Multifactor Models for Improved Prediction of Phenological Timing in Cold-climate Wine Grapes
An accurate predictive model for estimating the timing of seasonal phenological stages of grape (Vitis L.) would be a valuable tool for crop management. Currently the most used index for predicting the phenological timing of fruit crops is growing degree days (GDD), but the predictive accuracy of th...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
James A. Schrader [verfasserIn] Paul A. Domoto [verfasserIn] Gail R. Nonnecke [verfasserIn] Diana R. Cochran [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2020 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
In: HortScience - American Society for Horticultural Science (ASHS), 2020, 55(2020), 12, Seite 1912-1925 |
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| Übergeordnetes Werk: |
volume:55 ; year:2020 ; number:12 ; pages:1912-1925 |
| Links: |
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DOAJ061070815 |
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| 520 | |a An accurate predictive model for estimating the timing of seasonal phenological stages of grape (Vitis L.) would be a valuable tool for crop management. Currently the most used index for predicting the phenological timing of fruit crops is growing degree days (GDD), but the predictive accuracy of the GDD index varies from season-to-season and is considered unsatisfactory for grapevines grown in the midwestern United States. We used the methods of multiple regression to analyze and model the effects of multiple factors on the number of days remaining until each of four phenological stages (budbreak, bloom, veraison, and harvest maturity) for five cold-climate wine grape cultivars (Frontenac, La Crescent, Marquette, Petit Ami, and St. Croix) grown in central Iowa. The factors (predictor variables) evaluated in models included cultivar, numerical day of the year (DOY), DOY of soil thaw or the previous phenological stage, photoperiod, GDD with a base temperature of 10 °C (GDD 10), soil degree days with a base temperature of 5 °C (SDD 5), and solar accumulation. Models were evaluated for predictive accuracy and goodness of fit by calculating the coefficient of determination (R2), the corrected Akaike information criterion (AICc), and the Bayesian information criterion (BIC); testing for normal distribution of residuals; and comparing the actual number of days remaining until a phenological stage with the number of days predicted by models. The top-performing models from the training set were also tested for predictive accuracy on a validation dataset (a set of data not used to build the model), which consisted of environmental and phenological data recorded for one popular Midwest cultivar (Marquette) in 2019. At all four phenological stages, inclusion of multiple factors (cultivar and four to six additional factors) resulted in predictive models that were more accurate and consistent than models using cultivar and GDD 10 alone. Multifactor models generated from data of all five cultivars had high R2 values of 0.996, 0.985, 0.985, and 0.869 for budbreak, bloom, veraison, and harvest, respectively, whereas R2 values for models using only cultivar and GDD 10 were substantially lower (0.787, 0.904, 0.960, and 0.828, respectively). The average errors (differences from actual) for the top multifactor models were 0.70, 0.84, 1.77, and 3.80 days for budbreak, bloom, veraison, and harvest, respectively, and average errors for models that included only cultivar and GDD 10 were much larger (5.27, 2.24, 2.79, and 4.29 days, respectively). In the validation tests, average errors for budbreak, bloom, veraison, and harvest were 1.92, 1.31, 0.94, and 1.67 days, respectively, for the top multifactor models and 10.05, 2.54, 4.23, and 4.96 days, respectively, for models that included cultivar and GDD 10 only. Our results demonstrate the improved accuracy and utility of multifactor models for predicting the timing of phenological stages of cold-climate grape cultivars in the midwestern United States. Used together in succession, the models for budbreak, bloom, veraison, and harvest form a four-stage, multifactor calculator for improved prediction of phenological timing. Multifactor models of this type could be tailored for specific cultivars and growing regions to provide the most accurate predictions possible. | ||
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(DE-627)DOAJ061070815 (DE-599)DOAJa0193bc253d748f3977dd57729b4290a DE-627 ger DE-627 rakwb eng SB1-1110 James A. Schrader verfasserin aut Multifactor Models for Improved Prediction of Phenological Timing in Cold-climate Wine Grapes 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier An accurate predictive model for estimating the timing of seasonal phenological stages of grape (Vitis L.) would be a valuable tool for crop management. Currently the most used index for predicting the phenological timing of fruit crops is growing degree days (GDD), but the predictive accuracy of the GDD index varies from season-to-season and is considered unsatisfactory for grapevines grown in the midwestern United States. We used the methods of multiple regression to analyze and model the effects of multiple factors on the number of days remaining until each of four phenological stages (budbreak, bloom, veraison, and harvest maturity) for five cold-climate wine grape cultivars (Frontenac, La Crescent, Marquette, Petit Ami, and St. Croix) grown in central Iowa. The factors (predictor variables) evaluated in models included cultivar, numerical day of the year (DOY), DOY of soil thaw or the previous phenological stage, photoperiod, GDD with a base temperature of 10 °C (GDD 10), soil degree days with a base temperature of 5 °C (SDD 5), and solar accumulation. Models were evaluated for predictive accuracy and goodness of fit by calculating the coefficient of determination (R2), the corrected Akaike information criterion (AICc), and the Bayesian information criterion (BIC); testing for normal distribution of residuals; and comparing the actual number of days remaining until a phenological stage with the number of days predicted by models. The top-performing models from the training set were also tested for predictive accuracy on a validation dataset (a set of data not used to build the model), which consisted of environmental and phenological data recorded for one popular Midwest cultivar (Marquette) in 2019. At all four phenological stages, inclusion of multiple factors (cultivar and four to six additional factors) resulted in predictive models that were more accurate and consistent than models using cultivar and GDD 10 alone. Multifactor models generated from data of all five cultivars had high R2 values of 0.996, 0.985, 0.985, and 0.869 for budbreak, bloom, veraison, and harvest, respectively, whereas R2 values for models using only cultivar and GDD 10 were substantially lower (0.787, 0.904, 0.960, and 0.828, respectively). The average errors (differences from actual) for the top multifactor models were 0.70, 0.84, 1.77, and 3.80 days for budbreak, bloom, veraison, and harvest, respectively, and average errors for models that included only cultivar and GDD 10 were much larger (5.27, 2.24, 2.79, and 4.29 days, respectively). In the validation tests, average errors for budbreak, bloom, veraison, and harvest were 1.92, 1.31, 0.94, and 1.67 days, respectively, for the top multifactor models and 10.05, 2.54, 4.23, and 4.96 days, respectively, for models that included cultivar and GDD 10 only. Our results demonstrate the improved accuracy and utility of multifactor models for predicting the timing of phenological stages of cold-climate grape cultivars in the midwestern United States. Used together in succession, the models for budbreak, bloom, veraison, and harvest form a four-stage, multifactor calculator for improved prediction of phenological timing. Multifactor models of this type could be tailored for specific cultivars and growing regions to provide the most accurate predictions possible. muttiple regression mathematical modeling environmental monitoring weather data utilization viticulture interspecific hybrids crop management Plant culture Paul A. Domoto verfasserin aut Gail R. Nonnecke verfasserin aut Diana R. Cochran verfasserin aut In HortScience American Society for Horticultural Science (ASHS), 2020 55(2020), 12, Seite 1912-1925 (DE-627)1760614955 23279834 nnns volume:55 year:2020 number:12 pages:1912-1925 https://doi.org/10.21273/HORTSCI15367-20 kostenfrei https://doaj.org/article/a0193bc253d748f3977dd57729b4290a kostenfrei https://journals.ashs.org/hortsci/view/journals/hortsci/55/12/article-p1912.xml kostenfrei https://doaj.org/toc/2327-9834 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA AR 55 2020 12 1912-1925 |
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(DE-627)DOAJ061070815 (DE-599)DOAJa0193bc253d748f3977dd57729b4290a DE-627 ger DE-627 rakwb eng SB1-1110 James A. Schrader verfasserin aut Multifactor Models for Improved Prediction of Phenological Timing in Cold-climate Wine Grapes 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier An accurate predictive model for estimating the timing of seasonal phenological stages of grape (Vitis L.) would be a valuable tool for crop management. Currently the most used index for predicting the phenological timing of fruit crops is growing degree days (GDD), but the predictive accuracy of the GDD index varies from season-to-season and is considered unsatisfactory for grapevines grown in the midwestern United States. We used the methods of multiple regression to analyze and model the effects of multiple factors on the number of days remaining until each of four phenological stages (budbreak, bloom, veraison, and harvest maturity) for five cold-climate wine grape cultivars (Frontenac, La Crescent, Marquette, Petit Ami, and St. Croix) grown in central Iowa. The factors (predictor variables) evaluated in models included cultivar, numerical day of the year (DOY), DOY of soil thaw or the previous phenological stage, photoperiod, GDD with a base temperature of 10 °C (GDD 10), soil degree days with a base temperature of 5 °C (SDD 5), and solar accumulation. Models were evaluated for predictive accuracy and goodness of fit by calculating the coefficient of determination (R2), the corrected Akaike information criterion (AICc), and the Bayesian information criterion (BIC); testing for normal distribution of residuals; and comparing the actual number of days remaining until a phenological stage with the number of days predicted by models. The top-performing models from the training set were