AMMI and GGE biplot analysis of multi environment yield trial of soybeanin North Western Himalayan state Uttarakhand of India

2016 ◽  
Author(s):  
Anuradha Bhartiya ◽  
J. P. Aditya ◽  
Kamendra Singh ◽  
Pushpendra Pushpendra ◽  
J. P. Purwar ◽  
...  
Keyword(s):  
Seed Yield ◽  
Block Design ◽  
Principal Component ◽  
High Yield ◽  
Gge Biplot ◽  
Soybean Genotype ◽  
Genotype By Environment ◽  
Yield Trial ◽  
Soybean Genotypes ◽  
Ideal Genotype

The investigation was carried out to study Genotype × Environment (G×E) interaction for seed yield in 36 soybean genotypes including check PS1092 over 3 diverse environments represented by different altitudes in Uttarakhand. Grain yield performances of soybean genotypes were evaluated during Kharif 2013 season using a randomized complete block design. The AMMI analysis indicated that environment, genotypes and genotype by environment interactions had significantly affected seed yield and accounted for 9.76, 28.97 and 47.55% of the total variation, respectively. GGE biplot clearly displayed interrelationships between test locations as well as genotypes and facilitated visual comparisons based on Principal Component Analysis (PCA). The first two principal components PCI and PCII were used to create a two-dimensional GGE biplot that accounted for 45.68 and 38.88% variations respectively and based on discriminating and representative ability, E2 (Majhera) was most suitable location for selecting generally adapted genotypes. Soybean genotype C1 (PS1539) was identified as ideal genotype with high yield and low G×E interaction i.e. high stability.

scholarly journals Yield Stability of Soybean Genotypes in Tropical Environments based on Genotype and Genotype-by-Environment Biplot

2019 ◽  
Vol 46 (3) ◽  
pp. 231-239 ◽  
Author(s):  
Ayda Krisnawati ◽  
And Mochammad Muchlish Adie
Keyword(s):  
Seed Yield ◽  
Yield Stability ◽  
High Yield ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Ge Interaction ◽  
Tropical Environments ◽  
Genotype Environment Interaction ◽  
Soybean Genotypes ◽  
Ideal Genotype

Genotype × environment interaction is universal phenomenon when different genotypes are tested in a number of environments. The objective of this experiment was to determine the seed yield stability of soybean genotypes. Seven soybean genotypes and two check cultivars were evaluated at eight soybean production centers during the dry season 2015. Stability analysis on seed yield was based on the GGE biplot method. The combined analysis showed that yield and yield components were significantly affected by genotype (G), environments (E), and genotype × environment interaction (GEI), except for number of filled pods. The highest yield was G6 (3.07 ton ha-1), followed by G7 (2.93 ton ha-1). The “which-won-where” polygon mapping resulted two mega-environments. The best genotype for the first mega-environment was G1 (G511H/Anjasmoro//Anjasmoro-2-8) at E5 (Pasuruan2); and the second one was G6 (G511 H/Anj//Anj///Anj////Anjs-6-7) at E1 (Nganjuk), E2 (Mojokerto), E3 (Blitar), E4 (Pasuruan1), E6 (Jembrana), E7 (Tabanan), and E8 (Central Lombok). The G7 (G511 H/Anjasmoro-1-4-2) was closest to ideal genotype as indicated by relatively stable and produced high yield across environments. The analysis of multi-environment trials data using GGE is useful for determining mega-environment analysis and stability of genotype which focusing on overall performance to identify superior genotypes.Keywords: GE interaction, GGE biplot, Glycine max, seed yield

scholarly journals Morpho-physiological characterization of soybean genotypes under subtropical environment

Genetika ◽  
2017 ◽  
Vol 49 (1) ◽  
pp. 297-311
Author(s):  
Gaffar Al-Hadi ◽  
Rafiqul Islam ◽  
Abdul Karim ◽  
Tofazzal Islam
Keyword(s):  
Seed Yield ◽  
Plant Traits ◽  
Cropping Systems ◽  
Principal Component ◽  
Yield Potential ◽  
High Yield ◽  
Oilseed Crop ◽  
Wide Range ◽  
Soybean Genotypes ◽  
Cluster 2

