scholarly journals Corn Yield Loss Estimates Due to Diseases in the United States and Ontario, Canada from 2012 to 2015

2016 ◽  
Vol 17 (3) ◽  
pp. 211-222 ◽  
Author(s):  
Daren S. Mueller ◽  
Kiersten A. Wise ◽  
Adam J. Sisson ◽  
Tom W. Allen ◽  
Gary C. Bergstrom ◽  
...  
Keyword(s):  
United States ◽  
Yield Loss ◽  
Research Policy ◽  
Economic Loss ◽  
The United States ◽  
Gray Leaf Spot ◽  
Parasitic Nematodes ◽  
Corn Yield ◽  
Cost Of Disease ◽  
Northern United States

Annual decreases in corn yield caused by diseases were estimated by surveying members of the Corn Disease Working Group in 22 corn-producing states in the United States and in Ontario, Canada, from 2012 through 2015. Estimated loss from each disease varied greatly by state and year. In general, foliar diseases such as northern corn leaf blight, gray leaf spot, and Goss's wilt commonly caused the largest estimated yield loss in the northern United States and Ontario during non-drought years. Fusarium stalk rot and plant-parasitic nematodes caused the most estimated loss in the southern-most United States. The estimated mean economic loss due to yield loss by corn diseases in the United States and Ontario from 2012 to 2015 was $76.51 USD per acre. The cost of disease-mitigating strategies is another potential source of profit loss. Results from this survey will provide scientists, breeders, government, and educators with data to help inform and prioritize research, policy, and educational efforts in corn pathology and disease management. Accepted for publication 26 August 2016.

scholarly journals Corn Yield Loss Estimates Due to Diseases in the United States and Ontario, Canada, from 2016 to 2019

2020 ◽  
Vol 21 (4) ◽  
pp. 238-247
Author(s):  
Daren S. Mueller ◽  
Kiersten A. Wise ◽  
Adam J. Sisson ◽  
Tom W. Allen ◽  
Gary C. Bergstrom ◽  
...  
Keyword(s):  
United States ◽  
Leaf Spot ◽  
Yield Loss ◽  
Research Policy ◽  
Economic Loss ◽  
The United States ◽  
Gray Leaf Spot ◽  
Corn Yield ◽  
Puccinia Polysora ◽  
Northern United States

Annual reductions in corn (Zea mays L.) yield caused by diseases were estimated by university Extension-affiliated plant pathologists in 26 corn-producing states in the United States and in Ontario, Canada, from 2016 through 2019. Estimated loss from each disease varied greatly by state or province and year. Gray leaf spot (caused by Cercospora zeae-maydis Tehon & E.Y. Daniels) caused the greatest estimated yield loss in parts of the northern United States and Ontario in all years except 2019, and Fusarium stalk rot (caused by Fusarium spp.) also greatly reduced yield. Tar spot (caused by Phyllachora maydis Maubl.), a relatively new disease in the United States, was estimated to cause substantial yield loss in 2018 and 2019 in several northern states. Gray leaf spot and southern rust (caused by Puccinia polysora Underw.) caused the most estimated yield losses in the southern United States. Unfavorable wet and delayed harvest conditions in 2018 resulted in an estimated 2.5 billion bushels (63.5 million metric tons) of grain contaminated with mycotoxins. The estimated mean economic loss due to reduced yield caused by corn diseases in the United States and Ontario from 2016 to 2019 was US$55.90 per acre (US$138.13 per hectare). Results from this survey provide scientists, corn breeders, government agencies, and educators with data to help inform and prioritize research, policy, and educational efforts in corn pathology and disease management.

scholarly journals Soybean Yield Loss Estimates Due to Diseases in the United States and Ontario, Canada, from 2010 to 2014

2017 ◽  
Vol 18 (1) ◽  
pp. 19-27 ◽  
Author(s):  
Tom W. Allen ◽  
Carl A. Bradley ◽  
Adam J. Sisson ◽  
Emmanuel Byamukama ◽  
Martin I. Chilvers ◽  
...  
Keyword(s):  
United States ◽  
Soybean Cyst Nematode ◽  
Yield Loss ◽  
Research Policy ◽  
Economic Loss ◽  
Macrophomina Phaseolina ◽  
The United States ◽  
Yield Losses ◽  
Soybean Yield ◽  
Soybean Diseases

