Genotype-Environment interactions in rice (Oryza sativa L.) in temperate region using the Joint Regression Analysis and AMMI methods

Summary The objectives of the present study were to evaluate the genotype-environment interaction (GxE) in a temperate region of rice (Oryza sativa L.) using a low number of environments and to compare the Joint Regression Analysis (JRA) and AMMI (additive main effects and multiplicative interaction...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Asenjo, C. A. [verfasserIn] Bezus, R. Acciaresi, H. A.

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2003

Schlagwörter:	L genotype-environment interactions low number environment plant breeding

Anmerkung:	© Akadémiai Kiadó, Budapest 2003

Übergeordnetes Werk:	Enthalten in: Cereal research communications - Budapest : Akadémiai Kiadó, 1973, 31(2003), 1-2 vom: März, Seite 97-104
Übergeordnetes Werk:	volume:31 ; year:2003 ; number:1-2 ; month:03 ; pages:97-104

Links:	Volltext

DOI / URN:	10.1007/BF03543255

Katalog-ID:	SPR038766574

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allfields	10.1007/BF03543255 doi (DE-627)SPR038766574 (SPR)BF03543255-e DE-627 ger DE-627 rakwb eng Asenjo, C. A. verfasserin aut Genotype-Environment interactions in rice (Oryza sativa L.) in temperate region using the Joint Regression Analysis and AMMI methods 2003 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Akadémiai Kiadó, Budapest 2003 Summary The objectives of the present study were to evaluate the genotype-environment interaction (GxE) in a temperate region of rice (Oryza sativa L.) using a low number of environments and to compare the Joint Regression Analysis (JRA) and AMMI (additive main effects and multiplicative interactions) methods in the quantification of GxE. Grain yield (GY) of commercial genotypes and inbreed lines of rice sowing in Argentina were used. GxE and the environment sum of square (SS) was highly significant (p<0.001), meaning while genotype SS was non- significant. Regression heterogeneity of JRA was non-significant explaining only 16,8 % of SS. The first axis of principal component analysis of AMMI explained 73% of interactions SS. The AMMI model retained 70.62 % of the total SS, while only 57.9 % was explained by JRA. The AMMI model was more efficient that JRA to evaluate GxE in rice under low number of environments. Both, registered low radiation and temperature values near crop anthesis appear related to high expression of GxE in this marginal region. The growing cycle of genotypes is an important factor to take into account in the objectives of rice breeding program for temperate zones. L (dpeaa)DE-He213 genotype-environment interactions (dpeaa)DE-He213 low number environment (dpeaa)DE-He213 plant breeding (dpeaa)DE-He213 Bezus, R. aut Acciaresi, H. A. aut Enthalten in Cereal research communications Budapest : Akadémiai Kiadó, 1973 31(2003), 1-2 vom: März, Seite 97-104 (DE-627)528856642 (DE-600)2296169-0 1788-9170 nnns volume:31 year:2003 number:1-2 month:03 pages:97-104 https://dx.doi.org/10.1007/BF03543255 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_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 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_374 GBV_ILN_602 GBV_ILN_636 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_2018 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_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_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_2946 GBV_ILN_2949 GBV_ILN_2951 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 AR 31 2003 1-2 03 97-104
spelling	10.1007/BF03543255 doi (DE-627)SPR038766574 (SPR)BF03543255-e DE-627 ger DE-627 rakwb eng Asenjo, C. A. verfasserin aut Genotype-Environment interactions in rice (Oryza sativa L.) in temperate region using the Joint Regression Analysis and AMMI methods 2003 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Akadémiai Kiadó, Budapest 2003 Summary The objectives of the present study were to evaluate the genotype-environment interaction (GxE) in a temperate region of rice (Oryza sativa L.) using a low number of environments and to compare the Joint Regression Analysis (JRA) and AMMI (additive main effects and multiplicative interactions) methods in the quantification of GxE. Grain yield (GY) of commercial genotypes and inbreed lines of rice sowing in Argentina were used. GxE and the environment sum of square (SS) was highly significant (p<0.001), meaning while genotype SS was non- significant. Regression heterogeneity of JRA was non-significant explaining only 16,8 % of SS. The first axis of principal component analysis of AMMI explained 73% of interactions SS. The AMMI model retained 70.62 % of the total SS, while only 57.9 % was explained by JRA. The AMMI model was more efficient that JRA to evaluate GxE in rice under low number of environments. Both, registered low radiation and temperature values near crop anthesis appear related to high expression of GxE in this marginal region. The growing cycle of genotypes is an important factor to take into account in the objectives of