Soybean is a promising oilseed crop in rice-based cropping systems in South and Southeast Asia. In spite of immense scope of its expansion, the crop is not being popular to the farmers because of poor yield of the existing cultivars. Therefore, this study evaluated eighty-soybean genotypes of diverse growth habits with a view to searching genotype(s) of desirable morpho-physiological characters and high yield potential. Sixteen quantitative plant traits were evaluated to classify the genotypes into different groups using various multivariate methods. A wide range of variation was found in almost all qualitative plant traits. The study reveals that plants tend to become taller as the phenological cycle is longer. Seed yield was the product of the number of pods per plant, pod weight and seeds per pod. The first three components of principal component analysis explained 75% of the total variations of the soybean genotypes. Using Dendrogram from cluster analysis, the genotypes were grouped into six clusters. The maximum number of genotypes was concentrated in cluster 5 followed by clusters 4. The phenology, plant height, the number of pods and seed yield were the important discriminating variables in grouping the genotypes. The number of pods per plant displayed the principal role in explaining the maximum variance in the genotypes. The clustering pattern of the genotypes revealed that the genotypes under cluster 2 and cluster 6 were long statures, late maturing and produced higher seed yield. The genotype G00003 under cluster 2 is the best entry giving the highest seed yield. From cluster 6, the genotype G00209 could be the better choice for much better seed yield. The cluster 3 genotypes were comparatively early maturing and gave reasonable yield. It is concluded that the genotypes under clusters 2 and 6 and 3 can be important resources for developing a high yielding variety and sustainability of growing soybean in the subtropical conditions.

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

2022 ◽  
Author(s):  
P. Jagan Mohan Rao ◽  
N. Sandhyakishore ◽  
S. Sandeep ◽  
G. Neelima ◽  
A. Saritha ◽  
...  
Keyword(s):  
Block Design ◽  
High Yield ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Genotype By Environment ◽  
Genotype Environment Interaction ◽  
Single Location ◽  
Randomized Complete Block Design ◽  
Advanced Stages ◽  
Superior 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.

Evaluation of Performance and Yield Stability Analysis Based on AMMI and GGE-Biplot in Promising Pigeonpea [Cajanus cajan (L.) Millspaugh] Genotypes

2020 ◽  
Author(s):  
N. SandhyaKishore ◽  
P. Jagan Mohan Rao ◽  
S. Sandeep ◽  
G. Neelima ◽  
P. Madhukar Rao ◽  
...  
Keyword(s):  
Stability Analysis ◽  
Block Design ◽  
Crop Improvement ◽  
Interaction Model ◽  
Pigeon Pea ◽  
High Yield ◽  
Gge Biplot ◽  
Genotype By Environment ◽  
Wide Range ◽  
Minimum Interaction

Background: Pigeon pea is considered an excellent and affordable source of plant-based protein, essential amino and fatty acids, fibers, minerals and vitamins with consistent source of income and employment to small and marginal farmers and thus holds premier position in the world agriculture. Shifts in rainfall patterns and seasons due to climatic change require the development of varieties with stable and high yield over a wide range of environmental conditions became major objective of crop improvement. Methods: The present study was carried out to ascertain the stable genotypes, environments discrimination and genotype by environment crossovers using different stable models by conducting Multi-location pigeon pea trial in five environments during Kharif, 2018 in Randomized Complete Block Design. Stability analysis for grain yield was performed by deploying the AMMI (Additive Main Effects and Multiplicative Interaction) model and GGE (Genotype and Genotype by Environment) biplot method. The pigeon pea genotype WRG-330 was found superior among all the genotypes over checks over locations, while, WRG-327 exhibited almost minimum interaction with the environments convincing the reliability of the performance. The test environments at Adilabad and Tandur were observed representative with better discriminating ability. Conclusion: It is concluded that there is no large difference between the AMMI and GGE biplot analyses in evaluation of experimental pigeon pea genotypes in different locations and both methods revealed similar results convincing that both methods can be used equally.

scholarly journals Multi-Environmental Evaluation of Protein Content and Yield Stability among Tropical Soybean Genotypes Using GGE Biplot Analysis

Agronomy ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1265
Author(s):  
Tonny Obua ◽  
Julius Pyton Sserumaga ◽  
Bruno Awio ◽  
Fredrick Nganga ◽  
Thomas L. Odong ◽  
...  
Keyword(s):  
Grain Yield ◽  
Protein Content ◽  
Soybean Protein ◽  
Block Design ◽  
Gge Biplot ◽  
Test Environment ◽  
Soybean Genotype ◽  
Environmental Evaluation ◽  
Soybean Genotypes ◽  
The Relationship