Annual decreases in soybean (Glycine max L. Merrill) yield caused by diseases were estimated by surveying university-affiliated plant pathologists in 28 soybean-producing states in the United States and in Ontario, Canada, from 2010 through 2014. Estimated yield losses from each disease varied greatly by state or province and year. Over the duration of this survey, soybean cyst nematode (SCN) (Heterodera glycines Ichinohe) was estimated to have caused more than twice as much yield loss than any other disease. Seedling diseases (caused by various pathogens), charcoal rot (caused by Macrophomina phaseolina (Tassi) Goid), and sudden death syndrome (SDS) (caused by Fusarium virguliforme O’Donnell & T. Aoki) caused the next greatest estimated yield losses, in descending order. The estimated mean economic loss due to all soybean diseases, averaged across U.S. states and Ontario from 2010 to 2014, was $60.66 USD per acre. Results from this survey will provide scientists, breeders, governments, and educators with soybean yield-loss estimates to help inform and prioritize research, policy, and educational efforts in soybean pathology and disease management.

Predicted Corn Yield Loss Due to Weed Competition Prior to Postemergence Herbicide Application on Wisconsin Farms

2013 ◽  
Vol 27 (1) ◽  
pp. 54-62 ◽  
Author(s):  
Nathanael D. Fickett ◽  
Chris M. Boerboom ◽  
David E. Stoltenberg
Keyword(s):  
Weed Management ◽  
Yield Loss ◽  
Economic Loss ◽  
The United States ◽  
Yield Potential ◽  
Weed Competition ◽  
Corn Yield ◽  
Herbicide Application ◽  
Weed Species ◽  
Early Season

Approximately 50% of the genetically modified herbicide-resistant corn hectares in the United States are treated only with POST-applied herbicides for weed management. Although a high degree of efficacy can be obtained with POST-applied herbicides, delayed timing of application may result in substantial corn yield loss. Our goal was to characterize on-farm corn–weed communities prior to POST herbicide application and estimate potential corn-yield loss associated with early-season corn–weed competition. In 2008 and 2009, field surveys were conducted across 95 site-years in southern Wisconsin and recorded weed species, density, and height in addition to crop height, growth stage, and row spacing. WeedSOFT® was used to predict corn yield loss. Common lambsquarters, velvetleaf, dandelion, common ragweed, andAmaranthusspecies were the five most abundant broadleaf weed species across site-years, present in 92, 86, 59, 45, and 44% of all fields, respectively, at mean densities of 19, 3, 3, 4, and 3 plants m−2, respectively. Mean plant heights among these species were 17 cm or less. Grass and sedge species occurred in 96% of fields at a mean density of 25 plants m−2and height of 7 cm. The mean and median of total weed density across site-years were 96 and 52 plants m−2, with heights of 14 and 13 cm, respectively. Mean predicted corn yield loss was 4.5% with a mean economic loss of $62 ha−1. However, predicted yield loss was greater than 5% on one-third of the site-years, with a maximum of 26%. These results indicate that delayed application of POST herbicides has led to corn yield loss due to early-season weed-crop competition on a substantial number of fields across southern Wisconsin, and suggest that management tactics need to be improved to protect corn yield potential fully.

Potential Corn Yield Losses from Weeds in North America

2016 ◽  
Vol 30 (4) ◽  
pp. 979-984 ◽  
Author(s):  
Nader Soltani ◽  
J. Anita Dille ◽  
Ian C. Burke ◽  
Wesley J. Everman ◽  
Mark J. VanGessel ◽  
...  
Keyword(s):  
United States ◽  
Yield Loss ◽  
Commodity Price ◽  
The United States ◽  
Corn Yield ◽  
Yield Losses ◽  
Weed Interference ◽  
Net Returns ◽  
Small Plot ◽  
Statistics Canada

