Ground Cover Supplement : GC Supplement - Agronomy
7 AGRONOMY GROUND COVER WHEAT Cereal agronomy in the HRZ In the high-rainfall zone of south-east Australia models are showing growers the potential yield impact of their management decisions THE LONGER GROWING season generally experienced in the high-rainfall zones (HRZ) of south-east Australia provides greater management options for growers. At the same time more options can complicate the decision-making process. Researchers working in the HRZ are using modelling to help demonstrate to growers the interaction between their management practices and crop performance, especially in relation to variety maturity. To ensure the models provide regionally relevant information, the research team, led by Penny Riffkin -- of Angela Clough, Rob Harris and Garry O'Leary in Victoria, Geoff Dean and Tina Acuna in Tasmania, Trent Potter in South Australia -- held workshops with growers in each state in 2007. The team is working closely with the grower group Southern Farming Systems. At these workshops, topics that growers wanted to explore with the model were identified; these included time of sowing, nitrogen fertiliser, stubble management, grazing, irrigation and impacts of climate change. In 2008 trials to assess the impact of different practices relating to the identified topics were established at seven locations across the three states. For example, in Tasmania trials looked at time of sowing by nitrogen treatments, and grazing by nitrogen treatments. Detailed soil and water data was gathered at each trial site, as was climate data, above ground biomass at key growth stages and the dates these occurred, and details of all management applied. The APSIM model has accurately simulated yields at the Victorian sites (see Figure 2, left). The outputs have clearly shown the impact of different sowing dates on flowering date and yield. Similarly, the impact of nitrogen stress due to different soil nitrogen and the relationship with variety maturity has been shown. Regionally relevant data has been produced and presented to growers. In the final year of the trial the influence of stubble load on yield is being assessed, as well as the impacts of climate change. □ GRDC Research Code DAV00083 More information: Penny Riffkin, 03 5573 0926, firstname.lastname@example.org FIGURE 2 Optimum crop yields for three maturity groups of wheat -- early (SilverstarA), mid (CharaA) and late (MackellarA) -- sown on the 1st and 15th of each month. Simulations were run using 120 years of climate data. Climatic risks have not been accounted for and need to be considered alongside Figure 1. 0 1000 2000 3000 4000 5000 6000 0 1000 2000 3000 4000 5000 6000 0 1000 2000 3000 4000 5000 6000 Grain yield (kg/ha) Note: The model assumes optimal management and does not account for yield loss as a result of nutrient deficiencies or toxicities, weeds, disease or pest infestations. Yields are determined in the absence of frost (less than 0°C) and heat (greater than 30°C) stress. Model error based on validations from eight sites across south-west Victoria and south-east South Australia is estimated to be 640kg of grain per hectare. Early maturity 1 Jan 15 Jan 15 Feb 15 Mar 15 Apr 15 May 15 June 15 July 15 Aug 15 Sep 1 Sep 1 Aug 1 July 1 June 1 May 1 Apr 1 Mar 1 Feb 1 Dec 1 Nov 1 Oct 15 Dec 15 Nov 15 Oct Yields 90 to 100% of maximum Yields less than 80% of maximum Yields 80 to 89% of maximum Sowing date Mid maturity Late maturity 1 Jan 15 Jan 15 Feb 15 Mar 15 Apr 15 May 15 June 15 July 15 Aug 15 Sep 1 Sep 1 Aug 1 July 1 June 1 May 1 Apr 1 Mar 1 Feb 1 Dec 1 Nov 1 Oct 15 Dec 15 Nov 15 Oct 1 Jan 15 Jan 15 Feb 15 Mar 15 Apr 15 May 15 June 15 July 15 Aug 15 Sep 1 Sep 1 Aug 1 July 1 June 1 May 1 Apr 1 Mar 1 Feb 1 Dec 1 Nov 1 Oct 15 Dec 15 Nov 15 Oct The interaction between management in relation to variety maturity is being explored in the high-rainfall zone of south-east Australia.
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