Simulating Eyespot Disease Development and Yield Loss Using APSIM for UK Wheat
2015
Al-Azri, M. | Leibovici, D. | Karunaratne, A. | Ray, R.V.
A Global crop production is affected by seasonal and climatic variations in temperature, rainfall patterns or intensity and the occurrence of abiotic and biotic stresses. Climate change can alter pest and pathogen populations as well as pathogen complexes that pose an enormous risk to crop yields and future food security. Eyespot disease caused by Oculimacula yallundae and O. acuformis is associated with yield losses in UK wheat estimated in 1998 at £24 million. Crop simulation models have been validated as an important tool for the development of more resilient agricultural systems and improved decision making for growers. The Agricultural Production Systems Simulator (APSIM) is a software tool that enables sub-models to be incorporated for simulation of production in diverse agricultural systems. APSIM-wheat simulates crop growth and development, soil and management options. Modification of APSIM to incorporate epidemiological disease model for crop growth and yield under different disease intensities has not yet been undertaken in UK or elsewhere. Thus, the objective of this work was to develop epidemiological model for eyespot disease and incorporate it within APSIM for crop simulation under a range of disease and environmental conditions. Historical climatic data combined with 8 years of observed disease (2004-2012) data on incidence and severity of eyespot in UK field trials was used to develop epidemiological model, combining infection and severity, for the prediction of disease development in relation to crop growth stages. Crop growth characteristics, biomass and yield were measured separately and employed for eyespot yield loss or biomass reduction model in wheat based on disease severity. Current work is focused on modifying APSIM to simulate crop loss through the incorporation of the epidemiological disease and yield reduction components and further validation to confirm that empirical data were accurately simulated.
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