scholarly journals Yield Stability of Sweet Sorghum Genotypes for Bioenergy Production Under Contrasting Temperate and Tropical Environments

2018 ◽  
Vol 10 (12) ◽  
pp. 42
Author(s):  
Diana-Abasi Udoh ◽  
Søren K. Rasmussen ◽  
Sven-Erik Jacobsen ◽  
Godfrey A. Iwo ◽  
Walter de Milliano
Keyword(s):  
Grain Yield ◽  
Analysis Of Variance ◽  
Sweet Sorghum ◽  
Block Design ◽  
Yield Stability ◽  
Biofuel Production ◽  
Environment Interaction ◽  
Yield Attributes ◽  
Fresh Biomass ◽  
Sorghum Genotypes

Forty-three sweet sorghum accessions were grown in two contrasting environments; Nigeria (tropical environment) and Denmark (temperate environment). The objectives were to determine the interaction between genotype and environment on grain yield, fresh biomass and stem sugar, and to assess yield stability of sweet sorghum and identify the best genotypes for biofuel production. The sweet sorghum originating from a Dutch and ICRISAT collection was grown in randomized complete block design in three replicates for two years (2014 and 2015). The combined analysis of variance of the sweet sorghum genotypes in two years over the two contrasting environments revealed that year (Y), genotype (G), environment (E) and genotype by environment interaction (GEI) were significant in the entire biofuel yield attributes obtained from both Dutch and ICRISAT collections except the degree of Brix and fresh biomass respectively across the year. The year and genotype interaction (Y×G) was not significant in all the biofuel attributes of Dutch accessions. Additive main effect and multiplicative interaction (AMMI) analysis of variance showed significant effect of G, E and the GEI. The AMMI was used to identify the best performing, adaptable and more stable genotypes. Twenty-two genotypes of both ICRISAT and Dutch accessions were identified to be stable across the two locations with respect to different biofuel attributes. Nine, seven, and six genotypes were found to be stable for grain yield, biomass yield and brix value, respectively. The best performing genotypes for stem sugar across locations were identified. From the available data collected, the performance of the sweet sorghum was attributed to both genetic and environmental effects. High GE was observed to influence stability, hence will influence the selection criteria of the sweet sorghum genotypes.

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.

Additive Main Effect and Multiplicative Interaction analysis for grain yield of early maturing sorghum [Sorghum bicolor (L.) Moench] varieties in drought prone areas of Central Tigray, Northern Ethiopia

2020 ◽  
Author(s):  
Fantaye Belay ◽  
Hintsa Meresa ◽  
Shambel Syum
Keyword(s):  
Grain Yield ◽  
Analysis Of Variance ◽  
Interaction Analysis ◽  
Block Design ◽  
Northern Ethiopia ◽  
Environment Interaction ◽  
Multiplicative Interaction ◽  
Tigray Region ◽  
Early Maturing ◽  
Testing Environments

Abstract Shortage of widely adapted and high yielding variety is one of the major bottlenecks for production and productivity of sorghum in dry lowlands of Tigray region, northern Ethiopia. A field experiment was conducted during the main seasons of 2017and 2019 at four locations using randomized complete block design with three replications to evaluate the performance of ten early maturing sorghum genotypes for grain yield using AMMI (Additive Main Effects and Multiplicative Interaction) model. The combined analysis of variance revealed highly significant (P≤0.01) genotype (G), environment (E) and genotype × environment interaction (GEI). The significant genotype by environment interaction effects were further partitioned in to two significant interaction principal components by using AMMI model. The AMMI analysis of variance showed that the genotype, environment and interaction sum squares contributed 41.55 %, 28.67 % and 29.78 % to the treatment sum squares for grain yield respectively. In addition the first two IPCAs and interaction residual were significant. The first two IPCAs accounted for a total of 82.20 % of the interaction sum square. The results revealed that the observed yield variation among genotypes were due to genetic potential of genotypes and interaction rather than location differences. The highest yield was obtained from ESH-1 (3276 kg ha-1), while the lowest was from Grana-1 (2094 kg ha-1) and the average grain yield of genotypes was 2462 kg ha-1. Therefore, ESH-1 is selected as the best stable hybrid with consistent yielding performance across the testing environments in dry lowland areas of Abergelle and similar agro-ecologies in Tigray region, northern Ethiopia.

