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.

Rectification of modified AMMI stability value (MASV)

2020 ◽  
Vol 79 (04) ◽  
Author(s):  
B. C. Ajay ◽  
J. Aravind ◽  
R. Abdul Fiyaz ◽  
Narendra Kumar ◽  
Chuni Lal ◽  
...  
Keyword(s):  
Principal Components ◽  
Stability Index ◽  
Correct Version ◽  
Multiplicative Interaction ◽  
Genotype By Environment ◽  
Ammi Analysis ◽  
Correct Formula ◽  
Ammi Model ◽  
Main Effects ◽  
Original Formula

Additive main effects and multiplicative interaction (AMMI) analysis is widely used for analyzing data of multi-environment trials (METs) to model the genotype-by-environment interactions (GEIs). However, AMMI model do not rank genotypes which is required for aiding selection. In order to overcome these lacunae a stability index titled AMMI stability value (ASV) was proposed by Purchase et al. (1997) using first two interaction principal components (IPCA) from the results of AMMI analysis. Later, Zali et al. (2012) modified it and proposed Modified ASV (MASV) which used all significant IPCAs. However, Zali et al. (2012) read the original formula of ASV incorrectly while proposing MASV thus rendering it erroneous. Use of this erroneous MASV impacted genotype ranking significantly. Corrected version of MASV, i.e. MASV2 showed significant correlation with other stability models. Hence, we propose MASV2 as a correct formula for modified AMMI stability Value (MASV) and this correct version of MASV may be used instead of earlier formula proposed by Zali et al. (2012).

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.

AMMI based simultaneous selection for yield and stability of chickpea genotypes in south zone of India

2018 ◽  
Author(s):  
Hemant Kumar ◽  
G.P. Dixit ◽  
N.P. Singh ◽  
A.K. Srivastava
Keyword(s):  
Stability Index ◽  
Yield Component ◽  
Environment Interaction ◽  
Ammi Analysis ◽  
Genotypic Stability ◽  
Ammi Model ◽  
Selection For ◽  
Index Value ◽  
Main Effects ◽  
South Zone

Multi-environmental trials have generally significant genotype main effects and genotype x environment interaction (GEI) effect and, therefore different univariate and multivariate stability methods have been used to study the GEI. Among the multivariate methods, the additive main effects and multiplicative interaction (AMMI) analysis is widely used for GEI investigation. This method has been effective because it captures a large portion of the GEI sum of squares; it clearly separates main and interaction effects and often provides meaningful interpretation of data to support a breeding program such as genotypic stability. Based on the AMMI model, a stability index has been used to rank the genotypes. This index is the weightage of stability and yield component and higher the index value better is the genotypes. The index of 40 promising chickpea genotypes were calculated with two different weight of yield (50% and 75%) and stability component (50% and 25%). These genotypes were evaluated at seven locations viz. Hiriyur, Nandyal, Coimbtore, Dharwad, Lam, Bijapur and Gulbarga representing the south zone of All India Coordinated Research Project on Chickpea program during 2015-16. Ranking of genotypes are done based on two different weight of stability and yield component.

scholarly journals Stability Analysis for Seed Yield over Environments in Coriander

2016 ◽  
Vol 4 (11) ◽  
pp. 1014
Author(s):  
Sangeeta Yadav ◽  
Arun Kumar Barholia
Keyword(s):  
Stability Analysis ◽  
Seed Yield ◽  
High Fertility ◽  
Environment Interaction ◽  
Stability Parameters ◽  
Genotype Environment Interaction ◽  
Ammi Analysis ◽  
Back Ground ◽  
Ammi Model ◽  
Main Effects

