Publications by authors named "Zi-Long Su"
Article Synopsis
- Accurate forecasting of agricultural carbon emissions is crucial for achieving carbon peak and neutrality goals, but current methods have limitations.
- A study at a Shanghai farm used a Back Propagation neural network enhanced with a sparrow search algorithm to predict carbon emissions, achieving a high prediction accuracy of 96.14%.
- The analysis revealed that animal husbandry is the primary contributor to carbon emissions, indicating that better management in this sector could help reduce overall emissions.
Shelterbelt system is one of the main components of cultivated slope land in rolling hill black soil region of Northeast China, which plays an important role in the control of gully erosion. Based on the Quickbird high-resolution remote sensing image and the digital elevation model (DEM), and combining with field survey data, this paper analyzed the effects of shelterbelt system in a small watershed of rolling hill black soil region in Heshan Farm of Heilongjiang Province on the control of gully erosion in the cultivated slope land, and put forward an optimized scheme for gully erosion control based on the features of gully erosion in the cultivated slope land and their relations with the distribution of the shelterbelt system. In the study area, the current distribution of the shelterbelt system promoted the occurrence and development of shallow gully and gully directly and indirectly.
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