Prediction of strawberry fruit yield based on cultivar-specific growth models in the tunnel-type greenhouse
Abstract The strawberry growth and fruit yield of five Korean cultivars in the tunnel-type greenhouse predicted using their growth. The number of leaves, petiole length, leaf length and width, crown diameter, and the ratio of red and far-red (RFR) of the five Korean cultivars were measured during th...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Jo, Jung Su [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2022. corrected publication 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Horticulture, environment, and biotechnology - New York, NY : Springer, 2011, 63(2022), 4 vom: 11. Juli, Seite 467-476 |
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| Übergeordnetes Werk: |
volume:63 ; year:2022 ; number:4 ; day:11 ; month:07 ; pages:467-476 |
| Links: |
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| DOI / URN: |
10.1007/s13580-021-00416-0 |
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| Katalog-ID: |
SPR047885955 |
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| 520 | |a Abstract The strawberry growth and fruit yield of five Korean cultivars in the tunnel-type greenhouse predicted using their growth. The number of leaves, petiole length, leaf length and width, crown diameter, and the ratio of red and far-red (RFR) of the five Korean cultivars were measured during the cultivation period. The number of leaves of all cultivars exhibited a similar trend during this period; the plant and petiole length of ‘Maehyang’ were the longest, leaf length exhibited similar trends in all five cultivars except for ‘Jukhyang’, the leaf width of ‘Arihyang’, was the longest, and crown diameter of ‘Keumsil’ was the thickest. The leaf length, crown diameter, and RFR were associated with the fruit yield in the multiple linear regression. When a single model was used to predict the yield of all five cultivars, the correlation between expected yield and actual yield was r = 0.53. When cultivar-specific models were built for the prediction, the correlation increased to r = 0.77. The results indicated that the fruit yield of strawberry cultivars could be better predicted by considering cultivar-specific information, so it may be necessary to consider individual cultivars specifically rather than all cultivars simultaneously. | ||
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| 700 | 1 | |a Ahn, Su Ran |4 aut | |
| 700 | 1 | |a Kim, Sung Kyeom |0 (orcid)0000-0002-6911-8731 |4 aut | |
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10.1007/s13580-021-00416-0 doi (DE-627)SPR047885955 (SPR)s13580-021-00416-0-e DE-627 ger DE-627 rakwb eng Jo, Jung Su verfasserin aut Prediction of strawberry fruit yield based on cultivar-specific growth models in the tunnel-type greenhouse 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022. corrected publication 2022 Abstract The strawberry growth and fruit yield of five Korean cultivars in the tunnel-type greenhouse predicted using their growth. The number of leaves, petiole length, leaf length and width, crown diameter, and the ratio of red and far-red (RFR) of the five Korean cultivars were measured during the cultivation period. The number of leaves of all cultivars exhibited a similar trend during this period; the plant and petiole length of ‘Maehyang’ were the longest, leaf length exhibited similar trends in all five cultivars except for ‘Jukhyang’, the leaf width of ‘Arihyang’, was the longest, and crown diameter of ‘Keumsil’ was the thickest. The leaf length, crown diameter, and RFR were associated with the fruit yield in the multiple linear regression. When a single model was used to predict the yield of all five cultivars, the correlation between expected yield and actual yield was r = 0.53. When cultivar-specific models were built for the prediction, the correlation increased to r = 0.77. The results indicated that the fruit yield of strawberry cultivars could be better predicted by considering cultivar-specific information, so it may be necessary to consider individual cultivars specifically rather than all cultivars simultaneously. Cross-validation (dpeaa)DE-He213 Cultivar-specific modeling (dpeaa)DE-He213 Flowering (dpeaa)DE-He213 Fresh weight (dpeaa)DE-He213 Ratio of red and far-red (dpeaa)DE-He213 Kim, Dong Sub aut Jo, Won Jun aut Sim, Ha Seon aut Lee, Hye Jin aut Moon, Yu Hyun aut Woo, Ui Jeong aut Jung, Soo Bin aut Kim, Steven aut Mo, Xiaoming aut Ahn, Su Ran aut Kim, Sung Kyeom (orcid)0000-0002-6911-8731 aut Enthalten in Horticulture, environment, and biotechnology New York, NY : Springer, 2011 63(2022), 4 vom: 11. Juli, Seite 467-476 (DE-627)680322655 (DE-600)2642577-4 2211-3460 nnns volume:63 year:2022 number:4 day:11 month:07 pages:467-476 https://dx.doi.org/10.1007/s13580-021-00416-0 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A 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_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_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_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 AR 63 2022 4 11 07 467-476 |
