Selection of post-rainy sorghum landraces combining multi-traits mean performance and stability

Anisha, A.; Rajappa, P. V.; Parashuram, P.; Hemalatha, V.; Dhanyashree, R.; Tonapi, Vilas A.; Sujatha, K.; Girish, G.; Madhusudhana, R.

doi:10.1007/s10681-022-03127-4

Cited by 3 publications

(4 citation statements)

References 23 publications

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“…In this panel, a clear segregation between early-and late-flowering landraces and their geographical origin was reported by several authors [6,8,68]. In this case, this segregation could be related to genetic differentiation between the two groups of genotypes [24,69].…”

Section: Discussionsupporting

confidence: 73%

“…In this context, Ref. [6] mentioned a change in heritability for the same genotypes depending on the cultivation area. In this study, moderate estimates of heritability, coupled with a high GAM, were observed for PPW, SL, GY and Nnbr.…”

Section: Discussionmentioning

confidence: 99%

“…Flowering time is the trait that breeders make great efforts to ensure during cultivar development. The earliness is usually considered a drought escape mechanism since the post-flowering drought stress is more damaging than the pre-flowering stage, according to previous studies, and early-flowering genotypes could be more adapted to enhance the tolerance to terminal drought [5][6][7]. However, late-flowering genotypes are usually have better production under favorite growing conditions [8].…”

Section: Introductionmentioning

confidence: 99%

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Characterization and Trait Association Analysis of 27 Pearl Millet Landraces in Southern Tunisia

et al. 2023

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Pearl millet (Pennisetum glaucum (L.) R. Br., 2n = 2x = 14, Poaceae), is a cross-pollinated, warm-season crop grown worldwide. To select genotypes for breeding pearl millet cultivars that adapt to drought condition in southern Tunisia, we evaluated the grain yield (GY) and yield-related traits using a set of 27 landraces at two locations in southern Tunisia for two grown seasons (2019 and 2020). The genetic variability, phenotypic and genotypic association, and path coefficient (PC), based on grain yield (GY) and different yield-related agronomic traits, were evaluated. Analysis of variance and BLUPs value revealed a wide range of variability and the possibility of genetic selection for traits that are advantageous. Broad sense heritability (H) for all the traits ranged from 24.10% for grain yield (GY) to 57.11% for spike length (SL), indicating low to moderate inheritability. Genetic advance as a percentage of the mean (GAM) ranged from high (29.56%) for principal panicle weight (PPW) to moderate for all the traits except from plant high (PH) (7.31%). For all the traits, the phenotypic coefficient of variation (PCV) was higher than genotypic coefficient of variation (GCV), indicating the magnitude of environmental conditions. GY was significantly correlated with all the traits at the genotypic and phenotypic level. According to the path coefficient, the traits PPW and SL displayed the highest direct effects on GY. Heatmap analysis demonstrated a clear segregation between the early and late genotypes based on their geographic origin. Based on the cluster analysis and FAI-BLUPS analysis, genotypes G11, G13, G12, G17 and G18 were selected as the best-performing genotypes with the shortest cycle.

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Section: Discussionsupporting

confidence: 73%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Characterization and Trait Association Analysis of 27 Pearl Millet Landraces in Southern Tunisia

et al. 2023

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“…The metan package provides a comprehensive set of tools for MET data management, manipulating, analyzing, and visualizing the MET data. It has been successfully employed to quantify yield stability in various crops, including rice [ 14 ], lentil [ 22 ], wheat [ 23 ], soybean [ 24 , 25 ], sugarcane [ 26 ], and others [ 27 , 28 , 29 , 30 , 31 ].…”

Section: Introductionmentioning

confidence: 99%

Exploring Genetics by Environment Interactions in Some Rice Genotypes across Varied Environmental Conditions

