Quantitative trait loci (QTLs) of maize involved in mediating resistance to Peronosclerospora sorghi, the causative agent of sorghum downy mildew (SDM), were detected in a population of recombinant inbred lines (RILs) derived from the Zea mays L. cross between resistant (G62) and susceptible (G58) inbred lines. Field tests of 94 RILs were conducted over two growing seasons using artificial inoculation. Heritability of the disease reaction was high (around 70%). The mapping population of the RILs was also scored for restriction fragment length polymorphic (RFLP) markers. One hundred and six polymorphic RFLP markers were assigned to ten chromosomes covering 1648 cM. Three QTLs were detected that significantly affected resistance to SDM combined across seasons. Two of these mapped quite close together on chromosome 1, while the third one was on chromosome 9. The percentage of phenotypic variance explained by each QTL ranged from 12.4% to 23.8%. Collectively, the three QTLs identified in this study explained 53.6% of the phenotypic variation in susceptibility to the infection. The three resistant QTLs appeared to have additive effects. Increased susceptibility was contributed by the alleles of the susceptible parent. The detection of more than one QTL supports the hypothesis that several qualitative and quantitative genes control resistance to P. sorghi.

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The GIS-aided spatial interpolation was applied on collected groundwater data to predict selected parameters (i.e., pH, electrical conductivity, and temperature) for the selected water wells distributed over Mosul City in Iraq. A descriptive statistical analysis was conducted on collected samples to explore the statistical indices. The skewness test was also employed to test the distribution of data sets around their mean values. The natural logarithms function achieved least skewness values and thus was applied to transfer data sets in order to adjust normality of the data sets distribution. Among all applied semivariogram models, the J-Bessel semivariogram model was optimal in terms of root mean square error (RMSE) values. The average standard errors were 0.2217, 740.5, and 1.209 for pH, EC, and temperature, respectively.