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Record W2045202460 · doi:10.2134/agronj2006.0310s

Adaptation of Pulse Crops to the Changing Climate of the Northern Great Plains

2007· article· en· W2045202460 on OpenAlexaff
H. Cutforth, S. M. McGinn, Kevin McPhee, Perry R. Miller

Bibliographic record

VenueAgronomy Journal · 2007
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicClimate change impacts on agriculture
Canadian institutionsUniversity of LethbridgeAgriculture and Agri-Food Canada
Fundersnot available
KeywordsEnvironmental scienceClimate changePrecipitationFrost (temperature)Growing seasonMicroclimateAgricultureAgronomyClimatologyGeographyEcologyBiologyMeteorologyGeology

Abstract

fetched live from OpenAlex

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 CO 2 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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.690
Threshold uncertainty score0.712

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.029
GPT teacher head0.235
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations114
Published2007
Admission routes1
Has abstractyes

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