Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China.
Key Laboratory of Soil Environment and Pollution Remediation, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China.
Sci Total Environ. 2020 Sep 20;736:139565. doi: 10.1016/j.scitotenv.2020.139565. Epub 2020 May 23.
Cadmium (Cd) accumulations in crops and the effects of the related soil factors on them are critical to developing precise soil management measures for food safety. Traditionally-used non-spatial multiple linear regression (MLR) cannot adequately model the spatially varying effects of the related soil properties on Cd accumulations in crop (or soil). Moreover, the traditionally-used methods for exploring the spatial accumulation characteristics (e.g., ordinary kriging) and the effects of other factors on Cd accumulations (e.g., MLR) are sensitive to outliers. In this study, robust geostatistics, enrichment index, and bioavailability index were first used to explore the spatial accumulation characteristics of Cd in wheat grain (wheat-Cd), Cd in rice grain (rice-Cd), and soil DTPA-extractable Cd (DTPA-Cd) in Jintan County, a typical rice-wheat rotation area in China. Then, robust geographically weighted regression (RGWR), established in geographic space rather than variable space, was used to explore the spatially varying relationships between Cd accumulations and the corresponding main influential factors determined by stepwise regression. Last, the modelling accuracy of RGWR was compared with those of basic GWR and MLR. Results showed that (i) outliers affected the spatial predictions of soil total Cd, soil DTPA-Cd, wheat-Cd, and rice-Cd and robust variograms should be used; (ii) the enrichment index of wheat grain was significantly higher than that of rice grain in almost the whole study area; (iii) the areas with the high bioavailability index of soil Cd mainly located in the southeast, southwest, and centre of the study area; (iv) RGWR acquired higher modelling accuracy than GWR and MLR; (v) the spatially varying relationships between Cd accumulations and the corresponding influential factors were revealed by RGWR, which cannot be determined by MLR. The methods suggested in this study provided more precise spatial decision support for soil management measures to guarantee main agricultural product safety in large-scale areas.
镉（Cd）在作物中的积累以及相关土壤因素对其的影响，对于制定精确的食品安全土壤管理措施至关重要。传统的非空间多元线性回归（MLR）模型无法充分模拟相关土壤特性对作物（或土壤）中 Cd 积累的空间变化影响。此外，传统上用于探索 Cd 在小麦籽粒（小麦-Cd）、水稻籽粒（水稻-Cd）和土壤 DTPA 可提取 Cd（DTPA-Cd）中空间积累特征的方法（例如普通克里金法）以及其他因素对 Cd 积累的影响（例如 MLR）对异常值很敏感。在本研究中，首次使用稳健地统计学、富集指数和生物有效性指数来探索中国典型稻麦轮作区金坛县小麦籽粒（小麦-Cd）、水稻籽粒（水稻-Cd）和土壤 DTPA 可提取 Cd（DTPA-Cd）中 Cd 的空间积累特征。然后，在地理空间而不是变量空间中建立稳健地理加权回归（RGWR），以探索 Cd 积累与逐步回归确定的相应主要影响因素之间的空间变化关系。最后，将 RGWR 的建模精度与基本 GWR 和 MLR 的建模精度进行比较。结果表明：（i）异常值影响土壤总 Cd、土壤 DTPA-Cd、小麦-Cd 和水稻-Cd 的空间预测，应使用稳健变异函数；（ii）在几乎整个研究区域，小麦籽粒的富集指数明显高于水稻籽粒；（iii）土壤 Cd 生物有效性指数高的区域主要分布在研究区的东南部、西南部和中心；（iv）RGWR 比 GWR 和 MLR 获得更高的建模精度；（v）通过 RGWR 揭示了 Cd 积累与相应影响因素之间的空间变化关系，这是 MLR 无法确定的。本研究中提出的方法为保证大面积主要农产品安全的土壤管理措施提供了更精确的空间决策支持。
