Understanding the grassland ecosystem is crucial for improving grassland ecosystem functions and services such as climate regulation, water and soil conservation, carbon sequestration, and biodiversity and gene pool maintenance. However, a systematic and comprehensive review of the relevant literature is still unclear and lacking. The VOSviewer software and cluster analysis were used to visually analyze and perform dimension reduction classification on the 27,778 studies related to grassland ecosystem research based on the Web of Science database. The number of publications targeting grassland ecosystem increased rapidly from 2006 to 2021. Ecology, agronomy, and environmental science were the most popular research categories, and the top journal sources were Remote Sensing, Journal of Ecology, and Ecology and Evolution. The leading publishing countries were the United States, China, and Germany. The top three institutions were the Chinese Academy of Sciences, the University of Chinese Academy of Sciences, and Colorado State University. Cooperation between different countries and institutions had increased. Keyword co-occurrence network analysis showed that Biodiversity, Vegetation and Conservation were the most popular study areas, grassland management, climate change, land use pattern, and ecosystem impact were the hot research topics. All studies could be divided into three categories by cluster analysis: grassland ecological characteristics including basic physicochemical properties, vegetation community characteristics, aboveground and belowground biomass, and soil structural quality of grassland; driving mechanisms that demonstrated effects of human activities and climate change on grassland ecosystem function; and grassland ecosystem services that focused the influences of different grassland management strategies on ecological services, animal welfare and human well-being. The three topic categories of reviewed studies were interrelated and consistent with each other, and the performances were progressive. This paper reviewed the trend evolution through keyword hotspots and analyzed the future research directions to provide an important reference for scientists to better respond to the balance of herbage and sustainable utilization of grassland and maintenance of ecological security.

As the most extensive terrestrial ecosystem, grassland exhibits substantial ecological functions and scientific research significance. Conducting a scientific assessment of the soil fertility of grasslands is of paramount importance for attaining sustainable grassland management, especially for the Tibetan Plateau, which has the most vulnerable ecosystem. This study endeavors to evaluate the soil fertility and spatial differentiation patterns of the natural grasslands in the Tibetan Plateau. Initially, we developed a Soil Fertility Evaluation Index (SFEI) for natural grasslands by integrating three representative soil indicators (total nitrogen, soil organic matter, and bulk density) and a vegetation indicator (fractional vegetation cover). The selection of these indicators followed the Minimum Data Set (MDS) principle, ensuring both ecological relevance and consistent data availability across all sampling plots in the Tibetan Plateau. Subsequently, validation based on field sampling data showed an overall accuracy of 69.89%. Moreover, the evaluation result revealed a clear eastward-increasing gradient in soil fertility, with low fertility in the western regions (e.g., Ngari and Nagqu) and medium-to-high fertility in the central and eastern regions (e.g., Lhasa, Yushu, and Golog), consistent with regional hydrothermal patterns. The proposed method offers a novel and practical framework for assessing soil fertility of natural grassland in the Tibetan Plateau, with significant implications for differentiated grassland management and ecological restoration.

Hydrothermal variability, rising evaporative demand, and drought extremes increasingly threaten crop production in semi-arid regions, yet their relative contributions to maize yield variability remain unclear. Here, we examined maize yield responses to growing-season climatic conditions in Lyuliang City, North China, during 2005–2024 using yield statistics and ChinaMet climate data. Trend analysis, Pearson correlation, candidate regression models, standardized coefficients, and generalized additive models were used to identify dominant climatic predictors. Maize yield showed no significant temporal trend during the study period (Sen’s slope = 0.01 t ha⁻¹ yr⁻¹, p = 0.58), whereas growing-season potential evapotranspiration tended to increase (2.81 mm yr⁻¹, p = 0.06). Diurnal temperature range declined significantly (−0.04 °C yr⁻¹, p = 0.01), and minimum SPEI also decreased significantly (−0.04 yr⁻¹, p = 0.01), indicating intensified extreme dry conditions. Maize yield was most strongly correlated with aridity index (r = 0.72, p < 0.001) and water deficit (r = 0.72, p < 0.001), suggesting that hydrothermal balance explained yield variability better than precipitation or temperature alone. The highest-ranked regression model included aridity index, growing-season temperature, diurnal temperature range, and minimum SPEI, explaining 70% of interannual yield variation. Aridity index was the strongest positive predictor, whereas diurnal temperature range had a significant negative association with yield. Although extreme dry conditions intensified over time, minimum SPEI was not directly associated with annual yield, implying that drought impacts may depend on drought timing, crop phenology, and management buffering. These findings highlight the importance of maintaining favorable hydrothermal balance and reducing risks from increasing evaporative demand and temperature variability to support climate-resilient maize production in Lyuliang City and climatically similar rain-fed semi-arid regions of North China.