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Record W2809131341 · doi:10.3390/f9060369

Enrichment Planting and Soil Amendments Enhance Carbon Sequestration and Reduce Greenhouse Gas Emissions in Agroforestry Systems: A Review

2018· review· en· W2809131341 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueForests · 2018
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicAgroforestry and silvopastoral systems
Canadian institutionsAlberta Ministry of Agriculture and ForestryUniversity of AlbertaNatural Resources Canada
FundersAgriculture and Agri-Food Canada
KeywordsBiocharCarbon sequestrationGreenhouse gasEnvironmental scienceManureSoil carbonBiomass (ecology)Slash-and-charAgroforestryCompostAgronomySoil waterSoil organic matterWaste managementCarbon dioxideChemistryPyrolysisEngineeringSoil science

Abstract

fetched live from OpenAlex

Agroforestry practices that intentionally integrate trees with crops and/or livestock in an agricultural production system could enhance carbon (C) sequestration and reduce greenhouse gas (GHG) emissions from terrestrial ecosystems, thereby mitigating global climate change. Beneficial management practices such as enrichment planting and the application of soil amendments can affect C sequestration and GHG emissions in agroforestry systems; however, such effects are not well understood. A literature review was conducted to synthesize information on the prospects for enhancing C sequestration and reducing GHG emissions through enrichment (i.e., in-fill) tree planting, a common practice in improving stand density within existing forests, and the application of organic amendments to soils. Our review indicates that in agroforests only a few studies have examined the effect of enrichment planting, which has been reported to increase C storage in plant biomass. The effect of adding organic amendments such as biochar, compost and manure to soil on enhancing C sequestration and reducing GHG emissions is well documented, but primarily in conventional crop production systems. Within croplands, application of biochar derived from various feedstocks, has been shown to increase soil organic C content, reduce CO2 and N2O emissions, and increase CH4 uptake, as compared to no application of biochar. Depending on the feedstock used to produce biochar, biochar application can reduce N2O emission by 3% to 84% as compared to no addition of biochars. On the other hand, application of compost emits less CO2 and N2O as compared to the application of manure, while the application of pelleted manure leads to more N2O emission compared to the application of raw manure. In summary, enrichment planting and application of organic soil amendments such as compost and biochar will be better options than the application of raw manure for enhancing C sequestration and reducing GHG emissions. However, there is a shortage of data to support these practices in the field, and thus further research on the effect of these two areas of management intervention on C cycling will be imperative to developing best management practices to enhance C sequestration and minimize GHG emissions from agroforestry systems.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.890
Threshold uncertainty score0.796

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.046
GPT teacher head0.309
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it