Influence of droplet size and azoxystrobin insensitivity on frogeye leaf spot management in soybean

Butler, S.; Kelly, Heather Y.; Mueller, Thomas C.; Kruger, Greg R.; Cochran, Alicia; Raper, Tyson B.

doi:10.1016/j.cropro.2018.05.017

Cited by 12 publications

(5 citation statements)

References 24 publications

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“…FLS can undergo multiple cycles, with a higher incidence when the plant is in a humid environment (Dashiell et al 1991). Multiple infestations and disease multiplication may occur during soybean growth (Butler et al 2018). The pathogen (Cercospora sojina Hara) of FLS mainly acts on leaves but also infests stems, pods and seeds (Akem et al 1994).…”

Section: Introductionmentioning

confidence: 99%

Genome-wide association analysis of resistance to frogeye leaf spot China race 7 in soybean based on high-throughput sequencing

Chen¹,

Miao²,

Jiang³

et al. 2023

Theor Appl Genet

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Frogeye leaf spot (FLS) causes severe yield loss in soybean and has been found in several countries worldwide. Therefore, it is necessary to select and utilize FLS-resistant varieties for the management of FLS. In the present study, 335 representative soybean materials were assessed for partial resistance to FLS race 7. Quantitative trait nucleotide (QTN) and FLS race 7 candidate genes were identi ed using genome-wide association analysis (GWAS) based on a site-speci c ampli ed fragment sequencing (SLAF-seq) approach. A total of 23,156 single nucleotide polymorphisms (SNPs) were used to evaluate the level of linkage disequilibrium with a minor allele frequency ≥ 5 and deletion data < 3%. These SNPs covered about 947.01 MBP, nearly 86.09% of the entire soybean genome. In addition, a compressed mixed linear model was utilized to identify association signals for partial resistance to FLS race 7. A total of 15 QTNs associated with resistance were found to be novel for FLS race 7 resistance. A total of 217 candidate genes located in the 200 kb genomic region of these peak SNPs were identi ed. Based on gene association analysis, qRT-PCR, haplotype analysis and virusinduced gene silencing (VIGS) systems were used to further verify candidate genes Glyma.16G176800, Glyma.16G177300, Glyma.16G177400 and Glyma.16G182300. This indicates that these four candidate genes may participate in FLS race 7 resistance responses. Key messageFLS is a disease that causes severe yield reduction in soybean. In this study, four genes (Glyma.16G176800, Glyma.16G177300, Glyma.16G177400 and Glyma.16G182300) were tentatively con rmed to play an important role in the resistance of soybean to FLS race 7.

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Section: Introductionmentioning

confidence: 99%

Genome-wide association analysis of resistance to frogeye leaf spot China race 7 in soybean based on high-throughput sequencing

Chen¹,

Miao²,

Jiang³

et al. 2023

Theor Appl Genet

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“…FLS epidemics can cause yield losses up to 60%. FLS is a polycyclic disease in which infection, symptom development, and reproduction may all be repeated multiple times throughout a single season [5]. Therefore, it is essential to detect and assess the extent of disease to estimate its economic impact and apply control strategies.…”

Section: Introductionmentioning

confidence: 99%

Classification of soybean frogeye leaf spot disease using leaf hyperspectral reflectance

Liu

Sui

et al. 2021

PLoS ONE

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In this study, the feasibility of classifying soybean frogeye leaf spot (FLS) is investigated. Leaf images and hyperspectral reflectance data of healthy and FLS diseased soybean leaves were acquired. First, image processing was used to classify FLS to create a reference for subsequent analysis of hyperspectral data. Then, dimensionality reduction methods of hyperspectral data were used to obtain the relevant information pertaining to FLS. Three single methods, namely spectral index (SI), principal component analysis (PCA), and competitive adaptive reweighted sampling (CARS), along with a PCA and SI combined method, were included. PCA was used to select the effective principal components (PCs), and evaluate SIs. Characteristic wavelengths (CWs) were selected using CARS. Finally, the full wavelengths, CWs, effective PCs, SIs, and significant SIs were divided into 14 datasets (DS1–DS14) and used as inputs to build the classification models. Models’ performances were evaluated based on the classification accuracy for both the overall and individual classes. Our results suggest that the FLS comprised of five classes based on the proportion of total leaf surface covered with FLS. In the PCA and SI combination model, 5 PCs and 20 SIs with higher weight coefficient of each PC were extracted. For hyperspectral data, 20 CWs and 26 effective PCs were also selected. Out of the 14 datasets, the model input variables provided by five datasets (DS2, DS3, DS4, DS10, and DS11) were more superior than those of full wavelengths (DS1) both in support vector machine (SVM) and least squares support vector machine (LS-SVM) classifiers. The models developed using these five datasets achieved overall accuracies ranging from 91.8% to 94.5% in SVM, and 94.5% to 97.3% in LS-SVM. In addition, they improved the classification accuracies by 0.9% to 3.6% (SVM) and 0.9% to 3.7% (LS-SVM).

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“…japonica Hara, grown in a greenhouse (Noh et al, 2019). Butler et al (2018) showed residues of azoxystrobin, a commonly used QoI (strobilurin) fungicide, decreased exponentially on soybean leaves following a foliar application with a predicted half-life of 2.54 to 4.82 d, depending upon test conditions.…”

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confidence: 99%

Insecticide and Fungicide Residues Following Foliar Application to Cotton and Soybean

Lawson¹,

Steckel²,

Williams³

et al. 2020

JCS

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The control of target pests and impact on non-target arthropods, including pollinators, is affected by the persistence of pesticides on plants following an application. A study was conducted in Tennessee to investigate the levels of pesticide residues on cotton, Gossypium hirsutum L., and soybean, Glycine max (L.), following a foliar application made during early flowering. Residues of four classes of insecticides and three classes of fungicides were assessed at 1, 24, 72, 144, and 216 h after application on cotton leaves, anthers of cotton flowers, and soybean flowers. Active ingredients included acephate, imidacloprid, lambda-cyhalothrin, chlorantraniliprole, fluxapyroxad, pyraclostrobin, and propiconazole. Initial pesticide residues on cotton leaves were many times greater than those on cotton anthers or soybean flowers. With the exception of chlorantraniliprole on cotton leaves, fungicide residues persisted longer than insecticides. Also, pesticide residues on soybean flowers degraded more slowly than those on cotton leaves or anthers. For cotton leaves, insecticide residues decreased sharply within 24 h after application except for chlorantraniliprole. All pesticide residues on cotton anthers were dramatically lower 24 h after application, indicating little systemic movement to pollen. By 216 h after application, and considerably sooner in most scenarios, pesticide residues on cotton and soybean had diminished by 90% or more. The implications of these results on pest management and pollinator safety are discussed.

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Influence of droplet size and azoxystrobin insensitivity on frogeye leaf spot management in soybean

Cited by 12 publications

References 24 publications

Genome-wide association analysis of resistance to frogeye leaf spot China race 7 in soybean based on high-throughput sequencing

Genome-wide association analysis of resistance to frogeye leaf spot China race 7 in soybean based on high-throughput sequencing

Classification of soybean frogeye leaf spot disease using leaf hyperspectral reflectance

Insecticide and Fungicide Residues Following Foliar Application to Cotton and Soybean

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