Climate change impacts on rainfed agriculture and mitigation strategies for sustainable agricultural management: A case study of Prince Edward Island, Canada
Bibliographic record
Abstract
Abstract Rainfed agriculture in Prince Edward Island is affected by climatic changes like warming, seasonal shifts, varying precipitations, and reducing water availabilities, all questioning its sustainability. Projecting future scenarios was critically important; therefore, spatiotemporal variations in potential evapotranspiration (PET), crop water requirements (CWRs), effective rainfall (ER), supplemental irrigation requirements (SIR), and sustainable water availabilities (SWAs) were analyzed. Average annual PET was predicted to insignificantly increase (3% to 6%) under RCP4.5 during the next 30–60 years, with two to four times increase in colder months (January–April) and a significant reduction during August–December. Accordingly, historical CWR of potatoes (currently ~425 mm) would decrease by 5% to 9%, except for the least likely RCP8.5 scenario, which projects ~10% increase in 2051–2080. That, and changes in ER, would cause SIR to decrease to 50–90 mm in normal years, but two to three times higher during dry years, and almost no SIR in wet years. Spatially, SIR increases by ~40 mm from east to west and is expected to be higher in 2021–2050 than 2051–2080. Monthly SIR ranges in normal years would be July: 02–36 mm, August: 31–48 mm, and September 04–20 mm. Existing water policy allows pumping up to 20% of yearly recharges (annual SWA) and up to 35% of summer's streams baseflows (summer SWA). Despite insignificant reductions in annual SWA (3% to 17%) for the next 30–60 years, summer SWA may be reduced 38% to 50% due to temporal redistribution. Even in dry years, the reduced amounts would still be sufficient to fulfill SIR in the eastern forest‐dominated Bear River watershed and SIR in normal years in the central zone; however, they would not be enough to enable the entire cultivated area of western zone. In normal years, SIR would consume: 5% to 6%, 27% to 37%, and 63% to 79% of annual SWA in the eastern, central, and western zones, respectively. Sprinkler irrigation to meet SIR is challenging, more economic evaluations and policy adaptations are needed for sustainable agricultural management.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".