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A stratified random sampling design for paddy fields: Optimized stratification and sample allocation for effective spatial modeling and mapping of the impact of climate changes on agricultural system in Korea (농지 공간격자 자료의 층화랜덤샘플링: 농업시스템 기후변화 영향 공간모델링을 위한 국내 농지 최적 층화 및 샘플 수 최적화 연구)

  • Minyoung Lee;Yongeun Kim;Jinsol Hong;Kijong Cho
    • 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.

A study of analytical method for Benzo[a]pyrene in edible oils (식용유지 중 벤조피렌 분석법 비교 연구)

  • Min-Jeong Kim;jun-Young Park;Min-Ju Kim;Eun-Young Jo;Mi-Young Park;Nan-Sook Han;Sook-Nam Hwang
    • Analytical Science and Technology
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    • v.36 no.6
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    • pp.291-299
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    • 2023
  • The benzo[a]pyrene in edible oils is extracted using methods such as Liquid-liquid, soxhlet and ultrasound-assisted extraction. However these extraction methods have significant drawbacks, such as long extraction time and large amount of solvent usage. To overcome these drawbacks, this study attempted to improve the current complex benzo[a]pyrene analysis method by applying the QuEChERS (Quick, Easy, Cheap, Effective, Rugged and Safe) method that can be analyzed in a simple and short time. The QuEChERS method applied in this study includes extraction of benzo[a]pyrene into n-hexane saturated acetonitrile and n-hexane. After extraction and distribution using magnesium sulfate and sodium chloride, benzo[a]pyrene is analyzed by liquid chromatography with fluorescence detector (LC/FLR). As a result of method validation of the new method, the limit of detection (LOD) and quantification (LOQ) were 0.02 ㎍/kg and 0.05 ㎍/kg, respectively. The calibration curves were constructed using five levels (0.1~10 ㎍/kg) and coefficient (R2) was above 0.99. Mean recovery ratio was ranged from 74.5 to 79.3 % with a relative standard deviation (RSD) between 0.52 to 1.58 %. The accuracy and precision were 72.6~79.4 % and 0.14~7.20 %, respectively. All results satisfied the criteria ranges requested in the Food Safety Evaluation Department guidelines (2016) and AOAC official method of analysis (2023). Therefore, the analysis method presented in this study was a relatively simple pretreatment method compared to the existing analysis method, which reduced the analysis time and solvent use to 92 % and 96 %, respectively.

Analytical Method for Determination of Laccaic Acids in Foods with HPLC-PDA and Monitoring (식품 중 락카인산 성분 분리정제를 통한 분석법 확립 및 실태조사)

  • Jae Wook Shin;Hyun Ju Lee;Eunjoo Lim;Jung Bok Kim
    • Journal of Food Hygiene and Safety
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    • v.38 no.5
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    • pp.390-401
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    • 2023
  • Major components of lac coloring include laccaic acids A, B, C, and E. The Korean Food Additive Code regulates the use of lac coloring and prohibits its use in ten types of food products including natural food products. Since no commercial standards are available for laccaic acids A, B, C, and E, a standard for lac pigment itself was used to separate laccaic acids from the lac pigment molecule. A standard for each laccaic acid was then obtained by fractionation. To obtain pure lac pigment for use in food by High performance Liquid Chromatography Photo Diode Array (PDA), a C8 column yielded the best resolution among various tested columns and mobile phases. A qualitative analytical method using High Performance Liquid Chromatography (HPLC) Tandem Mass(LC-MS/MS) was developed. The conditions for fast and precise sample preparation begin with extraction using methanol and 0.3% ammonium phosphate, followed by concentration. The degree of precision observed for the analyses of ham, tomato juice and Red pepper paste was 0.3-13.1% (Relative Standard Deviation (RSD%)), degree of accuracy was 90.3-122.2% with r2=0.999 or above, and recovery rate was 91.6-114.9%. The limit of detection was 0.01-0.15 ㎍/mL, and the limits of quantitation ranged from 0.02 to 0.47 ㎍/mL. Lac pigment was not detected in 117 food products in the 10 food categories for which the use of lac pigment is banned. Multiple laccaic acids were detected in 105 food products in 6 food categories that are allowed to use lac color. Lac pigment concentrations range from 0.08 to 16.67 ㎍/mL.

Studies on the Derivation of the Instantaneous Unit Hydrograph for Small Watersheds of Main River Systems in Korea (한국주요빙계의 소유역에 대한 순간단위권 유도에 관한 연구 (I))