| spelling |
10.1007/s13580-021-00416-0 doi (DE-627)SPR047885955 (SPR)s13580-021-00416-0-e DE-627 ger DE-627 rakwb eng Jo, Jung Su verfasserin aut Prediction of strawberry fruit yield based on cultivar-specific growth models in the tunnel-type greenhouse 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022. corrected publication 2022 Abstract The strawberry growth and fruit yield of five Korean cultivars in the tunnel-type greenhouse predicted using their growth. The number of leaves, petiole length, leaf length and width, crown diameter, and the ratio of red and far-red (RFR) of the five Korean cultivars were measured during the cultivation period. The number of leaves of all cultivars exhibited a similar trend during this period; the plant and petiole length of ‘Maehyang’ were the longest, leaf length exhibited similar trends in all five cultivars except for ‘Jukhyang’, the leaf width of ‘Arihyang’, was the longest, and crown diameter of ‘Keumsil’ was the thickest. The leaf length, crown diameter, and RFR were associated with the fruit yield in the multiple linear regression. When a single model was used to predict the yield of all five cultivars, the correlation between expected yield and actual yield was r = 0.53. When cultivar-specific models were built for the prediction, the correlation increased to r = 0.77. The results indicated that the fruit yield of strawberry cultivars could be better predicted by considering cultivar-specific information, so it may be necessary to consider individual cultivars specifically rather than all cultivars simultaneously. Cross-validation (dpeaa)DE-He213 Cultivar-specific modeling (dpeaa)DE-He213 Flowering (dpeaa)DE-He213 Fresh weight (dpeaa)DE-He213 Ratio of red and far-red (dpeaa)DE-He213 Kim, Dong Sub aut Jo, Won Jun aut Sim, Ha Seon aut Lee, Hye Jin aut Moon, Yu Hyun aut Woo, Ui Jeong aut Jung, Soo Bin aut Kim, Steven aut Mo, Xiaoming aut Ahn, Su Ran aut Kim, Sung Kyeom (orcid)0000-0002-6911-8731 aut Enthalten in Horticulture, environment, and biotechnology New York, NY : Springer, 2011 63(2022), 4 vom: 11. Juli, Seite 467-476 (DE-627)680322655 (DE-600)2642577-4 2211-3460 nnns volume:63 year:2022 number:4 day:11 month:07 pages:467-476 https://dx.doi.org/10.1007/s13580-021-00416-0 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A 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_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_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_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 AR 63 2022 4 11 07 467-476 |
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10.1007/s13580-021-00416-0 doi (DE-627)SPR047885955 (SPR)s13580-021-00416-0-e DE-627 ger DE-627 rakwb eng Jo, Jung Su verfasserin aut Prediction of strawberry fruit yield based on cultivar-specific growth models in the tunnel-type greenhouse 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022. corrected publication 2022 Abstract The strawberry growth and fruit yield of five Korean cultivars in the tunnel-type greenhouse predicted using their growth. The number of leaves, petiole length, leaf length and width, crown diameter, and the ratio of red and far-red (RFR) of the five Korean cultivars were measured during the cultivation period. The number of leaves of all cultivars exhibited a similar trend during this period; the plant and petiole length of ‘Maehyang’ were the longest, leaf length exhibited similar trends in all five cultivars except for ‘Jukhyang’, the leaf width of ‘Arihyang’, was the longest, and crown diameter of ‘Keumsil’ was the thickest. The leaf length, crown diameter, and RFR were associated with the fruit yield in the multiple linear regression. When a single model was used to predict the yield of all five cultivars, the correlation between expected yield and actual yield was r = 0.53. When cultivar-specific models were built for the prediction, the correlation increased to r = 0.77. The results indicated that the fruit yield of strawberry cultivars could be better predicted by considering cultivar-specific information, so it may be necessary to consider individual cultivars specifically rather than all cultivars simultaneously. Cross-validation (dpeaa)DE-He213 Cultivar-specific modeling (dpeaa)DE-He213 Flowering (dpeaa)DE-He213 Fresh weight (dpeaa)DE-He213 Ratio of red and far-red (dpeaa)DE-He213 Kim, Dong Sub aut Jo, Won Jun aut Sim, Ha Seon aut Lee, Hye Jin aut Moon, Yu Hyun aut Woo, Ui Jeong aut Jung, Soo Bin aut Kim, Steven aut Mo, Xiaoming aut Ahn, Su Ran aut Kim, Sung Kyeom (orcid)0000-0002-6911-8731 aut Enthalten in Horticulture, environment, and biotechnology New York, NY : Springer, 2011 63(2022), 4 vom: 11. Juli, Seite 467-476 (DE-627)680322655 (DE-600)2642577-4 2211-3460 nnns volume:63 year:2022 number:4 day:11 month:07 pages:467-476 https://dx.doi.org/10.1007/s13580-021-00416-0 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A 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_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_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_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 AR 63 2022 4 11 07 467-476 |
