A CMIP5 Ensemble Assessment Of Durum Wheat Production & Climate Change In North Dakota, Usa
Bibliographic record
Abstract
In the United States (US), North Dakota is the largest producer of Durum Wheat (Triticum durum), hereinafter referred to as Durum. Durum grain has a high protein content and multiple utilities in food products. We investigated the historical trends in Durum production and yield as influenced by changes in precipitation (precip) and temperature (temp). The study accounted for variations in environmental conditions by running a dynamic crop model in thirteen Durum producing counties. The climate of North Dakota is representative of the highly productive agricultural lands of the Northern Great Plains, encompassing five US states and two Canadian provinces. The Eastern part of North Dakota has a humid continental climate while the western part is semi-arid. Creating a distinct West-to-East precip gradient across the state. Low mean average temps (cir. +4 °C), and high-temp variability lead to the relatively short growing season (cir. 130 days). Combined with limited rainfall (cir. 350 mm in the E and 560 mm in the W), it makes agriculture highly dependent on temp and precip. Accordingly, climate change has a high potential impact on crop production in the region. The ALMANAC crop growth model was used to simulate the production of Durum. Model performance was estimated by comparison of simulated yields with historical observations, and was found satisfactory using the Nash–Sutcliffe model efficiency coefficient (E) and Coefficient of determination (r2) (< 0.50). Uncertainty in projected future climate is addressed using an ensemble of 17 Global Circulation Models (GCMs) run under four scenarios. GCM output data were further downscaled using MarkSim weather, and daily weather was generated for two 30-year periods, characteristic of the 2020’s and the 2050’s.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".