Parameterization of FAO’s AquaCrop Model by Integrating a Hydrological Model and Climate Indices

Wednesday, 17 December 2014
Colin Langhorn, Stefan Werner Kienzle, Rufa Doria, Hester Jiskoot and Howard Cheng, University of Lethbridge, Lethbridge, AB, Canada
One of the greatest global challenges is to meet growing food demand under rapidly changing climate conditions. Continued global population growth increases the pressure on the agriculture sector to produce enough food to feed the world. In 2013, the province of Alberta, Canada, set a record high for principal field crop production of 34.5 million tonnes (Matejovsky, 2014). AquaCrop, a crop yield and water productivity model developed by the Land and Water Division of the Food and Agriculture Organization of the United Nations (FAO), attempts to balance the accuracy, simplicity and robustness of crop modelling (Steduto et al., 2009). The model is focused on the three components of the soil-plant-atmosphere continuum. AquaCrop is applied in this study for simulating hard red spring wheat and durum wheat yields, and simulated yields are verified against observed yields available from a crop insurer. One of the challenges of crop yield modelling is the selection of a realistic seeding date, which can vary by four to five weeks (end of March to end of April). In order to enable realistic simulation for the historical period 1950-2010 as well the future period 2041-2070, AquaCrop is coupled with the ACRU agro-hydrological modelling system to determine the soil moisture conditions after the spring snow melt, and with a WMO climate index which determines the climatological beginning of the growing season. Therefore, the selection of a realistic seeding data for individual years can be dynamically optimized, based on the combination of the beginning of the climatological growing season and soil moisture status. The results of the coupling of ACRU and calculated climate indices with AquaCrop will be presented to show how improvements of parameterization of the AquaCrop model can be used to simulate wheat yields in Southern Alberta under changing climate conditions.