also tested for predictive accuracy on a validation dataset (a set of data not used to build the model), which consisted of environmental and phenological data recorded for one popular Midwest cultivar (Marquette) in 2019. At all four phenological stages, inclusion of multiple factors (cultivar and four to six additional factors) resulted in predictive models that were more accurate and consistent than models using cultivar and GDD 10 alone. Multifactor models generated from data of all five cultivars had high R2 values of 0.996, 0.985, 0.985, and 0.869 for budbreak, bloom, veraison, and harvest, respectively, whereas R2 values for models using only cultivar and GDD 10 were substantially lower (0.787, 0.904, 0.960, and 0.828, respectively). The average errors (differences from actual) for the top multifactor models were 0.70, 0.84, 1.77, and 3.80 days for budbreak, bloom, veraison, and harvest, respectively, and average errors for models that included only cultivar and GDD 10 were much larger (5.27, 2.24, 2.79, and 4.29 days, respectively). In the validation tests, average errors for budbreak, bloom, veraison, and harvest were 1.92, 1.31, 0.94, and 1.67 days, respectively, for the top multifactor models and 10.05, 2.54, 4.23, and 4.96 days, respectively, for models that included cultivar and GDD 10 only. Our results demonstrate the improved accuracy and utility of multifactor models for predicting the timing of phenological stages of cold-climate grape cultivars in the midwestern United States. Used together in succession, the models for budbreak, bloom, veraison, and harvest form a four-stage, multifactor calculator for improved prediction of phenological timing. Multifactor models of this type could be tailored for specific cultivars and growing regions to provide the most accurate predictions possible. muttiple regression mathematical modeling environmental monitoring weather data utilization viticulture interspecific hybrids crop management Plant culture Paul A. Domoto verfasserin aut Gail R. Nonnecke verfasserin aut Diana R. Cochran verfasserin aut In HortScience American Society for Horticultural Science (ASHS), 2020 55(2020), 12, Seite 1912-1925 (DE-627)1760614955 23279834 nnns volume:55 year:2020 number:12 pages:1912-1925 https://doi.org/10.21273/HORTSCI15367-20 kostenfrei https://doaj.org/article/a0193bc253d748f3977dd57729b4290a kostenfrei https://journals.ashs.org/hortsci/view/journals/hortsci/55/12/article-p1912.xml kostenfrei https://doaj.org/toc/2327-9834 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA AR 55 2020 12 1912-1925 |
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(DE-627)DOAJ061070815 (DE-599)DOAJa0193bc253d748f3977dd57729b4290a DE-627 ger DE-627 rakwb eng SB1-1110 James A. Schrader verfasserin aut Multifactor Models for Improved Prediction of Phenological Timing in Cold-climate Wine Grapes 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier An accurate predictive model for estimating the timing of seasonal phenological stages of grape (Vitis L.) would be a valuable tool for crop management. Currently the most used index for predicting the phenological timing of fruit crops is growing degree days (GDD), but the predictive accuracy of the GDD index varies from season-to-season and is considered unsatisfactory for grapevines grown in the midwestern United States. We used the methods of multiple regression to analyze and model the effects of multiple factors on the number of days remaining until each of four phenological stages (budbreak, bloom, veraison, and harvest maturity) for five cold-climate wine grape cultivars (Frontenac, La Crescent, Marquette, Petit Ami, and St. Croix) grown in central Iowa. The factors (predictor variables) evaluated in models included cultivar, numerical day of the year (DOY), DOY of soil thaw or the previous phenological stage, photoperiod, GDD with a base temperature of 10 °C (GDD 10), soil degree days with a base temperature of 5 °C (SDD 5), and solar accumulation. Models were evaluated for predictive accuracy and goodness of fit by calculating the coefficient of determination (R2), the corrected Akaike information criterion (AICc), and the Bayesian information criterion (BIC); testing for normal distribution of residuals; and comparing the actual number of days remaining until a phenological stage with the number of days predicted by models. The top-performing models from the training set were also tested for predictive accuracy on a validation dataset (a set of data not used to build the model), which consisted of environmental and phenological data recorded for one popular Midwest cultivar (Marquette) in 2019. At all four phenological stages, inclusion of multiple factors (cultivar and four to six additional factors) resulted in predictive models that were more accurate and consistent than models using cultivar and GDD 10 alone. Multifactor models generated from data of all five cultivars had high R2 values of 0.996, 0.985, 0.985, and 0.869 for budbreak, bloom, veraison, and harvest, respectively, whereas R2 values for models using only cultivar and GDD 10 were substantially lower (0.787, 0.904, 0.960, and 0.828, respectively). The average errors (differences from actual) for the top multifactor models were 0.70, 0.84, 1.77, and 3.80 days for budbreak, bloom, veraison, and