Analysis of Profitability and Risk in New Agriculture Using Dynamic Non-Linear Programming Model
Bibliographic record
Abstract
Cropping pattern in the Himalayan region of India has undergone a significant change in the recent past. Introduction ofhorticultural crops such as vegetables, fruits and flowers has led to more intensive agriculture. Such a change, resultingin higher incomes and improvements of the overall living conditions has, however, been accompanied with increasedincome risk. This emphasizes the need for proper analysis of the cropping pattern, at an appropriate scale, such as amicro watershed. This was achieved by constructing a dynamic non-linear programming model incorporatingappropriate objective function, constraints and crop and livestock activity budgets along with risk component present inthe gross returns. The model was then solved under alternate policy scenarios by using General Algebraic ModelingSystems (GAMS) for the next 20 years. The optimum cropping plans were then compared with each other and with theexisting plan. Tomato and carnation are the preferred crops, if the sole objective is profit maximization. Optimum planwith risk consideration was also assessed by fixing the variance in gross returns at the current level. It reduced the areaunder tomato in rainy season by growing capsicum and beans. Similarly, peas replaced tomato in winter season andchrysanthemum replaced carnation. By comparing it with the existing plan, it can be inferred that the people are moreconcerned to risk than the profits. The profits and risks from floriculture are relatively very high as compared to othercrops. By removing constraints in credit availability, irrigation facilities, transportation and market yards, large scaleproduction of vegetables and flowers can help in raising the income level.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".