ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Crop and Product Physiology
Volume 16 - 2025 | doi: 10.3389/fpls.2025.1617775
A Data-driven Crop Model for Biomass Sorghum Growth Process Simulation
Provisionally accepted
- 1Oklahoma State University, Stillwater, Oklahoma, United States
- 2Iowa State University, Ames, Iowa, United States
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Accurate simulation of crop growth processes for predicting final yield is critical for optimizing resource management, particularly in regions with variable climates and limited resource availability. This paper proposes a novel data-driven crop model to simulate phenotypic changes during biomass sorghum growth. The model integrates a detailed physiological framework for sorghum development—tracking how phenotypes are determined by genotype, environment, management practices, and their interactions—with data-driven techniques to calibrate genotypic parameters using experimental data. Results demonstrate that the model achieves accurate biomass production predictions and successfully disentangles the effects of environmental and management factors on phenotypic development, even with limited data. This model enhances the accuracy and applicability of biomass sorghum growth and yield prediction models, offering valuable insights for precision agriculture.
Keywords: Biomass sorghum, Yield prediction, Data-driven Crop Model, Process-based crop model, Integrated Crop Model
Received: 24 Apr 2025; Accepted: 10 Oct 2025.
Copyright: © 2025 Chang, Ni, Panelo, Kemp, Salas Fernandez and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Maria G. Salas Fernandez, mgsalas@iastate.edu
Lizhi Wang, lizhi.wang@okstate.edu
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.