Cephalosporium stripe (caused by
Cephalosporium stripe of wheat is caused by the soil-borne fungal pathogen
The most typical and recognizable symptom, chlorotic leaf striping, is apparent on the younger, upper leaves during jointing and heading. Severely infected stems are stunted and prematurely ripen, producing a white and usually sterile head, containing sometimes just a few shriveled seeds. It is at this level of infection where the greatest amount of yield loss is observed [
Reducing incidence of Cephalosporium stripe has generally been accomplished by reducing inoculum in the soil via cultural controls such as crop rotation, management of crop residues, altering soil pH with lime applications, and fertilizer management [
The goals of this study were to estimate the magnitude of potential grain yield loss caused by Cephalosporium stripe under Oregon production conditions, its association with changes in test weight and kernel characteristics, and to estimate the level of host plant resistance required to attain minimal yield loss.
Varieties were included in the experiments based on commercial importance, performance in previous Cephalosporium stripe screening nurseries, and their range in disease response. Ten varieties were evaluated in the Pendleton trials. Stephens (CI 017596), Madsen (PI 511673), and Tubbs (PI 629114) are major cultivars grown in the region. The European cultivar Rossini and two derived breeding lines (OR9800919 and OR9800924, Rossini/Ysatis//Oracle) were included, as these were previously shown to have moderate-to-high levels of disease resistance. Three experimental lines with varying levels of resistance were also included. These originated from crosses between the Rossini-derived lines and adapted Oregon material (OR02F-B-46 (Tubbs//OR9800924/Weatherford), OR02F-C-169 (Tubbs//OR9800924/OR9900553) and OR02F-D-27 (OR9800924/Weatherford)). A highly resistant club winter wheat with an alien source of resistance (WA 7437, PI 561033) was included as a resistant check. At Moro, two new releases were added to the previous list of varieties to verify their performance to the disease (Skiles and ORSS-1757). Skiles had previously shown moderate-to-high levels of resistance, while ORSS-1757 (PVP 200500336) was considered moderately susceptible to the disease.
Field trials were conducted at the Columbia Basin Agricultural Research Center field stations near Pendleton, OR, during the 2005-2006 winter wheat season and in Moro, OR in 2005–2007. Both locations are in semiarid wheat-producing areas of the Columbia Plateau, with mean annual precipitation of 406 mm in Pendleton and 279 mm in Moro. These sites are representative of eastern Oregon winter wheat production areas where Cephalosporium stripe is frequent. A randomized complete block design with four replications was used at each location. Treatments consisted of a factorial of two levels of disease (inoculated and noninoculated), with 10 varieties in Pendleton and 12 in Moro; 10 varieties were common to both trials.
Differential disease levels were obtained by sowing autoclaved oat kernels that were previously infested with
Trials were sown into stubble mulch on 12 September 2005 in Pendleton and on 12 September 2006 in Moro. Early September sowing dates increase severity of Cephalosporium stripe at these sites. Each plot was four rows (1.5 m) × 6.1 m long. Border plots were included around each trial. A Hege 500 series plot drill (H&N Manufacturing, Colwich, KS) with deep furrow openers was used to place seed into moist soil. Fertilization and weed control practices were appropriate to commercial winter wheat production at the two sites. Hand weeding was necessary in Pendleton at postanthesis to keep weed pressure low. A spring application of fungicide (Bumper 41.8EC, propiconazole) was applied to avoid infection by
Cephalosporium stripe incidence was recorded on a plot basis through visual estimation of the percentage of tillers that were ripening prematurely, and which usually expressed complete or partial reduction of grainfill (whiteheads) [
Harvested grain was carefully cleaned using airflow to remove nongrain contamination. Grain weight per plot was measured with a precision digital scale. A 1 kg sample was taken from each bag to determine test weight (hectoliter weight), grain protein concentration (%), and grain moisture content (%). Test weight and grain moisture were measured with a Grain Analysis Computer (GAC) model 2100b (DICKEY-john Corporation, Auburn, IL). Protein content was measured with an Infratec 1241 Grain Analyzer (Foss, Eden Prairie, MN) with appropriate settings for soft white or hard red winter wheat varieties.
