Severe outbreaks of the disease can cause up to 30-50% yield loss in dent corn if the disease is established before tassel . 1996), giving breeding for NCLB resistance a high priority for disease control. Accordingly, indirect selection is superior to direct selection when LY is shorter than a certain fraction of LX, which depends on H2 of the target trait and the accuracy of genomic predictions. However, the development of accurate prediction models requires large training sets of genotyped and phenotyped individuals. Symptoms. All prediction approaches were applied to the same random splits of the data set into training and prediction set, and a paired t-test was used to determine the significance of differences in prediction accuracy observed between the “combined” and “within” prediction approaches. Northern corn leaf blight is found during warm, wet growing conditions. (2012). Compared with the increase in prediction accuracy when increasing Nt by adding individuals from the same group (e.g., moving from Nt = 25 to Nt = 50 within groups), the increase in prediction accuracy was only marginal, when the same increase in Nt was achieved by adding individuals from the other group. The lesions are initially bordered by gray-green margins. Leaf spots caused by Calonectria pseudonaviculata - Photo by Sandra Jensen, Cornell University, Bugwood.org. Wet, warm (64-81 °F) conditions favor infection and spore production by the fungus that causes northern leaf blight. Planting date. Extension is expanding its online education and resources to adapt to COVID-19 restrictions. Supporting information is available online at http://www.g3journal.org/lookup/suppl/doi:10.1534/g3.112.004630/-/DC1. Leaf lesions are long (1 to 6 inches) and elliptical, gray-green at first but then turn pale gray or tan. Symptoms: the major symptom that can be observed in plants with northern leaf blight â¦ The most economically important host is corn, but other forms may infect sorghum, Johnson grass, or sudangrass. The disease generally only causes limited damage, but it may lead to crop loss under certain conditions. The pathogen is the fungus Exserohilum turcicum (syn. (2012). After the 1930s, the disease declined in importance; however, since the turn of the â¦ The objectives of this study were to (1) assess the prospects of genomic prediction of NCLB resistance in maize and (2) compare the prediction accuracy of separate training sets for each heterotic group vs. combining both heterotic groups in a single training set. The process of generating training and prediction sets was repeated 100 times for all three levels of Nt in the manner described. Race T attacks leaves, husks, stalks, leaf sheaths, shanks, ears, and cobs. Following Falconer and Mackay (1996), this ratio can be calculated as(2)where iY is the selection intensity applied on the indirect trait and iX the selection intensity on the target trait, LY and LX are the cycle lengths of indirect and direct selection, respectively, HX is the square root of the heritability of the target trait, and HY the square root of the heritability of the indirect trait. There are two races of the pathogen. Genomic prediction, developed in dairy cattle breeding, uses all available marker data of a genotyped and phenotyped training set for building a prediction model without an intermediate QTL detection step (Meuwissen et al. Northern corn leaf blight (NCLB) symptoms usually appear first on the lower leaves. NOTE: We request your email address only to inform the recipient that it was you who recommended this article, and that it is not junk mail. Thus, always one group would be selected based on a training set of size 2Nt from the same group and one based on across group predictions. Note however, that the Wisconsin study found no relationship between anthracnose leaf blight â¦ Using our H2 estimates and the accuracies observed for the “combined” prediction approach at Nt = 75, selection for NCLB resistance based on genomic predictions would already be superior to phenotypic selection when LY is less than 81% (dent) or 86% (flint) of LX. The first 20,000 iterations of the chain were discarded as burn-in, and only every 20th sample of the remaining iterations was stored. Further, we observed a relative increase in accuracy from Nt = 50 to Nt = 75 that was considerably greater than the relative increase Lorenz et al. It has also been known as Helminthosporium turcicum. From equation (4), it can be seen that low Me combined with high H2 can lead to a high expected prediction accuracy at low Nt. Northern Corn Leaf Blight Angela Madeiras, UMass Extension Plant Diagnostic Laboratory At the turn of the 20th century, northern corn leaf blight (NCLB) was a common problem for corn growers in New â¦ 2011). We investigated the potential of the following three prediction approaches (Figure 1): (1) the “within” prediction approach, where lines used for fitting the model (training set) and lines to be predicted (prediction set) belonged to the same heterotic group; (2) the “across” prediction approach, where lines in the training set belonged to another heterotic group than lines in the predicting set and; (3) the “combined” prediction approach, where lines of both heterotic groups were combined in a training set to predict either lines in a flint or dent prediction set. Northern Corn Leaf Blight 7-15 1 Northern Corn Leaf Blight Northern corn leaf blight (NCLB) is a foliar disease of corn caused by the fungus Exserohilum turcicum. Lesions begin on the lower leaves and then spread to upper leaves. Northern corn leaf blight (NCLB) is a common leaf disease and occurs in all maize growing areas of the world. Our results and conclusions are based on rather low training set sizes Nt. 2003), our results showed that there is still consistent LD across the heterotic groups, even for markers at greater distances (i.e., the proportion of marker pairs with equal linkage phase was considerably greater than 0.5, the value representing independence). Infection at earlier growth stages will have a greater impact oâ¦ More detailed information on the history of this breeding program is given by Technow et al. Race O normally attacks only leaves. 