线性回归和相关矩阵在CERES3(玉米)评价中的应用

A. D. Toit, J. Booysen, J. J. Human
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引用次数: 13

摘要

利用6个品种和3个种植日期组成的历史数据集(土壤含水量、生长、物候和产量)对CERES3作物生长模型进行了评价。利用线性回归和相关矩阵对模型中需要标定的算法进行识别。结果表明,该模型在局部条件下对产量和核数的模拟精度较低。结果表明,单株穗数和吐丝前、吐丝期间的水分胁迫是导致吐丝精度低的主要因素。
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Use of linear regression and correlation matrix in the evaluation of CERES3 (Maize)
A historical data set (soil water content, growth, phenology and yield) consisting of six cultivars and three planting dates was used to evaluate the CERES3 crop growth model. Linear regression and correlation matrix were used to identify algorithms in the model in need of calibration. Results indicated that the model simulates yield and kernel number with low accuracy under local conditions. The number of ears per plant and water stress before and during silking were identified as factors that could explain the low accuracy.
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