Project Title: From Oceans to Farms: Integrated Management of Climate Variability

Project Supervisor: Dr. Andrew Ash

Funding Body: LWRRDC

Commencement Date: 01/07/98 Completion Date: 30/06/2001

1. Determine a set of skilful and industry targeted climate prediction indices based on ocean data (such as SST) and intermediate-complexity coupled ocean-atmosphere models (eg. Kleeman, Cane-Zebiak) for specific agricultural regions across Australia.
2. Optimise the use of statistical and intermediate coupled ocean-atmosphere forecasts in farming production/economics models for the extensive grazing, dryland grains and sugar industries. Evaluate the use of and potential improvements afforded by newer fully dynamic coupled ocean-atmosphere models and modern data assimilation techniques.
3. Assess the production, economic and resource-based value of these systems compared to current best-practice in the absence of climate forecasts.
4. Ensure industry participation in the application and assessment of these systems by close collaboration with Agribusiness, QCCA and other relevant industry bodies.

Annual gains and losses in agricultural production of around $1bn per year are associated with climatic variability. Good long-term forecasts will significantly improve agricultural gains in good years, and cut losses in bad years. They will also help land managers to minimise environmental impacts (eg. soil erosion, stream eutrophication, loss of biodiversity). However, current forecasting methodologies based on the SOI are at the limit of their capability, with short lead times, modest reliability and relevance mainly to eastern Australia.

This project aims to evaluate newer forecasts based on ocean data (such as SST) and intermediate-complexity coupled ocean-atmosphere models. New forecasts must be developed and evaluated in conjunction with industry needs, and because these needs vary by industry and location a collaborative approach is necessary. The value of these new forecasts will be tested for the grains, extensive grazing and sugar industries across Australia. The forecasts will be assessed using agricultural models that simulate production under a range of management strategies in a way that we can compare forecasts with no-forecasts. The output from the models will be used to assess economic impacts and to develop simple rules of thumb for adoption by industry.