The potential of using LAI time series to predict plant available water capacity (PAWC) of soils

Di He 1, Enli Wang 1

1 CSIRO Agriculture and Food, GPO Box 1700, Canberra ACT 2601, ACT, Australia, di.he@csiro.au, enil.wang@csrio.au

Abstract:

Plant available water holding capacity (PAWC) interacts with climate to determine crop yield and is thus a key factor for predicting spatial yield variations. However, PAWC data at the required spatial resolution are not available because direct soil measurement is expensive and time-consuming. Here, we explore a new approach to inversely estimate PAWC from crop LAI time series using process-based modelling with APSIM together with machine learning. We used the APSIM model to simulate daily LAI of wheat in response to a wide range of PAWC across Australia. Vegetation metrics are derived from simulated LAI time series and were used together with climatic variables to build a machine learning model for predicting PAWC. The model explained 29% to 83% variation of PAWC across ten sites with contrasting climate. This implies a potentially more effective way of PAWC estimation, an alternative to direct soil sampling method.

Host

The Australian Society of Agronomy is the professional body for agronomists in Australia. It has approximately 500 active members drawn from government, universities, research organisations and the private sector.

Photo Credits

David Marland Photography david_marland@hotmail.com Graham Centre for Agricultural Innovation, Charles Sturt University

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