Chia (Salvia hispanica L.) seeds are becoming increasingly popular as a superfood in Europe. However, broad experience in growing chia in temperate climates is missing. Crop simulation models can be helpful tools for management and decision‐making in crop production systems in different regions. The objective of this study was to adapt the CROPGRO model for simulating growth and yield of chia. Data sets from a field experiment conducted over 2 yr in southwestern Germany (48°74′ N, 08°92′ E, 475 m above sea level) were used for model adaptation. The initial starting point was the CROPGRO–soybean [Glycine max (L.) Merr.] model as a template for parameterizing temperature functions and setting tissue composition. Considerable iterations were made in optimizing growth, development, and photosynthesis parameters. After model calibration, the simulation of leaf area index (LAI) was reasonable for both years, slightly over‐predicting LAI with an average d‐statistic of 0.95 and root mean square error (RMSE) of 0.53. Simulations of final leaf number were close to the observed data with d‐statistic of 0.98 and RMSE of 1.36. Simulations were acceptable for total biomass (d‐statistic of 0.93), leaf (d‐statistic of 0.94), stem (d‐statistic of 0.94), pod mass (d‐statistic of 0.89), and seed yield (d‐statistic of 0.88). Pod harvest index (HI) showed good model performance (d‐statistic of 0.96 and RMSE of 0.08). Overall, the model adaptation resulted in a preliminarily adapted model with realistically simulated crop growth variables. Researchers can use the developed chia model to extend knowledge on the eco‐physiology of chia and to improve its production and adaption to other regions.

中文翻译：

---

修改CROPGRO模型以模拟Chia生长和产量

奇亚（Salvia hispanica L.）种子作为超级食品在欧洲变得越来越受欢迎。但是，缺少在温带气候下生长奇亚的广泛经验。作物模拟模型可以成为不同地区作物生产系统中管理和决策的有用工具。这项研究的目的是使CROPGRO模型适用于模拟Chia的生长和产量。来自德国西南部超过2年的野外实验数据集（北纬48°74′，东经08°92′，海拔475 m）用于模型自适应。最初的起点是CROPGRO-大豆[ Glycine max[L.）Merr。]模型作为参数化温度函数和设置组织成分的模板。在优化生长，发育和光合作用参数方面进行了大量的迭代。经过模型校准后，两年的叶面积指数（LAI）的模拟都是合理的，对LAI的预测有些过高，平均d统计量为0.95，均方根误差（RMSE）为0.53。最终叶片数的模拟与观察到的数据接近，d统计量为0.98，RMSE为1.36。对于总生物量（d统计量为0.93），叶片（d统计量为0.94），茎（d统计量为0.94），荚果质量（d统计量为0.89）和种子产量（d统计量为0.88）。荚果收获指数（HI）显示出良好的模型表现（d统计量为0.96，RMSE为0.08）。总的来说，模型的适应性产生了具有实际模拟的作物生长变量的初步适应性模型。研究人员可以使用已开发的Chia模型来扩展有关Chia生态生理学的知识，并改善其生产并适应其他地区。