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Record W3133148213 · doi:10.3390/land10020189

Greenhouse Gas Emissions from Forest Soils Reduced by Straw Biochar and Nitrapyrin Applications

2021· article· en· W3133148213 on OpenAlexafffundabout
Jinbiao Li, Jin‐Hyeob Kwak, Scott X. Chang, Xiaoqiang Gong, Zhengfeng An, Jinlin Chen

Bibliographic record

VenueLand · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaPriority Academic Program Development of Jiangsu Higher Education InstitutionsChina Scholarship Council
KeywordsBiocharSoil waterGreenhouse gasEnvironmental scienceBiomass (ecology)WindbreakAgronomyStrawSoil carbonCarbon sequestrationChemistryEnvironmental chemistryCarbon dioxideAgroforestrySoil scienceEcologyPyrolysis

Abstract

fetched live from OpenAlex

Forestlands are widely distributed in the dominantly agricultural landscape in western Canada, and they play important ecological functions; such forestlands (e.g., shelterbelts) accumulate soil organic matter and may receive a substantial amount of nitrogen in the form of surface and subsurface runoff from adjacent croplands and become a significant source of emissions of greenhouse gases (GHGs) such as CO2, N2O, and CH4. Biochar and nitrapyrin applications could potentially mitigate GHG emissions, but their co-application in forest soils has not been studied. We investigated the effect of the application of biochars produced at low (300 °C; BC300) and high temperatures (700 °C; BC700) using canola (Brassica napus L.) straw and the effect of their co-application with nitrapyrin on GHG emissions and soil properties in a 35-day laboratory incubation experiment using forest soils collected from five shelterbelt sites. Results showed no significant interaction effect of biochar and nitrapyrin on the global warming potential (GWP) of the GHG emissions, and the GWP was 15.8% lower in the soil with nitrapyrin than without nitrapyrin application treatments. The GWP was significantly enhanced by BC300 addition due to a 26.9% and 627.1% increase in cumulative CO2 and N2O emissions, respectively, over the 35-day incubation. The GWP significantly decreased by BC700 addition due to a 27.1% decrease in cumulative CO2 emissions. However, biochar addition did not affect CH4 emissions, while nitrapyrin decreased CH4 uptake by 50.5%. With BC300 addition, soil-dissolved organic carbon and microbial biomass carbon increased by 26.5% and 33.9%, respectively, as compared to no biochar addition (CK). Soil pH increased by 0.16 and 0.37 units after the addition of BC300 and BC700, respectively. Overall, the effect of biochar and nitrapyrin was independent in mitigating GHG emissions and was related to the type of biochar applied and changes in soil properties.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.012
GPT teacher head0.214
Teacher spread0.201 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations15
Published2021
Admission routes3
Has abstractyes

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