As climate change intensifies and countries actively pursue carbon peaking and carbon neutrality targets, agriculture has emerged as a significant source of carbon emissions. A comprehensive analysis of global agricultural carbon emission research can enhance the agricultural environment and achieve a mutually beneficial outcome for environmental protection and economic development. Despite the evolution of research domains and methodologies, the global context remains closely connected to the current state of the discipline. Drawing on the Web of Science core collection, this paper develops a knowledge network framework, examines the current status and hotspots of agricultural carbon emissions, forecasts future development trends, and analyzes the findings using CiteSpace visualization software. The findings indicate that the number of papers on agricultural carbon emissions has been increasing annually, with minor fluctuations; time series analysis and sustainable development have emerged as the current focal points, and relevant institutions are collaborating increasingly closely. However, cooperation among scholars requires further enhancement. Countries such as China, the United States, and Germany are the primary nations for paper publication. The hotspot analysis reveals a high frequency of keywords such as greenhouse gas emissions and climate change, indicating that research on agricultural carbon emissions has matured and the emphasis has shifted from accounting to management. This paper develops a domain knowledge framework to assist readers in understanding agricultural carbon emission patterns and provide resources for further research. Follow-up studies should enhance both comprehensiveness and breadth, promote interdisciplinary cooperation, provide a scientific foundation for policymakers, and outline future research directions.

Coffee leaves are a promising raw material for functional beverages due to their distinctive phytochemical profile. In this study, sun-dried Arabica coffee leaves were processed into coffee leaf dark tea using Pu-erh-style pile fermentation with three treatments: spontaneous pile fermentation (SPPF), Saccharomyces cerevisiae-inoculated fermentation (SIPF) and Aspergillus niger-inoculated fermentation (AIPF). Changes in basic components, tea pigments, key phytochemicals, antioxidant capacity, in vitro hypoglycemic and hypolipidemic activities, and sensory properties were evaluated over 25 days. Pile fermentation reduced water extract, free amino acids and several phenolic constituents, while promoting the formation of theabrownins, particularly in SIPF and AIPF. Total phenolics, flavonoids, and antioxidant activities increased initially and then declined, with higher bioactivities observed at intermediate fermentation times. Coffee leaf dark tea also exhibited in vitro α-amylase and α-glucosidase inhibition, glucose diffusion retardation, reduced starch digestibility, and pancreatic lipase inhibition, with SIPF and AIPF outperforming SPPF. Sensory evaluation showed that inoculated fermentations, especially AIPF at 15 days and SIPF at 10 days, produced teas with superior overall quality. These results suggest that Pu-erh-style pile fermentation with targeted microbial inoculation may be a feasible strategy to obtain coffee leaf dark tea with enhanced in vitro functional-related properties and desirable sensory characteristics.

The reference evapotranspiration (ETo) is essential for water-consuming in agriculture and land-water cycle research. The synoptic data from meteorological stations can provide reliable ground data for ETo estimation with the FAO-56 Penman-Monteith equation. However, the five primary variables this equation needs, including maximum temperature (Tmax), minimum temperature (Tmin), sunshine duration (SSD), wind speed (Wind), and relative humidity (RH), often experience severe data loss due to force majeure events in synoptic data. The data loss would directly introduce severe data gaps to the complex records for ETo. Machine learning algorithms can fill various data gaps with low error rates, however, to achieve high data quality, the algorithms must be selected properly to deal with the distinct types of data loss and train independently. Here, based on the data characters, we investigated and classified data gaps from the synoptic dataset into 2 major types: the common, minor data loss gaps including Tmax loss/Tmin loss/SSD loss/Wind loss/RH loss/Wind and SSD loss/Wind and RH loss, and the other 19 types of data loss which is more severe in information loss but barely occurred. Our results show that the XGBoost model achieved the best accuracy in all 3 machine learning models with high statistic levels. For the other 19 types of data gaps, the LSTM models were trained separately for each site and achieved average R², RMSE, and nRMSE at 0.9, 0.5 mm d^-1, and 38 % for the total 2419 stations. Thus, we propose a high-quality, gap-filled daily ETo dataset during 1951–2021 for China with the proportion of large errors (the data with daily ETo errors more than 1.5 mm d^-1) below 0.2 %. Our results also reveal that the entanglement degree between synoptic variables varies a lot from region to region in China. Although most research indicates that wind speed is not very important for ETo estimation with machine learning models, our findings reveal that wind speed played a more significant role in ETo estimation in most areas of China during the years before the 21st century. Still, the impact of wind speed on ETo has also been alleviated in recent years. This ETo dataset for China is available online at https://doi.org/10.5281/zenodo.11496932 (Zhou et al., 2024).