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Record W1995083247 · doi:10.2134/agronj2002.7670

Tillage Effects on Corn Production in a Coarse‐Textured Soil in Southern Ontario

2002· article· en· W1995083247 on OpenAlexaboutno aff
Jacqueline W. Schott, Peter H. White

Bibliographic record

VenueAgronomy Journal · 2002
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCrop Yield and Soil Fertility
Canadian institutionsnot available
Fundersnot available
KeywordsTillageAgronomyConventional tillageEnvironmental scienceProductivityGrain yieldBiology

Abstract

fetched live from OpenAlex

Minimum tillage has been shown to slow early corn ( Zea mays L.) growth and reduce grain yields in some soil types and under some climatic conditions. To overcome these limitations, the no‐till (NT) system can be modified by incorporating residues and loosening the soil in a zone over the center of the row while leaving the interrow area untilled. This study compares soil temperatures and corn growth and productivity under zone till (ZT), NT, and conventional tillage (CT) systems in a coarse‐textured soil (Psammentic Hapludalf) located in southwestern Ontario, Canada. Soil temperature at the 4‐cm depth decreased with decreasing tillage intensity from CT to NT during warmer years but was similar in CT and ZT during a cooler year. This resulted in reduced growing degree days in the seed zone with decreasing tillage. Lower soil temperatures in NT did not delay the initiation of corn seedling emergence but did reduce the rate of emergence compared with CT plots. Corn growth rates were found to be similar among tillage systems in the early part of the growing system but were higher for both the ZT and NT systems during late vegetative and early reproductive growth. Grain yields increased as tillage intensity decreased in a year with drier conditions at tasseling but were similar across tillage systems in the other 2 yr. These results suggest that converting a NT system to a ZT system would neither result in significantly higher yields, nor cause a serious grain yield reduction relative to CT.

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 score0.533
Threshold uncertainty score1.000

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)0.0010.000

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 head0.019
GPT teacher head0.188
Teacher spread0.170 · 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

Citations62
Published2002
Admission routes1
Has abstractyes

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