Agrometeorological models for estimating sweet cassava yield

Victor  Brunini Moreto; Lucas Eduardo  de Oliveira Aparecido; Glauco  de Souza Rolim; José Reinaldo  da Silva Cabral de Moraes

Agrometeorological models for estimating sweet cassava yield

Autores

Victor Brunini Moreto

Lucas Eduardo de Oliveira Aparecido

Glauco de Souza Rolim

José Reinaldo da Silva Cabral de Moraes

Editor

Universidade Federal de Goiás

Descripción

Brazil is the fourth largest producer of cassava in the world, with climate conditions being the main factor regulating its production. This study aimed to develop agrometeorological models to estimate the sweet cassava yield for the São Paulo state, as well as to identify which climatic variables have more influence on yield. The models were built with multiple linear regression and classified by the following statistical indexes: lower mean absolute percentage error, higher adjusted determination coefficient and significance (p-value < 0.05). It was observed that the mean air temperature has a great influence on the sweet cassava yield during the whole cycle for all regions in the state. Water deficit and soil water storage were the most influential variables at the beginning and final stages. The models accuracy ranged in 3.11 %, 6.40 %, 6.77 % and 7.15 %, respectively for Registro, Mogi Mirim, Assis and Jaboticabal.