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

Performance of extra-early maize cultivars based on GGE biplot and AMMI analysis

2011 ◽  
Vol 150 (4) ◽  
pp. 473-483 ◽  
Author(s):  
B. BADU-APRAKU ◽  
M. OYEKUNLE ◽  
K. OBENG-ANTWI ◽  
A. S. OSUMAN ◽  
S. G. ADO ◽  
...  
Keyword(s):  
West Africa ◽  
Grain Yield ◽  
Sum Of Squares ◽  
Gge Biplot ◽  
Maize Cultivars ◽  
Main Effect ◽  
Early Maturing ◽  
And Performance ◽  
Main Effects ◽  
Selection Of

SUMMARYMulti-environment trials (METs) in West Africa have demonstrated the existence of genotype×environment interactions (G×E), which complicate the selection of superior cultivars and the best testing sites for identifying superior and stable genotypes. Two powerful statistical tools available for MET analysis are the additive main effects and multiplicative interaction (AMMI) and the genotype main effect+G×E (known as GGE) biplot. The objective of the present study was to compare their effectiveness in identifying maize mega-environments and stable and superior maize cultivars with good adaptation to West Africa. Twelve extra-early maturing maize cultivars were evaluated at 17 locations in four countries in West Africa from 2006 to 2009. The effects of genotype (G), environments (E) and G×E were significant (P<0 01) for grain yield. Differences between E accounted for 0 75 of the total variation in the sum of squares for grain yield, whereas the G effects accounted for 0 03 and G×E for 0 22. The GGE biplot explained 0 74 of total variations in the sum of squares for grain yield and revealed three mega-environments and seven cultivar groups. The AMMI graph explained 0 13 and revealed four groups each of environments and cultivars. The two procedures provided similar results in terms of stability and performance of the cultivars. Both methods identified the cultivars 2004 TZEE-W Pop STR C4 and TZEE-W Pop STR C4 as superior across environments. Cultivar 2004 TZEE-W Pop STR C4 was the most stable. The GGE biplot was more versatile and flexible, and provided a better understanding of G×E than the AMMI graph. It identified Zaria, Ilorin, Ikenne, Ejura, Kita, Babile, Ina and Angaredebou as the core testing sites of the three mega-environments for testing the Regional Uniform Variety Trials-extra-early.

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.

scholarly journals R-AMMI-LM: Linear-fit Robust-AMMI model to analyze genotype-by environment interactions

2021 ◽  
Vol 81 (01) ◽  
pp. 87-92
Author(s):  
B. C. Ajay ◽  
K. T. Ramya ◽  
R. Abdul Fiyaz ◽  
G. Govindaraj ◽  
S. K. Bera ◽  
...  
Keyword(s):  
Sum Of Squares ◽  
Field Conditions ◽  
Common Phenomenon ◽  
Genotype By Environment Interactions ◽  
Genotype By Environment ◽  
Ammi Model ◽  
Linear Fit

Outliers are a common phenomenon when genotypes are evaluated over locations and years under field conditions and such outliers makes studying genotype-environment Interactions difficult. Robust-AMMI models which use a combination of robust fit and robust SVD approaches, denoted as ‘R-AMMI-RLM’ have been proposed to study GEI in presence of such outliers. Instead of ‘R-AMMI-RLM’ we propose a model which uses a combination of linear fit and robust SVD to study GEI in presence of outliers and we denote this model as ‘R-AMMI-LM’. Here we prove that ‘RAMMI-LM’ was superior over ‘R-AMMI-RLM’ as it recorded very low residual sum of squares and low RMSE values. Thus proposed, ‘R-AMMI-LM’ model could explain the GEI more precisely even in presence of outliers.

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.

ammistability: R package for ranking genotypes based on stability parameters derived from AMMI model

2019 ◽  
Vol 79 (02) ◽  
Author(s):  
B. C. Ajay ◽  
J. Aravind ◽  
R. Abdul Fiyaz
Keyword(s):  
R Package ◽  
Stability Parameters ◽  
Genotype By Environment ◽  
Stability Measure ◽  
Ammi Model ◽  
R Packages ◽  
Simultaneous Selection ◽  
Study Association ◽  
Target Environment ◽  
Selection Of

Selection of genotype for target environment is affected by genotype-by-environment interactions (GEI) and AMMI model is widely used tool to analyse GEI. AMMI does not quantify stability measure making it difficult to rank genotypes. To overcome this lacuna AMMI model output is used to quantify stability measures and rank genotypes. Of several stability measures available in literature, only AMMI stability value (ASV) is implemented in package ‘agricole’ and others have not been implemented in any other R packages. ‘ammistability’ uses output from ‘AMMI’ function in ‘agricolae’ package and computes various stability parameters for AMMI model. Further, genotypes are ranked on the basis of simultaneous selection of yield and stability (SSI). Package also helps to study association among several stability measures.

scholarly journals AMMI and Stability Analyses of Bunch Mass in Multilocational Testing of Musa Germplasm in Sub-Saharan Africa

1998 ◽  
Vol 123 (4) ◽  
pp. 623-627 ◽  
Author(s):  
Rodomiro Ortiz
Keyword(s):  
Environmental Changes ◽  
Sub Saharan Africa ◽  
Environment Interaction ◽  
Stability Analyses ◽  
Genotype Environment Interaction ◽  
Ammi Model ◽  
Sub Saharan ◽  
Main Effects ◽  
New Cultivars ◽  
Selection Of

There is a genuine need within a plantain and banana (Musa spp.) breeding program to assess thoroughly the experimental materials through a sequence of trials. This will result in the selection of promising clones as potential new cultivars in the targeted agroecozone. Stability analyses and the additive main effects and multiplicative interaction (AMMI) model provide together a means for the identification of clones with 1) homeostatic responses to environmental changes, 2) a genotypic response to environmental changes, and 3) adaptation to specific niches. Fourteen polyploid clones (10 tetraploid hybrids and 4 triploid cultivars) were evaluated in a broad range of environments in sub-Saharan Africa to determine the value of stability and AMMI analyses in Musa trials. The interpretation of the results, especially those concerning the genotype × environment interaction, was facilitated by the combination of stability and AMMI analyses. Tetraploid hybrids combining heavy and stable bunch mass were identified. The results also suggested that a clone should be assessed in the ratoon cycle because plantain and banana are perennial crops. Likewise, high yielding clones with specific adaptation should be selected in environments showing the respective environmental or biotic stress.

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).

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