Integrating pest population models with biophysical crop models to better represent the farming system

• Published in: Environmental modelling & software : with environment data news Vol. 72; pp. 418 - 425
• Main Authors: Whish, Jeremy P.M., Herrmann, Neville I., White, Neil A., Moore, Andrew D., Kriticos, Darren J.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.10.2015
• Subjects: Agro-ecological modelling; APSIM; Atmospheric models; Biotic constraints; Computer simulation; Crops; DYMEX; Farming; Farming systems modelling; Linking; Mathematical models; Modelling; Pests; Puccinia striiformis; Rusting; Stripe rust; Puccinia striiformis; APSIM; Agro-ecological modelling; Farming systems modelling; Modelling; Stripe rust; Biotic constraints; DYMEX
• ISSN: 1364-8152; 1873-6726
• DOI: 10.1016/j.envsoft.2014.10.010

Farming systems frameworks such as the Agricultural Production Systems simulator (APSIM) represent fluxes through the soil, plant and atmosphere of the system well, but do not generally consider the biotic constraints that function within the system. We designed a method that allowed population models built in DYMEX to interact with APSIM. The simulator engine component of the DYMEX population-modelling platform was wrapped within an APSIM module allowing it to get and set variable values in other APSIM models running in the simulation. A rust model developed in DYMEX is used to demonstrate how the developing rust population reduces the crop's green leaf area. The success of the linking process is seen in the interaction of the two models and how changes in rust population on the crop's leaves feedback to the APSIM crop modifying the growth and development of the crop's leaf area. This linking of population models to simulate pest populations and biophysical models to simulate crop growth and development increases the complexity of the simulation, but provides a tool to investigate biotic constraints within farming systems and further moves APSIM towards being an agro-ecological framework. •Modelling biotic constraints within farming system models.•The linking of multi-cohort population models and farming systems crop models.•Managing the complexity of combining population models and agro-ecological models.