Forecast models of coffee leaf rust symptoms and signs based on identified microclimatic combinations in coffee-based agroforestry systems in Costa Rica

Coffee leaf rust is a polycyclic disease that causes severe epidemics impacting yield over several years. For this reason, since the 1960s, more than 20 models have been developed to predict different indicators of the disease's development and help manage it. In existing models, standardized p...

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Main Authors:	Merle, Isabelle, Tixler, Philippe, Virginio Filho, Elias de Melo, Cilas, Christian, Avelino, Jacques
Format:	Artículo
Language:	Inglés
Published:	2020
Subjects:	COFFEA ARABICA HEMILEIA VASTATRIX ROYA DEL CAFÉ SÍNTOMAS MICROCLIMA UREDOSPORAS MODELOS SISTEMAS AGROFORESTALES COSTA RICA
Online Access:	https://repositorio.catie.ac.cr/handle/11554/9303 https://doi.org/10.1016/j.cropro.2019.105046

id	RepoCATIE9303
record_format	dspace
spelling	RepoCATIE93032023-11-16T16:36:09Z Forecast models of coffee leaf rust symptoms and signs based on identified microclimatic combinations in coffee-based agroforestry systems in Costa Rica Merle, Isabelle Tixler, Philippe Virginio Filho, Elias de Melo Cilas, Christian Avelino, Jacques COFFEA ARABICA HEMILEIA VASTATRIX ROYA DEL CAFÉ SÍNTOMAS MICROCLIMA UREDOSPORAS MODELOS SISTEMAS AGROFORESTALES COSTA RICA Coffee leaf rust is a polycyclic disease that causes severe epidemics impacting yield over several years. For this reason, since the 1960s, more than 20 models have been developed to predict different indicators of the disease's development and help manage it. In existing models, standardized periods of influence of the meteorological predictors of the disease are determined a priori, based on strong assumptions. However, the appearance of a symptom or sign can be influenced by complex combinations of meteorological variables acting at different times and for different durations. In our study, we monitored a total of 5400 coffee leaves during a year and a half, in different agroforestry systems, in order to detect the onset dates of the disease symptoms, such as lesion emergence, and signs, such as sporulation and infectious area increase. In these agroforestry systems, we also recorded microclimate. We statistically identified the complex combinations of microclimatic variables responsible for changes in lesion status to construct three models predicting lesion emergence probability, lesion sporulation probability and growth of its infectious area. Our method allowed the identification of different microclimatic variables that fit well with the knowledge about the coffee leaf rust biology. Minimum air temperature from 20 to 18 days before a lesion emergence explained the status change from healthy to emergence of visible lesion, possibly because the short germination phase is stimulated by low temperatures. We also found a unimodal effect of rainfall over a period of 10 days, 33 days before lesion emergence, with a maximum at 10 mm. Below this threshold, uredospore dispersal is efficient, increasing the lesion appearance probability; above this threshold, wash-off effects on uredospores probably occurs, decreasing the probability of lesion emergence. In addition, we identified microclimatic variables whose influence on coffee leaf rust had not been described before. These variables are likely to be involved in the internal development phases of the disease in the coffee leaves: (1) unimodal effects of maximum air temperature in different periods on sporulation and infectious area growth (2) positive and unimodal effects of rainfall in different periods on sporulation and (3) a negative effect of leaf thermal amplitude in different periods on lesion emergence, sporulation and infectious area growth. Although these models do not provide predictors of the level of disease attack, such as incidence, they provide valuable information for warning systems and for mechanistic model development. These models could also be used to forecast risks of infection, sporulation and infectious area growth and help optimize treatment recommendations. 2020-01-10T14:37:55Z 2020-01-10T14:37:55Z 2020 Artículo https://repositorio.catie.ac.cr/handle/11554/9303 https://doi.org/10.1016/j.cropro.2019.105046 en Crop Protection info:eu-repo/semantics/restrictedAccess application/pdf
institution	Centro Agronómico Tropical de Investigación y Enseñanza
collection	Repositorio CATIE
language	Inglés
topic	COFFEA ARABICA HEMILEIA VASTATRIX ROYA DEL CAFÉ SÍNTOMAS MICROCLIMA UREDOSPORAS MODELOS SISTEMAS AGROFORESTALES COSTA RICA
