GGE Biplot Analysis to Explore the Adaption Potential of Italian Common Wheat Genotypes

Given the substantial variation in global wheat yield, insufficient research in cultivar selection for climate change, and the lack of suitable germplasm in sustainable agroecosystems, there is a requisite for soft wheat genotypes, with stable grain yield as well as quality parameters. The present study was aimed at genotype evaluation (GGE biplot for “mean performance versus stability”) not only for yield, but also for technological, phytosanitary and functional quality parameters of 24 Triticum aestivum L. genotypes (eight landraces, old and modern varieties, respectively) within a single organic farm location (Argelato, Emilia-Romagna, Italy) over three consecutive years. Overall, high yield stability was shown for the landraces and old varieties. In particular, the landraces Piave and Gamba di Ferro, as well as the old variety Verna, showed high stability with above-average means for numerous quality parameters of interest. Additionally, relative stability combined with above-average mean for quality parameters was also demonstrated for the high-yielding Gentil Bianco and Guà 113. Aside from Verna, these “unrecognized” resilient genotypes were also shown to meet the requisites for suitable germplasm in sustainable agroecosystems. Future potential utilization of these more stable landraces in addressing climate change would also ultimately facilitate the survival of valuable genetic resources.

AMMI and GGE Biplot Analysis for Stability of Yield in Mid-early Pigeonpea [Cajanus cajan (L.) Millspaugh] Genotypes

Background: The genotype × environment interaction greatly influences the success of breeding and in multi-location trials complicates the identification of superior genotypes for a single location, due to magnitude of genotype by location interaction are often greater than genotype by year interaction. This necessitates genotype evaluation in multi environments trials in the advanced stages of selection. Methods: Nine elite pigeonpea genotypes of mid-early duration were evaluated in six diverse locations in randomized complete block design with three replications during kharif, 2019 to ascertain the stable genotypes, environments discrimination and genotype by environment crossovers using AMMI and GGE biplot stability models. Result: The results in the present investigation revealed that first two principal components explained 73.4% of variation interaction, while, 80.50% in GGE biplot. Both the models identified WRGE-126 (G6) as stable performer with high yield (1733 kg ha-1) and among the locations Tandur (E1) measured as the ideal environment. Whereas, the environments, Adilabad (E3) and Warangal (E4) were observed representative with better discriminating ability.

Genotype plus genotype by-environment interaction biplot and genetic diversity analyses on multi-environment trials data of yield and technological traits of cotton cultivars

ABSTRACT: Understanding the genetic diversity and overcoming genotype-by-environment interaction issues is an essential step in breeding programs that aims to improve the performance of desirable traits. This study estimated genetic diversity and applied genotype + genotype-by-environment (GGE) biplot analyses in cotton genotypes. Twelve genotypes were evaluated for fiber yield, fiber length, fiber strength, and micronaire. Estimation of variance components and genetic parameters was made through restricted maximum likelihood and the prediction of genotypic values was made through best linear unbiased prediction. The modified Tocher and principal component analysis (PCA) methods, were used to quantify genetic diversity among genotypes. GGE biplot was performed to find the best genotypes regarding adaptability and stability. The Tocher technique and PCA allowed for the formation of clusters of similar genotypes based on a multivariate framework. The GGE biplot indicated that the genotypes IMACV 690 and IMA08 WS were highly adaptable and stable for the main traits in cotton. The cross between the genotype IMACV 690 and IMA08 WS is the most recommended to increase the performance of the main traits in cotton crops.

