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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. [ABSTRACT FROM AUTHOR]

The selection based on multiple traits enhances the crop cultivars merit to farmers. In this regard, 19 breeding lines as well as two commercial cultivars were studied using a randomized complete block design (RCBD) with three replications in three locations during the 2020–2021 growing season. In this study, to identify the association among different traits and to select the best rapeseed lines based on multiple traits, genotype × trait (GT) and genotype × yield × trait (GYT) biplot analyses were used. The results showed that using GYT biplot is more efficient than GT biplot. Based on the GYT biplot and superiority index (SI), the breeding lines G16 and G18 were considered as superior genotypes in combination with the agronomical traits, that is, 1000‐seed weight, number of seeds per pod, number of pods per plant, number of lateral branches, plant height, and pod length with seed yield, which represents a genetic gain in rapeseed breeding program. Based on seed yield combination with phenological traits (early maturity), the breeding line G15 was selected as the best one. Moreover, the line G2 was defined as the superior one in combination of seed yield with pod length. The results indicated that there is a potential for simultaneous genetic improvement of the characteristics (i.e., plant height, number of seeds per pod, early maturity) in rapeseed. Generally, the graphical method of the GYT biplot represented an efficient and practical new way to recognize superior genotypes based on multiple traits in rapeseed breeding programs. [ABSTRACT FROM AUTHOR]

The multi‐environmental trials aid breeders in selecting the best genotypes for specific or general adaptability to different environments before commercial release. This study aimed to assess the stability of 13 new soybean pure lines, along with two controls, in terms of seed yield and important agronomic traits. The assessment was based on a completely randomized block design with three replications across four areas during 2020–2022. Various adaptability methods, including parametric, AMMI, GGE biplot, PCA, and SIIG were employed. The mixed analysis showed that the effects of environment, genotype, and genotype–environment (GE) interaction were significant for most studied traits. The AMMI showed the highest portion of environment (65.89%) in soybean seed yield. Based on all stability parameters, lines 2 and 5 were selected for their average seed yields of 3349 and 3142 kg ha−1, respectively. Additionally, lines 6 and 5 showed the most stability, yielding higher than the average, which were 2992 and 3142 kg ha−1, respectively, according to GGE biplot results. Furthermore, lines 2, 5, and 8 were identified as the ideal genotypes concerning seed yield and other agronomic traits, with high SIIG parameters and yields exceeding the average. Finally, the soybean line 5 was deemed the most suitable due to its higher yield, stability, and early maturity (128‐day growth period). Therefore, line 5 is considered appropriate for its high stability and earliness in various regions of Iran. [ABSTRACT FROM AUTHOR]

Sunflower is one of the most important oilseed plants in the world and its oil has nutritional and high economic value. Selection of high‐yielding hybrids is important in sunflower breeding. In this regard, 11 new hybrids along with four cultivars were evaluated in a randomized complete block design with four replications during the 2018–2020 growing seasons. The phenological and agronomic traits including days to flowering, days to ripening, plant height, stem diameter, head diameter, seed number per head, thousand‐seed weight, oil content, and seed yield were measured. In this study, the methods of genotype × trait (GT) and genotype × yield × trait biplot (GYT) were used to identify interrelationships between different traits and to select the best sunflower hybrids based on multiple traits. According to the results, GYT biplot method was more efficient compared to the GT biplot method. Considering both superiority index (SI) and GYT biplot, the genotypes G8, G11, G5, and G3 were superior in terms of agronomical attributes such as flowering and maturity times, stem and head diameter, plant height, thousand‐seed weight, and seed number per head in close relationship with grain yield. Oil content of the hybrids G8, G11, G5, and G3 was 47.9%, 46.4%, 45.8%, and 46.3%, respectively. The results indicated that there is a potential for simultaneous genetic improvement of the characteristics (i.e., plant height, thousand‐seed weight, seed number per head, early maturity) in sunflower. Overall, the GYT graphical biplot method provides a practical and efficient new approach for the identification of suitable hybrids according to the set of intended characteristics in sunflower improvement under multi‐years or multi‐locations. [ABSTRACT FROM AUTHOR]

Mega‐environment (ME) analysis is analysis of multi‐year, multi‐location crop variety trial data conducted in a target region of a crop to understand the magnitude and nature of genotype‐by‐environment interaction (GE) of the crop in the region. If repeatable GE patterns are identified, then the target region must be divided into subregions or MEs. Breeding and utilizing ME‐specific cultivars will convert the repeatable GE into genotypic main effect (G) within ME, thereby improving heritability (selection reliability) and selection gain and maximize regional and overall productivity. If no repeatable GE is found, then the target region must be treated as a single ME and the GE must be accommodated by testing adequately, that is, at a sufficient number of locations in a sufficient number of years. This paper presents a theoretical framework of ME analysis, describes graphical tools to reveal the which‐won‐where patterns in a genotype‐by‐environment dataset, and demonstrates LG (location‐grouping) biplot analysis for revealing repeatable GE patterns and delineating MEs. The concept of G + GE or GGE, that is, GE relative to G, is emphasized. It is the relative GE that is the basis for ME analysis and breeding for specific adaptation; absolute magnitude of GE has little relevance for these purposes. Breeding ME‐specific oat cultivars in Canada is demonstrated with a real‐world example. Core Ideas: Proper mega‐environment analysis is a prerequisite for effective cultivar development.Mega‐environment delineation must be based on repeatable genotype‐by‐environment interaction patterns.Location‐grouping (LG) biplot is an effective graphical too for mega‐environment analysis.Examples were provided for mega‐environment analysis.An example was provided for breeding mega‐environment‐specific oat cultivars. [ABSTRACT FROM AUTHOR]