A 300-seed subsample was randomly taken from each bulk and analyzed for kernel weight (mg) and diameter (mm), using a Single-Kernel Characterization System (SKCS) model 4100 (Perten Instruments, Springfield, IL). For each sample, the SKCS integrated computer software (Perten Instruments, Springfield, IL) provided the means and standard deviations of the 300 individual kernel determinations.
Statistical analyses were performed with the Statistical Analysis System (SAS) (SAS v9.1, SAS Institute Inc., Cary, NC, USA). Analyses of variance (ANOVA) for disease response, yield, test weight, and kernel related traits were conducted with PROC GLM to determine the level of variation between blocks and to test the significance of both treatment factors (disease and varieties) and their interaction. Type III F statistics were used to test the significance of variance sources. For ANOVA, whitehead percentages were square-root-transformed to meet the assumptions of normality and homogeneity of variance. The significance of the disease treatment on individual varieties was determined with the SLICE option in the LSMEANS statement.
Yield loss for genotypes was estimated as the reduction in grain yield between noninoculated and inoculated plots expressed as percentage relative to the yield in noninoculated plots. Similar calculations were done to estimate loss or change in test weight and kernel traits due to disease.
Pearson correlation coefficients among traits were estimated from genotype least square means using the PROC CORR procedure in SAS, pooling means from inoculated and noninoculated treatments together.
Linear regressions were fitted to estimate the relationship of grain yield loss and test weight loss to the susceptibility of genotypes to Cephalosporium stripe. The level of susceptibility was measured as the difference in whiteheads between inoculated and noninoculated plots. Grain yield loss was calculated for each cultivar as (grain yield inoculated − grain yield noninoculated) * 100/grain yield inoculated, test weight loss was estimated as well and was calculated similarly. Linear and quadratic regressions of yield and test weight loss on disease response differential were fitted using the PROC REG procedure in SAS.
Averaged over varieties, inoculation significantly decreased yield, test weight, kernel weight, and kernel diameter; grain protein at Pendleton and the standard deviations of kernel weight and kernel diameter at both locations were significantly increased by inoculation (Table
Analysis of variance for % whiteheads (square root transformed) and grain parameters for wheat genotypes grown in plots inoculated or not inoculated with
DF | Whiteheads | Yield | Test weight | Protein | Kernel weight | Kernel diameter | |||
Source of variation | avg | SD | avg | SD | |||||
Block | 3 | 0.31 | 3.12** | 8.88** | 0.714 | 9.40** | 0.677 | 0.030** | 0.0002 |
Inoculation | 1 | 113.34** | 21.65** | 212.23** | 5.274** | 77.15** | 42.506** | 0.143** | 0.0590** |
Genotype | 9 | 15.08** | 1.21** | 36.27** | 0.546 | 139.08** | 20.803** | 0.332** | 0.0277** |
Inoculation x genotype | 9 | 4.33** | 0.63** | 6.61** | 0.678* | 4.52** | 0.781* | 0.010** | 0.0017** |
Error | 57 | 0.20 | 0.20 | 1.34 | 0.320 | 1.61 | 0.330 | 0.003 | 0.0006 |
CV (%) | 22.0 | 9.3 | 1.6 | 5.3 | 3.6 | 5.9 | 2.3 | 4.9 | |
Block | 3 | 0.11 | 0.83** | 10.63** | 0.371 | 25.50** | 0.659 | 0.056** | 0.0011 |
Inoculation | 1 | 198.27** | 25.06** | 33.36** | 0.004 | 12.80* | 5.880** | 0.028* | 0.0093** |
Genotype | 11 | 7.25** | 1.37** | 30.81** | 2.318** | 96.50** | 8.978** | 0.160** | 0.0109** |
Inoculation x genotype | 11 | 2.37** | 0.35** | 1.34 | 1.179* | 3.33 | 1.209** | 0.006 | 0.0022** |
Error | 69 | 0.46 | 0.11 | 0.81 | 0.520 | 2.53 | 0.250 | 0.005 | 0.0005 |
CV (%) | 16.4 | 9.4 | 1.2 | 8.4 | 4.7 | 5.7 | 2.9 | 4.5 |
*Significant at the 0.05 probability level.
