The effect of earlier mating and improving fertility on greenhouse gas emissions intensity of beef production in northern Australian herds

Approximately 5% of Australian national greenhouse gas (GHG) emissions are derived from the northern beef industry. Improving the reproductive performance of cows has been identified as a key target for increasing profitability, and this higher efficiency is also likely to reduce the GHG emissions intensity of beef production. The effects of strategies to increase the fertility of breeding herds and earlier joining of heifers as yearlings were studied on two properties at Longreach and Boulia in western Queensland. The beef production, GHG emissions, emissions intensity and profitability were investigated and compared with typical management in the two regions. Overall weaning rates achieved on the two properties were 79% and 74% compared with typical herd weaning rates of 58% in both regions. Herds with high reproductive performance had GHG emissions intensities (t CO2-e t–1 liveweight sold) 28% and 22% lower than the typical herds at Longreach and Boulia, with most of the benefit from higher weaning rates. Farm gross margin analysis showed that it was more profitable, by $62 000 at Longreach and $38 000 at Boulia, to utilise higher reproductive performance to increase the amount of liveweight sold with the same number of adult equivalents compared with reducing the number of adult equivalents to maintain the same level of liveweight sold and claiming a carbon credit for lower farm emissions. These gains achieved at two case study properties which had different rainfall, country types, and property sizes suggest similar improvements can be made on-farm across the Mitchell Grass Downs bioregion of northern Australia.

How important is the coast? A survey of coastal objectives in an Australian regional city

Defining goals and objectives is a critical component of adaptive management of natural resources because they provide the basis on which management strategies can be designed and evaluated. The aims of this study are: (i) to apply and test a collaborative method to elicit goals and objectives for inshore fisheries and biodiversity in the coastal zone of a regional city in Australia; (ii) to understand the relative importance of management objectives for different community members and stakeholders; and (iii) to understand how diverse perceptions about the importance of management objectives can be used to support multiple-use management in Australia’s iconic Great Barrier Reef. Management goals and objectives were elicited and weighted using the following steps: (i) literature review of management objectives, (ii) development of a hierarchy tree of objectives, and (iii) ranking of management objectives using survey methods. The overarching goals identified by the community group were to: (1) protect and restore inshore environmental assets; (2) improve governance systems; and (3) improve regional (socio-economic) well-being. Interestingly, these goals differ slightly from the usual triple-bottom line objectives (environmental, social and economic) often found in the literature. The objectives were ranked using the Analytical Hierarchical Process, where a total of 141 respondents from industry, government agencies, and community from across Queensland State undertook the survey. The environment goal received the highest scores, followed by governance and lastly well-being. The approach to elicit and rank goals and objectives developed in this study can be used to effectively support coastal resource management by providing opportunities for local communities to participate in the setting of regional objectives.

How important is the coast? A survey of coastal objectives in an Australian regional city

Defining goals and objectives is a critical component of adaptive management of natural resources because they provide the basis on which management strategies can be designed and evaluated. The aims of this study are: (i) to apply and test a collaborative method to elicit goals and objectives for inshore fisheries and biodiversity in the coastal zone of a regional city in Australia; (ii) to understand the relative importance of management objectives for different community members and stakeholders; and (iii) to understand how diverse perceptions about the importance of management objectives can be used to support multiple-use management in Australia’s iconic Great Barrier Reef. Management goals and objectives were elicited and weighted using the following steps: (i) literature review of management objectives, (ii) development of a hierarchy tree of objectives, and (iii) ranking of management objectives using survey methods. The overarching goals identified by the community group were to: (1) protect and restore inshore environmental assets; (2) improve governance systems; and (3) improve regional (socio-economic) well-being. Interestingly, these goals differ slightly from the usual triple-bottom line objectives (environmental, social and economic) often found in the literature. The objectives were ranked using the Analytical Hierarchical Process, where a total of 141 respondents from industry, government agencies, and community from across Queensland State undertook the survey. The environment goal received the highest scores, followed by governance and lastly well-being. The approach to elicit and rank goals and objectives developed in this study can be used to effectively support coastal resource management by providing opportunities for local communities to participate in the setting of regional objectives.

