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.

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.

Methane (CH₄) is the second most important anthropogenic greenhouse gas (GHG) after carbon dioxide (CO₂), and paddy fields are among the largest sources of CH₄ emissions. Owing to the scarcity of observational data, the characteristics and influencing factors of CH₄ fluxes in paddy fields at different timescales need to be further investigated. Observations of CH₄ fluxes via eddy covariance (EC) data were performed over four seasons in two paddy fields in Nanchang, Jiangxi Province, China. The pattern, magnitude and biophysical controls of CH₄ emissions were explored by wavelet analysis and stepwise multiplicative modelling. The results revealed a distinct, single-peak diurnal pattern in CH₄ fluxes during the vegetative stage in all four rice growing seasons and the reproductive stage of early rice. Large seasonal variations in daily CH₄ emissions were observed in the two double-cropping paddy fields, and the soil temperature at a depth of 5 cm (T_s5) explained most of the seasonality of the CH₄ fluxes. At the inter-seasonal scale, under local farms’ traditional field management method, reducing the amount and frequency of irrigation during the vegetative stage could decrease CH₄ emissions in southern China. This study improves the understanding of CH₄ emissions and helps in developing GHG management strategies for paddy fields.

Direct-seeded rice (DSR) has received much attention because of its advantages in having low labor costs compared to the traditional transplanted rice (TPR). Investigating the differences in evapotranspiration (ET) partitioning and crop coefficients (K_c) between DSR and TPR is essential in understanding how agricultural water demand is affected by crop rotation. In this study, the water fluxes of two-year (2017–2018) growing seasons were collected from a pair of eddy covariance (EC) towers for DSR and TPR in the Poyang Lake Basin, Southern China. This study aims to compare the seasonal characteristics of the ET components (evaporation, E, and transpiration, T) and dual crop coefficients (basal crop coefficient, K_s·K_cb, and soil/water crop coefficient, K_e) of DSR with those of TPR. The ET values for the 2017 and 2018 growing seasons were 374 mm and 436 mm for the DSR, respectively, and 309 mm and 342 mm for the TPR. The seasonal T/ET values in 2017 and 2018 were 0.40 and 0.46 for the DSR, respectively, and 0.49 and 0.52 for the TPR, indicating that the higher ET values for the DSR can be mostly attributed to E. The DSR had overall higher K_c values than the TPR because of free water evaporation during the initial stage and a higher plant density. Our results enrich the K_c dataset for DSR and have great implications for the sustainable irrigation of the Poyang Lake Basin in the future.

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.

Knowledge of the responses of winter wheat yield to meteorological dryness/wetness variations is crucial for reducing yield losses in Henan province, China’s largest winter wheat production region, under the background of climate change. Data on climate, yield and atmospheric circulation indices were collected from 1987 to 2017, and monthly self-calibrating Palmer drought severity index (sc-PDSI) values were calculated during the winter wheat growing season. The main results were as follows: (1) Henan could be partitioned into four sub-regions, namely, western, central-western, central-northern and eastern regions, based on the evolution characteristics of the time series of winter wheat yield in 17 cities during the period of 1988–2017. Among them, winter wheat yield was high and stable in the central-northern and eastern regions, with a remarkable increasing trend (p < 0.05). (2) The sc-PDSI in February had significantly positive impacts on climate-driven winter wheat yield in the western and central-western regions (p < 0.05), while the sc-PDSI in December and the sc-PDSI in May had significantly negative impacts on climate-driven winter wheat yield in the central-northern and eastern regions, respectively (p < 0.05). (3) There were time-lag relationships between the sc-PDSI for a specific month and the atmospheric circulation indices in the four sub-regions. Furthermore, we constructed multifactorial models based on selected atmospheric circulation indices, and they had the ability to simulate the sc-PDSI for a specific month in the four sub-regions. These findings will provide scientific references for meteorological dryness/wetness monitoring and risk assessments of winter wheat production.