Within-field spatial variability of greenhouse gas fluxes from an extensive and intensive sheep-grazed pasture-猫眼课题宝

DOI：

10.1016/j.agee.2021.107355

发表时间：

2021-06

期刊：

Agriculture, Ecosystems & Environment

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

A. Charteris;P. Harris;K. Marsden;I. Harris;Ziwei Guo;D. Beaumont;H. Taylor;Gianmarco Sanfratello;Davey L. Jones;S. Johnson;M. Whelan;N. Howden;H. Sint;D. Chadwick;L. Cárdenas

放牧草地土壤的温室气体(GHG)通量在空间和时间上都有很大的变化，但调控这种变化的因素在数量上的重要性仍不清楚。我们的目的是在对比广泛(低投入)和集约化管理的绵羊放牧“案例研究”牧场上探索这种变异性。我们(通过标准的和空间信息的回归)量化了温室气体通量(一氧化二氮(N2O)、二氧化碳(CO2)和甲烷(CH4))与一系列土壤、田间和管理特征之间的统计关系。这三种温室气体在两个研究点的通量差异很大，但空间结构(即自相关)仅表现在集约区的N2O通量和广泛区的CO2通量的变异性上。回归分析确定了显著的温室气体预测变量：N2O的3号−(p&lt； 0.001)和植被类型(p&lt； 0.01)(R2= 0.57；p= 0.000)；二氧化碳的NH4+(p&lt； 0.05)、坡度(p&lt； 0.05)和海拔(p&lt； 0.01)(R2= 0.34；p= 0.000)；以及3号−(p&lt； 0.01)、NH4+(p&lt； 0.0 5)和土壤水分(p&lt； 0.0 5)(R2= 0.2；p= 0.005)。显著的温室气体预测变量是N2O的土壤水分(p&lt； )和容重(p&lt； 0.01)(R2= 0.27；p= 0.005)；二氧化碳的土壤水分(p&lt； 0.001)(R2= 0.31；p= 0.001)；而CH4没有发现(R2= 0.10；p= 0.655)。推动温室气体变化的关键因素既有现场的，也有温室气体的，通量受当地条件控制，导致限制因素的差异(甚至可能是在现场范围内)。我们的统计分析表明，可能需要更大范围的驱动变量(例如，空气和土壤温度或其他土壤化学性质，如总可提取氮)，才能更全面地捕捉到这里所考虑的温室气体过程中观察到的变异性，而且对于未来的分析来说，考虑空间和时间尺度上的非线性、非平稳和相互作用的关系也可能是卓有成效的。每个地点样本设计的适当性在温室气体进程中也发挥了关键的解释作用，需要通过额外的抽样运动进行进一步评估。

Greenhouse gas (GHG) fluxes from livestock grazed pasture soils are highly variable in both space and time but the quantitative importance of the factors regulating this variation remain poorly understood. Our aim was to explore this variability on contrasting extensively (low input) and intensively managed sheep-grazed ‘case-study’ pastures. We quantified (through standard and spatially-informed regressions) the statistical relationships between GHG fluxes (nitrous oxide (N2O), carbon dioxide (CO2) and methane (CH4)) and a range of soil, field and management characteristics. Fluxes of these three GHGs at two study sites were highly variable, but spatial structure (i.e. autocorrelation) was only observed in the variability of N2O fluxes across the intensive site and CO2fluxes across the extensive site. The regression analyses identified significant GHG predictor variables for the extensive site as: NO3−(p< 0.001) and vegetation-type (p< 0.01) for N2O (R2= 0.57;p= 0.000); NH4+(p< 0.05), slope (p< 0.05) and elevation (p< 0.01) for CO2(R2= 0.34;p= 0.000); and NO3−(p< 0.01), NH4+(p< 0.05) and soil moisture (p< 0.05) for CH4(R2= 0.25;p= 0.005). Significant GHG predictor variables for the intensive site were soil moisture (p< 0.01) and bulk density (p< 0.01) for N2O (R2= 0.27;p= 0.005); soil moisture (p< 0.001) for CO2(R2= 0.31;p= 0.001); while none were found for CH4(R2= 0.10;p= 0.655). Key factors driving GHG variation were both site- and GHG-specific, with fluxes controlled by local conditions leading to differences in limiting factors (possibly even at the within-site scale). Our statistical analyses suggest a larger range of driving variables (e.g. air and soil temperature or other soil chemical properties such as total extractable N) may be required to more fully capture the observed variability in the GHG processes considered here, and that it may also be fruitful for future analyses to consider non-linear, non-stationary and interacting relationships across space- and time-scales. Adequacies of each site’s sample design also played a key interpretive role in the GHG processes, requiring further evaluation through additional sampling campaigns.

Within-field spatial variability of greenhouse gas fluxes from an extensive and intensive sheep-grazed pasture

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