Overview This technical report examines the application of the statistical technique, small area estimation, to broadacre farm survey data. This modelling approach can be applied to provide estimates of farm performance and characteristics at a finer spatial scale than the survey regions designated in the original survey design. The report demonstrates the potential application and value of the approach. Key Issues * The Australian Agricultural and Grazing Industries Survey (AAGIS) collects detailed information on the physical characteristics and financial performance from a sample of around 1600 broadacre farms across Australia each year. The AAGIS is designed so that estimates of regional averages of the most important financial variables are generally reliable for 32 predefined AAGIS regions spanning the country. However, considerable heterogeneity in farm performance exists at a spatial scale finer than the AAGIS regions. This means that estimates at the AAGIS region level might not be representative of any of the component sub-areas that make up that region. Attempts to use the AAGIS data to calculate estimates for sub-regions smaller than specified by the survey design using conventional survey estimation methods would be expected to result in estimates with high variance because of insufficient sample sizes. * Small area estimation techniques are aimed at producing reliable estimation of quantities collected in surveys at a finer scale than is achievable by conventional, design-based survey estimation. This is achieved by modelling the values of the quantity of interest collected in the survey using covariates that are assumed to be known at the small area level. * This report describes an approach used to generate a set of estimates of average total cash costs and total cash receipts of broadacre farms in 2010-11 at the Statistical Local Area (SLA) level for a selection of 390 SLAs in south eastern Australia. The precision of the estimates is also estimated. The approach described incorporates auxiliary data sourced from the 2011 Agricultural Census run by the Australian Bureau of Statistics, as well as estimates of pasture growth from the Pastures from Space™ website. Similarity in economic performance of farms within SLAs and in neighbouring SLAs is also modelled. * The approach developed could be used to provide additional information derived from ABARES rich farm survey data tailored to clients' requests.