Application of GGE biplot graphs in multi-environment trials on selection of forest trees

In the studies on selection and population genetics of forest trees that include the analysis of genotype × environment interaction (GE), the use of biplot graphs is relatively rare. This article describes the models and analytic methods useful in the biplot graphs, which enable the analyses of mega-environments, selection of the testing environment, as well as the evaluation of genotype stability. The main method presented in the paper is the GGE biplot method (G – genotype effect, GE – genotype × environment interaction effect). At the same time, other methods have also been referred to, such as, SVD (singular value decomposition), PCA (principal component analysis), linear-bilinear SREG model (sites regression), linear-bilinear GREG model (genotypes regression) and AMMI (additive main effects multiplicative interaction). The potential of biplot method is presented based on the data on growth height of 20 European beech genotypes (Fagus sylvatica L.), generated from real data concerning selection trials and carried out in 5 different environments. The combined ANOVA was performed using fixed-effects, as well as mixed-effects models, and significant interaction GE was shown. The GGE biplot graphs were constructed using PCA. The first principal component (GGE1) explained 54%, and the second (GGE2) explained more than 23% of the total variation. The similarity between environments was evaluated by means of the AEC method, which allowed us to determine one mega-environment that comprised of 4 environments. None of the tested environments represented the ideal one for trial on genotype selection. The GGE biplot graphs enabled: (a) the detection of a stable genotype in terms of tree height (high and low), (b) the genotype evaluation by ranking with respect to the height and genotype stability, (c) determination of an ideal genotype, (d) the comparison of genotypes in 2 chosen environments

Yield and stability analysis of oat genotypes using graphical GGE method

W pracy wykonano analizę plonu rodów owsa oplewionego i nieoplewionego. Dane pochodziły z doświadczeń wstępnych przeprowadzonych w 2008 r. Badano 27 rodów owsa oplewionego i 2 wzorce w 6 miejscowościach oraz 12 rodów owsa nieoplewionego i 2 wzorce w 5 miejscowościach. Do analizy plonu wykorzystano metodę graficzną biplot typu GGE (na efekty GGE składają się efekty główne genotypów G oraz efekty interakcji genotypowo środowiskowej GEI). Na podstawie wykresów biplot typu GGE scharakteryzowano genotypy oraz wskazano te o największym efekcie GGE w każdym środowisku. Spośród rodów owsa nieoplewionego we wszystkich badanych miejscowościach najwyżej plonowały i były dobrze adaptowalne: STH6264, CHD1368, a w przypadku owsa oplewionego: CHD1534, STH149, STH6038, STH12, KREZUS, POB3107. Zbadano stabilność genotypów typu dynamicznego tzn. wskazano genotypy, które nie wykazywały interakcji genotypowo środowiskowej GEI. Najbardziej stabilnymi rodami owsa nieoplewionego były: STH6294, CHD1408, CHD1438, CHD2567, CHD1368, a najmniej stabilnymi: STH108 i STH6315. Wśród rodów owsa oplewionego najbardziej stabilnymi były: CHD1156, CHD3833, STH12, CHD1193, zaś najmniej STH132 i POB3672. Określono genotyp idealny. Wśród rodów owsa nieoplewionego idealnym genotypem był STH6264, a w przypadku rodów owsa oplewionego STH12.Under this study, a yield analysis of covered grain and naked grain oat strains was carried out. The data originated from the preliminary trial experiments accomplished in 2008. There were examined: 27 covered grain oat genotypes and 2 standards in 6 environments, and 12 naked grain oat genotypes and 2 standards in 5 environments. A graphical bi-plot method of GGE type was applied to the yield analysis (the GGE effects comprise a sum of main effects of G genotypes and the effects of GEI genotypic environmental interaction). Based on the GGE bi-plots, the genotypes were characterized and those showing the highest GGE effect in each environment were pointed out. From among the naked grain oat strains in all the environments studied, STH6264 and CHD1368 had the highest yield and were well adaptable, and as for the covered grain oat strains: CHD1534, STH149, STH6038, STH12, KREZUS, POB3107. A dynamic concept of stability was studied, i.e. those oat genotypes were identified, which did not show any genotypic environment interaction. The most stable naked grain oat strains were: STH6294, CHD1408, CHD1438, CHD2567, CHD1368 and the most unstable: STH108 and STH6315. The most stable covered grain oat strains were: CHD1156, CHD3833, STH12, CHD1193 and the most unstable STH132 and POB3672. An ideal genotype was determined. Among the naked grain oat strains, STH6264 was the most ideal genotype, whereas among the covered grain genotypes: STH12

