scholarly journals Rice cultivar selection in an agroforestry system through GGE-biplot and EBLUP

2021 ◽  
Vol 22 (11) ◽  
Author(s):  
Taufan Alam ◽  
Priyono Suryanto ◽  
Supriyanta Supriyanta ◽  
Panjisakti Basunanda ◽  
Rani Agustina Wulandari ◽  
...  
Keyword(s):  
Rice Cultivar ◽  
Agroforestry System ◽  
Agroforestry Systems ◽  
Soil Types ◽  
Environment Interaction ◽  
Rice Cultivars ◽  
Gge Biplot ◽  
Genotype By Environment ◽  
Cultivar Selection ◽  
Melaleuca Cajuputi

Abstract. Alam T, Suryanto P, Supriyanta, Basunanda P, Wulandari RA, Kastono D, Widyawan MH, Nurmansyah, Taryono. 2021. Rice cultivar selection in an agroforestry system through GGE-biplot and EBLUP. Biodiversitas 22: 4750-4757. Genotype-by-environment interaction (GEI) causes differences in the productivity of rice cultivars in agroforestry systems. For this reason, the stability of rice cultivars is an important aspect that should be considered before a cultivar is recommended to farmers. Superior genotypes and ideal environments are commonly identified using two statistical models, namely, genotype–genotype-by-environment biplot (GGE-biplot) and empirical best linear unbiased prediction (EBLUP). In this study, 15 rice cultivars were evaluated in terms of their productivity and stability in three soil types (Lithic Haplusterts, Ustic Epiaquerts, and Vertic Haplustalfs) in an agroforestry system with kayu putih (Melaleuca cajuputi) in 2019 and 2020 at the Menggoran Forest Resort, Playen Forest Section, Yogyakarta Forest Management District, Indonesia. The cultivars were treated as random effects to select and obtain the EBLUP of the best cultivars in each soil type. The EBLUP revealed that Situ Patenggang showed the highest yields of 4.887 and 5.456 tons ha?1 in Lithic Haplusterts and Vertic Haplustalfs, respectively. GM 28 exhibited the highest yield of 6.492 tons ha?1 in Ustic Epiaquerts. Ciherang, GM 2, GM 8, GM 11, GM 28, Inpari 6 Jete, Inpari 33, IR-64, and Way Apo Buru were classified as stable and fairly stable cultivars, whereas the other cultivars were unstable. Therefore, rice cultivars with high yields in specific soil types should be selected.

scholarly journals Assessment of soil quality parameters and yield of rice cultivars in Melaleuca cajuputi agroforestry system

2020 ◽  
Vol 21 (8) ◽  
Author(s):  
Priyono Suryanto ◽  
Taryono TARYONO ◽  
Supriyanta SUPRIYANTA ◽  
Dody Kastono ◽  
Eka Tarwaca Susila Putra ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Soil Quality ◽  
Stepwise Regression ◽  
Rice Yield ◽  
Agroforestry System ◽  
Quality Parameters ◽  
Limiting Factors ◽  
Soil Types ◽  
Rice Cultivars ◽  
Melaleuca Cajuputi

Abstract. Suryanto P, Taryono, Supriyanta, Kastono D, Putra ETS, Widyawan MH, Alam T. 2020. Assessment of soil quality parameters and yield of rice cultivars in Melaleuca cajuputi agroforestry system. Biodiversitas 21: 3463-3470.  Interactions between rice cultivars and soil quality parameters rises problems in the attempt of increasing rice yield. The objective of this study was to assess soil quality parameters that affect the yield of 15 rice cultivars in an agroforestry system of ‘kayu putih’ (Melaleuca cajuputi) situated in Menggoran forest, Yogyakarta, Indonesia which have three soil types namely Lithic Haplusterts, Ustic Epiaquerts, and Vertic Haplustalfs. The observation was conducted on 21 soil quality parameters and yield of rice cultivars. The data were analyzed by using ANOVA, factor analysis, and stepwise regression. The highest yield of rice per hectare was attained by GM 28 in Ustic Epiaquerts with 6.493 tons ha-1, while Situ Patenggang and GM 28 in Vertic Haplustalfs as high as5.549 and 5.401 tons ha-1, respectively, and Situ Patenggang in Lithic Haplusterts as high as 4.893 tons ha-1. Soil quality parameters that had significant effect on the yield of rice cultivars were Clay, SMC, pH, SOC, N, Mg, Fe, Fg, and Bae. We suggested that rice cultivars recommendations for Lithic Haplusterts, Ustic Epiaquerts, and Vertic Haplustalfs are Situ Patenggang, Situ Patenggang or GM 28, and GM 28, respectively, in addition to fertilization based on limiting factors of each rice cultivars.

