Silage review: Unique challenges of silages made in hot and cold regions
Bibliographic record
Abstract
Silage making can be conveniently divided into field, ensiling, storage, and feed-out phases. In all of these stages, controllable and uncontrollable components can affect silage quality. For instance, silages produced in hot or cold regions are strongly influenced by uncontrollable climate-related factors. In hot regions, crops for silage are influenced by (1) high temperatures negatively affecting corn yield (whole-crop and grain) and nutritive value, (2) butyric and alcoholic fermentations in warm-season grasses (Panicum, Brachiaria, and Pennisetum genera) and sugarcane, respectively, and (3) accelerated aerobic deterioration of silages. Ensiling expertise and economic factors that limit mechanization also impair silage production and utilization in hot environments. In cold regions, a short and cool growing season often limits the use of crops sensitive to cool temperature, such as corn. The fermentation triggered by epiphytic and inoculated microorganisms can also be functionally impaired at lower temperature. Although the use of silage inoculants has increased in Northern Europe, acid-based additives are still a good option in difficult weather conditions to ensure good fermentation quality, nutritive value, and high intake potential of silages. Acid-based additives have enhanced the quality of round bale silage, which has become a common method of forage preservation in Northern Europe. Although all abiotic factors can affect silage quality, the ambient temperature is a factor that influences all stages of silage making from production in the field to utilization at the feed bunk. This review identifies challenges and obstacles to producing silages under hot and cold conditions and discusses strategies for addressing these challenges.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".