scholarly journals Combining Experimental and Modeling Approaches to Understand Genotype x Sowing Date x Environment Interaction Effects on Emergence Rates and Grain Yield of Soybean

2020 ◽  
Vol 11 ◽  
Author(s):  
Jay Ram Lamichhane ◽  
Jean-Noël Aubertot ◽  
Luc Champolivier ◽  
Philippe Debaeke ◽  
Pierre Maury
Keyword(s):  
Grain Yield ◽  
Sowing Date ◽  
Interaction Effects ◽  
Environment Interaction ◽  
Modeling Approaches

Application of physiological understanding in soybean improvement. I. Understanding phenological constraints to adaptation and yield potential

2011 ◽  
Vol 62 (1) ◽  
pp. 1 ◽  
Author(s):  
R. J. Lawn ◽  
A. T. James
Keyword(s):  
Seed Yield ◽  
Environmental Control ◽  
Thermal Environment ◽  
Sowing Date ◽  
Companion Paper ◽  
Interaction Effects ◽  
Yield Potential ◽  
Environment Interaction ◽  
Sowing Dates ◽  
Eastern Australia

The purpose of this paper and its companion1 is to describe how, in eastern Australia, soybean improvement, in terms of both breeding and agronomy, has been informed and influenced over the past four decades by physiological understanding of the environmental control of phenology. This first paper describes how initial attempts to grow soybean in eastern Australia, using varieties and production practices from the southern USA, met with limited success due to large variety × environment interaction effects on seed yield. In particular, there were large variety × location, variety × sowing date, and variety × sowing date × density effects. These various interaction effects were ultimately explained in terms of the effects of photo-thermal environment on the phenology of different varieties, and the consequences for radiation interception, dry matter production, harvest index, and seed yield. This knowledge enabled the formulation of agronomic practices to optimise sowing date and planting arrangement to suit particular varieties, and underpinned the establishment of commercial production in south-eastern Queensland in the early 1970s. It also influenced the establishment and operation over the next three decades of several separate breeding programs, each targeting phenological adaptation to specific latitudinal regions of eastern Australia. This paper also describes how physiological developments internationally, particularly the discovery of the long juvenile trait and to a lesser extent the semi-dwarf ideotype, subsequently enabled an approach to be conceived for broadening the phenological adaptation of soybeans across latitudes and sowing dates. The application of this approach, and its outcomes in terms of varietal improvement, agronomic management, and the structure of the breeding program, are described in the companion paper.

Genotype and genotype × environment interaction effects on the grain yield performance of cowpea genotypes in dryland farming system in South Africa

Euphytica ◽  
2020 ◽  
Vol 216 (5) ◽  
Author(s):  
Abe Shegro Gerrano ◽  
Willem Sternberg Jansen van Rensburg ◽  
Isack Mathew ◽  
Admire I. T. Shayanowako ◽  
Michael Wolday Bairu ◽  
...  
Keyword(s):  
South Africa ◽  
Grain Yield ◽  
Farming System ◽  
Interaction Effects ◽  
Environment Interaction ◽  
Yield Performance ◽  
Genotype Environment Interaction ◽  
Dryland Farming

EFFECT OF SOWING DATE ON GRAIN YIELD OF CRAB GRASS

2001 ◽  
Vol 49 (3) ◽  
pp. 293-297
Author(s):  
S. O. Bakare ◽  
M. G. M. Kolo ◽  
J. A. Oladiran
Keyword(s):  
Grain Yield ◽  
Interaction Effect ◽  
Significant Interaction ◽  
Sowing Date ◽  
Yield Data ◽  
Significant Interaction Effect ◽  
Sowing Dates

There was a significant interaction effect between the variety and the sowing date for the number of productive tillers, indicating that the response to sowing date varied with the variety. A significant reduction in the number of productive tillers became evident when sowing was delayed till 26 June in the straggling variety as compared to sowing dates in May. Lower numbers of productive tillers were also recorded when the sowing of the erect variety was further delayed till 10 July. The grain yield data showed that it is not advisable to sow the straggling variety later than 12 June, while sowing may continue till about 26 June for the erect variety in the study area.

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).

Epistatic Effects and QTL×Environment Interaction Effects of QTLs for Yield and Agronomic Traits in Soybean

2014 ◽  
Vol 40 (1) ◽  
pp. 37
Author(s):  
Hui-Zhen LIANG ◽  
Yong-Liang YU ◽  
Hong-Qi YANG ◽  
Hai-Yang ZHANG ◽  
Wei DONG ◽  
...  
Keyword(s):  
Agronomic Traits ◽  
Interaction Effects ◽  
Environment Interaction ◽  
Epistatic Effects

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

Stability Analysis of Agronomic Traits for Maize (Zea Mays L.) Genotypes Based on Ammi Model

2021 ◽  
Vol 50 (2) ◽  
pp. 343-350
Author(s):  
Meijin Ye ◽  
Zhaoyang Chen ◽  
Bingbing Liu ◽  
Haiwang Yue
Keyword(s):  
Grain Yield ◽  
Agronomic Traits ◽  
Interaction Model ◽  
Genotype By Environment Interaction ◽  
Kernel Weight ◽  
Environment Interaction ◽  
Maize Hybrids ◽  
Genotype By Environment ◽  
Ammi Model ◽  
The Ideal

Stability and adaptability of promising maize hybrids in terms of three agronomic traits (grain yield, ear weight and 100-kernel weight) in multi-environments trials were evaluated. The analysis of AMMI model indicated that the all three agronomic traits showed highly significant differences (p < 0.01) on genotype, environment and genotype by environment interaction. Results showed that genotypes Hengyu321 (G9), Yufeng303 (G10) and Huanong138 (G3) were of higher stability on grain yield, ear weight and 100-kernel weight, respectively. Genotypes Hengyu1587 (G8) and Hengyu321 (G9) showed good performance in terms of grain yield, whereas Longping208 (G2) and Weike966 (G12) showed broad adaptability for ear weight. It was also found that the genotypes with better adaptability in terms of 100-kernel weight were Zhengdan958 (G5) and Weike966 (G12). The genotype and environment interaction model based on AMMI analysis indicated that Hengyu1587 and Hengyu321 were the ideal genotypes, due to extensive adaptability and high grain yield under both testing sites. Bangladesh J. Bot. 50(2): 343-350, 2021 (June)

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