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Dive into the research topics where Chad D. Lee is active.

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Featured researches published by Chad D. Lee.


Weed Technology | 2004

Use of Optical Remote Sensing for Detecting Herbicide Injury in Soybean

Kurt D. Thelen; Alexandra N. Kravchenko; Chad D. Lee

Experiments were conducted from 2000 to 2002 at two locations each year to determine if lactofen and imazethapyr injury to soybean could be detected using digital aerial imagery and ground-based optical remote sensing. Lactofen and imazethapyr were applied at base rates of 105 and 71 g/ha, respectively, and at 0, 2X, and 4X rates. Treated plots were evaluated between 7 and 21 d after treatment for crop injury using a ground-based radiometer and a system using computer analysis of digital aerial imagery. Both the ground-based radiometer and the digital aerial imagery were effective in detecting herbicide injury under most conditions. The digital aerial imagery system was found to be more sensitive in detecting herbicide injury than the ground-based radiometer system. Herbicide or herbicide rate had a significant effect on normalized differential vegetation indices (NDVI) derived from digital aerial imagery in four of four site-years. NDVI values derived from a multispectral ground-based radiometer were significant for herbicide or herbicide rate in four of six site-years. NDVI values from treated plots were subtracted from the NDVI value of the untreated check to generate a ΔNDVI. The resulting ΔNDVI values from the ground-based radiometer system were significant for herbicide or herbicide rate in six of six site-years. Neither optical remote-sensing system was effective at estimating actual application rates of lactofen and imazethapyr across a broad range of field and weather conditions due to temporal and spatial variability in crop response to the herbicides. Nomenclature: Imazethapyr; lactofen; soybean, Glycine max (L.) Merr. ‘Asgrow 2001’, ‘Becks 243’, ‘Becks 323’, ‘Mycogen 5251’, ‘Pioneer 19B91’, ‘Pioneer 92B38’. Additional index words: Aerial imagery, light reflectance, vegetation index. Abbreviations: Fc, fractional cover; LAI, leaf area index; NDVI, normalized differential vegetation index; NIR, near-infrared; SVI, spectral vegetation index.


Weed Science | 2003

Glyphosate and shade effects on glyphosate-resistant soybean defense response to Sclerotinia sclerotiorum

Chad D. Lee; Donald Penner; R. Hammerschmidt

Abstract Application of glyphosate in combination with planting soybeans in narrow rows is an effective practice for management of weeds in glyphosate-resistant soybean. Farmers in Michigan reported higher levels of Sclerotinia stem rot (caused by Sclerotinia sclerotiorum) in fields of glyphosate-resistant soybean. Studies were conducted to determine if glyphosate or shading reduced the defense response of glyphosate-resistant soybean to S. sclerotiorum. Glyphosate caused shikimate accumulation in glyphosate-susceptible cultivar GL2415 but not in glyphosate-resistant cultivar GL2600RR. Ethylacetate extracts containing the plant defense compound glyceollin inhibited S. sclerotiorum hyphae in a rate-dependent manner. Glyphosate had no effect on either baseline or induced levels of glyceollin in glyphosate-resistant soybean, indicating that glyphosate did not impair plant defense responses to S. sclerotiorum. Shade levels of 60 and 90% in the greenhouse did not inhibit the induction of glyceollin synthesis. Glyphosate herbicide and shading did not affect the glyphosate-resistant soybean defense response to S. sclerotiorum. Nomenclature: Glyphosate; lactofen; shikimate; soybean, Glycine max (L.) Merr. ‘Great Lakes GL2415′, ‘Great Lakes GL2600RR’, and ‘Pioneer 92B71′; Sclerotinia stem rot (white mold), Sclerotinia sclerotiorum (Lib.) de Bary.


Agronomy Journal | 2008

Soybean Response to Plant Population at Early and Late Planting Dates in the Mid-South

Chad D. Lee; D. B. Egli; Dennis M. TeKrony


Crop Science | 2014

Genetic Improvement of U.S. Soybean in Maturity Groups II, III, and IV

Keith Rincker; Randall L. Nelson; James E. Specht; D. A. Sleper; T. R. Cary; Silvia R. Cianzio; Shaun N. Casteel; Shawn P. Conley; Pengyin Chen; Vince M. Davis; Carolyn M. Fox; George L. Graef; Chad B. Godsey; David L. Holshouser; Guo-Liang Jiang; Stella K. Kantartzi; William Kenworthy; Chad D. Lee; Rouf Mian; Leah K. McHale; Seth L. Naeve; James H. Orf; Vaino Poysa; William T. Schapaugh; Grover Shannon; Robert Uniatowski; Dechun Wang; Brian W. Diers


Biomass & Bioenergy | 2016

Harvest index and straw yield of five classes of wheat

Jing Dai; Brent Bean; B. Brown; William P. Bruening; Jeff Edwards; Michael Flowers; Russell S. Karow; Chad D. Lee; Gaylon D. Morgan; Michael J. Ottman; Joel K. Ransom; J. J. Wiersma


Crop Management | 2006

Reducing Row Widths to Increase Yield: Why It Does Not Always Work

Chad D. Lee


Crop Science | 2016

High-Input Management Systems Effect on Soybean Seed Yield, Yield Components, and Economic Break-Even Probabilities

John M. Orlowski; Bryson J. Haverkamp; Randall G. Laurenz; David A. Marburger; Eric W. Wilson; Shaun N. Casteel; Shawn P. Conley; Seth L. Naeve; Emerson D. Nafziger; Kraig L. Roozeboom; William J. Ross; Kurt D. Thelen; Chad D. Lee


Crop Science | 2016

Early-Season Lactofen Application has Limited Effect on Soybean Branch and Mainstem Yield Components

John M. Orlowski; Gary L. Gregg; Chad D. Lee


Crop Management | 2012

Winter Wheat Yield Response to Wide Rows Varies By Year in the Southern Ohio River Valley

Chad D. Lee; James H. Herbek


Crop Science | 2016

Characterizing Genotype × Management Interactions on Soybean Seed Yield

David A. Marburger; Bryson J. Haverkamp; Randall G. Laurenz; John M. Orlowski; Eric W. Wilson; Shaun N. Casteel; Chad D. Lee; Seth L. Naeve; Emerson D. Nafziger; Kraig L. Roozeboom; William J. Ross; Kurt D. Thelen; Shawn P. Conley

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John M. Orlowski

Mississippi State University

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Kurt D. Thelen

Michigan State University

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Shawn P. Conley

University of Wisconsin-Madison

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David A. Marburger

University of Wisconsin-Madison

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