Sugarcane is the main feedstock for sugar production in the world and has emerged as the second major source of biofuel (Goldemberg et al., 2014). It’s a crop of significant social, economic and environmental importance in many developing countries where nearly 75% of global production is concentrated in Brazil, India, China, Thailand and Pakistan (FAO, 2019).

The SAMUCA model (Agronomic Modular Simulator for Sugarcane) is a process-based model initially developed by Marin and Jones (2014), focusing on the specific features of Brazilian farming systems, and due to the relatively small number of available sugarcane models for simulation ensembles (Marin et al., 2015; Marin, Jones and Boote, 2017). The updated version of SAMUCA added into the DSSAT platform is fully described by Vianna et al (2020), where potential and attainable crop growth can be simulated as a function of atmospheric CO2 concentration, solar radiation, temperature, soil moisture and genotypic parameters. 

New algorithms tested and added into the CSM-SAMUCA includes: (i) the biomass partitioning simulation at the phytomer level; (ii) the computation of structural and sugars components with a source-sink method; (iii) canopy carbon assimilation using measured rates of leaf assimilation and carboxylation efficiency; (iv) the distinction between air and soil temperature to simulate soil related processes such as tillering, root growth and shoot emergence. The latter is specifically important to account for the green cane trash blanket (GCTB) effect on sugarcane growth and development. The model has been tested for in-field variability (Pereira et al., 2021), data assimilation (Junior et al., 2022) and used in yield forecast systems in Brazil (Marin, 2017). Full details of model updates and new features, including the model parameters, uncertainties and opportunities for improvements can be found in Appendix A of Vianna et al (2020).


FAO, 2019. Food and Agriculture Organization Corporate Statistical Database. FAOSTAT. URL http://www.fao.org/faostat/en/#home (accessed 20-Dec-2019).

Goldemberg, J., Mello, F.F.C., Cerri, C.E.P., Davies, C.A., Cerri, C.C., 2014. Meeting the global demand for biofuels in 2021 through sustainable land use change policy. Energy Policy 69, 14–18.

Junior, I.M.F., dos Santos Vianna, M., Marin, F.R., 2022. Assimilating leaf area index data into a sugarcane process-based crop model for improving yield estimation. European Journal of Agronomy. https://doi.org/10.1016/j.eja.2022.126501

Marin, F., Jones, J.W., Boote, K.J., 2017. A Stochastic Method for Crop Models: Including Uncertainty in a Sugarcane Model. Agron. J. 109, 483–495.

Marin, F.R., 2017. Tempocampo: A System for Operational Forecasting of Brazilian Sugarcane and Soybean Yield, in: Managing Global Resources for a Secure Future. Presented at the ASA, CSSA, SSSA Annual Meeting, Tampa, USA.

Marin, F.R., Jones, J.W., 2014. Process-based simple model for simulating sugarcane growth and production. Sci. Agric. 71, 1–16.

Marin, F.R., Thorburn, P.J., Nassif, D.S.P., Costa, L.G., 2015. Sugarcane model intercomparison: Structural differences and uncertainties under current and potential future climates. Environmental Modelling & Software 72, 372–386.

Pereira, R.A. de A., Vianna, M. dos S., Nassif, D.S.P., Carvalho, K. dos S., Marin, F.R., 2021. Global sensitivity and uncertainty analysis of a sugarcane model considering the trash blanket effect. Eur. J. Agron. 130, 126371.

Vianna, M. dos S., Nassif, D.S.P., dos Santos Carvalho, K., Marin, F.R., 2020. Modelling the trash blanket effect on sugarcane growth and water use. Comput. Electron. Agric. 172, 105361.