Crop models are usually developed using a test set of data and simulations representing a range of environment, soil, management and genotype combinations. Previous studies demonstrated that errors in the configuration of test simulations and aggregation of observed data sets are common and can cause major problems for model development. However, the extent and effect of such errors using Agricultural Production system SIMulator Next Generation (APSIM) crop models are not usually considered as a source of model uncertainty. This is a methodological paper describing several approaches for testing the APSIM simulation configuration to detect anomalies in the input and observed data. In this study, we assess the simulation configuration process through (i) quality control analysis based on a standardised climate dataset (ii) outlier identification and (iii) a palette of visualization tools. A crop model – APSIM-Potato is described to demonstrate the main sources of error during the simulation configuration and data collation processes. Input data from 426 experiments conducted from 1970 to 2019 in 19 countries were collected and configured to run a model simulation. Plots were made comparing simulation configuration data and observed data across the entire test set so these values could be checked relative to others in the test set and with independent datasets. Errors were found in all steps of the simulation configuration process (climate, soil, crop management and observed data). We identified a surprising number of errors and inappropriate assumptions that had been made which could influence model predictions. The approach presented here moved the bulk of the effort from fitting model processes to setting up broad simulation configuration testing and detailed interrogation to identify current gaps for further model development.

作物模型通常是使用一组测试数据和模拟来开发的，这些数据和模拟代表了一系列环境、土壤、管理和基因型组合。先前的研究表明，测试模拟配置和观测数据集聚合中的错误很常见，可能会导致模型开发出现重大问题。然而，使用下一代农业生产系统模拟器 (APSIM) 作物模型的此类错误的程度和影响通常不被视为模型不确定性的来源。这是一篇方法论论文，描述了几种测试 APSIM 模拟配置以检测输入和观察数据异常的方法。在这项研究中，我们通过 (i) 基于标准化气候数据集的质量控制分析 (ii) 异常值识别和 (iii) 可视化工具调色板来评估模拟配置过程。作物模型 – APSIM-Potato 用于演示模拟配置和数据整理过程中的主要误差来源。收集了 1970 年至 2019 年在 19 个国家/地区进行的 426 次实验的输入数据，并将其配置为运行模型模拟。绘制了比较整个测试集中的模拟配置数据和观察数据的图，以便可以相对于测试集中的其他值和独立数据集检查这些值。在模拟配置过程的所有步骤（气候、土壤、作物管理和观测数据）中都发现了错误。我们发现了数量惊人的错误和不适当的假设，这些假设可能会影响模型预测。此处介绍的方法将大部分工作从拟合模型过程转移到设置广泛的模拟配置测试和详细询问，以确定当前的差距以进一步开发模型。