Phytophthora infestans Prediction for a Potato Crop
Abstract The present work studies the seasonal variation of Phytophthora infestans concentrations in the atmosphere of a potato crop grown in A Limia. Different models have also been tested to predict the attack of this pathogen in order to establish the necessary treatments. Sampling has been carri...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Iglesias, Isabel [verfasserIn] Escuredo, Olga [verfasserIn] Seijo, Carmen [verfasserIn] Méndez, Jose [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2009 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: American journal of potato research - New York, NY : Springer, 1923, 87(2009), 1 vom: 14. Okt., Seite 32-40 |
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| Übergeordnetes Werk: |
volume:87 ; year:2009 ; number:1 ; day:14 ; month:10 ; pages:32-40 |
| Links: |
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| DOI / URN: |
10.1007/s12230-009-9114-y |
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| Katalog-ID: |
SPR025504754 |
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| 520 | |a Abstract The present work studies the seasonal variation of Phytophthora infestans concentrations in the atmosphere of a potato crop grown in A Limia. Different models have also been tested to predict the attack of this pathogen in order to establish the necessary treatments. Sampling has been carried out during crop cycles in 2004, 2005 and 2007 by using a volumetric spore trap, located in the centre of the plot at a height of 1.5 m. The collection of meteorological data was done with an automatic gauge. The highest concentrations of Phytophthora infestans were registered during June and July, with maximum daily levels ranging from 82 to 145 spores/$ m^{3} $, as a result of the maximum temperature average, around 16–23°C. Three prediction models for Phytophthora infestans have been adjusted to our area of study. The Smith Periods model provides better results in years with low and medium levels of the oomycete inoculum. The NegFRY model is useful to adjust the day of first treatment application, when used together with the Negative Prognosis model. To observe the influence of meteorological parameters on Phytophthora infestans spore concentration, besides applying these models, a Spearman correlation analysis was carried out. This test allowed the establishment of a correlation between the temperature parameter and the oomycete, obtaining positive and significant correlations (p < 0.01). | ||
| 650 | 4 | |a Meteorological factors |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Smith Periods model |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Negative Prognosis model |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a NegFry model |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Escuredo, Olga |e verfasserin |4 aut | |
| 700 | 1 | |a Seijo, Carmen |e verfasserin |4 aut | |
| 700 | 1 | |a Méndez, Jose |e verfasserin |4 aut | |
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10.1007/s12230-009-9114-y doi (DE-627)SPR025504754 (SPR)s12230-009-9114-y-e DE-627 ger DE-627 rakwb eng 630 640 ASE Iglesias, Isabel verfasserin aut Phytophthora infestans Prediction for a Potato Crop 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The present work studies the seasonal variation of Phytophthora infestans concentrations in the atmosphere of a potato crop grown in A Limia. Different models have also been tested to predict the attack of this pathogen in order to establish the necessary treatments. Sampling has been carried out during crop cycles in 2004, 2005 and 2007 by using a volumetric spore trap, located in the centre of the plot at a height of 1.5 m. The collection of meteorological data was done with an automatic gauge. The highest concentrations of Phytophthora infestans were registered during June and July, with maximum daily levels ranging from 82 to 145 spores/$ m^{3} $, as a result of the maximum temperature average, around 16–23°C. Three prediction models for Phytophthora infestans have been adjusted to our area of study. The Smith Periods model provides better results in years with low and medium levels of the oomycete inoculum. The NegFRY model is useful to adjust the day of first treatment application, when used together with the Negative Prognosis model. To observe the influence of meteorological parameters on Phytophthora infestans spore concentration, besides applying these models, a Spearman correlation analysis was carried out. This test allowed the establishment of a correlation between the temperature parameter and the oomycete, obtaining positive and significant correlations (p < 0.01). Meteorological factors (dpeaa)DE-He213 Smith Periods model (dpeaa)DE-He213 Negative Prognosis model (dpeaa)DE-He213 NegFry model (dpeaa)DE-He213 Escuredo, Olga verfasserin aut Seijo, Carmen verfasserin aut Méndez, Jose verfasserin aut Enthalten in American journal of potato research New York, NY : Springer, 1923 87(2009), 1 vom: 14. Okt., Seite 32-40 (DE-627)54963410X (DE-600)2395546-6 1874-9380 nnns volume:87 year:2009 number:1 day:14 month:10 pages:32-40 https://dx.doi.org/10.1007/s12230-009-9114-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 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_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 87 2009 1 14 10 32-40 |
| spelling |
