Current status and adoption of mechanized agriculture in Pakistan- A review
Bibliographic record
Abstract
Presently about 0.94 million of tractors are working in Pakistan, offering a farm power of about 0.84hp/acre. Domestic tractor unit manufacturing in Pakistan risen nearly 14.6 percent during the 2017-18 fiscal year. Production increased to 63,054 tractor units by 19.6 percent compared to last year’s 54,992 units, with additional 901 thousand of chisel ploughs and 108 thousand mould board plough while tillage operation for soil bed planning is the only procedure that is nearly 100 percent mechanized in the country for almost all crops. The planting and spraying equipment industry has risen from 70 and 21 thousand in 2004 to 295 and 1438 thousand in 2014 respectively. Thresher market in Pakistan is estimated at 20,000-30,000 units per annum through sales resulting in nearly 100 percent mechanized cereal crop threshing operation. The average crop yield can be improved by raising the available horse power per hectare and adequate managing of agricultural machinery. It is concluded from the review of different survey and research conducted on farm mechanization that optimum level of mechanized agriculture is attained as per FAO optimum farm power requirement per unit area for crop production. However, it is still less than many developed and developing countries that’s why yield is also two to four times less than Japan, Europe, Canada, USA, etc. In Pakistan Tractors are commonly used in industry, building and road construction not contributing agriculture. To increase yield per unit area proper used of tractor with farm equipment should made compulsory.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".