Variability of the selected traits of Picea abies (L.) Karst. cones and seeds depending on their location in the crown

The objective of the study was to determine whether differences exist between the selected characteristics of Norway spruce cones and seeds depending on their location in the crown. The study was performed in two spruce tree stands growing under mountainous conditions (the Beskidy Mountains, southern Poland). In winter 2015, the cones were collected from 60 standing trees located in the two selected seed stands – Ujsoły and Rycerka. From each tree, the cones were collected from three crown zones: top, central and bottom differing in terms of light conditions. Two characteristics of the cones and six characteristics of the seeds were analysed. To determine quantitative and qualitative traits of seeds standard methods for seed testing were applied. Significant differences between the populations were determined for cone weight and fraction of full seeds, weight of 1000 seeds and germination capacity. The crown zones differed significantly in terms of fraction of cones in the parts of the crown and mass of one cone as well as weight of 1000 seeds, germination energy and germination capacity. Moreover, a significant intra−population variation was observed for a majority of the analysed characteristics (fraction of full seeds, weight of one cone, weight of 1000 seeds, germination energy, and germination capacity). However, no statistically significant interaction between population and crown zone was observed. A significantly lower contribution of cones in the bottom zone of the crown may affect the economic viability of the commercial harvest. On the other hand some of the important seeds traits (e.g. weight of 1000 seeds, germination energy and germination capacity) were significantly better in the bottom and central part of the crown than in the top zone. There are no justified circumstances (both qualitative and quantitative) to perform the cone harvest only from the top zone of the crown

Preparation and Some Properties of Carbon Nanotubes

Carbon nanotubes can be grown in the DC electric arc, they grow covered by a turbostratic graphite. The mechanism of the tubes growth is discussed. The magnetic susceptibility of samples, which contained aligned nanotubes exhibits anisotropic oscillatory behavior, which is ascribed to spatial quantization of the energy levels

Combining ability of extra‐early biofortified maize inbreds under Striga

Striga hermonthica (Del.) Benth parasitism, low soil N, and nutritional deficiencies of normal‐endosperm maize (Zea mays L.) threaten maize yield and exacerbate nutritional problems in sub‐Sahara Africa (SSA). This study was conducted (a) to evaluate genetic variation among extra‐early maturing maize hybrids with provitamin A and quality protein characteristics, (b) to investigate gene action governing the inheritance of Striga resistance, grain yield, low N tolerance, and other measured traits under low‐N, high‐N, and Striga‐infested environments, and (c) to identify hybrids with high yield and stability across environments. One hundred and fifty hybrids developed using North Carolina Design II were evaluated with six checks under low‐N, high‐N, and Striga‐infested environments in Nigeria. Mean squares for hybrids were highly significant (P < .01) for grain yield and other traits across environments. Only general combining ability (GCA) for female and/or male mean squares were significant for measured traits under low N. In addition to significant GCA effects for most traits, specific combining ability was significant (P < .05) for Striga emergence count under Striga infestation, and ear height and ears per plant under high N, indicating that additive and nonadditive genetic effects controlled the inheritance of few traits under Striga and high N, whereas additive genetic effect governed the inheritance of the traits under low N. Hybrids TZEEIORQ 55 × TZEEIORQ 26, TZEEIORQ 49 × TZEEIORQ 75, and TZEEIORQ 52 × TZEEIORQ 43 were high yielding and stable across environments and have potential for improving nutrition and maize yields in SSA