Temporal variability is a major source of uncertainty in soil carbon measurements

Stewart B. Wuest, Nicole Durfee
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Abstract

Monitoring changes in soil organic carbon over time is important to many agricultural and environmental goals. Despite decades of measurements, temporal variability in soil carbon measurements has not been studied extensively. In this report, we examine five sets of monthly samples extending up to 3 years each that were collected from field experiments at four locations representative of dryland farming in the Pacific Northwest. The variance from month-to-month was 15%–32% of the random error, averaging 20%. This was often greater than the variance between replicate experimental units (2%–42%, averaging 17%). At certain sites, sequential samples were found to be temporally autocorrelated, but no consistent trend patterned on seasonal factors like precipitation was found. This suggests that a single point-in-time sample can deviate substantially from the long-term average soil carbon at the site. We illustrate this problem with the results of repeated soil samples taken from 12 commercial farm fields. We recommend that confidence intervals for soil organic carbon estimates should include variance based on a large population of samples rather than from a single sample set at one timepoint.

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时间变异性是土壤碳测量不确定性的主要来源
监测土壤有机碳随时间的变化对许多农业和环境目标都很重要。尽管已经进行了几十年的测量,但土壤碳测量的时间变异性尚未得到广泛研究。在本报告中,我们研究了五组月度样本,每组样本的时间最长可达 3 年,这些样本都是从西北太平洋地区四个具有代表性的旱地农业田间试验中采集的。月与月之间的差异为随机误差的 15%-32%,平均为 20%。这往往大于重复实验单元之间的差异(2%-42%,平均 17%)。在某些地点,连续样本被发现具有时间自相关性,但没有发现与降水等季节性因素相关的一致趋势。这表明,单个时间点样本可能与该地点的长期平均土壤碳含量有很大偏差。我们用从 12 块商业农田重复采集土壤样本的结果来说明这个问题。我们建议,土壤有机碳估计值的置信区间应包括基于大量样本的方差,而不是基于一个时间点的单一样本。
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