scholarly journals Effect of Fungicide and Timing of Application on Soybean Rust Severity and Yield

Plant Disease ◽  
2009 ◽  
Vol 93 (3) ◽  
pp. 243-248 ◽  
Author(s):  
T. A. Mueller ◽  
M. R. Miles ◽  
W. Morel ◽  
J. J. Marois ◽  
D. L. Wright ◽  
...  
Keyword(s):  
United States ◽  
The United States ◽  
Field Trials ◽  
Soybean Rust ◽  
Growth Stages ◽  
Yield Losses ◽  
Treatment Timing ◽  
Progress Curve ◽  
Significant Yield ◽  
Rust Severity

Soybean rust, caused by Phakopsora pachyrhizi, is a devastating foliar disease of soybean that may cause significant yield losses if not managed by well-timed fungicide applications. To determine the effect of fungicide timing on soybean rust severity and soybean yield, field trials were completed in Paraguay (four locations), the United States (two locations), and Zimbabwe (one location) from 2005 to 2006. Treatments at each location included applications of tebuconazole, pyraclostrobin, or a combination of azoxystrobin + propiconazole, and in some locations pyraclostrobin + tebuconazole at the following soybean growth stages (GS): (i) GS R1 (beginning flowering), (ii) GS R3 (beginning pod), (iii) GS R5 (beginning seed), (iv) GS R1 + R3, (v) GS R3 + R5, and (vi) GS R1 + R3 + R5. Soybean yields from plots treated with fungicides were 16 to 114% greater than yields from no fungicide control plots in four locations in Paraguay, 12 to 55% greater in two locations in the United States, and 31% greater in Zimbabwe. In all locations, rust severity measured over time as area under the disease progress curve (AUDPC) was negatively correlated (r = –0.3, P < 0.0001) to yield. The effectiveness of any given treatment (timing of application and product applied) was often dependent on when rust was first detected and the intensity of its development. For example, when soybean rust was first observed before GS R3 (two locations in Paraguay), the plants in plots treated with a fungicide at GS R1 had the lowest AUPDC values and highest yields. When soybean rust was first observed after GS R3, plants treated with a fungicide at GS R3 and/or GS R5 had the lowest AUDPC values and highest yields with a few exceptions.

Fungicide efficacy on tar spot and yield of corn in the Midwestern United States

2022 ◽  
Author(s):  
Darcy E. P. Telenko ◽  
Martin I. Chilvers ◽  
Adam Byrne ◽  
Jill Check ◽  
Camila Rocco Da Silva ◽  
...  
Keyword(s):  
United States ◽  
Growth Stage ◽  
Field Trials ◽  
Corn Belt ◽  
Yield Losses ◽  
Single Application ◽  
Midwestern United States ◽  
Tar Spot ◽  
Significant Yield ◽  
Foliar Fungicide

Tar spot of corn caused by Phyllachora maydis has recently led to significant yield losses in the eastern corn belt of the Midwestern United States. Foliar fungicides containing quinone outside inhibitors(QoI), demethylation inhibitors(DMI), and succinate dehydrogenase inhibitors(SDHI) are commonly used to manage foliar diseases in corn. To mitigate the losses from tar spot thirteen foliar fungicides containing single or multiple modes of action (MOA/FRAC groups) were applied at their recommended rates in a single application at the standard tassel/silk growth stage timing to evaluate their efficacy against tar spot in a total of eight field trials in Illinois, Indiana, Michigan, and Wisconsin during 2019 and 2020. The single MOA fungicides included either a QoI or DMI. The dual MOA fungicides included a DMI with either a QoI or SDHI, and fungicides containing three MOAs included a QoI, DMI, and SDHI. Tar spot severity estimated as the percentage of leaf area covered by P. maydis stroma of the non-treated control at dent growth stage ranged from 1.6 to 23.3% on the ear leaf. Averaged across eight field trials all foliar fungicide treatments reduced tar spot severity, but only prothioconazole+trifloxystrobin, mefentrifluconazole+pyraclostrobin+fluxapyroxad, and mefentrifluconazole+pyraclostrobin significantly increased yield over the non-treated control. When comparing fungicide treatments by the number of MOAs foliar fungicide products that had two or three MOAs decreased tar spot severity over not treating and products with one MOA. The fungicide group that contained all three MOAs significantly increased yield over not treating with a fungicide or using a single MOA.

