Genotype and genotype × environment interaction effects on the grain yield performance of cowpea genotypes in dryland farming system in South Africa
Abstract The identification of stable and adaptable high yielding cowpeas (Vigna unguiculata (L.) Walp.) and highly discriminative environments will be useful for elite cultivar development in South Africa. Two statistical models, the Additive main effects multiplicative interaction (AMMI) and the g...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Gerrano, Abe Shegro [verfasserIn] Jansen van Rensburg, Willem Sternberg [verfasserIn] Mathew, Isack [verfasserIn] Shayanowako, Admire I. T. [verfasserIn] Bairu, Michael Wolday [verfasserIn] Venter, Sonja Louise [verfasserIn] Swart, Wijnand [verfasserIn] Mofokeng, Alina [verfasserIn] Mellem, John [verfasserIn] Labuschagne, Maryke [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Euphytica - Dordrecht [u.a.] : Springer Science + Business Media B.V., 1952, 216(2020), 5 vom: 28. Apr. |
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| Übergeordnetes Werk: |
volume:216 ; year:2020 ; number:5 ; day:28 ; month:04 |
| Links: |
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| DOI / URN: |
10.1007/s10681-020-02611-z |
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| Katalog-ID: |
SPR039557065 |
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| 520 | |a Abstract The identification of stable and adaptable high yielding cowpeas (Vigna unguiculata (L.) Walp.) and highly discriminative environments will be useful for elite cultivar development in South Africa. Two statistical models, the Additive main effects multiplicative interaction (AMMI) and the genotype, genotype by environment biplot analysis have been used extensively to identify superior genotypes and ideal testing environments. Hence, the objective of this study was to identify elite cowpea lines and testing environments using the two models to inform future cultivar development strategies. Fifteen cowpea genotypes were evaluated for yield performance and stability across 3 different locations during 2016 and 2017 growing seasons in South Africa. Genotype main effects were significant for grain yield, while genotype × environment interaction effect was insignificant for grain yield. However, genotypic perfomance for grain yield was significantly affected by seasonal variability. The AMMI analysis of variance showed that genotypes (G), environments (E) and their interaction were significant for grain yield. The G and GE effects accounted for about 10% of the total variation in grain yield, while the environment accounted for 66%. The high yielding genotypes, Vigna Onb, TVU-5431 and Kisumu mix, were adapted to Mafikeng and Potchefstroom sites. Kisumu mix, followed by Vigna Onb, were the most stable genotypes, while Veg cowpea 1, Veg cowpea dacama cream and Veg cowpea 2 were the least stable genotypes across the three sites and seasons. The analysis also showed that Environment 4 (Potchefstroom) was the most ideal site for cowpea production among the test sites. | ||
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| 650 | 4 | |a Genotype by environment interaction |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Stability |7 (dpeaa)DE-He213 | |
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| 700 | 1 | |a Mathew, Isack |e verfasserin |4 aut | |
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| 700 | 1 | |a Bairu, Michael Wolday |e verfasserin |4 aut | |
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| 700 | 1 | |a Swart, Wijnand |e verfasserin |4 aut | |
| 700 | 1 | |a Mofokeng, Alina |e verfasserin |4 aut | |
| 700 | 1 | |a Mellem, John |e verfasserin |4 aut | |
| 700 | 1 | |a Labuschagne, Maryke |e verfasserin |4 aut | |
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10.1007/s10681-020-02611-z doi (DE-627)SPR039557065 (SPR)s10681-020-02611-z-e DE-627 ger DE-627 rakwb eng 630 640 ASE 48.58 bkl Gerrano, Abe Shegro verfasserin aut Genotype and genotype × environment interaction effects on the grain yield performance of cowpea genotypes in dryland farming system in South Africa 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The identification of stable and adaptable high yielding cowpeas (Vigna unguiculata (L.) Walp.) and highly discriminative environments will be useful for elite cultivar development in South Africa. Two statistical models, the Additive main effects multiplicative interaction (AMMI) and the genotype, genotype by environment biplot analysis have been used