使用非线性增长函数进行作物产量和水生产率建模
Iman Hajirad1, Khaled Ahmadaali2, Abdolmajid Liaghat1
1Department of Irrigation and Reclamation Engineering, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.
非线性物流和戈珀茨模型准确地预测了灌玉米的生长,优化了干旱地区的灌和产量. 这些模型通过模拟不同温度和水的可用性下的作物动态来增强精确农业.
科学领域:
- 农业科学 农业科学
- 植物生理学 植物生理学
- 生物数学是生物数学.
背景情况:
- 精准农业依赖于了解植物生长动态,以获得最佳的生产力.
- 作物生长机制很复杂,需要精确的建模才能有效管理.
研究的目的:
- 采用非线性物流和戈珀茨模型来预测灌玉米的产量和水的生产力.
- 在干旱/半干旱地区的不同灌制度下,使用生长度日 (GDD) 评估模型性能.
主要方法:
- 使用了Logistic,Gompertz和sigmoid生长模型来模拟化玉米的生长.
- 应用赤字 (60%,80%) 和完全 (100%) 的灌制度 (W2,W3,W1).
- 使用R2,NRMSE和MAPE评估模型,GDD作为一个关键预测因素.
主要成果:
- 后勤和Gompertz模型表现出高准确度 (R2>99%脉冲,>80%连续灌).
- 在1014°C GDD (种植后50天) 发现的最大生物产量.
- 增长率模式不同:钟形 (Logistic) 和右倾 (Gompertz).
结论:
- 后勤和Gompertz模型有效地模拟了在不同灌和温度下灌玉米的生长.
- 这些模型为灌和精密管理提供定量预测和决策支持.
- 整合到智能农业增强资源优化和干旱地区的可持续农业.
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