**Significant at the 0.01 probability level.
Cephalosporium stripe occurred in noninoculated plots of both trials, but more so at Moro than at Pendleton (Tables
Percent whiteheads, grain yield, test weight, and grain protein of 10 wheat genotypes noninoculated (U) and inoculated (I) with
Whiteheads (%) | Grain yield (t ha−1) | Test weight (kg hl−1) | Grain protein (%) | |||||||||
U | I | Changea | U | I | Loss (%)b | U | I | Loss | U | I | Change | |
Stephens | 5.8 | 47.0 | 41.3** | 5.35 | 3.64 | 32.0** | 77.15 | 71.73 | 5.43** | 10.31 | 11.46 | 1.15** |
Madsen | 0.5 | 13.3 | 12.8** | 5.58 | 4.20 | 24.8** | 78.05 | 73.68 | 4.38** | 10.40 | 10.97 | 0.57 |
Tubbs | 2.9 | 35.8 | 32.9** | 5.66 | 4.03 | 28.9** | 76.95 | 72.10 | 4.85** | 10.17 | 10.92 | 0.75 |
OR9800919 | 0.6 | 9.0 | 8.4** | 5.77 | 4.95 | 14.2* | 74.28 | 71.10 | 3.18** | 10.47 | 10.88 | 0.42 |
OR9800924 | 0.2 | 1.1 | 0.9 | 4.90 | 4.82 | 1.7 | 75.80 | 74.38 | 1.43 | 10.74 | 10.92 | 0.18 |
Rossini | 1.1 | 17.5 | 16.4** | 5.91 | 4.50 | 23.8** | 77.63 | 73.93 | 3.70** | 10.02 | 10.63 | 0.61 |
OR02F-B-46 | 0.4 | 4.8 | 4.3** | 5.71 | 4.54 | 20.6** | 72.83 | 70.30 | 2.53** | 10.31 | 10.69 | 0.38 |
OR02F-C-169 | 0.5 | 11.8 | 11.2** | 4.70 | 3.38 | 28.1** | 78.30 | 72.88 | 5.43** | 10.29 | 11.59 | 1.31** |
OR02F-D-27 | 0.1 | 5.0 | 4.9** | 5.01 | 4.59 | 8.3 | 71.78 | 70.33 | 1.45 | 9.86 | 10.47 | 0.62 |
WA 7437 | 0.0 | 0.0 | 0.0 | 4.72 | 4.27 | 9.5 | 78.08 | 77.85 | 0.23 | 11.32 | 10.49 | −0.83* |
LSD (0.05) | 3.7 | 3.7 | 0.63 | 0.63 | 1.64 | 1.64 | 0.80 | 0.80 |
*Significant at the 0.05 probability level. **Significant at the 0.01 probability level.
aSignificance is based on percent whiteheads square root transformed.
bGrain yield loss (%) = (noninoculated − inoculated)/noninoculated * 100.
