大数据,小解释和预测能力:从农场产量变化的随机森林建模中得出的教训以及对数据驱动农业学的影响
João Vasco Silva1,2, Joost van Heerwaarden2, Pytrik Reidsma2
1Sustainable Agrifood Systems, CIMMYT, Harare, Zimbabwe.
概括
来自农场的大数据可以解释一些作物产量变化,但在多种不同的农业系统中难以预测. 机器学习模型表现出有限的成功,特别是在热带地区的小农场.
科学领域:
- 农业学是一种农业学.
- 数据科学数据科学数据科学
- 农业经济学 农业经济学
背景情况:
- 对农场生产数据的分析对于理解产量变化和提高资源利用效率至关重要.
- 由于复杂的环境和管理因素,全球农业系统在优化作物产量方面面临挑战.
研究的目的:
- 评估统计和机器学习方法在解释和预测各种国际农业系统的作物产量的有效性.
- 用农民现场数据的大型数据集来评估不同的建模方法的性能.
主要方法:
- 分析了来自三个不同农业强化水平的国家10940种田年组合.
- 应用随机效应模型用于产量变化分割和随机森林模型用于产量解释和预测.
- 利用与空间和时间数据重新采样的交叉验证来测试模型的稳定性.
主要成果:
- 在荷兰的小麦/大麦和埃塞俄比亚的小麦中,收益率的变化最低,在菲律宾的水中,收益率的变化中介,在埃塞俄比亚的玉米中,收益率的变化最高.
- 随机森林模型在荷兰解释了高达65%的产量变化,但在埃塞俄比亚和菲律宾不到45%.
- 变量的重要性因地区而异:埃塞俄比亚的作物管理,菲律宾的预测气候和荷兰的解释气候.
结论:
- 大规模的农场数据提供了对收益率变化的部分见解,但在预测不同时间和地点的收益率方面存在局限性.
- 这项研究表明,对农业学中的大数据和机器学习持谨慎乐观态度,特别注意热带小农系统的表现较差.
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