Agrometeorological Models For Forecasting Coffee Yield

dc.creatorde Oliveira Aparecido
dc.creatorLucas Eduardo; Rolim
dc.creatorGlauco de Souza; Camargo Lamparelli
dc.creatorRubens Augusto; de Souza
dc.creatorPaulo Sergio; dos Santos
dc.creatorEder Ribeiro
dc.date2017
dc.datejan-fev
dc.date2017-11-13T11:31:07Z
dc.date2017-11-13T11:31:07Z
dc.date.accessioned2018-03-29T05:46:09Z
dc.date.available2018-03-29T05:46:09Z
dc.identifierAgronomy Journal. Amer Soc Agronomy, v. 109, p. 249 - 258, 2017.
dc.identifier0002-1962
dc.identifier1435-0645
dc.identifierWOS:000396462000026
dc.identifier10.2134/agronj2016.03.0166
dc.identifierhttps://dl-sciencesocieties-org.ez88.periodicos.capes.gov.br/publications/aj/abstracts/109/1/249
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/325858
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1362864
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionSome forecasting techniques have been tested with crop models using various statistical analyses for generating future scenarios of yield (Y). Forecasting, however, can be achieved by simply using regression analysis and carefully selecting independent variables (IVs) with time displacement relative to the dependent variable. The early forecasting of Y is the vanguard of agronomic modeling, promoting improvements in planning, allowing more rational strategic decisions, and increasing food and economic security. Climatic variables are the most important factors controlling the yield and quality of coffee (Coffea arabica L.). We calibrated and tested agrometeorological models to forecast the annual Y of coffee for six traditional producing regions in the state of Minas Gerais, Brazil. We used multiple linear regressions, selecting IVs to maximize the period between the forecast of Y and the harvest for each locality. The IVs were monthly meteorological variables from 1997 to 2014: air temperature, rainfall, potential evapotranspiration, soil water storage, water deficit, and water surplus. The IVs were selected by testing all possible combinations in the domain and avoiding multicollinearity. The agrometeorological models were accurate for all regions, and the earliest forecasts were 6 and 5 mo before harvest for the producing locations of Guaxupe and Coromandel, respectively. The models for yield forecasting for Guaxupe included the water deficit in July and October and July precipitation for the high-yield season and the water deficit in April and September and October precipitation for the low-yield season. The models for yield forecasting for Coromandel included the November water surplus and February and September precipitation for the high-yield season and precipitation for January, April, and October for the low-yield season.
dc.description109
dc.description1
dc.description249
dc.description258
dc.descriptionSao Paulo Research Foundation (FAPESP) [2014/05025-4]
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.languageEnglish
dc.publisherAmer Soc Agronomy
dc.publisherMadison
dc.relationAgronomy Journal
dc.rightsfechado
dc.sourceWOS
dc.titleAgrometeorological Models For Forecasting Coffee Yield
dc.typeArtículos de revistas


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