Genotypes with significant grain yield loss also had significant reductions in test weight in Pendleton. The reduction in test weight ranged from 0.23 to 5.43 kg hl−1. SD of kernel weight and kernel diameter also were affected by increased disease levels. The same eight genotypes that showed an increase in whitehead scores had a significant increase in kernel weight SD, and six of these genotypes also showed an increase in kernel diameter SD, reflecting an increase in kernel size variability due to higher disease incidence (Tables
Kernel parameters of 10 wheat genotypes grown noninoculated (U) and inoculated (I) with
Kernel weight avg (mg) | Kernel weight SD | Kernel diameter avg (mm) | Kernel diameter SD | |||||||||
U | I | Changea | U | I | Change | U | I | Change | U | I | Change | |
Stephens | 40.59 | 37.42 | −3.17** | 10.93 | 13.41 | 2.48** | 2.784 | 2.650 | −0.135** | 0.582 | 0.667 | 0.085** |
Madsen | 33.18 | 30.96 | −2.22* | 8.62 | 9.94 | 1.32** | 2.437 | 2.355 | −0.082 | 0.444 | 0.509 | 0.065** |
Tubbs | 37.88 | 36.36 | −1.51 | 10.28 | 12.49 | 2.21** | 2.673 | 2.594 | −0.078 | 0.525 | 0.619 | 0.094** |
OR9800919 | 38.67 | 36.63 | −2.05* | 8.89 | 9.97 | 1.08** | 2.791 | 2.704 | −0.087* | 0.530 | 0.560 | 0.031 |
OR9800924 | 37.28 | 35.65 | −1.63 | 8.77 | 9.52 | 0.75 | 2.704 | 2.641 | −0.063 | 0.496 | 0.516 | 0.020 |
Rossini | 44.08 | 42.74 | −1.34 | 9.74 | 11.28 | 1.54** | 2.991 | 2.970 | −0.021 | 0.517 | 0.588 | 0.071** |
OR02F-B-46 | 34.04 | 31.38 | −2.66** | 8.90 | 10.61 | 1.71** | 2.523 | 2.390 | −0.134** | 0.474 | 0.514 | 0.040* |
OR02F-C-169 | 35.39 | 30.34 | −5.05** | 9.51 | 11.18 | 1.67** | 2.576 | 2.337 | −0.239** | 0.485 | 0.572 | 0.088** |
OR02F-D-27 | 30.64 | 31.07 | 0.42 | 7.59 | 8.99 | 1.40** | 2.382 | 2.388 | 0.006 | 0.460 | 0.487 | 0.027 |
WA 7437 | 30.62 | 30.19 | −0.43 | 6.21 | 6.61 | 0.40 | 2.359 | 2.346 | −0.013 | 0.405 | 0.426 | 0.022 |
LSD (0.05) | 1.80 | 1.80 | 0.81 | 0.81 | 0.078 | 0.078 | 0.035 | 0.035 |
*Significant at the 0.05 probability level. **Significant at the 0.01 probability level.
aChange = (inoculated − noninoculated).
Percent whiteheads, grain yield, test weight and grain protein of 12 wheat genotypes noninoculated (U) and inoculated (I) with
Whiteheads (%) | Grain yield (t ha−1) | Test weight (kg hl−1) | Grain protein (%) | |||||||||
U | I | Changea | U | I | Loss (%)b | U | I | Loss | U | I | Change | |
Stephens | 10.0 | 52.5 | 42.5** | 4.26 | 2.51 | 41.2** | 77.62 | 74.81 | 2.81** | 9.03 | 8.70 | −0.33 |
Madsen | 6.8 | 18.3 | 11.5** | 4.10 | 3.57 | 13.1* | 78.17 | 77.33 | 0.84 | 9.88 | 8.25 | −1.63** |
Tubbs | 11.8 | 45.0 | 33.3** | 3.82 | 2.88 | 24.6** | 76.03 | 74.16 | 1.87** | 9.00 | 8.75 | −0.25 |
OR9800919 | 2.8 | 31.8 | 29.0** | 5.01 | 3.51 | 30.0** | 74.19 | 72.45 | 1.74** | 7.43 | 8.15 | 0.73 |
