In a carbon constrained future, with more regular El Nino drying events and a growing Queensland population, sufficient food will need to be secured with optimal water and energy use.
Currently, the rising costs for direct and indirect energy is affecting the ability to deliver secure and efficient water supplies and economically viable food production.
Queensland has the highest electricity use per dollar of gross state product of the three largest Australian economies (DEWS Qld (2012), The 30-year electricity strategy- Directions paper, Queensland Department of Energy and Water Supply, Brisbane).
Direct energy use for urban water treatment and transport is anticipated to increase by 200-250% between 2010 and 2030, due largely to a combination of population growth and increased reliance on “climate resilient” water supplies, and gas prices are anticipated to increase as supply decreases.
A 2.5% reduction in water-related energy in urban Southeast Queensland would save more than $25 million/year in energy.
Pumping water for irrigation and storage demands about 70% of total energy supplied in rural areas.
Electricity prices have escalated significantly, in some cases this has resulted in growers reducing irrigation application below optimum requirements.
In addition to the stressors of climate variability and climate change, increasing energy costs and drivers for water, nutrient and eco-(GHG) efficiency, are further impacting farming communities.
Increasing energy costs (diesel and electricity) are one of the major challenges facing Australian agriculture. Further, the embodied water and energy in food supplies for the urban population is not considered in energy and water accounting.
With the increasing focus on agricultural expansion in northern Queensland, understanding these costs, and alternatives, is crucial. A key factor to underpin effective policy responses to these issues is establishing a thorough understanding of the agricultural ‘metabolism’ driven by the water-energy-food interactions (e.g. baseline data and optimal production capacities).