10.1007/s12230-009-9114-y doi (DE-627)SPR025504754 (SPR)s12230-009-9114-y-e DE-627 ger DE-627 rakwb eng 630 640 ASE Iglesias, Isabel verfasserin aut Phytophthora infestans Prediction for a Potato Crop 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The present work studies the seasonal variation of Phytophthora infestans concentrations in the atmosphere of a potato crop grown in A Limia. Different models have also been tested to predict the attack of this pathogen in order to establish the necessary treatments. Sampling has been carried out during crop cycles in 2004, 2005 and 2007 by using a volumetric spore trap, located in the centre of the plot at a height of 1.5 m. The collection of meteorological data was done with an automatic gauge. The highest concentrations of Phytophthora infestans were registered during June and July, with maximum daily levels ranging from 82 to 145 spores/$ m^{3} $, as a result of the maximum temperature average, around 16–23°C. Three prediction models for Phytophthora infestans have been adjusted to our area of study. The Smith Periods model provides better results in years with low and medium levels of the oomycete inoculum. The NegFRY model is useful to adjust the day of first treatment application, when used together with the Negative Prognosis model. To observe the influence of meteorological parameters on Phytophthora infestans spore concentration, besides applying these models, a Spearman correlation analysis was carried out. This test allowed the establishment of a correlation between the temperature parameter and the oomycete, obtaining positive and significant correlations (p < 0.01). Meteorological factors (dpeaa)DE-He213 Smith Periods model (dpeaa)DE-He213 Negative Prognosis model (dpeaa)DE-He213 NegFry model (dpeaa)DE-He213 Escuredo, Olga verfasserin aut Seijo, Carmen verfasserin aut Méndez, Jose verfasserin aut Enthalten in American journal of potato research New York, NY : Springer, 1923 87(2009), 1 vom: 14. Okt., Seite 32-40 (DE-627)54963410X (DE-600)2395546-6 1874-9380 nnns volume:87 year:2009 number:1 day:14 month:10 pages:32-40 https://dx.doi.org/10.1007/s12230-009-9114-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 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_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 87 2009 1 14 10 32-40 |
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10.1007/s12230-009-9114-y doi (DE-627)SPR025504754 (SPR)s12230-009-9114-y-e DE-627 ger DE-627 rakwb eng 630 640 ASE Iglesias, Isabel verfasserin aut Phytophthora infestans Prediction for a Potato Crop 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The present work studies the seasonal variation of Phytophthora infestans concentrations in the atmosphere of a potato crop grown in A Limia. Different models have also been tested to predict the attack of this pathogen in order to establish the necessary treatments. Sampling has been carried out during crop cycles in 2004, 2005 and 2007 by using a volumetric spore trap, located in the centre of the plot at a height of 1.5 m. The collection of meteorological data was done with an automatic gauge. The highest concentrations of Phytophthora infestans were registered during June and July, with maximum daily levels ranging from 82 to 145 spores/$ m^{3} $, as a result of the maximum temperature average, around 16–23°C. Three prediction models for Phytophthora infestans have been adjusted to our area of study. The Smith Periods model provides better results in years with low and medium levels of the oomycete inoculum. The NegFRY model is useful to adjust the day of first treatment application, when used together with the Negative Prognosis model. To observe the influence of meteorological parameters on Phytophthora infestans spore concentration, besides applying these models, a Spearman correlation analysis was carried out. This test allowed the establishment of a correlation between the temperature parameter and the oomycete, obtaining positive and significant correlations (p < 0.01). Meteorological factors (dpeaa)DE-He213 Smith Periods model (dpeaa)DE-He213 Negative Prognosis model (dpeaa)DE-He213 NegFry model (dpeaa)DE-He213 Escuredo, Olga verfasserin aut Seijo, Carmen verfasserin aut Méndez, Jose verfasserin aut Enthalten in American journal of potato research New York, NY : Springer, 1923 87(2009), 1 vom: 14. Okt., Seite 32-40 (DE-627)54963410X (DE-600)2395546-6 1874-9380 nnns volume:87 year:2009 number:1 day:14 month:10 pages:32-40 https://dx.doi.org/10.1007/s12230-009-9114-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 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_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 87 2009 1 14 10 32-40 |