scholarly journals Genotype by environment interaction and yield stability analysis of quality protein maize genotypes in Terai Region of Nepal

2013 ◽  
Vol 1 (2) ◽  
pp. 74-78 ◽  
Author(s):  
Jiban Shrestha
Keyword(s):  
Grain Yield ◽  
Block Design ◽  
Genotype By Environment Interaction ◽  
Yield Stability ◽  
Coefficient Of Determination ◽  
Quality Protein Maize ◽  
Environment Interaction ◽  
Maize Breeding ◽  
Maize Genotypes ◽  
Joint Regression Analysis

Grain yield stability for the new maize genotypes is an important target in maize breeding programs. The main objective of this study was to identify stable high yielding quality protein maize (QPM) genotypes under various locations and years in terai region of Nepal. Six quality protein maize genotypes along with Poshilo Makai-1 (Standard Check) and Farmer’s Variety (Local Check) were tested at three different locations namely Ayodhyapuri-2, Devendrapur, Madi, Chitwan; Rajahar-8, Bartandi, Rajahar,  Nawalparasi; Mangalpur-2, Rampur,  Chitwan during  2011 and 2012 spring and winter seasons under rainfed condition.  The experiment was conducted using Randomized Complete Block Design with two replications in farmer’s fields. There was considerable variation among genotypes and environments for grain yield. The analysis of variance showed that mean squares of environments (E) was highly significant and genotypes (G) and genotype x environment interaction (GEI) were non significant. The genotypes S03TLYQ-AB02 and RampurS03FQ02 respectively produced the higher mean grain yield 5422±564 kg/ha and 5274±603 kg/ha across the locations. Joint regression analysis showed that RampurS03FQ02 and S03TLYQ-AB02 with regression coefficient 1.10 and 1.22 respectively are the most stable genotypes over the tested environments. The coefficient of determination (R2) for genotypes Rampur S03FQ02 and S03TLYQ-AB02 were as high as 0.954, confirming their high predictability to stability. Further confirmation from GGE biplot analysis showed that maize genotype S03TLYQ-AB02 followed by Rampur S03FQ02 were more stable and adaptive genotypes across the tested environments. Thus these genotypes could be recommended to farmers for general cultivation.DOI: http://dx.doi.org/10.3126/ijasbt.v1i2.8202 Int J Appl Sci Biotechnol, Vol. 1(2): 75-79

scholarly journals Analysis of Genotype-Environment Interaction and Yield Stability of Introduced Upland Rice in the Groundnut Basin Agroclimatic Zone of Senegal

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Ghislain Kanfany ◽  
Mathieu Anatole Tele Ayenan ◽  
Yedomon Ange Bovys Zoclanclounon ◽  
Talla Kane ◽  
Malick Ndiaye ◽  
...  
Keyword(s):  
Grain Yield ◽  
Upland Rice ◽  
Block Design ◽  
Principal Component ◽  
Stability Index ◽  
Rice Production ◽  
Yield Stability ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Genotype Environment Interaction

Identification of highly performing varieties under Senegalese environment is crucial to sustain rice production. Genotype-environment interaction and stability performance on the grain yield of ten upland rice genotypes were investigated across 11 environments in Senegal during the rainy seasons of 2016 and 2017 to identify adapted varieties. The experiment was conducted using a randomized complete block design with three replications at each environment. Data on grain yield were recorded and analyzed using the additive main effects and multiplicative interaction (AMMI) model. The combined analysis of variance revealed that the grain yield was significantly affected by environment (67.9%), followed by genotype × environment (G × E) interaction (23.6%) and genotype (8.5%). The first two principal component axes were highly significant with 37.5 and 26% of the total observed G × E interaction variation, respectively. GGE biplot grouped the environments into four potential megaenvironments. Based on the yield stability index parameter and ranking GGE biplot, NERICA 8 and ART3-7-L9P8-1-B-B-1 were stable and high-yielding varieties compared to the local check NERICA 6. These varieties should be proposed for cultivation in order to sustain the rice production in the southern part of the groundnut basin of Senegal and used as parental lines in rice breeding program for grain yield improvement.

scholarly journals Genotype – environment interaction study in sugar beet (Beta vulgaris L.)

