Impact of foliar fungicide timing and fungicide class on corn yield response in the United States and Ontario, Canada

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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Palmer Amaranth (Amaranthus palmeri) Control by Glufosinate plus Fluometuron Applied Postemergence to WideStrike®Cotton

Weed Technology ◽

10.1614/wt-d-12-00158.1 ◽

2013 ◽

Vol 27 (2) ◽

pp. 291-297 ◽

Cited By ~ 14

Author(s):

Kelly A. Barnett ◽

A. Stanley Culpepper ◽

Alan C. York ◽

Lawrence E. Steckel

Keyword(s):

United States ◽

Weed Control ◽

Acetolactate Synthase ◽

The United States ◽

Palmer Amaranth ◽

Effective Control ◽

Yield Response ◽

Amaranthus Palmeri ◽

Cotton Yield ◽

Cotton Cultivar

Glyphosate-resistant (GR) weeds, especially GR Palmer amaranth, are very problematic for cotton growers in the Southeast and Midsouth regions of the United States. Glufosinate can control GR Palmer amaranth, and growers are transitioning to glufosinate-based systems. Palmer amaranth must be small for consistently effective control by glufosinate. Because this weed grows rapidly, growers are not always timely with applications. With widespread resistance to acetolactate synthase-inhibiting herbicides, growers have few herbicide options to mix with glufosinate to improve control of larger weeds. In a field study using a WideStrike®cotton cultivar, we evaluated fluometuron at 140 to 1,120 g ai ha−1mixed with the ammonium salt of glufosinate at 485 g ae ha−1for control of GR Palmer amaranth 13 and 26 cm tall. Standard PRE- and POST-directed herbicides were included in the systems. Glufosinate alone injured the WideStrike® cotton less than 10%. Fluometuron increased injury up to 25% but did not adversely affect yield. Glufosinate controlled 13-cm Palmer amaranth at least 90%, and there was no improvement in weed control nor a cotton yield response to fluometuron mixed with glufosinate. Palmer amaranth 26 cm tall was controlled only 59% by glufosinate. Fluometuron mixed with glufosinate increased control of the larger weeds up to 28% and there was a trend for greater yields. However, delaying applications until weeds were 26 cm reduced yield 22% relative to timely application. Our results suggest fluometuron mixed with glufosinate may be of some benefit when attempting to control large Palmer amaranth. However, mixing fluometuron with glufosinate is not a substitute for a timely glufosinate application.

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Meta-Analysis of Yield Response of Hybrid Field Corn to Foliar Fungicides in the U.S. Corn Belt

Phytopathology ◽

10.1094/phyto-03-11-0091 ◽

2011 ◽

Vol 101 (9) ◽

pp. 1122-1132 ◽

Cited By ~ 65

Author(s):

P. A. Paul ◽

L. V. Madden ◽

C. A. Bradley ◽

A. E. Robertson ◽

G. P. Munkvold ◽

...

Keyword(s):

United States ◽

Disease Severity ◽

Meta Analysis ◽

The United States ◽

Yield Response ◽

Foliar Disease ◽

Yield Increase ◽

Field Corn ◽

Yield Difference ◽

Meta Analyses

The use of foliar fungicides on field corn has increased greatly over the past 5 years in the United States in an attempt to increase yields, despite limited evidence that use of the fungicides is consistently profitable. To assess the value of using fungicides in grain corn production, random-effects meta-analyses were performed on results from foliar fungicide experiments conducted during 2002 to 2009 in 14 states across the United States to determine the mean yield response to the fungicides azoxystrobin, pyraclostrobin, propiconazole + trifloxystrobin, and propiconazole + azoxystrobin. For all fungicides, the yield difference between treated and nontreated plots was highly variable among studies. All four fungicides resulted in a significant mean yield increase relative to the nontreated plots (P < 0.05). Mean yield difference was highest for propiconazole + trifloxystrobin (390 kg/ha), followed by propiconazole + azoxystrobin (331 kg/ha) and pyraclostrobin (256 kg/ha), and lowest for azoxystrobin (230 kg/ha). Baseline yield (mean yield in the nontreated plots) had a significant effect on yield for propiconazole + azoxystrobin (P < 0.05), whereas baseline foliar disease severity (mean severity in the nontreated plots) significantly affected the yield response to pyraclostrobin, propiconazole + trifloxystrobin, and propiconazole + azoxystrobin but not to azoxystrobin. Mean yield difference was generally higher in the lowest yield and higher disease severity categories than in the highest yield and lower disease categories. The probability of failing to recover the fungicide application cost (ploss) also was estimated for a range of grain corn prices and application costs. At the 10-year average corn grain price of $0.12/kg ($2.97/bushel) and application costs of $40 to 95/ha, ploss for disease severity <5% was 0.55 to 0.98 for pyraclostrobin, 0.62 to 0.93 for propiconazole + trifloxystrobin, 0.58 to 0.89 for propiconazole + azoxystrobin, and 0.91 to 0.99 for azoxystrobin. When disease severity was >5%, the corresponding probabilities were 0.36 to 95, 0.25 to 0.69, 0.25 to 0.64, and 0.37 to 0.98 for the four fungicides. In conclusion, the high ploss values found in most scenarios suggest that the use of these foliar fungicides is unlikely to be profitable when foliar disease severity is low and yield expectation is high.

