Weather-based predictive modeling of orange rust of sugarcane in Florida
Epidemics of sugarcane orange rust (caused by Puccinia kuehnii) in Florida are largely influenced by prevailing weather conditions. In this study, we attempted to model the relationship between weather conditions and rust epidemics as a first step toward development of a decision aid for disease management. For this purpose, rust severity data were collected from 2014 through 2016 at the Everglades Research and Education Center, Belle Glade, Florida, by recording percentage of rust-affected area of the top visible dewlap leaf every 2 weeks from three orange rust susceptible cultivars. Hourly weather data for 10- to 40-day periods prior to each orange rust assessment were evaluated as potential predictors of rust severity under field conditions. Correlation and stepwise regression analyses resulted in the identification of nighttime (8 PM to 8 AM) accumulation of hours with average temperature 20 to 22°C as a key predictor explaining orange rust severity. The five best regression models for a 30-day period prior to disease assessment explained 65.3 to 76.2% of variation of orange rust severity. Prediction accuracy of these models was tested using a case control approach with disease observations collected in 2017 and 2018. Based on receiver operator characteristic curve analysis of these two seasons of test data, a single-variable model with the nighttime temperature predictor mentioned above gave the highest prediction accuracy of disease severity. These models have potential for use in quantitative risk assessment of sugarcane rust epidemics.
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Language: | eng |
Subjects: | H20 - Maladies des plantes, P40 - Météorologie et climatologie, Pucciniales, Saccharum officinarum, épidémiologie, conditions météorologiques, modèle de simulation, écologie, mycologie, Puccinia kuehnii, http://aims.fao.org/aos/agrovoc/c_31692, http://aims.fao.org/aos/agrovoc/c_6727, http://aims.fao.org/aos/agrovoc/c_2615, http://aims.fao.org/aos/agrovoc/c_29565, http://aims.fao.org/aos/agrovoc/c_24242, http://aims.fao.org/aos/agrovoc/c_2467, http://aims.fao.org/aos/agrovoc/c_5019, http://aims.fao.org/aos/agrovoc/c_ac7b37ec, http://aims.fao.org/aos/agrovoc/c_2985, |
Online Access: | http://agritrop.cirad.fr/595214/ http://agritrop.cirad.fr/595214/1/2020%20Chaulagain_Modeling%20sugarcane%20orange%20rust.pdf |
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dig-cirad-fr-5952142024-01-29T02:39:02Z http://agritrop.cirad.fr/595214/ http://agritrop.cirad.fr/595214/ Weather-based predictive modeling of orange rust of sugarcane in Florida. Chaulagain Bhim, Small Ian M., Shine James M., Fraisse Clyde W., Raid Richard Neil, Rott Philippe. 2020. Phytopathology, 110 (3) : 626-632.https://doi.org/10.1094/PHYTO-06-19-0211-R <https://doi.org/10.1094/PHYTO-06-19-0211-R> Weather-based predictive modeling of orange rust of sugarcane in Florida Chaulagain, Bhim Small, Ian M. Shine, James M. Fraisse, Clyde W. Raid, Richard Neil Rott, Philippe eng 2020 Phytopathology H20 - Maladies des plantes P40 - Météorologie et climatologie Pucciniales Saccharum officinarum épidémiologie conditions météorologiques modèle de simulation écologie mycologie Puccinia kuehnii http://aims.fao.org/aos/agrovoc/c_31692 http://aims.fao.org/aos/agrovoc/c_6727 http://aims.fao.org/aos/agrovoc/c_2615 http://aims.fao.org/aos/agrovoc/c_29565 http://aims.fao.org/aos/agrovoc/c_24242 http://aims.fao.org/aos/agrovoc/c_2467 http://aims.fao.org/aos/agrovoc/c_5019 http://aims.fao.org/aos/agrovoc/c_ac7b37ec Floride http://aims.fao.org/aos/agrovoc/c_2985 Epidemics of sugarcane orange rust (caused by Puccinia kuehnii) in Florida are largely influenced by prevailing weather conditions. In this study, we