This article assesses sugarcane yield gaps (YG) in Brazil to determine the degree to which production can be increased without land expansion. In our scenario assessments, we evaluated how much of the projected sugarcane demand to 2024 (for both sugar and bioethanol) can be satisfied through YG closure. The current national average yield is 62% of yield potential estimated for rainfed conditions (i.e., a YG of 38%). Continuing the historical rate of yield gain is not sufficient to meet the projected demand without an area expansion by 5% and 45% for low- and high-demand scenarios, respectively. Closing the exploitable YG to 80% of potential yield would meet future sugarcane demand, with an 18% reduction in sugarcane area for the low-demand scenario or a 13% expansion for the high-demand scenario. A focus on accelerating yield gains to close current exploitable YG is a high priority for meeting future demand while minimizing pressure on additional land requirements.

Dynamic simulation models can increase research efficiency and improve risk management of agriculture. Crop models are still little used for sugarcane (Saccharum spp.) because the lack of understanding of their capabilities and limitations, lack of experience in calibrating them, difficulties in evaluating and using models, and a general lack of model credibility. This paper describes the biophysics and shows a statistical evaluation of a simple sugarcane processbased model coupled with a routine for model calibration. Classical crop model approaches were used as a framework for this model, and fitted algorithms for simulating sucrose accumulation and leaf development driven by a source-sink approach were proposed. The model was evaluated using data from five growing seasons at four locations in Brazil, where crops received adequate nutrients and good weed control. Thirteen of the 27 parameters were optimized using a Generalized Likelihood Uncertainty Estimation algorithm using the leave-one-out cross-validation technique. Model predictions were evaluated using measured data of leaf area index, stalk and aerial dry mass, and sucrose content, using bias, root mean squared error, modeling efficiency, correlation coefficient and agreement index. The model well simulated the sugarcane crop in Southern Brazil, using the parameterization reported here. Predictions were best for stalk dry mass, followed by leaf area index and then sucrose content in stalk fresh mass.