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

Jones, Matthew R.; Singels, A.; Chinorumba, S.; Poser, Christophe; Christina, Mathias; Shine, James M.; Annandale, J. G.; Hammer, Graeme

doi:10.1016/j.fcr.2020.107983

Cited by 5 publications

(1 citation statement)

References 27 publications

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“…In these models, the accumulation of biomass is governed by two primary mechanisms, the first is the sugarcane canopy's growth and interception of solar radiation, and the second is the transformation of intercepted solar radiation into carbohydrates through photosynthesis. In conditions where there is no stress, the expansion of the canopy is driven by temperature, and the conversion of radiation each day is defined by the maximum possible radiation use efficiency (RUE) and air temperature, additionally, the APSIM-Sugar model accounts for carbohydrate availability from photosynthesis (JONES et al, 2021).…”

Section: Sugarcane Process-based Crop Modelsmentioning

confidence: 99%

A synergistic approach to sugarcane yield forecasting using machine learning, remote sensing, and process-based modeling

Grubert

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A synergistic approach to sugarcane yield forecasting using machine learning, remote sensing, and process-based modelingAccurate and precise crop yield forecasts are essential for farmers and decision-makers. This study aims to assess a hybrid approach involving remote sensing data, crop modeling with process-based models, and machine learning algorithms to improve sugarcane yield predictions. To achieve this, a hybrid yield forecasting approach was developed, combining various data sources, including simulated soil and plant variables from the APSIM model (a process-based crop model), meteorological data, and vegetation indices. These data were used as inputs in machine learning models to forecast end-season sugarcane yield. In this study, 16 regression models were evaluated to forecast sugarcane yield at the municipal level in the state of São Paulo, Brazil, during the period 2010-2020. The results indicated that the hybrid approach developed using the K-Neighbors Regressor algorithm showed the best statistical performance, resulting in the lowest Mean Absolute Error (MAE) of 3.26 t ha -1 , with a Mean Absolute Percentage Error (MAPE) of 4.54%. Sugarcane yield predictions were most accurate 1-2 months before harvesting. Furthermore, the study determined which variables had the greatest influence on sugarcane productivity prediction by partially excluding some variables from the prediction model. The results showed that adding variables simulated by the process-based model (APSIM) as input variables for machine learning models could reduce the Root Mean Square Error (RMSE) of yield prediction, ranging from 7.7% to 26.9%, while vegetation indices had the least impact on predictions. The analysis revealed that meteorological data had a greater impact on yield prediction when provided to the process-based model than when directly used in machine learning algorithms. This result suggests that the simulated variables provided by APSIM offer a more comprehensive biophysical description of the interaction between soil, plant, and atmosphere.

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Section: Sugarcane Process-based Crop Modelsmentioning

confidence: 99%