We present a molecular dynamics simulation of chemical vapor deposition of graphene. Single layer graphene growth on a Ni (100) facet was studied at different substrate temperatures, C flow rates, and C flow energies. Results show that a single layer graphene film grows through a combined deposition mechanism on a Ni substrate, rather than by surface segregation. These simulations suggest that high quality graphene deposition is theoretically possible on Ni (100) facet under high flux energy.

In this study, 12 maize hybrids were planted and evaluated to determine the effect of genotype and genotype-environment interaction (GEI) base GGE (genotype plus genotype-by-environment) using a Graphical biplot technique in four research stations (Arak, Birjand, Shiraz and Karaj) within two years using a Randomized Complete Blocks Design (RCBD). The combined analysis of variance showed that the effects of the environment, genotype and genotype-environment interaction (GEI) were significant in the one percent probability level. GGE biplot results indicated that the first and second principal components (PC1 and PC2) explained more than 83% of the grain performance variation. Simultaneous study of grain performance and hybrid stability using the biplot of average environment coordinates showed that the KSC705 genotype had the highest yield and stability. Polygon view divided the studied areas into two mega-environments (MEs) and identified the best genotypes in each mega-environment (ME). In the first mega-environment (ME1), the Karaj and Shiraz with KSC706 and KSC400 genotypes were detected, and were the best; and in the second mega-environment (ME2), Arak and Birjand with KSC704 and KSC707 genotypes performed better. The biplot graph for the correlation between the genotypes categorized the studied hybrids into four groups positively related to each other based on the angles between vectors. The KSC704 and KSC707 genotypes were desirable in the yield in Shiraz and Karaj and KSC706 were in Arak and Birjand. Additionally, Arak-Birjand, Karaj-Shiraz showed a positive and significant correlation. Birjand and Karaj had most genotype interaction with each other.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

To study the interaction of genotype and environment in canola crop, a study was carried out in 2010. Ten genotypes (Modena, Okapi, Hyola 401, Licord, Opera, Zarfam, RGS 003, SLM046, Sarigol, and Hyola 308) of canola were studied under normal conditions of irrigation in four locations (Karaj, Birjand, Shiraz, and Kashmar) using randomized complete block design with three replications. Using GGE biplot method, grain yield was investigated for each cultivar. According to analysis of variance, there was a very significant difference among the regions. According to the yield average and genotype stability, Licord, Hyola 308, Modena and Zarfam were the best cultivars. The graphs obtained from GGE biplot software indicated that Hyola 401, Opera, and Sarigol were better than the rest of the genotypes based on stability and yield performance. At location Shiraz, none of the genotype had appropriate stability or yield. Four locations were divided into three mega-environments including Karaj, Kashmar (first mega-environment), Birjand (second mega-environment), and Shiraz (third mega-environment). Moreover, Karaj was recognized as the best region of the classification and ranking of genotypes. The study indicated that the highest and lowest genotypic reaction rates belonged to Licord and SLM 046 cultivars, respectively.

Genotype-environment interaction for plant breeders has been important as it is a complex issue in breeding for high yield varieties and releasing new genotypes. In order to assess adaptability and stability of sunflower varieties in different climate conditions, twelve cultivars were investigated in Karaj, Shiraz, Birjand, Kashmar and Arak in randomized complete block designs with three replications. To study genotype by environment interaction, additive main effects and multiplicative interaction (AMMI) model was used. Analysis of additive main effects (analysis of variance) and multiplicative interaction effects (principal components analysis) revealed that the effect of environment at 1% probability level and effect of genotypeenvironment interaction at 5% probability level were significant and tow first interaction principal components explained 84% of the interaction sum squares. Biplot of first interaction principle and mean yield revealed that Progress genotype had adequate yield and lowest value for first principle component. Therefore this genotype selected as a high yield and stable genotype; Record and Gabur genotypes were stable and high yield following Progress genotype. AMMI2 graph indicated that Progress, Master and Zargol in Arak and Karaj, Favorit, Record and Azargol in Shiraz, Gabur and SHF81-90 in Kashmar and Armaverski, Lakomka, Zaria and Sor in Birjand had specific adaptability. Among all genotypes, Zaria, Lakomka, SHF81-90, Gabur and Zargol had the highest general adaptability.

