Sugarcane smut after three years: a policy retrospective.

The detection of sugarcane smut disease (Ustilago scitaminea) in the Bundaberg-Childers region of eastern Australia in 2006 triggered a comprehensive and united response from BSES Limited, Queensland Government and CANEGROWERS. The response to sugarcane smut in the Bundaberg-Childers area was the first test for the Emergency Plant Pest Response Deed, an agreement between Australian governments and plant industries to facilitate a response to a plant pest incursion. As part of this response and the subsequent inquiry, economic models of the likely pattern of spread and cost of the smut epidemic were prepared. This paper reviews the predictions of those models in the light of the subsequent three years' experience. It examines reasons for divergence from the modelled outcomes, some of which were good approximations of actual experience.

Determining Optimum Irrigation Scheduling Techniques for Key Wildflower Crops

Determining Optimum Irrigation Scheduling Techniques for Key Wildflower Crops.

Nitrogen requirements of Burdekin soils to inform Reef Policy legislation

Reef Project 20: Nitrogen fertiliser requirements of representative soils of the Burdekin (BRIA and Delta), and peaty soils of the Wet Tropics to inform the ReefWise farming Nutrient Calculator.

Plant based monitoring for irrigation scheduling in vegetable horticulture: A case study in South Australian onions

Direct measurement of plant water status for irrigation scheduling may be more sensitive, and promote better horticultural crop quality, than indirect methods such as soil moisture monitoring. In our research project, we sought to identify instances where direct methods of plant-water status previously used in horticultural crops in Australia. We present the outcomes, suitability or obstacles for adoption by horticultural producers.

AQUAMAN: a web-based decision support system for irrigation scheduling in peanuts

Peanut (Arachis hypogaea L.) is an economically important legume crop in irrigated production areas of northern Australia. Although the potential pod yield of the crop in these areas is about 8 t ha(-1), most growers generally obtain around 5 t ha(-1), partly due to poor irrigation management. Better information and tools that are easy to use, accurate, and cost-effective are therefore needed to help local peanut growers improve irrigation management. This paper introduces a new web-based decision support system called AQUAMAN that was developed to assist Australian peanut growers schedule irrigations. It simulates the timing and depth of future irrigations by combining procedures from the food and agriculture organization (FAO) guidelines for irrigation scheduling (FAO-56) with those of the agricultural production systems simulator (APSIM) modeling framework. Here, we present a description of AQUAMAN and results of a series of activities (i.e., extension activities, case studies, and a survey) that were conducted to assess its level of acceptance among Australian peanut growers, obtain feedback for future improvements, and evaluate its performance. Application of the tool for scheduling irrigations of commercial peanut farms since its release in 2004-2005 has shown good acceptance by local peanuts growers and potential for significantly improving yield. Limited comparison with the farmer practice of matching the pan evaporation demand during rain-free periods in 2006-2007 and 2008-2009 suggested that AQUAMAN enabled irrigation water savings of up to 50% and the realization of enhanced water and irrigation use efficiencies.

Good science for improving policy: greenhouse gas emissions from agricultural manures

Australia’s and New Zealand’s major agricultural manure management emission sources are reported to be, in descending order of magnitude: (1) methane (CH4) from dairy farms in both countries; (2) CH4 from pig farms in Australia; and nitrous oxide (N2O) from (3) beef feedlots and (4) poultry sheds in Australia. We used literature to critically review these inventory estimates. Alarmingly for dairy farm CH4 (1), our review revealed assumptions and omissions that when addressed could dramatically increase this emission estimate. The estimate of CH4 from Australian pig farms (2) appears to be accurate, according to industry data and field measurements. The N2O emission estimates for beef feedlots (3) and poultry sheds (4) are based on northern hemisphere default factors whose appropriateness for Australia is questionable and unverified. Therefore, most of Australasia’s key livestock manure management greenhouse gas (GHG) emission profiles are either questionable or are unsubstantiated by region-specific research. Encouragingly, GHG from dairy shed manure are relatively easy to mitigate because they are a point source which can be managed by several ‘close-to-market’ abatement solutions. Reducing these manure emissions therefore constitutes an opportunity for meaningful action sooner compared with the more difficult-to-implement and long-term strategies that currently dominate agricultural GHG mitigation research. At an international level, our review highlights the critical need to carefully reassess GHG emission profiles, particularly if such assessments have not been made since the compilation of original inventories. Failure to act in this regard presents the very real risk of missing the ‘low hanging fruit’ in the rush towards a meaningful response to climate change