Calibration and evaluation of the DSSAT/Canegro model for sugarcane cultivars under irrigation managements

Abstract

Model calibration is a fundamental factor to obtain high accuracy in the estimation of crop growth and yield. This study aimed to parameterize the genetic and ecotype coefficients of the DSSAT/Canegro model for five sugarcane cultivars kept under three water managements, besides evaluating the accuracy of the model in predicting sugarcane stalk yield, sugar yield and height. Experimental field data were obtained from two years (2016 and 2017) of cultivation at FCAV/Universidade Estadual Paulista, Jaboticabal, SP, Brazil. The cultivars were maintained under supplementary irrigation, deficit irrigation and no irrigation. Data of the supplementary irrigation treatment (without stress) were used for the parameterization of each cultivar. Model accuracy was assessed by Pearson correlation (r), root mean squared error (RMSE), mean bias error (MBE), index of agreement (d) and confidence coefficient (c). The DSSAT/Canegro model is highly accurate in predicting stalk and sugar yields of sugarcane grown under water regimes, presenting itself as a viable alternative in sugarcane yield simulation. For better performance of the DSSAT/Canegro model, it is necessary to parameterize the variables related to the ecotype of the cultivars, besides the specific coefficients of the cultivars.

Evaluating process-based sugarcane models for simulating genotypic and environmental effects observed in an international dataset

“…Jones et al (2019) found evidence for small but significant G differences in RUEmax. G-specific values for RUEo model trait parameters have been reported by Marin et al (2011b), Dias and Sentelhas (2017) and Coelho et al (2020); GxE rankings in RUEa were not revealed in these results, however. In contrast, RUEo parameter values were not changed during model calibration by Marin et al (2015) and Dias et al (2019).…”

Evaluation of the DSSAT‐CANEGRO model for simulating the growth of energy cane (Saccharum spp.), a biofuel feedstock crop

“…Coelho et al. (2020) reported APFMAX values ranging from 0.68 to 0.72 for sugarcane varieties. A prominent characteristic that differentiates energy cane from sugarcane is that energy cane allocates a significant portion of photosynthates to underground rhizomes compared with sugarcane (Matsuoka et al., 2014).…”

“…In our study, a sensitivity analysis for the parametrization of cultivar coefficients resulted in an APFMAX value of 0.60, which is the maximum fraction of dry mass increments that can be allocated to aerial dry mass. Coelho et al (2020) reported APFMAX values ranging from 0.68 to 0.72 for sugarcane varieties. A prominent characteristic that differentiates energy cane from sugarcane is that energy cane allocates a significant portion of photosynthates to underground rhizomes compared with sugarcane (Matsuoka et al, 2014).…”

A Bayesian sequential updating approach to predict phenology of silage maize

“…He et al, (2017b) found that data from different weather and site conditions are required to obtain a good calibrated parameter set for a particular cultivar. Improved crop model performance has been reported upon the inclusion of environment-specific parameters in calibration (Coelho et al, 2020). Cultivar-or genotype-and environment-specific parameters already exist in some models (Jones et al, 2003;Wang et al, 2019).…”