The yield and protein performance in a soybean genotype result from its interaction with the prevailing environmental conditions. This makes selecting the best genotypes under varied target production environments more complex. This study’s objectives were to determine protein content and protein stability of 30 elite soybean genotypes in major soybean-growing areas of Uganda, assess the yield performance and stability in soybeans and determine the relationship between the protein content and grain yield in soybeans. The genotypes were planted in a randomized complete block design of three replications for six seasons across eight locations in Uganda. Genotype and genotype-by-environment (GGE) biplot analyses classified the test locations into three mega-environments for soybean protein and grain yields. Genotype NII X GC 20.3 had the highest mean protein content of 43.0%, and BSPS 48A-9-2 and BSPS 48A-28 were superior for the mean grain yield (1207 kg ha−1). Bulindi was the most discriminating and representative test environment for soybean yield. A weak and negative correlation (r = −0.1**, d.f. = 29) was detected between the protein content (%) and yield (kg ha−1). The highest-yielding genotypes BSPS 48A-9-2, BSPS 48A-31, and Nam II × GC 44.2 are recommended for further evaluation under farmers’ production conditions for selection and release as new soybean varieties in Uganda.

scholarly journals Genotype by environment interaction for seed yield per plant in rapeseed using AMMI model

2011 ◽  
Vol 46 (2) ◽  
pp. 174-181 ◽  
Author(s):  
Ana Marjanović-Jeromela ◽  
Nevena Nagl ◽  
Jelica Gvozdanović-Varga ◽  
Nikola Hristov ◽  
Ankica Kondić-Špika ◽  
...  
Keyword(s):  
Seed Yield ◽  
Block Design ◽  
Total Yield ◽  
Interaction Model ◽  
Field Trials ◽  
Genotype By Environment Interaction ◽  
Yield Potential ◽  
High Yield ◽  
Environment Interaction ◽  
Genotype By Environment

The objective of this study was to assess genotype by environment interaction for seed yield per plant in rapeseed cultivars grown in Northern Serbia by the AMMI (additive main effects and multiplicative interaction) model. The study comprised 19 rapeseed genotypes, analyzed in seven years through field trials arranged in a randomized complete block design, with three replicates. Seed yield per plant of the tested cultivars varied from 1.82 to 19.47 g throughout the seven seasons, with an average of 7.41 g. In the variance analysis, 72.49% of the total yield variation was explained by environment, 7.71% by differences between genotypes, and 19.09% by genotype by environment interaction. On the biplot, cultivars with high yield genetic potential had positive correlation with the seasons with optimal growing conditions, while the cultivars with lower yield potential were correlated to the years with unfavorable conditions. Seed yield per plant is highly influenced by environmental factors, which indicates the adaptability of specific genotypes to specific seasons.

scholarly journals Characterization of 56 Soybean Accessions on Yield Components and Maturity-related Traits

2016 ◽  
Vol 21 (1) ◽  
pp. 25
Author(s):  
I Made Tasma ◽  
Puji Lestari ◽  
NFN Reflinur
Keyword(s):  
Cluster Analysis ◽  
Seed Yield ◽  
Yield Components ◽  
Block Design ◽  
Yield Potential ◽  
High Yield ◽  
Early Maturity ◽  
Soybean Genotype ◽  
Pod Number ◽  
High Yield Potential

<p>Peningkatan produktivitas kedelai nasional dapat dilakukan dengan penggunaan varietas produktivitas tinggi dan manipulasi indeks panen menggunakan varietas genjah. Pembentukan varietas di atas memerlukan plasma nutfah dengan potensi hasil tinggi dan berumur genjah. Tujuan dari penelitian ini adalah untuk mengidentifikasi aksesi kedelai yang mempunyai potensi hasil tinggi dan aksesi kedelai berumur genjah. Sebanyak 56 aksesi kedelai terdiri atas varietas elit dan introduksi, aksesi lokal dan galur-galur persilangan ditanam di Kebun Percobaan Cikeumeuh (250 m dpl) dan Pacet (1.200 m dpl) menggunakan Rancangan Acak Kelompok dengan tiga ulangan. Pengamatan dilakukan terhadap karakter morfologi, komponen hasil dan karakter reproduktif yang meliputi umur berbunga (fase R1, R3, R7, dan R8). Hasil penelitian menunjukkan 28 aksesi (50%) diuji di Cikeumeuh dan 43 aksesi (76,79%) diuji di Pacet menunjukkan jumlah polong/tanaman lebih dari 50. Sekitar 35,71% aksesi di Cikeumeuh dan 41,07% aksesi di Pacet menghasilkan biji/tanaman lebih dari 10 g/tanaman. Aksesi kedelai yang menunjukkan komponen hasil tinggi hanya di KP Cikeumeuh adalah B2981, B3517, dan B3628. Aksesi kedelai yang menunjukkan komponen hasil tinggi hanya di KP Pacet adalah B4441, B3628, B382, B4334, dan B3414. Aksesi yang menunjukkan komponen hasil tinggi di kedua lokasi (Cikeumeuh dan Pacet) adalah B3417. Aksesi B3417 diklasifikasikan sebagai aksesi dengan adaptasi luas karena berkeragaan komponen hasil tinggi di dataran rendah dan di dataran tinggi. Aksesi dengan umur panen genjah ditunjukkan oleh B2973 (74 hari setelah tanam, hst) yang tidak berbeda nyata dengan aksesi B1430 (75 hst), B3611 (76 hst), B4433 (77 hst), dan B4439 (80 hst) berdasarkan uji DMRT (p&lt;0,05). Berdasarkan karakter morfologi, sebagian besar plasma nutfah kedelai terbagi dalam tiga kelompok termasuk landraces tanpa mempertimbangkan asal daerahnya. Analisis klaster berdasarkan karakter agronomi mendukung analisis DMRT bahwa aksesi kedelai dengan hasil biji tinggi dan umur genjah dapat dibedakan. Aksesi kedelai dengan jumlah polong banyak, hasil biji tinggi, umur genjah potensial digunakan dalam program pemuliaan kedelai produktivitas tinggi dengan umur genjah.</p><p> </p><p><strong>Abstrak</strong></p><p>One effort to improve soybean production in Indonesia is by using high yielding and manipulating harvest index by using early maturing varieties. Such variety development requires the availability of soybean germplasm with high yield potential and early maturity. The objective of this study was to identify soybean genotypes showing high yield potential and early maturity. A total of 56 soybean accessions consisting of elite and introducing varieties, landraces, and breeding lines were characterized in the field with different altitudes i.e. Cikeumeuh (250 m above sea level) and Pacet (1.200 m asl). The experiments were arranged in a randomized block design using three replications. Characters observed were morphological chracters, yield components and maturity-related traits (days to R1, R3, R7, and R8). Results showed that 28 accessions (50%) tested at Cikeumeuh and 43 accessions (76.79%) tested at Pacet demonstrated pod number/plant more than 50. About 35.71% at Cikeumeuh and 41.07% at Pacet showed seed yield more than 10 g/plant. Soybean accessions demonstrating high yield components only at Cikeumeuh were B2981, B3517, and B3628. Soybean accessions demonstrating high yield components only at Pacet were B4441, B3628, B382, B4334, and B3414. The accession demonstrating high yield component performance at both locations (Cikeumeuh and Pacet) was shown by B3417. Accession B3417 is then classified as a broad adaptating soybean genotype. The early pod maturing accession was demonstrated by B2973 (74 dap) that was not significantly different from accessions B1430 (75 dap), B3611 (76 dap), B4433 (77 dap), and B4439 (80 dap) based on DMRT at p = 0.05. Three distinct clades were generated based on morpho-agronomical variables on both locations (Cikeumeuh dan Pacet). Cluster analysis of agronomical characters was able to distinguish accessions with high yield components in either one or both locations (B3417, B3628, and B2981), and accessions with early maturiy and least pod number (B4439 and B4433). Cluster analysis results were in well-agreement with the results based on DMRT. Soybean accessions having high pod number, high seed yield and early in maturity are potentially used for developing high yielding soybean varieties with early in maturity.</p>

Responses of soybean genotypes to intercropping with maize in the Southern Guinea Savanna, Nigeria

2004 ◽  
Vol 52 (2) ◽  
pp. 157-163
Author(s):  
C. U. Egbo ◽  
M. A. Adagba ◽  
D. K. Adedzwa
Keyword(s):  
Seed Yield ◽  
Block Design ◽  
Field Trials ◽  
Yield Response ◽  
Ecological Zone ◽  
Land Equivalent Ratio ◽  
Guinea Savanna ◽  
Days To Maturity ◽  
Soybean Genotypes ◽  
Location Interaction

