The nutritional feed gap: Seasonal variations in ruminant nutrition and knowledge gaps in relation to food security in Southern Africa
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
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.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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