Can nonlinear agrometeorological models estimate coffee foliation?

Area of Science:

• Agronomy and Crop Science
• Agricultural Meteorology
• Plant Physiology

Background:

• Pest and disease outbreaks in coffee plantations significantly reduce yield and bean quality.
• Accurate estimation of coffee foliation is crucial for effective crop management.
• Climate variability impacts coffee production, necessitating predictive models.

Purpose of the Study:

• To estimate arabica coffee foliation in Brazil's key coffee-producing regions.
• To develop nonlinear models correlating foliation with climatic variables.
• To provide a practical tool for coffee growers to assess crop health.

Main Methods:

• Utilized a 25-year historical dataset of Coffea arabica foliation from untreated cultivations.
• Incorporated daily climate data from NASA/POWER, including reference evapotranspiration (PET).
• Applied four-parameter sigmoidal logistic regression models to estimate foliation based on degree days.

Main Results:

• Developed a highly accurate (MAPE = 2.19%) and precise (adj. R² = 0.99) model for foliation estimation.
• The model demonstrated a sigmoidal reduction trend in foliation.
• Identified a potential foliation reduction to 0.009% without phytosanitary interventions.

Conclusions:

• High ambient temperatures correlate strongly with reduced coffee foliation (correlation = -0.94).
• Coffee foliation can be reliably estimated using degree days derived from air temperature.
• This method offers a convenient and accessible tool for growers, requiring only a thermometer.