Optimization of evaluation method for low nitrogen tolerance in soybean germplasm seedlings
Abstract Nitrogen is a critical macro-nutrient for growth and development of soybeans (Glycine max L.). Improving nitrogen use efficiency and developing low nitrogen tolerance varieties are important approaches to mitigate excessive fertilization and maximize production benefits. Precise identificat...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Guoxin, He [verfasserIn] Sujuan, Li [verfasserIn] Jian, Wang [verfasserIn] Yanjun, Li [verfasserIn] Xiaoyuan, Tao [verfasserIn] Zihong, Ye [verfasserIn] Guang, Chen [verfasserIn] Shengchun, Xu [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2024 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Nature B.V. 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Plant growth regulation - Springer Netherlands, 1982, 104(2024), 1 vom: 01. Juli, Seite 487-501 |
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| Übergeordnetes Werk: |
volume:104 ; year:2024 ; number:1 ; day:01 ; month:07 ; pages:487-501 |
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| DOI / URN: |
10.1007/s10725-024-01178-2 |
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SPR057665044 |
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| 520 | |a Abstract Nitrogen is a critical macro-nutrient for growth and development of soybeans (Glycine max L.). Improving nitrogen use efficiency and developing low nitrogen tolerance varieties are important approaches to mitigate excessive fertilization and maximize production benefits. Precise identification of low nitrogen tolerance germplasms serves as a crucial bridge for converting germplasm advantages into breeding advantages. In this study, we optimized a precise evaluation method for low-nitrogen tolerance in soybean seedlings based on Extreme Gradient Boosting (XGBoost) algorithm. Three hundred soybean germplasms were assessed for low-nitrogen tolerance under hydroponic conditions with normal (7.5 mM) and low (0.75 mM) nitrogen levels. Fourteen physiological traits related to low nitrogen tolerance, such as biomass, chlorophyll fluorescence, were measured. The XGBoost-based evaluation method was compared to a traditional fuzzy membership function comprehensive evaluation method for accuracy and applicability. Results showed that the XGBoost-based method ensured precision and reduced the number of determined physiological indicators compared to traditional methods. Furthermore, this approach reduces the number of traits required for precise identification, which reduces time and improves economic benefits. Consequently, the screening efficiency of soybean low nitrogen tolerance germplasms is improved, offering valuable insights for soybean breeding programs. | ||
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10.1007/s10725-024-01178-2 doi (DE-627)SPR057665044 (SPR)s10725-024-01178-2-e DE-627 ger DE-627 rakwb eng 333.7 580 VZ 48.50 bkl 42.41 bkl 48.52 bkl Guoxin, He verfasserin aut Optimization of evaluation method for low nitrogen tolerance in soybean germplasm seedlings 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Nitrogen is a critical macro-nutrient for growth and development of soybeans (Glycine max L.). Improving nitrogen use efficiency and developing low nitrogen tolerance varieties are important approaches to mitigate excessive fertilization and maximize production benefits. Precise identification of low nitrogen tolerance germplasms serves as a crucial bridge for converting germplasm advantages into breeding advantages. In this study, we optimized a precise evaluation method for low-nitrogen tolerance in soybean seedlings based on Extreme Gradient Boosting (XGBoost) algorithm. Three hundred soybean germplasms were assessed for low-nitrogen tolerance under hydroponic conditions with normal (7.5 mM) and low (0.75 mM) nitrogen levels. Fourteen physiological traits related to low nitrogen tolerance, such as biomass, chlorophyll fluorescence, were measured. The XGBoost-based evaluation method was compared to a traditional fuzzy membership function comprehensive evaluation method for accuracy and applicability. Results showed that the XGBoost-based method ensured precision and reduced the number of determined physiological indicators compared to traditional methods. Furthermore, this approach reduces the number of traits required for precise identification, which reduces time and improves economic benefits. Consequently, the screening efficiency of soybean low nitrogen tolerance germplasms is improved, offering valuable insights for soybean breeding programs. Soybeans (dpeaa)DE-He213 Low nitrogen tolerance (dpeaa)DE-He213 Germplasm screening (dpeaa)DE-He213 Optimization of evaluation system (dpeaa)DE-He213 Sujuan, Li verfasserin aut Jian, Wang verfasserin aut Yanjun, Li verfasserin aut Xiaoyuan, Tao verfasserin aut Zihong, Ye verfasserin aut Guang, Chen verfasserin aut Shengchun, Xu verfasserin (orcid)0000-0001-8135-0990 aut Enthalten in Plant growth regulation Springer Netherlands, 1982 104(2024), 1 vom: 01. Juli, Seite 487-501 (DE-627)270932941 (DE-600)1478400-2 1573-5087 nnns volume:104 year:2024 number:1 day:01 month:07 pages:487-501 https://dx.doi.org/10.1007/s10725-024-01178-2 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OLC-PHA 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_72 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_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_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2574 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 48.50 VZ 42.41 VZ 48.52 VZ AR 104 2024 1 01 07 487-501 |
