• 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.

• Korean Journal of Environmental Agriculture
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• v.38 no.3
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• pp.173-184
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• 2019

BACKGROUND: This study was carried out to investigate pesticide residues from fifty streams in Korea. Water samples were collected at two times. Thee first sampling was performed from april to may, which was the season for start of pesticide application and the second sampling event was from august to september, which was a period for spraying pesticides multiple times. METHODS AND RESULTS: The 136 pesticide residues were analyzed by LC-MS/MS and GC/ECD. As a result, eleven of the pesticide residues were detected at the first sampling. Twenty eight of the pesticide residues were detected at the second sampling. Seven pesticides were frequently detected from more than 10 water samples. Ecological risk assessment (ERA) was carried out by using residual and toxicological data. Four scenarios were applied for the ERA. Scenario 1 and 2 were performed using LC50 values and mean and maximum concentrations. Scenarios 3 and 4 were conducted by NOEC values and mean and maximum concentrations. CONCLUSION: Frequently detected pesticide residues tended to coincide with the period of preventing pathogen and pest at paddy rice. As a result of ERA, five pesticides (butachlor, carbendazim, carbofuran, chlorantranilprole, and oxadiazon) were assessed to be risks at scenario 4. However, only oxadiazon was assessed to be a risk at scenario 3 for the first sampling. Oxadiazon was not assessed to be a risk at the second sampling. It seems to be temporary phenomenon at the first sampling, because usage of herbicides such as oxadiazon increased from April to march for preventing weeds at paddy fields. However, this study suggested that five pesticides which were assessed to be risks need to be monitored continuously for the residues.