Spatiotemporal variations and driving factors of global terrestrial vegetation productivity gap under the changing of climate, CO2, landcover and N deposition.

Understanding the gap between potential productivity and the actual productivity of vegetation (vegetation productivity gap, VPG) is the basis to explore the potential productivity improvement and identify its constraints. In this study, we used the classification and regression tree model to simulate the potential net primary productivity (PNPP) based on the flux-observational maximum net primary productivity (NPP) across different vegetation types, that is, potential productivity. The actual NPP (ANPP) is obtained from the grid NPP averaged over five terrestrial biosphere models, and the VPG is subsequently calculated. On this basis, we used the variance decomposition method to separate the effects of climate change, land-use change, CO2, and nitrogen deposition on the trend and the interannual variability (IAV) of VPG from 1981 to 2010. Meanwhile, the spatiotemporal variation characteristics and influencing factors of VPG under future climate scenarios are analyzed. The results showed an increasing trend in PNPP and ANPP, while there was a decreasing trend of VPG in most parts of the world and this trend is more significant under representative concentration pathways (RCPs). The turning points (TP) of VPG variation are found under RCPs and the reduction trend of VPG before TP is more than that after TP. The VPG reduction in most regions was caused by the combined effects of PNPP and ANPP (41.68 %) from 1981 to 2010. However, the dominant factors of global VPG reduction are changing under RCPs, and the increment of NPP (39.71 % - 49.3 %) has become the dominating factor of VPG variation. CO2 plays a decisive role in the multi-year trend of VPG, while climate change is the main factor determining the IAV of VPG. Under changing climate, temperature and precipitation are negatively correlated with VPG in most parts of the world, and the relationship between radiation and VPG from weak negative to positive correlation.

A novel rapid web investigation method for ecological agriculture patterns in China.

The investigation of Ecological Agriculture (EA) patterns can reveal the differences, aggregation, and diversity of agricultural development, providing specific paths in agricultural development and environmental protection to achieve the Sustainable Development Goals. Although field surveys, literature analysis, and the method using administrative statistics can be employed to investigate EA records and determine EA distributions comprehensively, they still rely on manual operations that are generally unable to support the rapid and large-scale identification of EA patterns required by current agricultural sustainable researches. To address this issue, this paper proposes a novel and rapid approach for Ecological Agriculture Pattern Investigation Based on Web-text (WEAPI), with the ability to automatically acquire EA pattern records, including pattern type, occurrence time, precise location, and other relevant information. The proposed method is employed in a national-scale case study to investigate trends in Chinese Ecological Agriculture (CEA). Results of the study reveal WEAPI's ability to detect new trends in CEA via the latest news and the corresponding distributions. The WEAPI method can also exhibit the unknown patterns of the current Chinese agricultural development. Further validation experiments demonstrate that the proposed method achieves over 95 % precision in the pattern parse processes and an 87 % coverage rate at the town level of the official CEA pattern list. Moreover, WEAPI can provide dynamic changing analyses on the annual evolution of the EA patterns in each type. Despite limitations under sparse records in partial classes, the results reveal WEAPI as a promising and powerful tool for agricultural research and agricultural development planning.