Suitability of Early Blight Forecasting Systems for Detecting First Symptoms in Potato Crops of NW Spain
In recent years, early blight epidemics have been frequently causing important yield loses in potato crop. This fungal disease develops quickly when weather conditions are favorable, forcing the use of fungicides by farmers. A Limia is one of the largest areas for potato production in Spain. Usually...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/12/7/1611 |
| _version_ | 1797433969972609024 |
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| author | Laura Meno Isaac Kwesi Abuley Olga Escuredo M. Carmen Seijo |
| author_facet | Laura Meno Isaac Kwesi Abuley Olga Escuredo M. Carmen Seijo |
| author_sort | Laura Meno |
| collection | DOAJ |
| description | In recent years, early blight epidemics have been frequently causing important yield loses in potato crop. This fungal disease develops quickly when weather conditions are favorable, forcing the use of fungicides by farmers. A Limia is one of the largest areas for potato production in Spain. Usually, early blight epidemics are controlled using pre-established schedule calendars. This strategy is expensive and can affect the environment of agricultural areas. Decision support systems are not currently in place to be used by farmers for managing early blight. Thus, the objective of this research was to evaluate different early blight forecasting models based on plant or/and pathogen requirements and weather conditions to check their suitability for predicting the first symptoms of early blight, which is necessary to determine the timings of the first fungicide application. For this, weather, phenology and symptomatology of disease were monitored throughout five crop seasons. The first early blight symptoms appeared starting the flowering stage, between 37 and 40 days after emergence of plants. The forecasting models that were based on plants offered the best results. Specifically, the Wang-Engel model, with 1.4 risk units and Growing Degree-Days (361 cumulative units) offeredthe best prediction. The pathogen-based models showed a conservative forecast, whereas the models that integrated both plant and pathogen features forecasted the first early blight attack markedly later. |
| first_indexed | 2024-03-09T10:24:33Z |
| format | Article |
| id | doaj.art-aee6008943f54f4ca3ac8dad204b96d9 |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-09T10:24:33Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-aee6008943f54f4ca3ac8dad204b96d92023-12-01T21:47:19ZengMDPI AGAgronomy2073-43952022-07-01127161110.3390/agronomy12071611Suitability of Early Blight Forecasting Systems for Detecting First Symptoms in Potato Crops of NW SpainLaura Meno0Isaac Kwesi Abuley1Olga Escuredo2M. Carmen Seijo3Department of Vegetal Biology and Soil Sciences, Faculty of Sciences, University of Vigo, As Lagoas, 32004 Ourense, SpainFlakkebjerg Research Center, Department of Agroecology, Aarhus University, Forsøgsvej 1, 4200 Slagelse, DenmarkDepartment of Vegetal Biology and Soil Sciences, Faculty of Sciences, University of Vigo, As Lagoas, 32004 Ourense, SpainDepartment of Vegetal Biology and Soil Sciences, Faculty of Sciences, University of Vigo, As Lagoas, 32004 Ourense, SpainIn recent years, early blight epidemics have been frequently causing important yield loses in potato crop. This fungal disease develops quickly when weather conditions are favorable, forcing the use of fungicides by farmers. A Limia is one of the largest areas for potato production in Spain. Usually, early blight epidemics are controlled using pre-established schedule calendars. This strategy is expensive and can affect the environment of agricultural areas. Decision support systems are not currently in place to be used by farmers for managing early blight. Thus, the objective of this research was to evaluate different early blight forecasting models based on plant or/and pathogen requirements and weather conditions to check their suitability for predicting the first symptoms of early blight, which is necessary to determine the timings of the first fungicide application. For this, weather, phenology and symptomatology of disease were monitored throughout five crop seasons. The first early blight symptoms appeared starting the flowering stage, between 37 and 40 days after emergence of plants. The forecasting models that were based on plants offered the best results. Specifically, the Wang-Engel model, with 1.4 risk units and Growing Degree-Days (361 cumulative units) offeredthe best prediction. The pathogen-based models showed a conservative forecast, whereas the models that integrated both plant and pathogen features forecasted the first early blight attack markedly later.https://www.mdpi.com/2073-4395/12/7/1611<i>Alternaria</i> spp.disease forecastingphysiological ageGrowing Degree Days<i>Solanum tuberosum</i> |
| spellingShingle | Laura Meno Isaac Kwesi Abuley Olga Escuredo M. Carmen Seijo Suitability of Early Blight Forecasting Systems for Detecting First Symptoms in Potato Crops of NW Spain Agronomy <i>Alternaria</i> spp. disease forecasting physiological age Growing Degree Days <i>Solanum tuberosum</i> |
| title | Suitability of Early Blight Forecasting Systems for Detecting First Symptoms in Potato Crops of NW Spain |
| title_full | Suitability of Early Blight Forecasting Systems for Detecting First Symptoms in Potato Crops of NW Spain |
| title_fullStr | Suitability of Early Blight Forecasting Systems for Detecting First Symptoms in Potato Crops of NW Spain |
| title_full_unstemmed | Suitability of Early Blight Forecasting Systems for Detecting First Symptoms in Potato Crops of NW Spain |
| title_short | Suitability of Early Blight Forecasting Systems for Detecting First Symptoms in Potato Crops of NW Spain |
| title_sort | suitability of early blight forecasting systems for detecting first symptoms in potato crops of nw spain |
| topic | <i>Alternaria</i> spp. disease forecasting physiological age Growing Degree Days <i>Solanum tuberosum</i> |
| url | https://www.mdpi.com/2073-4395/12/7/1611 |
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