harvest, respectively, and average errors for models that included only cultivar and GDD 10 were much larger (5.27, 2.24, 2.79, and 4.29 days, respectively). In the validation tests, average errors for budbreak, bloom, veraison, and harvest were 1.92, 1.31, 0.94, and 1.67 days, respectively, for the top multifactor models and 10.05, 2.54, 4.23, and 4.96 days, respectively, for models that included cultivar and GDD 10 only. Our results demonstrate the improved accuracy and utility of multifactor models for predicting the timing of phenological stages of cold-climate grape cultivars in the midwestern United States. Used together in succession, the models for budbreak, bloom, veraison, and harvest form a four-stage, multifactor calculator for improved prediction of phenological timing. Multifactor models of this type could be tailored for specific cultivars and growing regions to provide the most accurate predictions possible. muttiple regression mathematical modeling environmental monitoring weather data utilization viticulture interspecific hybrids crop management Plant culture Paul A. Domoto verfasserin aut Gail R. Nonnecke verfasserin aut Diana R. Cochran verfasserin aut In HortScience American Society for Horticultural Science (ASHS), 2020 55(2020), 12, Seite 1912-1925 (DE-627)1760614955 23279834 nnns volume:55 year:2020 number:12 pages:1912-1925 https://doi.org/10.21273/HORTSCI15367-20 kostenfrei https://doaj.org/article/a0193bc253d748f3977dd57729b4290a kostenfrei https://journals.ashs.org/hortsci/view/journals/hortsci/55/12/article-p1912.xml kostenfrei https://doaj.org/toc/2327-9834 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA AR 55 2020 12 1912-1925 |
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(DE-627)DOAJ061070815 (DE-599)DOAJa0193bc253d748f3977dd57729b4290a DE-627 ger DE-627 rakwb eng SB1-1110 James A. Schrader verfasserin aut Multifactor Models for Improved Prediction of Phenological Timing in Cold-climate Wine Grapes 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier An accurate predictive model for estimating the timing of seasonal phenological stages of grape (Vitis L.) would be a valuable tool for crop management. Currently the most used index for predicting the phenological timing of fruit crops is growing degree days (GDD), but the predictive accuracy of the GDD index varies from season-to-season and is considered unsatisfactory for grapevines grown in the midwestern United States. We used the methods of multiple regression to analyze and model the effects of multiple factors on the number of days remaining until each of four phenological stages (budbreak, bloom, veraison, and harvest maturity) for five cold-climate wine grape cultivars (Frontenac, La Crescent, Marquette, Petit Ami, and St. Croix) grown in central Iowa. The factors (predictor variables) evaluated in models included cultivar, numerical day of the year (DOY), DOY of soil thaw or the previous phenological stage, photoperiod, GDD with a base temperature of 10 °C (GDD 10), soil degree days with a base temperature of 5 °C (SDD 5), and solar accumulation. Models were evaluated for predictive accuracy and goodness of fit by calculating the coefficient of determination (R2), the corrected Akaike information criterion (AICc), and the Bayesian information criterion (BIC); testing for normal distribution of residuals; and comparing the actual number of days remaining until a phenological stage with the number of days predicted by models. The top-performing models from the training set were also tested for predictive accuracy on a validation dataset (a set of data not used to build the model), which consisted of environmental and phenological data recorded for one popular Midwest cultivar (Marquette) in 2019. At all four phenological stages, inclusion of multiple factors (cultivar and four to six additional factors) resulted in predictive models that were more accurate and consistent than models using cultivar and GDD 10 alone. Multifactor models generated from data of all five cultivars had high R2 values of 0.996, 0.985, 0.985, and 0.869 for budbreak, bloom, veraison, and harvest, respectively, whereas R2 values for models using only cultivar and GDD 10 were substantially lower (0.787, 0.904, 0.960, and 0.828, respectively). The average errors (differences from actual) for the top multifactor models were 0.70, 0.84, 1.77, and 3.80 days for budbreak, bloom, veraison, and harvest, respectively, and average errors for models that included only cultivar and GDD 10 were much larger (5.27, 2.24, 2.79, and 4.29 days, respectively). In the validation tests, average errors for budbreak, bloom, veraison, and harvest were 1.92, 1.31, 0.94, and 1.67 days, respectively, for the top multifactor models and 10.05, 2.54, 4.23, and 4.96 days, respectively, for models that included cultivar and GDD 10 only. Our results demonstrate the improved accuracy and utility of multifactor models for predicting the timing of phenological stages of cold-climate grape cultivars in the midwestern United States. Used together in succession, the models for budbreak, bloom, veraison, and harvest form a four-stage, multifactor calculator for improved prediction of phenological timing. Multifactor models of this type could be tailored for specific cultivars and growing regions to provide the most accurate predictions possible. muttiple regression mathematical modeling environmental monitoring weather data utilization viticulture interspecific hybrids crop management Plant culture Paul A. Domoto verfasserin aut Gail R. Nonnecke verfasserin aut Diana R. Cochran verfasserin aut In HortScience American Society for Horticultural Science (ASHS), 2020 55(2020), 12, Seite 1912-1925 (DE-627)1760614955 23279834 nnns volume:55 year:2020 number:12 pages:1912-1925 https://doi.org/10.21273/HORTSCI15367-20 kostenfrei https://doaj.org/article/a0193bc253d748f3977dd57729b4290a kostenfrei https://journals.ashs.org/hortsci/view/journals/hortsci/55/12/article-p1912.xml kostenfrei https://doaj.org/toc/2327-9834 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA AR 55 2020 12 1912-1925 |