attempted to model the relationship between weather conditions and rust epidemics as a first step toward development of a decision aid for disease management. For this purpose, rust severity data were collected from 2014 through 2016 at the Everglades Research and Education Center, Belle Glade, Florida, by recording percentage of rust-affected area of the top visible dewlap leaf every 2 weeks from three orange rust susceptible cultivars. Hourly weather data for 10- to 40-day periods prior to each orange rust assessment were evaluated as potential predictors of rust severity under field conditions. Correlation and stepwise regression analyses resulted in the identification of nighttime (8 PM to 8 AM) accumulation of hours with average temperature 20 to 22°C as a key predictor explaining orange rust severity. The five best regression models for a 30-day period prior to disease assessment explained 65.3 to 76.2% of variation of orange rust severity. Prediction accuracy of these models was tested using a case control approach with disease observations collected in 2017 and 2018. Based on receiver operator characteristic curve analysis of these two seasons of test data, a single-variable model with the nighttime temperature predictor mentioned above gave the highest prediction accuracy of disease severity. These models have potential for use in quantitative risk assessment of sugarcane rust epidemics. article info:eu-repo/semantics/article Journal Article info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/595214/1/2020%20Chaulagain_Modeling%20sugarcane%20orange%20rust.pdf text Cirad license info:eu-repo/semantics/restrictedAccess https://agritrop.cirad.fr/mention_legale.html https://doi.org/10.1094/PHYTO-06-19-0211-R 10.1094/PHYTO-06-19-0211-R info:eu-repo/semantics/altIdentifier/doi/10.1094/PHYTO-06-19-0211-R info:eu-repo/semantics/altIdentifier/purl/https://doi.org/10.1094/PHYTO-06-19-0211-R |
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H20 - Maladies des plantes P40 - Météorologie et climatologie Pucciniales Saccharum officinarum épidémiologie conditions météorologiques modèle de simulation écologie mycologie Puccinia kuehnii http://aims.fao.org/aos/agrovoc/c_31692 http://aims.fao.org/aos/agrovoc/c_6727 http://aims.fao.org/aos/agrovoc/c_2615 http://aims.fao.org/aos/agrovoc/c_29565 http://aims.fao.org/aos/agrovoc/c_24242 http://aims.fao.org/aos/agrovoc/c_2467 http://aims.fao.org/aos/agrovoc/c_5019 http://aims.fao.org/aos/agrovoc/c_ac7b37ec http://aims.fao.org/aos/agrovoc/c_2985 H20 - Maladies des plantes P40 - Météorologie et climatologie Pucciniales Saccharum officinarum épidémiologie conditions météorologiques modèle de simulation écologie mycologie Puccinia kuehnii http://aims.fao.org/aos/agrovoc/c_31692 http://aims.fao.org/aos/agrovoc/c_6727 http://aims.fao.org/aos/agrovoc/c_2615 http://aims.fao.org/aos/agrovoc/c_29565 http://aims.fao.org/aos/agrovoc/c_24242 http://aims.fao.org/aos/agrovoc/c_2467 http://aims.fao.org/aos/agrovoc/c_5019 http://aims.fao.org/aos/agrovoc/c_ac7b37ec http://aims.fao.org/aos/agrovoc/c_2985 |
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H20 - Maladies des plantes P40 - Météorologie et climatologie Pucciniales Saccharum officinarum épidémiologie conditions météorologiques modèle de simulation écologie mycologie Puccinia kuehnii http://aims.fao.org/aos/agrovoc/c_31692 http://aims.fao.org/aos/agrovoc/c_6727 http://aims.fao.org/aos/agrovoc/c_2615 http://aims.fao.org/aos/agrovoc/c_29565 http://aims.fao.org/aos/agrovoc/c_24242 http://aims.fao.org/aos/agrovoc/c_2467 http://aims.fao.org/aos/agrovoc/c_5019 http://aims.fao.org/aos/agrovoc/c_ac7b37ec http://aims.fao.org/aos/agrovoc/c_2985 H20 - Maladies des plantes P40 - Météorologie et climatologie Pucciniales Saccharum officinarum épidémiologie conditions