ScholarWorks@경상국립대학교

초록

Soil test data, derived from laboratory analyses of chemical properties such as organic matter (OM), provide a critical foundation for soil management and guiding fertilizer use. In Korea, the Rural Development Administration (RDA) has maintained a national soil test database ("HeukToram") and associated soil attribute maps for commercial agricultural fields for several decades. However, the reliability of these data is often compromised by manual entry errors and reporting inconsistencies. This study evaluates the impact of error detection and correction on OM assessment at both sub-national and national scales. To address this, soil test records for paddy rice collected from the official dataset between 2022 and 2025 were analyzed and modeled (n = 913,984). The dataset was processed through systematic screening for apparent errors. At the national level, the most frequent issue was repeated soil test values across consecutive sampling dates (16.37% of records), followed by values outside agronomically valid ranges (0.81%), records with all variables missing (0.06%), and those with identical values across all variables (0.02%). Depending on the type and extent of these errors, mean OM estimates deviated by up to 0.153 g kg-1 between the datasets, potentially affecting field-level fertilizer recommendations. The results indicate that even relatively minor errors in large-scale soil test datasets can lead to substantial misestimations in OM stocks and nutrient management requirements, the magnitude of which often remains unknown. Continuous quality control is therefore essential to maintain reliable soil assessments and to guide effective crop and soil management.

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