Schillaci Calogero, Saia Sergio, Lipani Aldo, Perego Alessia, Zaccone Claudio, Acutis Marco
Department of Agricultural and Environmental Science, University of Milan, 20133, Milan, Italy.
Department of Veterinary Sciences, University of Pisa, Via delle Piagge 2, 56129, Pisa, Italy.
Carbon Balance Manag. 2021 Jun 7;16(1):19. doi: 10.1186/s13021-021-00182-7.
Legacy data are unique occasions for estimating soil organic carbon (SOC) concentration changes and spatial variability, but their use showed limitations due to the sampling schemes adopted and improvements may be needed in the analysis methodologies. When SOC changes is estimated with legacy data, the use of soil samples collected in different plots (i.e., non-paired data) may lead to biased results. In the present work, N = 302 georeferenced soil samples were selected from a regional (Sicily, south of Italy) soil database. An operational sampling approach was developed to spot SOC concentration changes from 1994 to 2017 in the same plots at the 0-30 cm soil depth and tested.
The measurements were conducted after computing the minimum number of samples needed to have a reliable estimate of SOC variation after 23 years. By applying an effect size based methodology, 30 out of 302 sites were resampled in 2017 to achieve a power of 80%, and an α = 0.05. A Wilcoxon test applied to the variation of SOC from 1994 to 2017 suggested that there was not a statistical difference in SOC concentration after 23 years (Z = - 0.556; 2-tailed asymptotic significance = 0.578). In particular, only 40% of resampled sites showed a higher SOC concentration than in 2017.
This finding contrasts with a previous SOC concentration increase that was found in 2008 (75.8% increase when estimated as differences of 2 models built with non-paired data), when compared to 1994 observed data (Z = - 9.119; 2-tailed asymptotic significance < 0.001). This suggests that the use of legacy data to estimate SOC concentration dynamics requires soil resampling in the same locations to overcome the stochastic model errors. Further experiment is needed to identify the percentage of the sites to resample in order to align two legacy datasets in the same area.
遗留数据是估算土壤有机碳（SOC）浓度变化和空间变异性的独特契机，但由于所采用的采样方案，其应用存在局限性，分析方法可能需要改进。当用遗留数据估算SOC变化时，使用在不同地块采集的土壤样本（即非配对数据）可能会导致结果有偏差。在本研究中，从一个区域（意大利南部的西西里岛）土壤数据库中选取了N = 302个地理参考土壤样本。开发了一种操作性采样方法，以确定1994年至2017年同一地块0 - 30厘米土壤深度处SOC浓度的变化并进行测试。
在计算出23年后对SOC变化进行可靠估算所需的最少样本数量后进行了测量。通过应用基于效应量的方法，2017年对302个地点中的30个进行了重新采样，以达到80%的功效，α = 0.05。对1994年至2017年SOC变化应用Wilcoxon检验表明，23年后SOC浓度没有统计学差异（Z = - 0.556；双侧渐近显著性 = 0.578）。特别是，只有40%的重新采样地点的SOC浓度高于2017年。
这一发现与2008年发现的SOC浓度增加情况形成对比（与1994年观测数据相比，用非配对数据构建的两个模型的差异估算时增加了75.8%，Z = - 9.119；双侧渐近显著性 < 0.001）。这表明使用遗留数据估算SOC浓度动态需要在相同地点进行土壤重新采样，以克服随机模型误差。需要进一步实验来确定重新采样地点的百分比以便在同一区域对齐两个遗留数据集。