Large‐scale genotypic information can be used to increase genetic gain in plant breeding programmes. In this research, we evaluated the following: (i) statistical models that could be useful in selection of superior tobacco genotypes in absence of phenotypic information; (ii) the applicability of genome‐wide selection (GWS) for predicting tobacco hybrid performance, and (iii) correlations between genetic divergence of parental lines and F1 hybrid performance. We crossed 13 inbred lines of flue‐cured Virginia tobacco crossed in a diallel scheme to generate 72 hybrid combinations and evaluated them in two field environments. Genotype by sequencing was used for single nucleotide polymorphism (SNP) marker generation, and prediction model validation was performed with different levels of missing information. Hybrid performance was predicted using only the genotypic and phenotypic information. We found genetic divergence among lines to be uncorrelated with hybrid performance or heterosis. Genotype × environment interaction affects GWS efficiency, however, and an index that incorporates both genotypic and phenotypic information improves selection accuracy. Tobacco hybrid prediction utilizing GWS data can be used as additional information to increase the response to selection. [ABSTRACT FROM AUTHOR]

Drought significantly affects foxtail millet [Setaria italica (L.) Beauv.] yield, and its adverse effects can be mitigated via genetic improvement to generate drought‐tolerant varieties. This study aimed to screen drought‐tolerant materials and genes in foxtail millet germplasm resources to improve the efficiency of breeding for drought tolerance. A field experiment was conducted in a randomized incomplete block design (α‐lattice design) and repeated thrice with 30 foxtail millet genotypes treated under drought stress or normal water supply (control) for 2 yr. The agronomic traits and drought tolerance coefficient (DC) of each genotype were assessed during the entire growth period. The soil water environment and genotype significantly affected the indices. Moreover, the interaction between genotype and soil water environment significantly affected the growth traits of foxtail millet. A genotype plus genotype × environment interaction (GGE) biplot revealed mixed correlations among the DC indices. Various indices had different effects on the drought tolerance of foxtail millet. The drought tolerance performance and evaluation indices of foxtail millet were ranked according to the distance from the "ideal" drought tolerance genotype and index. The drought tolerance was classified into five categories according to the comprehensive evaluation index (D‐value) cluster analysis. Discriminant analysis showed that the plant height and spike weight could be used as indices for identifying drought tolerance identification in foxtail millet. The GGE biplot results were consistent with the results based on the D‐value. Therefore, multiple agronomic traits combined with D‐value as an evaluation parameter can effectively and accurately identify drought‐tolerant foxtail millet. Core ideas: Evaluation of drought tolerance in foxtail millet based on agronomic traits was practical.The GGE biplot accurately visualized drought tolerance in foxtail millet.Plant height and spike weight could serve as drought tolerance indices for foxtail millet. [ABSTRACT FROM AUTHOR]

Many maize (Zea mays L.) breeding programs select genotypes considering just grain yield. However, this strategy may not be efficient due to undesirable associations between grain yield and other traits. The breeder's challenge is to combine good characteristics in one genotype. The aim of this study was to propose a new approach to select maize progenies considering multiple traits. One hundred forty‐one half‐sib progenies were evaluated for 14 agronomic traits in a 12 by 12 triple lattice design in two environments. Four progeny selection strategies were performed to increase yield. Strategy 1, considering just yield; Strategy 2, considering all traits in a selection index; Strategy 3, considering only traits that have effect in yield, based on path analysis; and Strategy 4, similar to Strategy 3, but disregarding any trait showing undesirable correlation with other traits. Strategies 2, 3, and 4 considered Smith and Hazel index. The progenies selected by Strategy 4 showed more balanced predicted gains considering all traits. Its predicted gain in all traits related to yield was higher than the of Strategies 2 and 3, and it led to smaller gains in height and flowering time, which breeders want to decrease its means. Strategy 1 showed the best gains in yield, as expected, but obtained undesirable phenotypes for the other traits. The use of path analysis to delineate the Smith and Hazel index is a very appealing approach for selecting maize progenies and the Strategy 4 was the most efficient at accomplishing balanced gains for multiple traits. [ABSTRACT FROM AUTHOR]

In order to investigate the interaction of genotype × trait and relationships among agronomic traits on 12 maize hybrids, an experiment was conducted in a randomized complete block design (RCBD) with three replicates in four regions of Karaj, Birjand, Shiraz, and Arak. Results of analysis of variance indicated that most of the genotypes were significantly different in terms of agronomic traits. Mean comparison by Duncan's method showed that KSC705 genotype was more favorable than other genotypes in all studied regions. SC604 genotype in Birjand and Karaj regions and KSC707 genotype in Shiraz region have higher rank than other genotypes. Correlation analysis was used to investigate the relationships between traits. In most of the studied regions, traits of number of grains in row and number of rows per ear were positively and significantly correlated with grain width and grain weight with grain yield. Graphical analysis was used to further investigate. Genotypes–trait interaction graph explained 59.27%, 61.22%, 59.17%, and 61.95% of total variance in Karaj, Birjand, Shiraz, and Arak, respectively. Based on the multivariate graph, KSC705, KSC706, and SC647 genotypes were identified as superior genotypes in all studied regions and KSC400 genotype did not show much response to change in traits. Correlation between grain width and number of rows in ear, plant height and grain length, one thousand grain weight and grain thickness, and ear diameter with number of grains in row was positive and significant. The results of classification graph of genotypes also divided the cultivars in to three groups as follows: KSC703, KSC400, and KSC706 genotypes in the first group; DC370, SC604, and SC301 in the second group; and KSC260, KSC704, KSC707, and SC301 in the third group. [ABSTRACT FROM AUTHOR]