extensively to identify superior genotypes and ideal testing environments. Hence, the objective of this study was to identify elite cowpea lines and testing environments using the two models to inform future cultivar development strategies. Fifteen cowpea genotypes were evaluated for yield performance and stability across 3 different locations during 2016 and 2017 growing seasons in South Africa. Genotype main effects were significant for grain yield, while genotype × environment interaction effect was insignificant for grain yield. However, genotypic perfomance for grain yield was significantly affected by seasonal variability. The AMMI analysis of variance showed that genotypes (G), environments (E) and their interaction were significant for grain yield. The G and GE effects accounted for about 10% of the total variation in grain yield, while the environment accounted for 66%. The high yielding genotypes, Vigna Onb, TVU-5431 and Kisumu mix, were adapted to Mafikeng and Potchefstroom sites. Kisumu mix, followed by Vigna Onb, were the most stable genotypes, while Veg cowpea 1, Veg cowpea dacama cream and Veg cowpea 2 were the least stable genotypes across the three sites and seasons. The analysis also showed that Environment 4 (Potchefstroom) was the most ideal site for cowpea production among the test sites. Cowpea (dpeaa)DE-He213 Genotype (dpeaa)DE-He213 Genotype by environment interaction (dpeaa)DE-He213 Stability (dpeaa)DE-He213 Jansen van Rensburg, Willem Sternberg verfasserin aut Mathew, Isack verfasserin aut Shayanowako, Admire I. T. verfasserin aut Bairu, Michael Wolday verfasserin aut Venter, Sonja Louise verfasserin aut Swart, Wijnand verfasserin aut Mofokeng, Alina verfasserin aut Mellem, John verfasserin aut Labuschagne, Maryke verfasserin aut Enthalten in Euphytica Dordrecht [u.a.] : Springer Science + Business Media B.V., 1952 216(2020), 5 vom: 28. Apr. (DE-627)312840098 (DE-600)2012322-X 1573-5060 nnns volume:216 year:2020 number:5 day:28 month:04 https://dx.doi.org/10.1007/s10681-020-02611-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_211 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 48.58 ASE AR 216 2020 5 28 04 |
| spelling |
10.1007/s10681-020-02611-z doi (DE-627)SPR039557065 (SPR)s10681-020-02611-z-e DE-627 ger DE-627 rakwb eng 630 640 ASE 48.58 bkl Gerrano, Abe Shegro verfasserin aut Genotype and genotype × environment interaction effects on the grain yield performance of cowpea genotypes in dryland farming system in South Africa 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The identification of stable and adaptable high yielding cowpeas (Vigna unguiculata (L.) Walp.) and highly discriminative environments will be useful for elite cultivar development in South Africa. Two statistical models, the Additive main effects multiplicative interaction (AMMI) and the genotype, genotype by environment biplot analysis have been used extensively to identify superior genotypes and ideal testing environments. Hence, the objective of this study was to identify elite cowpea lines and testing environments using the two models to inform future cultivar development strategies. Fifteen cowpea genotypes were evaluated for yield performance and stability across 3 different locations during 2016 and 2017 growing seasons in South Africa. Genotype main effects were significant for grain yield, while genotype × environment interaction effect was insignificant for grain yield. However, genotypic perfomance for grain yield was significantly affected by seasonal variability. The AMMI analysis of variance showed that genotypes (G), environments (E) and their interaction were significant for grain yield. The G and GE effects accounted for about 10% of the total variation in grain yield, while the environment accounted for 66%. The high yielding genotypes, Vigna Onb, TVU-5431 and Kisumu mix, were adapted to Mafikeng and Potchefstroom sites. Kisumu mix, followed by Vigna Onb, were the most stable genotypes, while Veg cowpea 1, Veg cowpea dacama cream and Veg cowpea 2 were the least stable genotypes across the three sites and seasons. The analysis also showed that Environment 4 (Potchefstroom) was the most ideal site for cowpea production among the test sites. Cowpea (dpeaa)DE-He213 Genotype (dpeaa)DE-He213 Genotype by environment interaction (dpeaa)DE-He213 Stability (dpeaa)DE-He213 Jansen van Rensburg, Willem Sternberg verfasserin aut Mathew, Isack verfasserin aut Shayanowako, Admire I. T. verfasserin aut Bairu, Michael Wolday verfasserin aut Venter, Sonja Louise verfasserin aut Swart, Wijnand verfasserin aut Mofokeng, Alina verfasserin aut Mellem, John verfasserin aut Labuschagne, Maryke verfasserin aut Enthalten in Euphytica Dordrecht [u.a.] : Springer Science + Business Media B.V., 1952 216(2020), 5 vom: 28. Apr. (DE-627)312840098 (DE-600)2012322-X 1573-5060 nnns volume:216 year:2020 number:5 day:28 month:04 https://dx.doi.org/10.1007/s10681-020-02611-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_211 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 48.58 ASE AR 216 2020 5 28 04 |
