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 period...

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Main Authors:	Merle, Isabelle, Tixier, Philippe, Virginio Filho, Elías de Melo, Gilas, Chistian, Avelino, Jacques
Format:	Artículo
Language:	Inglés
Published:	Elsevier, Ámsterdam (Países Bajos) 2020
Subjects:	CAFE ARABICA ROYA DEL CAFE MICROCLIMA SISTEMAS AGROFORESTALES AGRICULTORES PREVENCION DEL RIESGO CONTROL DE ENFERMEDADES AGRO METEREOLOGIA EXPERIMENTOS DE CAMPO HEMILEIA VASTATRIX COSTA RICA
Online Access:	https://doi.org/10.1016/j.cropro.2019.105046 https://repositorio.catie.ac.cr/handle/11554/9736

id	RepoCATIE9736
record_format	dspace
spelling	RepoCATIE97362023-11-16T16:35:21Z Forecast models of coffee leaf rust symptoms and signs based on identified microclimatic combinations in coffee-based agroforestry systems in Costa Rica Merle, Isabelle Tixier, Philippe Virginio Filho, Elías de Melo Gilas, Chistian Avelino, Jacques CAFE ARABICA ROYA DEL CAFE MICROCLIMA SISTEMAS AGROFORESTALES AGRICULTORES PREVENCION DEL RIESGO CONTROL DE ENFERMEDADES AGRO METEREOLOGIA EXPERIMENTOS DE CAMPO HEMILEIA VASTATRIX 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. 2020-10-22T19:21:37Z 2020-10-22T19:21:37Z 2019 Artículo https://doi.org/10.1016/j.cropro.2019.105046 https://repositorio.catie.ac.cr/handle/11554/9736 en Crop Protection info:eu-repo/semantics/openAccess application/pdf Elsevier, Ámsterdam (Países Bajos)
institution	Centro Agronómico Tropical de Investigación y Enseñanza
collection	Repositorio CATIE
language	Inglés
topic	CAFE ARABICA ROYA DEL CAFE MICROCLIMA SISTEMAS AGROFORESTALES AGRICULTORES PREVENCION DEL RIESGO CONTROL DE ENFERMEDADES AGRO METEREOLOGIA EXPERIMENTOS DE CAMPO HEMILEIA VASTATRIX COSTA RICA
spellingShingle	CAFE ARABICA ROYA DEL CAFE MICROCLIMA SISTEMAS AGROFORESTALES AGRICULTORES PREVENCION DEL RIESGO CONTROL DE ENFERMEDADES AGRO METEREOLOGIA EXPERIMENTOS DE CAMPO HEMILEIA VASTATRIX COSTA RICA Merle, Isabelle Tixier, Philippe Virginio Filho, Elías de Melo Gilas, Chistian 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.
format	Artículo
author	Merle, Isabelle Tixier, Philippe Virginio Filho, Elías de Melo Gilas, Chistian Avelino, Jacques
author_facet	Merle, Isabelle Tixier, Philippe Virginio Filho, Elías de Melo Gilas, Chistian 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
publisher	Elsevier, Ámsterdam (Países Bajos)
publishDate	2020
url	https://doi.org/10.1016/j.cropro.2019.105046 https://repositorio.catie.ac.cr/handle/11554/9736
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_version_	1808117679664398336

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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