Optimal Crop Choice, Irrigation Allocation, and the Impact of Contract Farming
Bibliographic record
Abstract
The changing climate and concerns over food security are prompting a new look at the supply chain reliability of products derived from agriculture, and the potential role of contract farming as a mechanism to address climate and price risk while contributing toward crop diversification and water use efficiency is also emerging. In this study, the decision problem of a farmer associated with allocating his land among different crops with varying water requirements is considered, given that a subset of the crops may be associated with a forward contract that is being offered by a buyer. The problem includes a decision to acquire a certain amount of irrigation water capacity prior to the season and to allocate this capacity as irrigation water to be applied during the season to each of the crops selected. Rainfall in the growing season and the market price of each crop at the end of the season are considered to be random variables. Two stochastic programming models are developed to consider facets of this problem and to understand how contracts that reduce market price uncertainty from the problem may change the farmer's decision. The structural properties of these models are discussed, and selected implications are illustrated through an application to data from the Ganganagar district in Rajasthan, 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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".