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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Weather-based coffee leaf rust apparent infection rate modeling

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Author(s):
Hinnah, Fernando Dill [1] ; Sentelhas, Paulo Cesar [1] ; Meira, Carlos Alberto Alves [2] ; Paiva, Rodrigo Naves [3]
Total Authors: 4
Affiliation:
[1] ESALQ, Sao Paulo - Brazil
[2] Embrapa Agr Informat, Campinas, SP - Brazil
[3] Procafe Fdn, Varginha - Brazil
Total Affiliations: 3
Document type: Journal article
Source: INTERNATIONAL JOURNAL OF BIOMETEOROLOGY; v. 62, n. 10, p. 1847-1860, OCT 2018.
Web of Science Citations: 0
Abstract

Brazil is the major coffee producer in the world, with 2 million hectares cropped, with 75% of this area with Coffea arabica and 25% with Coffea canephora. Coffee leaf rust (CLR) is one of the main diseases that cause yield losses by reducing healthy leaf area. As CLR is highly influenced by weather conditions, this study aimed to determine the best linearization model to estimate the CLR apparent infection rate, to correlate CLR infection rates with weather variables, and to develop and assess the performance of weather-based infection rate models to be used as a disease warning system. The CLR epidemic was analyzed for 88 site-seasons, while progress curves were assessed by linear, monomolecular, logistic, Gompertz, and exponential linearization models for apparent infection rate determination. Correlations between CLR infection rates and weather variables were conducted at different periods. From these correlations, multiple linear regressions were developed to estimate CLR infection rates, using the most weather-correlated variables. The Gompertz growth model had the best fit with CLR progress curves. Minimum temperature and relative humidity were the weather variables most correlated to infection rate and, therefore, chosen to compose a CLR forecast system. Among the models developed, the one for the condition of high coffee yield at a narrow row spacing was the best, with only 9.4% of false negative occurrences for all the months assessed. (AU)

FAPESP's process: 14/17781-8 - Development and application of phytosanitary warning systems for the management of coffee diseases
Grantee:Fernando Dill Hinnah
Support Opportunities: Scholarships in Brazil - Doctorate