Graphical analysis to determine the stability and adaptability of yield characteristics of dwarf cashew trees

The cashew culture provides jobs and boosts the economy of the Northeast region and is therefore of great socioeconomic importance. In genetic improvement programs, the existence of an interaction between genotypes and environments has been observed, making studies of adaptability and stability essential for effective selection. Thus, the objective was to study the performance of early dwarf cashew clones using the GGE Biplot in three agricultural years (2016–2018). The experimental design was in randomized blocks with 25 treatments (clones) and three replications. The plot consisted of four plants spaced 8 × 8 m apart. The variables evaluated were nut yield throughout the harvest (PRC), average nut weight (PMC), and percentage of nuts pierced (PCF). The clones most characterized as ideotypes were T25, T14, T7, T8, T2, T10, T15, and T22 for PRC; T12, T1, and T16 for PMC; and T5, T24, T21, and T8 for PCF, with coincidence for T8 in PC and PCF. Clones with values above the general average, with emphasis on stability, were T14 and T2 for PRC; T12, T1, T16, T8, and T22 for PMC; and T5 and T22 for PCF. The years with test characteristics, that is, those with average environmental factors for all years, were 2016 for PRC and 2018 for PCF, with no test year for PMC; the every years were more discriminating to PCF, with the exception of 2016 for PRC.

Assessment of repeatability and representativeness of testing sites for provitamin a maize in savanna agro-ecologies of Nigeria

Identification of ideal testing sites for selection of superior maize (Zea mays L.) germplam is vital to the success of a maize breeding programme. Sixteen provitamin A maize genotypes were evaluated at seven locations in savanna agro-ecologies of Nigeria for 3 yr to assess the representativeness, discriminating ability, and repeatability of the testing sites and to identify ideal testing sites for selection of superior maize germplasm. Location, year, and their interaction effects were significant for grain yield and mostmeasured traits while genotype and genotype ´x year interactive effects were significant for grain yield. The genotype main effects plus genotype ´x environment interaction (GGE) biplot analysis revealed PVA SYN-18 F2 as the highest-yielding and most stable genotype across environments. The GGE biplot identified Zaria, Saminaka, and Kaboji as the most discriminating locations. Also, the biplot identified Kaboji, Batsari, Saminaka, and Zaria as the most repeatable locations. Zaria and Saminaka, being among the most discriminating, representative and repeatable locations, were considered as the core testing sites for selection of superior maize genotypes for release and commercialization. The core testing sites identified in this study should facilitate the identification of stable and high-yielding maize germplasm adaptable to the savannas agro-ecologies of Nigeria.

GGE-BIPLOT OF MULTIVARIATE INDEX TO SELECT MAIZE PROGENIES FOR EFFICIENT ASSOCIATION WITH Azospirillum brasiliense1

ABSTRACT Maize is widely cultivated in Brazil, and nitrogen is a major nutrient for its yield. Azospirillum brasiliense bacteria help in plant nutrient supply; however, maize-Azospirillum symbiosis is not very efficient and requires selection of genotypes with a more efficient association. Multivariate indexes facilitate selection using a single value, and GGE-biplot analysis enables the visualization of the genotype-environment interaction from this value. The present study aimed to select progenies that effectively associate with the bacteria and study the efficiency of progeny selection using a multivariate index observed in the GGE-biplot method. The experiments were conducted in two cities in the state of Mato Grosso do Sul. In a simple 16 × 16 lattice, 256 genotypes were evaluated in the presence and absence of diazotrophic bacteria. PH, SL, SD, FI, HGM, SS, and GY were measured for the construction of a selection index. Genotypes exhibited significant genotype–environment interactions for all evaluated traits, allowing their use in the selection index. High-yield genotypes were not those with the highest selection index values. The traits GY, SD, HGM, SS, SL, and PH contributed the most to the construction of the index. The no-till system may have contributed to the weaker response of maize inoculated with Azospirillum brasiliense. Genotype 96 had the highest values of the characteristics used to calculate the GISI, along with the stability between environments.

