在加纳,随机森林机器学习用于玉米产量和农学效率预测
Eric Asamoah1,2,3,4, Gerard B M Heuvelink1,4, Ikram Chairi5
1Soil Geography and Landscape Group, Wageningen University & Research, PO Box 47, 6700, AA, Wageningen, the Netherlands.
Heliyon
|September 17, 2024
概括
机器学习准确地预测了加纳的玉米产量和营养使用效率. 土壤和气候因素是关键的驱动因素,为粮食安全提供可持续肥料建议.
科学领域:
- 农业科学 农业科学
- 机器学习 机器学习
- 农业学是一种农业学.
背景情况:
- 玉米 (Zea mays) 对于撒哈拉以南非洲的粮食安全至关重要,但产量增加往往取决于土地扩张,导致环境问题.
- 提高每单位面积的产量对于加纳的可持续玉米生产至关重要.
- 准确预测玉米产量和营养使用效率对于明智的决策至关重要.
研究的目的:
- 开发和评估一种随机森林机器学习模型,用于预测加纳的玉米产量和农业效率.
- 确定影响玉米生产的关键土壤,气候,环境和管理因素.
- 为改善可持续玉米生产的肥料建议提供见解.
主要方法:
- 在加纳 (1991-2020年) 训练了一种随机森林机器学习算法,使用来自482个玉米田间试验 (3136个地块) 的数据.
- 采用5x10倍嵌交叉验证方法进行模型校准和评估.
- 分析了预测变量 (土壤,气候,环境,管理) 对产量和农业效率的重要性.
主要成果:
- 随机森林模型显示了玉米产量的良好预测性能 (MEC=0.81).
- 在农学效率方面,预测性能达到中等水平 (AE-N:MEC=0.63,AE-P:MEC=0.55,AE-K:MEC=0.54).
- 土壤变量对于产量预测比气候变量更重要;温度对产量至关重要,降雨对农业效率至关重要.
结论:
- 随机森林模型增强了对热带气候中玉米产量和农业效率驱动因素的理解.
- 这些发现为优化肥料建议提供了宝贵的见解,以支持可持续的玉米生产.
- 这种方法有助于通过数据驱动的农业实践改善撒哈拉以南非洲的粮食安全.
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