The nutritional feed gap: Seasonal variations in ruminant nutrition and knowledge gaps in relation to food security in Southern Africa
Bibliographic record
Abstract
Livestock production is critical to food security and rural livelihoods across Southern Africa. Despite progress in livestock science research in recent years, the seasonal availability and quality of feed remains one of the key challenges to livestock productivity in Southern Africa. In particular, dry weather conditions, the lack of rain and lower temperatures in the dry season cause herbaceous plants to die back and browse species to defoliate, limiting the abundance, quality, and variety of feed available. This creates a 'Nutritional Feed Gap', defined here as the combined effect of the sharp reduction in both forage quantity and quality from the wet to the dry season and the risk that it poses to ruminant production systems and the food security of the people and communities reliant on them. Understanding the nature and extent of how seasonality impacts ruminant production potential can thus contribute towards mitigating negative impacts of extreme weather and climate change on food systems. In this review, we characterise this nutritional feed gap in terms of forage abundance and nutrition as well as discussing how climate change may shape the future nutritional landscape. Whilst some forage nutrient concentrations varied little by season, crude protein and phosphorus were consistently found to decrease from the wet season to the dry season. We also identify a shortfall in primary research that assess both forage quality and quantity simultaneously, which forms part of a broader knowledge gap of our limited understanding of the impact of limiting factors to ruminant production on short and long-term food security across Southern Africa.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".