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Willyerd, K. T., Li, C., Madden, L. V., Bradley, C. A., Bergstrom, G. C., Sweets, L. E., McMullen, M., Ransom, J. K., Grybauskas, A., Osborne, L., Wegulo, S. N., Hershman, D. E., Wise, K., Bockus, W. W., Groth, D., Dill-Macky, R., Milus, E., Esker, P. D., Waxman, K. D., Adee, E. A., Ebelhar, S. E., Young, B. G., and Paul, P. A. 2012 [MR_UT]) were used in multivariate meta-analyses, and mean log response ratios across trials were estimated and transformed to estimate mean percent control ( C ) due to the management combinations relative to S_UT. All combinations led to a significant reduction in index and DON (P < 0.001). MR_TR was the most effective combination, with a C of 76% for index and 71% for DON, followed by MS_TR (71 and 58%, respectively), MR_UT (54 and 51%, respectively), S_TR (53 and 39%, respectively), and MS_UT (43 and 30%, respectively). Calculations based on the principle of treatment independence showed that the combination of fungicide application and resistance was additive in terms of percent control for index and DON. Management combinations were ranked based on percent control relative to S_UT within each trial, and nonparametric analyses were performed to determine management combination stability across environments (trials) using the Kendall coefficient of concordance (W). There was a significant concordance of management combinations for both index and DON (P < 0.001), indicating a nonrandom ranking across environments and relatively low variability in the within-environment ranking of management combinations. MR_TR had the highest mean rank (best control relative to S_UT) and was one of the most stable management combinations across environments, with low rank stability variance (0.99 for index and 0.67 for DON). MS_UT had the lowest mean rank (poorest control) but was also one of the most stable management combinations. Based on Piepho's nonparametric rank-based variance homogeneity U test, there was an interaction of management combination and environment for index (P = 0.011) but not for DON (P = 0.147), indicating that the rank ordering for index depended somewhat on environment. In conclusion, although the magnitude of percent control will likely vary among environments, integrating a single tebuconazole + prothioconazole application at anthesis with cultivar resistance will be a more effective and stable management practice for both index and DON than either approach used alone.

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Existing crop monitoring programs determine the incidence and distribution of plant diseases and pathogens and assess the damage caused within a crop production region. These programs have traditionally used observed or predicted disease and pathogen data and environmental information to prescribe management practices that minimize crop loss. Monitoring programs are especially important for crops with broad geographic distribution or for diseases that can cause rapid and great economic losses. Successful monitoring programs have been developed for several plant diseases, including downy mildew of cucurbits, Fusarium head blight of wheat, potato late blight, and rusts of cereal crops. A recent example of a successful disease-monitoring program for an economically important crop is the soybean rust (SBR) monitoring effort within North America. SBR, caused by the fungus Phakopsora pachyrhizi, was first identified in the continental United States in November 2004. SBR causes moderate to severe yield losses globally. The fungus produces foliar lesions on soybean (Glycine max) and other legume hosts. P. pachyrhizi diverts nutrients from the host to its own growth and reproduction. The lesions also reduce photosynthetic area. Uredinia rupture the host epidermis and diminish stomatal regulation of transpiration to cause tissue desiccation and premature defoliation. Severe soybean yield losses can occur if plants defoliate during the mid-reproductive growth stages. The rapid response to the threat of SBR in North America resulted in an unprecedented amount of information dissemination and the development of a real-time, publicly available monitoring and prediction system known as the Soybean Rust-Pest Information Platform for Extension and Education (SBR-PIPE). The objectives of this article are (i) to highlight the successful response effort to SBR in North America, and (ii) to introduce researchers to the quantity and type of data generated by SBR-PIPE. Data from this system may now be used to answer questions about the biology, ecology, and epidemiology of an important pathogen and disease of soybean.

