GGE Biplot Analysis Research Articles

Stability analysis and genotype X environment interaction of cotton seed and fiber yield of some cotton (Gossypium hirsutum L.) genotypes in multi-environment in Mali

Cotton is a strategically important crop for Mali. Mainly used for export, it represents 50–60 % of the value of the country's exports. Genotype by environment interaction study was carried out to identify the most stable cotton genotype(s) and the most desirable for seed cotton yield and lint yield in 35 villages across six different environments(High Valley of the Niger, New Cotton Growing Zone, North Sudan-Sahel, Old Basin, Southern Extension Zone and Zone Sikasso-Bougouni) in a dispersed block design. Where each village represented a block, giving a total of 35 blocks or replications. A combined analysis of variance showed that yields of seed cotton and fiber were significantly affected by environments (p<0.05), but did not reveal any significant difference between genotypes or genotype-environment interactions. The result of GGE biplot analysis method showed that the polygonal view identified three mega-environments (ME1, ME2 and ME3) with three winning genotypes: BRS 293, NTA B149 and NTA L66, respectively, for seed cotton yield. For fiber yield, the winning genotypes were CG1, NTA L65 and BRS 293, respectively. Given that all the varieties tested have a mean fiber yield within the recommended norms, the two promising varieties NTA B149 and NTA L66 with good seed cotton yields and the standard check BRS 293 can be recommended for extension in the environments to which they have been assigned.

Deciphering G × E Interaction of Photo-insensitive Horsegram [Macrotyloma uniflorum (Lam.) Verdc.] Mutants using AMMI and GGE Biplot Models

Background: Photosensitivity in horsegram restricts horizontal expansion in its cultivable area. Using induced mutagenesis, photo-insensitive mutants of a popular variety PAIYUR 2 were evolved. Methods: The mutants were experimented with across seasons and locations. All the experiments were conducted in the randomized block design with five replications. The AMMI and GGE biplot models were employed to tag the best-yielding and stable genotype(s). Result: The ANOVA indicated significant effects of genotypes (G), environments (E) and their combined genotype × environment interaction (G × E) for all the experimental traits implying a large range of variation. The interaction effect in AMMI has been partitioned into several principal components. Of the six Principal Components (PC), the first PC explained the major variation. It is 60.20% for the number of clusters per plant, 99.60% for days to fifty percent flowering, 92.57% for the number of pods per cluster, 96.63% for the number of pods per plant, 99.66% for days to maturity and 96.34% for yield per hectare. AMMI and GGE biplot analyses helped to identify the best performing and stable photo-insensitive mutants TNAU-HG-DM-001 and TNAU-HG-DM-004 for further exploitation.

Unravelling the dynamics of genotype and environment interactions on chilli (Capsicum annuum L.) yield-related attributes in soilless planting systems

Evaluation of genotypes to identify high-yielding and stable varieties is crucial for chilli production sustainability and food security. These analyses are essential, particularly when the breeding program aims to select lines with great adaptability and stability. Thirty chilli genotypes were evaluated for yield stability under four soilless planting systems viz; fertigation, HydroStock (commercial hydrogel), BioHydrogel (biodegradable hydrogel), and hydroponic to study the influence of genotype by environment interaction. The research used a split-plot randomized complete block design (RCBD) with two cropping cycles and five replications. The GGE biplot analysis was employed to assess the mean versus stability perspective in explaining the variation in genotypic and genotype-by-environment effects on the yield-related attributes for yield per plant, fruit number, fruit length, and width. Stability analysis denoted genotypes G26 and G30 as the most stable for yield per plant, while G16, G22, and G30 were stable for the number of fruits per plant. Among the four planting systems evaluated, HydroStock and BioHydrogel outperformed the others in yield per plant, demonstrating the highest level of informativeness or discrimination. These findings offer critical insights for future crop breeding programs and the optimization of agricultural practices.