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10.1007/s10681-020-02611-z doi (DE-627)SPR039557065 (SPR)s10681-020-02611-z-e DE-627 ger DE-627 rakwb eng 630 640 ASE 48.58 bkl Gerrano, Abe Shegro verfasserin aut Genotype and genotype × environment interaction effects on the grain yield performance of cowpea genotypes in dryland farming system in South Africa 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The identification of stable and adaptable high yielding cowpeas (Vigna unguiculata (L.) Walp.) and highly discriminative environments will be useful for elite cultivar development in South Africa. Two statistical models, the Additive main effects multiplicative interaction (AMMI) and the genotype, genotype by environment biplot analysis have been used extensively to identify superior genotypes and ideal testing environments. Hence, the objective of this study was to identify elite cowpea lines and testing environments using the two models to inform future cultivar development strategies. Fifteen cowpea genotypes were evaluated for yield performance and stability across 3 different locations during 2016 and 2017 growing seasons in South Africa. Genotype main effects were significant for grain yield, while genotype × environment interaction effect was insignificant for grain yield. However, genotypic perfomance for grain yield was significantly affected by seasonal variability. The AMMI analysis of variance showed that genotypes (G), environments (E) and their interaction were significant for grain yield. The G and GE effects accounted for about 10% of the total variation in grain yield, while the environment accounted for 66%. The high yielding genotypes, Vigna Onb, TVU-5431 and Kisumu mix, were adapted to Mafikeng and Potchefstroom sites. Kisumu mix, followed by Vigna Onb, were the most stable genotypes, while Veg cowpea 1, Veg cowpea dacama cream and Veg cowpea 2 were the least stable genotypes across the three sites and seasons. The analysis also showed that Environment 4 (Potchefstroom) was the most ideal site for cowpea production among the test sites. Cowpea (dpeaa)DE-He213 Genotype (dpeaa)DE-He213 Genotype by environment interaction (dpeaa)DE-He213 Stability (dpeaa)DE-He213 Jansen van Rensburg, Willem Sternberg verfasserin aut Mathew, Isack verfasserin aut Shayanowako, Admire I. T. verfasserin aut Bairu, Michael Wolday verfasserin aut Venter, Sonja Louise verfasserin aut Swart, Wijnand verfasserin aut Mofokeng, Alina verfasserin aut Mellem, John verfasserin aut Labuschagne, Maryke verfasserin aut Enthalten in Euphytica Dordrecht [u.a.] : Springer Science + Business Media B.V., 1952 216(2020), 5 vom: 28. Apr. (DE-627)312840098 (DE-600)2012322-X 1573-5060 nnns volume:216 year:2020 number:5 day:28 month:04 https://dx.doi.org/10.1007/s10681-020-02611-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_211 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 48.58 ASE AR 216 2020 5 28 04 |
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10.1007/s10681-020-02611-z doi (DE-627)SPR039557065 (SPR)s10681-020-02611-z-e DE-627 ger DE-627 rakwb eng 630 640 ASE 48.58 bkl Gerrano, Abe Shegro verfasserin aut Genotype and genotype × environment interaction effects on the grain yield performance of cowpea genotypes in dryland farming system in South Africa 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The identification of stable and adaptable high yielding cowpeas (Vigna unguiculata (L.) Walp.) and highly discriminative environments will be useful for elite cultivar development in South Africa. Two statistical models, the Additive main effects multiplicative interaction (AMMI) and the genotype, genotype by environment biplot analysis have been used extensively to identify superior genotypes and ideal testing environments. Hence, the objective of this study was to identify elite cowpea lines and testing environments using the two models to inform future cultivar development strategies. Fifteen cowpea genotypes were evaluated