Simulation of biomass and sugar accumulation in sugarcane using a process-based model

A crop simulation model (QCANE) was developed to simulate growth and sugar accumulation in sugarcane. QCANE is based on crop growth and development processes including canopy development, photosynthesis, respiration, and the partitioning of carbohydrates to plant organs for growth and respiration as dictated by phenological development and changing environments. Seasonal temperature changes and shading in lower layers of the canopy are used to determine leaf senescence and canopy development. Photosynthesis is simulated by incorporating diurnal light variation and canopy light attenuation into the rectangular hyperbolic relationship between leaf-photosynthesis and light intensity. Simulation of respiration is related to temperature and biomass accumulation. Partitioning of carbohydrates into leaf, non-millable top, cane and root components of the crop uses temperature related functions which differ for different stages of crop development. A smoothing spline technique was used to account for the autocorrelation over time of sequential observations used for validation of the model. Root mean squared error ( RMSE) and ‘performance efficiency’ ( PE) were used for assessing the model performance. Validation of the model against data from an independent experiment at Bundaberg (a sub-tropical environment) resulted in RMSE values of 0.64 m 2 m −2, 231 g m −2, 279 g m −2 and 124 g m −2, respectively for leaf area index ( L), cane dry matter accumulation ( W c), sugar accumulation ( S c) and fibre accumulation ( F c). PE values indicated that model accounted for 86, 98, 91 and 95% of variance observed in L, W c, S c, and F c, respectively. The validation was extended to a tropical environment for an experiment conducted at Ingham. The result was RMSE values of 1.15 m 2 m −2, 722 g m −2, and 254 g m −2 and the simulation accounted for 48, 86 and 92% of variance observed in L, W c, and S c, respectively. The flexibility and capacity of model to be applied to the simulation of climate changes are discussed.