Yield related traits in some Persian walnut cultivars: Analysis of genetic and genetic by environment interaction

The most important trait in tree species, including walnut, is the yield. In this study, the effect of genotype and their interaction with year on Nut weight, Kernel weight, Kernel percentage, Fruit set, Nuts number on Scaffold (Canopy) Cross Area (SCA), Nut weight on SCA and Kernel weight on SCA were evaluated on Caspian, Persia, Alvand, and Chaldoran walnut cultivars. The results showed that the effects of year, genotype, and year × genotype interaction on all traits were significant. The results showed that Alvand had the highest number of nuts (41.8 per m2) and nut weight (472.1 g/m2) on (SCA). Heritability (H2b) for kernel weight and kernel percentage, were estimated 0.75 and 0.80, respectively. The lowest value of H2b (0.36) was belong to fruit set. The analyses of genetic and phenotypic correlations between traits showed that, the nut weight had (rg = 0.31, rp = 0.27) a moderate correlation with SCA same as kernel weight (rg = 0.34, rp = 0.29). The GGE biplot analysis explained most of the existing variations (>90%). The genetic effect (PC1) for all traits were higher respect to the genetic × environment interaction (PC2), especially for the kernel percentage (94.4%) and number and weight of nut and kernel on SCA (>90%). The lowest value of the PC1 was related to the fruit set (65.6%), which indicates the trait was more affected by genetic × environment interactions (21.8%). So, this result showed that the yield-related traits in walnut is highly relevant to environment(year in this study) and evaluation of the new cultivars needs careful attention in this case.

GGE biplot analysis of genotype × environment interaction and forage yield stability in grass pea (Lathyrus sativus) genotypes

In crop breeding programs, biplot analysis is a well-known statistical method. This study aimed to survey the genotype ? environment interaction (GEI) on grass pea genotypes in Iran. The experiment was conducted in twelve environments (four separate sites: Gachsaran, Kuhdasht, Mehran, and Shirvanchardavol) over three sequential years (2017, 2018, and 2019) with sixteen grass pea genotypes. The purpose of this research was to utilize the GGE biplot as a tool to identify the superior genotypes of grass peas. The results for the combined analysis of variance, genotypes, and the GEI revealed a significant impact (p < 0.001) on forage yield. Moreover, genotype ? environment interaction responded differently under various climatic conditions. The interaction components evaluated by the biplots revealed the genotypes' predominant effect and the significant genotype ? environment interactions (GEI). The first two principal components (PCs) interpreted up to 93.11% of the total variation in the GGE model (PC1 = 53.30%, PC2 = 37.80%). GGE biplot analysis categorized the studied environments into two mega-groups for forage yield. Genotype G11 (Russia) was superior in terms of mean forage yield (5.362 t/ha). The genotypes that performed best in each environment, were genotypes G11 (Russia) and G8 (Bangladesh-I). Among these genotypes, G11 (Russia) was the highest-yielding genotype in the field. The Kohdasht site was the most discerning and representative test environment for crop yield. The selected genotypes are recommended for breeding programs aimed to improve forage yield in the tested sites or similar agroecological areas.

Investigating the Stability of Bread Wheat Genotypes under Water Deficit Stress Conditions in Different Cold Climate Regions of using AMMI and GGE-Biplot Analysis

Investigating the Stability of Bread Wheat Genotypes under Water Deficit Stress Conditions in Different Cold Climate Regions of using AMMI and GGE-Biplot Analysis

Optimizing Chickpea Yield: GGE Biplot Analysis of Sowing Seasons

Maximizing chickpea production in varying environmental conditions is crucial for ensuring food security and agricultural sustainability. This study addresses this challenge by evaluating sixty-six chickpea genotypes across winter and spring sowing seasons over two years (2016-17 and 2017-18). Utilizing an Augmented Trial Design with three replications and six check varieties, the study reveals significant effects of genotype, sowing season, and their interaction on all examined traits (p≤0.01). Notably, winter sowing season led to a substantial increase in yield, demonstrating up to a 57.1% improvement compared to spring sowing season. The first two components of principal component analysis accounted for 67% of the total variation of chickpea genotypes. In the examined traits, the lowest variation was observed in 100 seed weight, followed by seed yield. Biplot analysis, Pearson’s correlation, and heatmap analysis indicated a positive association of seed yield with pod number of plant and seed number per plant, yet a negative association for plant height, first pod height and 100 seed weight. SMN56, SMN57, and SMN51 genotypes consistently performed well across both sowing and growing seasons for desired traits. Furthermore, genotypes SMN13, SMN20, SMN37, SMN38, and SMN54 excelled during spring sowing season, while Gülümser, İnci, Aydın 92, SMN14, SMN20, and SMN39 were superior in winter. This study not only underscores the advantage of winter sowing for improved yield and other traits but also demonstrates the effectiveness of GGE biplot analysis in genotype selection, offering valuable implications for future chickpea breeding efforts.