2016 ◽  
Vol 5 (3) ◽  
pp. 74-86
Author(s):  
Entessar Al Jbawi ◽  
Ahmad Fahd Al Raei ◽  
Ahmad Al Ali ◽  
Hussain Al Zubi
Keyword(s):  
Sugar Beet ◽  
Analysis Of Variance ◽  
Block Design ◽  
Error Variance ◽  
Genotype By Environment Interaction ◽  
Yield Stability ◽  
Environment Interaction ◽  
Combined Analysis ◽  
The Stability ◽  
Stability Statistics

The research was carried out to study the response of 16 cultivars of sugar beet in 3 seasons at one major sugar beet producing location, Hama, in Syria in autumn time, and assess genotype by environment interaction, and to estimate the stability of the varieties performance, according to the yield stability statistics (Ysi), for the studied traits of these varieties. A randomized complete block design with four replications was used. Data collected from each experiment were subjected to simple analysis of variance and after homogenization of error variance, combined analysis for four traits including Sucrose content (SC %), Purity (P %), Root yield (RY ton.ha-1), and Sugar yield (SY ton.ha-1) were carried out. Combined analysis of variance over years, exhibited significant differences (P≤0.05) among the varieties. Results of yield stability statistics (Ysi) revealed that five of the monogerm sugar beet varieties (Vico, Dita, Al Ceste, Chimene, and SR305) were stable for all of the studied traits, during three seasons, which is recommended to be planted in autumn time.International Journal of Environment Vol.5(3) 2016, pp.74-86

scholarly journals Agronomic evaluation of corn (Zea mays L.) genotypes in the warm dry region of Chiapas, Mexico

2020 ◽  
Vol 13 (9) ◽  
Author(s):  
Jesús Martínez-Sánchez ◽  
Néstor Espinosa-Paz ◽  
Pedro Cadena-Iñiguez ◽  
Rafael Ariza-Flores ◽  
Robertony Camas-Gómez
Keyword(s):  
Zea Mays ◽  
Grain Yield ◽  
Plant Height ◽  
Analysis Of Variance ◽  
Central Region ◽  
Zea Mays L ◽  
Block Design ◽  
Experimental Unit ◽  
Environment Interaction ◽  
Female Flowering

Objective. To evaluate the agronomic behavior of corn (Zea mays L.) experimental genotypes in three contrasting environments in the Central region of Chiapas, Mexico. Design / methodology / approach. The experiments took place during the 2016 spring-summer agricultural cycle at Francisco Villa, Villaflores (730 m); San Luis, Suchiapa (600 m) and Ocozocoautla (800 m), at the Central region in the state of Chiapas, Mexico. At the three assessed sites, the climate is warm subhumid with rains in summer and intra-stival drought during the second half of July and the first half of August. The genotypes XT-5614, XT-3402, XT-5610, XT-5612, XT-5627, and BG7415W from the Biogene Company were evaluated, which are used in commercial crops at the Center of Chiapas. All genotypes showed viability greater than 90%. Three experimental sites were evaluated, in a randomized complete block design with four replications. The experimental unit consisted of four 5 m long rows 0.8 m apart. The useful plot was formed by two central furrows. The evaluated variables were: days to male flowering (DMF), days to female flowering (DFF), plant height (PH), cob height (CH), cob length (CL), cob diameter (CD), rows per cob (RC), grains per row and grain yield (YLD) at 14% moisture. These were analyzed with an analysis of variance (ANOVA) and for the genotype x environment interaction (GEI) the additive main effects and multiplicative interaction model (AMMI) were used, with the SAS statistical software and the GEA-R software. Results: The combined analysis of variance detected differences between genotypes (G) for most of the variables except in grains per row. and days to male and female flowering; there were significant differences between environments (A) for all variables, while for the GEI, there were significant differences for the number of rows per cob. The CV was 1.26 (DFF) at 10% (YLD), which indicates an acceptable control (<20%) of the experimental variability. The results indicated genetic variation between evaluated genotypes, which allows the selection of the most outstanding ones. The evaluation environments showed differential effects and this condition is necessary for the evaluation of germplasm for a genetic improvement process. Study limitations/implications: Cob height registered acceptable values given that short plants favor rotting in hot climates when weeds are present before harvest. The flowering of the genotypes was considered acceptable and was earlier (55 d) at the Suchiapa site. Findings/conclusions: Among the assessed genotypes there were significant differences for grain yield, plant height, cob height, cob length and the number of rows; the genotype-by-environment interaction was not significant. The XT 5627 and XT 5610 genotypes showed higher stability, and the former showed higher grain yield. The highest yields were recorded in the environment from Francisco Villa, Villaflores, at the Frailesca region, Chiapas.

