Simulating Irrigation Requirements for Vegetable Crops in a Humid Region Considering a Changing Climate
Bibliographic record
Abstract
Highlights Water deficiency of 50% did not reduce yields. Potato needs a minimum of 247 mm of water by the 2080s. Irrigation requirements of squash are 124 mm for 2080s. Abstract. This study assessed the supplemental irrigation needs in a humid vegetable producing region of Quebec using the AquaCrop model. Three irrigation treatments were investigated, comprising a maximum allowable depletion (MAD) of 20%, 35%, and 50% of plant-available water (AW) for potato and squash cultivated on sandy soils. The AquaCrop models were calibrated to field measurements of soil moisture in 2022. Potato and squash models simulated soil moisture with a strong agreement to field sensor measurements, with a Willmott index of agreement of 0.91 and 0.84, respectively. Statistical analysis revealed that both the historical weather and the irrigation treatment (20%, 35%, or 50% depletion of AW) had a significant effect on net irrigation requirement. The MAD of 50% AW demonstrated significant water-saving potential, particularly in dry years where crop-water demand was significantly higher. Under the 50% MAD, the mean net irrigation requirement for potato and squash in historical dry years were 265 mm and 151 mm, respectively. The models were then used to predict the impact of climate change on irrigation requirements for the periods 2050 and 2080. The irrigation requirement for potato is predicted to significantly increase by the 2080s compared to the historical period (1997–2021), under the CMIP-6 SSP5-8.5 high emissions scenario. Projected irrigation requirements for squash remained stable. Irrigation treatment significantly impacted the net irrigation requirement, with an MAD set at 50% AW significantly reducing irrigation requirements across all climate periods. Keywords: Climate change, Crop modeling, Humid regions, Irrigation water requirement.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".