Genotype × environment interaction: trade-offs between the agronomic performance and stability of durum (Triticum turgidum) wheat to stem-rust resistance in Kenya.

Stem rust significantly threatens durum wheat production, often resulting in substantial yield losses. To better understand resistance mechanisms and the stability of durum lines in stem rust-prone environments, this study evaluated 49 durum genotypes over three seasons at the Kenya Agricultural and Livestock Research Organization in Njoro. Utilizing 7 × 7 alpha lattice design, we assessed adult-plant resistance, monitored disease progression through final disease score (FDS) and area under the disease progress curve (AUDPC), and evaluated agronomic performance. Statistical analyses revealed significant seasonal and genotypic effects on FDS, AUDPC, spike length, and grain yield (p≤0.01; p≤0.001), with important genotype-by-season interactions (p≤0.05; p≤0.001). Broad-sense heritability for AUDPC was high at 0.91 and moderate at 0.35 for kernels per spike, underscoring the genetic basis of these traits. Notably, we observed negative correlations between disease parameters and agronomic traits, suggesting potential trade-offs. GGE biplot analysis singled out the first season (main season of 2019) as crucial for evaluating stem rust resistance and identified several durum lines, such as G45 and G48, as consistently resistant across all conditions. Furthermore, this analysis highlighted G45, G48, G176 and G189 as the highest yielding and most stable lines. The discovery of these resistant and high-performing genotypes is critical for enhancing durum breeding programs, helping to mitigate the impact of stem rust and improve yield stability.

Demonstration the effectiveness of NDVI with GGE biplot model in determination of high yielding durum wheat (Triticum durum Desf.) genotypes

The use of spectral indices such as normalised difference vegetation index (NDVI) is becoming an important element in the evaluation of physiological traits in cereal crops. Determining the correlation between spectral readings taken at different phenological stages of wheat plants and grain yield (GY) is crucial. This study aimed to demonstrate the effectiveness of NDVI in combination with the GGE biplot model in identifying high yielding durum wheat genotypes. Field experiments were conducted in multiple environments and growth stages to evaluate the relationship between NDVI and grain yield (GY). Twenty-five durum wheat genotypes were tested for two years under rainfed and supplementary irrigation conditions using a split plot experimental design. The results showed significant positive correlations between NDVI and GY, especially at the generative stages under rain-fed conditions. In contrast, NDVI at the grain-filling stage showed a weaker relationship with GY under supplemental irrigation. Genotypes exhibiting high NDVI values up to the stem elongation stage tended to have lower grain yields, emphasizing the importance of considering growth stage dynamics. GGE biplot analysis provided visual information on genotype-trait relationships, helping to identify genotypes with consistent performance in different environments. This study highlights that NDVI can be used to predict yield potential and guide selection in durum wheat breeding programs and the GGE biplot model serves as a valuable tool for genotype evaluation and selection.

AMMI GGE biplot analysis of wheat genotypes under heat stress and heat drought environment

Wheat is the third most important cereal crop of Nepal. Climatic changes have been a major threat on overall production and productivity of wheat in Nepal. With the aim of evaluating twenty elite wheat genotypes under heat stress and heat drought environments, a field experiment was conducted using alpha lattice design at Bhairahawa, Rupandehi, Nepal. The analysis of variance (ANOVA) revealed significant differences in the yield across wheat growing environments (p<0.001). Environment explained 75.11% of the total variation in grain yield. NL 1404 was the most stable followed by NL 1368, and NL 1413. NL 1376, NL 1369. NL 1386 was the best adapted genotypes under heat stress environments whereas NL 1384, Gautam, and BL 4949 were best adapted to heat drought environments. NL 1346 was the best genotype. WWW model explained NL 1346 won under heat drought environment and NL 1384 under heat stress environment. Mean vs. stability model showed NL 1346 was the above yielder and stable genotype. In ranking, NL1179 was concluded to be the ideal genotype. From the study, NL 1368 was found to be the winning genotype under heat drought and heat stress environments. These genotypes should further be evaluated to release as a variety.