OR9800924 | 4.3 | 26.3 | 22.0** | 4.44 | 3.41 | 23.2** | 74.87 | 74.68 | 0.19 | 7.80 | 9.15 | 1.35** |
Rossini | 1.5 | 14.5 | 13.0** | 4.21 | 3.58 | 14.8* | 77.94 | 76.07 | 1.87** | 8.83 | 8.18 | −0.65 |
OR02F-B-46 | 22.5 | 44.3 | 21.8** | 3.41 | 2.71 | 20.7** | 72.71 | 72.77 | −0.06 | 8.80 | 9.23 | 0.42 |
OR02F-C-169 | 7.8 | 42.5 | 34.8** | 3.80 | 2.43 | 36.0** | 77.97 | 76.87 | 1.10 | 9.85 | 9.30 | −0.55 |
OR02F-D-27 | 14.0 | 34.3 | 20.3** | 4.12 | 2.81 | 31.6** | 73.58 | 73.19 | 0.39 | 7.68 | 8.15 | 0.48 |
WA 7437 | 7.5 | 12.8 | 5.3 | 3.15 | 2.76 | 12.3 | 76.94 | 76.00 | 0.94 | 8.53 | 8.50 | −0.03 |
Skiles | 6.8 | 35.0 | 28.3** | 4.65 | 3.41 | 26.6** | 79.43 | 78.00 | 1.42* | 8.38 | 8.80 | 0.43 |
ORSS-1757 | 5.0 | 37.5 | 32.5** | 4.07 | 3.10 | 23.6** | 77.91 | 76.87 | 1.03 | 7.93 | 7.80 | −0.13 |
LSD (0.05) | 8.6 | 8.6 | 0.47 | 0.47 | 1.27 | 1.27 | 1.02 | 1.02 |
*Significant at the 0.05 probability level. ** Significant at the 0.01 probability level.
aSignificance is based on percent whiteheads square root-transformed.
bGrain yield loss (%) = (noninoculated − inoculated)/noninoculated * 100.
Kernel parameters of 12 wheat genotypes grown noninoculated (U) and inoculated (I) with
Kernel weight avg (mg) | Kernel weight SD | Kernel diameter avg (mm) | Kernel diameter SD | |||||||||
U | I | Change | U | I | Change | U | I | Change | U | I | Change | |
Stephens | 37.34 | 35.22 | −2.12 | 8.42 | 10.77 | 2.35** | 2.628 | 2.534 | −0.094 | 0.473 | 0.563 | 0.090** |
Madsen | 31.26 | 31.31 | 0.05 | 7.63 | 8.17 | 0.53 | 2.396 | 2.441 | 0.045 | 0.420 | 0.468 | 0.048** |
Tubbs | 33.75 | 32.76 | −0.99 | 8.94 | 9.90 | 0.96** | 2.471 | 2.413 | −0.058 | 0.463 | 0.500 | 0.037* |
OR9800919 | 37.06 | 33.30 | −3.75** | 7.80 | 8.07 | 0.27 | 2.610 | 2.458 | −0.152** | 0.475 | 0.473 | −0.002 |
OR9800924 | 34.61 | 33.77 | −0.84 | 8.69 | 8.10 | −0.58 | 2.555 | 2.508 | −0.047 | 0.501 | 0.474 | −0.027 |
Rossini | 41.39 | 41.15 | −0.24 | 10.67 | 11.68 | 1.00** | 2.811 | 2.784 | −0.027 | 0.558 | 0.582 | 0.024 |
OR02F-B-46 | 30.55 | 32.01 | 1.46 | 8.05 | 8.86 | 0.80* | 2.360 | 2.397 | 0.037 | 0.437 | 0.468 | 0.032* |
OR02F-C-169 | 33.17 | 33.04 | −0.13 | 8.69 | 9.14 | 0.46 | 2.477 | 2.465 | −0.012 | 0.466 | 0.496 | 0.030 |
OR02F-D-27 | 32.43 | 32.49 | 0.06 | 8.40 | 8.53 | 0.13 | 2.425 | 2.429 | 0.004 | 0.471 | 0.497 | 0.026 |
WA 7437 | 27.36 | 26.97 | −0.39 | 7.12 | 6.66 | −0.46 | 2.207 | 2.173 | −0.034 | 0.450 | 0.422 | −0.028 |
Skiles | 37.43 | 36.08 | −1.35 | 9.15 | 9.04 | −0.11 | 2.538 | 2.482 | −0.056 | 0.526 | 0.513 | −0.013 |
ORSS-1757 | 34.99 | 34.48 | −0.52 | 8.62 | 9.19 | 0.58 | 2.517 | 2.499 | −0.018 | 0.483 | 0.503 | 0.019 |
LSD (0.05) | 2.24 | 2.24 | 0.71 | 0.71 | 0.100 | 0.100 | 0.032 | 0.032 |
*Significant at the 0.05 probability level. **Significant at the 0.01 probability level.
