Understanding the effects of land use changes on the spatiotemporal variation of soil organic carbon (SOC) can provide guidance for low carbon and sustainable agriculture. In this paper, based on the large-scale datasets of soil surveys in 1982 and 2009 for Pinggu District — an urban-rural ecotone of Beijing, China, the effects of land use and land use changes on both temporal variation and spatial variation of SOC were analyzed. Results showed that from 1982 to 2009 in Pinggu District, the following land use change mainly occurred: Grain cropland converted to orchard or vegetable land, and grassland converted to forestland. The SOC content decreased in region where the land use type changed to grain cropland (e.g., vegetable land to grain cropland decreased by 0.7 g kg^–1; orchard to grain cropland decreased by 0.2 g kg^–1). In contrast, the SOC content increased in region where the land use type changed to either orchard (excluding forestland) or forestland (e.g., grain cropland to orchard and forestland increased by 2.7 and 2.4 g kg^–1, respectively; grassland to orchard and forestland increased by 4.8 and 4.9 g kg^–1, respectively). The organic carbon accumulation capacity per unit mass of the soil increased in the following order: grain cropland soil

Green manure use in China has declined rapidly since the 1980s with the extensive use of chemical fertilizers. The deterioration of field environments and the demand for green agricultural products have resulted in more attention to green manure. Human intervention and policy-oriented behaviors likely have large impacts on promoting green manure planting. However, little information is available regarding on where, at what rates, and in which ways (i.e., intercropping green manure in orchards or rotating green manure in cropland) to develop green manure and what benefits could be gained by incorporating green manure in fields at the county scale. This paper presents the conversion of land use and its effects at small region extent (CLUE-S) model, which is specifically developed for the simulation of land use changes originally, to predict spatial distribution of green manure in cropland and orchards in 2020 in Pinggu District located in Beijing, China. Four types of land use for planting or not planting green manure were classified and the future land use dynamics (mainly croplands and orchards) were considered in the prediction. Two scenarios were used to predict the spatial distribution of green manure based on data from 2011: The promotion of green manure planting in orchards (scenario 1) and the promotion of simultaneous green manure planting in orchards and croplands (scenario 2). The predictions were generally accurate based on the receiver operating characteristic (ROC) and Kappa indices, which validated the effectiveness of the CLUE-S model in the prediction. In addition, the spatial distribution of the green manure was acquired, which indicated that green manure mainly located in the orchards of the middle and southern regions of Dahuashan, the western and southern regions of Wangxinzhuang, the middle region of Shandongzhuang, the eastern region of Pinggu and the middle region of Xiagezhuang under scenario 1. Green manure planting under scenario 2 occurred in orchards in the middle region of Wangxinzhuang, and croplands in most regions of Daxingzhuang, southern Pinggu, northern Xiagezhuang and most of Mafang. The spatially explicit results allowed for the assessment of the benefits of these changes based on different economic and ecological indicators. The economic and ecological gains of scenarios 1 and 2 were 175 691 900 and 143 000 300 CNY, respectively, which indicated that the first scenario was more beneficial for promoting the same area of green manure. These results can facilitate policies of promoting green manure and guide the extensive use of green manure in local agricultural production in suitable ways.

The spatial interpolation for soil texture does not necessarily satisfy the constant sum and nonnegativity constraints. Meanwhile, although numeric and categorical variables have been used as auxiliary variables to improve prediction accuracy of soil attributes such as soil organic matter, they (especially the categorical variables) are rarely used in spatial prediction of soil texture. The objective of our study was to comparing the performance of the methods for spatial prediction of soil texture with consideration of the characteristics of compositional data and auxiliary variables. These methods include the ordinary kriging with the symmetry logratio transform, regression kriging with the symmetry logratio transform, and compositional kriging (CK) approaches. The root mean squared error (RMSE), the relative improvement value of RMSE and Aitchison’s distance (DA) were all utilized to assess the accuracy of prediction and the mean squared deviation ratio was used to evaluate the goodness of fit of the theoretical estimate of error. The results showed that the prediction methods utilized in this paper could enable interpolation results of soil texture to satisfy the constant sum and nonnegativity constraints. Prediction accuracy and model fitting effect of the CK approach were better, suggesting that the CK method was more appropriate for predicting soil texture. The CK method is directly interpolated on soil texture, which ensures that it is optimal unbiased estimator. If the environment variables are appropriately selected as auxiliary variables, spatial variability of soil texture can be predicted reasonably and accordingly the predicted results will be satisfied.