Evaluation of three sugarcane simulation models and their ensemble for yield estimation in commercially managed fields

By Henrique Boriolo Dias, P. Sentelhas. 2017

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Abstract The sugarcane production system is very complex, involving a large number of variables, namely genotype, environmental conditions and crop management, which define yield level. Thus, estimation of sugarcane yield is also complex, nevertheless, highly important for planning and decision-making in the sugarcane industry. Crop simulation models calibrated to local conditions can be useful to estimate yield, since they can capture the effect of the crop management. On the other hand, recent studies have shown that the use of at least three simulation models in an ensemble can reduce simulation uncertainties, resulting in more reliable estimates than using a single model. Thus, the aims of this study were: i) to evaluate the performance of three sugarcane simulation models (FAO‐AZM, DSSAT/CANEGRO and APSIM‐Sugarcane), separately and in a multi-model approach, for commercially managed fields in Brazil; and ii) to propose a management factor (kdec) associated with the yield decline along successive crop cycles to improve performance of these models. Sugarcane yield and meteorological data were obtained for seven Brazilian states. The FAO‐AZM model was calibrated by changing the crop water deficit sensitivity coefficient values. For the DSSAT/CANEGRO and APSIM‐Sugarcane models, small adjustments were made to coefficients previously calibrated for Brazilian cultivars to improve their performances. The three models presented a weak performance, with high mean absolute error (MAE > 29 t ha‐1) and low precision (R2

Subfields: Plant Science, Soil Science

View Paper: https://www.semanticscholar.org/paper/a979ec1a434182f02b64c2cecff855ada0b01539