Herbage accumulation and nutritive value of new bermudagrass accessions and cultivars
Bibliographic record
Abstract
Abstract Bermudagrass [Cynodon dactylon (L.) Pers.] is an important forage source for ruminants in tropical and subtropical regions of the world; nevertheless, little is known about the productive and nutritional characteristics of new accessions and cultivars originating from breeding programs. Five bermudagrass cultivars (Tifton 85, Jiggs, Florida 44, Callie, and Newell) and five accessions (276, 282, 323, 286, and Missouri) were tested during the 2018 and 2019 growing seasons. Genotype × harvest interactions were detected for herbage accumulation (HA), crude protein (CP), and nitrogen yield (p < 0.05). In June, all bermudagrass genotypes showed significant variation in HA, with accession 286 being more productive than Jiggs (4.42 vs. 3.24 Mg DM ha−1 harvest−1, respectively, where DM is dry matter). In October, however, accession 323 had greater CP than Callie, accession 286, Newell, and Tifton 85, with average CP values of 155, 128, 136, and 137 g kg−1 DM, respectively. Average in vitro digestible organic matter for accession 323 (450 g kg−1 DM) was similar to that of Tifton 85 and Newell but greater than that of Missouri (393 g kg−1 DM). Genotypes displayed unique responses to all traits across harvest dates. According to the principal component analysis, the accession Missouri exhibited low productive and nutritive value properties. The accession 286 showed greater CP concentration while still productive; thus, this accession will be further examined for future release to livestock or hay producers in subtropical regions worldwide.
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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 | 0.001 | 0.001 |
| 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".