Agrometeorological models to forecast açaí (Euterpe oleracea Mart.) yield in the Eastern Amazon - da Silva Cabral de Moraes, José Reinaldo [UNESP]; Souza Rolim, Glauco de [UNESP]; Martorano, Lucieta Guerreiro; de Oliveira Aparecido, Lucas Eduardo [UNESP]; Padilha de Oliveira, Maria do Socorro; de Farias Neto, João Tome (Wiley, 2020)
BACKGROUND: The increasing demand in Brazil and the world for products derived from the açaí palm (Euterpe oleracea Mart) has generated changes in its production process, principally due to the necessity of maintaining yield in situations of seasonality and climate fluctuation. The objective of this study was to estimate açaí fruit yield in irrigated system (IRRS) and rainfed system or unirrigated (RAINF) using agrometeorological models in response to climate conditions in the eastern Amazon. Modeling was done using multiple linear regression using the ‘stepwise forward’ method of variable selection. Monthly air temperature (T) values, solar radiation (SR), vapor pressure deficit (VPD), precipitation + irrigation (P + I), and potential evapotranspiration (PET) in six phenological phases were correlated with yield. The thermal necessity value was calculated through the sum of accumulated degree days (ADD) up to the formation of fruit bunch, as well as the time necessary for initial leaf development, using a base temperature of 10 °C. RESULTS: The most important meteorological variables were T, SR, and VPD for IRRS, and for RAINF water stress had the greatest effect. The accuracy of the agrometeorological models, using maximum values for mean absolute percent error (MAPE), was 0.01 in the IRRS and 1.12 in the RAINF. CONCLUSION: Using these models yield was predicted approximately 6 to 9 months before the harvest, in April, May, November, and December in the IRRS, and January, May, June, August, September, and November for the RAINF. © 2019 Society of Chemical Industry.
Keywords: crop model; ECMWF; multiple linear regression; NASA POWER