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Record W1964489907 · doi:10.1080/07060660209507033

Effect of crop rotation and tillage system on sclerotinia stem rot on soybean

2002· article· en· W1964489907 on OpenAlexvenueno aff
Daren S. Mueller, W. L. Pedersen, G. L. Hartman

Bibliographic record

VenueCanadian Journal of Plant Pathology · 2002
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant pathogens and resistance mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsPloughAscocarpTillageAgronomySclerotiniaCrop rotationMulchCropPopulationMathematicsBiologyBotanyMedicine

Abstract

fetched live from OpenAlex

Sclerotinia stem rot (SSR) of soybean is a major disease in the North Central region of the United States. A 3-year study was done to determine if crop rotation and tillage, moldboard plowing then mulch tillage (MP + MT), mulch tillage (MT), and no-till (NT), affected the population density of sclerotia and apothecia, incidence of SSR, and soybean yield. Crop rotation did not significantly affect the number or distribution of sclerotia or SSR incidence, but did affect the number of apothecia and the yield. The highest number of apothecia per square metre was observed in the plots in the continuous soybean rotation. Tillage affected both the number and distribution of sclerotia in the soil profile. Moldboard plowing lowered the number of sclerotia per litre of soil, compared to MT and NT plots, and buried the sclerotia deeper than 10 cm into the soil. Tillage did not affect the total number of apothecia observed, but moldboard plowing did delay emergence of apothecia compared to no-till. The MP + MT plots had the lowest disease incidence and the highest yield, while the NT plots had the highest disease incidence and the lowest yields. One year of moldboard plowing will bury sclerotia at least 10 cm in soil and delay the production of apothecia. How this affects SSR development depends on the other factors involved with disease development.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.510
Threshold uncertainty score0.182

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 head0.015
GPT teacher head0.177
Teacher spread0.162 · 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 designBench or experimental
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

Citations52
Published2002
Admission routes1
Has abstractyes

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