Literature DB >> 34159603

Can nonlinear agrometeorological models estimate coffee foliation?

Lucas Eduardo de Oliveira Aparecido1, João A Lorençone2, Pedro A Lorençone2, Glauco de Souza Rolim3, Kamila C de Meneses3, José R da Silva Cabral de Moraes3, Guilherme B Torsoni2.   

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 (mean absolute percentage error = 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 degree days with accuracy and precision through the air temperature. This represents great convenience because crop foliation can be obtained using only a thermometer.
© 2021 Society of Chemical Industry. © 2021 Society of Chemical Industry.

Entities:  

Keywords:  Coffea arabica; air temperature; climate model; crop modeling; forecasting

Mesh:

Year:  2021        PMID: 34159603     DOI: 10.1002/jsfa.11387

Source DB:  PubMed          Journal:  J Sci Food Agric        ISSN: 0022-5142            Impact factor:   3.638


  1 in total

1.  An empirical model for estimating daily atmospheric column-averaged CO2 concentration above São Paulo state, Brazil.

Authors:  Luis Miguel da Costa; Gustavo André de Araújo Santos; Alan Rodrigo Panosso; Glauco de Souza Rolim; Newton La Scala
Journal:  Carbon Balance Manag       Date:  2022-06-11
  1 in total

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