EVALUATING WATER POLICY OPTIONS IN AGRICULTURE: A WHOLE‐FARM STUDY FOR THE BROYE RIVER BASIN (SWITZERLAND)
Bibliographic record
Abstract
ABSTRACT In this study, we evaluate the impact of an increased volumetric water price and the implementation of a water quota on management decisions, income, income risk and utility of an arable farmer in the Broye River Basin, western Switzerland. We develop a bio‐economic whole‐farm model, which couples the process‐based crop growth model CropSyst with an economic decision model at farm scale and use a genetic algorithm as optimization technique. This integrated modelling approach is employed to optimize the farmer's management decisions with regard to crop land use as well as crop‐specific nitrogen fertilization and irrigation intensities under different climate and water policy scenarios. Our results show that the farm's water demand will increase by almost 100% under climate change. However, both, an increased volumetric water price and a water quota, are under current and future expected climate conditions effective policy measures to reduce the farm's water consumption. At the same time, due to adjustments in the crop mix as well as in crop‐specific nitrogen fertilization and irrigation strategies, both policies lead to losses in farm income and in the farmer's utility of only about 10%. Nevertheless, a higher water price as well as a water quota increase under future expected climate conditions the crop farm's downside risk exposure (i.e. probability of low farm incomes). Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".