Grain yield potential and stability of soybean genotypes of different ages across diverse environments in southern Africa

Soybean [Glycine max (L.) Merrill] is an important crop in southern Africa where it is cultivated in a wide range of agro-ecologies. Both spatial and seasonal variability is high in the region. As a result, breeders aim to release varieties with a fine balance of high productivity potential and stab...

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Main Authors: Mushoriwa, H., Mathew, I., Gwata, E.T., Tongoona, Pangirayi, Derera, J.
Format: Journal Article
Language:Inglés
Published: MDPI 2022
Subjects:
Online Access:https://hdl.handle.net/10568/119777
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author Mushoriwa, H.
Mathew, I.
Gwata, E.T.
Tongoona, Pangirayi
Derera, J.
author_browse Derera, J.
Gwata, E.T.
Mathew, I.
Mushoriwa, H.
Tongoona, Pangirayi
author_facet Mushoriwa, H.
Mathew, I.
Gwata, E.T.
Tongoona, Pangirayi
Derera, J.
author_sort Mushoriwa, H.
collection Repository of Agricultural Research Outputs (CGSpace)
description Soybean [Glycine max (L.) Merrill] is an important crop in southern Africa where it is cultivated in a wide range of agro-ecologies. Both spatial and seasonal variability is high in the region. As a result, breeders aim to release varieties with a fine balance of high productivity potential and stability. Genotype × environment interaction (GEI) limits the selection of superior genotypes in heterogeneous environments consequently slowing down breeding progress. This study determined the magnitude of GEI effects and genotype superiority index of soybean genotypes of different ages across three countries in southern Africa. Forty-two soybean genotypes that were released between 1966 and 2013 were evaluated for two seasons at thirteen diverse locations across the three countries. Additive main effects and multiplicative interaction (AMMI) and genotype superiority index tools were used to analyse both productivity and stability performance of these genotypes. The AMMI analysis showed that grain yield variation due to genotypes, environments main effects and GEI were highly significant (p < 0.001). Environments explained the greatest proportion (77%) of the total treatment sum of squares followed by GEI (17.4%) and genotypes (5.6%), justifying the need for multi-environmental trials over many seasons in this region. The two methods were useful in discriminating and identifying common productive and stable genotypes of different ages. The top four high-yielding (>5.0 tha−1) genotypes displayed both stability and genotype superiority index. These findings have important implications for soybean genotype recommendations, breeding progress, and strategy.
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spelling CGSpace1197772025-12-08T10:29:22Z Grain yield potential and stability of soybean genotypes of different ages across diverse environments in southern Africa Mushoriwa, H. Mathew, I. Gwata, E.T. Tongoona, Pangirayi Derera, J. adaptation genotypes genotype environment interaction soybeans yields Soybean [Glycine max (L.) Merrill] is an important crop in southern Africa where it is cultivated in a wide range of agro-ecologies. Both spatial and seasonal variability is high in the region. As a result, breeders aim to release varieties with a fine balance of high productivity potential and stability. Genotype × environment interaction (GEI) limits the selection of superior genotypes in heterogeneous environments consequently slowing down breeding progress. This study determined the magnitude of GEI effects and genotype superiority index of soybean genotypes of different ages across three countries in southern Africa. Forty-two soybean genotypes that were released between 1966 and 2013 were evaluated for two seasons at thirteen diverse locations across the three countries. Additive main effects and multiplicative interaction (AMMI) and genotype superiority index tools were used to analyse both productivity and stability performance of these genotypes. The AMMI analysis showed that grain yield variation due to genotypes, environments main effects and GEI were highly significant (p < 0.001). Environments explained the greatest proportion (77%) of the total treatment sum of squares followed by GEI (17.4%) and genotypes (5.6%), justifying the need for multi-environmental trials over many seasons in this region. The two methods were useful in discriminating and identifying common productive and stable genotypes of different ages. The top four high-yielding (>5.0 tha−1) genotypes displayed both stability and genotype superiority index. These findings have important implications for soybean genotype recommendations, breeding progress, and strategy. 2022 2022-06-08T09:35:49Z 2022-06-08T09:35:49Z Journal Article https://hdl.handle.net/10568/119777 en Open Access application/pdf MDPI Mushoriwa, H., Mathew, I., Gwata, E.T., Tongoona, P. & Derera, J. (2022). Grain yield potential and stability of soybean genotypes of different ages across diverse environments in southern Africa. Agronomy, 12(5), 1-11.
spellingShingle adaptation
genotypes
genotype environment interaction
soybeans
yields
Mushoriwa, H.
Mathew, I.
Gwata, E.T.
Tongoona, Pangirayi
Derera, J.
Grain yield potential and stability of soybean genotypes of different ages across diverse environments in southern Africa
title Grain yield potential and stability of soybean genotypes of different ages across diverse environments in southern Africa
title_full Grain yield potential and stability of soybean genotypes of different ages across diverse environments in southern Africa
title_fullStr Grain yield potential and stability of soybean genotypes of different ages across diverse environments in southern Africa
title_full_unstemmed Grain yield potential and stability of soybean genotypes of different ages across diverse environments in southern Africa
title_short Grain yield potential and stability of soybean genotypes of different ages across diverse environments in southern Africa
title_sort grain yield potential and stability of soybean genotypes of different ages across diverse environments in southern africa
topic adaptation
genotypes
genotype environment interaction
soybeans
yields
url https://hdl.handle.net/10568/119777
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