Thirty five genotypes of coriander (Coriandrum sativum L.) were tested in four artificially created environments to judge their stability in performance of seed yield. The differences among genotypes and environments were significant for seed yield. Stability parameters varied considerably among the tested genotypes in all the methods used. The variation in result in different methods was due to non-fulfillment of assumption of different models. However, AMMI analysis provides the information on main effects as well as interaction effects and depiction of PCA score gives better understanding of the pattern of genotype – environment interaction. The sum of squares due to PCAs was also used for the computation of AMMI stability values for better understanding of the adaptability behavior of genotypes hence, additive main effects and multiplicative interaction (AMMI) model was most appropriate for the analysis of G x E interactions for seed yield in coriander. Genotypes RVC 15, RVC 19, RVC 22, RVC 25 and Panipat local showed wider adaptability while, Simpo S 33 exhibited specific adaptability to favourable conditions of high fertility. These genotypes could be utilized in breeding programmers to transfer the adaptability genes into high yielding genetic back ground of coriander.

scholarly journals ADAPTABILITY AND STABILITY FOR IRON AND ZINC IN COWPEA BY AMMI ANALYSIS

2021 ◽  
Vol 34 (3) ◽  
pp. 590-598
Author(s):  
CARLOS ENRIQUE CARDONA-AYALA ◽  
HERMES ARAMENDIZ-TATIS ◽  
MIGUEL MARIANO ESPITIA CAMACHO
Keyword(s):  
Genetic Improvement ◽  
Block Design ◽  
Environment Interaction ◽  
Improvement Program ◽  
Genotype Environment Interaction ◽  
Randomized Complete Block Design ◽  
Ammi Analysis ◽  
Iron And Zinc ◽  
Ammi Model ◽  
Main Effects

ABSTRACT Iron and zinc deficiency is one of the main problems affecting vulnerable populations in the Colombian Caribbean, thereby generating malnutrition from the consumption of foods with low content of essential minerals. The objective of this study was to evaluate the genotype-environment interaction for iron and zinc accumulation in grains in 10 cowpea bean genotypes by additive main effects and multiplicative interaction (AMMI) model and to select the most stable ones to stimulate their planting or as parents in the genetic improvement program. Nine promising lines and a commercial control were evaluated using the randomized complete block design with 10 treatments and four replications in 10 environments of the northern Colombia in the second semester of 2017 and first of 2018. The adaptability and stability analysis was done using AMMI model. The results showed highly significant differences at the level of environments, genotypes, and genotype-environment interaction for iron and zinc, demostrating a differential adaptability of genotypes in the test environments. Genotypes 2 and 3 expressed greater adaptability and stability for iron contents in the seed; while genotype 1, recorded it for zinc contents. These three genotypes outperformed the commercial control and, therefore, can be recommended for planting or be used as parents in the genetic improvement program.

scholarly journals Genotype × environment analysis of cowpea grain production in the forest and derived savannah cultivation ecologies

Agro-Science ◽  
2021 ◽  
Vol 20 (2) ◽  
pp. 20-24
Author(s):  
A.L. Nassir ◽  
M.O. Olayiwola ◽  
S.O. Olagunju ◽  
K.M. Adewusi ◽  
S.S. Jinadu
Keyword(s):  
Sum Of Squares ◽  
Grain Production ◽  
Environment Analysis ◽  
Late Season ◽  
Genotype By Environment ◽  
Differential Performance ◽  
Main Effect ◽  
Ammi Model ◽  
Main Effects ◽  
Selection Of

Differential performance of genotypes in different cultivation environments has remained a challenge to farmers and plant breeders, the emphasis being the selection of high yielding and stable genotypes, across similar ecologies. A set of nine cowpea genotypes were  cultivated in Ago-Iwoye and Ayetoro, two locations representing high and moderate moisture zones. Plantings were done with the early and late season rains in Ago-Iwoye and mid-late season rains of Ayetoro. Statistical analysis was done to understand genotype reaction to the different environments and the plant and environment factors mediating the performance. The Additive Main Effect and Multiplicative Interaction (AMMI) model captured 61.30% of the total sum of squares (TSS). The main effects: genotype (G) environment (E) and their interaction (GxE) were significant with the largest contribution of 28.70% by the environment while the interaction and genotype fractionscaptured 20.20% and 12.40%, respectively. The percentage contribution of the main effects and GxE to total sum of squares (TSS) for traits was not consistent. The Genotype plus Genotype-by-Environment (GGE) analysis summarized 91.30% of the variation in genotype performance across environment. The cultivation environments were separated into two, with IT 95M 118 as the vertex genotype in the Ayetoro while TVU 8905 was the topmost genotype in Ago-Iwoye. The two genotypes recorded the highest grain weight per plant (GWPP) but were also the most unstable The stable genotypes IT 95M 120 and IT 86 D 716 flowered relatively late compared to others, are taller, had higher vegetative score and are low grain producers. Key words: AMMI, drought, GGE, stability, Vigna unguiculata