Suppression of Soybean Yield Potential in the Continental United States by Plant Diseases from 2006 to 2009

2010 ◽  
Vol 11 (1) ◽  
pp. 5 ◽  
Author(s):  
Stephen R. Koenning ◽  
J. Allen Wrather
Keyword(s):  
United States ◽  
The United States ◽  
Plant Diseases ◽  
Soybean Rust ◽  
Yield Potential ◽  
Stem Rot ◽  
Yield Losses ◽  
Sclerotinia Stem Rot ◽  
Soybean Yield ◽  
The Impact

Research must focus on management of diseases that cause extensive losses, especially when funds for research are limited. Knowledge of the losses caused by various soybean diseases is essential when prioritizing research budgets. The objective of this project was to compile estimates of soybean yield potential losses caused by diseases for each soybean producing state in the United States from 2006 to 2009. This data is of special interest since the 4-year period summarized in this report, permits an examination of the impact of soybean rust that was first reported in the United States in 2004. Thus, in addition to the goal of providing this information to aid funding agencies and scientists in prioritizing research objectives and budgets, an examination of the impact of soybean rust on soybean yield losses relative to other diseases is warranted. Yield losses caused by individual diseases varied among states and years. Soybean cyst nematode caused more yield losses than any other disease during 2006 to 2009. Seedling diseases, Phytophthora root and stem rot, sudden death syndrome, Sclerotinia stem rot, and charcoal rot ranked in the top six of diseases that caused yield loss during these years. Soybean yield losses due to soybean rust and Sclerotinia stem rot varied greatly over years, especially when compared to other diseases. Accepted for publication 21 October 2010. Published 22 November 2010.

Yield losses of soybeans due to Phytophthora megasperma f.sp. Glycinea

1989 ◽  
Vol 40 (6) ◽  
pp. 1161 ◽  
Author(s):  
MJ Ryley ◽  
HF Mosetter ◽  
JL Rose
Keyword(s):  
The United States ◽  
Field Trials ◽  
Field Resistance ◽  
Growth Stages ◽  
Yield Losses ◽  
Phytophthora Megasperma ◽  
High Death Rate ◽  
High Death ◽  
Soybean Genotypes ◽  
Seed Yields

Field trials were conducted in two seasons to determine the influence of Phytophthora megasperma f.sp. glycinea on seed yields of soybean genotypes with different levels of resistance. The fungicide metalaxyl, applied as a seed dressing and soil drenches, was used to control the disease. The cultivar Davis, which has high field resistance, did not suffer significant losses in either year. whereas the yield depression of less resistant genotypes ranged up to 72%, depending on the genotype and year. In 198 1 - 82 high death rate of plants occurred early in the season, but yield losses were less than in 1982-83 when mortality occurred late in the season. The declining ability of surviving plants to compensate for disease losses during the latter growth stages may account for the differences in yield losses between seasons.Genotypes with immunity to P. megasperma f.sp. glycinea have been widely grown in the United States because they offer complete protection against some races. However, new pathogenic races have developed quickly, and previously immune genotypes have then suffered severe yield losses. The results of these trials demonstrate that yield losses can be prevented without resorting to fungicide treatments. It is likely that a stable long-term solution to phytophthora root and stem rot will be conferred by field resistance.

scholarly journals Similarity, Pattern, and Grouping of Soybean Fungal Diseases in the United States: Implications for the Risk of Soybean Rust

