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Can nonlinear agrometeorological models estimate coffee foliation?

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BACKGROUND The loss of coffee leaves caused by the attack of pests and diseases significantly reduces its production and bean quality. Thus, this study aimed to estimate foliation for regions… Click to show full abstract

BACKGROUND The loss of coffee leaves caused by the attack of pests and diseases significantly reduces its production and bean quality. Thus, this study aimed to estimate foliation for regions with the highest production of arabica coffee in Brazil using nonlinear models as a function of climate. A 25-year historical series (1995-2019) of Coffea arabica foliation (%) data was obtained by the Procafé Foundation in cultivations with no phytosanitary treatment. The climate data were obtained on a daily scale by NASA/POWER platform with a temporal resolution of 33 years (1987-2019) and a spatial resolution of approximately 106 km, thus allowing the calculation of the reference evapotranspiration (PET). Foliation estimation models were adjusted through regression analysis using four-parameter sigmoidal logistic models. The analysis of the foliation trend of coffee plantations was carried out from degrees-day for 70 locations. RESULTS The general model calibrated to estimate the arabica coffee foliation was accurate (MAPE = 2.19%) and precise (R2 adj  = 0.99) and can be used to assist decision-making by coffee growers. The model had a sigmoidal trend of reduction, with parameters ymax = 97.63%, ymin = 9%, Xo = 3517.41 DD, and p = 6.27%, showing that foliation could reach 0.009% if the necessary phytosanitary controls are not carried out. CONCLUSION Locations with high air temperatures over the year had low arabica coffee foliation, as shown by the correlation of -0.94. Therefore, coffee foliation can be estimated using degrees-day with accuracy and precision through the air temperature. It represents great ease because crop foliation can be obtained using only a thermometer. This article is protected by copyright. All rights reserved.

Keywords: agrometeorological models; coffee foliation; nonlinear agrometeorological; arabica coffee; foliation; coffee

Journal Title: Journal of the science of food and agriculture
Year Published: 2021

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