aChange = (inoculated − noninoculated).
There was a negative correlation of disease scores with grain yield, with
Pearson correlation coefficients among Cephalosporium stripe rating, grain yield, test weight, and several kernel traits of 10 varieties tested in Pendleton 2006 (below diagonal) and 12 varieties tested in Moro 2007 (above diagonal) under inoculated and noninoculated conditions.
Whiteheads | Whiteheads SQRT | Grain yield | Test weight | Grain protein | Kernel weight (avg) | Kernel weight (SD) | Kernel diameter (avg) | Kernel diameter (SD) | |
---|---|---|---|---|---|---|---|---|---|
Whiteheads | — | 0.986 | −0.809 | −0.435 | 0.134 | −0.155 | 0.290 | −0.190 | 0.217 |
*** | *** | * | ns | ns | ns | ns | ns | ||
Whiteheads SQRT | 0.952 | — | −0.820 | −0.447 | 0.142 | −0.217 | 0.228 | −0.249 | 0.145 |
*** | *** | * | ns | ns | ns | ns | ns | ||
Grain yield | −0.626 | −0.618 | — | 0.304 | −0.271 | 0.461 | −0.096 | 0.464 | −0.011 |
** | ** | ns | ns | * | ns | * | ns | ||
Test weight | −0.433 | −0.517 | 0.389 | — | 0.273 | 0.263 | 0.093 | 0.210 | 0.140 |
+ | * | + | ns | ns | ns | ns | ns | ||
Grain protein | 0.540 | 0.526 | −0.749 | −0.262 | — | −0.161 | −0.001 | −0.141 | −0.235 |
* | * | *** | ns | ns | ns | ns | ns | ||
Kernel weight (avg) | 0.176 | 0.211 | 0.419 | 0.229 | −0.266 | — | 0.747 | 0.971 | 0.794 |
ns | ns | + | ns | ns | *** | *** | *** | ||
Kernel weight (SD) | 0.793 | 0.881 | −0.378 | −0.413 | 0.332 | 0.442 | — | 0.730 | 0.910 |
*** | *** | ns | + | ns | * | *** | *** | ||
Kernel diam. (avg) | 0.123 | 0.161 | 0.428 | 0.183 | −0.262 | 0.988 | 0.382 | — | 0.751 |
ns | ns | + | ns | ns | *** | + | *** | ||
Kernel diam. (SD) | 0.812 | 0.879 | −0.376 | −0.429 | 0.367 | 0.520 | 0.946 | 0.486 | — |
*** | *** | ns | + | ns | * | *** | * |
+Significant at the 0.10 probability level. *Significant at the 0.05 probability level. **Significant at the 0.01 probability level. ***Significant at the 0.001 probability level.
