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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.

A timely and accurately predicted grain yield can ensure regional and global food security. The scientific community is gradually advancing the prediction of regional-scale maize yield. However, the combination of various datasets while predicting the regional-scale maize yield using simple and accurate methods is still relatively rare. Here, we have used multi-source datasets (climate dataset, satellite dataset, and soil dataset), lasso algorithm, and machine learning methods (random forest, support vector, extreme gradient boosting, BP neural network, long short-term memory network, and K-nearest neighbor regression) to predict China’s county-level maize yield. The use of multi-sourced datasets advanced the predicting accuracy of maize yield significantly compared to the single-sourced dataset. We found that the machine learning methods were superior to the lasso algorithm, while random forest, extreme gradient boosting, and support vector machine represented the most preferable methods for maize yield prediction in China (R2 ≥ 0.75, RMSE = 824–875 kg/ha, MAE = 626–651 kg/ha). The climate dataset contributed more to the prediction of maize yield, while the satellite dataset contributed to tracking the maize growth process. However, the methods’ accuracies and the dominant variables affecting maize growth varied with agricultural regions across different geographic locations. Our research serves as an important effort to examine the feasibility of multi-source datasets and machine learning techniques for regional-scale maize yield prediction. In addition, the methodology we have proposed here provides guidance for reliable yield prediction of different crops.

Dry Land Asia is the largest arid and semi-arid region in the northern hemisphere that suffers from land desertification. Over the period 1982–2011, there were both overall improvement and regional degeneration in the vegetation NDVI. We analyze future climate changes in these area using two ensemble-average methods from CMIP5 data. Bayesian Model Averaging shows a better capability to represent the future climate and less uncertainty represented by the 22-model ensemble than does the Simple Model Average. From 2006 to 2100, the average growing season temperature value will increase by 2.9 °C, from 14.4 °C to 17.3 °C under three climate scenarios (RCP 26, RCP 45 and RCP 85). We then conduct multiple regression analysis between climate changes compiled from the Climate Research Unit database and vegetation greenness from the GIMMS NDVI3g dataset. There is a general acceleration in the desertification trend under the RCP 85 scenario in middle and northern part of Middle Asia, northwestern China except Xinjiang and the Mongolian Plateau (except the middle part). The RCP 85 scenario shows a more severe desertification trend than does RCP 26. Desertification in dry land Asia, particularly in the regions highlighted in this study, calls for further investigation into climate change impacts and adaptations.

The soil chronosequence is a useful method for investigating pedological theories. Soil chemical, physical and mineralogical properties in chronosequences change over time and exhibit systematic and time-dependent trends, which can be used to analyze the rates and directions of pedogenic changes. The potential of soil spectroscopy as an emerging, rapid and cost-effective technique for predicting soil properties has been widely accepted and has motivated the application of spectroscopic techniques to the analysis of soil chronosequence. We present a soil chronosequence derived from 1000-year-old calcareous marine sediments and examine changes in six soil properties over this period. We evaluated the utility of a soil spectroscopic method to detect soil property changes and to predict the pedogenic properties and soil ages of the chronosequence. The results show that some soil pedogenic processes, such as soil organic matter accumulation, CaCO3 leaching and clay migration, can be identified in the millennium chronosequence. Power chronofunctions are formulated for soil organic matter (SOM) and Logarithmic chronofunctions are fitted for clay, CaCO3 and pH. These pedogenic processes are identified in the reflectance intensity and absorption features of soil spectroscopy, and pedogenic properties can be calibrated via soil reflectance spectroscopy. Profile ages can also be predicted via pseudo multi-depth spectra of soil profiles, and soil spectral curves for 0−30 cm generated the best prediction results (RPD = 1.85). We conclude that soil properties, changing due to weathering and soil formation, act as a bridge linking spectroscopy and weathering levels/pedogenic processes. The results imply that applying spectroscopy techniques to chronosequence study and mapping the degree of soil development in certain areas should be possible.

This study evaluates the performances of three global satellite datasets (Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS) and Satellite pour l’ observation de la Terre (SPOT) of the Mongolian Plateau, where in situ observation is insufficient to assess vegetation dynamics on terrestrial systems. We give a comprehensive assessment of the historical changes in vegetation dynamics by using comparative and correlation methods on the three archives using two indices: the growing season’s Normalized Difference Vegetation Index (NDVI) and the Start of the Season Index (SOS). The main findings are: (1) MODIS and SPOT have generally better comparability and consistency in the spatial-temporal trends of NDVI and SOS than AVHRR in this area; (2) all the three archives exhibit better consistency in Inner Mongolia than in Mongolia; (3) integration data analysis of AVHRR (1982–1997) and SPOT (1998–2012) shows that the dynamics of vegetation growth has three distinct phases: enhanced before 1994; a flatter/slightly decreasing trend before 2001; and, then, a rapid recovery between 2001 and 2012 with remarkable spatial heterogeneity, with Inner Mongolia experiencing a significant greening in vegetation NDVI compared with no obvious changes in Mongolia; (4) the temporal average SOS showed no significant “earlier spring” onset during the past 31 years, on the middle and northern Mongolian Plateau.

This paper discussed the analysis of the survey on sustainability of bioenergy conducted in the Philippines, India and China. It acquired general perceptions of the people by asking them (a) specific questions about their level of familiarity with bioenergy; (b) relationship of their work to bioenergy; and (c) their opinion on contribution of various feedstock on the economy and impact of bioenergy production on food security. In addition to these questions, we estimated preference weights of various feedstock based on the conjoint choices on bioenergy’s contribution to social stability, social welfare and ecological balance. The estimates revealed significant trade-offs not only among these three dimensions of sustainability but also the relative importance of energy security, food security and ecosystem capacity to other economic, social and environmental objectives. The types of first generation feedstock that are currently used for biofuel production in the respective countries and those that offer alternative household use are perceived as important to the economy and preferred bioenergy feedstock. Based on the results of the study, the preferred role of bioenergy for sustainable development reflects the social and economic concerns in the respective Asian countries, e.g., energy security in China, food security in India, and ecosystem degradation in the Philippines.