Relationships of Environmental and Cultural Factors with Severity of Gray Leaf Spot in Maize

Quantitative trait locus analysis of gray leaf spot resistance in the maize IBM Syn10 DH population

KEY MESSAGE

The exploration and dissection of a set of QTLs and candidate genes for gray leaf spot disease resistance using two fully assembled parental genomes may help expedite maize resistance breeding. The fungal disease of maize known as gray leaf spot (GLS), caused by Cercospora zeae-maydis and Cercospora zeina, is a significant concern in China, Southern Africa, and the USA. Resistance to GLS is governed by multiple genes with an additive effect and is influenced by both genotype and environment. The most effective way to reduce the cost of production is to develop resistant hybrids. In this study, we utilized the IBM Syn 10 Doubled Haploid (IBM Syn10 DH) population to identify quantitative trait loci (QTLs) associated with resistance to gray leaf spot (GLS) in multiple locations. Analysis of seven distinct environments revealed a total of 58 QTLs, 49 of which formed 12 discrete clusters distributed across chromosomes 1, 2, 3, 4, 8 and 10. By comparing these findings with published research, we identified colocalized QTLs or GWAS loci within eleven clustering intervals. By integrating transcriptome data with genomic structural variations between parental individuals, we identified a total of 110 genes that exhibit both robust disparities in gene expression and structural alterations. Further analysis revealed 19 potential candidate genes encoding conserved resistance gene domains, including putative leucine-rich repeat receptors, NLP transcription factors, fucosyltransferases, and putative xyloglucan galactosyltransferases. Our results provide a valuable resource and linked loci for GLS marker resistance selection breeding in maize.

Impact of different types of green manure on pests and disease incidence and severity as well as growth and yield parameters of maize

The emergence of pests and diseases, including the maize streak virus, leaf blight, the African stem borer, and gray leaf spot, poses a persistent threat to maize production (Zea mays L. cv: DMR-ESR-Yellow) around the world. A field experiment was conducted at the School of Agriculture experimental site, Njala University, Sierra Leone, during a two-year period (2020-2021) to assess the effects of green manure on pest and disease incidence and severity as well as growth and yield parameters of maize. The experiment was laid out in a randomized complete block design (RCBD) with three replications and four treatments: Cal. 3 t.ha^-1, Cal. 6 t.h^-1, Pan. 3 t.h^-1, Pan 6 t.ha^-1 and a control plot amended with 200 kg ha^-1 of N (urea) and NPK 15:15:15 ha^-1 split application. The study showed that gray leaf spot damage was the most severe infection among all treatments. Therefore, the effects of the most severe disease and pest of maize in Sierra Leone can be minimized by applying green manure. Moreover, results reveal that Calopogonium- Pueraria mixture amended plots showed significant performance in the measured growth parameters viz. highest leaf number, large leaf area stem girth, superior plant height, best ear height (64.6-78.5 cm), higher cob yield (1.2-1.4 t.ha^-1) ear (1.8-2.1 t.ha^-1) and dry grain yield (0.5-0.7 ha^-1). Panicum green manure results showed that prompt and adequate application, as well as decomposition of green manures, is imperative for the successful conservation and sustainability of maize farming systems. The findings of this research could improve the efficiency of green manure use in pest, disease, and crop management systems.

Effect of Maize Hybrid and Foliar Fungicides on Yield Under Low Foliar Disease Severity Conditions

Foliar fungicide use in the U.S. Corn Belt increased in the last decade; however, questions persist pertaining to its value and sustainability. Multistate field trials were established from 2010 to 2012 in Illinois, Iowa, Ohio, and Wisconsin to examine how hybrid and foliar fungicide influenced disease intensity and yield. The experimental design was in a split-split plot with main plots consisting of hybrids varying in resistance to gray leaf spot (caused by Cercospora zeae-maydis) and northern corn leaf blight (caused by Setosphaera turcica), subplots corresponding to four application timings of the fungicide pyraclostrobin, and sub-subplots represented by inoculations with either C. zeae-maydis, S. turcica, or both at two vegetative growth stages. Fungicide application (VT/R1) significantly reduced total disease severity relative to the control in five of eight site-years (P<0.05). Disease was reduced by approximately 30% at Wisconsin in 2011, 20% at Illinois in 2010, 29% at Iowa in 2010, and 32 and 30% at Ohio in 2010 and 2012, respectively. These disease severities ranged from 0.2 to 0.3% in Wisconsin in 2011 to 16.7 to 22.1% in Illinois in 2010. The untreated control had significantly lower yield (P<0.05) than the fungicide-treated in three site-years. Fungicide application increased the yield by approximately 6% at Ohio in 2010, 5% at Wisconsin in 2010 and 6% in 2011. Yield differences ranged from 8,403 to 8,890 kg/ha in Wisconsin 2011 to 11,362 to 11,919 kg/ha in Wisconsin 2010. Results suggest susceptibility to disease and prevailing environment are important drivers of observed differences. Yield increases as a result of the physiological benefits of plant health benefits under low disease were not consistent.