for yield performance and stability across 3 different locations during 2016 and 2017 growing seasons in South Africa. Genotype main effects were significant for grain yield, while genotype × environment interaction effect was insignificant for grain yield. However, genotypic perfomance for grain yield was significantly affected by seasonal variability. The AMMI analysis of variance showed that genotypes (G), environments (E) and their interaction were significant for grain yield. The G and GE effects accounted for about 10% of the total variation in grain yield, while the environment accounted for 66%. The high yielding genotypes, Vigna Onb, TVU-5431 and Kisumu mix, were adapted to Mafikeng and Potchefstroom sites. Kisumu mix, followed by Vigna Onb, were the most stable genotypes, while Veg cowpea 1, Veg cowpea dacama cream and Veg cowpea 2 were the least stable genotypes across the three sites and seasons. The analysis also showed that Environment 4 (Potchefstroom) was the most ideal site for cowpea production among the test sites. Cowpea (dpeaa)DE-He213 Genotype (dpeaa)DE-He213 Genotype by environment interaction (dpeaa)DE-He213 Stability (dpeaa)DE-He213 Jansen van Rensburg, Willem Sternberg verfasserin aut Mathew, Isack verfasserin aut Shayanowako, Admire I. T. verfasserin aut Bairu, Michael Wolday verfasserin aut Venter, Sonja Louise verfasserin aut Swart, Wijnand verfasserin aut Mofokeng, Alina verfasserin aut Mellem, John verfasserin aut Labuschagne, Maryke verfasserin aut Enthalten in Euphytica Dordrecht [u.a.] : Springer Science + Business Media B.V., 1952 216(2020), 5 vom: 28. Apr. (DE-627)312840098 (DE-600)2012322-X 1573-5060 nnns volume:216 year:2020 number:5 day:28 month:04 https://dx.doi.org/10.1007/s10681-020-02611-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_211 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 48.58 ASE AR 216 2020 5 28 04 |
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10.1007/s10681-020-02611-z doi (DE-627)SPR039557065 (SPR)s10681-020-02611-z-e DE-627 ger DE-627 rakwb eng 630 640 ASE 48.58 bkl Gerrano, Abe Shegro verfasserin aut Genotype and genotype × environment interaction effects on the grain yield performance of cowpea genotypes in dryland farming system in South Africa 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The identification of stable and adaptable high yielding cowpeas (Vigna unguiculata (L.) Walp.) and highly discriminative environments will be useful for elite cultivar development in South Africa. Two statistical models, the Additive main effects multiplicative interaction (AMMI) and the genotype, genotype by environment biplot analysis have been used extensively to identify superior genotypes and ideal testing environments. Hence, the objective of this study was to identify elite cowpea lines and testing environments using the two models to inform future cultivar development strategies. Fifteen cowpea genotypes were evaluated for yield performance and stability across 3 different locations during 2016 and 2017 growing seasons in South Africa. Genotype main effects were significant for grain yield, while genotype × environment interaction effect was insignificant for grain yield. However, genotypic perfomance for grain yield was significantly affected by seasonal variability. The AMMI analysis of variance showed that genotypes (G), environments (E) and their interaction were significant for grain yield. The G and GE effects accounted for about 10% of the total variation in grain yield, while the environment accounted for 66%. The high yielding genotypes, Vigna Onb, TVU-5431 and Kisumu mix, were adapted to Mafikeng and Potchefstroom sites. Kisumu mix, followed by Vigna Onb, were the most stable genotypes, while Veg cowpea 1, Veg cowpea dacama cream and Veg cowpea 2 were the least stable genotypes across the three sites and seasons. The analysis also showed that Environment 4 (Potchefstroom) was the most ideal site for cowpea production among the test sites. Cowpea (dpeaa)DE-He213 Genotype (dpeaa)DE-He213 Genotype by environment interaction (dpeaa)DE-He213 Stability (dpeaa)DE-He213 Jansen van Rensburg, Willem Sternberg verfasserin aut Mathew, Isack verfasserin aut Shayanowako, Admire I. T. verfasserin aut Bairu, Michael Wolday verfasserin aut Venter, Sonja Louise verfasserin aut Swart, Wijnand verfasserin aut Mofokeng, Alina verfasserin aut Mellem, John verfasserin aut Labuschagne, Maryke verfasserin aut Enthalten in Euphytica Dordrecht [u.a.] : Springer Science + Business Media B.V., 1952 216(2020), 5 vom: 28. Apr. (DE-627)312840098 (DE-600)2012322-X 1573-5060 nnns volume:216 year:2020 number:5 day:28 month:04 https://dx.doi.org/10.1007/s10681-020-02611-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_211 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 48.58 ASE AR 216 2020 5 28 04 |