AMMI and GGE biplot analysis of yield performance of wheat genotypes under irrigated, heat stress and heat drought environments

Wheat is the third most important cereal crop in Nepal. The impact of global warming is threatening global wheat production and food security. The terminal heat stress reduces the grain quality of wheat. However, the drought is affecting more than 15% of global wheat productivity. To find out the stable and high- yielding wheat genotype the experiment was carried out in Rupandehi, Nepal with twenty genotypes under three different environmental conditions namely heat drought, heat stress and irrigated in an alpha lattice design with two replications in each environment. The AMMI (Additive mean effect multiplicative interaction) biplot analysis shows differences in 20 different genotypes in terms of yield and stability. The analysis of variance model showed the share of GE (genotype and environment) interaction in the variation in grain yield of twenty wheat genotypes. The grain yield of genotype varied significantly with environmental impact (p ). The AMMI stability value (ASV) examined NL1387 as the most stable line. The tested environments were discriminative for genotype and showed negative correlation between them. The GGE biplot analysis was conducted to find out the best performing line under different environments and the stable line in diverse environments. The NL1420 was found stable genotype in all three tested environment. The NL1376 line is most ideal ranking first in the ranking biplot. The mean versus stability model indicated NL1369 and NL1376 as elite genotypes and NL 1404, BL4919 and NL1387 can be recommended as new cultivars.

Yield Stability Analysis of Some Promising Barley Genotypes under Different Environmental Conditions

To mitigate the impact of climate change on barley production, adaptation strategies are crucial including developing and promoting drought-tolerant barley genotypes that can withstand higher temperatures. This study aimed to evaluate twenty different barley genotypes under different conditions, specifically normal, heat stress, and rain-fed conditions during 2019/2020 and 2020/2021 growing seasons. This study was conducted as part of breeding programs aimed at developing agricultural genotypes that can adapt to changing environments. The experiment involved evaluating the performance of the twenty barley genotypes at three different locations: Sakha for the normal condition, New Valley Station for the heat stress condition, and Marsa Matruh for the rain-fed condition. The analysis of variance showed that there were significant differences in grain yield among the different genotypes and locations, as well as the interaction between genotypes and locations. This indicates that the genotypes responded differently to the different locations, and further analysis is needed to determine their stability. The average grain yield ranged from 9.9 to 16.0 arddab fad-1 (Faddan=4200m2) for Giza 135 and Line 5, respectively, across all three locations in both seasons, this shows that there is variation among the genotypes. Considering GGE biplot and stability analyses, Giza 126, Giza 2000, Giza 134, Giza 137, Giza 138, Line1, Line 2, Line 3, Line 4 and Line 5 showed the best performances, suggesting their adaptation to a wide range of environments. This suggests that these genotypes perform well in relation to the linear component of the genotype-by-environment interaction. Overall, this information can be used by plant breeders to select genotypes that are not only high-yielding but also stable across different environments.

Evaluation of garlic genotypes for yield performance and stability using GGE biplot analysis and genotype by environment interaction

AbstractIn Ethiopia, the low productivity of garlic is largely due to a shortage of high yielding and stable varieties. To address this issue, thirteen garlic genotypes were evaluated for yield stability and performance across six different environments in central and southeast Ethiopia. The genotypes were analysed using Additive Main effects and Multiplicative Interaction (AMMI) and genotype-by-environment interaction (G × E) methods. Results showed that the genotypes differed significantly for bulb yield performance and morphological traits, with G-020/03 and G-054/03 producing the highest bulb yield with 25.39% and 18.39% yield advantages over the check variety, Kuriftu, respectively. Furthermore, G-020/03 demonstrated better yield stability across most environments, while G-054/03 exhibited specific adaptability. GGE biplot analysis confirmed that these two genotypes were among the three winning genotypes in terms of yield performance and relative stability. Therefore, G-020/03 and G-054/03 have been selected as candidate varieties for release, potentially improving garlic productivity in Ethiopia.

Genetic Enhancement for Biotic Stress Resistance in Basmati Rice through Marker-Assisted Backcross Breeding.

