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.

Potential yield loss in grain sorghum (Sorghum bicolor) with weed interference in the United States

2020 ◽  
Vol 34 (4) ◽  
pp. 624-629 ◽  
Author(s):  
J. Anita Dille ◽  
Phillip W. Stahlman ◽  
Curtis R. Thompson ◽  
Brent W. Bean ◽  
Nader Soltani ◽  
...  
Keyword(s):  
United States ◽  
Weed Control ◽  
Weed Management ◽  
Yield Loss ◽  
Science Research ◽  
The United States ◽  
Grain Sorghum ◽  
Control Cost ◽  
Potential Yield ◽  
Weed Interference

AbstractPotential yield losses in grain sorghum due to weed interference based on quantitative data from the major grain sorghum-growing areas of the United States are reported by the WSSA Weed Loss Committee. Weed scientists and extension specialists who researched weed control in grain sorghum provided data on grain sorghum yield loss due to weed interference in their region. Data were requested from up to 10 individual experiments per calendar year over 10 yr between 2007 and 2016. Based on the summarized information, farmers in Arkansas, Kansas, Missouri, Nebraska, South Dakota, and Texas would potentially lose an average of 37%, 38%, 30%, 56%, 61%, and 60% of their grain sorghum yield with no weed control, and have a corresponding annual monetary loss of US $19 million, 302 million, 7 million, 32 million, 25 million, and 314 million, respectively. The overall average yield loss due to weed interference was estimated to be 47% for this grain sorghum-growing region. Thus, US farmers would lose approximately 5,700 million kg of grain sorghum valued at approximately US $953 million annually if weeds are not controlled. With each dollar invested in weed management (based on estimated weed control cost of US $100 ha−1), there would be a return of US $3.80, highlighting the return on investment in weed management and the importance of continued weed science research for sustaining high grain sorghum yield and profitability in the United States.

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.

scholarly journals An analysis of ozone damage to historical maize and soybean yields in the United States

2015 ◽  
Vol 112 (46) ◽  
pp. 14390-14395 ◽  
Author(s):  
Justin M. McGrath ◽  
Amy M. Betzelberger ◽  
Shaowen Wang ◽  
Eric Shook ◽  
Xin-Guang Zhu ◽  
...  
Keyword(s):  
United States ◽  
Food Production ◽  
Yield Loss ◽  
Cost Benefit ◽  
Ground Level ◽  
The United States ◽  
Yield Losses ◽  
Ground Level Ozone ◽  
Food And Feed ◽  
Actual Yield

Numerous controlled experiments find that elevated ground-level ozone concentrations ([O3]) damage crops and reduce yield. There have been no estimates of the actual yield losses in the field in the United States from [O3], even though such estimates would be valuable for projections of future food production and for cost–benefit analyses of reducing ground-level [O3]. Regression analysis of historical yield, climate, and [O3] data for the United States were used to determine the loss of production due to O3 for maize (Zea mays) and soybean (Glycine max) from 1980 to 2011, showing that over that period production of rain-fed fields of soybean and maize were reduced by roughly 5% and 10%, respectively, costing approximately $9 billion annually. Maize, thought to be inherently resistant to O3, was at least as sensitive as soybean to O3 damage. Overcoming this yield loss with improved emission controls or more tolerant germplasm could substantially increase world food and feed supply at a time when a global yield jump is urgently needed.

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.

Potential yield loss in sugar beet due to weed interference in the United States and Canada

2018 ◽  
Vol 32 (6) ◽  
pp. 749-753 ◽  
Author(s):  
Nader Soltani ◽  
J. Anita Dille ◽  
Darren E. Robinson ◽  
Christy L. Sprague ◽  
Don W. Morishita ◽  
...  
Keyword(s):  
United States ◽  
North America ◽  
Sugar Beet ◽  
Weed Control ◽  
Quantitative Data ◽  
Yield Loss ◽  
The United States ◽  
Committee Report ◽  
Potential Yield ◽  
Weed Interference

