Integrated water resources management: a case study of on-farm water use for potato processing
Bibliographic record
Abstract
Integrated water resources management (IWRM) is described as a holistic approach to manage water efficiently, equitably, and sustainably. This paper presents a case study where cooperative strategy building among diverse stakeholders (researchers, potato farmers, and government regulators) resulted in significant water conservation for the on-farm washing of potatoes on a large potato operation (31% reduction per unit of potatoes sold). Water was reduced by applying modified IWRM methods, including (i) goal setting, where common goals with all three parties were outlined; (ii) initial assessment, where farm water use was monitored in detail for one year; (iii) cooperative strategy building, where monitoring results were presented and potential water-use reduction strategies were brainstormed; (iv) implementation, where strategies were put into place on the farm; and (v) final assessment, where water use was monitored for a second year, after conservation strategies were in place, and the efficacy of the strategies was determined. This case study demonstrates the value of IWRM, through cooperation among researchers, farmers, and the regulators, for improving water management in agriculture.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| 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 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".