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10.1007/s13580-021-00416-0 doi (DE-627)SPR047885955 (SPR)s13580-021-00416-0-e DE-627 ger DE-627 rakwb eng Jo, Jung Su verfasserin aut Prediction of strawberry fruit yield based on cultivar-specific growth models in the tunnel-type greenhouse 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022. corrected publication 2022 Abstract The strawberry growth and fruit yield of five Korean cultivars in the tunnel-type greenhouse predicted using their growth. The number of leaves, petiole length, leaf length and width, crown diameter, and the ratio of red and far-red (RFR) of the five Korean cultivars were measured during the cultivation period. The number of leaves of all cultivars exhibited a similar trend during this period; the plant and petiole length of ‘Maehyang’ were the longest, leaf length exhibited similar trends in all five cultivars except for ‘Jukhyang’, the leaf width of ‘Arihyang’, was the longest, and crown diameter of ‘Keumsil’ was the thickest. The leaf length, crown diameter, and RFR were associated with the fruit yield in the multiple linear regression. When a single model was used to predict the yield of all five cultivars, the correlation between expected yield and actual yield was r = 0.53. When cultivar-specific models were built for the prediction, the correlation increased to r = 0.77. The results indicated that the fruit yield of strawberry cultivars could be better predicted by considering cultivar-specific information, so it may be necessary to consider individual cultivars specifically rather than all cultivars simultaneously. Cross-validation (dpeaa)DE-He213 Cultivar-specific modeling (dpeaa)DE-He213 Flowering (dpeaa)DE-He213 Fresh weight (dpeaa)DE-He213 Ratio of red and far-red (dpeaa)DE-He213 Kim, Dong Sub aut Jo, Won Jun aut Sim, Ha Seon aut Lee, Hye Jin aut Moon, Yu Hyun aut Woo, Ui Jeong aut Jung, Soo Bin aut Kim, Steven aut Mo, Xiaoming aut Ahn, Su Ran aut Kim, Sung Kyeom (orcid)0000-0002-6911-8731 aut Enthalten in Horticulture, environment, and biotechnology New York, NY : Springer, 2011 63(2022), 4 vom: 11. Juli, Seite 467-476 (DE-627)680322655 (DE-600)2642577-4 2211-3460 nnns volume:63 year:2022 number:4 day:11 month:07 pages:467-476 https://dx.doi.org/10.1007/s13580-021-00416-0 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A 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_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_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_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 AR 63 2022 4 11 07 467-476 |
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10.1007/s13580-021-00416-0 doi (DE-627)SPR047885955 (SPR)s13580-021-00416-0-e DE-627 ger DE-627 rakwb eng Jo, Jung Su verfasserin aut Prediction of strawberry fruit yield based on cultivar-specific growth models in the tunnel-type greenhouse 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022. corrected publication 2022 Abstract The strawberry growth and fruit yield of five Korean cultivars in the tunnel-type greenhouse predicted using their growth. The number of leaves, petiole length, leaf length and width, crown diameter, and the ratio of red and far-red (RFR) of the five Korean cultivars were measured during the cultivation period. The number of leaves of all cultivars exhibited a similar trend during this period; the plant and petiole length of ‘Maehyang’ were the longest, leaf length exhibited similar trends in all five cultivars except for ‘Jukhyang’, the leaf width of ‘Arihyang’, was the longest, and crown diameter of ‘Keumsil’ was the thickest. The leaf length, crown diameter, and RFR were associated with the fruit yield in the multiple linear regression. When a single model was used to predict the yield of all five cultivars, the correlation between expected yield and actual yield was r = 0.53. When cultivar-specific models were built for the prediction, the correlation increased to r = 0.77. The results indicated that the fruit yield of strawberry cultivars could be better predicted by considering cultivar-specific information, so it may be necessary to consider individual cultivars specifically rather than all cultivars simultaneously. Cross-validation (dpeaa)DE-He213 Cultivar-specific modeling (dpeaa)DE-He213 Flowering (dpeaa)DE-He213 Fresh weight (dpeaa)DE-He213 Ratio of red and far-red (dpeaa)DE-He213 Kim, Dong Sub aut Jo, Won Jun aut Sim, Ha Seon aut Lee, Hye Jin aut Moon, Yu Hyun aut Woo, Ui Jeong aut Jung, Soo Bin aut Kim, Steven aut Mo, Xiaoming aut Ahn, Su Ran aut Kim, Sung Kyeom (orcid)0000-0002-6911-8731 aut Enthalten in Horticulture, environment, and biotechnology New York, NY : Springer, 2011 63(2022), 4 vom: 11. Juli, Seite 467-476 (DE-627)680322655 (DE-600)2642577-4 2211-3460 nnns volume:63 year:2022 number:4 day:11 month:07 pages:467-476 https://dx.doi.org/10.1007/s13580-021-00416-0 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A 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_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_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_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 AR 63 2022 4 11 07 467-476 |