Regressions were fitted to estimate % grain yield and test weight loss as a function of increasing susceptibility to Cephalosporium stripe. The response of grain yield to whiteheads difference between inoculated and noninoculated plots in Pendleton followed a polynomial regression that included a quadratic term (Figure
Yield loss caused by inoculation of wheat genotypes with
The relationship between whiteheads and reductions in test weight was nonlinear for both locations. The best fit was a polynomial regression with significant quadratic terms (
Reduction of grain test weight caused by inoculation of wheat genotypes with
The general objective of yield loss studies is to provide quantitative estimates regarding effect of disease on its host crop [
Despite major differences in rainfall, soil fertility, and environmental stress, susceptible and resistant varieties performed similarly at both Pendleton and Moro. The range of differences in whiteheads between inoculated and noninoculated plots was also similar among locations, with a maximum increase of 40%. Lines with intermediate levels of Cephalosporium stripe showed more variability between locations, however. OR9800924 performed similarly to WA 7437 (resistant check), in Pendleton. In Moro, however, the same variety had a mean 22.0% increase in whiteheads and 23.2% grain yield loss under inoculation. This is not surprising, as significant year × treatment interaction has been reported for Cephalosporium stripe response and associated yield losses by Bockus et al. [
Regressions of grain yield loss on whitehead change were similar for both environments, although fitted models were not the same. At Pendleton, a quadratic polynomial provided the best fit, while at Moro the relationship was linear. Intercepts of the two models indicated similar yield loss at zero whitehead change (6.44 and 7.49% at Pendleton and Moro, resp.), though statistical support for the intercepts was not high in either case (
At-low-to intermediate disease levels, the relationship between disease and yield loss was linear for both environments. Regression coefficients for Pendleton (0.6) and Moro (0.7) suggest that for each additional unit increase in disease pressure, there is a loss of 0.6 to 0.7% in grain yield (Figure
Analysis of test weight is often included in yield-loss studies to evaluate the effect of disease on grain quality, which can be an important component of the monetary value of the crop. The inclusion of a quadratic effect increased the overall fit of the regressions however, shapes of the curves differed between the two sites. For Pendleton, the function was parabolic while, for Moro, there was a hyperbolic relationship between loss of test weight and whitehead increase. Maximum reduction in test weight was recorded for Stephens and OR02F-C-169 at Pendleton and was 5.43 kg hl−1.
Test weight loss increased linearly at a rate of 0.32 kg hl−1 for each unit increase in whiteheads in Pendleton. As whiteheads increased to 15 to 20%, the slope decreased, meaning the rate of change in test weight was less at higher disease levels. In contrast, results from Moro indicated that test weight losses were not substantial until more than 25% whiteheads change was observed. The maximum loss observed at Moro was about half of that observed in Pendleton. In studies on take-all, which is another soil-borne pathogen that affects wheat and also produces whiteheads, test weight was usually inversely related to disease severity and responded to take-all intensity similarly to grain yield [
In Pendleton, Cephalosporium stripe not only affected grain yield and test weight, but also had a significant impact on uniformity of kernel size and weight. Morton and Mathre [
The role and impact of plant pathogens are not static, but change in relation to varieties, environments, management, and cropping systems. Understanding potential damage, risk, and vulnerability from pathogens is important to prioritizing breeding objectives and allocating resources to crossing, selection, and screening of germplasm. It also has direct impact on release decisions, in that new cultivars should have low risk of yield loss from major diseases that occur in the target region. For producers, risk of losses from pathogens are important considerations in many management decisions, including choice of tillage practices, planting date, crop rotations, and choice of varieties. For example, potential yield gains from early fall seeding dates can be far outweighed by increased risk of damage from soil diseases.
Cephalosporium stripe is known to cause significant damage to wheat grown in the Pacific Northwest. Economic damage has been erratic, often inconsistent within fields, and generally reduced by avoiding early plantings. Cephalosporium stripe often is lumped into the category of “chronic diseases,” for which modest resources have been allocated for prevention and breeding for resistance. In this study, there was evidence for yield reduction in presence of the disease before whitehead symptoms were significant. Yield losses of nearly 50% were found in the most susceptible varieties. Intermediate levels of resistance were shown to be valuable in reducing economic damage from the pathogen. Varieties with intermediate resistances should be sufficient for most production situations, especially as the disease is generally not highly aggressive. However, higher levels of resistance, as observed in WA 7437, are needed to avoid losses with high inoculum levels and favorable environmental conditions.
The authors thank Erling Jacobsen, Karl Rhinhart, Kathryn Sackett, and LaRae Wallace for their contributions to the establishment and maintenance of the field plots.