Agricultural Mechanization Status for Some Crops in Irrigated Sector in River Nile State, Sudan
Bibliographic record
Abstract
Agricultural mechanization and it is impact on agricultural productivity was studied by many authors in different areas in the world. Irrigated agriculture in the Sudan, have played a significant role in expanding agricultural mechanization, and the major mechanized operation is the land preparation, operations such as planting, spraying, fertilizer application, mechanical weeding and harvesting are still largely carried out manually. A baseline survey on mechanization status was implemented in River Nile State, focuses on mechanization status for production of wheat as strategic crop, legumes as food crops, onion and alfalfa as cash crops in smallholder farms. The analysis of respondents answers show that tillage operation has the high percent (90.5-93.3%) of mechanical power among other operations for production of the selected crops, where wheat has considerable percent of using mechanical power in sowing and harvesting operations compare to the three rest crops. For legumes and alfalfa broadcasting of seeds for sowing and cutting and binding at harvest operations, still manual activity prevailing, where for onion transplanting are 100% carried out manually.The mechanization level range between 0.2-0.58, which reflect the less number of tractors to the cultivated areas in the state. Concerning the mechanization index as the ratio of mechanical power to the total power input in term of MJ/ha for each crop range from 0.03- 0.07, shows that manual and animal power still exerted to produce such crops.
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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 | 0.000 | 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 | 0.001 | 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) | 0.002 | 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".