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Record W2166456975 · doi:10.1614/wt-d-12-00115.1

Weed Interference Impacts and Yield Recovery after Four Years of Variable Crop Inputs in No-Till Barley and Canola

2013· article· en· W2166456975 on OpenAlexaffabout
K. Neil Harker, John T. O’Donovan, T. Kelly Turkington, Robert E. Blackshaw, Eric N. Johnson, Stu Brandt, H. R. Kutcher, George W. Clayton

Bibliographic record

VenueWeed Technology · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWeed Control and Herbicide Applications
Canadian institutionsUniversity of SaskatchewanNational Association of Friendship CentresLethbridge CollegeAgriculture and Agri-Food Canada
Fundersnot available
KeywordsCanolaAgronomyWeedWeed controlCropFertilizerBiomass (ecology)Yield (engineering)GlyphosateBiologyCrop yieldEnvironmental science

Abstract

fetched live from OpenAlex

A 2-yr (2009 to 2010), no-till (direct-seeded) “follow-up” study was conducted at five western Canada sites to determine weed interference impacts and barley and canola yield recovery after 4 yr of variable crop inputs (seed, fertilizer, herbicide). During the initial period of the study (2005 to 2008), applying fertilizer in the absence of herbicides was often worse than applying no optimal inputs; in the former case, weed biomass levels were at the highest levels (2,788 to 4,294 kg ha −1 ), possibly due to better utilization of nutrients by the weeds than by the crops. After optimal inputs were restored (standard treatment), most barley and canola plots recovered to optimal yield levels after 1 yr. However, 4 yr with all optimal inputs but herbicides led to only 77% yield recovery for both crops. At most sites, when all inputs were restored for 2 yr, all plots yielded similarly to the standard treatment combination. Yield “recovery” occurred despite high weed biomass levels (> 4,000 kg ha −1 ) prior to the first recovery year and despite high wild oat seedbank levels (> 7,000 seeds m −2 ) at the end of the second recovery year. In relatively competitive narrow-row crops such as barley and canola, the negative effects of high soil weed seedbanks can be mitigated if growers facilitate healthy crop canopies with appropriate seed and fertilizer rates in combination with judicious herbicide applications to adequately manage recruited weeds.

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)0.000
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 score0.973
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.008
GPT teacher head0.184
Teacher spread0.175 · 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

Citations14
Published2013
Admission routes2
Has abstractyes

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