• Parasites, Hosts and Diseases
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• v.54 no.5
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• pp.555-563
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• 2016

For several years, we have conducted a series of studies on the patterns of ancient parasitism prevailing in the soil of rural and urban areas of past Kingdom of Korea. Actually, during our survey of paleoparasitology in archaeological sites of Korean peninsula, numerous ancient parasite eggs were discovered in the samples from the city districts of Hansung (Joseon) and Buyeo (Baikje), the palace moat at Gyeongju (Silla), shell-midden site at Bonghwang-dong (Silla to Joseon), and the reservoir found in Hwawangsansung fortress (Silla). By the paleoparasitological studies, with respect to parasitism in the high-density populations of ancient towns and cities, we have managed to catch glimpses of the patterns prevalent therein: a serious parasitic contamination of the soil in ancient urban areas, but not in rural areas of the past. Our historical research also proposed the plausible mechanism of parasite infection very serious indeed among urban populations in Korean history. Although city dwelling doubtless has accrued significant benefits for people and populations with agriculture, it can be equally supposed that living in such highly populated areas might have facilitated the spread of parasite infection.

• Korean Journal of Environmental Biology
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• v.39 no.4
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• pp.526-535
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• 2021

Spatial sampling design plays an important role in GIS-based modeling studies because it increases modeling efficiency while reducing the cost of sampling. In the field of agricultural systems, research demand for high-resolution spatial databased modeling to predict and evaluate climate change impacts is growing rapidly. Accordingly, the need and importance of spatial sampling design are increasing. The purpose of this study was to design spatial sampling of paddy fields (11,386 grids with 1 km spatial resolution) in Korea for use in agricultural spatial modeling. A stratified random sampling design was developed and applied in 2030s, 2050s, and 2080s under two RCP scenarios of 4.5 and 8.5. Twenty-five weather and four soil characteristics were used as stratification variables. Stratification and sample allocation were optimized to ensure minimum sample size under given precision constraints for 16 target variables such as crop yield, greenhouse gas emission, and pest distribution. Precision and accuracy of the sampling were evaluated through sampling simulations based on coefficient of variation (CV) and relative bias, respectively. As a result, the paddy field could be optimized in the range of 5 to 21 strata and 46 to 69 samples. Evaluation results showed that target variables were within precision constraints (CV<0.05 except for crop yield) with low bias values (below 3%). These results can contribute to reducing sampling cost and computation time while having high predictive power. It is expected to be widely used as a representative sample grid in various agriculture spatial modeling studies.