The ability to estimate sugarcane yield is an important factor to improving the planning capacity of public and private sectors, and so food and energy security. One way of achieving this is by employing process-based crop models (PBM), which can be coupled to data assimilation (DA) algorithms to correct predictions along the crop season. While the application of PBMs often need careful parameterization or genotype-specific parameters, few studies focus on understanding the impacts of crop parametrization with different crop genotypes with DA. Moreover, dimensioning the number and timing of observations is key to effectively improve predictions with DA. This study assess the performance of a new sugarcane PBM (DSSAT/SAMUCA) coupled to three DA methods, and when the genotype-specific parameters are available or not. Data from 22 field experiments is utilized to compare the performance of using the ensemble Kalman filter (EnKF), ensemble smoother (ES) and weighted mean (WM) for assimilating leaf area index (LAI) to improve yields estimates. We also quantify the impact of using one genotype-specific calibration (cv. RB867515) on yield predictions of four non-calibrated genotypes (cv. NCo376, SP832847, R570, RB72454). Simulations of DA methods had better performance than employing the PBM without DA, so called open-loop (OP). The ES method resulted in the best performance (R² = 0.498 and RMSE = 20.268 Mg ha⁻¹) followed by EnKF and WM. Utilizing a genotype-specific calibration showed substantially smaller RMSE for the three DA methods (EnKF = 16.76, ES = 16.70 and WM = 15.36 Mg ha⁻¹) compared to non-calibrated (EnKF = 21.44–26.23, ES = 21.50–26.27 and WM = 23.38–28.37 Mg ha⁻¹). Nevertheless, we also verified a higher improvement of model performance when applying EnKF and ES method to experiments where the cultivar does not match the genotype-specific calibration employed. While the WM had the opposite results, with the calibrated cultivar showing a higher improvement of model performance. As the number of LAI data assimilation increases, the DA methods tend to outperform the OP, but observations at late crop phenological stage of development showed a higher positive influence on SFY predictions.

估算甘蔗产量的能力是提高公共和私营部门规划能力以及粮食和能源安全的重要因素。实现这一目标的一种方法是采用基于过程的作物模型 (PBM)，它可以与数据同化 (DA) 算法相结合，以纠正作物季节的预测。虽然 PBM 的应用通常需要仔细的参数化或特定于基因型的参数，但很少有研究关注使用 DA 来了解作物参数化对不同作物基因型的影响。此外，确定观察的数量和时间是有效改进 DA 预测的关键。本研究评估了与三种 DA 方法相结合的新型甘蔗 PBM (DSSAT/SAMUCA) 的性能，以及基因型特定参数何时可用或不可用。来自 22 个现场实验的数据用于比较使用集成卡尔曼滤波器 (EnKF)、集成平滑器 (ES) 和加权平均 (WM) 来同化叶面积指数 (LAI) 以改进产量估计的性能。我们还量化了使用一种基因型特异性校准（cv. RB867515）对四种非校准基因型（cv. NCo376、SP832847、R570、RB72454）产量预测的影响。DA 方法的模拟比使用没有 DA 的 PBM 具有更好的性能，即所谓的开环 (OP)。ES 方法产生了最佳性能（R² = 0.498 和 RMSE = 20.268 Mg ha 我们还量化了使用一种基因型特异性校准（cv. RB867515）对四种非校准基因型（cv. NCo376、SP832847、R570、RB72454）产量预测的影响。DA 方法的模拟比使用没有 DA 的 PBM 具有更好的性能，即所谓的开环 (OP)。ES 方法产生了最佳性能（R² = 0.498 和 RMSE = 20.268 Mg ha 我们还量化了使用一种基因型特异性校准（cv. RB867515）对四种非校准基因型（cv. NCo376、SP832847、R570、RB72454）产量预测的影响。DA 方法的模拟比使用没有 DA 的 PBM 具有更好的性能，即所谓的开环 (OP)。ES 方法产生了最佳性能（R² = 0.498 和 RMSE = 20.268 Mg ha^-1 ) 其次是 EnKF 和 WM。与未校准的（EnKF = 21.44–26.23、ES = 21.50–26.27^和WM = 23.38–28.37 Mg ha ^-1）。尽管如此，我们还验证了在将 EnKF 和 ES 方法应用于品种与所采用的基因型特异性校准不匹配的实验时，模型性能的更高改进。虽然 WM 的结果相反，但经过校准的品种显示出更高的模型性能改进。随着 LAI 数据同化数量的增加，DA 方法往往优于 OP，但在作物物候发育后期的观察显示，对 SFY 预测具有更高的积极影响。