Integrated animal and cropping systems in single and multi-objective frameworks for enhancing the livelihood security of farmers and agricultural sustainability in Northern India
Bibliographic record
Abstract
Fast degrading and declining land, water availability, biodiversity, environment and other natural resources, together with shrinking farm returns and reduced crop productivity caused by continuous and intensive cultivation of rice-wheat systems, necessitate diversification of farming in Northern India. Integrated farming systems (IFS) involving animals (livestock, fish, etc.) and cropping (cereals, trees, etc.) are recognised as an alternative for preserving ecosystems and enhancing livelihood security. A study was therefore undertaken under Northern Indian conditions to develop IFS models for various sizes of farm and to compare these models with the existing rice-wheat system for sustainability and profitability. The IFS models were developed in single objective (using linear programming) and multi-objective (using compromise programming) frameworks. Multi-objective analysis provides deeper insight into the problem as it caters directly for the multi-faceted needs of the farmers. These parallel methodologies offer a novel approach to modelling IFS to draw different farming scenarios for comparison. The IFS strategies developed show the potential to generate a greater farm income than with existing rice-wheat cropping for all sizes of farm. The study revealed that IFS offer more perspectives for an economically viable and sustainable agriculture for typical farms in Northern India.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".