rice breeding program for temperate zones. L (dpeaa)DE-He213 genotype-environment interactions (dpeaa)DE-He213 low number environment (dpeaa)DE-He213 plant breeding (dpeaa)DE-He213 Bezus, R. aut Acciaresi, H. A. aut Enthalten in Cereal research communications Budapest : Akadémiai Kiadó, 1973 31(2003), 1-2 vom: März, Seite 97-104 (DE-627)528856642 (DE-600)2296169-0 1788-9170 nnns volume:31 year:2003 number:1-2 month:03 pages:97-104 https://dx.doi.org/10.1007/BF03543255 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_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 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_374 GBV_ILN_602 GBV_ILN_636 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_2018 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_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_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_2946 GBV_ILN_2949 GBV_ILN_2951 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 AR 31 2003 1-2 03 97-104
allfields_unstemmed	10.1007/BF03543255 doi (DE-627)SPR038766574 (SPR)BF03543255-e DE-627 ger DE-627 rakwb eng Asenjo, C. A. verfasserin aut Genotype-Environment interactions in rice (Oryza sativa L.) in temperate region using the Joint Regression Analysis and AMMI methods 2003 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Akadémiai Kiadó, Budapest 2003 Summary The objectives of the present study were to evaluate the genotype-environment interaction (GxE) in a temperate region of rice (Oryza sativa L.) using a low number of environments and to compare the Joint Regression Analysis (JRA) and AMMI (additive main effects and multiplicative interactions) methods in the quantification of GxE. Grain yield (GY) of commercial genotypes and inbreed lines of rice sowing in Argentina were used. GxE and the environment sum of square (SS) was highly significant (p<0.001), meaning while genotype SS was non- significant. Regression heterogeneity of JRA was non-significant explaining only 16,8 % of SS. The first axis of principal component analysis of AMMI explained 73% of interactions SS. The AMMI model retained 70.62 % of the total SS, while only 57.9 % was explained by JRA. The AMMI model was more efficient that JRA to evaluate GxE in rice under low number of environments. Both, registered low radiation and temperature values near crop anthesis appear related to high expression of GxE in this marginal region. The growing cycle of genotypes is an important factor to take into account in the objectives of rice breeding program for temperate zones. L (dpeaa)DE-He213 genotype-environment interactions (dpeaa)DE-He213 low number environment (dpeaa)DE-He213 plant breeding (dpeaa)DE-He213 Bezus, R. aut Acciaresi, H. A. aut Enthalten in Cereal research communications Budapest : Akadémiai Kiadó, 1973 31(2003), 1-2 vom: März, Seite 97-104 (DE-627)528856642 (DE-600)2296169-0 1788-9170 nnns volume:31 year:2003 number:1-2 month:03 pages:97-104 https://dx.doi.org/10.1007/BF03543255 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_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 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_374 GBV_ILN_602 GBV_ILN_636 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_2018 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_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_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_2946 GBV_ILN_2949 GBV_ILN_2951 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 AR 31 2003 1-2 03 97-104
allfieldsGer	10.1007/BF03543255 doi (DE-627)SPR038766574 (SPR)BF03543255-e DE-627 ger DE-627 rakwb eng Asenjo, C. A. verfasserin aut Genotype-Environment interactions in rice (Oryza sativa L.) in temperate region using the Joint Regression Analysis and AMMI methods 2003 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Akadémiai Kiadó, Budapest 2003 Summary The objectives of the present study were to evaluate the genotype-environment interaction (GxE) in a temperate region of rice (Oryza sativa L.) using a low number of environments and to compare the Joint Regression Analysis (JRA) and AMMI (additive main effects and multiplicative interactions) methods in the quantification of GxE. Grain yield (GY) of commercial genotypes and inbreed lines of rice sowing in Argentina were used. GxE and the environment sum of square (SS) was highly significant (p<0.001), meaning while genotype SS was non- significant. Regression heterogeneity of JRA was non-significant explaining only 16,8 % of SS. The first axis of principal component analysis of AMMI explained 73% of interactions SS. The AMMI model retained 70.62 % of the total SS, while only 57.9 % was explained by JRA. The AMMI model was more efficient that JRA to evaluate GxE in rice under low number of environments. Both, registered low radiation and temperature values near crop anthesis appear related to high expression of GxE in this marginal region. The growing cycle of genotypes is an important factor to take into account in the objectives of rice breeding program for temperate zones. L (dpeaa)DE-He213 genotype-environment