Performance of the DSSAT MANIHOT-Cassava model for cassava cultivation in the Recôncavo Baiano

Abstract

The aim of the present study was to calibrate the DSSAT MANIHOT-Cassava model with information from cassava varieties grown in the Recôncavo Baiano region, Bahia state, Brazil. The database used to calibrate the model was obtained in the dry sub-humid tropical climate in Cruz das Almas city, from 2019 to 2020. The model was calibrated with experimental data obtained under irrigated and rainfed conditions for the BRS Novo Horizonte and Eucalipto varieties. The calibration was carried out by adjusting parameters related to the characteristics of each variety. Model performance was evaluated with statistical indices that indicate the precision and accuracy of the simulations, such as the root mean square error and coefficients of determination, the Willmott index, and the performance index. The model, regardless of the variety, adequately simulated most of the variables studied during the calibration and validation stage, with reliability considered excellent or optimal and minor errors than the default variety for most of the variables simulated. An exception was simulation of the leaf area index, which did not properly represent the leaf senescence or regrowth phase 180 days after planting, showing overestimations for the BRS Novo Horizonte variety and underestimations for the Eucalipto variety. The model can be applied for reliably simulating performance of the cassava varieties under the sub-humid conditions of the Recôncavo Baiano.