scholarly journals Assessment of Genotype-Environment interaction using additive main effects and multiplicative interaction model (AMMI) in maize (Zea mays L.) Hybrids

2017 ◽  
Vol 8 (4) ◽  
pp. 1223 ◽  
Author(s):  
G. Usha Rani ◽  
V. Satyanarayana Rao ◽  
M Lal Ahmad ◽  
K.L. Narasimha Rao
Keyword(s):  
Zea Mays ◽  
Zea Mays L ◽  
Interaction Model ◽  
Environment Interaction ◽  
Multiplicative Interaction ◽  
Genotype Environment Interaction ◽  
Main Effects

scholarly journals Effects of NP Fertilizer Placement Depth by Year Interaction on the Number of Maize (Zea mays L.) Plants after Emergence Using the Additive Main Effects and Multiplicative Interaction Model

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1543
Author(s):  
Piotr Szulc ◽  
Jan Bocianowski ◽  
Kamila Nowosad ◽  
Henryk Bujak ◽  
Waldemar Zielewicz ◽  
...  
Keyword(s):  
Zea Mays ◽  
Zea Mays L ◽  
Interaction Model ◽  
Fertilizer Application ◽  
Fertilizer Placement ◽  
Multiplicative Interaction ◽  
Maize Cultivation ◽  
Main Effects ◽  
Placement Depth ◽  
Np Fertilizer

Field experiments were carried out at the Department of Agronomy of the Poznań University of Life Sciences to determine the effect of the depth of NP fertilization placement in maize cultivation on the number of plants after emergence. The adopted assumptions were verified based on a six-year field experiment involving four depths of NP fertilizer application (A1—0 cm (broadcast), A2—5 cm (in rows), A3—10 cm (in rows), A4—15 cm (in rows)). The objective of this study was to assess NP fertilizer placement depth, in conjunction with the year, on the number of maize (Zea mays L.) plants after emergence using the additive main effects and multiplicative interaction model. The number of plants after emergence decreased with the depth of NP fertilization in the soil profile, confirming the high dependence of maize on phosphorus and nitrogen availability, as well as greater subsoil loosening during placement. The number of plants after emergence for the experimental NP fertilizer placement depths varied from 7.237 to 8.201 plant m−2 during six years, with an average of 7.687 plant m−2. The 61.51% of variation in the total number of plants after emergence was explained by years differences, 23.21% by differences between NP fertilizer placement depths and 4.68% by NP fertilizer placement depths by years interaction. NP fertilizer placement depth 10 cm (A3) was the most stable (ASV = 1.361) in terms of the number of plants after emergence among the studied NP fertilizer placement depths. Assuming that the maize kernels are placed in the soil at a depth of approx. 5 cm, the fertilizer during starter fertilization should be placed 5 cm to the side and below the kernel. Deeper NP fertilizer application in maize cultivation is not recommended. The condition for the use of agriculture progress, represented by localized fertilization, is the simultaneous recognition of the aspects of yielding physiology of new maize varieties and the assessment of their reaction to deeper seed placement during sowing.

scholarly journals Evaluación de híbridos de maíz (Zea mays L.) de grano blanco y amarillo en ambientes de Centroamérica, Panamá y el Caribe en 1996.

2016 ◽  
Vol 9 (1) ◽  
pp. 28 ◽  
Author(s):  
Adán Aguiluz
Keyword(s):  
Zea Mays ◽  
Zea Mays L ◽  
Environment Interaction ◽  
Genotype Environment Interaction ◽  
Ammi Model ◽  
White Grain ◽  
National Programs ◽  
And Control ◽  
Main Effects

