Proper estimate of residue input as condition for understanding drivers of soil carbon dynamics

Proper estimate of residue input as condition for understanding drivers of soil carbon dynamics
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正确估计残留物输入作为了解土壤碳动态驱动因素的条件

DOI:
10.1111/gcb.13822
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发表时间:
2017
影响因子:
11.6
通讯作者:
J. Leifeld
J. Leifeld
中科院分区:
环境科学与生态学1区
文献类型:
--
作者:
S. G. Keel;J. Hirte;S. Abiven;Chloé Wüst;J. Leifeld

文献摘要

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对于农业生态系统来说,土壤碳(C)输入与土壤有机碳(SOC)储量之间的密切联系已有充分的记录(Buya-novsky &瓦格纳,1998年)。因此,正确计算和无偏的C输入是研究和解释土壤C动态的关键。在最近的一篇论文中,Luo、Feng、Luo、巴尔多克和Wang(2017年)证明,农业系统中的SOC动态主要由植物残体向土壤输入的C的数量和质量(按重要性顺序排列)驱动。气候和土壤性质,包括土壤C的数量和组成。虽然我们同意Luo等人(2017)的观点,即平均碳输入量是对SOC变化率影响最大的变量,但他们的实验设置无法准确量化其重量。他们应用了路径分析,并利用了一个广泛的数据集,其中包括来自90个澳大利亚田间试验的详细土壤特性。在气象站记录气象变量或对其进行插值,并测量土壤特性。相反,来自例如残茬的植物C输入未被测量,而是通过测量的产量和收获指数(HI)来近似。所采用的估计方法可能导致重大偏差和重大遗漏。我们对这种做法感到关切,原因如下:
The close link between carbon (C) inputs to the soil and soil organic carbon (SOC) stocks is well documented for agroecosystems (Buya-novsky & Wagner, 1998). Hence, correctly calculated and unbiased C inputs are critical for the study and interpretation of soil C dynamics. In a recent paper, Luo, Feng, Luo, Baldock, and Wang (2017) demonstrate that SOC dynamics in agricultural systems are mainly driven by, in order of importance, the amount and quality of C inputs to the soil from plant residues, climate, and soil properties including the amount and composition of soil C. While we concur with Luo et al. (2017) that the average C input amount is the most influential variable on SOC change rate, their experimental set-up did not enable its weight to be accurately quantified. They apply a path analysis and make use of an extensive data set that comprises detailed soil properties from 90 Australian field trials. Meteorological variables were recorded at weather stations or were interpolated, and soil properties were measured. In contrast, plant C inputs from, for example, stubble were not measured, but were approximated by measured yields and harvest indices (HI ’ s). The method of estimation employed may have led to important biases and critical omissions. We are concerned about this approach for following reasons: