The use of an AMMI model and its parameters to analyse yield stability in multi-environment trials

2008 ◽  
Vol 146 (5) ◽  
pp. 571-581 ◽  
Author(s):  
N. SABAGHNIA ◽  
S. H. SABAGHPOUR ◽  
H. DEHGHANI
Keyword(s):  
Stability Analysis ◽  
Crop Improvement ◽  
Principal Component ◽  
Environmental Variance ◽  
Combined Analysis ◽  
Genotype By Environment ◽  
Breeding Programmes ◽  
Ammi Model ◽  
Index Measure ◽  
Stability Statistics

SUMMARYGenotype by environment (G×E) interaction effects are of special interest for breeding programmes to identify adaptation targets and test locations. Their assessment by additive main effect and multiplicative interaction (AMMI) model analysis is currently defined for this situation. A combined analysis of two former parametric measures and seven AMMI stability statistics was undertaken to assess G×E interactions and stability analysis to identify stable genotypes of 11 lentil genotypes across 20 environments. G×E interaction introduces inconsistency in the relative rating of genotypes across environments and plays a key role in formulating strategies for crop improvement. The combined analysis of variance for environments (E), genotypes (G) and G×E interaction was highly significant (P<0·01), suggesting differential responses of the genotypes and the need for stability analysis. The parametric stability measures of environmental variance showed that genotype ILL 6037 was the most stable genotype, whereas the priority index measure indicated genotype FLIP 82-1L to be the most stable genotype. The first seven principal component (PC) axes (PC1–PC7) were significant (P<0·01), but the first two PC axes cumulatively accounted for 71% of the total G×E interaction. In contrast, the AMMI stability statistics suggested different genotypes to be the most stable. Most of the AMMI stability statistics showed biological stability, but the SIPCF statistics of AMMI model had agronomical concept stability. The AMMI stability value (ASV) identified genotype FLIP 92-12L as a more stable genotype, which also had high mean performance. Such an outcome could be regularly employed in the future to delineate predictive, more rigorous recommendation strategies as well as to help define stability concepts for recommendations for lentil and other crops in the Middle East and other areas of the world.

scholarly journals AMMISOFT for AMMI Analysis with Best Practices

2019 ◽  
Author(s):  
Hugh G. Gauch ◽  
David R. Moran
Keyword(s):  
Best Practices ◽  
Open Source Software ◽  
Crop Improvement ◽  
Genotype By Environment ◽  
Ammi Analysis ◽  
Ammi Model ◽  
Model Diagnosis ◽  
Main Effects ◽  
Free Open Source ◽  
Yield Trials

ABSTRACTThe Additive Main effects and Multiplicative Interaction (AMMI) model has been used extensively for analysis of multi-environment yield trials for two main purposes: understanding complex genotype-by-environment interactions and increasing accuracy. A 2013 paper in Crop Science presented a protocol for AMMI analysis with best practices, which has four steps: (i) analysis of variance, (ii) model diagnosis, (iii) mega-environment delineation, and (iv) agricultural recommendations. This preprint announces free open-source software, called AMMISOFT, which makes it easy to implement this protocol and thereby to accelerate crop improvement.

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 Stability of Expression and Concentration of Ascorbic Acid in North American Potato Germplasm

HortScience ◽  
2004 ◽  
Vol 39 (1) ◽  
pp. 156-160 ◽  
Author(s):  
Stephen L. Love ◽  
Thomas Salaiz ◽  
Bahman Shafii ◽  
William J. Price ◽  
Alvin R. Mosley ◽  
...  
Keyword(s):  
Ascorbic Acid ◽  
North American ◽  
Principal Component ◽  
Environment Interaction ◽  
Interaction Patterns ◽  
Potato Germplasm ◽  
Plot Analysis ◽  
Essential Nutrient ◽  
Ammi Model ◽  
Stability Statistics

Ascorbic acid (vitamin C) is an essential nutrient in the human diet and potatoes are a valuable source. As a first step in breeding for potatoes (Solanum tuberosum L.) with higher levels of ascorbic acid, 75 clones from 12 North American potato-breeding programs were evaluated for concentration, and 10 of those for stability of expression. Trials were grown in Idaho, Oregon, and Washington in 1999 and 2000, tubers sampled, and ascorbic acid quantified. There were significant differences among clones and clone by environment interaction was also significant. Concentration of ascorbic acid of the clones was continuously distributed over a range of 11.5 to 29.8 mg/100 g. A subgroup of 10 clones was analyzed using an additive main effects and multiplicative interaction (AMMI) model, to diagnose interaction patterns and measure clone stability. The first two principal component axes accounted for over 80% of the variability. Bi-plot analysis showed `Ranger Russet' to be highly unstable across the environments tested. A plot of Tai's stability statistics found six of the 10 clones to be stable for ascorbic acid expression. Appropriate evaluation methods for ascorbic acid concentration must involve multi-year testing.

Phenotypic Stability Analysis of Oil Yield in Sesame (Sesamum indicum L.) Verities across the Awash Valleys in Ethiopia

2015 ◽  
Vol 5 (2) ◽  
pp. 650-657
Author(s):  
Mohammed Abate Dawud ◽  
Firew Mekbib Alemu
Keyword(s):  
Stability Analysis ◽  
High Performance ◽  
Block Design ◽  
Principal Component ◽  
Oil Yield ◽  
Yield Potential ◽  
Ammi Model ◽  
Testing Environments ◽  
High Yielding Varieties ◽  
Sesame Genotypes

A study was conducted to estimate the nature and magnitude of G x E Interaction (GEI) for oil yield in sesame varieties and to identify stable and promising varieties for general and specific adaptations. The experiment was carried out at three locations across the areas of the Awash Valley in Ethiopia; namely Assaita, Melkassa and Werer over two seasons during the 2011 cropping and 2012 off seasons. Ten improved sesame varieties were planted in a randomized complete block design (RCBD) replicated trice in each location and year. Analysis of variance using AMMI model revealed significant differences (P<0.01) for genotype, environment, GEI and interaction principal component (IPCA1), suggesting differential response of varieties across testing environments and the need for stability analysis. Stability analysis using Biplot graph and AMMI stability value were done to further shed light on the GEI of oil yield. The study revealed that the environment Wr1 (Werer season-I) had relatively little interaction effects with above average mean oil yield per environment. Hence, it can be recommended as ideal environment for growing the present set of sesame genotypes for breeding programme. Ranking of genotypes based on the different stability indices identified the varieties Adi and Serkamo to be the most stable genotypes across all environments. Therefore, these varieties can be recommended as promising cultivars for oil yield of sesame across diverse agro-ecologies of the Awash Valley to exploit their yield potential. On the other hand, the two high yielding varieties Abasena and Tate were found to be highly interactive and they are recommended for cultivation under favorable environmental conditions for oil yield. Moreover, the study indicated that high performance of genotypes for oil yield recorded in season two (2012). Hence, the off season generally is suggested as the best environment for oil yield of sesame across the areas of the Awash Valley. In this study, AMMI analysis with two IPCA was the best predictive model to reveal the maximum GEI for oil yield in sesame.

Using AMMI approach to delineate genotype by environment interaction and stability of barley (Hordeum vulgare L.) genotypes under northern Indian shivalik hill conditions

2020 ◽  
Vol 80 (03) ◽  
Author(s):  
Om Prakash Yadav ◽  
A. K. Razdan ◽  
Bupesh Kumar ◽  
Praveen Singh ◽  
Anjani K. Singh
Keyword(s):  
Hordeum Vulgare ◽  
Grain Yield ◽  
Principal Component ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Hordeum Vulgare L ◽  
Genotype By Environment ◽  
Biplot Analysis ◽  
Principal Component Axis ◽  
Ammi Analysis

Genotype by environment interaction (GEI) of 18 barley varieties was assessed during two successive rabi crop seasons so as to identify high yielding and stable barley varieties. AMMI analysis showed that genotypes (G), environment (E) and GEI accounted for 1672.35, 78.25 and 20.51 of total variance, respectively. Partitioning of sum of squares due to GEI revealed significance of interaction principal component axis IPCA1 only On the basis of AMMI biplot analysis DWRB 137 (41.03qha–1), RD 2715 (32.54qha–1), BH 902 (37.53qha–1) and RD 2907 (33.29qha–1) exhibited grain yield superiority of 64.45, 30.42, 50.42 and 33.42 per cent, respectively over farmers’ recycled variety (24.43qha–1).

scholarly journals Tackling G × E × M interactions to close on-farm yield-gaps: creating novel pathways for crop improvement by predicting contributions of genetics and management to crop productivity

2021 ◽  
Author(s):  
Mark Cooper ◽  
Kai P. Voss-Fels ◽  
Carlos D. Messina ◽  
Tom Tang ◽  
Graeme L. Hammer
Keyword(s):  
Climate Change ◽  
Crop Improvement ◽  
Target Population ◽  
Crop Productivity ◽  
Climate Change Scenarios ◽  
Global Food Security ◽  
Genotype By Environment ◽  
Improvement Strategies ◽  
Yield Gaps ◽  
On Farm