Evaluation of sugarcane genotypes with respect to sucrose yield across three crop cycles using GGE biplot analysis

2021 ◽  
pp. 1-13
Author(s):  
Aliya Momotaz ◽  
Per H. McCord ◽  
R. Wayne Davidson ◽  
Duli Zhao ◽  
Miguel Baltazar ◽  
...  
Keyword(s):  
Block Design ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Genotype By Environment ◽  
Biplot Analysis ◽  
Genotype Environment Interaction ◽  
Area 5 ◽  
Randomized Complete Block Design ◽  
Main Effects

Summary The experiment was carried out in three crop cycles as plant cane, first ratoon, and second ratoon at five locations on Florida muck soils (histosols) to evaluate the genotypes, test locations, and identify the superior and stable sugarcane genotypes. There were 13 sugarcane genotypes along with three commercial cultivars as checks included in this study. Five locations were considered as environments to analyze genotype-by-environment interaction (GEI) in 13 genotypes in three crop cycles. The sugarcane genotypes were planted in a randomized complete block design with six replications at each location. Performance was measured by the traits of sucrose yield tons per hectare (SY) and commercial recoverable sugar (CRS) in kilograms of sugar per ton of cane. The data were subjected to genotype main effects and genotype × environment interaction (GGE) analyses. The results showed significant effects for genotype (G), locations (E), and G × E (genotype × environment interaction) with respect to both traits. The GGE biplot analysis showed that the sugarcane genotype CP 12-1417 was high yielding and stable in terms of sucrose yield. The most discriminating and non-representative locations were Knight Farm (KN) for both SY and CRS. For sucrose yield only, the most discriminating and non-representative locations were Knight Farm (KN), Duda and Sons, Inc. USSC, Area 5 (A5), and Okeelanta (OK).

scholarly journals GENOTYPE BY ENVIRONMENT INTERACTION IN COWPEA LINES USING GGE BIPLOT METHOD

2018 ◽  
Vol 31 (1) ◽  
pp. 64-71 ◽  
Author(s):  
MASSAINE BANDEIRA E SOUSA ◽  
KAESEL JACKSON DAMASCENO-SILVA ◽  
MAURISRAEL DE MOURA ROCHA ◽  
JOSÉ ÂNGELO NOGUEIRA DE MENEZES JÚNIOR ◽  
LAÍZE RAPHAELLE LEMOS LIMA
Keyword(s):  
Maximum Yield ◽  
Genotype By Environment Interaction ◽  
Superior Performance ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Brazilian Cerrado ◽  
Genotype By Environment ◽  
Biplot Analysis ◽  
Adjusted Means ◽  
Selection Of

ABSTRACT The GGE Biplot method is efficien to identify favorable genotypes and ideal environments for evaluation. Therefore, the objective of this work was to evaluate the genotype by environment interaction (G×E) and select elite lines of cowpea from genotypes, which are part of the cultivation and use value tests of the Embrapa Meio-Norte Breeding Program, for regions of the Brazilian Cerrado, by the GGE-Biplot method. The grain yield of 40 cowpea genotypes, 30 lines and 10 cultivars, was evaluated during three years (2010, 2011 and 2012) in three locations: Balsas (BAL), São Raimundo das Mangabeiras (SRM) and Primavera do Leste (PRL). The data were subjected to analysis of variance, and adjusted means were obtained to perform the GGE-Biplot analysis. The graphic results showed variation in the performance of the genotypes in the locations evaluated over the years. The performance of the lines MNC02-675F-4-9 and MNC02-675F-4-10 were considered ideal, with maximum yield and good stability in the locations evaluated. There mega-environments were formed, encompassing environments correlated positively. The lines MNC02-675F-4-9, MNC02-675F-9-3 and MNC02-701F-2 had the best performance within each mega-environment. The environment PRL10 and lines near this environment, such as MNC02-677F-2, MNC02-677F-5 and the control cultivar (BRS-Marataoã) could be classified as those of greater reliability, determined basically by the genotypic effects, with reduced G×E. Most of the environments evaluated were ideal for evaluation of G×E, since the genotypes were well discriminated on them. Therefore, the selection of genotypes with adaptability and superior performance for specific environments through the GGE-Biplot analysis was possible.

scholarly journals Adaptability and Stability Study of Selected Sweet Sorghum Genotypes for Ethanol Production under Different Environments Using AMMI Analysis and GGE Biplots

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Justice Kipkorir Rono ◽  
Erick Kimutai Cheruiyot ◽  
Jacktone Odongo Othira ◽  
Virginia Wanjiku Njuguna ◽  
Joseph Kinyoro Macharia ◽  
...  
Keyword(s):  
Sweet Sorghum ◽  
Ethanol Yield ◽  
Block Design ◽  
Cane Yield ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Genotype By Environment ◽  
Cane Juice ◽  
Juice Yield ◽  
Sorghum Genotypes

The genotype and environment interaction influences the selection criteria of sorghum (Sorghum bicolor) genotypes. Eight sweet sorghum genotypes were evaluated at five different locations in two growing seasons of 2014. The aim was to determine the interaction between genotype and environment on cane, juice, and ethanol yield and to identify best genotypes for bioethanol production in Kenya. The experiments were conducted in a randomized complete block design replicated three times. Sorghum canes were harvested at hard dough stage of grain development and passed through rollers to obtain juice that was then fermented to obtain ethanol. Cane, juice, and ethanol yield was analyzed using the additive main effect and multiplication interaction model (AMMI) and genotype plus genotype by environment (GGE) biplot. The combined analysis of variance of cane and juice yield of sorghum genotypes showed that sweet sorghum genotypes were significantly (P<0.05) affected by environments (E), genotypes (G) and genotype by environment interaction (GEI). GGE biplot showed high yielding genotypes EUSS10, ACFC003/12, SS14, and EUSS11 for cane yield; EUSS10, EUSS11, and SS14 for juice yield; and EUSS10, SS04, SS14, and ACFC003/12 for ethanol yield. Genotype SS14 showed high general adaptability for cane, juice, and ethanol 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 Analysis of Genotype by Environment Interaction on Cocoa Hybrids (Theobroma cacao L.) Resistance to Phytophthora Pod Rot