scholarly journals Capturing GEI Patterns for Quality Traits in Biparental Wheat Populations

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1022
Author(s):  
Ivana Plavšin ◽  
Jerko Gunjača ◽  
Ruđer Šimek ◽  
Dario Novoselović
Keyword(s):  
Genotype By Environment Interaction ◽  
Grain Protein Content ◽  
Environment Interaction ◽  
Quality Traits ◽  
Dominant Source ◽  
Genotype By Environment ◽  
Ammi Model ◽  
Superior Genotypes ◽  
Main Effects ◽  
Selection Of

Genotype-by-environment interaction (GEI) is often a great challenge for breeders since it makes the selection of stable or superior genotypes more difficult. In order to reduce drawbacks caused by GEI and make the selection for wheat quality more effective, it is important to properly assess the effects of genotype, environment, and GEI on the trait of interest. In the present study, GEI patterns for the selected quality and mixograph traits were studied using the Additive Main Effects and Multiplicative Interaction (AMMI) model. Two biparental wheat populations consisting of 145 and 175 RILs were evaluated in six environments. The environment was the dominant source of variation for grain protein content (GPC), wet gluten content (WGC), and test weight (TW), accounting for approximately 40% to 85% of the total variation. The pattern was less consistent for mixograph traits for which the dominant source of variation has been shown to be trait and population-dependent. Overall, GEI has been shown to play a more important role for mixograph traits compared to other quality traits. Inspection of the AMMI2 biplot revealed some broadly adapted RILs, among which, MG124 is the most interesting, being the prevalent “winner” for GPC and WGC, but also the “winner” for non-correlated trait TW in environment SB10.

Selection of locations for safflower cultivar trials on the Canadian prairies by using the AMMI procedure

1995 ◽  
Vol 75 (4) ◽  
pp. 767-774
Author(s):  
H.-H. Mündel ◽  
T. Entz ◽  
J. P. Braun ◽  
F. A. Kiehn
Keyword(s):  
Weather Conditions ◽  
Model Fit ◽  
Test Weight ◽  
Data Sets ◽  
Data Set ◽  
Canadian Prairies ◽  
Ammi Analysis ◽  
Ammi Model ◽  
Main Effects ◽  
Selection Of

Additive main effects and multiplicative interaction (AMMI) analysis of Safflower Cooperative Registration Test (SCRT) data gathered from 1984 to 1991 across the Canadian prairies was used to assess the possibility of reducing the number of locations for cultivar evaluation. The cultivars Saffire, Hartman, S-208, and S-541 were included in the 1984–1986 data set; and Saffire, AC Stirling, S-208, and S-541 in the 1988–1991 set. Seed yield, percent oil, days to maturity, and test weight were measured at 12 locations, although due to weather conditions, data were sometimes not available for all locations in any given year. The AMMI model fit the data well for all four traits, and indicated that among-year variability at a given location was usually higher than inter-location variability in a given year. Cultivar interaction effects for all four characteristics assessed were usually large for both data sets, indicating that differences among cultivars at a given location can vary considerably over years. Intra-location variability was not consistent for the four traits and no clear grouping of locations or locations with cultivars over years was evident. These results suggest that local environmental factors significantly influence safflower traits, and potential cultivars need to be evaluated at as many locations as resources permit. Key words:Carthamus tinctorius, cultivar × environment interactions, yield, oil, maturity, test weight

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).

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