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10.1007/s12230-009-9114-y doi (DE-627)SPR025504754 (SPR)s12230-009-9114-y-e DE-627 ger DE-627 rakwb eng 630 640 ASE Iglesias, Isabel verfasserin aut Phytophthora infestans Prediction for a Potato Crop 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The present work studies the seasonal variation of Phytophthora infestans concentrations in the atmosphere of a potato crop grown in A Limia. Different models have also been tested to predict the attack of this pathogen in order to establish the necessary treatments. Sampling has been carried out during crop cycles in 2004, 2005 and 2007 by using a volumetric spore trap, located in the centre of the plot at a height of 1.5 m. The collection of meteorological data was done with an automatic gauge. The highest concentrations of Phytophthora infestans were registered during June and July, with maximum daily levels ranging from 82 to 145 spores/$ m^{3} $, as a result of the maximum temperature average, around 16–23°C. Three prediction models for Phytophthora infestans have been adjusted to our area of study. The Smith Periods model provides better results in years with low and medium levels of the oomycete inoculum. The NegFRY model is useful to adjust the day of first treatment application, when used together with the Negative Prognosis model. To observe the influence of meteorological parameters on Phytophthora infestans spore concentration, besides applying these models, a Spearman correlation analysis was carried out. This test allowed the establishment of a correlation between the temperature parameter and the oomycete, obtaining positive and significant correlations (p < 0.01). Meteorological factors (dpeaa)DE-He213 Smith Periods model (dpeaa)DE-He213 Negative Prognosis model (dpeaa)DE-He213 NegFry model (dpeaa)DE-He213 Escuredo, Olga verfasserin aut Seijo, Carmen verfasserin aut Méndez, Jose verfasserin aut Enthalten in American journal of potato research New York, NY : Springer, 1923 87(2009), 1 vom: 14. Okt., Seite 32-40 (DE-627)54963410X (DE-600)2395546-6 1874-9380 nnns volume:87 year:2009 number:1 day:14 month:10 pages:32-40 https://dx.doi.org/10.1007/s12230-009-9114-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 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_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 87 2009 1 14 10 32-40 |
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10.1007/s12230-009-9114-y doi (DE-627)SPR025504754 (SPR)s12230-009-9114-y-e DE-627 ger DE-627 rakwb eng 630 640 ASE Iglesias, Isabel verfasserin aut Phytophthora infestans Prediction for a Potato Crop 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The present work studies the seasonal variation of Phytophthora infestans concentrations in the atmosphere of a potato crop grown in A Limia. Different models have also been tested to predict the attack of this pathogen in order to establish the necessary treatments. Sampling has been carried out during crop cycles in 2004, 2005 and 2007 by using a volumetric spore trap, located in the centre of the plot at a height of 1.5 m. The collection of meteorological data was done with an automatic gauge. The highest concentrations of Phytophthora infestans were registered during June and July, with maximum daily levels ranging from 82 to 145 spores/$ m^{3} $, as a result of the maximum temperature average, around 16–23°C. Three prediction models for Phytophthora infestans have been adjusted to our area of study. The Smith Periods model provides better results in years with low and medium levels of the oomycete inoculum. The NegFRY model is useful to adjust the day of first treatment application, when used together with the Negative Prognosis model. To observe the influence of meteorological parameters on Phytophthora infestans spore concentration, besides applying these models, a Spearman correlation analysis was carried out. This test allowed the establishment of a correlation between the temperature parameter and the oomycete, obtaining positive and significant correlations (p < 0.01). Meteorological factors (dpeaa)DE-He213 Smith Periods model (dpeaa)DE-He213 Negative Prognosis model (dpeaa)DE-He213 NegFry model (dpeaa)DE-He213 Escuredo, Olga verfasserin aut Seijo, Carmen verfasserin aut Méndez, Jose verfasserin aut Enthalten in American journal of potato research New York, NY : Springer, 1923 87(2009), 1 vom: 14. Okt., Seite 32-40 (DE-627)54963410X (DE-600)2395546-6 1874-9380 nnns volume:87 year:2009 number:1 day:14 month:10 pages:32-40 https://dx.doi.org/10.1007/s12230-009-9114-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 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_4012 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 87 2009 1 14 10 32-40 |
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Enthalten in American journal of potato research 87(2009), 1 vom: 14. Okt., Seite 32-40 volume:87 year:2009 number:1 day:14 month:10 pages:32-40 |
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Iglesias, Isabel @@aut@@ Escuredo, Olga @@aut@@ Seijo, Carmen @@aut@@ Méndez, Jose @@aut@@ |
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Different models have also been tested to predict the attack of this pathogen in order to establish the necessary treatments. Sampling has been carried out during crop cycles in 2004, 2005 and 2007 by using a volumetric spore trap, located in the centre of the plot at a height of 1.5 m. The collection of meteorological data was done with an automatic gauge. The highest concentrations of Phytophthora infestans were registered during June and July, with maximum daily levels ranging from 82 to 145 spores/$ m^{3} $, as a result of the maximum temperature average, around 16–23°C. Three prediction models for Phytophthora infestans have been adjusted to our area of study. The Smith Periods model provides better results in years with low and medium levels of the oomycete inoculum. The NegFRY model is useful to adjust the day of first treatment application, when used together with the Negative Prognosis model. To observe the influence of meteorological parameters on Phytophthora infestans spore concentration, besides applying these models, a Spearman correlation analysis was carried out. 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Iglesias, Isabel |
