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.

Genetic Divergence and GGE Bi-plot Analysis of Multi-environment Trial Data of Barley (Hordeum vulgare L.) to Identify “Ideal” Genotype

The present research revealed the study of genetic divergence and genotype (G) main effect and genotype by the environment (GE) interaction (G × GE) bi-plot analysis for multi-environmental trial data using yield data of three years. Since, genotypes were planted in 2017 in two dates like early and lates own condition hence, there was very slight differences in their yield so both the environments come together as compared to third environment (2018) which for from the two locations of year 2017. The objective of this study was to determine the effects of genotype, environment and their interaction on grain yield and to identify stable barley genotypes. The field experiment comprising of 69 barley genotypes laid out in a Randomized Block Design with three replications during Rabi 2016-2017. The extent of genetic variability, association between yield and yield components, frequency distribution of 25 top best genotypes in response to yield in three different environments, yield stability analysis and genetic diversity was studied. For stability analysis yield data of current year for one location and yield data of two locations/environments of previous year have been used. Field observations were recorded on six important characters days to 50% flowering, days to maturity, effective tillers per plant, plant height with awn, plant height without awn and 1000 grain weight (g). The result of bi-plot analysis using yield data of three years revealed that AXIS1 explained 57.6 per cent variation while AXIS2 was explained 31.07 per cent variation. Since, genotypes were planted in 2017 in two dates like early and late sown condition hence, there was very slight differences in their yield so both the environments come together (Figure1) as compared to third environment (2018) which for from the two locations of year 2017. Our result indicate that line G69 recommended as most stable genotype for yield potential and stability whereas lines G9, G55, G67 and G68 were consider as superior genotypes.

Assessment of Genotype × Environment Interaction of Safflower (Carthamus tinctorius L.) Genotypes by Parametric and Non-Parametric Methods

Genotype-environment interaction is a significant factor for finding and selecting stable and productive varieties in safflower breeding programs. This study was conducted at three locations over two years (2016-2017) to determine the extent of genotype by environment (GE) interaction in seed and oil yield. 20 safflower lines and cultivars were evaluated in terms of stability in 3 environments. Considering the stability and performance, the most suitable genotypes were determined as Remzibey-05 and Genotype-125 in seed yield, Genotype-8 and Genotype-155 in oil yield. In terms of stability and performances of genotypes, the environment of Ikizce 2017 (E4) was prominent. Correlation analysis among parametric and nonparametric features was given only for seed yield. The following stability parameters were calculated: the coefficient of variation (CV), regression constant (ai), regression coefficient (bi), mean deviation squares from regression (S2di), coefficient of determination (Ri2), stability variance (σi2), ecovalance value (Wi), stability index (Pi) and as nonparametric stability measures Si(1) and Si(2) values. This analysis indicated that seed yield was significantly positively correlated only with Pi (P<0.01). CV showed a positively significant correlation with ai. S2di and ri2 had a positive association with Ri2, σi2, Wi, Pi, Si(1), Si(2), and between each other.

Application of AMMI model in zoning of FAO 400-500 maize hybrids

Identification of high yielding and stable genotypes is one of the main goals in all breeding programmes. Estimation of hybrids is often aggravated due to the presence of genotype x environment (GE) interaction. One of the ways to eliminate negative effect of this interaction is the application of reliable statistical models such as AMMI model, which singles out high yielding and stable genotypes that have positive reaction to the improvement of production environments. This research aimed to establish specific maize hybrids interactions in different environments in two years by AMMI analysis. Twelve KWS maize hybrids belonging to FAO 400-500, were examined in two years over eight locations in Vojvodina. The highest yield in both years had the hybrid KWS2 (12.764 kg ha-1). Based on the AMMI1 model, hybrid KWS9 showed the highest stability and adaptability at all locations. According to AMMI2, hybrids KWS1, KWS3 and KWS12, had the highest stability and adaptability, while hybrids KWS2, KWS6, KWS8, KWS9 and KWS10 showed a satisfactory level of stability and it is necessary to pay attention to which locations they have positive interactions in order to be recommended in such regions. Nevertheless, based on the AMMI2, locations Temerin, Kikinda and Zrenjanin, showed similar interaction response, which points out that the number of trial locations could be reduced. Obtained results would contribute to the more precise decision in hybrids recommendation for the certain region, but also in defining further aims in maize breeding.

