With increasing concentrations of atmospheric greenhouse gases, the interaction between climate change and agriculture is receiving widespread attention as part of food security and sustainable human development. However, a comprehensive summary of knowledge in the field of climate change and agriculture from a scientometric perspective is still lacking. Here, we selected 25,872 papers related to climate change and agriculture from the Web of Science Core Collection database for the period 1985 to 2023 and used VOSviewer software to reveal the research status and trends. The main results were as follows: (1) the number of papers in this field showed a rapidly increasing trend after 2007, with a clear interdisciplinary characteristic; (2) The United States was the most influential country in this field with 6819 papers and 363,109 citations. China had the second highest number of papers (3722 papers), but the Chinese Academy of Sciences was the most influential institution with the most papers. On an author level, Pete Smith was the most influential; (3) All keywords were divided into four different research topics, such as the impact of climate change on agriculture, climate change mitigation and adaptation in agriculture, and crop growth in response to climate change. Among them, some keywords related to climate change adaptation were the most recent topics of interest in this field. These findings provide effective scientific references for relevant scientists and practitioners to better conduct future theoretical and practical research on climate change and agriculture.

Inappropriate human activities contribute to the degradation of ecosystems in arid or semi-arid regions. Therefore, emphasizing the importance of strategies for restoring vegetation in these areas cannot be overstated. However, there has been insufficient research on how to develop effective restoration strategies at minimal cost. This paper addresses this gap by studying how optimizing the spatiotemporal distribution of human activities through local and boundary controls can reduce the level of desertification in vegetation pattern structures, thereby facilitating the recovery of arid land vegetation. The results indicate that vegetation restoration depends on the proportion and number of human activity areas, with a trade-off between them. Furthermore, consistent conclusions were obtained on circular regions, demonstrating the robustness of the approach to boundary shapes. This paper aims to offer new insights into the restoration of arid land vegetation and the prevention of catastrophic ecosystem changes from the perspective of optimal control.

Spring phenology is often considered the start of season (SOS) for vegetation, which can affect ecosystem photosynthesis, respiration, and evapotranspiration. However, the long-run variation of SOS remains unclear at the regional scale. In this research, the long-term variation of SOS in northern China was explored by using the updated normalized difference vegetation index and monthly climatic data during 1982–2014. Furthermore, the relative importance of climatic factors on SOS was analyzed through partial correlation and multivariate regression methods. The main results were as follows: (1) average SOS largely ranged between day 120 and 165 of the year and varied widely for different vegetation types; (2) SOS during 1982–2014 showed an advancing trend, but it appeared to be reversed after 1998; (3) preseason minimum temperature was a dominant factor controlling SOS in most pixels in northern China, followed by maximum temperature (Tmx). However, impacts of radiation and precipitation on the trend of SOS primarily depended on vegetation types; (4) impacts of climatic factors on SOS declined in the period after 1998, especially for Tmx. These findings provide important support for modeling vegetation phenology and growth in northern China.

Vegetation plays a crucial role in maintaining the balance between nature, water and soil resources. However, understanding its impact mechanisms in arid and semi-arid areas remains limited. This study aims to analyze the spatial–temporal characteristics of the vegetation leaf area index (LAI) and climate elements in typical regions of northwest China and the correlations between LAI and climate elements; it also aims to explore the influence of regional vegetation growth on climate change. The results reveal significant correlations between LAI and various climate elements. Specifically, within the same region, surface temperature, precipitation, vegetation transpiration, and total evaporation show positive correlations with the LAI, whereas surface albedo shows a negative correlation. Vegetation may affect climate through both heat and water exchange between the land and atmosphere. Increased vegetation leads to the enhanced absorption of solar radiation by the land surface, elevating surface temperature. Increased levels of vegetation also increase vegetation transpiration and total evaporation, increasing the water vapor content in the atmosphere and thus leading to increased surface precipitation. Therefore, vegetation distribution plays a role in climate change, and ecological restoration projects in the northwest region hold significant potential for addressing ecological challenges in its arid and semi-arid areas.

Understanding the dynamics of vegetation autumn phenology (i.e., the end of growing season, EOS) is crucial for evaluating impacts of climate change on vegetation growth. Nevertheless, responses of the EOS to climatic factors were unclear at the regional scale. In this study, northern China was chosen for our analysis, which is a typical ecologically fragile area. Using the Enhanced Vegetation Index (EVI) and climatic data from 1982 to 2016, we extracted the EOS and analyzed its trends in northern China by using the linear least-squares regression and the Bayesian change-point detection method. Furthermore, the partial correlation analysis and multivariate regression analysis were used to determine which climatic factor was more influential on EOS. The main findings were as follows: (1) multi-year average of EOS mainly varied between 275 and 305 day of year (DOY) and had complicated spatial differences for different vegetation types; (2) the percentage of the pixel showing delaying EOS (65.50%) was larger than that showing advancing EOS (34.50%), with a significant delaying trend of 0.21 days/year at the regional scale during the study period. As for different vegetation types, their EOS trends were similar in sign but different in magnitude; (3) temperature showed a dominant role in governing EOS trends from 1982 to 2016. The increase in minimum temperature led to the delayed EOS, whereas the increase in maximum temperature reversed the EOS trends. In addition to temperature, the impacts of precipitation and radiation on EOS trends were more complex and largely depended on the vegetation types. These findings can provide a crucial support for developing vegetation dynamics models in northern China.