Thesis: Farm-Scale Modelling for Predicting Carbon Sequestration, Active Soil Depth and other Productive Soil Success Measures for South Western Australia.
A significant number of Soil Organic Carbon (SOC) models are utilised world-wide, with extensive research completed to define the accuracy of these models for various soils, climates and land management practices. Techniques to accurately define a soil's total carbon content, components, behaviour and influences have been proposed and examined. Substantial government funding globally has been directed towards rewarding improved SOC storage. But are models, measures and triggered incentives capturing the true value of (or appropriately directing activity towards) sustained improvements based on the soil success measures of stakeholders?
Achieving a soil's maximum attainable SOC (one success measure / incentive provider) coincident with maximum productivity (alternate success measure / income driven) is not practical, but an elevation across both may be deemed a success (especially when considering the potential other secondary and tertiary benefits). Utilising existing research into SOC (modelling, analysis, behaviour) coupled with agreed soil success measures will enable known current and extrapolated future farm characteristics (soil, topography, climate) to translate research information into farming practice.
It is hypothesised that the value currently obtained from rainfall in south Western Australia is the common driver behind stakeholders' success measures. This value provides an indication of the impact Australia's Climate Change Projections will have on productivity should no change occur in existing farming practices. In creating a simplified analysis tool for comparing differing water utilisation across a specific farming system relative to agreed soil success measures, without the need for extensive analysis, farmers gain an indication of their potential return (production versus subsidy) for action rather than SOC outcomes based rewards.
Why my research is important
This research combines local knowledge of farming land with existing research, publicly available data and simplified modelling tools, to support the translation of relatively poorer performing areas into more healthy, productive systems. The ultimate aim is to deliver the existing research associated with the identified success measures to the “farm gate” in a location specific and practically implementable form.