Plant Disease ◽  
2009 ◽  
Vol 93 (2) ◽  
pp. 162-169 ◽  
Author(s):  
X. Li ◽  
X. B. Yang
Keyword(s):  
United States ◽  
Downy Mildew ◽  
Geographical Distribution ◽  
Leaf Spot ◽  
The United States ◽  
Fungal Diseases ◽  
Brown Spot ◽  
Soybean Rust ◽  
Yield Losses ◽  
Similarity Coefficients

Ten biological or ecological characteristics of pathogens/diseases were used to quantitatively describe 34 soybean (Glycine max) fungal diseases in the United States. These characteristics included optimal temperatures for disease development, host ranges, characteristics of disease cycle, and the pathogens' survival capacity. Gower's general similarity coefficients for pairs of diseases were determined and used in principal coordinate analysis (PCoA) to project the diseases into a two-dimensional space, in which significant patterns were identified for some of the characteristic variables, e.g., means of pathogen dispersal. Similarity coefficients indicated that soybean rust (Phakopsora pachyrhizi) resembled soybean downy mildew (Peronospora manshurica) and Leptosphaerulina leaf spot (Leptosphaerulina trifolii). Cluster analysis with multiscale bootstrapping identified two major clusters with high significance level (P > 0.95). In a loose cluster (P = 0.86), soybean rust was grouped with brown spot (Septoria glycines), frogeye leaf spot (Cercospora sojina), Phyllosticta leaf spot (Phyllosticta sojicola), purple seed stain (Cercospora kikuchii), downy mildew, and Leptosphaerulina leaf spot. Estimated soybean yield losses in the United States from 1996 to 2005 and the geographical distribution information of the diseases in this cluster implied that the potential geographical distribution range of soybean rust may include most U.S. soybean production regions and that yield losses would be light in the north but moderate in the south if environmental conditions are conducive.

scholarly journals Effect of Transplant Age, Tobacco Cultivar, Acibenzolar-S-Methyl, and Imidacloprid on Tomato Spotted Wilt Infection in Flue-Cured Tobacco

Plant Disease ◽  
2008 ◽  
Vol 92 (11) ◽  
pp. 1524-1528 ◽  
Author(s):  
C. Nischwitz ◽  
A. S. Csinos ◽  
S. W. Mullis ◽  
L. L. Hickman ◽  
K. L. Stevenson ◽  
...  
Keyword(s):  
United States ◽  
Tomato Spotted Wilt Virus ◽  
The United States ◽  
Field Trials ◽  
Disease Progress Curve ◽  
Tomato Spotted Wilt ◽  
Progress Curve ◽  
Tobacco Cultivar ◽  
Systemic Infections ◽  
Wilt Virus

Tomato spotted wilt virus (TSWV) has become the most serious problem in flue-cured tobacco in Georgia and is a growing problem in other tobacco-growing areas in the United States. The effects of transplant age (6 to 10 weeks), tobacco cultivar (K-326 and NC-71), and preplant applications of acibenzolar-S-methyl (ASM) and the insecticide imidacloprid (IMD) were evaluated on levels of TSWV infection, number of symptomatic plants, and yield in field trials over 4 years. In all 4 years and in four of five trials, treatment of transplants with ASM and IMD resulted in fewer symptomatic plants, smaller areas under the disease progress curve (AUDPC), and higher yields compared with the nontreated controls. There were no consistent effects of transplant age or cultivar on number of symptomatic plants or systemic infections, AUDPC, or yield. Treatment of transplants with ASM and IMD can significantly reduce the number of symptomatic plants in the field and substantially increase yields and value per hectare.

scholarly journals Integrated Management of Important Soybean Pathogens of the United States in Changing Climate

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Mitchell G Roth ◽  
Richard W Webster ◽  
Daren S Mueller ◽  
Martin I Chilvers ◽  
Travis R Faske ◽  
...  
Keyword(s):  
United States ◽  
Soybean Cyst Nematode ◽  
The United States ◽  
Yield Losses ◽  
Sclerotinia Stem Rot ◽  
Susceptible Host ◽  
Soybean Production ◽  
Significant Yield ◽  
Soybean Diseases ◽  
The Impact

