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利用本地化的有机物质与有机碳方程改进菲律宾红树林土壤碳估算

Improving soil carbon estimates of Philippine mangroves using localized organic matter to organic carbon equations.

作者信息

Salmo Severino G, Manalo Sean Paul B, Jacob Precious B, Gerona-Daga Maria Elisa B, Naputo Camila Frances P, Maramag Mareah Wayne A, Basyuni Mohammad, Sidik Frida, MacKenzie Richard

机构信息

Institute of Biology, University of the Philippines Diliman, 1101, Quezon City, Philippines.

University of the Philippines Tacloban College, 6500, Tacloban City, Philippines.

出版信息

Carbon Balance Manag. 2024 Sep 11;19(1):31. doi: 10.1186/s13021-024-00276-y.

Abstract

BACKGROUND

Southeast Asian (SEA) mangroves are globally recognized as blue carbon hotspots. Methodologies that measure mangrove soil carbon stock (SCS) are either accurate but costly (i.e., elemental analyzers), or economical but less accurate (i.e., loss-on-ignition [LOI]). Most SEA countries estimate SCS by measuring soil organic matter (OM) through the LOI method then converting it into organic carbon (OC) using a conventional conversion equation (%C = 0.415 * % LOI + 2.89, R = 0.59, n = 78) developed from Palau mangroves. The local site conditions in Palau does not reflect the wide range of environmental settings and disturbances in the Philippines. Consequently, the conventional conversion equation possibly compounds the inaccuracies of converting OM to OC causing over- or under-estimated SCS. Here, we generated a localized OM-OC conversion equation and tested its accuracy in computing SCS against the conventional equation. The localized equation was generated by plotting % OC (from elemental analyzer) against the % OM (from LOI). The study was conducted in different mangrove stands (natural, restored, and mangrove-recolonized fishponds) in Oriental Mindoro and Sorsogon, Philippines from the West and North Philippine Sea biogeographic regions, respectively. The OM:OC ratios were also statistically tested based on (a) stand types, (b) among natural stands, and (c) across different ages of the restored and recolonized stands. Increasing the accuracy of OM-OC conversion equations will improve SCS estimates that will yield reasonable C emission reduction targets for the country's commitments on Nationally Determined Contributions (NDC) under the Paris Agreement.

RESULTS

The localized conversion equation is %OC = 0.36 * % LOI + 2.40 (R = 0.67; n = 458). The SOM:OC ratios showed significant differences based on stand types (x = 19.24; P = 6.63 × 10), among natural stands (F = 23.22; p = 1.17 × 10), and among ages of restored (F = 5.14; P = 0.03) and recolonized stands (F = 3.4; P = 0.02). SCS estimates using the localized (5%) and stand-specific equations (7%) were similar with the values derived from an elemental analyzer. In contrast, the conventional equation overestimates SCS by 20%.

CONCLUSIONS

The calculated SCS improves as the conversion equation becomes more reflective of localized site conditions. Both localized and stand-specific conversion equations yielded more accurate SCS compared to the conventional equation. While our study explored only two out of the six marine biogeographic regions in the Philippines, we proved that having a localized conversion equation leads to improved SCS measurements. Using our proposed equations will make more realistic SCS targets (and therefore GHG reductions) in designing mangrove restoration programs to achieve the country's NDC commitments.

摘要

背景

东南亚(SEA)红树林是全球公认的蓝碳热点地区。测量红树林土壤碳储量(SCS)的方法要么准确但成本高昂(即元素分析仪),要么经济但准确性较低(即烧失量法[LOI])。大多数东南亚国家通过烧失量法测量土壤有机质(OM),然后使用从帕劳红树林得出的传统换算方程(%C = 0.415 * %LOI + 2.89,R = 0.59,n = 78)将其转换为有机碳(OC)来估算土壤碳储量。帕劳当地的场地条件并不能反映菲律宾广泛的环境状况和干扰情况。因此,传统换算方程可能会加剧将有机质转换为有机碳时的不准确性,导致土壤碳储量估算过高或过低。在此,我们生成了一个本地化的有机质 - 有机碳换算方程,并针对传统方程测试了其在计算土壤碳储量方面的准确性。本地化方程是通过将(来自元素分析仪的)%OC与(来自烧失量法的)%OM作图生成的。该研究分别在菲律宾西菲律宾海和北菲律宾海生物地理区域的东民都洛省和索索贡省的不同红树林林分(天然林、恢复林和重新定殖的鱼塘红树林)中进行。还基于(a)林分类型、(b)天然林分之间以及(c)恢复林和重新定殖林分的不同年龄对有机质与有机碳的比率进行了统计检验。提高有机质 - 有机碳换算方程的准确性将改善土壤碳储量估算,从而为该国在《巴黎协定》下的国家自主贡献(NDC)承诺制定合理的碳排放减排目标。

结果

本地化换算方程为%OC = 0.36 * %LOI + 2.40(R = 0.67;n = 458)。基于林分类型(x = 19.24;P = 6.63×10)、天然林分之间(F = 23.22;p = 1.17×10)以及恢复林(F = 5.

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0bc8/11391756/44a7805ce4c9/13021_2024_276_Fig1_HTML.jpg

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