Energy and Economic Evaluation of Farm Operations in Crop Production
Bibliographic record
Abstract
The present research work has been carried out at Central Research Station farm of Dr. PDKV, Akola and atKatkheda and Sutala village of the Akola and Bulbhana district respectively. The operations considered were landpreparation, sowing, intercultural, harvesting and crop residue management etc. The inputs like human power,bullock power for traditional operation were studied in entire work of the research. Similarly, for the same cropsthese operations were carried out by the mechanized practice for the exact quantification of the operational energyinput. The study reflects the energy use patterns in mechanized and traditional farming and optimized energyefficient cropping system through mechanized farming over traditional farming. The practices evaluated for thecrop production which resulted in the high yielding of crop and the crop residues.On the basis of results obtained, it was observed that the traditional operational energy requirement increases from2680.78 MJ/ha in traditional method to 3130.72 MJ/ha in mechanized method for green gram crop. While, there isdecrease in cost of operation from Rs 8407.5/ha in traditional method to Rs 5147.0/ha in mechanized system.Similar trend was observed in cotton, soybean, sorghum and wheat crop. For all the crops seed bed preparation isdone by tractors in traditional as well as mechanized method except in mechanized method land smoothening isdone by self propelled tiller instead of bullock drawn blade harrow. In most of the crops the farm operations weremechanized with different implements except harvesting operation, due to unavailability of appropriate machine for harvesting of crops except wheat crop. Overall it seen that the application of modern implements andmachineries for the crop production over the traditional practices reduces the cost of production which surelyimpact on the crop production and the net income of the farmers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".