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Enthalten in Horticulture, environment, and biotechnology 63(2022), 4 vom: 11. Juli, Seite 467-476 volume:63 year:2022 number:4 day:11 month:07 pages:467-476 |
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Jo, Jung Su @@aut@@ Kim, Dong Sub @@aut@@ Jo, Won Jun @@aut@@ Sim, Ha Seon @@aut@@ Lee, Hye Jin @@aut@@ Moon, Yu Hyun @@aut@@ Woo, Ui Jeong @@aut@@ Jung, Soo Bin @@aut@@ Kim, Steven @@aut@@ Mo, Xiaoming @@aut@@ Ahn, Su Ran @@aut@@ Kim, Sung Kyeom @@aut@@ |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR047885955</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230519143618.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">220819s2022 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s13580-021-00416-0</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR047885955</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s13580-021-00416-0-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="100" ind1="1" ind2=" "><subfield code="a">Jo, Jung Su</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Prediction of strawberry fruit yield based on cultivar-specific growth models in the tunnel-type greenhouse</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</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) 2022. corrected publication 2022</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The strawberry growth and fruit yield of five Korean cultivars in the tunnel-type greenhouse predicted using their growth. The number of leaves, petiole length, leaf length and width, crown diameter, and the ratio of red and far-red (RFR) of the five Korean cultivars were measured during the cultivation period. The number of leaves of all cultivars exhibited a similar trend during this period; the plant and petiole length of ‘Maehyang’ were the longest, leaf length exhibited similar trends in all five cultivars except for ‘Jukhyang’, the leaf width of ‘Arihyang’, was the longest, and crown diameter of ‘Keumsil’ was the thickest. The leaf length, crown diameter, and RFR were associated with the fruit yield in the multiple linear regression. When a single model was used to predict the yield of all five cultivars, the correlation between expected yield and actual yield was r = 0.53. When cultivar-specific models were built for the prediction, the correlation increased to r = 0.77. The results indicated that the fruit yield of strawberry cultivars could be better predicted by considering cultivar-specific information, so it may be necessary to consider individual cultivars specifically rather than all cultivars simultaneously.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Cross-validation</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Cultivar-specific modeling</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Flowering</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Fresh weight</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Ratio of red and far-red</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kim, Dong Sub</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Jo, Won Jun</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sim, Ha Seon</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lee, Hye Jin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Moon, Yu Hyun</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Woo, Ui Jeong</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Jung, Soo Bin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kim, Steven</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mo, Xiaoming</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ahn, Su Ran</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kim, Sung Kyeom</subfield><subfield code="0">(orcid)0000-0002-6911-8731</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Horticulture, environment, and biotechnology</subfield><subfield code="d">New York, NY : Springer, 2011</subfield><subfield code="g">63(2022), 4 vom: 11. 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Jo, Jung Su |
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Jo, Jung Su misc Cross-validation misc Cultivar-specific modeling misc Flowering misc Fresh weight misc Ratio of red and far-red Prediction of strawberry fruit yield based on cultivar-specific growth models in the tunnel-type greenhouse |
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Prediction of strawberry fruit yield based on cultivar-specific growth models in the tunnel-type greenhouse Cross-validation (dpeaa)DE-He213 Cultivar-specific modeling (dpeaa)DE-He213 Flowering (dpeaa)DE-He213 Fresh weight (dpeaa)DE-He213 Ratio of red and far-red (dpeaa)DE-He213 |
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Prediction of strawberry fruit yield based on cultivar-specific growth models in the tunnel-type greenhouse |
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Jo, Jung Su Kim, Dong Sub Jo, Won Jun Sim, Ha Seon Lee, Hye Jin Moon, Yu Hyun Woo, Ui Jeong Jung, Soo Bin Kim, Steven Mo, Xiaoming Ahn, Su Ran Kim, Sung Kyeom |
