An analysis of observed and predicted extreme heat and precipitation trends across four pulse producing regions in North America: North Dakota, Montana, Saskatchewan, and Northeastern United States
Bibliographic record
Abstract
Abstract The consumption of plant-based proteins in lieu of animal proteins is the most important dietary shift that would be needed to keep the world under 2 °C of warming, and this shift would require a dramatic increase in the percentage of cropland devoted to nuts and pulses (Peters et al 2016 Elementa 4 000116). As the demand for plant-based proteins, like pulse crops, continues to grow, it is critical to understand the impact of climate change on crop production. In this paper, we study two climate-related stressors for pulse production in North America: extreme heat and excess moisture during harvest. Pulses must be dried on the plant before harvest, requiring a 7 day dry spell before harvest or the use of Roundup (glyphosate) to kill the plants quickly. However, little is known about the changes in frequency of hot extremes or dry spells during harvest in pulse-growing regions. We analyze climate trends using the Unprecedented Simulated Extreme Ensemble method with historical data, and we compare results to the average future change in the ssp370 scenario, which is a medium-high climate change scenario of 40 models of the the Coupled Model Intercomparison Project (CMIP6) in four pulse growing regions across North America: Montana, North Dakota, Saskatchewan, and the Northeast USA. We find that temperature extremes have increased in all regions, with extreme events 3–4 times more likely today than in 1981, increasing the risk of crop loss. August and September rainfall during the harvest months has been decreasing in the Midwestern regions and it is projected to continue to decrease in the future; however, the likelihood of a wet August in the Northeast has nearly doubled. Even with this drying trend, farmers cannot assume that they will have a 7 day consecutive dry spell that would enable natural drying of pulses without synthetic drying agents like glyphosate. Future expansion of pulse production should incorporate adaptation measures to manage extreme heat and the potential for rain events during harvest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".