Abstract Quickly determining accurate crop genetic parameters for crop model applications can be difficult. In this study, we coupled the independent automatic parameter optimization tool PEST (Parameter ESTimation) with the crop growth model of DSSAT (Decision Support System for Agrotechnology Transfer) using the R programming language. A new DSSAT-PEST package was developed to perform automatic optimization of the crop genetic parameters. In addition, the PEST tool was modified to reduce problems associated with local optima and model runtime. The DSSAT-PEST package was used to estimate the genetic coefficients for five crops (i.e., maize (Zea mays L.), soybean (Glycine max L. Merrill), wheat (Triticum aestivum L.), rice (Oryza sativa L.), and cotton (Gossypium hirsutum L.)) based on existing experiments in the DSSAT database. Three parameter optimization methods were compared based on their efficiency and accuracy for estimating crop genetic parameters: 1) the traditional trial-and-error method (default crop genetic parameters in the DSSAT database); 2) DSSAT-GLUE (general likelihood uncertainty estimation, an existing parameter estimation package in DSSAT), and 3) DSSAT-PEST. The DSSAT-PEST optimization method produced reasonably accurate optimization results and improved optimization efficiency compared with the other two methods. For example, the average absolute relative error (AREs) between relevant field observations and model simulations obtained with DSSAT-PEST were 12 %, 7 %, 18 %, 4 %, and 19 % for the five crops, respectively, which were similar to or better than the results with DSSAT-GLUE and the default method. Additionally, average runtime for DSSAT-PEST was about 65 % of the runtime for DSSAT-GLUE. In general, the DSSAT-PEST package performed similarly to or better than the traditional trial-and-error method and DSSAT-GLUE in terms of both optimization efficiency and accuracy, which should promote wider application of the DSSAT model in agricultural and environmental research.

中文翻译：

---

使用改进的 PEST 软件估计 DSSAT 的作物遗传参数

摘要 为作物模型应用快速确定准确的作物遗传参数可能很困难。在本研究中，我们使用R编程语言将独立的自动参数优化工具PEST（Parameter ESTimation）与DSSAT（农业技术转移决策支持系统）的作物生长模型相结合。开发了一个新的 DSSAT-PEST 包来执行作物遗传参数的自动优化。此外，修改了 PEST 工具以减少与局部最优和模型运行时相关的问题。DSSAT-PEST 包用于估计五种作物（即玉米（Zea mays L.）、大豆（Glycine max L. Merrill）、小麦（Triticum aestivum L.）、水稻（Oryza sativa L.）的遗传系数和棉花（Gossypium hirsutum L.））基于 DSSAT 数据库中的现有实验。根据估计作物遗传参数的效率和准确性，比较了三种参数优化方法：1）传统的试错法（DSSAT 数据库中的默认作物遗传参数）；2) DSSAT-GLUE（一般似然不确定性估计，DSSAT 中现有的参数估计包），和 3) DSSAT-PEST。与其他两种方法相比，DSSAT-PEST 优化方法产生了相当准确的优化结果并提高了优化效率。例如，使用 DSSAT-PEST 获得的相关实地观察和模型模拟之间的平均绝对相对误差 (ARE) 分别为 12 %、7 %、18 %、4 % 和 19 %，这与或优于 DSSAT-GLUE 和默认方法的结果。此外，DSSAT-PEST 的平均运行时间约为 DSSAT-GLUE 运行时间的 65%。总的来说，DSSAT-PEST 包在优化效率和准确性方面的表现与传统的试错法和 DSSAT-GLUE 相似或更好，这将促进 DSSAT 模型在农业和环境研究中的更广泛应用。