Crop models can improve our understanding of crop responses to environmental conditions and farming practices. However, uncertainties in model inputs can notably impact the quality of the outputs. This study aimed at quantifying the uncertainty in soil information and analyse how it propagates through the Quantitative Evaluation of Fertility of Tropical Soils model to affect yield and fertilizer recommendation rates using Monte Carlo simulation. Additional objectives were to analyse the uncertainty contributions of the individual soil inputs to model output uncertainty and discuss strategies to communicate uncertainty to end-users. The results showed that the impact of soil input uncertainty on model output uncertainty was significant and varied spatially. Comparison of the results of a deterministic model run with the mean of the Monte Carlo simulation runs showed systematic differences in yield predictions, with Monte Carlo simulations on average predicting a yield that was 0.62 tonnes ha⁻¹ lower than the deterministic run. Similar systematic differences were observed for fertilizer recommendations, with Monte Carlo simulations recommending up to 59, 42, and 20 kg ha⁻¹ lower nitrogen (N), phosphorous (P), and potassium (K) fertilizer applications, respectively. Stochastic sensitivity analysis showed that pH was the main source of uncertainty for K fertilizer (81.6%) and that soil organic carbon contributed most to the uncertainty of N fertilizer application (97%). Uncertainty in P fertilizer application mostly came from uncertainty in extractable phosphorus (55%) and exchangeable potassium (20%). A threshold probability map designed using statistical predictions served as a visual aid that could enable farmers to swiftly make informed decisions about fertilizer application locations. The study highlights the importance of refining the accuracy of soil maps as well as incorporating uncertainty in input data, which improves QUEFTS model predictions and offers valuable insights into the relationship between soil information accuracy and reliable crop modeling for sustainable agricultural decisions.

作物模型可以提高我们对作物对环境条件和耕作方式的反应的理解。然而，模型输入的不确定性会显著影响输出的质量。本研究旨在量化土壤信息的不确定性，并使用蒙特卡洛模拟分析其如何通过热带土壤肥力定量评估模型传播以影响产量和肥料推荐率。其他目标是分析单个土壤输入的不确定性贡献，以模拟输出不确定性，并讨论将不确定性传达给最终用户的策略。结果表明：土壤输入不确定性对模式输出不确定性的影响显著且在空间上存在差异。将确定性模型运行的结果与蒙特卡洛模拟运行的结果进行比较，显示产量预测存在系统性差异，蒙特卡洛模拟平均预测的产量比确定性运行低 0.62 吨 ^ha-1。在肥料推荐方面也观察到类似的系统差异，蒙特卡洛模拟建议分别使用高达 59、42 和 20 kg ^ha-1 的氮 （N）、磷 （P） 和钾 （K） 肥料。随机敏感性分析表明，pH 值是钾肥施用不确定性的主要来源 （81.6%），土壤有机碳对氮肥施用不确定性的贡献最大 （97%）。磷肥施用的不确定性主要来自可提取磷 （55%） 和可交换钾 （20%） 的不确定性。 使用统计预测设计的阈值概率图起到了视觉辅助作用，使农民能够迅速做出有关施肥位置的明智决策。该研究强调了改进土壤地图准确性以及在输入数据中纳入不确定性的重要性，这改进了 QUEFTS 模型预测，并为土壤信息准确性与可持续农业决策的可靠作物建模之间的关系提供了有价值的见解。