2016 ◽  
Vol 32 (3) ◽  
pp. 162-170
Author(s):  
Agung Wahyu Soesilo ◽  
Indah Anita Sari ◽  
Bayu Setyawan
Keyword(s):  
Block Design ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Genotype By Environment ◽  
Experimental Station ◽  
Stability Performance ◽  
Stable Performance ◽  
Wet Climate ◽  
The Stability

Phenomenon of genotype by environment interaction was able to influence the stability performance of cocoa resistance to Phytophthora pod rot (PPR). This research had an objective to evaluate the effect of genotype by environment interaction on resistance of cocoa hybrids to PPR. The tested hybrids were F1 crosses between selected clones of TSH 858, Sulawesi 1, Sulawesi 2, NIC 7, ICS 13, KEE 2 and KW 165. There were 14 tested hybrids and an open pollinated hybrid of ICS 60 x Sca 12 was used as control in multilocation trials at four different agroclimatic locations, namely Jatirono Estate ((highland-wet climate), Kalitelepak Estate (lowland-wet climate), Kaliwining Experimental Station (low land-dry climate) and Sumber Asin Experimental Station (highland-dry climate). Trials were established in the randomized complete block design with four replications. Resistance to PPR were evaluated based on the percentage of infected pod for the years during wet climate of 2010 in Jatirono, Kalitelepak and Kaliwining followed in dry climate of 2011–2015 in Kaliwining and Sumber Asin. Variance of data were analyzed for detecting the effect of genotype by environment interaction (GxE) then visualized with a graph of genotype main effect and genotype by environment interaction (a graph of GGE) biplot. There was consistently no interaction effect between hybrid and location to PPR incidence which was affected by single factor of hybrid, year, location and interaction between year and location. The effect of year indicated yearly change of weather was more important to PPR incidence than location difference. A graph of GGE biplot indicated a stable performance of the tested hybrids among locations.

scholarly journals Assessment of ecological stability in yield for breeding of spring barley cultivars with increased adaptive potential

2020 ◽  
Vol 11 (3) ◽  
pp. 425-430
Author(s):  
V. M. Hudzenko ◽  
O. A. Demydov ◽  
V. P. Kavunets ◽  
L. M. Kachan ◽  
V. A. Ishchenko ◽  
...  
Keyword(s):  
Spring Barley ◽  
Genotype By Environment Interaction ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Ecological Stability ◽  
Breeding Lines ◽  
Barley Cultivars ◽  
Genotype By Environment ◽  
Barley Breeding ◽  
Agroclimatic Zones

Increasing crop adaptability in terms of ensuring a stable level of productivity in the genotype – environment interaction is still the central problem of plant breeding theory and practice. The aim of the present study is to theoretically substantiate and practically test a scheme of multi-environment trials, as well as interpret experimental data using modern statistical tools for evaluation of the genotype by environment interaction, and highlight the best genotypes with combining yield performance and ecological stability at the final stage of the spring barley breeding process. For this purpose in the first year of competitive testing (2016) at the V. M. Remeslo Myronivka Institute of Wheat of the National Academy of Agrarian Sciences of Ukraine we selected nine promising spring barley breeding lines. In 2017 and 2018 these breeding lines were additionally tested in two other scientific institutions located in different agroclimatic zones of Ukraine. For a more reliable assessment, the breeding lines were compared not only with standard cultivar, but also with ten spring barley cultivars widespread in agricultural production of Ukraine. Thus, for three years of competitive testing, we received experimental genotype-environmental data from seven environments, which represent a combination of contrasting agroclimatic zones (Central part of the Forest-Steppe, Polissia and Northern Steppe of Ukraine) and different years (2016–2018). Our results revealed significant variability of mean yield of genotypes, as well as cross-over genotype by environment interaction. The first two principal components of both AMMI and GGE biplot explained more than 80% of the genotype by environment interaction. In general, the peculiarities we revealed indicate the effectiveness of the proposed combination of spatial (agroclimatic zones) and temporal (years) gradients to identify the best spring barley genotypes with the optimal combination of yield performance and ecological stability. Using AMMI and GGE biplot models was effective for the comprehensive differentiation of genotypes in terms of wide and specific adaptability, as well as for qualitative characterization of test environments and providing mega-environment analysis. As a practical result of the multi-environment trial, four spring barley breeding lines have been submitted to the State Variety Testing of Ukraine as new cultivars MIP Sharm, MIP Tytul, MIP Deviz and MIP Zakhysnyk, respectively.

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.

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