The APEC Climate Center (APCC) research team, led by Dr. Shin Yonghee, announced the successful refinement of existing agricultural impact indices into five core indices that maximize field usability. This was achieved through precise diagnoses and consultations with experts from the Korea National University of Agriculture and Fisheries, the National Institute of Animal Science, and the National Institute of Horticultural and Herbal Science. The previously utilized eight indices faced limitations in reflecting actual field demands, using vague terms, and having significant data compatibility challenges, such as requiring hourly meteorological data for daily scenarios. This recent achievement represents a paradigm shift from a simple listing of meteorological statistics to precise prediction models that effectively reflect the biological characteristics of crops.

The specific improvements of the five core indices are as follows:
▶ Scientific Standardization and Integration of Crop Growth Indices: Overlapping concepts were unified into 'Growing Degree Days (GDD)' with standardized units.  Vague terms were abolished and replaced with 'Growing Season Length (GSL)' which has clear, quantitative calculation criteria to precisely predict changes in cultivation periods.
▶ Efficiency in Facility Agriculture Energy Management and Advancement of Fruit Tree Models: 'Facility Heating Degree Days (HDD)' was introduced to accurately calculate heating demand and support farmers in reducing energy costs. For fruit trees, a phenological model using daily data was applied, introducing 'Chill Days' and 'Anti-Chill Days' to precisely predict dormancy break and flowering times.
▶ Strengthening Response Capabilities in Livestock Sector: The simple index was upgraded to the 'Livestock Heat Index (THI)'. This enhanced index provides precise 5-level risk warnings (Normal to Fatal) by livestock type (cattle, pigs, chickens) to support preemptive measures against mass mortality during summer heatwaves.

These enhanced indices go beyond mere statistics, providing practical decision-support information. They will enable data-driven precision agriculture planning, significant energy cost reductions for facility farmers, and the minimization of agricultural damage from meteorological disasters, thereby stabilizing farm household income and strengthening national food security.

(made by AI)