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10.1007/s10725-024-01178-2 doi (DE-627)SPR057665044 (SPR)s10725-024-01178-2-e DE-627 ger DE-627 rakwb eng 333.7 580 VZ 48.50 bkl 42.41 bkl 48.52 bkl Guoxin, He verfasserin aut Optimization of evaluation method for low nitrogen tolerance in soybean germplasm seedlings 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Nitrogen is a critical macro-nutrient for growth and development of soybeans (Glycine max L.). Improving nitrogen use efficiency and developing low nitrogen tolerance varieties are important approaches to mitigate excessive fertilization and maximize production benefits. Precise identification of low nitrogen tolerance germplasms serves as a crucial bridge for converting germplasm advantages into breeding advantages. In this study, we optimized a precise evaluation method for low-nitrogen tolerance in soybean seedlings based on Extreme Gradient Boosting (XGBoost) algorithm. Three hundred soybean germplasms were assessed for low-nitrogen tolerance under hydroponic conditions with normal (7.5 mM) and low (0.75 mM) nitrogen levels. Fourteen physiological traits related to low nitrogen tolerance, such as biomass, chlorophyll fluorescence, were measured. The XGBoost-based evaluation method was compared to a traditional fuzzy membership function comprehensive evaluation method for accuracy and applicability. Results showed that the XGBoost-based method ensured precision and reduced the number of determined physiological indicators compared to traditional methods. Furthermore, this approach reduces the number of traits required for precise identification, which reduces time and improves economic benefits. Consequently, the screening efficiency of soybean low nitrogen tolerance germplasms is improved, offering valuable insights for soybean breeding programs. Soybeans (dpeaa)DE-He213 Low nitrogen tolerance (dpeaa)DE-He213 Germplasm screening (dpeaa)DE-He213 Optimization of evaluation system (dpeaa)DE-He213 Sujuan, Li verfasserin aut Jian, Wang verfasserin aut Yanjun, Li verfasserin aut Xiaoyuan, Tao verfasserin aut Zihong, Ye verfasserin aut Guang, Chen verfasserin aut Shengchun, Xu verfasserin (orcid)0000-0001-8135-0990 aut Enthalten in Plant growth regulation Springer Netherlands, 1982 104(2024), 1 vom: 01. Juli, Seite 487-501 (DE-627)270932941 (DE-600)1478400-2 1573-5087 nnns volume:104 year:2024 number:1 day:01 month:07 pages:487-501 https://dx.doi.org/10.1007/s10725-024-01178-2 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OLC-PHA 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_72 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_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_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2574 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 48.50 VZ 42.41 VZ 48.52 VZ AR 104 2024 1 01 07 487-501 |
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10.1007/s10725-024-01178-2 doi (DE-627)SPR057665044 (SPR)s10725-024-01178-2-e DE-627 ger DE-627 rakwb eng 333.7 580 VZ 48.50 bkl 42.41 bkl 48.52 bkl Guoxin, He verfasserin aut Optimization of evaluation method for low nitrogen tolerance in soybean germplasm seedlings 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Nitrogen is a critical macro-nutrient for growth and development of soybeans (Glycine max L.). Improving nitrogen use efficiency and developing low nitrogen tolerance varieties are important approaches to mitigate excessive fertilization and maximize production benefits. Precise identification of low nitrogen tolerance germplasms serves as a crucial bridge for converting germplasm advantages into breeding advantages. In this study, we optimized a precise evaluation method for low-nitrogen tolerance in soybean seedlings based on Extreme Gradient Boosting (XGBoost) algorithm. Three hundred soybean germplasms were assessed for low-nitrogen tolerance under hydroponic conditions with normal (7.5 mM) and low (0.75 mM) nitrogen levels. Fourteen physiological traits related to low nitrogen tolerance, such as biomass, chlorophyll fluorescence, were measured. The XGBoost-based evaluation method was compared to a traditional fuzzy membership function comprehensive evaluation method for accuracy and applicability. Results showed that the XGBoost-based method ensured precision and reduced the number of determined physiological indicators compared to traditional methods. Furthermore, this approach reduces the number of traits required for precise identification, which reduces time and improves