Evidence map›Paper›PMID 32772191›Full record

ArticleEnvironmental monitoring and assessment2020

Assessment of forest carbon stocks for REDD+ implementation in the muyong forest system of Ifugao, Philippines.

Ram Avtar, Kenichi Tsusaka, Srikantha Herath

Abstract read
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In one paragraph

Article in Environmental monitoring and assessment, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Ram AvtarFaculty of Environmental Earth Science, Hokkaido University, Sapporo, Japan. ram.envjnu@gmail.com.ORCID http://orcid.org/0000-0003-3653-5771
Kenichi TsusakaInstitute for the Advanced Studies of Sustainability (UNU-IAS), United Nations University, Tokyo, Japan.
Srikantha HerathMinistry of Megapolis and Western Development, Battaramulla, Sri Lanka.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Forests hold significant potential for carbon sequestration and climate change mitigation. Forest biomass estimation is vital for sustainable forest management, providing critical input data for implementing the United Nations Reducing Emissions from Deforestation and forest Degradation-plus (REDD+) mechanism. This study investigates the total carbon pools-aboveground biomass (AGB), belowground biomass (BGB), forest floor biomass, and soil carbon-using field-based information in the muyong forest management system, which is native to Ifugao in the Philippines. This study reveals that a difference may be observed between the total carbon stock of the private woodlots (muyong) and that of the communal forest (bilid). The results indicate that the bilid forest has trees with a small diameter at breast height (DBH) and high tree density in contrast to the muyong, which has trees with high DBH and low tree density. The average carbon stock per unit area is higher in muyong (150.8 tC/ha) than in bilid (126.1 tC/ha). These findings are valuable in determining whether Ifugao's muyong forest system should be included under the REDD+ framework. Human mediation and management helps forests to sequester a greater amount of carbon than they would without human intervention. Implementation of REDD+ should promote Ifugao's ecosystem and biodiversity conservation and agroforestry practices in addition to protecting traditional agricultural practices and livelihoods in relation to rice terraces.

Indexed as

EcosystemCarbonConservation of Natural ResourcesEnvironmental MonitoringForestsPhilippinesCarbonClimate change mitigationForest carbon stocksMuyongREDD+

Identifiers

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.