Modelling Climate Change Impact on Irrigation Water Requirement and Yield of Winter Wheat (Triticum aestivum L.), Barley (Hordeum vulgare L.), and Fodder Maize (Zea mays L.) in the Semi-Arid Qazvin Plateau, Iran
Why this work is in the frame
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Bibliographic record
Abstract
It is very important to determine the irrigation water requirement (IR) of crops for optimal irrigation scheduling under the changing climate. This study aimed to investigate the impact of climate change on the future IR and yield of three strategic crops (winter wheat, barley, fodder maize) in the semi-arid Qazvin Plateau, Iran, for the periods 2016–2040, 2041–2065, and 2066–2090. The Canadian Earth System Model (CanESM2), applying IPCC scenarios rcp2.6, rcp4.5, and rcp8.5, was used to project the monthly maximum and minimum temperatures and monthly precipitation of the region. The results indicated that the maximum and minimum temperatures will increase by 1.7 °C and 1.2 °C, respectively, under scenario rcp8.5 in the period 2066–2090. The precipitation will decrease (1%–13%) under all scenarios in all months of the future periods, except in August, September, and October. The IR of winter wheat and barley will increase by 38%–79% under scenarios rcp2.6 and rcp8.5 in the future periods. The increase in the IR of fodder maize will be very slight (0.7%–4.1%). The yield of winter wheat and barley will decrease by ~50%–100% under scenarios rcp2.6 and rcp8.5 in the future periods. The reduction in the yield of maize will be ~4%. Serious attention has to be paid to the water resources management of the region. The use of drought-tolerant cultivars in the region can be a good strategy to deal with the predicted future climatic conditions.
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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 it