Herd-level associations between somatic cell counts and economic performance indicators in Brazilian dairy herds
Bibliographic record
Abstract
ABSTRACT The aims of the present study were to provide a portrait of the techno-economic status of dairy herds in Minas Gerais, Brazil, particularly with respect to bulk-tank somatic cell count (BTSCC) data, and to examine the herd-level associations of BTSCC with various economic performance indicators (EPI). Data from 543 herds, 1,052 herd-year records in total, spread over 3 years (2015–2017), from the South and Southwest mesoregions of Minas Gerais State were provided by the Brazilian Support Agency to Micro and Small Companies Division Minas Gerais (SEBRAE). Herds had an average of 82 lactating cows per herd, milk yield of 17 L/cow per day, and availability of financial information via routine monthly economic surveys. The EPI data (revenue, gross margin, GM; net margin, NM; profit; break-even point; and operational profitability) of each herd was measured monthly by SEBRAE personnel, and herd-year averages of all variables were computed. Bulk-tank data (SCC, total bacterial count, content of crude protein and fat) taken by producers or dairy processors were recorded by SEBRAE personal; and corresponding herd-year averages were calculated and included in the SEBRAE database. There were 209 selected herds, which passed all edit checks, and which had data for all 3 years. The EPI (all expressed on a per-cow basis, $/cow per year) were analyzed, including the effects of region, year, log (ln) BTSCC, production level, and herd size, together with the random effect of herd nested within region. A high proportion of herds (94.6%) presented data records (herd-years) with an average BTSCC > 200 × 103 cells/mL: 37.8% of herd-year records had BTSCC between >200 and ≤400, 14.5% with BTSCC between >400 and ≤500, 25% with BTSCC between >500 and ≤750, and 17.3% with BTSCC >750. For each unit increase in ln BTSCC, revenue declined by $228.5/cow per year, GM by $155.6/cow per year, and profit by $138.6/cow per year. Herds with cows of lower production (<14 kg/d) presented lower GM ($286.8/cow per year) compared with herds containing cows producing ≥14 kg/d (≥14 and <19 kg/d = $446.5, and ≥19 kg/d = $601.9). The small-scale milk producers (<39 lactating cows) presented lower revenue ($1,914.9/cow per year) and GM ($274.5/cow per year) and consequently a negative profit (−$224.1/cow per year) compared with other herd size categories (≥39 lactating cows). The reduction in milk yield was 641 L/cow per lactation for each unit increase in ln BTSCC; this represented 9.4% of the milk yield per lactation, assuming an average milk production of 6,843.3 L/cow per lactation of cows from herds that had BTSCC ≤ 200 × 103 cells/mL. Consequently, we found a negative association of BTSCC with profit; profit declining from $227.0 to −53.1/cow per year when the BTSCC increased from 100 to 750 × 103 cell/mL. In short, the lower the BTSCC, the greater the revenue, GM and NM, profit, and operational profitability of the herds. The reduction of milk yield was the main factor associated with higher BTSCC.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".