Economic and Productive Assessment of an Ordinary Small-Sized Dairy Enterprise in Southeast Brazil: A Multi-Year Study
Bibliographic record
Abstract
The objectives were to analyze the economic performance over time of a dairy enterprise located in southeast Brazil and to identify the key production parameters that contributed to economic performance, using a 10-year database. Two distinct approaches to evaluate production cost were analyzed. Briefly, the first approach involves variable and fixed costs (more traditional economic analysis), and the second involves total operating cost, consisting of effective operating costs and depreciation. From these two distinct approaches, we obtain as economic indicators the profitability I and profitability II, respectively. In addition, correlation between economic and productivity parameters was performed. Considering the first approach, revenue was not sufficient to cover the total cost and on average profitability I was negative. During three years, the break-even point was not achieved. Considering the second approach, gross profit margin was positive throughout the period, and consequently profitability II was positive. In general, production parameters were within the ordinary range observed in small-sized Brazilian dairy farms. From the correlations between economic and production parameters, we noted that correlation between average milk production per lactating cow and both measures of profit was present, indicating that if the average milk production per lactating cow was high, profit was positive. We conclude that this type of evaluation is important to assess the performance of a business, and consequently, for decision-making of dairy producers.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".