economic benefits. Consequently, the screening efficiency of soybean low nitrogen tolerance germplasms is improved, offering valuable insights for soybean breeding programs. Soybeans (dpeaa)DE-He213 Low nitrogen tolerance (dpeaa)DE-He213 Germplasm screening (dpeaa)DE-He213 Optimization of evaluation system (dpeaa)DE-He213 Sujuan, Li verfasserin aut Jian, Wang verfasserin aut Yanjun, Li verfasserin aut Xiaoyuan, Tao verfasserin aut Zihong, Ye verfasserin aut Guang, Chen verfasserin aut Shengchun, Xu verfasserin (orcid)0000-0001-8135-0990 aut Enthalten in Plant growth regulation Springer Netherlands, 1982 104(2024), 1 vom: 01. Juli, Seite 487-501 (DE-627)270932941 (DE-600)1478400-2 1573-5087 nnns volume:104 year:2024 number:1 day:01 month:07 pages:487-501 https://dx.doi.org/10.1007/s10725-024-01178-2 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OLC-PHA 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_72 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_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_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2574 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 48.50 VZ 42.41 VZ 48.52 VZ AR 104 2024 1 01 07 487-501 |
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10.1007/s10725-024-01178-2 doi (DE-627)SPR057665044 (SPR)s10725-024-01178-2-e DE-627 ger DE-627 rakwb eng 333.7 580 VZ 48.50 bkl 42.41 bkl 48.52 bkl Guoxin, He verfasserin aut Optimization of evaluation method for low nitrogen tolerance in soybean germplasm seedlings 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Nitrogen is a critical macro-nutrient for growth and development of soybeans (Glycine max L.). Improving nitrogen use efficiency and developing low nitrogen tolerance varieties are important approaches to mitigate excessive fertilization and maximize production benefits. Precise identification of low nitrogen tolerance germplasms serves as a crucial bridge for converting germplasm advantages into breeding advantages. In this study, we optimized a precise evaluation method for low-nitrogen tolerance in soybean seedlings based on Extreme Gradient Boosting (XGBoost) algorithm. Three hundred soybean germplasms were assessed for low-nitrogen tolerance under hydroponic conditions with normal (7.5 mM) and low (0.75 mM) nitrogen levels. Fourteen physiological traits related to low nitrogen tolerance, such as biomass, chlorophyll fluorescence, were measured. The XGBoost-based evaluation method was compared to a traditional fuzzy membership function comprehensive evaluation method for accuracy and applicability. Results showed that the XGBoost-based method ensured precision and reduced the number of determined physiological indicators compared to traditional methods. Furthermore, this approach reduces the number of traits required for precise identification, which reduces time and improves economic benefits. Consequently, the screening efficiency of soybean low nitrogen tolerance germplasms is improved, offering valuable insights for soybean breeding programs. Soybeans (dpeaa)DE-He213 Low nitrogen tolerance (dpeaa)DE-He213 Germplasm screening (dpeaa)DE-He213 Optimization of evaluation system (dpeaa)DE-He213 Sujuan, Li verfasserin aut Jian, Wang verfasserin aut Yanjun, Li verfasserin aut Xiaoyuan, Tao verfasserin aut Zihong, Ye verfasserin aut Guang, Chen verfasserin aut Shengchun, Xu verfasserin (orcid)0000-0001-8135-0990 aut Enthalten in Plant growth regulation Springer Netherlands, 1982 104(2024), 1 vom: 01. Juli, Seite 487-501 (DE-627)270932941 (DE-600)1478400-2 1573-5087 nnns volume:104 year:2024 number:1 day:01 month:07 pages:487-501 https://dx.doi.org/10.1007/s10725-024-01178-2 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OLC-PHA 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_72 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_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_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2574 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 48.50 VZ 42.41 VZ 48.52 VZ AR 104 2024 1 01 07 487-501 |
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10.1007/s10725-024-01178-2 doi (DE-627)SPR057665044 (SPR)s10725-024-01178-2-e DE-627 ger DE-627 rakwb eng 333.7 580 VZ 48.50 bkl 42.41 bkl 48.52 bkl Guoxin, He verfasserin aut Optimization of evaluation method for low nitrogen tolerance in soybean germplasm seedlings 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Nitrogen is a critical macro-nutrient for growth and development of soybeans (Glycine max L.). Improving nitrogen use efficiency and developing low nitrogen tolerance varieties are important approaches to mitigate excessive fertilization and maximize production benefits. Precise identification of low nitrogen tolerance germplasms serves as a crucial bridge for converting germplasm advantages into breeding advantages. In this study, we optimized a precise evaluation method for low-nitrogen tolerance in soybean seedlings based on Extreme Gradient Boosting (XGBoost) algorithm. Three hundred soybean germplasms were assessed for low-nitrogen tolerance