Sustainable Agriculture Irrigation Management: The Water-Energy-Food Nexus in Pajaro Valley, California
Bibliographic record
Abstract
The water-energy-food (WEF) nexus is quickly becoming one of the most critical global environmental challenges of the twenty first century. However, WEF systems are inherently complex; they typically are dynamic and span multiple land or agro-ecosystems at a regional or global scale. Addressing this challenge requires a systems approach to optimal and sustainable resource management across multiple dimensions. To that end, using Pajaro Valley (California) as a case study, our research aims to (1) highlight synergies and tradeoffs in food and water production, (2) build a dynamic framework capable of examining intertemporal resource relationships, and (3) detail the steps required to develop incentive-compatible financing of the resulting management plans when benefits are not distributed uniformly across users. Using a stylized model, we find that in the long run, inland growers benefit from the halting of seawater intrusion (SWI) due to overpumping of groundwater. We also calculate that the water provided by the proposed College Lake Multi-Objective Management Program-a plan designed to halt SWI and support sustainable water and agricultural development in the region-will generate net revenue of $40-58 million per year, compared to an annualized cost of less than $3 million. An equal cost-sharing plan would be desirable if the benefit of the project exceeded $1,268 per year for each well owner. Since this may not necessarily be the case for smaller well owners, one possible alternative is to allocate costs in proportion to expected benefits for each user.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".