scholarly journals Influence of Three Resistance Sources in Winter Wheat Derived from TAM 107 on Yield Response to Russian Wheat Aphid

2005 ◽  
Vol 98 (2) ◽  
pp. 389-394 ◽  
Author(s):  
Terri L. Randolph ◽  
Frank B. Peairs ◽  
Michael Koch ◽  
Cynthia B. Walker ◽  
James S. Quick
Keyword(s):  
Winter Wheat ◽  
Russian Wheat Aphid ◽  
Yield Response ◽  
Resistance Sources

Yield Response and Categories of Resistance to Russian Wheat Aphid in Four Dn4 Hard Red Winter Wheat Cultivars

2005 ◽  
Vol 98 (2) ◽  
pp. 588-594 ◽  
Author(s):  
Terri L. Randolph ◽  
Frank B. Peairs ◽  
Michael Koch ◽  
Cynthia B. Walker ◽  
Jesse R. Stubbs ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Russian Wheat Aphid ◽  
Yield Response ◽  
Wheat Cultivars ◽  
Hard Red Winter Wheat ◽  
Winter Wheat Cultivars ◽  
Red Winter Wheat

scholarly journals Meta-Analysis of the Effects of Triazole-Based Fungicides on Wheat Yield and Test Weight as Influenced by Fusarium Head Blight Intensity

2010 ◽  
Vol 100 (2) ◽  
pp. 160-171 ◽  
Author(s):  
P. A. Paul ◽  
M. P. McMullen ◽  
D. E. Hershman ◽  
L. V. Madden
Keyword(s):  
Winter Wheat ◽  
Fusarium Head Blight ◽  
Wheat Yield ◽  
Test Weight ◽  
Response To Treatment ◽  
Yield Response ◽  
Head Blight ◽  
Yield Increase ◽  
Soft Red Winter Wheat ◽  
Red Winter Wheat

Multivariate random-effects meta-analyses were conducted on 12 years of data from 14 U.S. states to determine the mean yield and test-weight responses of wheat to treatment with propiconazole, prothioconazole, tebuconazole, metconazole, and prothioconazole+tebuconazole. All fungicides led to a significant increase in mean yield and test weight relative to the check (D; P < 0.001). Metconazole resulted in the highest overall yield increase, with a D of 450 kg/ha, followed by prothioconazole+tebuconazole (444.5 kg/ha), prothioconazole (419.1 kg/ha), tebuconazole (272.6 kg/ha), and propiconazole (199.6 kg/ha). Metconazole, prothioconazole+tebuconazole, and prothioconazole also resulted in the highest increases in test weight, with D values of 17.4 to 19.4 kg/m3, respectively. On a relative scale, the best three fungicides resulted in an overall 13.8 to 15.0% increase in yield but only a 2.5 to 2.8% increase in test weight. Except for prothioconazole+tebuconazole, wheat type significantly affected the yield response to treatment; depending on the fungicide, D was 110.0 to 163.7 kg/ha higher in spring than in soft-red winter wheat. Fusarium head blight (FHB) disease index (field or plot-level severity) in the untreated check plots, a measure of the risk of disease development in a study, had a significant effect on the yield response to treatment, in that D increased with increasing FHB index. The probability was estimated that fungicide treatment in a randomly selected study will result in a positive yield increase (p+) and increases of at least 250 and 500 kg/ha (p250 and p500, respectively). For the three most effective fungicide treatments (metconazole, prothioconazole+tebuconazole, and prothioconazole) at the higher selected FHB index, p+ was very large (e.g., ≥0.99 for both wheat types) but p500 was considerably lower (e.g., 0.78 to 0.92 for spring and 0.54 to 0.68 for soft-red winter wheat); at the lower FHB index, p500 for the same three fungicides was 0.34 to 0.36 for spring and only 0.09 to 0.23 for soft-red winter wheat.

Palweed:Wheat: A Bioeconomic Decision Model for Postemergence Weed Management in Winter Wheat (Triticum aestivum)

Weed Science ◽  
1995 ◽  
Vol 43 (4) ◽  
pp. 595-603 ◽  
Author(s):  
Tae-Jin Kwon ◽  
Douglas L. Young ◽  
Frank L. Young ◽  
Chris M. Boerboom
Keyword(s):  
Winter Wheat ◽  
Weed Management ◽  
Decision Model ◽  
Damage Function ◽  
Yield Response ◽  
Density Functions ◽  
Yield Model ◽  
Weed Density ◽  
Annual Grasses ◽  
Winter Annual

Based on six years of data from a field experiment near Pullman, WA, a bioeconomic decision model was developed to annually estimate the optimal post-emergence herbicide types and rates to control multiple weed species in winter wheat under various tillage systems and crop rotations. The model name, PALWEED:WHEAT, signifies a Washington-Idaho Palouse region weed management model for winter wheat The model consists of linear preharvest weed density functions, a nonlinear yield response function, and a profit function. Preharvest weed density functions were estimated for four weed groups: summer annual grasses, winter annual grasses, summer annual broadleaves, and winter annual broadleaves. A single aggregated weed competition index was developed from the four density functions for use functions for use in the yield model. A yield model containing a logistic damage function performed better than a model containing a rectangular hyperbolic damage function. Herbicides were grouped into three categories: preplant nonselective, postemergence broadleaf, and postemergence grass. PALWEED:WHEAT was applied to average conditions of the 6-yr experiment to predict herbicide treatments that maximized profit. In comparison to average treatment rates in the 6-yr experiment, the bioeconomic decision model recommended less postemergence herbicide. The weed management recommendations of PALWEED:WHEAT behaved as expected by agronomic and economic theory in response to changes in assumed weed populations, herbicide costs, crop prices, and possible restrictions on herbicide application rates.