scholarly journals Evaluasi Stabilitas Hasil Jagung Hibrida Menggunakan Metode Genotype and Genotype by Environment Interaction Biplot (GGE BIPLOT)

2017 ◽  
Vol 1 (2) ◽  
pp. 97
Author(s):  
Slamet Bambang Priyanto ◽  
Roy Efendi ◽  
Bunyamin Z. ◽  
M. Azrai ◽  
M. Syakir
Keyword(s):  
Grain Yield ◽  
Block Design ◽  
Genotype By Environment Interaction ◽  
Yield Stability ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Maize Hybrids ◽  
Genotype By Environment ◽  
North Sulawesi ◽  
South Sulawesi

<p class="Abstrak">Visualization of GGE biplot analyses was able to explain the genotype by environment interaction. This research was aimed to determine the yield stability of promising experimental maize hybrids in eight locations based GGE biplot method. Ten promising experimental maize hybrids and two commercial hybrid varieties as check, namely: HBSTK01, HBSTK03, HBSTK05, HBSTK06, HBSTK07, HBSTK08, HBSTK09, HBSTK10, HBSTK11, HBSTK13 and Bima 16 and Pertiwi 3 were evaluated in eight locations, ie. Bangka (Bangka Belitung), Probolinggo (East Java), Minahasa Utara (North Sulawesi), Donggala (Central Sulawesi), Soppeng, South Sulawesi, Gowa (South Sulawesi, Konawe (Southeast Sulawesi)and Lombok Barat (West Nusa Tenggara) from May to October 2013. The treatments were arranged in a randomized complete block design (RCBD) with 3 replications. Variable measured was grain yield. Analysis of variance was performed for data from each study site, to determine the performance of each genotype at each location. Yield stability analysis was performed by GGE biplot method using PB tools software. Results showed that genotype H9 (HBSTK11) had the highest biological stability with grain yield of 10.37 t/ha, higer than the overall mean yield. The best hybrid with the highest yield and good stability was hybrid H6 (HBSTK08) of 11.08 t/ha. This experimental hybrid is considered potential to be released as new hybrid variety. North Minahasa is considered the most suitable location for testing, whereas Konawe and West Lombok are least suitable, compared with the other locations.</p>

scholarly journals Genotype X Environment Interaction and Yield Stability in Early-Maturing Cowpea (Vigna unguiculata (L.) Walp.) Landraces in Ethiopia

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yirga Kindie ◽  
Bulti Tesso ◽  
Berhanu Amsalu
Keyword(s):  
Grain Yield ◽  
Analysis Of Variance ◽  
Vigna Unguiculata ◽  
Regression Coefficient ◽  
Yield Stability ◽  
Environment Interaction ◽  
Genotype X Environment ◽  
Genotype Environment Interaction ◽  
Cropping Season ◽  
Early Maturing

The study was conducted to estimate the effects of genotype, environment, and genotype × environment interaction on grain yield and yield-related traits and to identify stability genotype. At six environments, twenty-four cowpea landraces and one check were evaluated in a 5 × 5 triple lattice during the 2019 cropping season. Data were collected on yield and yield-related traits. The analysis of variance for each environment and across environments showed significant differences among genotypes, environments, and GEI for most traits including yield. Environment, genotype, and GEI showed 27.45%, 20.9%, and 49.55% contribution to the total sum of squares, respectively, for grain yield. This indicated that the environments were diverse and most of the variation in grain yield was caused due to interaction and environmental means. G24 (2632 kg ha−1) and G16 (2290 kg ha−1) were the highest yielder and stable genotypes with mean grain yields above the grand mean (2049.28 kg ha−1) and standard check (2273 kg ha−1). G24 and G16 were the most stable genotypes according to cultivar superiority, Wricke’s ecovalence, regression coefficient, and devotion from regression stability models.