météorologiques modèle de simulation écologie mycologie Puccinia kuehnii http://aims.fao.org/aos/agrovoc/c_31692 http://aims.fao.org/aos/agrovoc/c_6727 http://aims.fao.org/aos/agrovoc/c_2615 http://aims.fao.org/aos/agrovoc/c_29565 http://aims.fao.org/aos/agrovoc/c_24242 http://aims.fao.org/aos/agrovoc/c_2467 http://aims.fao.org/aos/agrovoc/c_5019 http://aims.fao.org/aos/agrovoc/c_ac7b37ec http://aims.fao.org/aos/agrovoc/c_2985 Chaulagain, Bhim Small, Ian M. Shine, James M. Fraisse, Clyde W. Raid, Richard Neil Rott, Philippe Weather-based predictive modeling of orange rust of sugarcane in Florida |
description |
Epidemics of sugarcane orange rust (caused by Puccinia kuehnii) in Florida are largely influenced by prevailing weather conditions. In this study, we attempted to model the relationship between weather conditions and rust epidemics as a first step toward development of a decision aid for disease management. For this purpose, rust severity data were collected from 2014 through 2016 at the Everglades Research and Education Center, Belle Glade, Florida, by recording percentage of rust-affected area of the top visible dewlap leaf every 2 weeks from three orange rust susceptible cultivars. Hourly weather data for 10- to 40-day periods prior to each orange rust assessment were evaluated as potential predictors of rust severity under field conditions. Correlation and stepwise regression analyses resulted in the identification of nighttime (8 PM to 8 AM) accumulation of hours with average temperature 20 to 22°C as a key predictor explaining orange rust severity. The five best regression models for a 30-day period prior to disease assessment explained 65.3 to 76.2% of variation of orange rust severity. Prediction accuracy of these models was tested using a case control approach with disease observations collected in 2017 and 2018. Based on receiver operator characteristic curve analysis of these two seasons of test data, a single-variable model with the nighttime temperature predictor mentioned above gave the highest prediction accuracy of disease severity. These models have potential for use in quantitative risk assessment of sugarcane rust epidemics. |
format |
article |
topic_facet |
H20 - Maladies des plantes P40 - Météorologie et climatologie Pucciniales Saccharum officinarum épidémiologie conditions météorologiques modèle de simulation écologie mycologie Puccinia kuehnii http://aims.fao.org/aos/agrovoc/c_31692 http://aims.fao.org/aos/agrovoc/c_6727 http://aims.fao.org/aos/agrovoc/c_2615 http://aims.fao.org/aos/agrovoc/c_29565 http://aims.fao.org/aos/agrovoc/c_24242 http://aims.fao.org/aos/agrovoc/c_2467 http://aims.fao.org/aos/agrovoc/c_5019 http://aims.fao.org/aos/agrovoc/c_ac7b37ec http://aims.fao.org/aos/agrovoc/c_2985 |
author |
Chaulagain, Bhim Small, Ian M. Shine, James M. Fraisse, Clyde W. Raid, Richard Neil Rott, Philippe |
author_facet |
Chaulagain, Bhim Small, Ian M. Shine, James M. Fraisse, Clyde W. Raid, Richard Neil Rott, Philippe |
author_sort |
Chaulagain, Bhim |
title |
Weather-based predictive modeling of orange rust of sugarcane in Florida |
title_short |
Weather-based predictive modeling of orange rust of sugarcane in Florida |
title_full |
Weather-based predictive modeling of orange rust of sugarcane in Florida |
title_fullStr |
Weather-based predictive modeling of orange rust of sugarcane in Florida |
title_full_unstemmed |
Weather-based predictive modeling of orange rust of sugarcane in Florida |
title_sort |
weather-based predictive modeling of orange rust of sugarcane in florida |
url |
http://agritrop.cirad.fr/595214/ http://agritrop.cirad.fr/595214/1/2020%20Chaulagain_Modeling%20sugarcane%20orange%20rust.pdf |
work_keys_str_mv |
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_version_ |
1792499916928450560 |