Pusa Basmati 1509 (PB1509) is one of the major foreign-exchange-earning varieties of Basmati rice; it is semi-dwarf and early maturing with exceptional cooking quality and strong aroma. However, it is highly susceptible to various biotic stresses including bacterial blight and blast. Therefore, bacterial blight resistance genes, namely, xa13 + Xa21 and Xa38, and fungal blast resistance genes Pi9 + Pib and Pita were incorporated into the genetic background of recurrent parent (RP) PB1509 using donor parents, namely, Pusa Basmati 1718 (PB1718), Pusa 1927 (P1927), Pusa 1929 (P1929) and Tetep, respectively. Foreground selection was carried out with respective gene-linked markers, stringent phenotypic selection for recurrent parent phenotype, early generation background selection with Simple sequence repeat (SSR) markers, and background analysis at advanced generations with Rice Pan Genome Array comprising 80K SNPs. This has led to the development of Near isogenic lines (NILs), namely, Pusa 3037, Pusa 3054, Pusa 3060 and Pusa 3066 carrying genes xa13 + Xa21, Xa38, Pi9 + Pib and Pita with genomic similarity of 98.25%, 98.92%, 97.38% and 97.69%, respectively, as compared to the RP. Based on GGE-biplot analysis, Pusa 3037-1-44-3-164-20-249-2 carrying xa13 + Xa21, Pusa 3054-2-47-7-166-24-261-3 carrying Xa38, Pusa 3060-3-55-17-157-4-124-1 carrying Pi9 + Pib, and Pusa 3066-4-56-20-159-8-174-1 carrying Pita were identified to be relatively stable and better-performing individuals in the tested environments. Intercrossing between the best BC3F1s has led to the generation of Pusa 3122 (xa13 + Xa21 + Xa38), Pusa 3124 (Xa38 + Pi9 + Pib) and Pusa 3123 (Pi9 + Pib + Pita) with agronomy, grain and cooking quality parameters at par with PB1509. Cultivation of such improved varieties will help farmers reduce the cost of cultivation with decreased pesticide use and improve productivity with ensured safety to consumers.

Multi-environment screening of Linum germplasm collection for dissecting the potential of bud fly (Dasyneura lini Barnes) resistance and assembling a reference set for efficient utilization in genetic improvement

Linseed or flaxseed (Linum usitatissimum L.) is one of the most ancient industrial crops domesticated for oil and natural fiber. Bud fly (Dasyneura lini Barnes) susceptibility is serious biotic stress affecting its production both quantitatively and qualitatively. It has economic significance particularly in Asian countries, causing substantial crop damage leading to significant yield penalty and deterioration of seed quality. In the present study, a large collection of 2598 linseed germplasm accessions from the Indian National Genebank were screened for bud fly infestation at two natural hot-spot locations in India. The maximum bud fly incidence across all four environments ranged from 47.93% to 90.47%. Based on average bud fly infestation over the environments, a reference set comprising 195 accessions including 46 resistant, 94 moderately resistant and 55 moderately susceptible/susceptible germplasm was developed. Further, a selected subset of 26 accessions having a common reaction against linseed bud fly at both locations was validated for resistance over multi-location-year environments (four environments over two years) through GGE biplot analysis. The GGE biplot revealed that two accessions, G22 (EC0993391) and G23 (IC0633096) were highly resistant (<10% bud fly damage) and most stable over the tested environments against bud fly infestation. Additionally, two accessions namely G15 (IC0498795) and G24 (EC0099001) were considered resistant as they showed consistent performance and low bud fly infestation in comparison to the resistant check (Neela). Moreover, these accessions had a great buffering capacity to perceive environmental signals for developing adaptive capacities in a wide range of environments. The identified germplasm accessions may serve as potential donors for accelerating the resistance breeding programme to impart a long-lasting, broad-spectrum resistance against linseed bud fly.

