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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Main Authors: Chaulagain, Bhim, Small, Ian M., Shine, James M., Fraisse, Clyde W., Raid, Richard Neil, Rott, Philippe
Format: article biblioteca
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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spelling 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
institution CIRAD FR
collection DSpace
country Francia
countrycode FR
component Bibliográfico
access En linea
databasecode dig-cirad-fr
tag biblioteca
region Europa del Oeste
libraryname Biblioteca del CIRAD Francia
language eng
topic 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
spellingShingle 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
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