Comparing fixed and flexible stocking as adaptations to inter-annual rainfall variability in the extensive beef industry of northern Australia

Many beef producers within the extensive cattle industry of northern Australia attempt to maintain a constant herd size from year-to-year (fixed stocking), whereas others adjust stock numbers to varying degrees annually in response to changes in forage supply. The effects of these strategies on pasture condition and cattle productivity cannot easily be assessed by grazing trials. Simulation studies, which include feedbacks of changes to pasture condition on cattle liveweight gain, can extend the results of grazing trials both spatially and temporally. They can compare a large number of strategies, over long periods of time, for a range of climate periods, at locations which differ markedly in climate. This simulation study compared the pasture condition and cattle productivity achieved by fixed stocking at the long-term carrying capacity with that of 55 flexible stocking strategies at 28 locations across Queensland and the Northern Territory. Flexible stocking strategies differed markedly in the degree they increased or decreased cattle stocking rates after good and poor pasture growing seasons, respectively. The 28 locations covered the full range in average annual rainfall and inter-annual rainfall variability experienced across northern Australia. Constrained flexibility, which limited increases in stocking rates after good growing seasons to 10% but decreased them by up to 20% after poor growing seasons, provides sustainable productivity gains for cattle producers in northern Australia. This strategy can improve pasture condition and increase cattle productivity relative to fixed stocking at the long-term carrying capacity, and its capacity to do this was greatest in the semiarid rangeland regions that contain the majority of beef cattle in northern Australia. More flexible stocking strategies, which also increased stocking rates after good growing seasons by only half as much as they decreased them after poor growing seasons, were equally sustainable and more productive than constrained flexibility, but are often impractical at property and industry scales. Strategies with the highest limits (e.g. 70%) for both annual increases and decreases in stocking rates could achieve higher cattle productivity, but this was at the expense of pasture condition and was not sustainable. Constrained flexible stocking, with a 10% limit for increases and a 20% limit for decreases in stocking rates annually, is a risk-averse adaptation to high and unpredictable rainfall variability for the extensive beef industry of northern Australia. © Australian Rangeland Society 2016.

Statistical forecasting of the Australian macadamia crop

Three types of forecasts of the total Australian production of macadamia nuts (t nut-in-shell) have been produced early each year since 2001. The first is a long-term forecast, based on the expected production from the tree census data held by the Australian Macadamia Society, suitably scaled up for missing data and assumed new plantings each year. These long-term forecasts range out to 10 years in the future, and form a basis for industry and market planning. Secondly, a statistical adjustment (termed the climate-adjusted forecast) is made annually for the coming crop. As the name suggests, climatic influences are the dominant factors in this adjustment process, however, other terms such as bienniality of bearing, prices and orchard aging are also incorporated. Thirdly, industry personnel are surveyed early each year, with their estimates integrated into a growers and pest-scouts forecast. Initially conducted on a 'whole-country' basis, these models are now constructed separately for the six main production regions of Australia, with these being combined for national totals. Ensembles or suites of step-forward regression models using biologically-relevant variables have been the major statistical method adopted, however, developing methodologies such as nearest-neighbour techniques, general additive models and random forests are continually being evaluated in parallel. The overall error rates average 14% for the climate forecasts, and 12% for the growers' forecasts. These compare with 7.8% for USDA almond forecasts (based on extensive early-crop sampling) and 6.8% for coconut forecasts in Sri Lanka. However, our somewhatdisappointing results were mainly due to a series of poor crops attributed to human reasons, which have now been factored into the models. Notably, the 2012 and 2013 forecasts averaged 7.8 and 4.9% errors, respectively. Future models should also show continuing improvement, as more data-years become available.