GGE biplot technique is one of the appropriate methods for investigating the genotype–environment interaction. An experiment was conducted to examine and evaluate the stability and adaptability of grain yield of 12 sunflower genotypes using the randomized complete block design (RCBD) with three replications in five regions including Karaj, Birjand, Firooz‐Abad, Kashmar, and Arak within two agricultural years. Analysis of variance indicated that the effect of location, year, location × year, genotype, and genotype × location was significant at 1% probability level. Results of biplot analysis showed that the first and second principle components accounted 50.6% and 22.8%, respectively, and in total 73.4% of grain yield variance. In this study, genotype, location, year, year × location, genotype × location, genotype × year, and genotype × year × location explained 2.75%, 17.36%, 5.47%, 17%, 10.8%, 1.04%, and 7.48% of total variance, respectively. Investigating the polygon view led to the identification of three top genotypes and also three mega‐environment. The first mega‐environment included Karaj, Birjand, and Kashmar. The second was Arak, and the third was Firooz‐Abad. To study the kernel yield and stability of genotypes simultaneously, average coordinate view of environments was used and it was determined that genotype Zaria with the highest grain yield has high yield stability also. Ranking the cultivars based on the ideal genotype introduced the genotype Zaria as the best genotype. The highest grain yield belonged to Zaria cultivar at 3.34 t/ha followed by Favorite with 3.23 t/ha. Results obtained from ranking the environments based on the ideal environment introduced Kashmar and Birjand regions as the best environments. Examining the biplot figure for testing environments correlation confirms the positive correlation among Karaj, Birjand, and Kashmar. Correlation between Karaj with Arak, Karaj with Firooz‐Abad, and Arak with Firooz‐Abad was negative. Arak and Firooz‐Abad were highly discriminating and representative and would be used to identification of superior genotypes.

The present study investigated the stability and adaptability of maize (Zea mays L.) hybrids. In this study, 12 maize hybrids were planted and examined considering the grain yield. The experiment was arranged in a randomized complete block design (RCBD) with three replications in four research stations in Iran during two crop years. The combined analysis of variance showed that genotype-environment interactions were significant at one percent probability level. The grain yield can stabilize, and hybrids with specific adaptability are recommended to each environment. Hybrids with specific adaptability can be recommended to all types of the environment. Means comparison yield of the genotypes identified DC370 as a high-yield genotype. Regarding AMMI analysis, genotype × environment interactions (GEIs) and two first components were found significant. The SC647 genotype was identified as the most stable genotype. Regarding the stability parameters, SC647 and KSC705 genotypes were selected as the most stable genotypes. From AMMI1 and AMMI2 graphs, the SC647 genotype was identified as the most stable genotype compared with other hybrids.

Stem rust is one of the most important diseases, threatening global wheat production. Identifying genomic regions associated with resistance to stem rust at the seedling stage may contribute wheat breeders to introduce durably resistant varieties. Genome‐wide association study (GWAS) approach was applied to detect stem rust (Sr) resistance genes/QTLs in a set of 282 Iranian bread wheat varieties and landraces. Germplasms evaluated for infection type and latent period in four races of Puccinia graminis f. sp. tritici (Pgt). A total of 3 QTLs for infection type (R2 values from 9.54% to 10.76%) and 4 QTLs for the latent period (R2 values from 8.97% to 12.24%) of studied Pgt races were identified in the original dataset. However, using the imputed SNPs dataset, the number of QTLs for infection type increased to 10 QTLs (R2 values from 8.12% to 11.19%), and for the latent period increased to 44 QTLs (R2 values from 9.47% to 26.70%). According to the results, the Iranian wheat germplasms are a valuable source of resistance to stem rust which can be used in wheat breeding programs. Furthermore, new information for the selection of resistant genotypes against the disease through improving marker‐assisted selection efficiency has been suggested.