Effects of the Russian wheat aphid on osmotic potential and fructan content of winter wheat seedlings

Euphytica ◽  
1993 ◽  
Vol 65 (1) ◽  
pp. 9-14 ◽  
Author(s):  
E.W. Storlie ◽  
L.E. Talbert ◽  
G.A. Taylor ◽  
H.A. Ferguson ◽  
J.H. Brown
Keyword(s):  
Winter Wheat ◽  
Osmotic Potential ◽  
Russian Wheat Aphid ◽  
Wheat Seedlings

Radiation Use Efficiency and Yield Response of Winter Wheat to Planting Patterns and Irrigation in Northern China

2014 ◽  
Vol 106 (1) ◽  
pp. 168-174 ◽  
Author(s):  
Y. Y. Han ◽  
G. Y. Wang ◽  
X. B. Zhou ◽  
Y. H. Chen ◽  
P. Liu
Keyword(s):  
Winter Wheat ◽  
Northern China ◽  
Radiation Use Efficiency ◽  
Yield Response ◽  
Use Efficiency ◽  
Radiation Use

scholarly journals Winter Wheat Grain Yield Response to Fungicide Application is Influenced by Cultivar and Rainfall

2019 ◽  
Vol 35 (1) ◽  
pp. 63-70
Author(s):  
Emmanuel Byamukama ◽  
Shaukat Ali ◽  
Jonathan Kleinjan ◽  
Dalitso N. Yabwalo ◽  
Christopher Graham ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Grain Yield ◽  
Yield Response ◽  
Wheat Grain ◽  
Fungicide Application

scholarly journals Response of Lithuanian Winter Wheat Advanced Lines to Common Bunt (Tilletia tritici (BJERK.) WINT)

2009 ◽  
Vol 63 (1-2) ◽  
pp. 51-56
Author(s):  
Vytautas Ruzgas ◽  
Žilvinas Liatukas
Keyword(s):  
Winter Wheat ◽  
Disease Incidence ◽  
Common Bunt ◽  
Infection Level ◽  
Breeding Lines ◽  
Tilletia Tritici ◽  
Resistance Sources ◽  
Resistant Lines ◽  
High Infection ◽  
Very High

Response of Lithuanian Winter Wheat Advanced Lines to Common Bunt (Tilletia tritici (BJERK.) WINT) The study was carried out at the Lithuanian Institute of Agriculture in an artificially inoculated nursery during 2006-2007. Resistance to common bunt in 2006 was tested for 71, in 2007 for 118 breeding lines of Lithuanian winter wheat from the competitive trial nursery. Additionally, 148 promising lines were selected and tested from the check nursery, which possessed some resistance in their pedigree ancestors. The average disease incidence in 2006 and 2007 was 80.9 and 63.5%, respectively. The very high infection level highlighted the genotypes with the most effective resistance under conditions highly favourable for common bunt. There were no lines without infected ears. Among the 29 breeding lines tested in the two years, two lines Bill/Aspirant and Dream/Lut.9329 were infected the least, 17.2% and 1.9% in 2006 and 18.5% and 7.8% in 2007, respectively. Most of the breeding lines were highly susceptible. Lines with disease incidence over 50% accounted for over 90% in 2006 and 80% in 2007 of the total lines tested. The most resistant lines had in their pedigrees the following resistance sources: genotypes Bill, Lut.9329, Strumok, Lut.9313, Lut.9358, Tommi as well as Dream, Haldor, 91002G2.1, 96/101, Bezenchiukskaya380.

scholarly journals Molecular characterization of Russian wheat aphid consumption by spiders in winter wheat

2012 ◽  
Vol 40 (1) ◽  
pp. 71-77 ◽  
Author(s):  
Lauren M. Kerzicnik ◽  
Eric G. Chapman ◽  
James D. Harwood ◽  
Frank B. Peairs ◽  
Paula E. Cushing
Keyword(s):  
Winter Wheat ◽  
Molecular Characterization ◽  
Russian Wheat Aphid ◽  
Aphid Consumption

Comparisons of anhydrous ammonia with solid ammonium nitrate for spring barley and winter wheat 1967–70

1973 ◽  
Vol 81 (1) ◽  
pp. 47-53
Author(s):  
J. B. A. Rodger ◽  
W. D. Gill ◽  
G. K. Shukla
Keyword(s):  
Winter Wheat ◽  
Ammonium Nitrate ◽  
Spring Barley ◽  
Unit Cost ◽  
Early Growth ◽  
Yield Response ◽  
Injection Equipment ◽  
Anhydrous Ammonia ◽  
N Level ◽  
N Input

SummaryFourteen trials on spring barley and eleven on winter wheat, grown in the east of Scotland, compared the effects on yield of liquified anhydrous ammonia and solid ammonium nitrate at various levels of application.For grain yield, the optimum N level in these trials was about 100–113 kg N/ha. The object of including in the trials N input levels higher than optimum was attained. Yield response to the two forms of N was similar for both wheat and barley at the different N input levels.Winter injection of anhydrous ammonia was less efficient than spring application. Injection of anhydrous ammonia into young wheat resulted in frequent reduction of plant population and, on occasion, loss of yield.At equivalent rates, anhydrous ammonia caused less lodging than ammonium nitrate; it also appeared to be less readily leached from the soil. Ammonium nitrate gave more rapid early growth and this led to a greater proneness to leaf disease.Considering the complexity of storage and injection equipment required to handle anhydrous ammonia, it is questionable if these agronomic advantages justify its use on cereals where rates of use do not also confer the benefits of cheaper unit cost of nitrogen.

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