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Record W2588827342 · doi:10.1080/14735903.2017.1288518

Soil CO <sub>2</sub> emission and carbon budget of a wheat/maize annual double-cropped system in response to tillage and residue management in the North China Plain

2017· article· en· W2588827342 on OpenAlexaff
Lanfang Wu, Binbin Li, Yue Qin, E. G. Gregorich

Bibliographic record

VenueInternational Journal of Agricultural Sustainability · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsAgriculture and Agri-Food Canada
FundersNational Natural Science Foundation of China
KeywordsCrop residueAgronomyTillageConventional tillageCropMultiple croppingGrowing seasonSoil carbonNo-till farmingEnvironmental scienceSoil waterResidue (chemistry)Cropping systemBiologySowingSoil fertilityAgricultureSoil science

Abstract

fetched live from OpenAlex

To investigate the impacts of tillage and crop residue managements on soil CO2 emission and C budget in a wheat (Triticum aestivum L.)/maize (Zea mays L.) double-cropped system in the North China Plain (NCP), a field experiment was conducted consisting of four treatments: tillage with crop residues retention (CT+), tillage with crop residues removal (CT−), no-till with crop residues retention (NT+), and no-till with crop residues removal (NT−). Daily soil CO2 fluxes changed with crop growing stage and peaked during the most vigorous growth of period, fluxes in maize season were higher than those in wheat season. Compared to the tilled soils, cumulative CO2 emissions were significantly lower under no-till treatments. The largest cumulative CO2 emission occurred under CT+ (65 g CO2-C m−2 y−1) and the smallest was under NT+ (39 g CO2-C m−2 y−1). After 5 years of the experiment, soil organic carbon (SOC) sequestration were greater with crop residues retention (CT+ and NT+) than with crop residues removal (CT− and NT−), the maximum SOC stock was in NT+ (5940 g C m−2) while the minimum was in CT− (3635 g C m−2). NT+ could help to mitigate CO2 emission in the annual wheat/maize double-cropping system of the area.

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 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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.293

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.006
GPT teacher head0.239
Teacher spread0.233 · 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 teacher head, 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

Citations22
Published2017
Admission routes1
Has abstractyes

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