Quantifying changes in soil organic carbon density from 1982 to 2020 in Chinese grasslands using a random forest model.

China has the second-largest grassland area in the world. Soil organic carbon storage (SOCS) in grasslands plays a critical role in maintaining carbon balance and mitigating climate change, both nationally and globally. Soil organic carbon density (SOCD) is an important indicator of SOCS. Exploring the spatiotemporal dynamics of SOCD enables policymakers to develop strategies to reduce carbon emissions, thus meeting the goals of "emission peak" in 2030 and "carbon neutrality" in 2060 proposed by the Chinese government. The objective of this study was to quantify the dynamics of SOCD (0-100 cm) in Chinese grasslands from 1982 to 2020 and identify the dominant drivers of SOCD change using a random forest model. The results showed that the mean SOCD in Chinese grasslands was 7.791 kg C m-2 in 1982 and 8.525 kg C m-2 in 2020, with a net increase of 0.734 kg C m-2 across China. The areas with increased SOCD were mainly distributed in the southern (0.411 kg C m-2), northwestern (1.439 kg C m-2), and Qinghai-Tibetan (0.915 kg C m-2) regions, while those with decreased SOCD were mainly found in the northern (0.172 kg C m-2) region. Temperature, normalized difference vegetation index, elevation, and wind speed were the dominant factors driving grassland SOCD change, explaining 73.23% of total variation in SOCD. During the study period, grassland SOCS increased in the northwestern region but decreased in the other three regions. Overall, SOCS of Chinese grasslands in 2020 was 22.623 Pg, with a net decrease of 1.158 Pg since 1982. Over the past few decades, the reduction in SOCS caused by grassland degradation may have contributed to soil organic carbon loss and created a negative impact on climate. The results highlight the urgency of strengthening soil carbon management in these grasslands and improving SOCS towards a positive climate impact.

Response of leaf stoichiometry of Potentilla anserina to elevation in China's Qilian Mountains.

Plants adapt to changes in elevation by regulating their leaf ecological stoichiometry. Potentilla anserina L. that grows rapidly under poor or even bare soil conditions has become an important ground cover plant for ecological restoration. However, its leaf ecological stoichiometry has been given little attention, resulting in an insufficient understanding of its environmental adaptability and growth strategies. The objective of this study was to compare the leaf stoichiometry of P. anserina at different elevations (2,400, 2,600, 2,800, 3,000, 3,200, 3,500, and 3,800 m) in the middle eastern part of Qilian Mountains. With an increase in elevation, leaf carbon concentration [(C)leaf] significantly decreased, with the maximum value of 446.04 g·kg-1 (2,400 m) and the minimum value of 396.78 g·kg-1 (3,500 m). Leaf nitrogen concentration [(N)leaf] also increased with an increase in elevation, and its maximum and minimum values were 37.57 g·kg-1 (3,500 m) and 23.71 g·kg-1 (2,800 m), respectively. Leaf phosphorus concentration [(P)leaf] was the highest (2.79 g·kg-1) at 2,400 m and the lowest (0.91 g·kg-1) at 2,800 m. The [C]leaf/[N]leaf decreased with an increase in elevation, while [N]leaf/[P]leaf showed an opposite trend. The mean annual temperature, mean annual precipitation, soil pH, organic carbon, nitrogen, and phosphorus at different elevations mainly affected [C]leaf, [N]leaf, and [P]leaf. The growth of P. anserina in the study area was mainly limited by P, and this limitation was stronger with increased elevation. Progressively reducing P loss at high elevation is of great significance to the survival of P. anserina in this specific region.

Driving Factors and Future Prediction of Land Use and Cover Change Based on Satellite Remote Sensing Data by the LCM Model: A Case Study from Gansu Province, China.

Land use and cover change (LUCC) is an important issue affecting the global environment, climate change, and sustainable development. Detecting and predicting LUCC, a dynamic process, and its driving factors will help in formulating effective land use and planning policy suitable for local conditions, thus supporting local socioeconomic development and global environmental protection. In this study, taking Gansu Province as a case study example, we explored the LUCC pattern and its driving mechanism from 1980 to 2018, and predicted land use and cover in 2030 using the integrated LCM (Logistic-Cellular Automata-Markov chain) model and data from satellite remote sensing. The results suggest that the LUCC pattern was more reasonable in the second stage (2005 to 2018) compared with that in the first stage (1980 to 2005). This was because a large area of green lands was protected by ecological engineering in the second stage. From 1980 to 2018, in general, natural factors were the main force influencing changes in land use and cover in Gansu, while the effects of socioeconomic factors were not significant because of the slow development of economy. Landscape indices analysis indicated that predicted land use and cover in 2030 under the ecological protection scenario would be more favorable than under the historical trend scenario. Besides, results from the present study suggested that LUCC in arid and semiarid area could be well detected by the LCM model. This study would hopefully provide theoretical instructions for future land use planning and management, as well as a new methodology reference for LUCC analysis in arid and semiarid regions.