Field trials were conducted in the wet seasons of 1997 and 1998 at Makurdi, Otukpo and Yandev in the Southern Guinea Savanna ecological zone of Nigeria to study the responses of ten soybean genotypes to intercropping. The experiment was laid out in a randomised complete block design. The genotypes TGX 1807-19F, NCRI-Soy2, Cameroon Late and TGX 1485-1D had the highest grain yield. All the Land Equivalent Ratio (LER) values were higher than unity, indicating that there is great advantage in intercropping maize with soybean. The yield of soybean was positively correlated with the days to 50% flowering, days to maturity, plant height, pods/plant and leaf area, indicating that an improvement in any of these traits will be reflected in an increase in seed yield. There was a significant genotype × yield × location interaction for all traits. This suggests that none of these factors acted independently. Similarly, the genotype × location interaction was more important than the genotype × year interaction for seed yield, indicating that the yield response of the ten soybean genotypes varied across locations rather than across years. Therefore, using more testing sites for evaluation may be more important than the number of years.

Canola seed yield and phenological responses to plant density

2016 ◽  
Vol 96 (1) ◽  
pp. 151-159 ◽  
Author(s):  
Gan Yantai ◽  
K. Neil Harker ◽  
H. Randy Kutcher ◽  
Robert H. Gulden ◽  
Byron Irvine ◽  
...  
Keyword(s):  
Seed Yield ◽  
Plant Density ◽  
Block Design ◽  
Positive Association ◽  
High Yield ◽  
Western Canada ◽  
Flowering Period ◽  
Canola Seed ◽  
Randomized Complete Block Design ◽  
Plant Densities

Optimal plant density is required to improve plant phenological traits and maximize seed yield in field crops. In this study, we determined the effect of plant density on duration of flowering, post-flowering phase, and seed yield of canola in diverse environments. The field study was conducted at 16 site-years across the major canola growing area of western Canada from 2010 to 2012. The cultivar InVigor® 5440, a glufosinate-resistant hybrid, was grown at five plant densities (20, 40, 60, 80, and 100 plants m−2) in a randomized complete block design with four replicates. Canola seed yield had a linear relationship with plant density at 8 of the 16 site-years, a quadratic relationship at 4 site-years, and there was no correlation between the two variables in the remaining 4 site-years. At site-years with low to medium productivity, canola seed yield increased by 10.2 to 14.7 kg ha−1 for every additional plant per square metre. Averaged across the 16 diverse environments, canola plants spent an average of 22% of their life cycle flowering and another 27% of the time filling seed post-flowering. Canola seed yield had a negative association with duration of flowering and a positive association with the days post-flowering but was not associated with number of days to maturity. The post-flowering period was 12.7, 14.7, and 12.6 d (or 55, 68, and 58%) longer in high-yield experiments than in low-yield experiments in 2010, 2011, and 2012, respectively. We conclude that optimization of plant density for canola seed yield varies with environment and that a longer post-flowering period is critical for increasing canola yield in western Canada.

scholarly journals Grouping sunflower genotypes for yield, oil content, and reaction to Alternaria leaf spot using GGE biplot

2015 ◽  
Vol 50 (8) ◽  
pp. 649-657 ◽  
Author(s):  
Regina Maria Villas Bôas de Campos Leite ◽  
Maria Cristina Neves de Oliveira
Keyword(s):  
Principal Component Analysis ◽  
Disease Severity ◽  
Principal Components ◽  
Leaf Spot ◽  
Oil Content ◽  
Principal Component ◽  
Component Analysis ◽  
High Yield ◽  
Gge Biplot ◽  
Alternaria Leaf Spot

Abstract:The objective of this work was to evaluate the suitability of the multivariate method of principal component analysis (PCA) using the GGE biplot software for grouping sunflower genotypes for their reaction to Alternaria leaf spot disease (Alternariaster helianthi), and for their yield and oil content. Sixty-nine genotypes were evaluated for disease severity in the field, at the R3 growth stage, in seven growing seasons, in Londrina, in the state of Paraná, Brazil, using a diagrammatic scale developed for this disease. Yield and oil content were also evaluated. Data were standardized using the software Statistica, and GGE biplot was used for PCA and graphical display of data. The first two principal components explained 77.9% of the total variation. According to the polygonal biplot using the first two principal components and three response variables, the genotypes were divided into seven sectors. Genotypes located on sectors 1 and 2 showed high yield and high oil content, respectively, and those located on sector 7 showed tolerance to the disease and high yield, despite the high disease severity. The principal component analysis using GGE biplot is an efficient method for grouping sunflower genotypes based on the studied variables.

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