PSXIV-21 Effect of high-tannin sorghum grain on rumen fermentation and methane production in vitro
Bibliographic record
Abstract
Abstract Tannins are natural compounds with potential to reduce enteric methane (CH4) emissions in ruminants. High-tannin sorghum (HTS) varieties are cultivated in different regions of the world, including Mexico. The objective of this study was to evaluate the effects of HTS on rumen fermentation and CH4 production using a batch culture system. The experiment was set up as a completely randomized block design using a 3 × 5 factorial arrangement with 3 grains [HTS, low-tannin sorghum (LTS), and corn, with 19.6, 0.07 and 0 g of condensed tannins (CT)/kg dry matter (DM) respectively] and 5 grain to forage (alfalfa hay) proportions (20:80, 40:60, 60:40, 80:20 and 100:0). Data were analyzed using the MIXED procedure of SAS. Contrasts were used to determine linear and quadratic effects of grain:forage proportions. A decrease of 2.96% in CH4 concentration was observed for sorghum treatments compared to corn (P < 0.01). However, HTS and LTS were not different from each other (P = 0.76). Furthermore, CH4 production (mL/d) decreased (P < 0.01) by 19.13 and 8.53% with HTS compared with corn and LTS respectively. This reduction was associated with a decrease (P < 0.01) of 14.52 and 10.04% in total gas production with HTS as compared to corn and LTS. Both, CH4 and gas production decrease, were associated with reduced DM digestibility for HTS, which can be a negative effect of CT in HTS. Sorghum treatments produced 14.56% less CH4 (mL/g DM; P < 0.01) than corn. This was partially explained by a 7.98% decrease in DMD with sorghum treatments. As expected, gas production linearly increased and rumen pH linearly decreased with increasing levels of grain (P < 0.01). In conclusion, HTS had no advantage over LTS in terms of CH4 production. Nonetheless, sorghum grain, substituted for corn, can be an option to decrease CH4 production in ruminants, but digestibility might be compromised.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".