Streamlining Process-Based Wheat Models: A Framework for Efficient Data Collection And Crop Model Calibration

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Authors

Luis Vargas-Rojas¹ , Diane R Wang¹ , Matthew Paul Reynolds²

1. Department of Agronomy, Purdue University, West Lafayette, IN, United States
2. Wheat Physiology, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico

Conference / event

3rd International Wheat Congress, September 2024 (Perth, Australia)

Poster summary

This poster presents an innovative approach to enhancing crop model (CM) calibration for wheat breeding programs. It addresses challenges in wheat production and current breeding limitations by integrating proximal and remote sensing (PRS) data with field measurements. The study, conducted at CIMMYT's CENEB in Mexico, compared 14 wheat genotypes under various environmental conditions. Using DSSAT's GLUE tool, the research evaluated two parameterization scenarios: integrated PRS-field data and field-based measurements alone. Results show that the integrated approach improves CM accuracy, with lower RMSE and higher R² values in most conditions. The poster highlights the importance of time-series data for improving crop growth simulations and suggests implementing PEST software for future calibration of PRS time-series data. This research demonstrates the potential of integrating PRS data and CMs to enhance wheat breeding efficiency and expand crop performance predictions across diverse environments.

Keywords

Wheat Breeding, Crop Models, Proximal and Remote Sensing, Parameterization, Data Integration, Time-Series Data

Research areas

Agriculture, Computer and Information Science , Remote Sensing, Plant Sciences, Genetics

References

1. Hoogenboom, G., Jones, J. W., Traore, P. C. S., & Boote, K. J. (2012). Experiments and Data for Model Evaluation and Application (J. Kihara, D. Fatondji, J. W. Jones, G. Hoogenboom, R. Tabo, & A. Bationo, Eds.; pp. 9–18). Springer Netherlands. https://doi.org/10.1007/978-94-007-2960-5_2
2. Kasampalis, D. A., Alexandridis, T. K., Deva, C., Challinor, A., Moshou, D., & Zalidis, G. (2018). Contribution of Remote Sensing on Crop Models: A Review. Journal of Imaging, 4(4). https://doi.org/10.3390/jimaging4040052

Funding

1. HedWIC
2. Consejo Nacional de Humanidades, Ciencias y Tecnologías (Conahcyt)

Supplemental files

No data provided

Additional information

Competing interests

No competing interests were disclosed.

Data availability statement

The datasets generated during and / or analyzed during the current study are available from the corresponding author on reasonable request.

Creative Commons license

Copyright © 2024 Vargas-Rojas et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.