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Record W1972259091 · doi:10.4141/cjps08086

Impact of tillage on field pea following spring wheat

2009· article· en· W1972259091 on OpenAlexvenueno aff
Patrick M. Carr, Glenn B. Martin, Richard D. Horsley

Bibliographic record

VenueCanadian Journal of Plant Science · 2009
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgronomic Practices and Intercropping Systems
Canadian institutionsnot available
Fundersnot available
KeywordsField peaTillageAgronomySativumConventional tillagePisumRandomized block designBiologyLegumeNo-till farmingCropHorticultureSoil waterSoil fertility

Abstract

fetched live from OpenAlex

Tillage is being reduced in semiarid regions. The impact of changing tillage practices on field pea (Pisum sativum L.) performance has not been considered in a major pea-producing area within the US northern Great Plains. A study was conducted from 2000 through 2005 to determine how field pea performance compared following spring wheat (Triticum aestivum L.) in clean-till (CT), reduced-till (RT), and no-till (NT) systems arranged in a randomized complete block at Dickinson in southwestern North Dakota. Seed yield increased over 1600 kg ha -1 in 2000 and almost 400 kg ha -1 in 2003 under NT compared with CT, and by 960 kg ha -1 in 2000 under NT compared with RT (P < 0.05). Differences in seed yield were not detected between tillage systems in other years. Plant establishment was improved as tillage was reduced, averaging 66 plants m -2 under NT and RT compared with 60 plants m -2 under CT management. The soil water conservation that can occur after adopting NT may explain the increased seed yields that occurred in some years. These results suggest that field pea seed yield can be increased by eliminating tillage in semiarid areas of the US northern Great Plains, particularly when dry conditions develop and persist. Key words: Zero tillage, field pea, cropping system, N-fixation, legume

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 score0.881
Threshold uncertainty score0.993

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 head0.022
GPT teacher head0.244
Teacher spread0.222 · 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

Citations11
Published2009
Admission routes1
Has abstractyes

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