Taylor's power law and the stability of crop yields
Taylor's power law and the stability of crop yields
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DOI：
10.1016/j.fcr.2015.08.005
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发表时间：
2015-11-01
影响因子：
5.8
通讯作者：
Cohen, Joel E.
中科院分区：
文献类型：
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作者：
Doering, Thomas F.;Knapp, Samuel;Cohen, Joel E.
泰勒幂律（TPL）描述了经验关系sigma(2)=a mu(b)，其中sigma(2)是样本方差，mu是数据集中数据子集的样本均值。同样，TPL表示样本方差的对数是不同数据子集样本均值对数的线性函数。在这里，我们展示了作物产量在不同情况下的几个已发表数据集中遵循这种关系。研究表明，TPL对构建数据的各种因素（包括品种、作物品种、试验环境或国家）经常有效，但并非总是有效。我们提出log（sigma(2)）对log（mu）的线性回归的残差可以用作稳定性的度量，称为POLAR（幂律残差）。我们将POLAR稳定性与其他常用的稳定性度量进行比较，并表明POLAR稳定性比一些常用的稳定性度量具有优势。(C) 2015 Elsevier B.V.版权所有
Taylor's power law (TPL) describes the empirical relationship sigma(2)=a mu(b) where sigma(2) are sample variances and mu are sample means in subsets of data in a data set. Equivalently, TPL states that the logarithm of the sample variance is a linear function of the logarithm of the sample mean across different subsets of data. Here we show that crop yields follow this relationship in several published data sets from varied situations. We show that TPL is frequently, but not always, valid for various factors structuring the data including varieties, crop species, trial environments or countries. We propose that the residuals from the linear regression of log(sigma(2)) against log(mu) can be used as a measure of stability, called POLAR (Power Law Residuals). We compare POLAR stability with other commonly used measures of stability, and show that POLAR stability offers an advantage over some frequently used stability measures. (C) 2015 Elsevier B.V. All rights reserved.