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In an effort to characterize the association between weather variables and inoculum of Gibberella zeae in wheat canopies, spikes were sampled and assayed for pathogen propagules from plots established in Indiana, North Dakota, Ohio, Pennsylvania, South Dakota, and Manitoba between 1999 and 2005. Inoculum abundance was quantified as the daily number of colony forming units per spike (CFU/spike). A total of 49 individual weather variables for 24-h periods were generated from measurements of ambient weather data. Polynomial distributed lag regression analysis, followed by linear mixed model analysis, was used to (i) identify weather variables significantly related to log-transformed CFU/spike (the response variable; Y), (ii) determine the time window (i.e., lag length) over which each weather variable affected Y, (iii) determine the form of the relationship between each weather variable and Y (defined in terms of the polynomial degree for the relationship between the parameter weights for the weather variables and the time lag involved), and (iv) account for location-specific effects and random effects of years within locations on the response variable. Both location and year within location affected the magnitude of Y, but there was no consistent trend in Y over time. Y on each day was significantly and simultaneously related to weather variables on the day of sampling and on the 8 days prior to sampling (giving a 9-day time window). The structural relationship corresponded to polynomial degrees of 0, 1, or 2, generally showing a smooth change in the parameter weights and time lag. Moisture- (e.g., relative humidity-) related variables had the strongest relationship with Y, but air temperature- and rainfall-related variables also significantly affected Y. The overall marginal effect of each weather variable on Y was positive. Thus, local weather conditions can be utilized to improve estimates of spore density on wheat spikes around the time of flowering.

Wheat curl mite (WCM)-transmitted viruses—namely, Wheat streak mosaic virus (WSMV), Triticum mosaic virus (TriMV), and the High Plains virus (HPV)—are three of the wheat-infecting viruses in the central Great Plains of the United States. TriMV is newly discovered and its prevalence and incidence are largely unknown. Field surveys were carried out in Colorado, Kansas, Nebraska, and South Dakota in spring and fall 2010 and 2011 to determine TriMV prevalence and incidence and the frequency of TriMV co-infection with WSMV or HPV in winter wheat. WSMV was the most prevalent and was detected in 83% of 185 season–counties (= s-counties), 73% of 420 season–fields (= s-fields), and 35% of 12,973 samples. TriMV was detected in 32, 6, and 6% of s-counties, s-fields, and samples, respectively. HPV was detected in 34, 15, and 4% of s-counties, s-fields, and samples, respectively. TriMV was detected in all four states. In all, 91% of TriMV-positive samples were co-infected with WSMV, whereas WSMV and HPV were mainly detected as single infections. The results from this study indicate that TriMV occurs in winter wheat predominantly as a double infection with WSMV, which will complicate breeding for resistance to WCM-transmitted viruses.

Kandel, Y. R., Glover, K. D., Tande, C. A., and Osbome, L. E. 2012. Evaluation of spring wheat germplasm for resistance to bacterial leaf streak caused by Xanthomonas campestris pv. translucens. Plant Dis. 96:1743-1748.Bacterial leaf streak (caused by Xanthomonas campestris pv. translucens) has reemerged as a potential threat in spring wheat {Triticum aestivum) production areas in the northern Great Plains. As with other foliar bacterial diseases, chemical control under field situations is neither economical nor practical. Development of resistant genotypes will be needed for adequate management of the disease. There is currently limited information on sources of resistance in hard spring wheat germplasm. The main objective was to develop and apply a robust screening tool for evaluating germplasm against bacterial leaf streak, and to identify resistance sources for this disease. Inoculated field experiments were conducted in Brookings and Codington Counties, SD in 2009 and 2010 using a virulent local isolate {XctSD-017) inocu-lated after tillering stage. Eorty-five hard red spring wheat genotypes with diverse genetic backgrounds were evaluated for disease severity, with ratings made at 7-day intervals from heading through dough stage. Results of this study showed clear differences in level of resistance among the 45 genotypes, with no immunity expressed.