Abstract Soybean (Glycine max L.) is a major crop grown in the United States but is susceptible to many diseases that cause significant yield losses each year. Consistent threats exist across both northern and southern production regions and include the soybean cyst nematode, charcoal rot, and seedling diseases. In contrast, significant soybean diseases like Phytophthora stem and root rot, sudden death syndrome, and Sclerotinia stem rot (white mold) are intermittent threats that can be heavily influenced by environmental factors. Additional threats to soybean production that have emerged in recent years as more common problems in soybean production include root-knot and reniform nematodes, frogeye leaf spot, and Diaporthe diseases. Disease in any crop will only occur when the three components of the disease triangle are present: a susceptible host, a virulent pathogen, and a conducive environment. If an environment is becoming more conducive for a particular disease, it is important that farmers and practitioners are prepared to manage the problem. The information in this review was compiled to help assist agriculturalists in being proactive in managing new soybean diseases that may be emerging in new areas. To do this, we provide: 1) an overview of the impact and disease cycle for major soybean diseases currently causing significant yield losses in the United States, 2) a comprehensive review of the current management strategies for each soybean disease, and 3) insights into the epidemiology of each pathogen, including the likelihood of outbreaks and expansion to additional geographic regions based on current trends in climate change.

scholarly journals Quantifying Alfalfa Yield Losses Caused by Foliar Diseases in Iowa, Ohio, Wisconsin, and Vermont

Plant Disease ◽  
2002 ◽  
Vol 86 (3) ◽  
pp. 269-277 ◽  
Author(s):  
F. W. Nutter ◽  
J. Guan ◽  
A. R. Gotlieb ◽  
L. H. Rhodes ◽  
C. R. Grau ◽  
...  
Keyword(s):  
United States ◽  
Remote Sensing ◽  
Disease Severity ◽  
Yield Loss ◽  
Disease Incidence ◽  
The United States ◽  
Yield Losses ◽  
Fungicide Treatment ◽  
Foliar Diseases ◽  
Significant Yield

Although foliar diseases of alfalfa occur throughout the United States wherever alfalfa is grown, little work has been done to quantify yield losses caused by foliar pathogens since the late 1980s. To quantify the yield losses caused by foliar diseases of alfalfa, field experiments were performed in Iowa, Ohio, Vermont, and Wisconsin from 1995 to 1998. Different fungicides and fungicide application frequencies were used to obtain different levels of foliar disease in alfalfa. Visual disease and remote sensing assessments were performed weekly to determine the relationships between disease assessments and alfalfa yield. Visual disease assessments of percentage of defoliation, disease incidence, and disease severity were performed weekly, approximately five to six times during each alfalfa growth cycle. Remote sensing assessments also were obtained weekly by measuring the percentage of sunlight reflected from alfalfa canopies using handheld, multispectral radiometers. Yield loss estimates were calculated as the yield difference between the fungicide treatment with the highest yield and the nonfungicide control, divided by the yield obtained from the highest yielding fungicide treatment × 100. Over the 4-year period, significant alfalfa yield losses (P ≤ 0.05) occurred on 22 of the 48 harvest dates for the four states. The average significant yield loss for the 22 harvests was 19.3%. Both visual and percentage of reflectance assessments were used as independent variables in linear regression models to quantify the relationships between assessments and alfalfa yield. From 1995 to 1998, visual disease assessments were performed for a total of 209 dates and remote sensing assessments were performed on 198 dates from the four states. Yield models were developed for each of these assessment dates. There were 26/209, 26/209, and 17/209 significant yield models based on percentage of defoliation, disease incidence, and disease severity, respectively. Most of the significant models were for disease assessments performed on or within 1 or 2 weeks of the date of alfalfa harvest. When the significant models were averaged, percentage of defoliation, disease incidence, and disease severity explained 51, 55, and 52% of the variation in alfalfa yield, respectively. There were a total of 68/198 significant alfalfa yield models based on remote sensing assessments, and the significant models (averaged) explained 62% of the variation in alfalfa yield. Alfalfa foliar diseases continue to have a significant negative impact on alfalfa yields in the United States and remote sensing appears to offer a better means to quantify the impact of foliar diseases on alfalfa yield compared with visual assessment methods.