interactions (dpeaa)DE-He213 low number environment (dpeaa)DE-He213 plant breeding (dpeaa)DE-He213 Bezus, R. aut Acciaresi, H. A. aut Enthalten in Cereal research communications Budapest : Akadémiai Kiadó, 1973 31(2003), 1-2 vom: März, Seite 97-104 (DE-627)528856642 (DE-600)2296169-0 1788-9170 nnns volume:31 year:2003 number:1-2 month:03 pages:97-104 https://dx.doi.org/10.1007/BF03543255 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_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 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_374 GBV_ILN_602 GBV_ILN_636 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_2018 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_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_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_2946 GBV_ILN_2949 GBV_ILN_2951 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 AR 31 2003 1-2 03 97-104
allfieldsSound	10.1007/BF03543255 doi (DE-627)SPR038766574 (SPR)BF03543255-e DE-627 ger DE-627 rakwb eng Asenjo, C. A. verfasserin aut Genotype-Environment interactions in rice (Oryza sativa L.) in temperate region using the Joint Regression Analysis and AMMI methods 2003 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Akadémiai Kiadó, Budapest 2003 Summary The objectives of the present study were to evaluate the genotype-environment interaction (GxE) in a temperate region of rice (Oryza sativa L.) using a low number of environments and to compare the Joint Regression Analysis (JRA) and AMMI (additive main effects and multiplicative interactions) methods in the quantification of GxE. Grain yield (GY) of commercial genotypes and inbreed lines of rice sowing in Argentina were used. GxE and the environment sum of square (SS) was highly significant (p<0.001), meaning while genotype SS was non- significant. Regression heterogeneity of JRA was non-significant explaining only 16,8 % of SS. The first axis of principal component analysis of AMMI explained 73% of interactions SS. The AMMI model retained 70.62 % of the total SS, while only 57.9 % was explained by JRA. The AMMI model was more efficient that JRA to evaluate GxE in rice under low number of environments. Both, registered low radiation and temperature values near crop anthesis appear related to high expression of GxE in this marginal region. The growing cycle of genotypes is an important factor to take into account in the objectives of rice breeding program for temperate zones. L (dpeaa)DE-He213 genotype-environment interactions (dpeaa)DE-He213 low number environment (dpeaa)DE-He213 plant breeding (dpeaa)DE-He213 Bezus, R. aut Acciaresi, H. A. aut Enthalten in Cereal research communications Budapest : Akadémiai Kiadó, 1973 31(2003), 1-2 vom: März, Seite 97-104 (DE-627)528856642 (DE-600)2296169-0 1788-9170 nnns volume:31 year:2003 number:1-2 month:03 pages:97-104 https://dx.doi.org/10.1007/BF03543255 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_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 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_374 GBV_ILN_602 GBV_ILN_636 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_2018 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_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_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_2946 GBV_ILN_2949 GBV_ILN_2951 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4346 GBV_ILN_4393 GBV_ILN_4700 AR 31 2003 1-2 03 97-104
language	English
source	Enthalten in Cereal research communications 31(2003), 1-2 vom: März, Seite 97-104 volume:31 year:2003 number:1-2 month:03 pages:97-104
sourceStr	Enthalten in Cereal research communications 31(2003), 1-2 vom: März, Seite 97-104 volume:31 year:2003 number:1-2 month:03 pages:97-104
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	L genotype-environment interactions low number environment plant breeding
isfreeaccess_bool	false
container_title	Cereal research communications
authorswithroles_txt_mv	Asenjo, C. A. @@aut@@ Bezus, R. @@aut@@ Acciaresi, H. A. @@aut@@
publishDateDaySort_date	2003-03-01T00:00:00Z
hierarchy_top_id	528856642
id	SPR038766574
language_de	englisch
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author	Asenjo, C. A.
spellingShingle	Asenjo, C. A. misc L misc genotype-environment interactions misc low number environment misc plant breeding Genotype-Environment interactions in rice (Oryza sativa L.) in temperate region using the Joint Regression Analysis and AMMI methods
authorStr	Asenjo, C. A.
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topic_title	Genotype-Environment interactions in rice (Oryza sativa L.) in temperate region using the Joint Regression Analysis and AMMI methods L (dpeaa)DE-He213 genotype-environment interactions (dpeaa)DE-He213 low number environment (dpeaa)DE-He213 plant breeding (dpeaa)DE-He213
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title	Genotype-Environment interactions in rice (Oryza sativa L.) in temperate region using the Joint Regression Analysis and AMMI methods
