Climate change affects agricultural ecosystems in various ways. Not only crop productivity but also other components of agricultural ecosystems such as pest impact and the emission of greenhouse gasses. To model such complex interactions, especially in a crop-pest relationship, we developed an integrated sectoral model for agriculture. The PPI(Pest Pressure Index) which is simple indexing system that representing overall performance of pest species in geographic space was designed for integrating sectoral model. The DSSAT (Decision Support System for Agrotechnology Transfer), PPI, and DNDC (DeNitrification-DeComposition) models were coupled in the form of data interchanges especially in planting and harvesting dates. We assumed farmers always decide the best planting and harvesting date for maximizing crop productivity.
As a case study, we ran this integrated model for the Republic of Korea. Future rice yield and the PPI of the rice leaf folder, Cnaphalocrocis medinalis Guenée, in ROK were predicted in 1km spatial scale with RCP 8.5 climate change scenario. The results showed that the mean of maximum yield in ROK was predicted to be increased in RCP 8.5 scenario despite the increasing potential yield reduction by PPI. More studies on multiple pest systems in this method will be needed to build realistic model system. This results could be utilized for further studies of agricultural vulnerability and risk assessment of climate change.