THE ANALYSIS OF GENOTYPE × ENVIRONMENT INTERACTION USING RAPESEED (BRASSICA NAPUS L.) BY GGE BIPLOT METHOD

This study was aimed to asses seed yield performances of 16 rapeseed genotypes in randomized complete block designs (RCBD) with three replications at four Agricultural Research Stations of cold and mid-cold regions over two years in Iran (2015-2017). GGE biplot analysis indicated that the first two components explained 83% of seed yield variations. Genotype, location and their interaction explained 18%, 52% and 30%of the total GE variation, respectively. In this research, a graphically represented GGE biplot analysis enabled selection of stable and high-yielding genotypes for all investigated locations, as well as genotypes with specific adaptability. The GGE biplot analysis was adequate in explaining GE interaction for seed yield in rapeseed. It can be concluded that genotypes G2, G4 and G13 had the highest mean seed yield and stability in four investigated locations. For specific adaptability, G13 was recommended for Isfahan, Karaj and Kermanshah and G4 for Mashhad.

Stability Analysis for Yield of Advanced Potato Genotypes for Commercial Cultivation in Bangladesh

Five advanced potato genotypes, along with three check varieties were evaluated at six locations in order to find out the stable varieties for commercial cultivation in Bangladesh. Combined analysis exhibited a highly significant variability among the genotypes, locations and GEI. The average tuber yield at 90 days after planting (DAP) of the genotypes ranged from 31.18 t ha-1 in the check BARI Alu-28 to 43.8 t ha-1 in the clone 9.35. The result of AMMI analysis of tuber yield at 90 DAP also showed that the first IPCA1 captured 55.30% of the interaction SS. IPCA2, IPCA3 and IPCA4 explained 29.01, 8.55 and 7.08% of GE interaction SS, respectively. In general, the AMMI2 model contained 99.94% of the treatment SS, while the residual contained 0.06%. In ASV method, the clone 9.125, followed by the check BARI Alu-28 and clone 9.91 were more stable than 9.35. Biplot of IPCA1 and IPCA2 covers 84.3% of GE interaction. Biplot analysis also showed that clone 9.44 and 9.35 and the environment of Gazipur and Jashore had a better result in the GE interaction. Clone 9.44 had specific adaptation with the environment of Gazipur and Jashore, while 9.35 had specific adaptation with Gazipur and Jamalpur. Clone 9.125, 9.91, BARI Alu-28, BARI Alu-8 and clone 9.112 were located toward the center of the biplot and can be considered as stable. Based on the GSI the most desirable genotype for both stability and high tuber yield is the clone 9.125 followed by clones 9.112, 9.91 and 9.35. SAARC J. Agri., 18(1): 73-86 (2020)

Development and Proof-of-Concept Application of Genome-Enabled Selection for Pea Grain Yield under Severe Terminal Drought

Terminal drought is the main stress limiting pea (Pisum sativum L.) grain yield in Mediterranean environments. This study aimed to investigate genotype × environment (GE) interaction patterns, define a genomic selection (GS) model for yield under severe drought based on single nucleotide polymorphism (SNP) markers from genotyping-by-sequencing, and compare GS with phenotypic selection (PS) and marker-assisted selection (MAS). Some 288 lines belonging to three connected RIL populations were evaluated in a managed-stress (MS) environment of Northern Italy, Marchouch (Morocco), and Alger (Algeria). Intra-environment, cross-environment, and cross-population predictive ability were assessed by Ridge Regression best linear unbiased prediction (rrBLUP) and Bayesian Lasso models. GE interaction was particularly large across moderate-stress and severe-stress environments. In proof-of-concept experiments performed in a MS environment, GS models constructed from MS environment and Marchouch data applied to independent material separated top-performing lines from mid- and bottom-performing ones, and produced actual yield gains similar to PS. The latter result would imply somewhat greater GS efficiency when considering same selection costs, in partial agreement with predicted efficiency results. GS, which exploited drought escape and intrinsic drought tolerance, exhibited 18% greater selection efficiency than MAS (albeit with non-significant difference between selections) and moderate to high cross-population predictive ability. GS can be cost-efficient to raise yields under severe drought.

