Cultivated land horizontal ecological compensation is an essential means of reconciling agricultural ecosystem protection and regional economic development. It is important to design a horizontal ecological compensation standard for cultivated land. Unfortunately, there are some defects in the existing quantitative assessments of horizontal cultivated land ecological compensation. In order to raise the accuracy of ecological compensation amounts, this study established an improved ecological footprint model based on the ecosystem service function, focused on estimating the value of ecosystem service function, ecological footprint, ecological carrying capacity, ecological balance index and ecological compensation values of cultivated land in all cities of Jiangxi province. It then analyzed the rationality of ecological compensation amounts in Jiangxi province, which is one of the 13 provinces of major grain-producing areas in China. The results show the following: (1) The total value of soil conservation service function, carbon sequestration and oxygen release service function and ecosystem service function in Jiangxi province showed a spatial distribution trend of “gradually increasing around Poyang Lake Basin”. (2) The cultivated land ecological deficit areas in Jiangxi province are Nanchang City, Jiujiang City and Pingxiang City; ecological surplus areas are Yichun City, Ji’an City and eight other cities; and there is an obvious “Spatial Agglomeration” phenomenon in ecological deficit and ecological surplus areas where ecological deficit areas are mainly concentrated in the northwest region of Jiangxi. (3) The amount needed to attain fair ecological compensation for cultivated land is 5.2 times the payment amount for cultivated land; this indicated there is larger arable land, a favorable condition for agricultural cultivation, and better supply capacity of ecosystem services in most of the cities of Jiangxi. (4) The compensation amount for cultivated land ecological surplus areas in Jiangxi province is generally higher than the cost of ecological protection, and its proportion in GDP, fiscal revenue and agriculture-related expenditure is significantly higher than that in ecological deficit areas; this indicated that the compensation value of cultivated land could play the driving role in the protective behavior for cultivated land. The results provide a theoretical and methodological reference for the construction of horizontal ecological compensation standards for cultivated land.

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Measuring the soil salinity using visible and near-infrared (vis–NIR) reflectance spectra is considered a fast and cost-effective method. For monitoring purposes, estimating soils with low salinity measured as electrical conductivity (EC) using vis–NIR spectra is still understudied. In this research, 399 legacy soil samples from six regions of Southern Xinjiang, China with low EC values were used. Reflectance spectra were measured in the laboratory on dried and ground soil samples using a portable vis–NIR spectrometer. By using 10-fold cross-validation, three algorithms–partial least-squares regression (PLSR), random forest (RF), and Cubist–were employed to develop statistical models of EC. The model performance evaluation was obtained by the relative importance of variants. In terms of accuracy assessment of soil EC prediction, the results demonstrated that the Cubist model performed better (R2 = 0.67, RMSE = 0.16 mS/cm, RPIQ = 2.28) than both PLSR and RF. Despite similar variants for modelling, the RF model performed somewhat better than that of the PLSR. Additionally, the 610 nm and 790 nm wavelengths only demonstrated significant promise for predicting low soil EC values when used in the Cubist mode. The current research recommends the use of Cubist to estimate the low soil salinity using the vis–NIR reflectance spectra.

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Cadmium (Cd) pollution in a soil–rice system is closely related to widely concerning issues, such as food security and health risk due to exposure to heavy metals. Therefore, modeling the Cd content in a soil–rice system and identifying related controls could provide critical information for ensuring food security and reducing related health risks. To archive this goal, in this study, we collected 217 pairs of soil–rice samples from three subareas in Zhejiang Province in the Yangtze River Delta of China. All soil–rice samples were air-dried and conducted for chemical analysis. The Pearson’s correlation coefficient, ANOVA, co-occurrence network, multiple regression model, and nonlinear principal component analysis were then used to predict the Cd content in rice and identify potential controls for the accumulation of Cd in rice. Our results indicate that although the mean total concentration of Cd in soil samples was higher than that of the background value in Zhejiang Province, the mean concentration of Cd in rice was higher than that of the national regulation value. Furthermore, a significant difference was detected for Cd content in rice planted in different soil groups derived from different parental materials. In addition, soil organic matter and total Cd in the soil are essential factors for predicting Cd concentrations in rice. Additionally, specific dominant factors resulting in Cd accumulation in rice planted at different subareas were identified via nonlinear principal component analysis. Our study provides new insights and essential implications for policymakers to formulate specific prevention and control strategies for Cd pollution and related health risks.