Selection of Winter Season Crop Pattern for Environmental-Friendly Agricultural Practices in India
Bibliographic record
Abstract
Owing to the sudden changes in climatic conditions, monsoon failure, and scarce availability of resources because of population hike, yielding a minimum profit has become a challenge for Indian farmers. This is a severe problem for India, as a major part of the Nation’s Gross Domestic Product (GDP) depends on agriculture. To change this dreadful situation, Indian farmers must employ sustainable agricultural practices in farming, as it will help them to meet their agricultural needs and economic stability. Here, we have built a framework for selecting the ideal crop pattern for Winter Cropping Season (Rabi Season), as crop pattern plays a vital role in the effective function of sustainable agricultural practices. We have used the rough AHP-TOPSIS (Analytical Hierarchy Process-Technique for Order Preference by Similarity to Ideal Solution) method for finding the best crop pattern for the Rabi season, by considering all the influential criteria in terms of agriculture sustainability. Our study demonstrates an overall idea to the farmers and stakeholders about attaining maximum crop productivity with optimum use of available resources, without compromising the economic, social, and ecological aspects of agriculture.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".