Thirty white-grain hybrids in 1210cations and 20 yellow-grain hybrids in 14 locations were evaluated in 1996. The control HB-83 was used for white grains, and control HB-46 was used for yellow grains. Measurement of genotype/environment interaction was obtained through the analysis of additive main effects and multiplicative in~eractions (AMMI model). White hybrids A- 7573, H-53, HN-951, A-7530, CB- XHS-7GMl and CML- 9XCML-47 equaled or surpassed the HB-83 control in 17,8%; 0,1%; 1,1%; 9,2%; 8,1% and 18,7% ofthe cases, respectively, showing little interaction with the environment (AMMI values close to O). Two of these hybrids are from national programs, and three from private seed companies. As for the yellow grains, only the CB-XHS-8GM3, HS-6 and DK-888A hybrids surpassed the control HA-46 in yield, and showed AMMI scores c10se to O: (0,20; -0,07 and -0,12, respectively); all of these belong to private seed companies.

scholarly journals Genotype by environment interaction for physiological traits in sugar beet (Beta vulgaris L.) parents and hybrids using additive main effects and multiplicative interaction model

2021 ◽  
Author(s):  
Zahra Abbasi ◽  
Jan Bocianowski
Keyword(s):  
Sugar Beet ◽  
Beta Vulgaris ◽  
Interaction Model ◽  
Genotype By Environment Interaction ◽  
Physiological Traits ◽  
Environment Interaction ◽  
Extraction Coefficient ◽  
Multiplicative Interaction ◽  
Genotype By Environment ◽  
Main Effects

AbstractThe objective of this study was to assess genotype by environment interaction for 21 physiological traits in sugar beet (Beta vulgaris L.) parents and hybrids grown in Rodasht Agricultural Research Station in Iran by the additive main effects and multiplicative interaction model. The study comprised of 51 sugar beet genotypes [10 multigerm pollen parents, four monogerm seed parents and 36 F1 hybrids], evaluated at four environments in a randomized complete block design, with three replicates. The additive main effects and multiplicative interaction analyses revealed significant environment main effects with respect to all observed traits, except extraction coefficient of sugar. The additive main effects and multiplicative interaction stability values ranged from 0.009 (G17 for leaf Ca2+) to 9.698 (G09 for extraction coefficient of sugar). The parental forms 2 7233-P.29 (G38) and C CMS (G49) as well as hybrids 2(6)*C (G27) and 5*C (G33) are recommended for further inclusion in the breeding programs because of their stability and good average values of observed traits.

scholarly journals Genotype by environment interaction for seeds yield in pea (Pisum sativum L.) using additive main effects and multiplicative interaction model

Euphytica ◽  
2019 ◽  
Vol 215 (11) ◽  
Author(s):  
Jan Bocianowski ◽  
Jerzy Księżak ◽  
Kamila Nowosad
Keyword(s):  
Pisum Sativum ◽  
Block Design ◽  
Interaction Model ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Ge Interaction ◽  
Multiplicative Interaction ◽  
Genotype By Environment ◽  
Randomized Complete Block Design ◽  
Main Effects

Abstract The objective of this study was to evaluate the genotype by environment interaction using the additive main effects and multiplicative interaction model for seeds yield of pea cultivars grown in Poland. Twelve pea (Pisum sativum L.) cultivars: Bohun, Boruta, Cysterski, Ezop, Kavalir, Lasso, Medal, Santana, Tarchalska, Terno, Wenus and Zekon were evaluated in 20 environments (ten locations in 2 years). The experiment was laid out as randomized complete block design with three replicates. Seeds yield ranged from 26.10 dt ha−1 (for Wenus in Radostowo 2011) to 79.73 dt ha−1 (for Lasso in Słupia 2010), with an average of 50.70 dt ha−1. AMMI analyses revealed significant genotype and environmental effects as well as genotype-by-environment interaction with respect to seeds yield. In the analysis of variance, 89.19% of the total seeds yield variation was explained by environment, 1.65% by differences between genotypes, and 8.33% by GE interaction. The cultivar Terno is the highest stability. The cultivar Tarchalska is recommended for further inclusion in the breeding program because its stability and the highest averages of seeds yield.

scholarly journals Identification of pigeonpea genotypes with wider adaptability to rainfed environments through AMMI and GGE biplot analyses