scholarly journals Field Evaluations of Leaf Spot Resistance and Yield in Peanut Genotypes in the United States and Bolivia

Plant Disease ◽  
2011 ◽  
Vol 95 (3) ◽  
pp. 263-268 ◽  
Author(s):  
S. K. Gremillion ◽  
A. K. Culbreath ◽  
D. W. Gorbet ◽  
B. G. Mullinix ◽  
R. N. Pittman ◽  
...  
Keyword(s):  
United States ◽  
Disease Resistance ◽  
Leaf Spot ◽  
Field Experiments ◽  
Disease Incidence ◽  
The United States ◽  
Yield Potential ◽  
Cercospora Arachidicola ◽  
Breeding Lines ◽  
Progress Curve

Field experiments were conducted in 2002 to 2006 to characterize yield potential and disease resistance in the Bolivian landrace peanut (Arachis hypogaea) cv. Bayo Grande, and breeding lines developed from crosses of Bayo Grande and U.S. cv. Florida MDR-98. Diseases of interest included early leaf spot, caused by the fungus Cercospora arachidicola, and late leaf spot, caused by the fungus Cercosporidium personatum. Bayo Grande, MDR-98, and three breeding lines, along with U.S. cvs. C-99R and Georgia Green, were included in split-plot field experiments in six locations across the United States and Bolivia. Whole-plot treatments consisted of two tebuconazole applications and a nontreated control. Genotypes were the subplot treatments. Area under the disease progress curve (AUDPC) for percent defoliation due to leaf spot was lower for Bayo Grande and all breeding lines than for Georgia Green at all U.S. locations across years. AUDPC for disease incidence from one U.S. location indicated similar results. Severity of leaf spot epidemics and relative effects of the genotypes were less consistent in the Bolivian experiments. In Bolivia, there were no indications of greater levels of disease resistance in any of the breeding lines than in Bayo Grande. In the United States, yields of Bayo Grande and the breeding lines were greater than those of the other genotypes in 1 of 2 years. In Bolivia, low disease intensity resulted in the highest yields in Georgia Green, while high disease intensity resulted in comparable yields among the breeding lines, MDR-98, and C-99R. Leaf spot suppression by tebuconazole was greater in Bolivia than in the United States. This result indicates a possible higher level of fungicide resistance in the U.S. population of leaf spot pathogens. Overall, data from this study suggest that Bayo Grande and the breeding lines may be desirable germplasm for U.S. and Bolivian breeding programs or production.

scholarly journals Pathogenic Variation of Phakopsora pachyrhizi Isolates on Soybean in the United States from 2006 to 2009

Plant Disease ◽  
2012 ◽  
Vol 96 (1) ◽  
pp. 75-81 ◽  
Author(s):  
M. Twizeyimana ◽  
G. L. Hartman
Keyword(s):  
United States ◽  
Resistance Genes ◽  
The United States ◽  
Soybean Rust ◽  
Phakopsora Pachyrhizi ◽  
Soybean Genotype ◽  
Pathogenic Variation ◽  
Differential Set ◽  
Soybean Genotypes ◽  
Soybean Leaves