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prediction of strawberry fruit yield based on cultivar-specific growth models in the tunnel-type greenhouse |
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Prediction of strawberry fruit yield based on cultivar-specific growth models in the tunnel-type greenhouse |
| abstract |
Abstract The strawberry growth and fruit yield of five Korean cultivars in the tunnel-type greenhouse predicted using their growth. The number of leaves, petiole length, leaf length and width, crown diameter, and the ratio of red and far-red (RFR) of the five Korean cultivars were measured during the cultivation period. The number of leaves of all cultivars exhibited a similar trend during this period; the plant and petiole length of ‘Maehyang’ were the longest, leaf length exhibited similar trends in all five cultivars except for ‘Jukhyang’, the leaf width of ‘Arihyang’, was the longest, and crown diameter of ‘Keumsil’ was the thickest. The leaf length, crown diameter, and RFR were associated with the fruit yield in the multiple linear regression. When a single model was used to predict the yield of all five cultivars, the correlation between expected yield and actual yield was r = 0.53. When cultivar-specific models were built for the prediction, the correlation increased to r = 0.77. The results indicated that the fruit yield of strawberry cultivars could be better predicted by considering cultivar-specific information, so it may be necessary to consider individual cultivars specifically rather than all cultivars simultaneously. © The Author(s) 2022. corrected publication 2022 |
| abstractGer |
Abstract The strawberry growth and fruit yield of five Korean cultivars in the tunnel-type greenhouse predicted using their growth. The number of leaves, petiole length, leaf length and width, crown diameter, and the ratio of red and far-red (RFR) of the five Korean cultivars were measured during the cultivation period. The number of leaves of all cultivars exhibited a similar trend during this period; the plant and petiole length of ‘Maehyang’ were the longest, leaf length exhibited similar trends in all five cultivars except for ‘Jukhyang’, the leaf width of ‘Arihyang’, was the longest, and crown diameter of ‘Keumsil’ was the thickest. The leaf length, crown diameter, and RFR were associated with the fruit yield in the multiple linear regression. When a single model was used to predict the yield of all five cultivars, the correlation between expected yield and actual yield was r = 0.53. When cultivar-specific models were built for the prediction, the correlation increased to r = 0.77. The results indicated that the fruit yield of strawberry cultivars could be better predicted by considering cultivar-specific information, so it may be necessary to consider individual cultivars specifically rather than all cultivars simultaneously. © The Author(s) 2022. corrected publication 2022 |
| abstract_unstemmed |
Abstract The strawberry growth and fruit yield of five Korean cultivars in the tunnel-type greenhouse predicted using their growth. The number of leaves, petiole length, leaf length and width, crown diameter, and the ratio of red and far-red (RFR) of the five Korean cultivars were measured during the cultivation period. The number of leaves of all cultivars exhibited a similar trend during this period; the plant and petiole length of ‘Maehyang’ were the longest, leaf length exhibited similar trends in all five cultivars except for ‘Jukhyang’, the leaf width of ‘Arihyang’, was the longest, and crown diameter of ‘Keumsil’ was the thickest. The leaf length, crown diameter, and RFR were associated with the fruit yield in the multiple linear regression. When a single model was used to predict the yield of all five cultivars, the correlation between expected yield and actual yield was r = 0.53. When cultivar-specific models were built for the prediction, the correlation increased to r = 0.77. The results indicated that the fruit yield of strawberry cultivars could be better predicted by considering cultivar-specific information, so it may be necessary to consider individual cultivars specifically rather than all cultivars simultaneously. © The Author(s) 2022. corrected publication 2022 |
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4 |
| title_short |
Prediction of strawberry fruit yield based on cultivar-specific growth models in the tunnel-type greenhouse |
| url |
https://dx.doi.org/10.1007/s13580-021-00416-0 |
| remote_bool |
true |
| author2 |
Kim, Dong Sub Jo, Won Jun Sim, Ha Seon Lee, Hye Jin Moon, Yu Hyun Woo, Ui Jeong Jung, Soo Bin Kim, Steven Mo, Xiaoming Ahn, Su Ran Kim, Sung Kyeom |
| author2Str |
Kim, Dong Sub Jo, Won Jun Sim, Ha Seon Lee, Hye Jin Moon, Yu Hyun Woo, Ui Jeong Jung, Soo Bin Kim, Steven Mo, Xiaoming Ahn, Su Ran Kim, Sung Kyeom |
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| doi_str |
10.1007/s13580-021-00416-0 |
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2024-07-03T15:39:02.285Z |
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| score |
7.400321 |