On-farm multi-location evaluation of genotype by environment interactions for seed yield and cooking time in common bean

Common bean is the most significant pulse in Mediterranean countries and high yield performance is required to become commercially successful. Seven common bean genotypes were evaluated for yield and stability along with their interrelationship with agronomical, physicochemical and quality characteristics. An analysis of variance was conducted to test main effects and interactions between plant traits and environments. Significant variation among genotypes occurred for seed yield and a strong positive correlation was observed between seed yield and pods m-2. High Genetic Coefficient of Variation (GCV) values combined with high heritability for traits as seed yield, cooking time, hydration capacity and protein content were recorded. The GGE biplot analysis indicated two cultivars as superior genotypes that combine high yield, stability, short cooking time and high protein content. Furthermore, a local population assessed as promising genetic material for the selection of elite lines with high yield and short cooking time.

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Investigation of Genotype by Environment Interactions for Seed Zinc and Iron Concentration and Iron Bioavailability in Common Bean

Frontiers in Plant Science ◽

10.3389/fpls.2021.670965 ◽

2021 ◽

Vol 12 ◽

Author(s):

Dennis N. Katuuramu ◽

Jason A. Wiesinger ◽

Gabriel B. Luyima ◽

Stanley T. Nkalubo ◽

Raymond P. Glahn ◽

...

Keyword(s):

Common Bean ◽

Zinc Concentration ◽

Phenotypic Variability ◽

Crop Improvement ◽

Iron Concentration ◽

Iron Bioavailability ◽

Environment Interaction ◽

Genotype By Environment Interactions ◽

Genotype By Environment ◽

Iron And Zinc

Iron and zinc malnutrition are global public health concerns afflicting mostly infants, children, and women in low- and middle-income countries with widespread consumption of plant-based diets. Common bean is a widely consumed staple crop around the world and is an excellent source of protein, fiber, and minerals including iron and zinc. The development of nutrient-dense common bean varieties that deliver more bioavailable iron and zinc with a high level of trait stability requires a measurement of the contributions from genotype, environment, and genotype by environment interactions. In this research, we investigated the magnitude of genotype by environment interaction for seed zinc and iron concentration and seed iron bioavailability (FeBIO) using a set of nine test genotypes and three farmers’ local check varieties. The research germplasm was evaluated for two field seasons across nine on-farm locations in three agro-ecological zones in Uganda. Seed zinc concentration ranged from 18.0 to 42.0 μg g–1 and was largely controlled by genotype, location, and the interaction between location and season [28.0, 26.2, and 14.7% of phenotypic variability explained (PVE), respectively]. Within a genotype, zinc concentration ranged on average 12 μg g–1 across environments. Seed iron concentration varied from 40.7 to 96.7 μg g–1 and was largely controlled by genotype, location, and the interaction between genotype, location, and season (25.7, 17.4, and 13.7% of PVE, respectively). Within a genotype, iron concentration ranged on average 28 μg g–1 across environments. Seed FeBIO ranged from 8 to 116% of Merlin navy control and was largely controlled by genotype (68.3% of PVE). The red mottled genotypes (Rozi Koko and Chijar) accumulated the most seed zinc and iron concentration, while the yellow (Ervilha and Cebo Cela) and white (Blanco Fanesquero) genotypes had the highest seed FeBIO and performed better than the three farmers’ local check genotypes (NABE-4, NABE-15, and Masindi yellow). The genotypes with superior and stable trait performance, especially the Manteca seed class which combine high iron and zinc concentrations with high FeBIO, would serve as valuable parental materials for crop improvement breeding programs aimed at enhancing the nutritional value of the common bean.

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Genotype by environment interactions for seed yield and oil content in sunflower (H. annuus L.) using AMMI model

Helia ◽

10.2298/hel1154079m ◽

2011 ◽

Vol 34 (54) ◽

pp. 79-88 ◽

Cited By ~ 4

Author(s):

R. Marinkovic ◽

M. Jockovic ◽

A. Marjanovic-Jeromela ◽

S. Jocic ◽

M. Ciric ◽

...