The introduction of Phakopsora pachyrhizi, the cause of soybean rust, into the United States is a classic case of a pathogen introduction that became established in a new geographical region overwintering on a perennial host (kudzu, Pueraria lobata). The objective of our study was to classify the pathogenic variation of P. pachyrhizi isolates collected in the United States, and to determine the spatial and temporal associations. In total, 72 isolates of P. pachyrhizi collected from infected kudzu and soybean leaves in the United States were purified, then established and increased on detached soybean leaves. These isolates were tested for virulence and aggressiveness on a differential set of soybean genotypes that included six genotypes with known resistance genes (Rpp), one resistant genotype without any known characterized resistance gene, and a susceptible genotype. Three pathotypes were identified among the 72 U.S. P. pachyrhizi isolates based on the virulence of these isolates on the genotypes in the differential set. Six aggressiveness groups were established based on sporulating-uredinia production recorded for each isolate on each soybean genotype. All three pathotypes and all six aggressiveness groups were found in isolates collected from the southern region and from both hosts (kudzu or soybean) in 2008. Shannon's index based on the number of pathotypes indicated that isolates from the South region were more diverse (H = 0.83) compared with the isolates collected in other regions. This study establishes a baseline of pathogenic variation of P. pachyrhizi in the United States that can be further compared with variation reported in other regions of the world and in future studies that monitor P. pachyrhizi virulence in association to deployment of rust resistance genes.

scholarly journals Accuracies Achieved in Classifying Five Leading World Crop Types and their Growth Stages Using Optimal Earth Observing-1 Hyperion Hyperspectral Narrowbands on Google Earth Engine

2018 ◽  
Vol 10 (12) ◽  
pp. 2027 ◽  
Author(s):  
Itiya Aneece ◽  
Prasad Thenkabail
Keyword(s):  
United States ◽  
Remote Sensing ◽  
Hyperspectral Imaging ◽  
The United States ◽  
Google Earth ◽  
Growth Stages ◽  
Agricultural Crops ◽  
Spectral Library ◽  
Crop Types ◽  
Google Earth Engine

As the global population increases, we face increasing demand for food and nutrition. Remote sensing can help monitor food availability to assess global food security rapidly and accurately enough to inform decision-making. However, advances in remote sensing technology are still often limited to multispectral broadband sensors. Although these sensors have many applications, they can be limited in studying agricultural crop characteristics such as differentiating crop types and their growth stages with a high degree of accuracy and detail. In contrast, hyperspectral data contain continuous narrowbands that provide data in terms of spectral signatures rather than a few data points along the spectrum, and hence can help advance the study of crop characteristics. To better understand and advance this idea, we conducted a detailed study of five leading world crops (corn, soybean, winter wheat, rice, and cotton) that occupy 75% and 54% of principal crop areas in the United States and the world respectively. The study was conducted in seven agroecological zones of the United States using 99 Earth Observing-1 (EO-1) Hyperion hyperspectral images from 2008–2015 at 30 m resolution. The authors first developed a first-of-its-kind comprehensive Hyperion-derived Hyperspectral Imaging Spectral Library of Agricultural crops (HISA) of these crops in the US based on USDA Cropland Data Layer (CDL) reference data. Principal Component Analysis was used to eliminate redundant bands by using factor loadings to determine which bands most influenced the first few principal components. This resulted in the establishment of 30 optimal hyperspectral narrowbands (OHNBs) for the study of agricultural crops. The rest of the 242 Hyperion HNBs were redundant, uncalibrated, or noisy. Crop types and crop growth stages were classified using linear discriminant analysis (LDA) and support vector machines (SVM) in the Google Earth Engine cloud computing platform using the 30 optimal HNBs (OHNBs). The best overall accuracies were between 75% to 95% in classifying crop types and their growth stages, which were achieved using 15–20 HNBs in the majority of cases. However, in complex cases (e.g., 4 or more crops in a Hyperion image) 25–30 HNBs were required to achieve optimal accuracies. Beyond 25–30 bands, accuracies asymptote. This research makes a significant contribution towards understanding modeling, mapping, and monitoring agricultural crops using data from upcoming hyperspectral satellites, such as NASA’s Surface Biology and Geology mission (formerly HyspIRI mission) and the recently launched HysIS (Indian Hyperspectral Imaging Satellite, 55 bands over 400–950 nm in VNIR and 165 bands over 900–2500 nm in SWIR), and contributions in advancing the building of a novel, first-of-its-kind global hyperspectral imaging spectral-library of agricultural crops (GHISA: www.usgs.gov/WGSC/GHISA).

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