Adaptive Cropping System Laboratory, USDA-ARS, Beltsville, MD, 20705, USA.
Nebraska Water Center, Robert B. Daugherty Water for Food Global Institute, 2021 Transformation Drive, University of Nebraska, Lincoln, NE, 68588, USA.
Sci Rep. 2023 May 5;13(1):7314. doi: 10.1038/s41598-023-34378-3.
GOSSYM, a mechanistic, process-level cotton crop simulation model, has a two-dimensional (2D) gridded soil model called Rhizos that simulates the below-ground processes daily. Water movement is based on gradients of water content and not hydraulic heads. In GOSSYM, photosynthesis is calculated using a daily empirical light response function that requires calibration for response to elevated carbon dioxide (CO). This report discusses improvements made to the GOSSYM model for soil, photosynthesis, and transpiration processes. GOSSYM's predictions of below-ground processes using Rhizos are improved by replacing it with 2DSOIL, a mechanistic 2D finite element soil process model. The photosynthesis and transpiration model in GOSSYM is replaced with a Farquhar biochemical model and Ball-Berry leaf energy balance model. The newly developed model (modified GOSSYM) is evaluated using field-scale and experimental data from SPAR (soil-plant-atmosphere-research) chambers. Modified GOSSYM better predicted net photosynthesis (root mean square error (RMSE) 25.5 versus 45.2 g CO m day; index of agreement (IA) 0.89 versus 0.76) and transpiration (RMSE 3.3 versus 13.7 L m day; IA 0.92 versus 0.14) and improved the yield prediction by 6.0%. Modified GOSSYM improved the simulation of soil, photosynthesis, and transpiration processes, thereby improving the predictive ability of cotton crop growth and development.
GOSSYM 是一个机械的、过程水平的棉花作物模拟模型，它有一个二维（2D）的网格化土壤模型 Rhizos，每天模拟地下过程。水的运动是基于含水量的梯度，而不是水力头。在 GOSSYM 中，光合作用是根据每日经验光响应函数计算的，该函数需要对二氧化碳（CO）升高的响应进行校准。本报告讨论了对 GOSSYM 模型的土壤、光合作用和蒸腾过程的改进。通过用 2DSOIL 替换 Rhizos，GOSSYM 对地下过程的预测得到了改善，2DSOIL 是一种机械的 2D 有限元土壤过程模型。GOSSYM 中的光合作用和蒸腾模型被 Farquhar 生化模型和 Ball-Berry 叶片能量平衡模型所取代。新开发的模型（修改后的 GOSSYM）使用来自 SPAR（土壤-植物-大气-研究）室的田间尺度和实验数据进行评估。修改后的 GOSSYM 更好地预测了净光合作用（根均方误差（RMSE）为 25.5 比 45.2 g CO m day; 一致性指数（IA）为 0.89 比 0.76）和蒸腾（RMSE 为 3.3 比 13.7 L m day; IA 为 0.92 比 0.14），并将产量预测提高了 6.0%。修改后的 GOSSYM 改进了土壤、光合作用和蒸腾过程的模拟，从而提高了棉花作物生长和发育的预测能力。