Crop losses from weed interference have a significant effect on net returns for producers. Herein, potential corn yield loss because of weed interference across the primary corn-producing regions of the United States and Canada are documented. Yield-loss estimates were determined from comparative, quantitative observations of corn yields between nontreated and treatments providing greater than 95% weed control in studies conducted from 2007 to 2013. Researchers from each state and province provided data from replicated, small-plot studies from at least 3 and up to 10 individual comparisons per year, which were then averaged within a year, and then averaged over the seven years. The resulting percent yield-loss values were used to determine potential total corn yield loss in t ha−1 and bu acre−1 based on average corn yield for each state or province, as well as corn commodity price for each year as summarized by USDA-NASS (2014) and Statistics Canada (2015). Averaged across the seven years, weed interference in corn in the United States and Canada caused an average of 50% yield loss, which equates to a loss of 148 million tonnes of corn valued at over U.S.$26.7 billion annually.

scholarly journals Dissecting the economic impact of soybean diseases in the United States over two decades

2019 ◽  
Author(s):  
Ananda Y. Bandara ◽  
Dilooshi K. Weerasooriya ◽  
Carl A. Bradley ◽  
Tom W. Allen ◽  
Paul D. Esker
Keyword(s):  
United States ◽  
Soybean Cyst Nematode ◽  
Research Policy ◽  
Economic Loss ◽  
The United States ◽  
Soybean Rust ◽  
Soybean Yield ◽  
Southern Blight ◽  
Soybean Diseases ◽  
The U.S

ABSTRACTSoybean (Glycine max L. Merrill) is a key commodity for United States agriculture. Here we analyze the economic impacts of 23 common soybean diseases in 28 soybean-producing states in the U.S., from 1996 to 2016. From 1996 to 2016, the total estimated economic loss due to soybean diseases in the U.S. was $81.39 billion, with $68.98 billion and $12.41 billion accounting for the northern and southern U.S. losses, respectively. Across states and years, soybean cyst nematode, charcoal rot, and seedling diseases were the most economically damaging pathogens/diseases while soybean rust, bacterial blight, and southern blight were the least economically damaging. Significantly positive linear correlation of mean soybean yield loss with the mean state-wide soybean production (MT) and mean soybean yield (kg ha−1) indicated that high production zones are more vulnerable to soybean diseases-associated yield losses. Our findings provide useful insights into how research, policy, and educational efforts should be prioritized in soybean disease management.

scholarly journals “How Do We Actually Do Convergence” for Disaster Resilience? Cases from Australia and the United States

2021 ◽  
Author(s):  
Shefali Juneja Lakhina ◽  
Elaina J. Sutley ◽  
Jay Wilson
Keyword(s):  
United States ◽  
Research Policy ◽  
Well Being ◽  
The United States ◽  
First Century ◽  
Urban Resilience ◽  
South Wales ◽  
Systemic Risks ◽  
Work Community ◽  
Research Findings

AbstractIn recent years there has been an increasing emphasis on achieving convergence in disaster research, policy, and programs to reduce disaster losses and enhance social well-being. However, there remain considerable gaps in understanding “how do we actually do convergence?” In this article, we present three case studies from across geographies—New South Wales in Australia, and North Carolina and Oregon in the United States; and sectors of work—community, environmental, and urban resilience, to critically examine what convergence entails and how it can enable diverse disciplines, people, and institutions to reduce vulnerability to systemic risks in the twenty-first century. We identify key successes, challenges, and barriers to convergence. We build on current discussions around the need for convergence research to be problem-focused and solutions-based, by also considering the need to approach convergence as ethic, method, and outcome. We reflect on how convergence can be approached as an ethic that motivates a higher order alignment on “why” we come together; as a method that foregrounds “how” we come together in inclusive ways; and as an outcome that highlights “what” must be done to successfully translate research findings into the policy and public domains.

scholarly journals Comparative Genetic Analysis of Magnaporthe oryzae Isolates Causing Gray Leaf Spot of Perennial Ryegrass Turf in the United States and Japan

