Strategic vegetable crop distribution in Himachal Pradesh for sustainable economic and environmental outcomes
Bibliographic record
Abstract
India, the second-largest global producer of fruits and vegetables, faces challenges in translating productivity into profitability, particularly in vegetable farming. This study examined resource use efficiency in Himachal Pradesh, focusing on optimizing land allocation using Multi-Objective Programming (MOP) to maximize income and employment while minimizing fertilizer and pesticide use. Primary data were gathered from Solan and Sirmaur districts through structured questionnaires employing a multi-stage sampling design. Optimization models were developed annually for marginal, small, and medium farmers over three cropping seasons. The study demonstrated significant productivity, profitability, and sustainability improvements through optimization techniques. For marginal farmers, income increased from Rs 229,359 to Rs 280,058 per hectare, employment peaked at 252 man-days from 190, and fertilizer costs reduced from Rs 6180 to Rs 4420 per hectare, with the MOP approach allocating Chinese cabbage (March-May), cauliflower (August-November), and peas (November-March). Small farmers saw income rise from Rs 238,095 to Rs 296,214 per hectare, employment reaches 263 man-days from 217, and fertilizer costs decrease from Rs 6686 to Rs 3977 per hectare, with the MOP approacgh allocating Chinese cabbage (March-May), beans (August-November), and peas (November-March). For medium farmers, income maximized from Rs 256,071 to Rs 374,673 per hectare, employment at 253 man-days from 219, and fertilizer costs minimized from Rs 7092 to Rs 4428 per hectare, with the MOP approach allocating Chinese cabbage (March-May), beans (August-November), and peas (November-March). The study highlighted the need for efficient resource use and strategic crop selection for sustainable agriculture. Key policy recommendations include targeted support programs, resource efficiency initiatives, customized training, improved access to credit, and infrastructure development. These measures can boost productivity, profitability, and sustainability, enhancing farmer livelihoods and contributing to regional economic growth.
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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 | 0.000 |
| 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".