Spatio-Temporal Dynamics of Arabica Coffee Berry Disease Caused by Colletotrichum kahawae on a Plot Scale

Coffee berry disease (CBD) is caused by Colletotrichum kahawae. This pathogen only attacks green berries; it causes cherry rot and premature fruit fall. The disease leads to major harvest losses in the western highland region of Cameroon. The origin of the primary inoculum and the beginning of epidemics are unknown. The interactions between the pathogen and its host were studied at locations where CBD was known to cause severe disease. The disease was monitored weekly in uniform plots of adjacent coffee trees at Santa (1,750 m) in 2003 and 2004 and Bafou (1,820 m) in 2004 and 2005. The logistic model provided good fit of the epidemic's temporal dynamics. The spatial distribution of CBD over time indicated that plants in a plot were contaminated stepwise from the first infected coffee tree. An analysis of semi-variograms and the disease dispersal maps obtained by kriging revealed primary infection foci at both sites. They were observed from the 8th to the 10th week after flowering at Bafou and from the 11th to the 13th week at Santa. CBD affected the entire plots 3 weeks after the foci first appeared. These results suggest that inoculum from previous epidemics survives at points in the initial foci in a coffee plantation.

Heritability and Components of Resistance to Cercospora zeae-maydis Derived from Maize Inbred VO613Y

ABSTRACT Gray leaf spot (GLS), caused by the fungus Cercospora zeae-maydis, is one of the most important foliar diseases of maize. This study was undertaken to estimate heritability of C. zeae-maydis resistance and examine the relationship between previously identified resistance loci and certain components of resistance including incubation period, lesion number, and maximum lesion length. Partially inbred progenies arising from hybridization between maize inbred lines VO613Y (high level of partial resistance) and Pa405 (susceptible) were examined in Ohio and South Africa. Heritability estimates of resistance were calculated based on severity and incubation period values. The range of heritability estimates based on severity was broad, with values ranging from approximately 0.46 to 0.81 (mean = 0.59). Estimates of mean heritability for incubation period were lowest (0.18), indicating that this component would likely be unsuitable for selection of germ plasm intended for deployment in diverse regions. Length of GLS lesions was significantly affected by host genotype, with resistant genotypes having shorter lesions from one site in Ohio during two seasons. Genotype also had a significant effect on incubation period and lesion number; the lower values for these components also were associated with resistant genotypes. The combined action of these resistance components resulted in lower overall disease severity.

Regression and artificial neural network modeling for the prediction of gray leaf spot of maize

ABSTRACT Regression and artificial neural network (ANN) modeling approaches were combined to develop models to predict the severity of gray leaf spot of maize, caused by Cercospora zeae-maydis. In all, 329 cases consisting of environmental, cultural, and location-specific variables were collected for field plots in Iowa between 1998 and 2002. Disease severity on the ear leaf at the dough to dent plant growth stage was used as the response variable. Correlation and regression analyses were performed to select potentially useful predictor variables. Predictors from the best 9 of 80 regression models were used to develop ANN models. A random sample of 60% of the cases was used to train the networks, and 20% each for testing and validation. Model performance was evaluated based on coefficient of determination (R(2)) and mean square error (MSE) for the validation data set. The best models had R(2) ranging from 0.70 to 0.75 and MSE ranging from 174.7 to 202.8. The most useful predictor variables were hours of daily temperatures between 22 and 30 degrees C (85.50 to 230.50 h) and hours of nightly relative humidity >/=90% (122 to 330 h) for the period between growth stages V4 and V12, mean nightly temperature (65.26 to 76.56 degrees C) for the period between growth stages V12 and R2, longitude (90.08 to 95.14 degrees W), maize residue on the soil surface (0 to 100%), planting date (in day of the year; 112 to 182), and gray leaf spot resistance rating (2 to 7; based on a 1-to-9 scale, where 1 = most susceptible to 9 = most resistant).

A model-based approach to preplanting risk assessment for gray leaf spot of maize

ABSTRACT Risk assessment models for gray leaf spot of maize, caused by Cercospora zeae-maydis, were developed using preplanting site and maize genotype data as predictors. Disease severity at the dough/dent plant growth stage was categorized into classes and used as the response variable. Logistic regression and classification and regression tree (CART) modeling approaches were used to predict severity classes as a function of planting date (PD), amount of maize soil surface residue (SR), cropping sequence, genotype maturity and gray leaf spot resistance (GLSR) ratings, and longitude (LON). Models were development using 332 cases collected between 1998 and 2001. Thirty cases collected in 2002 were used to validate the models. Preplanting data showed a strong relationship with late-season gray leaf spot severity classes. The most important predictors were SR, PD, GLSR, and LON. Logistic regression models correctly classified 60 to 70% of the validation cases, whereas the CART models correctly classified 57 to 77% of these cases. Cases misclassified by the CART models were mostly due to overestimation, whereas the logistic regression models tended to misclassify cases by underestimation. Both the CART and logistic regression models have potential as management decision-making tools. Early quantitative assessment of gray leaf spot risk would allow for more sound management decisions being made when warranted.