一个数据驱动的作物模型用于生物质生长过程模拟
Yanbin Chang1, Zheng Ni1, Juan S Panelo2,3
1School of Industrial Engineering and Management, Oklahoma State University, Stillwater, OK, United States.
Frontiers in plant science
|December 1, 2025
概括
这项研究提出了一个新的数据驱动作物模型,用于准确的生物质麦产量预测. 该模型有效模拟作物生长,区分环境和管理影响,用于精准农业.
科学领域:
- 农业科学 农业科学
- 植物生理学 植物生理学
- 计算生物学 计算生物学
背景情况:
- 准确的作物产量预测对于资源管理至关重要,特别是在变化的气候条件下.
- 生物质生长模拟需要能够考虑复杂的基因型-环境-管理相互作用的模型.
研究的目的:
- 开发一种新的数据驱动作物模型来模拟生物质的表型变化.
- 提高生物质生长和产量预测的准确性.
- 解开环境和管理因素对作物发展的影响.
主要方法:
- 整合了一个详细的生理土发展框架.
- 用实验数据进行基因型参数校准的数据驱动技术的应用.
- 仿真由基因型,环境和管理影响的表型变化.
主要成果:
- 该模型准确地预测了的生物质生产.
- 该模型成功地区分了环境和管理因素对表型的影响.
- 即使使用有限的实验数据,也可以实现有效的模型校准.
结论:
- 开发的模型提高了生物质预测的准确性和适用性.
- 该模型为优化精准农业战略提供了有价值的见解.
- 这种方法提供了一种可靠的方法来模拟不同条件下的作物生长.
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