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Agrometeorological Models for Forecasting Coffee Yield
Author(s) -
Oliveira Aparecido Lucas Eduardo,
Souza Rolim Glauco,
Camargo Lamparelli Rubens Augusto,
Souza Paulo Sergio,
Santos Eder Ribeiro
Publication year - 2017
Publication title -
agronomy journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.752
H-Index - 131
eISSN - 1435-0645
pISSN - 0002-1962
DOI - 10.2134/agronj2016.03.0166
Subject(s) - yield (engineering) , evapotranspiration , multicollinearity , hydric soil , crop yield , environmental science , coffea arabica , regression analysis , mathematics , linear regression , agricultural engineering , agronomy , statistics , soil water , ecology , soil science , materials science , horticulture , metallurgy , biology , engineering
Multiple linear regression can efficiently forecast crop yields related to climatic conditions. We can forecast coffee yield at least 5 mo prior to harvesting. An increase in T during vegetative growth was inversely proportional to coffee yield. Coffee yield in southern Minas Gerais is controlled by all meteorological elements. Coffee yield in Cerrado Mineiro is controlled by hydric conditions.Some 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 Guaxupé and Coromandel, respectively. The models for yield forecasting for Guaxupé 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.