结合输入数据源分辨率和聚合对高产特征对模拟小麦产量的贡献的影响
Ehsan Eyshi Rezaei1, Babacar Faye2,3, Frank Ewert2,4
1Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany. EhsanEyshi.Rezaei@zalf.de.
Scientific reports
|October 5, 2024
概括
像辐射利用效率 (RUE) 这样的高收益性质可以提高作物产量,但它们的有效性取决于数据分辨率和环境条件. 优化特征选择需要仔细考虑输入数据质量,以便准确的大规模影响评估.
科学领域:
- 农业科学 农业科学
- 适应气候变化 适应气候变化
- 作物建模作物建模
背景情况:
- 高产量特征在气候变化中提供了改善作物表现的潜力.
- 当前的作物模型经常使用聚合数据,限制了特定地点的特征验证.
- 了解数据解析影响对于可靠的大规模特征评估至关重要.
研究的目的:
- 调查使用不同输入数据分辨率和来源用于作物模型评估高产特征的后果.
- 量化德国不同行政单位对模拟产量的聚合效应.
- 评估特定特征 (RUE,K,FE) 和环境因素对产量改善的影响.
主要方法:
- 在整个德国 (2001-2010年) 应用了SIMPLACE建模平台,使用从现场到50公里的输入数据分辨率.
- 模拟了三种特征的1881组合:辐射使用效率 (RUE),光灭绝系数 (K) 和果实效率 (FE).
- 对行政单位进行集成的网格级模拟,以评估数据聚合效应和特征影响.
主要成果:
- 最大RUE特征值增加了1.43.1t ha-1的模拟产量,而K和FE单独显示了最小的改善 (<0.4t ha-1).
- 现场规模的输入数据最大化了每单位RUE的收益率提高.
- 与水相关的输入分辨率 (土壤,降水) 显著影响了产量,超过了温度,但没有改变特征效应.
- 数据聚合对模拟产量产生了很小的影响;温暖干燥的条件减少了特征的好处.
结论:
- 辐射利用效率 (RUE) 是提高产量的关键特征,特别是高分辨率输入数据.
- 高产品特征的有效性取决于环境,在温暖干燥的条件下下降.
- 对作物特征进行准确的大规模影响评估需要仔细考虑输入数据的分辨率和源质量.
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