Recently, the Innovation Team of Smart Agriculture at the Institute of Agricultural Resources and Regional Planning of the Chinese Academy of Agricultural Sciences has conducted research on a novel method for estimating crop yield for oil crops such as soybeans and rapeseed using a general crop model. The relevant research findings have been published in the agricultural and forestry journals "Computers and Electronics in Agriculture" and "Plant Phenomics."

Accurate estimation of crop yields is crucial for ensuring national food security and sustainable development. Crop growth models are one of the primary methods for yield estimation and have been widely applied in simulating crop growth and development, yield formation, and analyzing the impact of environmental factors. However, many existing studies is focused on Gramineae crops such as wheat, rice, and maize, with less attention given to oil crops like rapeseed and soybeans. While the canopy of Gramineae crops is mainly composed of leaves, leaves are not the only organs capable of photosynthesis in crops. Stems, siliques, pods, and even immature fruits can also perform photosynthesis, which are collectively referred to as non-foliar green organs. For oil crops such as rapeseed and soybeans with active non-foliar green organs, the traditional parameter calibration method based on Leaf Area Index (LAI) cannot meet the high-precision yield estimation requirements.

The research team has proposed a new method for calibrating a general crop model to estimate the yields of rapeseed and soybeans. This method considers the process of non-foliar green organs and the succession process of crop photosynthetic organs, constructing a Total Photosynthetic Area Index (TPAI). Using rapeseed and soybeans as the study subjects, the proposed model calibration method was employed for yield simulation. The results showed that compared to the model calibration method based on LAI, the TPAI-based calibration method for the WOFOST model improved the accuracy of crop yield simulation. For soybeans, the simulation R² of the calibration and validation points for TWSO (grain weight) were 0.741 and 0.926, respectively. The accuracy of rapeseed TWSO simulation R² increased from 0.73 (using LAI) to above 0.90 (using TPAI), with the calibration and validation points for TWSO simulation R² having 0.910 and 0.922, respectively. These results validate the feasibility and effectiveness of the proposed parameter calibration method. Therefore, the general crop model calibration method proposed in this study is significant for accurately estimating the yields of soybeans and rapeseed with active non-foliar green organs, promoting the application of general crop models across different types of crops, and achieving high-precision crop yield estimation.

Doctoral student Cao Hong from the Institute of Agricultural Resources and Regional Planning of the Chinese Academy of Agricultural Sciences in 2024 and Master's student Ruan Shiwei from the School of Information and Communication Engineering at North University of China in 2022 are the co-first authors, with Associate Researcher Wu Shangrong as the corresponding author of the paper. The research was supported by the State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, the National Key Research and Development Program of China, the National Natural Science Foundation of China, and the Youth Innovation Program of the Chinese Academy of Agricultural Sciences.

General crop model parameter calibration framework considering the non-foliar green organs

Original Article Links: 

https://www.sciencedirect.com/science/article/pii/S0168169924007531   

https://doi.org/10.34133/plantphenomics.0253