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Enthalten in Euphytica 216(2020), 5 vom: 28. Apr. volume:216 year:2020 number:5 day:28 month:04 |
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Cowpea Genotype Genotype by environment interaction Stability |
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Gerrano, Abe Shegro @@aut@@ Jansen van Rensburg, Willem Sternberg @@aut@@ Mathew, Isack @@aut@@ Shayanowako, Admire I. T. @@aut@@ Bairu, Michael Wolday @@aut@@ Venter, Sonja Louise @@aut@@ Swart, Wijnand @@aut@@ Mofokeng, Alina @@aut@@ Mellem, John @@aut@@ Labuschagne, Maryke @@aut@@ |
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Walp.) and highly discriminative environments will be useful for elite cultivar development in South Africa. Two statistical models, the Additive main effects multiplicative interaction (AMMI) and the genotype, genotype by environment biplot analysis have been used extensively to identify superior genotypes and ideal testing environments. Hence, the objective of this study was to identify elite cowpea lines and testing environments using the two models to inform future cultivar development strategies. Fifteen cowpea genotypes were evaluated for yield performance and stability across 3 different locations during 2016 and 2017 growing seasons in South Africa. Genotype main effects were significant for grain yield, while genotype × environment interaction effect was insignificant for grain yield. However, genotypic perfomance for grain yield was significantly affected by seasonal variability. 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Gerrano, Abe Shegro |
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Gerrano, Abe Shegro ddc 630 bkl 48.58 misc Cowpea misc Genotype misc Genotype by environment interaction misc Stability Genotype and genotype × environment interaction effects on the grain yield performance of cowpea genotypes in dryland farming system in South Africa |
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630 640 ASE 48.58 bkl Genotype and genotype × environment interaction effects on the grain yield performance of cowpea genotypes in dryland farming system in South Africa Cowpea (dpeaa)DE-He213 Genotype (dpeaa)DE-He213 Genotype by environment interaction (dpeaa)DE-He213 Stability (dpeaa)DE-He213 |
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Genotype and genotype × environment interaction effects on the grain yield performance of cowpea genotypes in dryland farming system in South Africa |
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Gerrano, Abe Shegro Jansen van Rensburg, Willem Sternberg Mathew, Isack Shayanowako, Admire I. T. Bairu, Michael Wolday Venter, Sonja Louise Swart, Wijnand Mofokeng, Alina Mellem, John Labuschagne, Maryke |
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genotype and genotype × environment interaction effects on the grain yield performance of cowpea genotypes in dryland farming system in south africa |
| title_auth |
Genotype and genotype × environment interaction effects on the grain yield performance of cowpea genotypes in dryland farming system in South Africa |
| abstract |
Abstract The identification of stable and adaptable high yielding cowpeas (Vigna unguiculata (L.) Walp.) and highly discriminative environments will be useful for elite cultivar development in South Africa. Two statistical models, the Additive main effects multiplicative interaction (AMMI) and the genotype, genotype by environment biplot analysis have been used extensively to identify superior genotypes and ideal testing environments. Hence, the objective of this study was to identify elite cowpea lines and testing environments using the two models to inform future cultivar development strategies. Fifteen cowpea genotypes were evaluated for yield performance and stability across 3 different locations during 2016 and 2017 growing seasons in South Africa. Genotype main effects were significant for grain yield, while genotype × environment interaction effect was insignificant for grain yield. However, genotypic perfomance for grain yield was significantly affected by seasonal variability. The AMMI analysis of variance showed that genotypes (G), environments (E) and their interaction were significant for grain yield. The G and GE effects accounted for about 10% of the total variation in grain yield, while the environment accounted for 66%. The high yielding genotypes, Vigna Onb, TVU-5431 and Kisumu mix, were adapted to Mafikeng and Potchefstroom sites. Kisumu mix, followed by Vigna Onb, were the most stable genotypes, while Veg cowpea 1, Veg cowpea dacama cream and Veg cowpea 2 were the least stable genotypes across the three sites and seasons. The analysis also showed that Environment 4 (Potchefstroom) was the most ideal site for cowpea production among the test sites. |
