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Record W2943661510 · doi:10.5539/jas.v11n6p329

Drying Time of Systemic and Protectant Fungicides on Coffee Leaves Exposed to Artificial Rain for the Control of Leaf Rust

2019· article· en· W2943661510 on OpenAlexvenueno aff
Eduardo Granados, Laércio Zambolim

Bibliographic record

VenueJournal of Agricultural Science · 2019
Typearticle
Languageen
FieldMedicine
TopicCoffee research and impacts
Canadian institutionsnot available
FundersFundação de Amparo à Pesquisa do Estado de Minas GeraisConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsCopperAlkylphenolRust (programming language)ChemistryFungicideHorticultureBiologyOrganic chemistry

Abstract

fetched live from OpenAlex

The drying time of pesticides on the leaves and the resistance of the deposits to rain removal are essential for success in controlling plant diseases. Because the fungicide application is performed during the rainy season, it is crucial to study the resistance of chemical deposits on the leaf surface for the control of the coffee leaf rust caused by Hemileia vastatrix. Thus, the present work analyzed the effect of an simulated rain (30 min), after several drying time of the deposits, on the removal of (i) copper oxychloride alone and associated to mineral and the adjuvant polyoxyethylene alkylphenol ether and also (ii) before several drying time, on the removal of the epoxiconazol + pyraclostrobin formulation mixed with copper hydroxide and micronutrients applied on coffee leaves, for the control of leaf rust. Around 85% and 45% of the treatments had copper retention > 30% after 120 min and 480 min, respectively, of drying time before the rain. At 480 min, retention of copper oxychloride + 0.75% mineral oil was > 30%, whereas copper oxychloride + adjuvant polyoxyethylene alkylphenol ether at 0.05% and 0.1%, respectively was 40% or higher. When copper oxychloride was added to 0.5% mineral oil at 0.05% to 0.20% of adjuvant polyoxyethylene alkylphenol ether at 480 min of drying time, copper retention was > 30%. When copper oxychloride was added to 0.75% mineral oil, copper retention was > 30% at 0.05% and 0.10 % of adjuvant polyoxyethylene alkylphenol ether. Five of the treatments with copper retention > 50% with 120 min of drying time were reduced to only two (copper oxychloride + 0.75% mineral oil; copper oxychloride + (1.0% mineral oil + 0.1% adjuvant polyoxyethylene alkylphenol ether) with copper retention > 30%, at the drying time of 480 min. All the treatments differed from the control but did not differ between each other for the coffee leaf rust control. The percentage of control ranged from 94.6% to 100% and from 71.3% to 99.5% at 180 min and 480 min of drying time, respectively. Copper retention varied from 32.2% to 55.2% and from 23.7% to 51.5%, at 180 min and 480 min of drying time, respectively, for the best treatments. The treatment with the highest copper retention was copper oxychloride + 0.75% mineral oil, both at 180 min and 480 min of drying time. The best treatments for leaf rust control were not always those with the highest copper retention by the leaves. The highest copper retention by the leaf surface was achieved with copper oxychloride mixed with 0.75% mineral oil; this treatment resulted in > 97% of coffee leaf rust control. Adding adjuvant polyoxyethylene alkylphenol ether to the mixture does not necessarily favor the tenacity and efficiency of the copper oxychloride for the coffee leaf rust control. We concluded that the copper oxychloride acts differently for the coffee leaf rust control, depending on the concentration of added mineral oil and adjuvant polyoxyethylene alkylphenol ether time zero. Pyraclostrobin + epoxiconazol alone or mixed with copper hidroxide controlled 100% of the disease after 30 min, 120 min and 480 min of spraying, but when micronutrients was added to pyraclostrobin + epoxiconazol and copper hidroxide control of coffee leaf rust reached 100% at drying time of 120 and 480 min.

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)0.001
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 score0.255
Threshold uncertainty score0.145

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.025
GPT teacher head0.283
Teacher spread0.258 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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