GGE biplot analysis of biofortification traits in relation to grain yield in landraces of tetraploid wheat (Triticum turgidum ssp. dicoccum)

GGE biplot analysis of biofortification traits in relation to grain yield in landraces of tetraploid wheat (Triticum turgidum ssp. dicoccum)

AMMI an GGE biplot analysis of grain yield for drought-tolerant maize hybrid selection in Inner Mongolia

Due to the ongoing global warming, maize production worldwide is expected to be heavily inflicted by droughts. The grain yield of maize hybrids is an important factor in evaluating their suitability and stability. In this study, we utilized the AMMI model and GGE biplot to analyze grain yield of 20 hybrids from the three tested environments in Inner Mongolia in 2018 and 2019, aiming at selecting drought-tolerant maize hybrids. AMMI variance analysis revealed highly significant difference on main effects for genotype, environment, and their interaction. Furthermore, G11 (DK159) and G15 (JKY3308) exhibited favorable productivity and stability across all three test environments. Moreover, G10 (LH1) emerged as the most stable hybrid according to the AMMI analysis and the GGE biplot. Bayannur demonstrated the highest identification ability among the three tested sites. Our study provides accurate identification for drought-resilient maize hybrids in different rain-fed regions. These findings can contribute to the selection of appropriate hybrids that exhibit productivity, stability, and adaptability in drought-prone conditions.

Yield Adaptability and Stability in Field Pea Genotypes Using AMMI, GGE, and GYT Biplot Analyses

Pea (Pisum sativum L.) is a vital leguminous crop farmed worldwide. Pea plays an essential role in China’s crop rotation system, but the major restrictions to its cultivation are stability and low yield. Breeding for promising cultivars with a significantly high yield will impact the sustainability of pea production. Additionally, diverse environment trials are crucial in determining the best genotype. The new cultivar “Yunwan 52” was developed by hybridization and subsequently evaluated through yield trials among six pea genotypes across 14 environments during the 2016–2018 growing seasons. The results showed that the average yield of “Yunwan 52” for all tested environments was 2.64 t ha−1 compared to the control cultivar (Yunwan 18, 1.83 t ha−1). Analysis of AMMI variance showed significant differences (p &lt; 0.01) between genotypes, environments, and their interaction. Based on the GGE biplot, some genotypes possessed wide and narrow adaptability to environments, such as Yunwan 52 was considered the most stable and ideal gen-otype across all tested environments. GYT biplot analysis also revealed that this realized cultivar was a superior and stable genotype that can be identified visually by combining all characters in breeding programs. Yunwan 52 distinguishes with purple blossoms and seed coat peas. It is possible to infer that the newly released cultivar “Yunwan 52” has outstanding yield performance and wide adaptability to multiple environmental conditions (resilience to abiotic stress). It will contribute to developing nutritional pea genotypes and increase pea production in irrigated areas.

Identification of Stable and Drought-Tolerant Soybean Cultivars using AMMI and GGE Biplot Analysis

Identification of Stable and Drought-Tolerant Soybean Cultivars using AMMI and GGE Biplot Analysis

Multi environmental evaluation for selection of stable and high yielding sugarcane (Saccharum officinarum L.) clones based on AMMI and GGE biplot models

The present study was aimed to evaluate the G x E (genotype x environment) interaction using AMMI and GGE biplot analysis to identify sugarcane (Saccharum officinarum L.) genotypes that combine high yield and stability in multi-location trials. Twelve advanced clones generated in the sugarcane breeding programs along with three standards (Co 86032, CoC 671 and Co 09004) were evaluated for cane and sugar yield using randomized complete block design with three replicates over two plant crops and one ratoon during 2019 to 2021 across two years and locations. Data was collected on yield traits and sugar quality characters. Stability parameters were determined. The data was also investigated using GGE biplot and the AMMI models. The results of combined analyses of variance exhibited significant differences among genotypes at the three test environments and when locations were combined. The GE interaction was also significant for all the traits, indicating inconsistency of performance of the genotypes over the locations and seasons. Pooled data analysis of 15 genotypes indicated the superiority of Co 15017 and CoN 15071 for CCS yield across six environments. Two clones, Co 15017 and CoSnk 15102 recorded higher mean cane yield as compared with three standards across the six experimental conditions. Multivariate (AMMI and GGE biplot) statistical analysis indicated that CoSnk 15102 exhibited higher and more stable commercial cane sugar yield and cane yield per hectare, indicating this genotype’s suitability across the test locations. The results of such multi-environmental trials with the elucidation of GE interaction using AMMI and GGE are of great significance in guiding the selection and recommendation of stable and superior varieties in sugarcane production zones.