Climate Services for Organic Fruit Production in Valencia Region: Early Frost Forecasting
The increased occurrence of extreme weather events due to climate change has heightened the need to develop support decision systems that can help farmers to mitigate losses in agriculture. Environmental hazards, such as frost, have a relevant economic impact on crops since they may cause damage and...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-02-01
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| Series: | Chemistry Proceedings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4583/10/1/70 |
| _version_ | 1797612755095650304 |
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| author | Omjyoti Dutta Freddy Rivas |
| author_facet | Omjyoti Dutta Freddy Rivas |
| author_sort | Omjyoti Dutta |
| collection | DOAJ |
| description | The increased occurrence of extreme weather events due to climate change has heightened the need to develop support decision systems that can help farmers to mitigate losses in agriculture. Environmental hazards, such as frost, have a relevant economic impact on crops since they may cause damage and injuries to sensitive crops and, therefore, lead to production losses. The probability of frost occurrences is heavily influenced by local climate conditions. In addition, the extent of damage due to frost also depends on the phenology stages of the crops present in the area of interest. Hence, an early frost warning system at a local scale has the potential to minimize damage to the crops as one can deploy protective mechanisms. In this article, we present models for early forecasting (24 and 48 h) of frost occurrences using stacked machine learning models. We trained the machine-learning models with hourly historical data from a local weather station. The trained model is validated within the timeframe when the crops (organic fruits) are most susceptible to frost for the area of study. We also show the applicability of the model by extrapolating it to a new region. This development is carried out within the framework of the H2020 CYBELE project. |
| first_indexed | 2024-03-11T06:46:28Z |
| format | Article |
| id | doaj.art-358c0a24e22347b8901f4564a5ad57d8 |
| institution | Directory Open Access Journal |
| issn | 2673-4583 |
| language | English |
| last_indexed | 2024-03-11T06:46:28Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Chemistry Proceedings |
| spelling | doaj.art-358c0a24e22347b8901f4564a5ad57d82023-11-17T10:17:44ZengMDPI AGChemistry Proceedings2673-45832022-02-011017010.3390/IOCAG2022-12218Climate Services for Organic Fruit Production in Valencia Region: Early Frost ForecastingOmjyoti Dutta0Freddy Rivas1Data Scientist, GMV Aerospace and Defense S.A., Isaac Newton, 11. P.T.M. Tres Cantos, 28760 Madrid, SpainProject Manager, Spatial Data Analyst, GMV Aerospace and Defense S.A., Santiago Grisolía, 4. P.T.M. Tres Cantos, 28760 Madrid, SpainThe increased occurrence of extreme weather events due to climate change has heightened the need to develop support decision systems that can help farmers to mitigate losses in agriculture. Environmental hazards, such as frost, have a relevant economic impact on crops since they may cause damage and injuries to sensitive crops and, therefore, lead to production losses. The probability of frost occurrences is heavily influenced by local climate conditions. In addition, the extent of damage due to frost also depends on the phenology stages of the crops present in the area of interest. Hence, an early frost warning system at a local scale has the potential to minimize damage to the crops as one can deploy protective mechanisms. In this article, we present models for early forecasting (24 and 48 h) of frost occurrences using stacked machine learning models. We trained the machine-learning models with hourly historical data from a local weather station. The trained model is validated within the timeframe when the crops (organic fruits) are most susceptible to frost for the area of study. We also show the applicability of the model by extrapolating it to a new region. This development is carried out within the framework of the H2020 CYBELE project.https://www.mdpi.com/2673-4583/10/1/70agriculturedamageextreme weathermachine learningrisksensor |
| spellingShingle | Omjyoti Dutta Freddy Rivas Climate Services for Organic Fruit Production in Valencia Region: Early Frost Forecasting Chemistry Proceedings agriculture damage extreme weather machine learning risk sensor |
| title | Climate Services for Organic Fruit Production in Valencia Region: Early Frost Forecasting |
| title_full | Climate Services for Organic Fruit Production in Valencia Region: Early Frost Forecasting |
| title_fullStr | Climate Services for Organic Fruit Production in Valencia Region: Early Frost Forecasting |
| title_full_unstemmed | Climate Services for Organic Fruit Production in Valencia Region: Early Frost Forecasting |
| title_short | Climate Services for Organic Fruit Production in Valencia Region: Early Frost Forecasting |
| title_sort | climate services for organic fruit production in valencia region early frost forecasting |
| topic | agriculture damage extreme weather machine learning risk sensor |
| url | https://www.mdpi.com/2673-4583/10/1/70 |
| work_keys_str_mv | AT omjyotidutta climateservicesfororganicfruitproductioninvalenciaregionearlyfrostforecasting AT freddyrivas climateservicesfororganicfruitproductioninvalenciaregionearlyfrostforecasting |