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

Abstract

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..