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Record W4387823012 · doi:10.3390/ani13203280

Assessment of the Effects of Commercial or Locally Engineered Biochars Produced from Different Biomass Sources and Differing in Their Physical and Chemical Properties on Rumen Fermentation and Methane Production In Vitro

2023· article· en· W4387823012 on OpenAlexafffund
C. Benchaar, F. Hassanat, Cristiano Côrtes

Bibliographic record

VenueAnimals · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRuminant Nutrition and Digestive Physiology
Canadian institutionsAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food Canada
KeywordsBiocharRumenChemistryFermentationPropionateMonensinMethanogenesisMethaneBiomass (ecology)Anaerobic digestionMoistureFood scienceAgronomyPyrolysisBiochemistryBiologyOrganic chemistry

Abstract

fetched live from OpenAlex

In recent years, interest in using biochar as feed additives to mitigate enteric methane (CH4) emissions from ruminants has increased. It has been suggested that the mitigating potential of biochar is influenced by its physical (e.g., porosity-related) and chemical (e.g., redox-potential-related) properties. Thus, the aim of this in vitro study was to evaluate the effects of commercial or locally engineered biochars, produced from different biomass sources and differing in their physical and chemical characteristics, on rumen fermentation and CH4 production. For this purpose, a 24 h batch culture of ruminal fluid incubations was conducted in a complete randomized block design (repeated three times) that included a negative control (no additive), a positive control (monensin, 10 mg/mL), and four commercial and three locally engineered biochars, each evaluated at 1%, 2%, or 5% of the substrate’s (i.e., the total mixed ration) dry matter. The evaluated biochars greatly differ in their chemical (i.e., moisture, ash, pH, redox potential, volatiles, carbon, fixed carbon, hydrogen, and sulfur) and physical (i.e., fine particles < 250 µm, bulk density, true density, porosity, electrical conductivity, specific surface area, and absorbed CO2) properties. Despite these differences and compared with the negative control, none of the biochars evaluated (regardless of the inclusion rate) influenced gas and CH4 production, volatile fatty acid characteristics (total concentration and profile), or ammonia-nitrogen (NH3-N) concentrations. As expected, monensin (i.e., the positive control) decreased (p < 0.05) CH4 production mainly because of a decreased (p < 0.05) acetate-to-propionate ratio. The results of this study reveal that despite the large differences in the physical and chemical properties of the biochars evaluated, their inclusion at different rates in vitro failed to modify rumen fermentation and decrease CH4 production. Based on these in vitro findings, it was concluded that biochar does not represent a viable strategy for mitigating enteric CH4 emissions.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.480
Threshold uncertainty score0.109

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.025
GPT teacher head0.245
Teacher spread0.221 · 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 designBench or experimental
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

Citations4
Published2023
Admission routes2
Has abstractyes

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