1961-2019年全球作物特异性施肥数据集
Fernando Coello1,2,3, Thomas Decorte4, Iris Janssens5
1Universitat Autònoma de Barcelona, 08193, Bellaterra, Spain. f.coello@creaf.uab.cat.
Scientific data
|January 9, 2025
概括
这项研究使用机器学习开发了精确的肥料应用率预测. 由此产生的数据集有助于理解受精趋势及其对粮食安全和气候变化减缓的驱动因素.
科学领域:
- 农业科学 农业科学
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 随着全球肥料使用的增加,需要高质量的数据来做出明智的决策.
- 现有的数据集存在差距,阻碍了对肥料应用趋势的全面分析.
研究的目的:
- 通过预测 (N),氧化 (P2O5) 和氧化 (K2O) 应用率来填补数据缺口.
- 创建一个高分辨率的数据集,用于1961年至2019年的13个主要作物组的肥料应用率.
- 确定肥料使用的社会经济,农业和环境驱动因素.
主要方法:
- 采用了 eXtreme渐变增强和HistGradientBoosting机器学习模型.
- 生成了肥料应用率的5arcmin分辨率地图.
- 根据现有数据库验证了预测,并使用了SHapley添加式扩展 (SHAP) 来评估驱动器.
主要成果:
- 为N,P2O5和K2O应用率制造了精确的国家级预测.
- 创建了主要作物组的综合历史数据集 (1961-2019).
- 确定了影响肥料应用率的关键驱动因素.
结论:
- 生成的数据集是评估受精趋势的宝贵资源.
- 能够分析社会经济,农业和环境驱动肥料使用的因素.
- 支持环境建模,因果分析和预测中的应用.
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