Scholarly record
A KPI-BASED ECONOMIC MODEL FOR CLIMATE RISK ASSESSMENT IN REGIONAL GRAIN PRODUCTION
Abstract
Background: Climate-related variability increasingly affects the stability and economic sustainability of agricultural production, particularly in grain-producing regions where weather conditions during key phenological stages strongly influence production outcomes. This study addresses the need for an applicable regional model that connects meteorological pressure with economic interpretation in the context of sustainable development. Methods: A key performance indicator-based economic model was developed using daily meteorological data for Dobrich, Bulgaria, for the period 2021–2025 [1, 2]. Seven indicators were selected to represent major climate-related pressures on regional grain production: Hot Days Frequency, Frost Days Frequency, Consecutive Dry Days, Extreme Precipitation Days, Temperature Variability Index, Critical Growth Period Water Deficit Indicator, and Harvest Period Rainfall Risk Indicator. The indicators were normalized and aggregated into a Composite Climate Risk Index using a weighted procedure, and annual risk levels were classified as low, moderate, or high. Results: The model identified clear interannual differences in the intensity and structure of climate-related risk. Among the analyzed years, 2024 showed the highest composite risk level and was classified as high risk, reflecting the combined influence of heat stress, prolonged dry periods, increased temperature variability, and strong water deficit during the critical growth period. The years 2022, 2023, and 2025 were classified as moderate risk, while 2021 had the lowest overall risk level. The findings suggest that regional grain production vulnerability is driven not by a single meteorological factor, but by the interaction of multiple risk dimensions. Conclusions: The proposed model demonstrates that publicly available meteorological data can be used to build a transparent and reproducible framework for regional climate risk assessment in grain production. Although it does not estimate observed farm-level financial losses, the model provides an indicator-based economic interpretation of production vulnerability and adaptive pressure. The approach has practical value for sustainable agricultural management and may support regional planning and climate adaptation strategies.
Publication details
References7
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