scholarly journals Grain Yield Stability of Rice Genotypes

2020 ◽  
Vol 3 (2) ◽  
pp. 116-126
Author(s):  
Jiban Shrestha ◽  
Ujjawal Kumar Singh Kushwaha ◽  
Bidhya Maharjan ◽  
Manoj Kandel ◽  
Suk Bahadur Gurung ◽  
...  
Keyword(s):  
Grain Yield ◽  
Climatic Factors ◽  
Block Design ◽  
Genotype By Environment Interaction ◽  
Yield Stability ◽  
Soil Conditions ◽  
Environment Interaction ◽  
Gge Biplot ◽  
Rice Genotypes ◽  
B 31

Stability analysis identifies the adaptation of a crop genotype in different environments. The objective of this study was to evaluate promising rice genotypes for yield stability at different mid-hill environments of Nepal. The multilocation trials were conducted in 2017 and 2018 at three locations viz Lumle, Kaski; Pakhribas, Dhankuta; and Kabre, Dolakha. Seven rice genotypes namely NR11115-B-B-31-3, NR11139-B-B-B-13-3, NR10676-B-5-3, NR11011-B-B-B-B-29, NR11105-B-B-27, 08FAN10, and Khumal-4 were evaluated in each location. The experiment was laid out in a randomized complete block design with three replications. The rice genotype NR10676-B-5-3 produced the highest grain yield (6.72 t/ha) among all genotypes. The growing environmental factors (climate and soil conditions) affect the grain yield performance of rice genotypes. The variation in climatic factors greatly contributed to the variation in grain yield. Polygon view of genotypic main effect plus genotype-by-environment interaction (GGE) biplot showed that the genotypes NR10676-B-53 and NR11105-B-B-27 were suitable for Lumle; NR11115-B-B-31-3 and NR11139-B-B-B-13-3 for Pakhribas; and 08FAN10 and NR11011-B-B-B-B-29 for Kabre. The GGE biplot showed that genotype NR10676-B-5-3 was stable hence it was near to the point of ideal genotype. This study suggests that NR10676-B-5-3 can be grown for higher grain yield production in mid-hills of Nepal.

scholarly journals Genotype X Environment Interaction of Food Barley

2015 ◽  
Vol 21 ◽  
pp. 41-48
Author(s):  
Gebremedhin Welu
Keyword(s):  
Grain Yield ◽  
Block Design ◽  
Genotype X Environment Interaction ◽  
Environment Interaction ◽  
Genotype X Environment ◽  
Genotype Effect ◽  
Environment Effects ◽  
Randomized Complete Block Design ◽  
Barley Genotypes

The objective of this experiment was to estimate the magnitude of genotype X environment interaction on grain yield and yield related traits. Twelve varieties of food barley were included in the study planted in randomized complete block design with three replications. The ANOVA of combined and individual location revealed significant differences among the food barley genotypes for grain yield and other traits. The results of ANOVA for grain yield showed highly significant (p≤0.01) differences among genotypes evaluated for grain yield at Maychew and significant (p≤0.05) differences in Korem, Alage and Mugulat. The ANOVA over locations showed a highly significant (p≤0.01) variation for the genotype effect, environment effects, genotype X environment interaction (GEI) effect and significant (p≤0.05) variation for GEI effect of yield and for most of the yield related traits of food barley genotypes. Haftysene, Yidogit, Estayish and Basso were the genotypes with relatively high mean grain yield across all locations and they are highly performing genotypes to the area. Among locations, the highest mean grain yield was recorded at Korem and it was a suited environment to all the genotypes whereas Mugulat is unfavoured one. ECOPRINT 21: 41-48, 2014DOI: http://dx.doi.org/10.3126/eco.v21i0.11903

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