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Iglesias, Isabel ddc 630 misc Meteorological factors misc Smith Periods model misc Negative Prognosis model misc NegFry model Phytophthora infestans Prediction for a Potato Crop |
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phytophthora infestans prediction for a potato crop |
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Phytophthora infestans Prediction for a Potato Crop |
| abstract |
Abstract The present work studies the seasonal variation of Phytophthora infestans concentrations in the atmosphere of a potato crop grown in A Limia. Different models have also been tested to predict the attack of this pathogen in order to establish the necessary treatments. Sampling has been carried out during crop cycles in 2004, 2005 and 2007 by using a volumetric spore trap, located in the centre of the plot at a height of 1.5 m. The collection of meteorological data was done with an automatic gauge. The highest concentrations of Phytophthora infestans were registered during June and July, with maximum daily levels ranging from 82 to 145 spores/$ m^{3} $, as a result of the maximum temperature average, around 16–23°C. Three prediction models for Phytophthora infestans have been adjusted to our area of study. The Smith Periods model provides better results in years with low and medium levels of the oomycete inoculum. The NegFRY model is useful to adjust the day of first treatment application, when used together with the Negative Prognosis model. To observe the influence of meteorological parameters on Phytophthora infestans spore concentration, besides applying these models, a Spearman correlation analysis was carried out. This test allowed the establishment of a correlation between the temperature parameter and the oomycete, obtaining positive and significant correlations (p < 0.01). |
| abstractGer |
Abstract The present work studies the seasonal variation of Phytophthora infestans concentrations in the atmosphere of a potato crop grown in A Limia. Different models have also been tested to predict the attack of this pathogen in order to establish the necessary treatments. Sampling has been carried out during crop cycles in 2004, 2005 and 2007 by using a volumetric spore trap, located in the centre of the plot at a height of 1.5 m. The collection of meteorological data was done with an automatic gauge. The highest concentrations of Phytophthora infestans were registered during June and July, with maximum daily levels ranging from 82 to 145 spores/$ m^{3} $, as a result of the maximum temperature average, around 16–23°C. Three prediction models for Phytophthora infestans have been adjusted to our area of study. The Smith Periods model provides better results in years with low and medium levels of the oomycete inoculum. The NegFRY model is useful to adjust the day of first treatment application, when used together with the Negative Prognosis model. To observe the influence of meteorological parameters on Phytophthora infestans spore concentration, besides applying these models, a Spearman correlation analysis was carried out. This test allowed the establishment of a correlation between the temperature parameter and the oomycete, obtaining positive and significant correlations (p < 0.01). |
| abstract_unstemmed |
Abstract The present work studies the seasonal variation of Phytophthora infestans concentrations in the atmosphere of a potato crop grown in A Limia. Different models have also been tested to predict the attack of this pathogen in order to establish the necessary treatments. Sampling has been carried out during crop cycles in 2004, 2005 and 2007 by using a volumetric spore trap, located in the centre of the plot at a height of 1.5 m. The collection of meteorological data was done with an automatic gauge. The highest concentrations of Phytophthora infestans were registered during June and July, with maximum daily levels ranging from 82 to 145 spores/$ m^{3} $, as a result of the maximum temperature average, around 16–23°C. Three prediction models for Phytophthora infestans have been adjusted to our area of study. The Smith Periods model provides better results in years with low and medium levels of the oomycete inoculum. The NegFRY model is useful to adjust the day of first treatment application, when used together with the Negative Prognosis model. To observe the influence of meteorological parameters on Phytophthora infestans spore concentration, besides applying these models, a Spearman correlation analysis was carried out. This test allowed the establishment of a correlation between the temperature parameter and the oomycete, obtaining positive and significant correlations (p < 0.01). |
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Phytophthora infestans Prediction for a Potato Crop |
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https://dx.doi.org/10.1007/s12230-009-9114-y |
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Escuredo, Olga Seijo, Carmen Méndez, Jose |
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Escuredo, Olga Seijo, Carmen Méndez, Jose |
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10.1007/s12230-009-9114-y |
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2024-07-03T16:20:21.410Z |
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| score |
7.399584 |