Ghazy,

Abdelrahman,

El-Agoury

et al. 2023

Plants

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Rice production faces challenges related to diverse climate change processes. Heat stress combined with low humidity, water scarcity, and salinity are the foremost threats in its cultivation. The present investigation aimed at identifying the most resilient rice genotypes with yield stability to cope with the current waves of climate change. A total of 34 rice genotypes were exposed to multilocation trials. These locations had different environmental conditions, mainly normal, heat stress with low humidity, and salinity-affected soils. The genotypes were assessed for their yield stability under these conditions. The newly developed metan package of R-studio was employed to perform additive main effects and multiplicative interactions modelling and genotype-by-environment modelling. The results indicated that there were highly significant differences among the tested genotypes and environments. The main effects of the environments accounted for the largest portion of the total yield sum of squared deviations, while different sets of genotypes showed good performance in different environments. AMMI1 and GGE biplots confirmed that Giza179 was the highest-yielding genotype, whereas Giza178 was considered the most-adopted and highest-yielding genotype across environments. These findings were further confirmed by the which–won–where analysis, which explained that Giza178 has the greatest adaptability to the different climatic conditions under study. While Giza179 was the best under normal environments, N22 recorded the uppermost values under heat stress coupled with low humidity, and GZ1968-S-5-4 manifested superior performance regarding salinity-affected soils. Giza 177 was implicated regarding harsh environments. The mean vs. stability-based rankings indicated that the highest-ranked genotypes were Giza179 > Giza178 > IET1444 > IR65600-77 > GZ1968-S-5-4 > N22 > IR11L236 > IR12G3213. Among them, Giza178, IR65600-77, and IR12G3213 were the most stable genotypes. Furthermore, these results were confirmed by cluster-analysis-based stability indices. A significant and positive correlation was detected between the overall yield under all the environments with panicle length, number of panicles per plant, and thousand grain weight. Our study sheds light on the notion that the Indica/Japonica and Indica types have greater stability potential over the Japonica ones, as well as the potential utilization of genotypes with wide adaptability, stability, and high yield, such as Giza178, in the breeding programs for climate change resilience in rice.

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Adaptability and stability of soybean [Glycine max (L.) Merrill] strains in Central-West Brazil

Silva,

Menezes Filho,

Reis

et al. 2024

Braz. J. of Sci.

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Soybean (Glycine max (L.) Merril) is one of the most important seed legumes in the world due to its high protein and vegetable oil content, being widely used in the food industry and animal feed. However, environmental changes, especially those related to rising global temperatures due to CO2 emissions, are impacting plant productivity, including soybeans. In this context, genetic improvement programs have been fundamental to develop cultivars that are more resistant to abiotic stresses, such as droughts and intense rains. To evaluate the adaptability and stability of soybean genotypes in different environments, statistical methods such as AMMI (Multiplicative Model of Interpretation and Intersection) and BLUP (Best Linear Unbased Prediction) have been widely used. The AMMI model is used to analyze the interaction between genotype and environment, while the BULP considers random genetic effects, providing a more accurate estimate of genetic value. Furthermore, the weighting between stability (WAASBY) (Weighted Average WAASB) index has been used to identify stable, high-performance genotypes, combining stability and yield characteristics. In the research carried out, eleven improved soybean lines were evaluated in five municipalities in the central-western region of Brazil. Data were analyzed using statistical techniques such as linear mixed model, GGE biplot and AMMI and BLUP models. The results indicated that soybean lines showed significant variations in productivity in different environments, highlighting specific genotypes for each location. The productivity prediction analysis showed that the BLUP model was more accurate compared to the AMMI model. Furthermore, the GGE biplot identified the most suitable genotypes for each environment, considering both average performance and stability. Finally, the combination of characteristics from the AMMI and BLUP techniques, using the WAASBY index, made it possible to identify genotypes with high potential for yield and stability. In summary, the results of this research contribute to the development of soybean cultivars more adapted to variable environmental conditions, providing valuable information for genetic improvement programs and agricultural practices in the central-western region of Brazil. The integration of different statistical methods and evaluation indices has been fundamental to improving the selection of genotypes with high performance and stability, contributing to food security and the sustainability of agricultural production.

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Selection of post-rainy sorghum landraces combining multi-traits mean performance and stability

Cited by 3 publications

References 23 publications

Characterization and Trait Association Analysis of 27 Pearl Millet Landraces in Southern Tunisia

Characterization and Trait Association Analysis of 27 Pearl Millet Landraces in Southern Tunisia

Exploring Genetics by Environment Interactions in Some Rice Genotypes across Varied Environmental Conditions

Adaptability and stability of soybean [Glycine max (L.) Merrill] strains in Central-West Brazil

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