Methane in Australian agriculture: current emissions, sources and sinks, and potential mitigation strategies

Methane is a potent greenhouse gas with a global warming potential ∼28 times that of carbon dioxide. Consequently, sources and sinks that influence the concentration of methane in the atmosphere are of great interest. In Australia, agriculture is the primary source of anthropogenic methane emissions (60.4% of national emissions, or 3260kt-1methaneyear-1, between 1990 and 2011), and cropping and grazing soils represent Australia's largest potential terrestrial methane sink. As of 2011, the expansion of agricultural soils, which are ∼70% less efficient at consuming methane than undisturbed soils, to 59% of Australia's land mass (456Mha) and increasing livestock densities in northern Australia suggest negative implications for national methane flux. Plant biomass burning does not appear to have long-term negative effects on methane flux unless soils are converted for agricultural purposes. Rice cultivation contributes marginally to national methane emissions and this fluctuates depending on water availability. Significant available research into biological, geochemical and agronomic factors has been pertinent for developing effective methane mitigation strategies. We discuss methane-flux feedback mechanisms in relation to climate change drivers such as temperature, atmospheric carbon dioxide and methane concentrations, precipitation and extreme weather events. Future research should focus on quantifying the role of Australian cropping and grazing soils as methane sinks in the national methane budget, linking biodiversity and activity of methane-cycling microbes to environmental factors, and quantifying how a combination of climate change drivers will affect total methane flux in these systems.

‘How to’ guide for measuring fan performance and efficiency in meat chicken sheds

This ‘how to’ guide provides readers with method to measure fan performance and energy efficiency of fans installed in meat chicken sheds. These methods are also useful for identifying fans that are under-performing or require maintenance. For more information about fan energy efficiency, a complementary report is available on the RIRDC website ‘Review of fan efficiency in meat chicken sheds’ (RIRDC Publication No. 15/018). A spreadsheet was also developed under this project for comparing and ranking fans against others in terms of energy efficiency, air flow and costs (‘Tunnel Ventilation Fan Comparison Spreadsheet’), and is available on the RIRDC website.

Molecular Breeding for Complex Adaptive Traits: How Integrating Crop Ecophysiology and Modelling Can Enhance Efficiency

Progress in crop improvement is limited by the ability to identify favourable combinations of genotypes (G) and management practices (M) in relevant target environments (E) given the resources available to search among the myriad of possible combinations. To underpin yield advance we require prediction of phenotype based on genotype. In plant breeding, traditional phenotypic selection methods have involved measuring phenotypic performance of large segregating populations in multi-environment trials and applying rigorous statistical procedures based on quantitative genetic theory to identify superior individuals. Recent developments in the ability to inexpensively and densely map/sequence genomes have facilitated a shift from the level of the individual (genotype) to the level of the genomic region. Molecular breeding strategies using genome wide prediction and genomic selection approaches have developed rapidly. However, their applicability to complex traits remains constrained by gene-gene and gene-environment interactions, which restrict the predictive power of associations of genomic regions with phenotypic responses. Here it is argued that crop ecophysiology and functional whole plant modelling can provide an effective link between molecular and organism scales and enhance molecular breeding by adding value to genetic prediction approaches. A physiological framework that facilitates dissection and modelling of complex traits can inform phenotyping methods for marker/gene detection and underpin prediction of likely phenotypic consequences of trait and genetic variation in target environments. This approach holds considerable promise for more effectively linking genotype to phenotype for complex adaptive traits. Specific examples focused on drought adaptation are presented to highlight the concepts.