Evaluation of genotype × environment relationship in silage maize genotypes using biplot analysis and stability index

This study was conducted to evaluate the adaptability and stability of silage maize hybrids determing herbage and dry matter yield using Biplot analysis and some stability indexes. The studies were carried out using five registered corn varieties under irrigated conditions for six years (2013-2018) in Çarşamba plain of Samsun province, Turkey. The experimental layout was a Randomized Complete Block Design with four replications. Finlay and Wilkenson's regression and Eberhart and Russel's deviation from regression (S2d) coefficients were used in statistical analysis. Genotype (G) x environment (E) interactions were studied using the additive main effects and multiplicative interaction (AMMI) and G + GE (GGE) biplot models. The combined analysis of variance revealed significant (P<0.01) effects of G, E and G × E interaction on herbage and dry matter yields. The analysis of variance indicated that 62.70% of variation in the herbage yield explained by E, 29.79% by the differences in G and 7.49% by the GE interaction. The analysis of variance indicated that 62.80% of the total variation in the dry matter yield accounted for E, 30.20% of the total variation by G and G × E interaction explained only 7.00% of the total variation in the data. The results of AMMI and GGE biplot models and stability analyses (R2, bi and S2di) revealed that PR31Y43 and Burak hybrids were stable in both herbage yield and dry matter yield.

Genotype by environment interaction for seeds yield in pea (Pisum sativum L.) using additive main effects and multiplicative interaction model

The objective of this study was to evaluate the genotype by environment interaction using the additive main effects and multiplicative interaction model for seeds yield of pea cultivars grown in Poland. Twelve pea (Pisum sativum L.) cultivars: Bohun, Boruta, Cysterski, Ezop, Kavalir, Lasso, Medal, Santana, Tarchalska, Terno, Wenus and Zekon were evaluated in 20 environments (ten locations in 2 years). The experiment was laid out as randomized complete block design with three replicates. Seeds yield ranged from 26.10 dt ha−1 (for Wenus in Radostowo 2011) to 79.73 dt ha−1 (for Lasso in Słupia 2010), with an average of 50.70 dt ha−1. AMMI analyses revealed significant genotype and environmental effects as well as genotype-by-environment interaction with respect to seeds yield. In the analysis of variance, 89.19% of the total seeds yield variation was explained by environment, 1.65% by differences between genotypes, and 8.33% by GE interaction. The cultivar Terno is the highest stability. The cultivar Tarchalska is recommended for further inclusion in the breeding program because its stability and the highest averages of seeds yield.

Multi-environment Manifestation of Heterosis for Morphological and Quality Traits in Maize (Zea mays L.)

An investigation was carried out with 10 parents, their 45 hybrids mated in half-diallel fashion and 4 checks in RBD for estimation of heterosis in two environment (Kharif, 2014 and Rabi, 2014-15) at the Instructional farm, RCA, MPUAT, Udaipur, India. The average productivity of maize in USA is 11.07 MT ha-1 while, in India it is only 3.02 MT ha-1 during 2018-19. We lag far behind USA and are still not able to harness the optimum yield potential in maize that could partly be addressed by evolving hybrid maize cultivar. The present study aims to is identify heterotic hybrids that could yield stably with better grain quality over a range of environments. Environment wise Analysis of variance revealed that mean squares due to genotypes were significant for all the traits in both the environments. Partitioning of total genotypic variance indicated significant contribution of parents for all the traits. Mean squares due to crosses were significant for all the traits under study except for days to 50% tasseling. Mean squares due to parents vs. crosses were significant for all the traits except for days to 75% brown husk. Bartlett test showed homogeneity of error variance only for one trait i.e. grain oil content. The pooled analysis for grain oil content revealed significant differences between the environments. The mean squares due to genotypes including parents, crosses and parents vs. crosses were also significant. Significance of GE interaction for oil content indicates the influence of environments on the expression of this trait. The partitioning of GE interaction into those that by parents×environment and crosses×environment interaction were also significant. Two hybrids in E1, viz., P3×P6 and P2×P4 has shown significant negative economic heterosis for days to 50% tasseling, days to 50% silking and days to 75% brown husk. None of the hybrids has shown significant negative economic heterosis for these traits in E2 environment. The hybrid P3×P5 exhibited maximum positive economic heterosis (6.71%) for grain oil content over the best check HQPM-5 on pooled basis. The hybrid P5×P7 (2.78%) in E1 and P5×P10 (2.58%) in E2 exhibited maximum positive economic heterosis for grain starch content over the best check HQPM-1. The maximum positive economic heterosis for grain protein content was exhibited by hybrid P5×P8 (4.14 %) in E1 and by P3×P5 (2.49%) in E2 over the best check HQPM-5.