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(DE-627)DOAJ061070815 (DE-599)DOAJa0193bc253d748f3977dd57729b4290a DE-627 ger DE-627 rakwb eng SB1-1110 James A. Schrader verfasserin aut Multifactor Models for Improved Prediction of Phenological Timing in Cold-climate Wine Grapes 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier An accurate predictive model for estimating the timing of seasonal phenological stages of grape (Vitis L.) would be a valuable tool for crop management. Currently the most used index for predicting the phenological timing of fruit crops is growing degree days (GDD), but the predictive accuracy of the GDD index varies from season-to-season and is considered unsatisfactory for grapevines grown in the midwestern United States. We used the methods of multiple regression to analyze and model the effects of multiple factors on the number of days remaining until each of four phenological stages (budbreak, bloom, veraison, and harvest maturity) for five cold-climate wine grape cultivars (Frontenac, La Crescent, Marquette, Petit Ami, and St. Croix) grown in central Iowa. The factors (predictor variables) evaluated in models included cultivar, numerical day of the year (DOY), DOY of soil thaw or the previous phenological stage, photoperiod, GDD with a base temperature of 10 °C (GDD 10), soil degree days with a base temperature of 5 °C (SDD 5), and solar accumulation. Models were evaluated for predictive accuracy and goodness of fit by calculating the coefficient of determination (R2), the corrected Akaike information criterion (AICc), and the Bayesian information criterion (BIC); testing for normal distribution of residuals; and comparing the actual number of days remaining until a phenological stage with the number of days predicted by models. The top-performing models from the training set were also tested for predictive accuracy on a validation dataset (a set of data not used to build the model), which consisted of environmental and phenological data recorded for one popular Midwest cultivar (Marquette) in 2019. At all four phenological stages, inclusion of multiple factors (cultivar and four to six additional factors) resulted in predictive models that were more accurate and consistent than models using cultivar and GDD 10 alone. Multifactor models generated from data of all five cultivars had high R2 values of 0.996, 0.985, 0.985, and 0.869 for budbreak, bloom, veraison, and harvest, respectively, whereas R2 values for models using only cultivar and GDD 10 were substantially lower (0.787, 0.904, 0.960, and 0.828, respectively). The average errors (differences from actual) for the top multifactor models were 0.70, 0.84, 1.77, and 3.80 days for budbreak, bloom, veraison, and harvest, respectively, and average errors for models that included only cultivar and GDD 10 were much larger (5.27, 2.24, 2.79, and 4.29 days, respectively). In the validation tests, average errors for budbreak, bloom, veraison, and harvest were 1.92, 1.31, 0.94, and 1.67 days, respectively, for the top multifactor models and 10.05, 2.54, 4.23, and 4.96 days, respectively, for models that included cultivar and GDD 10 only. Our results demonstrate the improved accuracy and utility of multifactor models for predicting the timing of phenological stages of cold-climate grape cultivars in the midwestern United States. Used together in succession, the models for budbreak, bloom, veraison, and harvest form a four-stage, multifactor calculator for improved prediction of phenological timing. Multifactor models of this type could be tailored for specific cultivars and growing regions to provide the most accurate predictions possible. muttiple regression mathematical modeling environmental monitoring weather data utilization viticulture interspecific hybrids crop management Plant culture Paul A. Domoto verfasserin aut Gail R. Nonnecke verfasserin aut Diana R. Cochran verfasserin aut In HortScience American Society for Horticultural Science (ASHS), 2020 55(2020), 12, Seite 1912-1925 (DE-627)1760614955 23279834 nnns volume:55 year:2020 number:12 pages:1912-1925 https://doi.org/10.21273/HORTSCI15367-20 kostenfrei https://doaj.org/article/a0193bc253d748f3977dd57729b4290a kostenfrei https://journals.ashs.org/hortsci/view/journals/hortsci/55/12/article-p1912.xml kostenfrei https://doaj.org/toc/2327-9834 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA AR 55 2020 12 1912-1925 |