  • 이순혁
    • Magazine of the Korean Society of Agricultural Engineers
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    • v.19 no.1
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    • pp.4296-4311
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    • 1977
  • This study was conducted to derive an Instantaneous Unit Hydrograph for the accurate and reliable unitgraph which can be used to the estimation and control of flood for the development of agricultural water resources and rational design of hydraulic structures. Eight small watersheds were selected as studying basins from Han, Geum, Nakdong, Yeongsan and Inchon River systems which may be considered as a main river systems in Korea. The area of small watersheds are within the range of 85 to 470$\textrm{km}^2$. It is to derive an accurate Instantaneous Unit Hydrograph under the condition of having a short duration of heavy rain and uniform rainfall intensity with the basic and reliable data of rainfall records, pluviographs, records of river stages and of the main river systems mentioned above. Investigation was carried out for the relations between measurable unitgraph and watershed characteristics such as watershed area, A, river length L, and centroid distance of the watershed area, Lca. Especially, this study laid emphasis on the derivation and application of Instantaneous Unit Hydrograph (IUH) by applying Nash's conceptual model and by using an electronic computer. I U H by Nash's conceptual model and I U H by flood routing which can be applied to the ungaged small watersheds were derived and compared with each other to the observed unitgraph. 1 U H for each small watersheds can be solved by using an electronic computer. The results summarized for these studies are as follows; 1. Distribution of uniform rainfall intensity appears in the analysis for the temporal rainfall pattern of selected heavy rainfall event. 2. Mean value of recession constants, Kl, is 0.931 in all watersheds observed. 3. Time to peak discharge, Tp, occurs at the position of 0.02 Tb, base length of hlrdrograph with an indication of lower value than that in larger watersheds. 4. Peak discharge, Qp, in relation to the watershed area, A, and effective rainfall, R, is found to be {{{{ { Q}_{ p} = { 0.895} over { { A}^{0.145 } } }}}} AR having high significance of correlation coefficient, 0.927, between peak discharge, Qp, and effective rainfall, R. Design chart for the peak discharge (refer to Fig. 15) with watershed area and effective rainfall was established by the author. 5. The mean slopes of main streams within the range of 1.46 meters per kilometer to 13.6 meter per kilometer. These indicate higher slopes in the small watersheds than those in larger watersheds. Lengths of main streams are within the range of 9.4 kilometer to 41.75 kilometer, which can be regarded as a short distance. It is remarkable thing that the time of flood concentration was more rapid in the small watersheds than that in the other larger watersheds. 6. Length of main stream, L, in relation to the watershed area, A, is found to be L=2.044A0.48 having a high significance of correlation coefficient, 0.968. 7. Watershed lag, Lg, in hrs in relation to the watershed area, A, and length of main stream, L, was derived as Lg=3.228 A0.904 L-1.293 with a high significance. On the other hand, It was found that watershed lag, Lg, could also be expressed as {{{{Lg=0.247 { ( { LLca} over { SQRT { S} } )}^{ 0.604} }}}} in connection with the product of main stream length and the centroid length of the basin of the watershed area, LLca which could be expressed as a measure of the shape and the size of the watershed with the slopes except watershed area, A. But the latter showed a lower correlation than that of the former in the significance test. Therefore, it can be concluded that watershed lag, Lg, is more closely related with the such watersheds characteristics as watershed area and length of main stream in the small watersheds. Empirical formula for the peak discharge per unit area, qp, ㎥/sec/$\textrm{km}^2$, was derived as qp=10-0.389-0.0424Lg with a high significance, r=0.91. This indicates that the peak discharge per unit area of the unitgraph is in inverse proportion to the watershed lag time. 8. The base length of the unitgraph, Tb, in connection with the watershed lag, Lg, was extra.essed as {{{{ { T}_{ b} =1.14+0.564( { Lg} over {24 } )}}}} which has defined with a high significance. 9. For the derivation of IUH by applying linear conceptual model, the storage constant, K, with the length of main stream, L, and slopes, S, was adopted as {{{{K=0.1197( {L } over { SQRT {S } } )}}}} with a highly significant correlation coefficient, 0.90. Gamma function argument, N, derived with such watershed characteristics as watershed area, A, river length, L, centroid distance of the basin of the watershed area, Lca, and slopes, S, was found to be N=49.2 A1.481L-2.202 Lca-1.297 S-0.112 with a high significance having the F value, 4.83, through analysis of variance. 10. According to the linear conceptual model, Formular established in relation to the time distribution, Peak discharge and time to peak discharge for instantaneous Unit Hydrograph when unit effective rainfall of unitgraph and dimension of watershed area are applied as 10mm, and $\textrm{km}^2$ respectively are as follows; Time distribution of IUH {{{{u(0, t)= { 2.78A} over {K GAMMA (N) } { e}^{-t/k } { (t.K)}^{N-1 } }}}} (㎥/sec) Peak discharge of IUH {{{{ {u(0, t) }_{max } = { 2.78A} over {K GAMMA (N) } { e}^{-(N-1) } { (N-1)}^{N-1 } }}}} (㎥/sec) Time to peak discharge of IUH tp=(N-1)K (hrs) 11. Through mathematical analysis in the recession curve of Hydrograph, It was confirmed that empirical formula of Gamma function argument, N, had connection with recession constant, Kl, peak discharge, QP, and time to peak discharge, tp, as {{{{{ K'} over { { t}_{ p} } = { 1} over {N-1 } - { ln { t} over { { t}_{p } } } over {ln { Q} over { { Q}_{p } } } }}}} where {{{{K'= { 1} over { { lnK}_{1 } } }}}} 12. Linking the two, empirical formulars for storage constant, K, and Gamma function argument, N, into closer relations with each other, derivation of unit hydrograph for the ungaged small watersheds can be established by having formulars for the time distribution and peak discharge of IUH as follows. Time distribution of IUH u(0, t)=23.2 A L-1S1/2 F(N, K, t) (㎥/sec) where {{{{F(N, K, t)= { { e}^{-t/k } { (t/K)}^{N-1 } } over { GAMMA (N) } }}}} Peak discharge of IUH) u(0, t)max=23.2 A L-1S1/2 F(N) (㎥/sec) where {{{{F(N)= { { e}^{-(N-1) } { (N-1)}^{N-1 } } over { GAMMA (N) } }}}} 13. The base length of the Time-Area Diagram for the IUH was given by {{{{C=0.778 { ( { LLca} over { SQRT { S} } )}^{0.423 } }}}} with correlation coefficient, 0.85, which has an indication of the relations to the length of main stream, L, centroid distance of the basin of the watershed area, Lca, and slopes, S. 14. Relative errors in the peak discharge of the IUH by using linear conceptual model and IUH by routing showed to be 2.5 and 16.9 percent respectively to the peak of observed unitgraph. Therefore, it confirmed that the accuracy of IUH using linear conceptual model was approaching more closely to the observed unitgraph than that of the flood routing in the small watersheds.

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