Improving the representation of soil productivity/constraints in existing DSS and modelling platforms

Photo by CRC webpage

Farmers often face complex and multiple soil constraints to agricultural production which are difficult and costly to diagnose, assess and ameliorate. The site-specific nature of such multiple soil constraints and significant cost to address means amelioration decisions an ideal use case for Decision Support Systems (DSS) and models. Unfortunately, the suite of models and DSS used in Australian agriculture have a limited ability to represent a diversity of soil constraints and how they interact to impact crop and pasture production. Essentially, only nitrogen fertility and soil water dynamics in dryland environments are well represented. Improvements to DSS and biophysical model soil components such as phosphorus (a key macronutrient) present the opportunity to enhance the current DSS and models to provide increased reliability of predictions that can be used in the paddock. This project will improve already exiting and widely used DSSs (e.g. ARM Online, Yield Prophet and Soil Water App) though developing and improving soil constraint modules in their underlying analysis engines (e.g. APSIM and HowLeaky?) behind them. By focusing on DSS with existing user bases will ensure early and rapid adoption of the project’s science outcomes and will provide enhanced decision support to the agricultural sector for addressing complex soil productivity/constraint challenges that limit farm productivity. Ultimately, this will help with farm decision-makers formulate interventions and new management strategies to improve farm productivity.


University of Southern Queensland (Assoc Prof Keith Pembleton, Mr Roy Anderson and Chloe Lai), Federation University (Dr Nathan Robinson), NSW DPI (Dr Simon Clarendon), Burdekin Productivity Services (Mr Rob Milla), West Midlands Group (Dr Nathan Craig), Riverine Plains Inc (Dr Cassandra Schefe).

Funded by

CRC for High Performance Soils Ltd. Australia

Grant amount


Jonathan Ojeda (Jony)
Crop Ecophysiologist - Cropping Systems Modeller - Data Scientist

I use crop models to understand GxExM interactions and quantify sources of uncertainties in agricultural predictions.