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Schrader</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Multifactor Models for Improved Prediction of Phenological Timing in Cold-climate Wine Grapes</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</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="520" ind1=" " ind2=" "><subfield code="a">An accurate predictive model for estimating the timing of seasonal phenological stages of grape (Vitis L.) would be a valuable tool for crop management. Currently the most used index for predicting the phenological timing of fruit crops is growing degree days (GDD), but the predictive accuracy of the GDD index varies from season-to-season and is considered unsatisfactory for grapevines grown in the midwestern United States. We used the methods of multiple regression to analyze and model the effects of multiple factors on the number of days remaining until each of four phenological stages (budbreak, bloom, veraison, and harvest maturity) for five cold-climate wine grape cultivars (Frontenac, La Crescent, Marquette, Petit Ami, and St. Croix) grown in central Iowa. The factors (predictor variables) evaluated in models included cultivar, numerical day of the year (DOY), DOY of soil thaw or the previous phenological stage, photoperiod, GDD with a base temperature of 10 °C (GDD 10), soil degree days with a base temperature of 5 °C (SDD 5), and solar accumulation. Models were evaluated for predictive accuracy and goodness of fit by calculating the coefficient of determination (R2), the corrected Akaike information criterion (AICc), and the Bayesian information criterion (BIC); testing for normal distribution of residuals; and comparing the actual number of days remaining until a phenological stage with the number of days predicted by models. The top-performing models from the training set were also tested for predictive accuracy on a validation dataset (a set of data not used to build the model), which consisted of environmental and phenological data recorded for one popular Midwest cultivar (Marquette) in 2019. At all four phenological stages, inclusion of multiple factors (cultivar and four to six additional factors) resulted in predictive models that were more accurate and consistent than models using cultivar and GDD 10 alone. Multifactor models generated from data of all five cultivars had high R2 values of 0.996, 0.985, 0.985, and 0.869 for budbreak, bloom, veraison, and harvest, respectively, whereas R2 values for models using only cultivar and GDD 10 were substantially lower (0.787, 0.904, 0.960, and 0.828, respectively). The average errors (differences from actual) for the top multifactor models were 0.70, 0.84, 1.77, and 3.80 days for budbreak, bloom, veraison, and harvest, respectively, and average errors for models that included only cultivar and GDD 10 were much larger (5.27, 2.24, 2.79, and 4.29 days, respectively). In the validation tests, average errors for budbreak, bloom, veraison, and harvest were 1.92, 1.31, 0.94, and 1.67 days, respectively, for the top multifactor models and 10.05, 2.54, 4.23, and 4.96 days, respectively, for models that included cultivar and GDD 10 only. Our results demonstrate the improved accuracy and utility of multifactor models for predicting the timing of phenological stages of cold-climate grape cultivars in the midwestern United States. Used together in succession, the models for budbreak, bloom, veraison, and harvest form a four-stage, multifactor calculator for improved prediction of phenological timing. 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Multifactor Models for Improved Prediction of Phenological Timing in Cold-climate Wine Grapes |
| abstract |
An accurate predictive model for estimating the timing of seasonal phenological stages of grape (Vitis L.) would be a valuable tool for crop management. Currently the most used index for predicting the phenological timing of fruit crops is growing degree days (GDD), but the predictive accuracy of the GDD index varies from season-to-season and is considered unsatisfactory for grapevines grown in the midwestern United States. We used the methods of multiple regression to analyze and model the effects of multiple factors on the number of days remaining until each of four phenological stages (budbreak, bloom, veraison, and harvest maturity) for five cold-climate wine grape cultivars (Frontenac, La Crescent, Marquette, Petit Ami, and St. Croix) grown in central Iowa. The factors (predictor variables) evaluated in models included cultivar, numerical day of the year (DOY), DOY of soil thaw or the previous phenological stage, photoperiod, GDD with a base temperature of 10 °C (GDD 10), soil degree days with a base temperature of 5 °C (SDD 5), and solar accumulation. Models were evaluated for predictive accuracy and goodness of fit by calculating the coefficient of determination (R2), the corrected Akaike information criterion (AICc), and the Bayesian information criterion (BIC); testing for normal distribution of residuals; and comparing the actual number of days remaining until a phenological stage with the number of days predicted by models. The top-performing models from the training set were also tested for predictive accuracy on a validation dataset (a set of data not used to build the model), which consisted of environmental and phenological data recorded for one popular Midwest cultivar (Marquette) in 2019. At all four phenological