under hydroponic conditions with normal (7.5 mM) and low (0.75 mM) nitrogen levels. Fourteen physiological traits related to low nitrogen tolerance, such as biomass, chlorophyll fluorescence, were measured. The XGBoost-based evaluation method was compared to a traditional fuzzy membership function comprehensive evaluation method for accuracy and applicability. Results showed that the XGBoost-based method ensured precision and reduced the number of determined physiological indicators compared to traditional methods. Furthermore, this approach reduces the number of traits required for precise identification, which reduces time and improves economic benefits. Consequently, the screening efficiency of soybean low nitrogen tolerance germplasms is improved, offering valuable insights for soybean breeding programs. Soybeans (dpeaa)DE-He213 Low nitrogen tolerance (dpeaa)DE-He213 Germplasm screening (dpeaa)DE-He213 Optimization of evaluation system (dpeaa)DE-He213 Sujuan, Li verfasserin aut Jian, Wang verfasserin aut Yanjun, Li verfasserin aut Xiaoyuan, Tao verfasserin aut Zihong, Ye verfasserin aut Guang, Chen verfasserin aut Shengchun, Xu verfasserin (orcid)0000-0001-8135-0990 aut Enthalten in Plant growth regulation Springer Netherlands, 1982 104(2024), 1 vom: 01. Juli, Seite 487-501 (DE-627)270932941 (DE-600)1478400-2 1573-5087 nnns volume:104 year:2024 number:1 day:01 month:07 pages:487-501 https://dx.doi.org/10.1007/s10725-024-01178-2 X:SPRINGER Resolving-System lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER SSG-OLC-PHA 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_72 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_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_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 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_2574 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 48.50 VZ 42.41 VZ 48.52 VZ AR 104 2024 1 01 07 487-501 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR057665044</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20241005064708.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">241005s2024 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10725-024-01178-2</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR057665044</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s10725-024-01178-2-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">333.7</subfield><subfield code="a">580</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">48.50</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">42.41</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">48.52</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Guoxin, He</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Optimization of evaluation method for low nitrogen tolerance in soybean germplasm seedlings</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2024</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer Nature B.V. 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Nitrogen is a critical macro-nutrient for growth and development of soybeans (Glycine max L.). Improving nitrogen use efficiency and developing low nitrogen tolerance varieties are important approaches to mitigate excessive fertilization and maximize production benefits. Precise identification of low nitrogen tolerance germplasms serves as a crucial bridge for converting germplasm advantages into breeding advantages. In this study, we optimized a precise evaluation method for low-nitrogen tolerance in soybean seedlings based on Extreme Gradient Boosting (XGBoost) algorithm. Three hundred soybean germplasms were assessed for low-nitrogen tolerance under hydroponic conditions with normal (7.5 mM) and low (0.75 mM) nitrogen levels. Fourteen physiological traits related to low nitrogen tolerance, such as biomass, chlorophyll fluorescence, were measured. The XGBoost-based evaluation method was compared to a traditional fuzzy membership function comprehensive evaluation method for accuracy and applicability. Results showed that the XGBoost-based method ensured precision and reduced the number of determined physiological indicators compared to traditional methods. Furthermore, this approach reduces the number of traits required for precise identification, which reduces time and improves economic benefits. 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Guoxin, He |
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Guoxin, He ddc 333.7 bkl 48.50 bkl 42.41 bkl 48.52 misc Soybeans misc Low nitrogen tolerance misc Germplasm screening misc Optimization of evaluation system Optimization of evaluation method for low nitrogen tolerance in soybean germplasm seedlings |
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333.7 580 VZ 48.50 bkl 42.41 bkl 48.52 bkl Optimization of evaluation method for low nitrogen tolerance in soybean germplasm seedlings Soybeans (dpeaa)DE-He213 Low nitrogen tolerance (dpeaa)DE-He213 Germplasm screening (dpeaa)DE-He213 Optimization of evaluation system (dpeaa)DE-He213 |
