Irrigation Decision Support Systems (IDSS) for California’s Water–Nutrient–Energy Nexus
Bibliographic record
Abstract
California has unsustainable use of agricultural water and energy, as well as problems of severe drought, nitrate pollution and groundwater salinity. As the leading producer and exporter of agricultural produce in the United States, 5.6 percent of California’s energy is currently used for pumping groundwater. These problems and new regulatory policies (e.g., Sustainable Groundwater Management Act, Irrigated Lands Regulatory Program) pressure growers to schedule, account and maintain records of water, energy and nutrients needed for crop and soil management. Growers require varying levels of decision support to integrate different irrigation strategies into farm operations. Decision support can come from the public or private sector, where there are many tradeoffs between cost, underlying science, user friendliness and overall challenges in farm integration. Thus, effective irrigation management requires clear definitions, decision support and guidelines for how to incorporate and evaluate the water–nutrient–energy nexus benefits of different practices and combinations of practices under shifting water governance. The California Energy Commission-sponsored Energy Product Evaluation Hub (Cal-EPE Hub) project has a mission of providing science-based evaluation of energy-saving technologies as a direct result of improved water management for irrigation in agriculture, including current and future irrigation decision support systems in California. This project incorporates end-user perceptions into evaluations of existing decision support tools in partnership with government, agricultural and private stakeholders. In this article, we review the policy context and science underlying the available irrigation decision support systems (IDSS), discuss the benefits/tradeoffs and report on their efficacy and ease of use for the most prevalent cropping systems in California. Finally, we identify research and knowledge-to-action gaps for incorporating irrigation decision support systems into new incentives and requirements for reporting water and energy consumption as well as salinity and nitrogen management in the state of California.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".