CSM-CROPGRO模型模拟了松花的现象发展和产量
Obaid Afzal1, Mukhtar Ahmed2, Fayyaz-Ul-Hassan1
1Department of Agronomy, Pir Mehr Ali Shah Arid Agriculture University, Rawalpindi, 46300, Pakistan.
International journal of biometeorology
|March 28, 2024
概括
DSSAT-CSM-CROPGRO-Safflower模型准确地预测了在不同气候条件下树叶的生长和产量. 这种经过验证的作物模拟工具有助于优化松花种植和将其整合到各种农业生态区域.
科学领域:
- 农业科学 农业科学
- 农业学是一种农业学.
- 作物建模作物建模
背景情况:
- 作物模拟模型对于农业决策和作物改进至关重要.
- CSM-CROPGRO模型整合了基因型,环境和管理,以模拟作物表现.
- 了解红杉对不同气候的反应对于提高其生产力至关重要.
研究的目的:
- 在不同气候条件下评估DSSAT-CSM-CROPGRO-Safflower模型 (4.8.2版本) 的性能.
- 校准和验证模型使用现场观测现象学,生物质和松花谷物产量 (SGY) 的模型.
- 评估模型对松花生长和产量的预测能力.
主要方法:
- 模型校准使用2016-17年现场数据用于表态学,生物质和SGY.
- 使用GLUE (遗传可能性不确定性估计) 程序估计遗传系数.
- 模拟结果与观察数据的验证,使用统计指数,如RMSE和d-index.
主要成果:
- 该模型显示了良好的统计指数,可以预测到开花和成熟的日期 (低RMSE,高d指数).
- 在开花和成熟时的松花生物量得到了合理的预测 (低RMSE,高d指数).
- 该模型在验证跨基因型和条件的松花谷物产量 (低RMSE,高d指数) 中表现出强的表现.
结论:
- DSSAT-CSM-CROPGRO-Safflower模型是一种可靠的工具,用于模拟松花生长和产量.
- 松花表现出对不同环境的弹性,表明其作为替代作物的潜力.
- 该模型可以为将叶绿花纳入现有作物系统的决策提供信息.
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