Models for simulating the frequency of pests and diseases of Coffea arabica L

We developed models for simulating trends over time as functions of the thermal index and models for estimating the levels of infestation of the coffee leaf miner and coffee berry borer and the severity of disease for coffee leaf rust and cercospora, the main phytosanitary problems in coffee crops a...

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Bibliographic Details
Published in:International journal of biometeorology 2020-07, Vol.64 (7), p.1063-1084
Main Authors: de Oliveira Aparecido, Lucas Eduardo, de Souza Rolim, Glauco
Format: Article
Language:English
Subjects:
Air temperature
Animal Physiology
Biological and Medical Physics
Biophysics
Borers
Climatic data
Coffea arabica
Coffee
Computer simulation
Dependent variables
Disease
Earth and Environmental Science
Environment
Environmental Health
Foliage
Fruits
Independent variables
Infestation
Leaf rust
Leaves
Meteorology
Original Paper
Pests
Plant Physiology
Regression analysis
Trends
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Summary:We developed models for simulating trends over time as functions of the thermal index and models for estimating the levels of infestation of the coffee leaf miner and coffee berry borer and the severity of disease for coffee leaf rust and cercospora, the main phytosanitary problems in coffee crops around the world. We used historical series of climatic data and levels of pest infestation and disease severity in Coffea arabica for high and low yields for seven locations in the two main coffee-producing regions in the state of Minas Gerais in Brazil, Sul de Minas Gerais and Cerrado Mineiro. We conducted two analyses: (a) we simulated the trends of the progress of diseases and pests over time using non-linear models. We only used the thermal index because air temperature is commonly measured by farmers in the regions. (b) We estimated the levels of pest infestation and disease severity using multiple linear regression, with the levels of diseases and pests as dependent variables and accumulated degree days (DD), coffee foliage (LF) estimated by DD and the number of nodes (NN) estimated by DD as independent variables. We used DD and LF = f (DD) and NN = f (DD) to predict diseases and pests with accuracy. MAPEs were 19.6, 5.7, 9.5, and 15.8% for rust, cercospora, leaf miner, and berry borer, respectively, for Sul de Minas Gerais. Establishing phytosanitary alerts using only air temperature was possible with these models.
ISSN:0020-7128
1432-1254
DOI:10.1007/s00484-020-01881-5