Integrated Agriculture Production Systems for Meeting Household Food, Fodder and Fuel Security
Bibliographic record
Abstract
Agriculture including crop production and animal husbandry provides for the food, fodder, and fuel needs in rural regions of many countries such as India. Using the knowledge pertinent to complex mixed cropping-livestock systems at the village level, the goal of this study is to develop a rational method for crop selection, such that the capacity for production of food, fodder and biomass fuel can be examined under various cropping patterns. An agricultural survey is carried out in November 2007 for three villages located in the dryland agro-ecozone of Karnataka State, India. Various demands, including human food energy and protein requirements, and constraints, including land area, are modeled for optimal cropping pattern. A clear recommendation of the study is that a substantial shift in village-wide area planted to cereal crops, in all cases over 50%, is necessary to satisfy human and livestock demands. Additionally, there are visible and growing population pressures on the resources in the dryland, semi-arid regions of India, and these strategies will need to be supplemented by improved agronomic practices directed toward increased productivity.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".