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Quantifying the impacts of compound extremes on agriculture

Hydrology and Earth System Sciences ◽

10.5194/hess-25-551-2021 ◽

2021 ◽

Vol 25 (2) ◽

pp. 551-564

Author(s):

Iman Haqiqi ◽

Danielle S. Grogan ◽

Thomas W. Hertel ◽

Wolfram Schlenker

Keyword(s):

Heat Stress ◽

Water Stress ◽

Agricultural Production ◽

Hydrological Model ◽

The United States ◽

Food Prices ◽

Yield Response ◽

Corn Yield ◽

Comprehensive Understanding ◽

Wet Heat

Abstract. Agricultural production and food prices are affected by hydroclimatic extremes. There has been a growing amount of literature measuring the impacts of individual extreme events (heat stress or water stress) on agricultural and human systems. Yet, we lack a comprehensive understanding of the significance and the magnitude of the impacts of compound extremes. This study combines a fine-scale weather product with outputs of a hydrological model to construct functional metrics of individual and compound hydroclimatic extremes for agriculture. Then, a yield response function is estimated with individual and compound metrics, focusing on corn in the United States during the 1981–2015 period. Supported by statistical evidence, the findings suggest that metrics of compound hydroclimatic extremes are better predictors of corn yield variations than metrics of individual extremes. The results also confirm that wet heat is more damaging than dry heat for corn. This study shows the average yield damage from heat stress has been up to four times more severe when combined with water stress.

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White mold incidence, severity and lima bean (Phaseolus lunatus) yield response to fungicide application timing in the mid-Atlantic region of the United States

Crop Protection ◽

10.1016/j.cropro.2021.105890 ◽

2021 ◽

pp. 105890

Author(s):

Habtamu B. Demissie ◽

Bahram Momen ◽

Kathryne L. Everts

Keyword(s):

United States ◽

Lima Bean ◽

The United States ◽

Phaseolus Lunatus ◽

White Mold ◽

Yield Response ◽

Atlantic Region ◽

Application Timing ◽

Fungicide Application

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Evaluation of Mesotrione in Mississippi Delta Corn Production

Weed Technology ◽

10.1614/wt-03-260r1 ◽

2004 ◽

Vol 18 (4) ◽

pp. 1111-1116 ◽

Cited By ~ 22

Author(s):

Daniel O. Stephenson ◽

Jason A. Bond ◽

Eric R. Walker ◽

Mohammad T. Bararpour ◽

Lawrence R. Oliver

Keyword(s):

United States ◽

Weed Control ◽

Mississippi Delta ◽

Field Studies ◽

The United States ◽

Corn Yield ◽

Corn Production ◽

Pitted Morningglory ◽

Excellent Control ◽

Prickly Sida

Field studies were conducted in Arkansas in 1999, 2000, and 2001 to evaluate mesotrione applied preemergence (PRE) and postemergence (POST) for weed control in corn grown in the Mississippi Delta region of the United States. Mesotrione was applied PRE (140, 210, and 280 g/ha) alone and POST (70, 105, and 140 g/ha), alone or in tank mixtures with atrazine (280 g/ha). Standard treatments for comparison were S-metolachlor/atrazine PRE and S-metolachlor plus atrazine PRE followed by atrazine POST. All PRE treatments controlled velvetleaf, pitted morningglory, entireleaf morningglory, prickly sida, and broadleaf signalgrass 95% 2 wk after emergence (WAE). Mesotrione controlled velvetleaf 89% or more 4 and 6 WAE. Control of morningglory species by mesotrione POST averaged 92% 6 WAE. Prickly sida was controlled at least 90% by all treatments 4 WAE. Mesotrione applied alone PRE and POST controlled broadleaf signalgrass 83 to 91% 4 WAE. All treatments controlled broadleaf signalgrass less than 90% 6 WAE, except treatments that contained S-metolachlor, which gave 94% or greater control. Corn yield ranged from 10.5 to 12.4 Mg/ha and did not differ among treatments. Mesotrione PRE and POST provided excellent control of broadleaf weeds, but S-metolachlor was needed for broadleaf signalgrass control.

