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Record W7056303922

THE EFFECT OF PLANT CUTTING AND BURNDOWN HERBICIDES ON WEED MANAGEMENT IN DOUBLE-CROP SOYBEANS FOLLOWING WINTER WHEAT

2021· article· en· W7056303922 on OpenAlexaboutno aff

Bibliographic record

VenueOpenSIUC (Southern Illinois University Carbondale) · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsWeed controlWeedAmbrosia artemisiifoliaWinter wheatAgricultureCropRagweedCrop yield
DOInot available

Abstract

fetched live from OpenAlex

AN ABSTRACT OF THE THESIS OFColton P. Carmody for the Master of Science degree in Plant, Soil, and Agricultural Systems, presented on May 3, 2019, at Southern Illinois University Carbondale. TITLE: THE EFFECT OF PLANT CUTTING AND BURNDOWN HERBICIDES ON WEED MANAGEMENT IN DOUBLE-CROP SOYBEANS FOLLOWING WINTER WHEAT MAJOR PROFESSOR: Dr. Karla L. Gage The agricultural practice of ‘double-cropping,’ harvesting two crops from the same piece of land in a single growing season, is a popular practice in Southern Illinois where growers often plant soybeans (Glycine max (L.) Merr.) soon after winter wheat (Triticum aestivum L.) harvest. Horseweed (Conyza canadensis L. Cronq.) competition and lack of control can cause detrimental effects on yield for double-crop producers in Illinois due to the development of multiple herbicide resistance in this weed species. To combat this problem of herbicide resistance, new herbicide-tolerant soybean technologies and new herbicide formulations have been developed, but these technologies will quickly lose efficacy unless stewarded properly. Therefore, our objective for this study was to evaluate the control of horseweed and other weeds and crop yield as influenced by weed-cutting height, herbicide treatments, timing of herbicide application in three studies (a greenhouse study and in-crop and non-crop field studies) with the goal of improving weed management in double-crop soybean. Data obtained from the non-crop study that focused on the evaluation of different cutting heights, herbicide treatments and application timings was observed to be significantly different by weed species evaluated: horseweed, common ragweed (Ambrosia artemisiifolia L.), and Canada goldenrod (Solidago canadensis L.). Horseweed control and biomass accumulation was observed to be strongly influenced by cutting height, where cutting to 15 cm resulted in the greatest control efficacy and reduction in overall biomass irrespective of herbicide treatment used. While application timing following cutting in relation to control efficacy and biomass accumulation was only observed to be significant based upon the translocation properties of the herbicide applied, i.e. contact or systemic. Differences in the perennial weed species Canada goldenrod were observed compared to horseweed; cutting in combination with herbicide treatments resulted in a decrease in control compared to herbicide applications made to noncut Canada goldenrod plants. Data obtained in this study suggest that cutting in combination with the use of effective burndown herbicides may lead to increased control of certain weed species and could be a component of herbicide technology stewardship in double-crop soybeans. The in-crop study focused on evaluating yield potential of burndown herbicides in double-crop soybean systems. Observations from this study revealed that similarly to full-season beans, yield in double-crop soybeans was limited dependent upon the burndown herbicide programs ability to achieve broad-spectrum weed control. When effective burndown herbicides dicamba, glufosinate, and paraquat were combined with herbicides that possessed the ability to provide soil residual activity, such as saflufenacil, chlorimuron, chloransulam, metribuzin, sulfentrazone or flumioxazin, yield potential was maximized compared to these active ingredients applied as standalone treatments. Data from this study further stresses the importance of utilizing multiple effective sites of actions to achieve higher yields while maintaining good herbicide stewardship practices.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.563
Threshold uncertainty score0.999

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)0.0020.000

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 head0.007
GPT teacher head0.213
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.

Study designNot applicable
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

Citations0
Published2021
Admission routes1
Has abstractyes

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