Principal investigators: Daniel Rodriguez
Associated with: Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Australia
Expected increases in food demand and the need to limit the incorporation of new lands into agriculture to curtail emissions, highlight the urgency to bridge productivity gaps, increase farmers profits and manage risks in rainfed cropping. A way to bridge those gaps is to identify optimum combination of genetics (G), and agronomic managements (M) i.e. crop designs (GxM), for the prevailing and expected environment (E). Our understanding of crop stress physiology indicates that in hindsight, those optimum crop designs are known, while the main problem is to predict relevant attributes of the E, at the time of sowing, so that optimum GxM combinations could be informed. Across most agricultural systems the dynamic of water and nitrogen availability during the crop cycle are the key elements determining final grain yields and efficiency measures, and is closely governed by GxMxE factors. In this project, we are developing more integrative GxExM approaches to inform optimum nitrogen management strategies by linking tested crop models (APSIM) with a skilful seasonal climate forecasting system (ACCESS-S1). So far results showed that linking skillful and reliable seasonal climate forecasts with crop models can inform optimum crop designs to increase farmers’ profits, manage risks and significantly increase water and nutrient efficiency indicators.