2012; Poland et al. Setosphaeria turcica . The northern corn leaf blight (NCLB) fungal pathogen overwinters as conidia (external spores) and mycelia (vegetative part of a fungus) in and on previously infected corn residue. The posterior means of β and ui were used to predict the genotypic values. This might hamper the application of traditional marker assisted breeding approaches. It thrives in places … The University of Minnesota is an equal opportunity educator and employer. Northern Corn Leaf Blight in Northern New York Kitty O'Neil, Team Leader, Field Crops & Soils Specialist North Country Regional Ag Team. 2010). In conclusion, our results encourage the application of genomic prediction in a NCLB resistance breeding program. These authors found this approach to have the potential of increasing the prediction accuracies for small breeds. Northern corn leaf blight (NLB) is caused by the fungus Exserohilum turcicum, previously classified as Helminthosporium turcicum. There are many races or strains of the fungus. Though, there seems to be some significant tolerance within â¦ Sign up to receive alert notifications of new articles. Symptoms: the major symptom that can be observed in plants with northern leaf blight is the long lesions that are cigar-shaped. Nonetheless, under a fixed budget that has to be allocated to all heterotic groups, increasing Nt within one group can only be achieved by decreasing it in another. We used a uniform, improper prior for β. Fungicides may be warranted on inbreds for seed production during the early stages of this disease. NCLB can cause yield loss if it develops before or during the tasseling and silking phases of corn development. Several races of this pathogen are known that interact differently with different resistance genes. Northern corn leaf blight lesions are usually larger, tan to gray, and cigar-shaped. Northern corn leaf blight (NCLB) is a disease of corn caused by the fungus, Exserohilum turcicum. 2012; Erbe et al. The proportion of marker pairs with the same linkage phase in both heterotic groups showed trends similar to the LD (Figure 2B). In contrast, the same increase in Nt for the populations of Lorenz et al. Regents of the University of Minnesota. 2010a; de Roos et al. The mean pairwise relationship coefficient, from the A matrix computed for the “combined” prediction approach, between dent lines was 0.46 with standard deviation of 0.38, and between flint lines 0.49 with standard deviation of 0.32. Large elliptical-shaped lesions on corn leaves herald this fungal disease. Late infections may have less of an â¦ Disease is not in every field, is at low levels in â¦ 2012). The northern corn leaf blight (NCLB) fungal pathogen overwinters as conidia (external spores) and mycelia (vegetative part of a fungus) in and on previously infected corn residue. Additionally, timely planting can be useful for avoiding conditions that favor the disease. It typically appears at or after silking, but the disease is usually more severe when infection occurs earlier. Lesions are tan, somewhat rectangular in shape, and have reddish-brown margins. Subsequently, this model is used to predict genotypic values of nonphenotyped individuals for which only marker data are available. Forages. Due to the migration of Boxwood Blight into the areas of Lake and Cook Counties, â¦ Afterward, it decreased slowly toward the value 0.5 but nonetheless remained slightly above this value over the whole range of Δ values considered. September 6, 2018. This was because the information added in the latter case was much lower than in the former, as is exemplified in the low linkage phase consistency between the heterotic groups. Northern Corn Leaf Blight. Environment plays a significant role in disease development (see the disease triangle below). Usually many spots will merge into one to form the blights. Midwestern corn growers know the symptoms of northern corn leaf blight all too well: greenish-gray lesions on the leaves that can add up to major yield losses if not detected and treated â¦ The resulting allocation of resources to parallel breeding programs challenges the establishment of sufficiently sized training sets within groups. The “prediction accuracy” was calculated by dividing the correlation between the predicted genotypic values and observed phenotypic values by , following common practice (Legarra et al. The principal component analysis showed a very clear genetic distinction of the dent and flint heterotic groups (Figure 4). Therefore, combining training sets is still worthwhile, since the gain in prediction accuracy obtained is essentially cost neutral and does not lead to a negligence of the other group. USE PLANTIX NOW! As the disease develops, the lesions spread to all leafy structures, including the husks, and produce dark gray spores, giving lesions as di… Likely, this was because such algorithms require much larger training set sizes to overcome the added complexity of the model due to greater dimensionality and redundancy of the predictor set. In general, the LD within the group of dent lines was slightly greater compared with the group of flint lines, whereas the LD across the set of dent and flint lines was lowest (Figure 2A). NCLB can be efficiently controlled through cultivation of resistant varieties (Dingerdissen et al. Get free diagnosis on WhatsApp! Again, second-order natural smoothing spline regression was used to visualize this proportion as a function of the distance between the center values of the bins. Yield losses are typically minimal, but can become significant with susceptible hybrids or inbreds. Severe symptoms can progress rapidly, resulting in blighted leaves and only 20th. 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