Keyword(s):

Seed Yield ◽

Oil Content ◽

Genotype By Environment Interactions ◽

Genotype By Environment ◽

Ammi Model

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Tackling G × E × M interactions to close on-farm yield-gaps: creating novel pathways for crop improvement by predicting contributions of genetics and management to crop productivity

Theoretical and Applied Genetics ◽

10.1007/s00122-021-03812-3 ◽

2021 ◽

Author(s):

Mark Cooper ◽

Kai P. Voss-Fels ◽

Carlos D. Messina ◽

Tom Tang ◽

Graeme L. Hammer

Keyword(s):

Climate Change ◽

Crop Improvement ◽

Target Population ◽

Crop Productivity ◽

Climate Change Scenarios ◽

Global Food Security ◽

Genotype By Environment ◽

Improvement Strategies ◽

Yield Gaps ◽

On Farm

Abstract Key message Climate change and Genotype-by-Environment-by-Management interactions together challenge our strategies for crop improvement. Research to advance prediction methods for breeding and agronomy is opening new opportunities to tackle these challenges and overcome on-farm crop productivity yield-gaps through design of responsive crop improvement strategies. Abstract Genotype-by-Environment-by-Management (G × E × M) interactions underpin many aspects of crop productivity. An important question for crop improvement is “How can breeders and agronomists effectively explore the diverse opportunities within the high dimensionality of the complex G × E × M factorial to achieve sustainable improvements in crop productivity?” Whenever G × E × M interactions make important contributions to attainment of crop productivity, we should consider how to design crop improvement strategies that can explore the potential space of G × E × M possibilities, reveal the interesting Genotype–Management (G–M) technology opportunities for the Target Population of Environments (TPE), and enable the practical exploitation of the associated improved levels of crop productivity under on-farm conditions. Climate change adds additional layers of complexity and uncertainty to this challenge, by introducing directional changes in the environmental dimension of the G × E × M factorial. These directional changes have the potential to create further conditional changes in the contributions of the genetic and management dimensions to future crop productivity. Therefore, in the presence of G × E × M interactions and climate change, the challenge for both breeders and agronomists is to co-design new G–M technologies for a non-stationary TPE. Understanding these conditional changes in crop productivity through the relevant sciences for each dimension, Genotype, Environment, and Management, creates opportunities to predict novel G–M technology combinations suitable to achieve sustainable crop productivity and global food security targets for the likely climate change scenarios. Here we consider critical foundations required for any prediction framework that aims to move us from the current unprepared state of describing G × E × M outcomes to a future responsive state equipped to predict the crop productivity consequences of G–M technology combinations for the range of environmental conditions expected for a complex, non-stationary TPE under the influences of climate change.

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Genotype by environment interactions and combining ability for strawberry families grown in diverse environments

Euphytica ◽

10.1007/s10681-017-1892-6 ◽

2017 ◽

Vol 213 (5) ◽

Cited By ~ 10

Author(s):

Megan M. Mathey ◽

Sonali Mookerjee ◽

Lise L. Mahoney ◽

Kazim Gündüz ◽

Umesh Rosyara ◽

...

Keyword(s):

Combining Ability ◽

Genotype By Environment Interactions ◽

Genotype By Environment

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Genetic Parameters and Genotype-by-Environment Interactions in Regional Progeny Tests of Pinus taeda L. in the Southern USA

Forest Science ◽

10.1093/forsci/fxaa035 ◽

2020 ◽

Author(s):

Edwin Lauer ◽

Andrew Sims ◽

Steven McKeand ◽

Fikret Isik

Keyword(s):

Genetic Variation ◽

Pinus Taeda ◽

Genetic Parameters ◽

Genetic Correlations ◽

Breeding Program ◽

Model Fit ◽

Genotype By Environment Interactions ◽

Genotype By Environment ◽

Progeny Tests ◽

Pinus Taeda L

Abstract Genetic parameters were estimated using a five-series multienvironment trial of Pinus taeda L. in the southern USA. There were 324 half-sib families planted in five test series across 37 locations. A set of six variance/covariance matrices for the genotype-by-environment (G × E) effect for tree height and diameter were compared on the basis of model fit. In single-series analysis, extended factor analytical models provided generally superior model fit to simpler models for both traits; however, in the combined-series analysis, diameter was optimally modeled using simpler variance/covariance structures. A three-way compound term for modeling G × E interactions among and within series yielded substantial improvements in terms of model fit and standard errors of predictions. Heritability of family means ranged between 0.63 and 0.90 for both height and diameter. Average additive genetic correlations among sites were 0.70 and 0.61 for height and diameter, respectively, suggesting the presence of some G × E interaction. Pairs of sites with the lowest additive genetic correlations were located at opposite ends of the latitude range. Latent factor regression revealed a small number of parents with large factor scores that changed ranks significantly between southern and northern environments. Study Implications Multienvironmental progeny tests of loblolly pine (Pinus taeda L.) were established over 10 years in the southern United States to understand the genetic variation for the traits of economic importance. There was substantial genetic variation between open-pollinated families, suggesting that family selection would be efficient in the breeding program. Genotype-by-environment interactions were negligible among sites in the deployment region but became larger between sites at the extremes of the distribution. The data from these trials are invaluable in informing the breeding program about the genetic merit of selection candidates and their potential interaction with the environment. These results can be used to guide deployment decisions in the southern USA, helping landowners match germplasm with geography to achieve optimal financial returns and conservation outcomes.

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