| abstractGer |
Abstract The identification of stable and adaptable high yielding cowpeas (Vigna unguiculata (L.) Walp.) and highly discriminative environments will be useful for elite cultivar development in South Africa. Two statistical models, the Additive main effects multiplicative interaction (AMMI) and the genotype, genotype by environment biplot analysis have been used extensively to identify superior genotypes and ideal testing environments. Hence, the objective of this study was to identify elite cowpea lines and testing environments using the two models to inform future cultivar development strategies. Fifteen cowpea genotypes were evaluated for yield performance and stability across 3 different locations during 2016 and 2017 growing seasons in South Africa. Genotype main effects were significant for grain yield, while genotype × environment interaction effect was insignificant for grain yield. However, genotypic perfomance for grain yield was significantly affected by seasonal variability. The AMMI analysis of variance showed that genotypes (G), environments (E) and their interaction were significant for grain yield. The G and GE effects accounted for about 10% of the total variation in grain yield, while the environment accounted for 66%. The high yielding genotypes, Vigna Onb, TVU-5431 and Kisumu mix, were adapted to Mafikeng and Potchefstroom sites. Kisumu mix, followed by Vigna Onb, were the most stable genotypes, while Veg cowpea 1, Veg cowpea dacama cream and Veg cowpea 2 were the least stable genotypes across the three sites and seasons. The analysis also showed that Environment 4 (Potchefstroom) was the most ideal site for cowpea production among the test sites. |
| abstract_unstemmed |
Abstract The identification of stable and adaptable high yielding cowpeas (Vigna unguiculata (L.) Walp.) and highly discriminative environments will be useful for elite cultivar development in South Africa. Two statistical models, the Additive main effects multiplicative interaction (AMMI) and the genotype, genotype by environment biplot analysis have been used extensively to identify superior genotypes and ideal testing environments. Hence, the objective of this study was to identify elite cowpea lines and testing environments using the two models to inform future cultivar development strategies. Fifteen cowpea genotypes were evaluated for yield performance and stability across 3 different locations during 2016 and 2017 growing seasons in South Africa. Genotype main effects were significant for grain yield, while genotype × environment interaction effect was insignificant for grain yield. However, genotypic perfomance for grain yield was significantly affected by seasonal variability. The AMMI analysis of variance showed that genotypes (G), environments (E) and their interaction were significant for grain yield. The G and GE effects accounted for about 10% of the total variation in grain yield, while the environment accounted for 66%. The high yielding genotypes, Vigna Onb, TVU-5431 and Kisumu mix, were adapted to Mafikeng and Potchefstroom sites. Kisumu mix, followed by Vigna Onb, were the most stable genotypes, while Veg cowpea 1, Veg cowpea dacama cream and Veg cowpea 2 were the least stable genotypes across the three sites and seasons. The analysis also showed that Environment 4 (Potchefstroom) was the most ideal site for cowpea production among the test sites. |
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Genotype and genotype × environment interaction effects on the grain yield performance of cowpea genotypes in dryland farming system in South Africa |
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Jansen van Rensburg, Willem Sternberg Mathew, Isack Shayanowako, Admire I. T. Bairu, Michael Wolday Venter, Sonja Louise Swart, Wijnand Mofokeng, Alina Mellem, John Labuschagne, Maryke |
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| score |
7.3989573 |