A biophysical model of Sugarcane growth

• Published in: Global change biology. Bioenergy 2012-01, Vol.4 (1), p.36-48
• Main Authors: CUADRA, S. V., COSTA, M. H., KUCHARIK, C. J., DA ROCHA, H. R., TATSCH, J. D., INMAN-BAMBER, G., DA ROCHA, R. P., LEITE, C. C., CABRAL, O. M. R.
• Format: Article
• Language: English
• Subjects: Agricultural ecosystems; Agricultural land; Agricultural production; Agriculture; Bias; Biodiesel fuels; biofuel; Biofuels; Biogeochemical cycles; Biogeochemistry; Biology; Biomass; Biosphere; Carbon dioxide; Climate change; Corn; Crop production; Crop yield; crop yield and energy budget; Crops; Ecosystem biology; Ecosystem models; Environmental economics; Environmental research; Food production; Geoengineering; Heat flux; land surface model; Land use; Latent heat; Meteorology; Photosynthesis; Physical sciences; Plant sciences; Respiration; Soil sciences; Sugarcane; sugarcane model; Vegetation; Working groups
• ISSN: 1757-1693; 1757-1707
• DOI: 10.1111/j.1757-1707.2011.01105.x

Scientists predict that global agricultural lands will expand over the next few decades due to increasing demands for food production and an exponential increase in crop‐based biofuel production. These changes in land use will greatly impact biogeochemical and biogeophysical cycles across the globe. It is therefore important to develop models that can accurately simulate the interactions between the atmosphere and important crops. In this study, we develop and validate a new process‐based sugarcane model (included as a module within the Agro‐IBIS dynamic agro‐ecosystem model) which can be applied at multiple spatial scales. At site level, the model systematically under/overestimated the daily sensible/latent heat flux (by −10.5% and 14.8%, H and λE, respectively) when compared against the micrometeorological observations from southeast Brazil. The model underestimated ET (relative bias between −10.1% and –12.5%) when compared against an agro‐meteorological field experiment from northeast Australia. At the regional level, the model accurately simulated average yield for the four largest mesoregions (clusters of municipalities) in the state of São Paulo, Brazil, over a period of 16 years, with a yield relative bias of −0.68% to 1.08%. Finally, the simulated annual average sugarcane yield over 31 years for the state of Louisiana (US) had a low relative bias (−2.67%), but exhibited a lower interannual variability than the observed yields.