spellingShingle	COFFEA ARABICA HEMILEIA VASTATRIX ROYA DEL CAFÉ SÍNTOMAS MICROCLIMA UREDOSPORAS MODELOS SISTEMAS AGROFORESTALES COSTA RICA Merle, Isabelle Tixler, Philippe Virginio Filho, Elias de Melo Cilas, Christian Avelino, Jacques Forecast models of coffee leaf rust symptoms and signs based on identified microclimatic combinations in coffee-based agroforestry systems in Costa Rica
description	Coffee leaf rust is a polycyclic disease that causes severe epidemics impacting yield over several years. For this reason, since the 1960s, more than 20 models have been developed to predict different indicators of the disease's development and help manage it. In existing models, standardized periods of influence of the meteorological predictors of the disease are determined a priori, based on strong assumptions. However, the appearance of a symptom or sign can be influenced by complex combinations of meteorological variables acting at different times and for different durations. In our study, we monitored a total of 5400 coffee leaves during a year and a half, in different agroforestry systems, in order to detect the onset dates of the disease symptoms, such as lesion emergence, and signs, such as sporulation and infectious area increase. In these agroforestry systems, we also recorded microclimate. We statistically identified the complex combinations of microclimatic variables responsible for changes in lesion status to construct three models predicting lesion emergence probability, lesion sporulation probability and growth of its infectious area. Our method allowed the identification of different microclimatic variables that fit well with the knowledge about the coffee leaf rust biology. Minimum air temperature from 20 to 18 days before a lesion emergence explained the status change from healthy to emergence of visible lesion, possibly because the short germination phase is stimulated by low temperatures. We also found a unimodal effect of rainfall over a period of 10 days, 33 days before lesion emergence, with a maximum at 10 mm. Below this threshold, uredospore dispersal is efficient, increasing the lesion appearance probability; above this threshold, wash-off effects on uredospores probably occurs, decreasing the probability of lesion emergence. In addition, we identified microclimatic variables whose influence on coffee leaf rust had not been described before. These variables are likely to be involved in the internal development phases of the disease in the coffee leaves: (1) unimodal effects of maximum air temperature in different periods on sporulation and infectious area growth (2) positive and unimodal effects of rainfall in different periods on sporulation and (3) a negative effect of leaf thermal amplitude in different periods on lesion emergence, sporulation and infectious area growth. Although these models do not provide predictors of the level of disease attack, such as incidence, they provide valuable information for warning systems and for mechanistic model development. These models could also be used to forecast risks of infection, sporulation and infectious area growth and help optimize treatment recommendations.
format	Artículo
author	Merle, Isabelle Tixler, Philippe Virginio Filho, Elias de Melo Cilas, Christian Avelino, Jacques
author_facet	Merle, Isabelle Tixler, Philippe Virginio Filho, Elias de Melo Cilas, Christian Avelino, Jacques
author_sort	Merle, Isabelle
title	Forecast models of coffee leaf rust symptoms and signs based on identified microclimatic combinations in coffee-based agroforestry systems in Costa Rica
title_short	Forecast models of coffee leaf rust symptoms and signs based on identified microclimatic combinations in coffee-based agroforestry systems in Costa Rica
title_full	Forecast models of coffee leaf rust symptoms and signs based on identified microclimatic combinations in coffee-based agroforestry systems in Costa Rica
title_fullStr	Forecast models of coffee leaf rust symptoms and signs based on identified microclimatic combinations in coffee-based agroforestry systems in Costa Rica
title_full_unstemmed	Forecast models of coffee leaf rust symptoms and signs based on identified microclimatic combinations in coffee-based agroforestry systems in Costa Rica
title_sort	forecast models of coffee leaf rust symptoms and signs based on identified microclimatic combinations in coffee-based agroforestry systems in costa rica
publishDate	2020
url	https://repositorio.catie.ac.cr/handle/11554/9303 https://doi.org/10.1016/j.cropro.2019.105046
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Forecast models of coffee leaf rust symptoms and signs based on identified microclimatic combinations in coffee-based agroforestry systems in Costa Rica

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