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<b>Potential carbon sequestration in rubber tree plantations in the northwestern region of the Paraná State, Brazil

2014· article· en· W1970406593 on OpenAlexaff
Selma Regina Maggiotto, Dalziza de Oliveira, Celso Jamil Marur, Sônia Maria Soares Stivari, Monique Y. Leclerc, Claudia Wagner‐Riddle

Bibliographic record

VenueActa Scientiarum Agronomy · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsEnvironmental scienceBiomass (ecology)Soil carbonCarbon sequestrationPastureBrachiariaNatural rubberSoil waterAgronomyAgroforestryCarbon fibersForestryCarbon dioxideSoil scienceBiologyEcologyGeographyChemistryForageMathematics

Abstract

fetched live from OpenAlex

Rubber tree plantations may improve the soil’s physical and chemical properties, and they may sequester atmospheric carbon in the biomass or the soil. However, the potential role of these plantations in sequestering carbon in the soil and plant biomass has not been fully evaluated. This study evaluated rubber tree plantations at Paranapoema, which is located in the northwestern region of the Paraná state of Brazil, to measure the biomass in plantations of different ages and to determine the organic carbon content and d13C in the soils. Biomass accumulation was evaluated using the destructive method in plantations of different ages. The total carbon stock in the top 60 cm of the soil was 63.4 Mg C ha-1 for the pasture adjacent to the plantations and 66.8 and 79.3 Mg C ha-1 for the 4- and 15-year-old rubber tree plantations, respectively. These values are equivalent to an annual increase in soil carbon stocks of 0.85 and 1.06 Mg ha-1, respectively, and they do not include the accumulation of carbon as tree woody biomass. The soil d13C indicated a relatively fast conversion from the previous C4-C (pasture; Brachiaria-Urochloa brizantha) to C3-C (rubber tree). The results from this study suggest that rubber tree plantations have untapped potential to sequester carbon over a relatively short time period.

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)0.000
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 score0.104
Threshold uncertainty score0.910

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.010
GPT teacher head0.201
Teacher spread0.191 · 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

Citations56
Published2014
Admission routes1
Has abstractyes

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