Silage review: Unique challenges of silages made in hot and cold regions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it