Environmental Stratification in Trials of Unbalanced Multiyear Soybean (Glycine max (l.) Merril) via the Integration of GGE Biplot Graphs and Networks of Environmental Similarity

Genotype x enviroment (GE) interaction can difficult soybean breeding programs to atieve the aim of obtain more productive cultivars. Enviroment stratification is a way to circunvent this problem. This work aimed to gather GGE Biplot graphs of a network of trials unbalance multiyear soybean via matrices of coincidence and networks of enviroment to optimize environmental stratification. Data from an experimental network of 43 trials was used, these experiments were implanted during the crop seasons of 2011/12, 2012/13, 2013/14 and 2015/16 in Brazil. The GE interaction were statistically significant for all 43 trials. The step by step of our analses was: GGE Biplots graphs were obtained; the enviroment coincidence matrices were calculated; the values of matrices were used for to obtain the networks of environmental similarity. The study demonstrated that by the method was possible to identify, using unbalanced multiyear data, the formation of four mega-environments. Therefore, integrating GGE Biplot graphs and networks of environmental similarity is an efficient method to optimize a soybean program by environment stratification.

AMMI and GGE biplot analysis for yield performance and stability assessment of selected Bambara groundnut (Vigna subterranea L. Verdc.) genotypes under the multi-environmental trails (METs)

The stability and high yielding of Vigna subterranea L. Verdc. genotype is an important factor for long-term development and food security. The effects of G × E interaction on yield stability in 30 Bambara groundnut genotypes in four different Malaysian environments were investigated in this research. The experiment used a randomized complete block design with three replications in each environment. Over multiple harvests, yield component traits such as the total number of pods per plant, fresh pods weight (g), hundred seeds weight (g), and yield per hectare were evaluated in the main and off-season in 2020 and 2021. Stability tests for multivariate stability parameters were performed based on analyses of variance. For all the traits, the pooled analysis of variance revealed highly significant (p < 0.01) variations between genotypes, locations, seasons, and genotypes by environment (G × E interaction). A two-dimensional GGE biplot was generated using the first two principal components (axis 1 and axis 2), which accounted for 94.97% and 3.11% difference in GEI for yield per hectare, respectively. Season and location were found to be the most significant causes of yield heterogeneity, accounting for 31.13% and 14.02% of overall G + E + G × E variation, respectively, according to the combined study of variance. The GGE biplot revealed that the three winning genotypes G1, G3, and G5 appear across environments whereas AMMI model exposed genotypes viz G18, G14, G7, G3, G1, and G5 as best performer. Based on ideal genotype ranking genotype G1 was the best performer, with a high mean yield and high stability in the tested environment. According to the AEC line, genotypes G1 and G3 were extremely stable, while genotypes G2 and G4 were low stable, with a high average yielding per hectare. A GGE and AMMI biplot graphically showed the interrelationships between the tested environment and genotypes, classified genotypes into three categories as well as simplifying visual evaluations, according to this investigation. According to our results, breeding could improve yield production, and the genotypes discovered could be recommended for commercial cultivation.

Genotype by Trait Interaction (GT) in Maize Hybrids on Complete Fertilizer

We investigated the interaction between genotype by trait, and an experiment was conducted at the University of Debrecen. Two maize cultivars, FAO340 and FAO410, were studied in a randomized complete block design with four replications. This experiment was applied to the six fertilization treatments. Fertilizer levels were NPK0 (control) (N:0, P2O5:0, K2O:0), NPK1 (N:30, P2O5:23, K2O:27), NPK2 (N:60, P2O5:46, K2O:54), NPK3 (N:90, P2O5:69, K2O:81), NPK4 (N:120, P2O5:92, K2O:108), and NPK5 (N:150, P2O5:115, K2O:135). The first principal component showed 54.24%, and the second principal component showed 20.75%, which explained the total squares interaction using the AMMI model in the case of the FAO410 hybrid. As regards the FAO340 hybrid, the first principal component showed 58.18%, and the second principal component showed 18.04%, explaining the total squares interaction using the AMMI model in the FAO410 hybrid. In the GGE biplot on FAO410, the first and the second principal components covered 91.20% of the total data in this analysis. Accordingly, the desirable treatment was NPK5, followed by NPK4, NPK2, NPK3, NPK1, and NPK0. NPK4 and NPK5 had the most desirable treatments for the number of seeds per row, chlorophyll, weight of 1000 seeds, and stem diameter in the case of the FAO410 hybrid.