Competencies in Dairy Production Needed by Dairy Farmers of Kuku Cooperative Dairy Society in Khartoum State, Sudan
Bibliographic record
Abstract
The dairy farmers of Sudan are facing the lack of some competencies needed for improving the dairy productivity of their cattle. Therefore it is very important to study the farmers competencies in order to put plans for improving the farmers conditions and go for better productivity. The main objective of this study was to identify the competencies in dairy production of Kuku Cooperative Dairy Society (KCDS) in Khartoum State. A random sample of 81 dairy farmers was drawn from the total 162 members of KCDS. A questionnaire was carefully prepared that included a list of 8 understandings and 27 important abilities in the field of dairy production. A rating scale was provided with a 0 to 4 range of the abilities and understandings. The personal interviews with the farmers in the sample were conducted during January, 2013. The data was analyzed using the Statistical Package for Social Science (SPSS). It was concluded that the dairy farmers need more competencies in dairy production. Some competencies need more emphasis than others. The dairy farmers felt that they possessed fewer competencies in dairy production than their actual need. Farmers indicated that they need more competence in areas related to calculating net farm income, selecting sires with high proofs and high repeatability, identifying mastitis problems and treating mastitic cows, recognizing symptoms of sick animals, following the vaccination program, the proper management and the adequacy of feeding calves and herd replacements, and the identification of common parasites.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".