Changes in the carbon (C) stock of grassland soil in response to land use change will increase atmospheric CO 2 , and consequently affect the climate. In this study we investigated the effects of land use change on soil organic C (SOC) and nitrogen (N) along a cultivation chronosequence in the Xilin River Basin, China. The chronosequence consisted of an undisturbed meadow steppe, a 28-year-old cropland and a 42-year-old cropland (abbreviated as Steppe, Crop-28 Y and Crop-42Y, respectively). Crop-28Y and Crop-42Y were originally created on the meadow steppe in 1972 and 1958, respectively. The soil samples, in ten replications from three depth increments (0-10, 10-20 and 20-30 cm), were collected, respectively, in the two cropland fields and the adjacent undisturbed steppe. Bulk density, SOC, total N and 2 m KCl-extractable mineral N including ammonium and nitrate were measured. Our results showed that the greatest changes in the measurements occurred in the 0-10 cm soil depth. The SOC stock in the upper 30-cm soil decreased by 9.83 Mg C ha −1 in Crop-28Y and 21.87 Mg C ha −1 in Crop-42Y, which indicated that approximately 10 and 25% of the original SOC of the steppe had been emitted over 28 and 42 years, respectively. Similarly, the total N lost was 0.66 Mg N ha −1 and 1.18 Mg N ha −1 , corresponding to approximately 9% and 16%, respectively, of the original N at the same depth and cropping duration as those noted for SOC. The mineral N concentration in the soil of both the two croplands was greater than that in the steppe soil, and the ammonium-N was less affected by cultivation than the nitrate-N. The extent of these changes depended on soil depth and cropland age. These effects of cultivation were much greater in the top 10 cm of soil than in deeper soil, and also greater in Crop-42Y than in Crop-28Y. The findings are significant for assessing the C and N sequestration potential of the land use changes associated with grassland conversion, and suggest that improved management practices are needed to sequester SOC and total N in the cropped soil in a semi-arid grassland.

The purpose of land ecological security (LES) assessment is to evaluate the influence of land use and human activities on the land ecosystem. Its ultimate objective is to offer decision-making assistance and direction for safeguarding and rejuvenating the well-being and effectiveness of the land ecosystem. However, it is important to note that there are still significant uncertainties associated with current land ecological safety assessments. This paper presents a comprehensive evaluation model that combines the strengths of subjective and objective weighting methods. The model is built upon an index system developed using the Pressure-State-Response (PSR) framework. To verify the level of LES, theThe results of classifying the total ecosystem service valueTotal Ecosystem Service Value are utilized to verify the level of LES. Furthermore, spatial distribution patterns of regional land ecological safety levels are analyzed using statistical techniques, such as Moran’s I, Mann–Whitney U-test, and Kruskal–Wallis H-test. The findings indicate that: (1) theThe evaluation model developed in this paper achieves a validation accuracy of 75.55%, indicating that it provides a more accurate reflection of the level of land ecological safety in the region; (2) The ecological security index is generally safe, with a mean value in the moderate safety range. It experienced a turning point in 2010, showing initial deterioration followed by improvement, mainly due to the transition between unsafe and relatively safe zones. (3) The level of economic development, topography, and urban-–rural structure are significant factors influencing the spatial concentration of LES in the region, ultimately shaping the spatial pattern of LES in the Chengdu Plain region.

Abstract. Modeling ecosystem carbon cycle on the regional and global scales is crucial to the prediction of future global atmospheric CO2 concentration and thus global temperature which features large uncertainties due mainly to the limitations in our knowledge and in the climate and ecosystem models. There is a growing body of research on parameter estimation against available carbon measurements to reduce model prediction uncertainty at regional and global scales. However, the systematic errors with the observation data have rarely been investigated in the optimization procedures in previous studies. In this study, we examined the feasibility of reducing the impact of systematic errors on parameter estimation using normalization methods, and evaluated the effectiveness of three normalization methods (i.e. maximum normalization, min-max normalization, and z-score normalization) on inversing key parameters, for example the maximum carboxylation rate (Vcmax,25) at a reference temperature of 25°C, in a process-based ecosystem model for deciduous needle-leaf forests in northern China constrained by the leaf area index (LAI) data. The LAI data used for parameter estimation were composed of the model output LAI (truth) and various designated systematic errors and random errors. We found that the estimation of Vcmax,25 could be severely biased with the composite LAI if no normalization was taken. Compared with the maximum normalization and the min-max normalization methods, the z-score normalization method was the most robust in reducing the impact of systematic errors on parameter estimation. The most probable values of estimated Vcmax,25 inversed by the z-score normalized LAI data were consistent with the true parameter values as in the model inputs though the estimation uncertainty increased with the magnitudes of random errors in the observations. We concluded that the z-score normalization method should be applied to the observed or measured data to improve model parameter estimation, especially when the potential errors in the constraining (observation) datasets are unknown.