新技术识别“最优饲草带”助力保障北方干旱区饲草安全

Core Insights - The research team led by Wang Shudong at the Chinese Academy of Sciences has made breakthroughs in artificial intelligence and remote sensing technology, successfully identifying optimal forage belts at kilometer scales in arid and semi-arid regions of northern China [1][3] - The results have been published in the international journal "Water Research" and are expected to provide scientific support for ecological protection in the Yellow River basin and national forage security [1] Group 1 - The team integrated satellite remote sensing, ecological hydrological models, and ground measurement data to reduce reliance on high-density ground sampling [1] - Key production factors such as irrigation water usage and vegetation productivity have been improved to over 90% accuracy through the use of ensemble learning and transfer learning techniques [1] - The introduction of regional bias correction technology has increased the accuracy of optimal forage belt identification to over 85% [1] Group 2 - Unlike traditional assessments that focus on single indicators, this technology constructs a three-dimensional collaborative optimization model of "water resource consumption - soil carbon sequestration - forage productivity" [3] - The unified evaluation system incorporates ecological, economic, and water cost factors, presenting results in a "one-map visualization" format [3] - This allows managers to directly identify priority planting areas and input-output ratios, providing precise decision-making support for resource allocation [3]