AbstractThe objective of this WSSA Weed Loss Committee report is to provide quantitative data on the potential yield loss in sugar beet due to weed interference from the major sugar beet growing areas of the United States and Canada. Researchers and extension specialists who conducted research on weed control in sugar beet in the United States and Canada provided quantitative data on sugar beet yield loss due to weed interference in their regions. Specifically, data were requested from weed control studies in sugar beet from up to 10 individual studies per calendar year over a 15-yr period between 2002 and 2017. Data collected indicated that if weeds are left uncontrolled under optimal agronomic practices, growers in Idaho, Michigan, Minnesota, Montana, Nebraska, North Dakota, Ontario, Oregon, and Wyoming would potentially lose an average of 79%, 61%, 66%, 68%, 63%, 75%, 83%, 78%, and 77% of the sugar beet yield. The corresponding monetary loss would be approximately US$234, US$122, US$369, US$43, US$40, US$211, US$12, US$14, and US$32 million, respectively. The average yield loss due to weed interference for the primary sugar beet growing areas of North America was estimated to be 70%. Thus, if weeds are not controlled, growers in the United States would lose approximately 22.4 million tonnes of sugar beet yield valued at approximately US$1.25 billion, and growers in Canada would lose approximately 0.5 million tonnes of sugar beet yield valued at approximately US$25 million. The high return on investment in weed management highlights the importance of continued weed science research for sustaining high crop yield and profitability of sugar beet production in North America.

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 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.

Potential Yield Loss in Dry Bean Crops Due to Weeds in the United States and Canada

2018 ◽  
Vol 32 (3) ◽  
pp. 342-346 ◽  
Author(s):  
Nader Soltani ◽  
J. Anita Dille ◽  
Robert H. Gulden ◽  
Christy L. Sprague ◽  
Richard K. Zollinger ◽  
...  
Keyword(s):  
United States ◽  
Weed Management ◽  
Yield Loss ◽  
Census Data ◽  
Field Studies ◽  
The United States ◽  
Yield Losses ◽  
Potential Yield ◽  
Dry Bean ◽  
Bean Yield

AbstractEarlier reports have summarized crop yield losses throughout various North American regions if weeds were left uncontrolled. Offered here is a report from the current WSSA Weed Loss Committee on potential yield losses due to weeds based on data collected from various regions of the United States and Canada. Dry bean yield loss estimates were made by comparing dry bean yield in the weedy control with plots that had >95% weed control from research studies conducted in dry bean growing regions of the United States and Canada over a 10-year period (2007 to 2016). Results from these field studies showed that dry bean growers in Idaho, Michigan, Montana, Nebraska, North Dakota, South Dakota, Wyoming, Ontario, and Manitoba would potentially lose an average of 50%, 31%, 36%, 59%, 94%, 31%, 71%, 56%, and 71% of their dry bean yield, respectively. This equates to a monetary loss of US $36, 40, 6, 56, 421, 2, 18, 44, and 44 million, respectively, if the best agronomic practices are used without any weed management tactics. Based on 2016 census data, at an average yield loss of 71.4% for North America due to uncontrolled weeds, dry bean production in the United States and Canada would be reduced by 941,000,000 and 184,000,000 kg, valued at approximately US $622 and US $100 million, respectively. This study documents the dramatic yield and monetary losses in dry beans due to weed interference and the importance of continued funding for weed management research to minimize dry bean yield losses.

scholarly journals Corn Yield Loss Due to Volunteer Soybean

Weed Science ◽  
2016 ◽  
Vol 64 (3) ◽  
pp. 495-500 ◽  
Author(s):  
Jill Alms ◽  
Sharon A. Clay ◽  
David Vos ◽  
Michael Moechnig
Keyword(s):  
Loss Function ◽  
Growth Stage ◽  
Yield Loss ◽  
Corn Yield ◽  
Yield Losses ◽  
Herbicide Resistant ◽  
Growing Seasons ◽  
Previous Season ◽  
Control Volunteer ◽  
Soybean Plants

The widespread adoption of glyphosate-resistant corn and soybean in cropping rotations often results in volunteer plants from the previous season becoming problem weeds that require alternative herbicides for control. Corn yield losses due to season-long volunteer soybean competition at several densities in two growing seasons were used to define a hyperbolic yield loss function. The maximum corn yield loss observed at high volunteer soybean densities was about 56%, whereas, the incremental yield loss (I) at low densities was 3.2%. Corn yield loss at low volunteer soybean densities was similar to losses reported for low densities of velvetleaf and redroot pigweed, with 10% yield loss estimated to occur at 3 to 4 volunteer soybean plants m−2. Several herbicides, including dicamba with or without diflufenzopyr applied at the V2 growth stage of volunteer soybean, provided > 90% control, demonstrating several economical options to control volunteer glyphosate-resistant soybean in glyphosate-resistant corn. Reevaluation of control recommendations may be needed with commercialization of other genetically modified herbicide-resistant soybean varieties.

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.

Sign in / Sign up

Export Citation Format

Share Document