Determination of Competitiveness of a Dairy Production System in Family Farming by Management Systematization as an Extension Practice
Bibliographic record
Abstract
Through a case study, the goals of this paper were: First, to provide whether the technical and financial results were adequate to determine the competitiveness of family farming unit to response for enhancing its dairy production system. Second, to propose an alternative analysis methodology titled concentrate rationing to measure the financial performance of the dairy cows. Third, to provide a method to analyze the performance of technical assistance and rural extension. Data were collected by control spreadsheets on family farming unit in Alegre, Espírito Santo, Brazil. Indicators related to income and production costs were analyzed by Excel® software spreadsheets as well the concentrate rationing tool. Financial analysis evidenced that the intensification process resulted in an income increase of 92.5% in dairy sales and a reduction of 38% in the total cost of dairy production; however, the scale of production can be an obstacle for farmers to meet their business opportunities. The best financial results were achieved with the cows that showed the highest milk production and were fed the highest amount of concentrate, implying that the concentrate feeding can be an ally for the family farmer. The intensification process demonstrated be positive in meeting the production costs requirements provided that there is scale; the concentrate rationing tool sets a new perspective for the financial evaluation of a dairy farm; and the interaction between the extension technician and the farmer should bring knowledge so that the family farming becomes the main character in the production structure of the dairy supply chain.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".