Goo-Bok Jung

This study evaluated the decadal trends and spatial distribution of four irrigation suitability indices—Electrical Conductivity (EC), Sodium Adsorption Ratio (SAR), Magnesium Hazard (MH), and Kelley’s Ratio (KR)—using agricultural groundwater data collected from 157 monitoring sites across Korea between 2015 and 2024. Internationally recognized classification criteria were applied, long-term trends were analyzed using the Mann–Kendall test and Sen’s slope estimator, and spatial distributions for 2015, 2020, and 2024 were visualized using Inverse Distance Weighting (IDW). The results showed that EC and SAR remained at generally low absolute levels but exhibited statistically significant increasing trends with Sen’s slopes of +0.0038 and +0.0053/year, respectively, indicating the necessity of long-term salinization management. KR remained largely stable throughout the study period. In contrast, MH displayed a distinct pattern, with unsuitable levels concentrated in Jeju Island—approximately 15% of monitoring sites were classified as unsuitable for irrigation. This was interpreted as the combined effect of the basaltic aquifer’s geological and hydrological characteristics, seawater intrusion, and the relatively high mobility of Mg compared with Ca. This study uniquely integrates temporal trend tests with spatial mapping at a national scale and offers a mechanistic interpretation of MH vulnerability in Jeju’s volcanic aquifers. These findings emphasize the need for tailored regional management centered on groundwater abstraction control and continuous monitoring to ensure the sustainable use of agricultural groundwater.

Pearson correlation matrix among CCME-WQI, key water quality parameters, farmland ratio, and cumulative precipitation across five provinces in Korea.

The objective of this study was to predict future phenological changes of Hydrochara affinis and Sternolophus rufipes in agro-ecosystem under climate change scenarios. Environmental shifts driven by climate change are affecting the phenology of species inhabiting agricultural ecosystems. A quantitative analysis of phenological change is necessary because these phenological changes can impact agricultural productivity. In this study, we monitored the adult flight phenology of Hydrochara affinis and Sternolophus rufipes, which were selected as climate change indicator species of agricultural ecosystem in South Korea, and simulated future phenological changes using SSP scenarios. The results indicate that the first flight of Hydrochara affinis and Sternolophus rufipes are expected to occur earlier in the future compared to past by 4-49 days and 4-50 days, respectively, under the SSP5-8.5 scenario. Phenological shifts were found to be more pronounced in SSP5-8.5 compared to SSP2-4.5. The analysis showed that, compared to the results of the RCP 8.5 scenario from previous studies, the flight timing of Hydrochara affinis and Sternolophus rufipes has been further advanced under the SSP5-8.5 scenario. Long-term monitoring is essential to analyze the impacts of climate change on agricultural ecosystems. The prediction methods and results of phenological changes in agricultural ecosystems presented in this study are expected to contribute to the development of climate adaptation strategies in agriculture.

Scientific, evidence-based vulnerability assessments are vital for predicting and mitigating the climate change risks poses to national systems. This study proposes a climate change vulnerability assessment method by combining a process-based model and machine learning, using nutrient runoff vulnerability in agricultural land as an example. A training dataset was generated using the process-based model APEX (Agricultural Policy and Environmental eXtender) to train a nonlinear regression machine learning model. The trained nonlinear regression model accurately replicated the APEX results, with an R² value of 0.53 for nitrogen in paddy, and 0.72 for nitrogen and 0.71 for phosphorus in upland. This model was then applied to quantify nitrogen and phosphorus discharge at the si (city) and gun (county) levels. The results for each indicator were normalized, and weights were assigned using the Analytic Hierarchy Process (AHP). Vulnerability assessments were then conducted for mid-term (2041 ~ 2070) and long-term (2071 ~ 2100) future scenarios, and compared with the baseline period (1981 ~ 2010). The results showed that the average nutrient discharge vulnerability score for si and gun units was 31.4 in the baseline period, with a minimum of 0.6 and a maximum of 70.0. Under the SSP5-8.5 scenario, the average score increased to 43.6 in the mid-term future and 54.7 in the long-term future. However, when scenarios of compliance with standard fertilization rates for paddy and a 20% reduction in fertilization rates for upland were applied, the average score dropped to 33.3 in the mid-term future and 44.5 in the long-term future. These results highlight potential policy measures to mitigate the increasing vulnerability due to climate change in the agricultural sector and provide a rapid reference for policymakers when designing effective adaptation strategies.

Evaluation on Heavy Metal Contents in Agricultural Soils around Industrial Complexes in Korea-Heavy metal,Industrial complexes,Soil

Assessment of Human Bioavailability Quotient for the Heavy Metal in Paddy Soils Below Part of the Closed Metalliferous Mine-Bioavailability,Closed metalliferous mine,Heavy metal,Paddy soil,SBET

Assessment of the Heavy Metal Contamination in Paddy Soils Below Part of the Closed Metalliferous Mine-Closed metalliferous mine,Contamination assessment,Heavy metal,Paddy soil