Journal article

Machine learning produces higher prediction accuracy than the Jarvis-type model of climatic control on stomatal conductance in a dryland wheat agro-ecosystem

Alireza Houshmandfar, Garry O'Leary, Glenn J Fitzgerald, Yang Chen, Sabine Tausz-Posch, Kurt Benke, Shihab Uddin, Michael Tausz



We compared Support Vector Machine (SVM) and Random Forest (RF) machine learning approaches with the widely used Jarvis-type phenomenological model for predicting stomatal conductance (gs) in wheat (Triticum aestivum L.) using historical measurements collected in the Australian Grains Free-Air CO2 Enrichment (AGFACE) facility. The machine learning-based methods produced greater accuracy than the Jarvis-type model in predicting gs from leaf age, atmospheric [CO2], photosynthetically active radiation, vapour pressure deficit, temperature, time of day, and soil water availability (i.e. phenological and environmental variables determining gs). The R2 was 0.76 for the Jarvis-type but 0.92 for SVM..

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Funding Acknowledgements

This work uses data produced in the AGFACE programme which was jointly run by the University of Melbourne and Agriculture Victoria with funding from the Grains Research Development Corporation (GRDC) and the Australian Commonwealth Government Department of Agriculture and Water Resources.