Abstract Key message Climate change and Genotype-by-Environment-by-Management interactions together challenge our strategies for crop improvement. Research to advance prediction methods for breeding and agronomy is opening new opportunities to tackle these challenges and overcome on-farm crop productivity yield-gaps through design of responsive crop improvement strategies. Abstract Genotype-by-Environment-by-Management (G × E × M) interactions underpin many aspects of crop productivity. An important question for crop improvement is “How can breeders and agronomists effectively explore the diverse opportunities within the high dimensionality of the complex G × E × M factorial to achieve sustainable improvements in crop productivity?” Whenever G × E × M interactions make important contributions to attainment of crop productivity, we should consider how to design crop improvement strategies that can explore the potential space of G × E × M possibilities, reveal the interesting Genotype–Management (G–M) technology opportunities for the Target Population of Environments (TPE), and enable the practical exploitation of the associated improved levels of crop productivity under on-farm conditions. Climate change adds additional layers of complexity and uncertainty to this challenge, by introducing directional changes in the environmental dimension of the G × E × M factorial. These directional changes have the potential to create further conditional changes in the contributions of the genetic and management dimensions to future crop productivity. Therefore, in the presence of G × E × M interactions and climate change, the challenge for both breeders and agronomists is to co-design new G–M technologies for a non-stationary TPE. Understanding these conditional changes in crop productivity through the relevant sciences for each dimension, Genotype, Environment, and Management, creates opportunities to predict novel G–M technology combinations suitable to achieve sustainable crop productivity and global food security targets for the likely climate change scenarios. Here we consider critical foundations required for any prediction framework that aims to move us from the current unprepared state of describing G × E × M outcomes to a future responsive state equipped to predict the crop productivity consequences of G–M technology combinations for the range of environmental conditions expected for a complex, non-stationary TPE under the influences of climate change.

Stability Analysis of Agronomic Traits for Maize (Zea Mays L.) Genotypes Based on Ammi Model

2021 ◽  
Vol 50 (2) ◽  
pp. 343-350
Author(s):  
Meijin Ye ◽  
Zhaoyang Chen ◽  
Bingbing Liu ◽  
Haiwang Yue
Keyword(s):  
Grain Yield ◽  
Agronomic Traits ◽  
Interaction Model ◽  
Genotype By Environment Interaction ◽  
Kernel Weight ◽  
Environment Interaction ◽  
Maize Hybrids ◽  
Genotype By Environment ◽  
Ammi Model ◽  
The Ideal

Stability and adaptability of promising maize hybrids in terms of three agronomic traits (grain yield, ear weight and 100-kernel weight) in multi-environments trials were evaluated. The analysis of AMMI model indicated that the all three agronomic traits showed highly significant differences (p < 0.01) on genotype, environment and genotype by environment interaction. Results showed that genotypes Hengyu321 (G9), Yufeng303 (G10) and Huanong138 (G3) were of higher stability on grain yield, ear weight and 100-kernel weight, respectively. Genotypes Hengyu1587 (G8) and Hengyu321 (G9) showed good performance in terms of grain yield, whereas Longping208 (G2) and Weike966 (G12) showed broad adaptability for ear weight. It was also found that the genotypes with better adaptability in terms of 100-kernel weight were Zhengdan958 (G5) and Weike966 (G12). The genotype and environment interaction model based on AMMI analysis indicated that Hengyu1587 and Hengyu321 were the ideal genotypes, due to extensive adaptability and high grain yield under both testing sites. Bangladesh J. Bot. 50(2): 343-350, 2021 (June)

Efficient High-throughput Techniques for the Analysis of Disease-Resistant Plant Varieties and Detection of Food Adulteration

2021 ◽  
Vol 23 ◽  
Author(s):  
Romesh Kumar Salgotra ◽  
Rafiq Ahmad Bhat ◽  
Deyue Yu ◽  
Javaid Akhter Bhat
Keyword(s):  
Multiplex Pcr ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Low Cost ◽  
Crop Improvement ◽  
Small Sample ◽  
Food Crops ◽  
Genomic Resources ◽  
Breeding Programmes ◽  
Next Generation Sequencing Ngs

Abstract: Over the past two decades, the advances in the next generation sequencing (NGS) platforms have led to the identification of numerous genes/QTLs at high-resolution for their potential use in crop improvement. The genomic resources generated through these high-throughput sequencing techniques have been efficiently used in screening of particular gene of interest particularly for numerous types of plant stresses and quality traits. Subsequently, the identified-markers linked to a particular trait have been used in marker-assisted backcross breeding (MABB) activities. Besides, these markers are also being used to catalogue the food crops for detection of adulteration to improve the quality of food. With the advancement of technologies, the genomic resources are originating with new markers; however, to use these markers efficiently in crop breeding, high-throughput techniques (HTT) such as multiplex PCR and capillary electrophoresis (CE) can be exploited. Robustness, ease of operation, good reproducibility and low cost are the main advantages of multiplex PCR and CE. The CE is capable of separating and characterizing proteins with simplicity, speed and small sample requirements. Keeping in view the availability of vast data generated through NGS techniques and development of numerous markers, there is a need to use these resources efficiently in crop improvement programmes. In summary, this review describes the use of molecular markers in the screening of resistance genes in breeding programmes and detection of adulterations in food crops using high-throughput techniques.

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