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The impact of development on the ecological environment is a matter of concern, especially in the ecologically fragile agro-pastoral ecotone. The agro-pastoral ecotone of northern Shaanxi (ANS) is a representative region of the agro-pastoral ecotone in northern China. The agricultural use of sand land (AUSL) is an effective way to respond to the development needs of this region. The AUSL will certainly have an impact on water yield services in the ANS, where water resources are already scarce. In this study, the Integration Valuation of Ecosystem Services and Tradeoffs Tool (InVEST) model was used to simulate the water yield of the ANS under different intensity scenarios of AUSL. The results showed that AUSL reduces the regional water yield, but the impact is limited. At the maximum scale of development, the total regional water yield decreased by 1.35% compared to the base year of 2020. In terms of distribution pattern, water yield decreased the most in Yuyang District and the least in Fugu County due to the uneven distribution of sand land. At the land use and land cover (LULC) scale, the AUSL has a diminishing effect on the water yield of both cultivated land and sand land due to the more intense evapotranspiration from cropland. This study can provide data and decision support for the ANS region for building the resource-economical and environment-friendly society. [ABSTRACT FROM AUTHOR]

[Display omitted] • SOC prediction in the agro-pastoral transitional zone of northwest China was realised using VIS-NIRS and chemometrics. • Ten spectral transforms coupled with correlation analysis and the Boruta algorithm for feature wavelength selection. • SOC content prediction model based on Boruta-FD-RF was developed. • Integrating spectral preprocessing, feature selection and chemometrics strategies has good prediction accuracy and stability. Rapidly and accurately grasp the change of soil organic carbon content in farmland, which is of great significance in guiding the timely and effective mastery of farmland soil fertility and improvement of soil physical properties. In this study, an ASD FieldSpec 4 spectrometer was used to collect spectral reflectance data on 128 agricultural soil samples taken from Jingbian County, Yulin City, Shaanxi Province, China. Firstly, descriptive statistics of the SOC in the study area were performed, and secondly, after 10 spectral transformations were performed, the correlation analysis and the Boruta algorithm were used to extract the characteristic wavebands of soil organic carbon, respectively, in order to reduce the redundancy of the data. Finally, by comparing the accuracies of different strategies, we constructed a spectral prediction model of soil organic carbon in farmland of the Northwest Agricultural and Animal Husbandry Intertwined Zone that integrates the optimal preprocessing, feature selection strategy and modelling method. The results indicate that: 1) The mean SOC content of the farmland in the study area was low and at the nutrient deficient level, with the standard errors and coefficients of variation for the modelling and validation sets were 1.596 g kg−1, 1.457 g kg−1, 54 % and 52 %, respectively; 2) The shape and trend of spectral special curves with different SOC contents show consistency, and the SOC content is negatively correlated with spectral reflectance; 3) CA selects more feature bands, but the feature bands are more homogeneous, while the Boruta algorithm can effectively remove irrelevant variables and improve the SOC feature selection effect; 4) The SOC prediction model based on Boruta-FD-RF can be better for soil organic carbon estimation, with R2 of 0.899 and 0.748 for the training set and validation set, respectively, RMSE of 1.432 g kg−1 and 1.967 g kg−1, and RPD of 2.557 and 1.647, respectively. The results show that the SOC model established by integrating optimal spectral pre-processing, feature selection strategy and chemometrics strategy has obvious improvement in prediction accuracy and stability, and this study provides an important reference for the fast and accurate estimation of SOC content in farmland of Agro-pastoral Transitional zone in northwest China. [ABSTRACT FROM AUTHOR]