Milk quality and production under climate change uncertainty: case of the Algerian cattle breed
Bibliographic record
Abstract
Algerian indigenous cattle breeds are well adapted to the harsh local arid and semi-arid environments. This study aims to summarize livestock practices, milk quality, and discuss the potential of local cattle breeds to maintain production capacity in the face of global warming conditions. A total of 175 smallholder farmers who practice the breeding of the Algerian local cattle breed were interviewed using a formal questionnaire. Following that, 122 milk samples were collected for physicochemical and bacteriological analyses. Climate data variability in the study area was evaluated. Results reveal that between 1980 and 2018, the average annual temperature rose by 0.3 ± 0.001 °C per year. Predictions suggest that by 2081 to 2100, temperatures could increase by 1.18°C under SSP1-2.6, 2.33°C under SSP2-4.5, and 4.59°C under SSP5-8.5. In the same period from 1980 to 2018, annual precipitation decreased by -0.99 ± 0.24 mm per year. Projections indicate a further decline of 22.5 mm for SSP1-2.6, 44.4 mm for SSP2-4.5, and 95.2 mm for SSP5-8.5 from 1980-2000 to 2081-2100. These changes in temperature and precipitation coincided with an expansion of cropland, which increased by 90.3% from 1992 to 2005. Conversely, pasture areas decreased by 53.7% between 1993 and 2009. A socio-demographic survey revealed that breeders have a low educational level (39.4% are unlettered). They own a small herd (6.84 ± 8.66 cattle). Moreover, the average daily milk production was 4.13 ± 2.12 Liters/cow, with acceptable physicochemical quality but poor bacteriological quality. Considering the climate change vulnerability of the study area, we can conclude that the exploitation of local breeds seems to be the best adaptation strategy to climate change effects. Conservation programs for local breeds can enhance biodiversity and ecosystem balance. Concurrently, genetic improvement programs have the potential to boost productivity and profitability, making substantial contributions to social equity and local economies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".