APSIM

This study investigates the performance of the APSIM and CANEGRO crop models in simulating sugarcane growth and yield under different growing environment and fertilizer application. Field experimental data from the 2023–24 season were employed for model calibration, while validation was conducted using data from the 2024–25 season. During calibration, the CANEGRO model demonstrated limited responsiveness to variations in fertilizer dosage, producing uniform outputs across different nutrient treatments. Model validation results indicate that APSIM outperformed CANEGRO in simulating key agronomic parameters including cane yield, aerial dry biomass, and days to emergence. APSIM achieved a higher coefficient of determination (R² = 0.93), D-index (0.95), and lower RMSE (6.01 t/ha) for cane yield compared to CANEGRO (R² = 0.88, D-index = 0.77, RMSE = 6.29 t/ha).

This study investigates the sensitivity of the APSIM and CANEGRO crop models to key climatic parameters and genetic coefficients in simulating sugarcane growth and yield. Sensitivity analyses to genetic coefficient identified critical genetic parameters influencing crop performance. In the CANEGRO model, MaxPARCE, APFMX, and STKPFMAX were the most influential for yield, biomass, and sucrose content, while LFMAX and SER0 significantly impacted LAI and stalk height. These findings suggest that calibration efforts should prioritize phenological, growth, and yield-related parameters. In contrast, global sensitivity analysis using the APSIM model highlighted RUE4, MSS, and GLN as the most impactful parameters affecting cane yield, commercial cane sugar (CCS), and sucrose accumulation, with RUE and MSS emerging as key contributors to both biomass production and sugar content. Additionally, both models exhibited sensitivity to climatic variables. A rise in temperature resulted in a decline in cane yield, more pronounced in CANEGRO (31.35% reduction at +6 °C) compared to APSIM (15.81%). Increases in solar radiation enhanced cane yield and sucrose dry mass, while reduced radiation had adverse effects. Overall, the findings provide actionable insights for improving model calibration and support cultivar selection and management practices under projected climate change scenarios.