavy metal(loid)s concentration for agricultural land in Dongli: a comparison of regression and random forest, Ecological Indicators, 119, (2020)
  • [19] Jia K L T, Yu H, Feng W Q, Et al., Effect of different N, P and K fertilizers on soil pH and available Cd under waterlogged conditions, Environmental Science, 30, 11, pp. 3414-3421, (2009)
  • [20] Wang J, Fu B J, Qiu Y, Et al., Spatial heterogeneity of soil nutrients in a small catchment of the Loess Plateau, Acta Ecologica Sinica, 22, 8, pp. 1173-1178, (2002)
12345