ABSTRACT In yield experiments conducted at different environments, assessment of Genotype × Environment interactions for investigated traits is a quite significant issue for both agronomists and breeders. GGE biplot analysis was employed in this study to assess the Genotype × Trait, Environment × Trait and Trait Association × Environment of five different traits (silage yield [SY], stem diameter [SD], green leaf weight ratio &91;GLWR], green stem weight ratio [GSWR], green corn cob ratio [GCCR] and plant height [PH]) of 25 silage maize (Zea mays L.) genotypes grown in six environments. The biplot graphs created in this study to assess Genotype × Trait, Environment × Trait and Environment × Trait correlation interactions were able to explain respectively 86%, 92%, and 83% of total variation of experiments. Current findings revealed that the genotype G18 (Safak), with the greatest silage yield in Genotype Trait biplot (GT biplot) also had the greatest SD; DIY14 (DIYARBAKIR-2014) with the greatest distance from the origin over Environment Trait (ET-biplot) graph was the most distinctive environment; SD with the greatest vector length was the most distinctive trait; DIY14 and DIY15 environments were the best environments for PH, GSWR, SY and SD. It was concluded that GGE biplot method with different perspectives could reliably be used in assessment of silage characteristics of maize genotypes grown in different environments.

Genotype (G), environment (E) and their interaction (GEI) play an important role in the final expression of grain yield and quality attributes. A multi-environment trial in wheat was conducted to evaluate the magnitude of G, E and GEI effects on grain yield and quality of wheat genotypes under the three rainfed locations (hereafter environment) of Central Anatolian Plateau of Turkey, during the 2012-2013 cropping season. Grain yield (GY) and analyses of test weight (TW), protein content (PC), wet gluten content (WGC), grain hardness (GH), thousand kernel weight (TKW) and Zeleny sedimentation volume (ZSV) were determined. Allelic variations of high and low molecular weight glutenin subunits (HMW-GS and LMW-GS) and 1B/1R translocation were determined in all genotypes evaluated. Both HMW-Glu-1, 17+18, 5+10 and LMW-Glu-3 b, b, b corresponded to genotypes possessing medium to good quality attributes. Large variability was found among most of the quality attributes evaluated; wider ranges of quality traits were observed in the environments than among the genotypes. The importance of the growing environment effects on grain quality was proved, suggesting that breeders' quality objectives should be adapted to the targeted environments.

Evaluation of performance stability and high yield is essential for yield trials conducted in different environments. We determined the stability of 10 bread wheat (Triticum aestivum L.) genotypes (5 cultivars and 5 advanced lines) using nonparametric stability measures and compared nonparametric stability statistics across 19 environments in the Central Anatolian Region of Turkey. Experiments were setup in a randomized complete block design with four replications. Nonparametric stability measures revealed that AL-2, Karahan-99 and Altay-2000 were the most stable and well adapted genotypes across the 19 environments. In addition, it was concluded that graphs of mean grain yield (t ha-1) vs. nonparametric measures (Si(¹), Si(²), S(³), Si(6) and σgy) values and kr vs. σr values could enhance visual efficiency of genotype selection based on genotype by environment interaction. Furthermore, high TOP values, Si(³) and Si(6) were associated with high grain yield, but the other nonparametric stability measures were not positively correlated with grain yield and instead characterized a static concept of stability. Choice of genotype for increased grain yield in bread wheat would, therefore, be expected to change yield stability by increasing the values of the Si(³), Si(6) and TOP nonparametric stability statistics.