Scholarlink

<p>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 (<i>n</i> = 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&#xa0;g kg<sup>−1</sup> 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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Data errors in soil tests compromise large-scale assessment of soil organic matter in Korea

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月17日

查看全文
  • Juhwan Lee

摘要

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.