Adaptation of Pulse Crops to the Changing Climate of the Northern Great Plains
Bibliographic record
Abstract
Climate over the northern Great Plains has generally warmed over the last 60 yr. The rate of warming has varied temporally and spatially, confounding trend analysis for climate indicators such as increased length of the growing season. Change in precipitation has been even more variable. Despite this variability, present‐day trends in temperature and precipitation generally coincide with the predicted direction of climate change. The synchrony of current and future trends reinforces the need for investigating adaptation in agriculture to changing climate. Our review is focused on sustainability of pulse crops in the northern Great Plains and the repercussions of climate change, focusing on the growth and yield response to temperature and water, and the climate restrictions that define their current geographic locations. The resilience of pulse crops to present‐day weather extremes such as drought, excess water, heat, cool weather during grain filling, and early frost are considered to predict adaptation to future climate change. Features discussed include changes to crop water‐use efficiency brought on by increased CO2 fertilization, accelerated growth rates resulting from higher air temperatures, and total crop failures caused by an increased occurrence and magnitude of weather extremes. Adaptation strategies that are discussed include earlier seeding of pulse crops, use of winter pulses, crop sequencing within crop rotations, and alterations to the microclimate such as direct seeding into standing stubble.
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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 | 0.000 |
| 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) | 0.001 | 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".