stages, inclusion of multiple factors (cultivar and four to six additional factors) resulted in predictive models that were more accurate and consistent than models using cultivar and GDD 10 alone. Multifactor models generated from data of all five cultivars had high R2 values of 0.996, 0.985, 0.985, and 0.869 for budbreak, bloom, veraison, and harvest, respectively, whereas R2 values for models using only cultivar and GDD 10 were substantially lower (0.787, 0.904, 0.960, and 0.828, respectively). The average errors (differences from actual) for the top multifactor models were 0.70, 0.84, 1.77, and 3.80 days for budbreak, bloom, veraison, and harvest, respectively, and average errors for models that included only cultivar and GDD 10 were much larger (5.27, 2.24, 2.79, and 4.29 days, respectively). In the validation tests, average errors for budbreak, bloom, veraison, and harvest were 1.92, 1.31, 0.94, and 1.67 days, respectively, for the top multifactor models and 10.05, 2.54, 4.23, and 4.96 days, respectively, for models that included cultivar and GDD 10 only. Our results demonstrate the improved accuracy and utility of multifactor models for predicting the timing of phenological stages of cold-climate grape cultivars in the midwestern United States. Used together in succession, the models for budbreak, bloom, veraison, and harvest form a four-stage, multifactor calculator for improved prediction of phenological timing. Multifactor models of this type could be tailored for specific cultivars and growing regions to provide the most accurate predictions possible. |
| abstractGer |
An accurate predictive model for estimating the timing of seasonal phenological stages of grape (Vitis L.) would be a valuable tool for crop management. Currently the most used index for predicting the phenological timing of fruit crops is growing degree days (GDD), but the predictive accuracy of the GDD index varies from season-to-season and is considered unsatisfactory for grapevines grown in the midwestern United States. We used the methods of multiple regression to analyze and model the effects of multiple factors on the number of days remaining until each of four phenological stages (budbreak, bloom, veraison, and harvest maturity) for five cold-climate wine grape cultivars (Frontenac, La Crescent, Marquette, Petit Ami, and St. Croix) grown in central Iowa. The factors (predictor variables) evaluated in models included cultivar, numerical day of the year (DOY), DOY of soil thaw or the previous phenological stage, photoperiod, GDD with a base temperature of 10 °C (GDD 10), soil degree days with a base temperature of 5 °C (SDD 5), and solar accumulation. Models were evaluated for predictive accuracy and goodness of fit by calculating the coefficient of determination (R2), the corrected Akaike information criterion (AICc), and the Bayesian information criterion (BIC); testing for normal distribution of residuals; and comparing the actual number of days remaining until a phenological stage with the number of days predicted by models. The top-performing models from the training set were also tested for predictive accuracy on a validation dataset (a set of data not used to build the model), which consisted of environmental and phenological data recorded for one popular Midwest cultivar (Marquette) in 2019. At all four phenological stages, inclusion of multiple factors (cultivar and four to six additional factors) resulted in predictive models that were more accurate and consistent than models using cultivar and GDD 10 alone. Multifactor models generated from data of all five cultivars had high R2 values of 0.996, 0.985, 0.985, and 0.869 for budbreak, bloom, veraison, and harvest, respectively, whereas R2 values for models using only cultivar and GDD 10 were substantially lower (0.787, 0.904, 0.960, and 0.828, respectively). The average errors (differences from actual) for the top multifactor models were 0.70, 0.84, 1.77, and 3.80 days for budbreak, bloom, veraison, and harvest, respectively, and average errors for models that included only cultivar and GDD 10 were much larger (5.27, 2.24, 2.79, and 4.29 days, respectively). In the validation tests, average errors for budbreak, bloom, veraison, and harvest were 1.92, 1.31, 0.94, and 1.67 days, respectively, for the top multifactor models and 10.05, 2.54, 4.23, and 4.96 days, respectively, for models that included cultivar and GDD 10 only. Our results demonstrate the improved accuracy and utility of multifactor models for predicting the timing of phenological stages of cold-climate grape cultivars in the midwestern United States. Used together in succession, the models for budbreak, bloom, veraison, and harvest form a four-stage, multifactor calculator for improved prediction of phenological timing. Multifactor models of this type could be tailored for specific cultivars and growing regions to provide the most accurate predictions possible. |
| abstract_unstemmed |