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optimization of evaluation method for low nitrogen tolerance in soybean germplasm seedlings |
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Optimization of evaluation method for low nitrogen tolerance in soybean germplasm seedlings |
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Abstract Nitrogen is a critical macro-nutrient for growth and development of soybeans (Glycine max L.). Improving nitrogen use efficiency and developing low nitrogen tolerance varieties are important approaches to mitigate excessive fertilization and maximize production benefits. Precise identification of low nitrogen tolerance germplasms serves as a crucial bridge for converting germplasm advantages into breeding advantages. In this study, we optimized a precise evaluation method for low-nitrogen tolerance in soybean seedlings based on Extreme Gradient Boosting (XGBoost) algorithm. Three hundred soybean germplasms were assessed for low-nitrogen tolerance under hydroponic conditions with normal (7.5 mM) and low (0.75 mM) nitrogen levels. Fourteen physiological traits related to low nitrogen tolerance, such as biomass, chlorophyll fluorescence, were measured. The XGBoost-based evaluation method was compared to a traditional fuzzy membership function comprehensive evaluation method for accuracy and applicability. Results showed that the XGBoost-based method ensured precision and reduced the number of determined physiological indicators compared to traditional methods. Furthermore, this approach reduces the number of traits required for precise identification, which reduces time and improves economic benefits. Consequently, the screening efficiency of soybean low nitrogen tolerance germplasms is improved, offering valuable insights for soybean breeding programs. © The Author(s), under exclusive licence to Springer Nature B.V. 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Abstract Nitrogen is a critical macro-nutrient for growth and development of soybeans (Glycine max L.). Improving nitrogen use efficiency and developing low nitrogen tolerance varieties are important approaches to mitigate excessive fertilization and maximize production benefits. Precise identification of low nitrogen tolerance germplasms serves as a crucial bridge for converting germplasm advantages into breeding advantages. In this study, we optimized a precise evaluation method for low-nitrogen tolerance in soybean seedlings based on Extreme Gradient Boosting (XGBoost) algorithm. Three hundred soybean germplasms were assessed for low-nitrogen tolerance under hydroponic conditions with normal (7.5 mM) and low (0.75 mM) nitrogen levels. Fourteen physiological traits related to low nitrogen tolerance, such as biomass, chlorophyll fluorescence, were measured. The XGBoost-based evaluation method was compared to a traditional fuzzy membership function comprehensive evaluation method for accuracy and applicability. Results showed that the XGBoost-based method ensured precision and reduced the number of determined physiological indicators compared to traditional methods. Furthermore, this approach reduces the number of traits required for precise identification, which reduces time and improves economic benefits. Consequently, the screening efficiency of soybean low nitrogen tolerance germplasms is improved, offering valuable insights for soybean breeding programs. © The Author(s), under exclusive licence to Springer Nature B.V. 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract Nitrogen is a critical macro-nutrient for growth and development of soybeans (Glycine max L.). Improving nitrogen use efficiency and developing low nitrogen tolerance varieties are important approaches to mitigate excessive fertilization and maximize production benefits. Precise identification of low nitrogen tolerance germplasms serves as a crucial bridge for converting germplasm advantages into breeding advantages. In this study, we optimized a precise evaluation method for low-nitrogen tolerance in soybean seedlings based on Extreme Gradient Boosting (XGBoost) algorithm. Three hundred soybean germplasms were assessed for low-nitrogen tolerance under hydroponic conditions with normal (7.5 mM) and low (0.75 mM) nitrogen levels. Fourteen physiological traits related to low nitrogen tolerance, such as biomass, chlorophyll fluorescence, were measured. The XGBoost-based evaluation method was compared to a traditional fuzzy membership function comprehensive evaluation method for accuracy and applicability. Results showed that the XGBoost-based method ensured precision and reduced the number of determined physiological indicators compared to traditional methods. Furthermore, this approach reduces the number of traits required for precise identification, which reduces time and improves economic benefits. Consequently, the screening efficiency of soybean low nitrogen tolerance germplasms is improved, offering valuable insights for soybean breeding programs. © The Author(s), under exclusive licence to Springer Nature B.V. 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| score |
7.397743 |