Benchmarking to improve long-term carrying capacity estimates for extensive grazing properties in Queensland

Safe carrying capacity information can assist producers in making stocking rate decisions to ensure minimal decline in land condition over the long-term. FORAGE, a modelling framework which uses the GRASP pasture growth model, spatial data, remote sensing and climate data, provides long-term carrying capacities for individual paddocks and land types for grazing properties in Queensland. Applying the framework across Queensland’s diverse grazing lands and capturing the large range of land types and climates is challenging. To overcome this challenge, we will collate on-ground data and expert-knowledge for reference properties to help validate the modelling framework and ensure the best-available safe carrying capacity information is provided

Bio-economic modelled outcomes of stocking rate and drought recovery strategies in the Mitchell grass region

Extreme year-to-year rainfall variability, long periods of drought, and temporal variability in forage supply pose significant challenges for the sustainable and profitable management of extensive grazing enterprises in Northern Australia. The impact of climate variability on a range of stocking rate and herd management strategies applicable to the Mitchell grass region of central Queensland were simulated by integrating output from the GRASP pasture growth model with the Breedcow and Dynama herd models

Improved grazing management practices in the catchments of the Great Barrier Reef, Australia: Does climate variability influence their adoption by landholders?

The declining health of the Great Barrier Reef from diffuse source pollutants has resulted in substantial policy attention on increasing the adoption of improved management practices by agricultural producers. Although economic modelling indicates that many improved management practices are financially rewarding, landholders with dated management practices remain hesitant to change. This research involved bio-economic modelling to understand the variance in private returns for grazing enterprises across a climate cycle. Results show that financial returns to landholders can vary substantially across different 20-year periods of a climate cycle, demonstrating that the variability in expected returns may be an important reason why landholders are cautious about changing their management practices. Although previous research has separately identified financial returns and attitudes to risk and uncertainty of landholders as key influences on decisions concerning adoption of improved management practices, this research demonstrates that it is the interaction between these factors that is important to understand when designing policy settings. © Australian Rangeland Society

Incorporating expert opinion and fine-scale vegetation mapping into statistical models of faunal distribution

1. Abiotic environmental predictors and broad-scale vegetation have been used widely to model the regional distributions of faunal species within forested regions of Australia. These models have been developed using stepwise statistical procedures but incorporate only limited expert involvement of the type sometimes advocated in distribution modelling. The objectives of this study were twofold. First, to evaluate techniques for incorporating fine-scaled vegetation and growth-stage mapping into models of species distribution. Secondly, to compare methods that incorporate expert opinion directly into statistical models derived using stepwise statistical procedures. 2. Using faunal data from north-east New South Wales, Australia, logistic regression models using fine-scale vegetation and expert opinion were compared with models employing only abiotic and broad vegetation variables. 3. Vegetation and growth-stage information was incorporated into models of species distribution in two ways, both of which used expert opinion to derive new explanatory variables. The first approach amalgamated fine-scaled vegetation classes into broader classes of ecological relevance to fauna. In the second approach, ordinal habitat indices were derived from vegetation and growth-stage mapping using rules specified by an expert panel. These indices described habitat features thought to be relevant to the faunal groups studied (e.g. tree hollow availability, fleshy fruit production). Landscape composition was calculated using these new variables within a 500-m and 2-km radius of each site. Each habitat index generated a spatially neutral variable and two spatial context variables. 4. Expert opinion was incorporated during the pre-modelling, model-fitting and post -modelling stages. At the pre-modelling stage experts developed new explanatory variables based on mapped fine-scale vegetation and growth-stage information. At the model-fitting stage an expert panel selected a subset of potential explanatory variables from the available set. At the post-modelling stage expert opinion modified or refined maps of predicted species distribution generated by statistical models. For comparative purposes expert opinion was also used to develop maps of species distribution by defining rules within a geographical information system, without the aid of statistical modelling. 5. Predictive accuracy was not improved significantly by incorporating habitat indices derived by applying expert opinion to fine-scaled vegetation and growth-stage mapping. Use of expert input at the pre-modelling stage to derive and select potential explanatory variables therefore does not provide more information than that provided by remotely mapped vegetation. 6. The incorporation of expert opinion at the model-fitting or post-modelling stages resulted in small but insignificant gains in predictive accuracy. The predictive accuracy of purely expert models was less than that achieved by approaches based on statistical modelling. 7. The study, one of few available evaluations of expert opinion in models of species distribution, suggests that expert modification of fitted statistical models should be confined to species for which models are grossly in error, or for which insufficient data exist to construct solely statistical models