2021 ◽  
Vol 81 (01) ◽  
pp. 63-73
Author(s):  
M. V. Nagesh Kumar ◽  
V. Ramya ◽  
C. V. Sameer Kumar ◽  
T. Raju ◽  
N. M. Sunil Kumar ◽  
...  
Keyword(s):  
Cajanus Cajan ◽  
Large Scale ◽  
Interaction Model ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Rainfed Agriculture ◽  
Genotype By Environment ◽  
Genotype Environment Interaction ◽  
Wide Range ◽  
Main Effects

Pigeonpea [Cajanus cajan (L.) Millspaugh] is an important pulse crop grown under Indian rainfed agriculture. Twenty eight pigeonpea genotypes were tested for stability and adaptability across ten rainfed locations in the States of Telangana and Karnataka, India using AMMI (additive main effects and multiplicative interaction) model and GGE (genotype and genotype by environment) biplot method. The grain yields were significantly affected by environment (56.8%) followed by genotype × environment interaction (27.6%) and genotype (18.6%) variances. Two mega environments were identified with several winning genotypes viz., ICPH 2740 (G15), TS 3R (G10), PRG 176 (G8) and ICPL 96058 (G22). E2 (Gulbarga, Karnataka), E3 (Bidar, Karnataka) and E6 (Vikarabad, Telangana) were the most discriminating environments. Genotypes, ICPH 2740, PRG 176 and TS 3R were the best cultivars in all the environments whereas PRG 158 (G9), ICPL 87119 (G12), ICPL 20098 (G19) and ICPL 96058 (G22) were suitable across a wide range of environments. Genotypes, ICPH 2740 and PRG 176 can be recommended on a large scale to the farmers with small holdings to enhance pigeonpea productivity and improve the food security

scholarly journals Genome-based trait prediction in multi- environment breeding trials in groundnut

2020 ◽  
Vol 133 (11) ◽  
pp. 3101-3117 ◽  
Author(s):  
Manish K. Pandey ◽  
Sunil Chaudhari ◽  
Diego Jarquin ◽  
Pasupuleti Janila ◽  
Jose Crossa ◽  
...  
Keyword(s):  
Complex Traits ◽  
Prediction Accuracy ◽  
Agronomic Traits ◽  
Total Yield ◽  
Snp Array ◽  
Interaction Model ◽  
Cost Effective ◽  
Environment Interaction ◽  
Genotype Environment Interaction ◽  
Main Effects

Abstract Key message Comparative assessment identified naïve interaction model, and naïve and informed interaction GS models suitable for achieving higher prediction accuracy in groundnut keeping in mind the high genotype × environment interaction for complex traits. Abstract Genomic selection (GS) can be an efficient and cost-effective breeding approach which captures both small- and large-effect genetic factors and therefore promises to achieve higher genetic gains for complex traits such as yield and oil content in groundnut. A training population was constituted with 340 elite lines followed by genotyping with 58 K ‘Axiom_Arachis’ SNP array and phenotyping for key agronomic traits at three locations in India. Four GS models were tested using three different random cross-validation schemes (CV0, CV1 and CV2). These models are: (1) model 1 (M1 = E + L) which includes the main effects of environment (E) and line (L); (2) model 2 (M2 = E + L + G) which includes the main effects of markers (G) in addition to E and L; (3) model 3 (M3 = E + L + G + GE), a naïve interaction model; and (4) model 4 (E + L + G + LE + GE), a naïve and informed interaction model. Prediction accuracy estimated for four models indicated clear advantage of the inclusion of marker information which was reflected in better prediction accuracy achieved with models M2, M3 and M4 as compared to M1 model. High prediction accuracies (> 0.600) were observed for days to 50% flowering, days to maturity, hundred seed weight, oleic acid, rust@90 days, rust@105 days and late leaf spot@90 days, while medium prediction accuracies (0.400–0.600) were obtained for pods/plant, shelling  %, and total yield/plant. Assessment of comparative prediction accuracy for different GS models to perform selection for untested genotypes, and unobserved and unevaluated environments provided greater insights on potential application of GS breeding in groundnut.

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