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Cotton yield response to soil applied potassium across the United States cotton belt

Agronomy Journal ◽

10.1002/agj2.20719 ◽

2021 ◽

Author(s):

Katie Lewis ◽

Gaylon Morgan ◽

William Hunter Frame ◽

Daniel Fromme ◽

Darrin M Dodds ◽

...

Keyword(s):

United States ◽

The United States ◽

Yield Response ◽

Cotton Yield ◽

Cotton Belt

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Analyses of Yield and Economic Response from Foliar Fungicide and Insecticide Applications to Soybean in the North Central United States

Plant Health Progress ◽

10.1094/php-rs-16-0038 ◽

2016 ◽

Vol 17 (4) ◽

pp. 232-238 ◽

Cited By ~ 12

Author(s):

Yuba R. Kandel ◽

Daren S. Mueller ◽

Chad E. Hart ◽

Nathan R. C. Bestor ◽

Carl A. Bradley ◽

...

Keyword(s):

United States ◽

Field Experiments ◽

Foliar Application ◽

The United States ◽

Insect Management ◽

North Central ◽

The North ◽

Central United States ◽

Foliar Fungicide ◽

The Impact

Foliar disease and insect management on soybean (Glycine max L. Merrill) in the North Central region of the United States has been increasingly accomplished through foliar fungicide and insecticide application. Data from research trials conducted in Illinois, Indiana, Iowa, and Nebraska were compiled from 2008 to 2014 to determine the impact of fungicide, insecticide, and fungicide + insecticide applications on soybean yield and profitability. In each state, field experiments occurred each year in two to seven locations. All treatments were applied at the R3 growth stage. Disease and insect pressure were very low in all states and years. A foliar application of fungicide, insecticide, or the combination, increased yield in seven out of 14 total site-years (P < 0.10). Economic analysis using an average soybean price of $0.42 per kilogram and average application cost of $62 per hectare indicated that fungicide applications were only profitable in 14% of the trial site-years. Insecticide alone and fungicide + insecticide was profitable in 39% and 45% of site-years, respectively. Effect of fungicide class on yield was inconsistent. Our results indicate that although yield increases can occur with foliar fungicide and/or insecticide treatments, current market prices and application costs may limit profitability when disease and/or insect pressure is low. Accepted for publication 22 September 2016.

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ESTIMATING CORN YIELD IN THE UNITED STATES WITH MODIS EVI AND MACHINE LEARNING METHODS

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsannals-iii-8-131-2016 ◽

2016 ◽

Vol III-8 ◽

pp. 131-136 ◽

Cited By ~ 8

Author(s):

K. Kuwata ◽

R. Shibasaki

Keyword(s):

Neural Network ◽

United States ◽

Machine Learning ◽

Crop Yield ◽

The United States ◽

Machine Learning Algorithms ◽

Corn Yield ◽

County Level ◽

Entire Area ◽

Modis Evi

Satellite remote sensing is commonly used to monitor crop yield in wide areas. Because many parameters are necessary for crop yield estimation, modelling the relationships between parameters and crop yield is generally complicated. Several methodologies using machine learning have been proposed to solve this issue, but the accuracy of county-level estimation remains to be improved. In addition, estimating county-level crop yield across an entire country has not yet been achieved. In this study, we applied a deep neural network (DNN) to estimate corn yield. We evaluated the estimation accuracy of the DNN model by comparing it with other models trained by different machine learning algorithms. We also prepared two time-series datasets differing in duration and confirmed the feature extraction performance of models by inputting each dataset. As a result, the DNN estimated county-level corn yield for the entire area of the United States with a determination coefficient (R2) of 0.780 and a root mean square error (RMSE) of 18.2 bushels/acre. In addition, our results showed that estimation models that were trained by a neural network extracted features from the input data better than an existing machine learning algorithm.

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