As one of the extreme climatic events, the frequency and intensity of drought have great impacts on regional water resource. Water is a main limiting factor for plant growth in arid and semi-arid regions. Therefore, it is of great scientific significance to explore the spatiotemporal variations and future tendency of drought for the ecological environment in the Loess Plateau. Based on grid data of monthly precipitation and temperature from 1986 to 2019, we calculated standardized precipitation evapotranspiration index (SPEI) and drought frequency. The spatiotemporal patterns and its variations were analyzed at the seasonal and annual scales in the Loess Plateau using the Mann-Kendall test and Sen's slope estimation method. Finally, the future trend of drought was analyzed in the Loess Plateau by the NAR neural network combined with Hurst index. Results showed that the trend of aridification became more significant in the Loess Plateau, and that the frequency of droughts events exhibited great spatial variations at the interannual and seasonal scales during the study period. Specifically, the highest frequency of drought in the interannual, spring and winter was found in the southeast and west of the Loess Plateau, whereas the frequency of drought in summer and autumn was higher in the northwest. The frequency of moderate drought was the highest in summer compared with other seasons while the frequency of slight drought was the highest in interannual and other seasons. The Loess Plateau showed a trend of aridification in spring and summer, but this trend in autumn and winter became weaker in most areas of the study area. The SPEI value in the interannual, spring, and summer exhibited a decline trend in a future period in the Loess Plateau. The aridification would be enhanced. The Hurst index value was the largest and the persis-tence of its change remained stronger in summer. The possibility of continuous drought in summer would be higher than that in other seasons in the future.
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http://dx.doi.org/10.13287/j.1001-9332.202102.012 | DOI Listing |
Pest Manag Sci
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Key Laboratory of Plant Protection Resources and Pest Management of Ministry of Education, Key Laboratory of Integrated Pest Management on the Loess Plateau of Ministry of Agriculture and Rural Affairs, College of Plant Protection, Northwest A&F University, Yangling, China.
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Inner Mongolia Agricultural University, No. 275, XinJian East Street, Hohhot, 010019, China.
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Department of Renewable Resources, University of Alberta, Edmonton, Canada.
Soil microorganisms transform plant-derived C (carbon) into particulate organic C (POC) and mineral-associated C (MAOC) pools. While microbial carbon use efficiency (CUE) is widely recognized in current biogeochemical models as a key predictor of soil organic carbon (SOC) storage, large-scale empirical evidence is limited. In this study, we proposed and experimentally tested two predictors of POC and MAOC pool formation: microbial necromass (using amino sugars as a proxy) and CUE (by O-HO approach).
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Guangdong Provincial Key Laboratory of Applied Botany, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China.
Understanding the complex interactions of plants and soils in the face of global food security and environmental degradation challenges is critical to the future of sustainable agriculture. This review discusses the important link between soil health and crop productivity by providing and comprehensive assessment of soil properties and management methods. By examining the physical, chemical, and biological properties of soil, it uncovers the key limitations posed by the soil environment on crop growth.
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January 2025
College of Resources and Environment, Shanxi University of Finance and Economics, Taiyuan 030006, China.
The Loess Plateau in northwest China features fragmented terrain and is prone to landslides. However, the complex environment of the Loess Plateau, combined with the inherent limitations of convolutional neural networks (CNNs), often results in false positives and missed detection for deep learning models based on CNNs when identifying landslides from high-resolution remote sensing images. To deal with this challenge, our research introduced a CNN-transformer hybrid network.
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