根据共同的社会经济路径,对未来农业管理和作物选择的预测
Sitong Wang1,2,3, Xiuming Zhang4, Ouping Deng1,2,5
1State Key Laboratory of Soil Pollution Control and Safety, Zhejiang University, Hangzhou, 310058, China.
Scientific data
|November 18, 2025
概括
这项研究引入了一个新的全球数据集,预测到2100年,在197个国家为10种主要作物收获的作物面积和农业投入. 它使用数据驱动方法在各种气候和社会经济情景下建模未来的农业景观.
科学领域:
- 农业科学 农业科学
- 环境科学 环境科学
- 社会经济学 社会经济学
背景情况:
- 未来的农业景观受到社会经济因素和环境条件的影响.
- 现有的模型经常使用理想化的假设,限制了它们代表现实世界的复杂性的能力.
- 需要数据驱动的方法来补充基于理论的模型.
研究的目的:
- 开发一套新的全球数据集,用于十个主要作物组的预计收获面积分配.
- 根据六种共享社会经济路径-代表性度路径 (SSP-RCP) 情景,提供2020年至2100年的年度预测.
- 支持对粮食安全,贸易政策和环境影响的评估.
主要方法:
- 使用了基于历史数据校准的统计固定效应模型.
- 为197个国家和地区开发了一个全新的全球数据集.
- 纳入气应用,灌和机械化水平的预测.
主要成果:
- 对于十个主要作物组的收获面积分配的年度预测.
- 根据六种SSP-RCP情景,详细说明农业管理投入的未来发展轨迹.
- 为每个场景提供预计的作物份额.
结论:
- 该数据集为分析未来农业景观提供了一个一致的,数据驱动的基础.
- 它可以全面评估全球粮食安全,贸易动态和环境影响.
- 这种方法提高了对塑造未来农业的复杂相互作用的理解.
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