Plant Disease ◽  
2007 ◽  
Vol 91 (5) ◽  
pp. 517-524 ◽  
Author(s):  
Y. Tosa ◽  
W. Uddin ◽  
G. Viji ◽  
S. Kang ◽  
S. Mayama
Keyword(s):  
United States ◽  
Perennial Ryegrass ◽  
Magnaporthe Oryzae ◽  
Leaf Spot ◽  
Genetic Relationships ◽  
The United States ◽  
Gray Leaf Spot ◽  
Genetic Lineage ◽  
Type I ◽  
Type Ii

Gray leaf spot caused by Magnaporthe oryzae is a serious disease of perennial ryegrass (Lolium perenne) turf in golf course fairways in the United States and Japan. Genetic relationships among M. oryzae isolates from perennial ryegrass (prg) isolates within and between the two countries were examined using the repetitive DNA elements MGR586, Pot2, and MAGGY as DNA fingerprinting probes. In all, 82 isolates of M. oryzae, including 57 prg isolates from the United States collected from 1995 to 2001, 1 annual ryegrass (Lolium multiflorum) isolate from the United States collected in 1972, and 24 prg isolates from Japan collected from 1996 to 1999 were analyzed in this study. Hybridization with the MGR586 probe resulted in approximately 30 DNA fragments in 75 isolates (designated major MGR586 group) and less than 15 fragments in the remaining 7 isolates (designated minor MGR586 group). Both groups were represented among the 24 isolates from Japan. All isolates from the United States, with the exception of one isolate from Maryland, belonged to the major MGR586 group. Some isolates from Japan exhibited MGR586 fingerprints that were identical to several isolates collected in Pennsylvania. Similarly, fingerprinting analysis with the Pot2 probe also indicated the presence of two distinct groups: isolates in the major MGR586 group showed fingerprinting profiles comprising 20 to 25 bands, whereas the isolates in the minor MGR586 group had less than 10 fragments. When MAGGY was used as a probe, two distinct fingerprint types, one exhibiting more than 30 hybridizing bands (type I) and the other with only 2 to 4 bands (type II), were identified. Although isolates of both types were present in the major MGR586 group, only the type II isolates were identified in the minor MGR586 group. The parsimony tree obtained from combined MGR586 and Pot2 data showed that 71 of the 82 isolates belonged to a single lineage, 5 isolates formed four different lineages, and the remaining 6 (from Japan) formed a separate lineage. This study indicates that the predominant groups of M. oryzae associated with the recent outbreaks of gray leaf spot in Japan and the United States belong to the same genetic lineage.

scholarly journals Statistical Weather-Impact Models: An Application of Neural Networks and Mixed Effects for Corn Production over the United States

2016 ◽  
Vol 55 (11) ◽  
pp. 2509-2527 ◽  
Author(s):  
Jordane A. Mathieu ◽  
Filipe Aires
Keyword(s):  
United States ◽  
The United States ◽  
Weather Data ◽  
Corn Yield ◽  
Mixed Effect ◽  
Local Conditions ◽  
Weather Impact ◽  
Yield Forecasting ◽  
The Impact ◽  
Impact Models

AbstractStatistical meteorological impact models are intended to represent the impact of weather on socioeconomic activities, using a statistical approach. The calibration of such models is difficult because relationships are complex and historical records are limited. Often, such models succeed in reproducing past data but perform poorly on unseen new data (a problem known as overfitting). This difficulty emphasizes the need for regularization techniques and reliable assessment of the model quality. This study illustrates, in a general way, how to extract pertinent information from weather data and exploit it in impact models that are designed to help decision-making. For a given socioeconomic activity, this type of impact model can be used to 1) study its sensitivity to weather anomalies (e.g., corn sensitivity to water stress), 2) perform seasonal forecasting (yield forecasting) for it, and 3) quantify the longer-term (several decades) impact of weather on it. The size of the training database can be increased by pooling data from various locations, but this requires statistical models that are able to use the localization information—for example, mixed-effect (ME) models. Linear, neural-network, and ME models are compared, using a real-world application: corn-yield forecasting over the United States. Many challenges faced in this paper may be encountered in many weather-impact analyses: these results show that much care is required when using space–time data because they are often highly spatially correlated. In addition, the forecast quality is strongly influenced by the training spatial scale. For the application that is described herein, learning at the state scale is a good trade-off: it is specific to local conditions while keeping enough data for the calibration.

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