Assessment of Climate Suitability for Italian Ryegrass Using Fuzzy Logic.

Assessment of climatic suitability for forage crop production would help design of cropping systems for improvement of ecosystem services and adaptation of climate change in a region. Simple approaches, e.g., the EcoCrop model, have been used to predict climatic suitability for forage crops. Although those simple models require relatively small number of input data, e.g., monthly climate data, they usually have relatively low reliability in prediction of climate suitability compared with the crop growth models for major grain crops, e.g., DSSAT. The objective of this study was to apply fuzzy logic to the existing simple model to improve assessment of climatic suitability for Italian ryegrass (IRG). To determine the degree of temperature and precipitation conditions favorable for the IRG, the EcoCrop model and its alternative model used t-norm  and t-conorm, respectively. Our results indicated that the alternative algorithm based on fuzzy logic and t-conorm had more reliable prediction of climatic suitability than the EcoCrop model did. When climatic suitability for Italian ryegrass was assessed in three regions that have different climate conditions including Korea, Ireland, and Belgium, t-conorm based model explained variability in crop yield more (51%) than the EcoCrop model did (9%). These results suggested that a simple model derived from fuzzy logic and altertive algorithm would be useful for identification of potential production areas for forage crops under a given climate condition, which merits further studies on different forages crops for which the state-of-art models are rarely developed.