Key message

This study investigates efficient strategies for fine tree root sampling, in terms of estimating root trait parameters with desired confidence intervals for least effort and cost.

Abstract

Sampling tree roots is difficult and costly with high variation among samples and wide confidence intervals for parameter estimates. Efficient strategies for fine tree root sampling will estimate root trait parameters with the desired confidence interval for least effort and cost. We compared alternative strategies to sample and estimate fine root surface area density; (1) collecting samples at intervals of 10 cm to a depth of 150 cm for entire tree root systems versus (2) independently taking samples from different randomly-selected 10-cm depth intervals around different trees. We also quantified the pilot sample size needed to reliably estimate the number of samples that would achieve the desired confidence interval. Efficiency of sampling entire tree root systems versus independent samples depended on the structure of the sample data. Pilot sample sizes > 5 per 10-cm soil depth can give reliable estimates of sample sizes required to achieve a 95% confidence interval of ± 10% of the sample mean. The statistical strategies in this paper are not particularly novel or difficult, but are seldom applied to root studies. We contend that they should be used, both to guide efficiency in sampling design and also to assess how realistic it is to expect that estimated sample means will be reliable, in the sense of having confidence intervals of the required width.

中文翻译：

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样本大小估计，以实现树木细根性状参数估计所需的不确定性

关键信息

这项研究调查了有效的策略，以最佳的树根采样，以最小的努力和成本估算具有所需置信区间的根性状参数。

抽象

采样树根很难而且成本高昂，因为采样之间的差异很大，并且参数估计的置信区间很宽。细树根采样的有效策略将以所需的置信区间估算根性状参数，以最小的努力和成本。我们比较了替代策略来采样和估计细根表面积的密度；（1）在整个树根系统中以10厘米到150厘米深度的间隔收集样本，而（2）从不同树木周围随机选择的10厘米深度间隔中独立获取样本。我们还量化了可靠估计需要达到预期置信区间的样本数量所需的试验样本大小。相对于独立样本，对整个树根系统进行抽样的效率取决于样本数据的结构。每10厘米土壤深度> 5的先行样本量可以可靠地估算出达到95％置信区间（平均值为±10％）所需的样本量。本文中的统计策略并不是特别新颖或困难，但很少应用于根研究。我们认为应该使用它们，既可以指导抽样设计的效率，也可以评估在具有所需宽度的置信区间的意义上，期望估计的抽样方式可靠的现实程度。