Nutrient balance strategies to build soil carbon in farming systems



     

Crop nutrition in agriculture is a complex interplay of crop physiology and timing and availability of nutrients, with both demand and supply affected by climate, soil, and management. Its study requires consideration of a multi-layered system in time and space. We suspect that insufficient crop nutrition (nitrogen in particular) is the biggest cause of the current “yield gap” in dryland agriculture (difference between actual and potential yield).  At the same time, we know that soil “nutrient mining” through inadequate nutrition drives the loss of carbon (organic matter) in soil.  Fine tuning and de-risking our nutrient management to maintain a nutrient balance at the system’s level is the key to dealing with these two critical but linked national goals. More deeply understanding the nutrient balance will have impacts not only towards crop productivity, but also carbon accounting and environmental impacts on soils, water, and landscapes.

The PhD project will use the APSIM model to refine, redefine and test hypotheses relating to fertilisation strategies, system level nutrient balance and soil carbon pool dynamics. The project will develop an in-depth understanding of soil carbon and nutrient dynamics in cropping systems, drawing on data from CSIRO’s Boorowa Agricultural Research Station (BARS) and other long-term cropping trials for verification and improvement of the simulated dynamics.

The PhD project will be supported by a multidisciplinary team of researchers with experience in farming systems modelling, agronomy, and soil nutrient and carbon dynamics. It will be integrated with the new long-term farming systems trial at BARS which is focussed on developing innovative solutions for resilient farming. 

Supervisors: Julianne Lilley (CSIRO), John Kirkegaard (CSIRO), Alan Richardson (CSIRO), Kirsten Verburg (CSIRO), Craig Strong (ANU), Rachael Rodney Harris (ANU) and Martin Amidy (ANU).

PhD Training

1) CSIRO farming systems scientists will provide training in cropping systems simulation and associated data analysis and machine learning to further explore model output. A background including coding in either R or Python would be desirable.

2) General field and laboratory work methods including soil sampling, soil physical and chemical analyses, and related analysis of data.

3) In-depth understanding of soil carbon and nutrient dynamics in cropping systems.

4) Transferable core scientific skills including data management, analysis, presentations, paper writing, and peer review, as well as working in a multi-disciplinary team.