An accurate predictive model for estimating the timing of seasonal phenological stages of grape (Vitis L.) would be a valuable tool for crop management. Currently the most used index for predicting the phenological timing of fruit crops is growing degree days (GDD), but the predictive accuracy of the GDD index varies from season-to-season and is considered unsatisfactory for grapevines grown in the midwestern United States. We used the methods of multiple regression to analyze and model the effects of multiple factors on the number of days remaining until each of four phenological stages (budbreak, bloom, veraison, and harvest maturity) for five cold-climate wine grape cultivars (Frontenac, La Crescent, Marquette, Petit Ami, and St. Croix) grown in central Iowa. The factors (predictor variables) evaluated in models included cultivar, numerical day of the year (DOY), DOY of soil thaw or the previous phenological stage, photoperiod, GDD with a base temperature of 10 °C (GDD 10), soil degree days with a base temperature of 5 °C (SDD 5), and solar accumulation. Models were evaluated for predictive accuracy and goodness of fit by calculating the coefficient of determination (R2), the corrected Akaike information criterion (AICc), and the Bayesian information criterion (BIC); testing for normal distribution of residuals; and comparing the actual number of days remaining until a phenological stage with the number of days predicted by models. The top-performing models from the training set were also tested for predictive accuracy on a validation dataset (a set of data not used to build the model), which consisted of environmental and phenological data recorded for one popular Midwest cultivar (Marquette) in 2019. At all four phenological stages, inclusion of multiple factors (cultivar and four to six additional factors) resulted in predictive models that were more accurate and consistent than models using cultivar and GDD 10 alone. Multifactor models generated from data of all five cultivars had high R2 values of 0.996, 0.985, 0.985, and 0.869 for budbreak, bloom, veraison, and harvest, respectively, whereas R2 values for models using only cultivar and GDD 10 were substantially lower (0.787, 0.904, 0.960, and 0.828, respectively). The average errors (differences from actual) for the top multifactor models were 0.70, 0.84, 1.77, and 3.80 days for budbreak, bloom, veraison, and harvest, respectively, and average errors for models that included only cultivar and GDD 10 were much larger (5.27, 2.24, 2.79, and 4.29 days, respectively). In the validation tests, average errors for budbreak, bloom, veraison, and harvest were 1.92, 1.31, 0.94, and 1.67 days, respectively, for the top multifactor models and 10.05, 2.54, 4.23, and 4.96 days, respectively, for models that included cultivar and GDD 10 only. Our results demonstrate the improved accuracy and utility of multifactor models for predicting the timing of phenological stages of cold-climate grape cultivars in the midwestern United States. Used together in succession, the models for budbreak, bloom, veraison, and harvest form a four-stage, multifactor calculator for improved prediction of phenological timing. Multifactor models of this type could be tailored for specific cultivars and growing regions to provide the most accurate predictions possible. |
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Schrader</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Multifactor Models for Improved Prediction of Phenological Timing in Cold-climate Wine Grapes</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</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="520" ind1=" " ind2=" "><subfield code="a">An accurate predictive model for estimating the timing of seasonal phenological stages of grape (Vitis L.) would be a valuable tool for crop management. 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Models were evaluated for predictive accuracy and goodness of fit by calculating the coefficient of determination (R2), the corrected Akaike information criterion (AICc), and the Bayesian information criterion (BIC); testing for normal distribution of residuals; and comparing the actual number of days remaining until a phenological stage with the number of days predicted by models. The top-performing models from the training set were also tested for predictive accuracy on a validation dataset (a set of data not used to build the model), which consisted of environmental and phenological data recorded for one popular Midwest cultivar (Marquette) in 2019. At all four phenological stages, inclusion of multiple factors (cultivar and four to six additional factors) resulted in predictive models that were more accurate and consistent than models using cultivar and GDD 10 alone. Multifactor models generated from data of all five cultivars had high R2 values of 0.996, 0.985, 0.985, and 0.869 for budbreak, bloom, veraison, and harvest, respectively, whereas R2 values for models using only cultivar and GDD 10 were substantially lower (0.787, 0.904, 0.960, and 0.828, respectively). The average errors (differences from actual) for the top multifactor models were 0.70, 0.84, 1.77, and 3.80 days for budbreak, bloom, veraison, and harvest, respectively, and average errors for models that included only cultivar and GDD 10 were much larger (5.27, 2.24, 2.79, and 4.29 days, respectively). In the validation tests, average errors for budbreak, bloom, veraison, and harvest were 1.92, 1.31, 0.94, and 1.67 days, respectively, for the top multifactor models and 10.05, 2.54, 4.23, and 4.96 days, respectively, for models that included cultivar and GDD 10 only. 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