Soil quality (SQ) is critical to sustainable agricultural development. It is sensitive to the crop yield, especially in thin-layer black-soil regions, which have experienced severe degradation in recent years. However, how to evaluate the SQ and its influence on land productivity is not clear in regions with thin black-soil coverage. Therefore, an integrated soil quality index (SQI) was constructed using diverse datasets along a 30 km transect in a typical thin-layer black-soil region of China. The results showed that obvious soil degradation was observed in this area. Black-soil thickness (BST), soil organic matter (SOM), and the total nitrogen (TN) content were the most strongly correlated with corn yield among the 13 investigated indexes, with Pearson coefficients of 0.65, 0.39 and 0.34, respectively. The minimum-dataset-based SQI using six soil properties within 0–30 cm was the optimal solution for SQ evaluation in the study area. The good performance of the established SQI using the optimal method was supported by its strong correlation with the corn yield, with a Pearson coefficient and linear R² of 0.75 and 0.56, respectively. The BST identified by differences in colour across the soil profile provided powerful information for the SQI, the value of which would be underestimated by 8% if this index were ignored. The linear R² between the SQI and corn yield decreased from 0.56 to 0.49 when the BST index was removed. This study showed the significance of improving the SQ in thin-layer black-soil regions. The core of soil management is to prevent the losses of surface black soil and improve the SOM content in this region. These findings can help farmers and decision makers adopt proper measures to improve SQ and thereby crop yield.

As a vital indicator for measuring the coupled carbon–water cycle of an ecosystem, water use efficiency (WUE) can also reflect the adaptive capacity of plants in different ecosystems. Located in Southwest China, the Li River Basin has a representative karst landform, and the uneven rainfall in the region leads to severe water shortage. In this study, we analyzed the spatial–temporal transformation characteristics of the WUE of the basin and its relationship with different influencing factors from 2001 to 2020 based on a correlation analysis and trend analysis. The main conclusions are as follows: (1) The average value of WUE in the Li River Basin was 1.8251 gC· mm⁻¹·m⁻², and it kept decreasing at a rate of 0.0072 gC· mm⁻¹·m⁻²·a⁻¹ in the past 20 years. With respect to the spatial distribution of the multi-year average of WUE, it exhibits a gradual increasing trend from west to east. (2) Between gross primary productivity (GPP) and evapotranspiration (ET), it was found that ET was the primary influencing factor of WUE. Precipitation was positively correlated with WUE in the Li River Basin, accounting for 67.22% of the total area of the basin. The air temperature was negatively correlated with WUE, and the area was negatively correlated with WUE, accounting for 92.67% of the basin area. (3) The normalized difference vegetation index (NDVI) and leaf area index (LAI) were negatively correlated with WUE, and the proportions of negatively correlated areas to the total area of the basin were similar; both were between 60 and 70%. The growth of vegetation inhibited the increase in WUE in the basin to a certain extent. Regarding Vapor Pressure Deficit (VPD), the proportions of positive and negative correlation areas with WUE were similar, accounting for 49.58% and 50.42%, respectively. (4) The occurrence of drought events and the enhancement in its degree led to a continuous increase in WUE in the basin; for different land cover types, the correlation of the standardized precipitation evapotranspiration index (SPEI) was in the following order from strongest to weakest: grassland > cropland > forest > shrubland.

Heavy metal pollution poses significant environmental challenges, and understanding how plants and endophytic bacteria interact to mitigate these challenges is of utmost importance. In this study, we investigated the roles of endophytic bacteria, particularly Chryseobacterium and Comamonas, in Leersia hexandra Swartz (L. hexandra) in response to chromium and nickel co-pollution. Our results demonstrated the remarkable tolerance of Chryseobacterium and Comamonas to heavy metals, and their potential to become dominant species in the presence of co-pollution. We observed a close relationship between these endophytic bacteria and the significant differences in metabolites, particularly carbohydrates, flavonoids, and amino acids in L. hexandra. These findings shed light on the potential of endophytic bacteria to promote the production of aspartic acid and other metabolites in plants as a response to abiotic stressors. Furthermore, our study presents a new direction for plant and bioremediation strategies in heavy metal pollution and enhances our understanding of L. hexandra’s mechanisms for heavy metal tolerance.

In this study, the hydrochemistry-runoff method and remote sensing estimation method were used to calculate the karst carbon sink flux (KCSF) and the forest vegetation carbon sequestration flux (FVCSF) in Darongjiang (DRJ) and Lingqu (LQ) watersheds. The results show the following: (1) The KCSF in DRJ and LQ watersheds is 238.43 × 10⁵ t·y⁻¹ and 353.44 × 10⁵ t·y⁻¹, respectively. Influenced by changes in flow rate, the two watersheds both show that their KCSF is higher in the rainy season than in the dry season. (2) The FVCSF in DRJ and LQ watersheds was 680.78 × 10⁷ t·y⁻¹ and 229.63 × 10⁷ t·y⁻¹, respectively. Through comparison, it can be seen that the FVCSF is much higher than the KCSF in both watersheds, but the FVCSF and the KCSF are at the same order of magnitude. (3) Through further analysis of the influence factors on the FVCSF and the KCSF, we found that the highest values of FVCSF are mainly distributed around Kitten Mountain on the upper reach and along the LQ watershed. That may be because most of the upper reach areas are mountainous forestland, and the strong weathering of rocks in the LQ watershed promotes the development of forest vegetation carbon sequestration. Therefore, it can be inferred that there is a certain synergistic effect between the karst carbon sink (KCS) and the forest vegetation carbon sequestration (FVCS) in the study area, and such a synergistic effect is caused by rock weathering.

Historical surveys and current soil maps can identify soil differences and establish carbon monitoring baselines. We compared 330 samples from 1980 to 1983 with 376 independent samples from 2025 in Pingle County, China. Random forest models used 11 common covariates and nested spatial cross-validation. Land-cover maps supported comparisons of predicted soil organic matter (SOM). Cropland and forest sample means were 4.70 and 4.97 g/kg lower in 2025. Within the common valid area, predicted means were 26.43 and 23.54 g/kg. The mean predicted difference was −2.89 g/kg, with a conditional block-resampling range of −5.61 to −0.13 g/kg. Mean differences were −2.31, −3.47, and −4.81 g/kg for forest–forest, cropland–cropland, and forest–cropland combinations. With water bodies and built-up land excluded, a 20 cm depth and 0.58 conversion factor gave a 2025 soil organic carbon stock of 6.180 Tg C. Mean density was 3.35 kg/m², and the joint-model resampling range was 5.805–6.497 Tg C. Forest and cropland contributed 74.01% and 25.97%. Unpaired sampling and unverified analytical comparability prevent direct inference of temporal change or conversion effects. Conditional ranges exclude potential analytical bias between periods. These estimates provide a spatial basis for identifying priorities for soil carbon protection and soil quality resampling at the county scale. They can support sustainable land management and future soil carbon monitoring in karst landscapes.