Opportunities to improve goat production and food security in Botswana through forage nutrition and the use of supplemental feeds
Bibliographic record
Abstract
Abstract Goats fulfil a central role in food and nutritional security across Africa with over half of households owning or rearing goats in rural areas. However, goat performance is poor and mortality high. This study assessed the nutritional quality of commonly used feeds and proposes feed-baskets to enhance goat nutrition and health. Feeds were collected from 11 areas within the Central District of Botswana, and macronutrient analyses were conducted, including crude protein, fibre fractions, ash, and metabolizable energy (ME). Forage nutrition was compared across seasons and soil types. Additionally, seasonal supplementation trials were conducted to evaluate consumption rates of various supplements, including crop residues, pellets, Lablab purpureus , and Dichrostachys cinerea . Each supplement was provided ad libitum for a 24-h period, and consumption rates determined. Findings revealed significant differences in nutrition among various feed sources, across seasons, and in relation to soil types (p < 0.001). Consumption rates of supplements were higher during the dry season, possibly due to reduced forage availability. Supplement consumption rates varied across supplement type, with crop residues accounting for approximately 1% of dry matter intake, compared to up to 45% for pellets, 13% for L. purpureus , and 15% for D. cinerea . While wet season feed baskets exhibited higher ME values compared to dry-season feed-baskets, the relative impact of supplementation was more pronounced during the dry season. These results highlight the potential for optimizing goat diets through improved grazing and browsing management, especially during the reduced nutritional availability in the dry season in Botswana. Such diet optimisation may improve goat health and productivity, which may positively impact the food and financial security of smallholders by providing both increased yields and increased resilience. Importantly, rural communities can experience some of the lowest food security levels in the region. The interventions explored in this study utilise natural capital, often freely available, which can be deployed through existing husbandry systems, potentially making them accessible and practical to smallholders.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".