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title_full	Genotype-Environment interactions in rice (Oryza sativa L.) in temperate region using the Joint Regression Analysis and AMMI methods
author_sort	Asenjo, C. A.
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author_browse	Asenjo, C. A. Bezus, R. Acciaresi, H. A.
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author-letter	Asenjo, C. A.
doi_str_mv	10.1007/BF03543255
title_sort	genotype-environment interactions in rice (oryza sativa l.) in temperate region using the joint regression analysis and ammi methods
title_auth	Genotype-Environment interactions in rice (Oryza sativa L.) in temperate region using the Joint Regression Analysis and AMMI methods
abstract	Summary The objectives of the present study were to evaluate the genotype-environment interaction (GxE) in a temperate region of rice (Oryza sativa L.) using a low number of environments and to compare the Joint Regression Analysis (JRA) and AMMI (additive main effects and multiplicative interactions) methods in the quantification of GxE. Grain yield (GY) of commercial genotypes and inbreed lines of rice sowing in Argentina were used. GxE and the environment sum of square (SS) was highly significant (p<0.001), meaning while genotype SS was non- significant. Regression heterogeneity of JRA was non-significant explaining only 16,8 % of SS. The first axis of principal component analysis of AMMI explained 73% of interactions SS. The AMMI model retained 70.62 % of the total SS, while only 57.9 % was explained by JRA. The AMMI model was more efficient that JRA to evaluate GxE in rice under low number of environments. Both, registered low radiation and temperature values near crop anthesis appear related to high expression of GxE in this marginal region. The growing cycle of genotypes is an important factor to take into account in the objectives of rice breeding program for temperate zones. © Akadémiai Kiadó, Budapest 2003
abstractGer	Summary The objectives of the present study were to evaluate the genotype-environment interaction (GxE) in a temperate region of rice (Oryza sativa L.) using a low number of environments and to compare the Joint Regression Analysis (JRA) and AMMI (additive main effects and multiplicative interactions) methods in the quantification of GxE. Grain yield (GY) of commercial genotypes and inbreed lines of rice sowing in Argentina were used. GxE and the environment sum of square (SS) was highly significant (p<0.001), meaning while genotype SS was non- significant. Regression heterogeneity of JRA was non-significant explaining only 16,8 % of SS. The first axis of principal component analysis of AMMI explained 73% of interactions SS. The AMMI model retained 70.62 % of the total SS, while only 57.9 % was explained by JRA. The AMMI model was more efficient that JRA to evaluate GxE in rice under low number of environments. Both, registered low radiation and temperature values near crop anthesis appear related to high expression of GxE in this marginal region. The growing cycle of genotypes is an important factor to take into account in the objectives of rice breeding program for temperate zones. © Akadémiai Kiadó, Budapest 2003
abstract_unstemmed	Summary The objectives of the present study were to evaluate the genotype-environment interaction (GxE) in a temperate region of rice (Oryza sativa L.) using a low number of environments and to compare the Joint Regression Analysis (JRA) and AMMI (additive main effects and multiplicative interactions) methods in the quantification of GxE. Grain yield (GY) of commercial genotypes and inbreed lines of rice sowing in Argentina were used. GxE and the environment sum of square (SS) was highly significant (p<0.001), meaning while genotype SS was non- significant. Regression heterogeneity of JRA was non-significant explaining only 16,8 % of SS. The first axis of principal component analysis of AMMI explained 73% of interactions SS. The AMMI model retained 70.62 % of the total SS, while only 57.9 % was explained by JRA. The AMMI model was more efficient that JRA to evaluate GxE in rice under low number of environments. Both, registered low radiation and temperature values near crop anthesis appear related to high expression of GxE in this marginal region. The growing cycle of genotypes is an important factor to take into account in the objectives of rice breeding program for temperate zones. © Akadémiai Kiadó, Budapest 2003
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container_issue	1-2
title_short	Genotype-Environment interactions in rice (Oryza sativa L.) in temperate region using the Joint Regression Analysis and AMMI methods
url	https://dx.doi.org/10.1007/BF03543255
remote_bool	true
author2	Bezus, R. Acciaresi, H. A.
author2Str	Bezus, R. Acciaresi, H. A.
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doi_str	10.1007/BF03543255
up_date	2024-07-03T19:51:29.380Z
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Genotype-Environment interactions in rice (Oryza sativa L.) in temperate region using the Joint Regression Analysis and AMMI methods

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