산양삼은 산림청 특별관리 임산물로 지정되어 관리되고 있지만 체계적인 연구 나 표준 재배지침이 현장의 재배 현실과는 상이한 부분이 많은 실정이다. 이에 본 연구에서는 평창지역의 산양삼 재배지 환경조건에 따른 진세노사이드 함량이 어 떠한가를 알아보기 위하여 산양삼 시료를 각각 10본씩 채취하여 실험을 통해 확 인하였다. 산양삼 입지환경은 경사, 사면 방향, 해발고, 나무 종류, 흉고 직경과 수 고 등 모두 6가지를 조사하여 국립산림과학원의 분류기준을 참고하여 침엽과 활 엽으로 구분하였다. 산양삼 재배지의 토양산도를 포함한 9가지 토양이화학성 분석 에서는 전체적으로 침엽지역보다 활엽지역에서 높은 함량을 보였다. 통계분석 결 과에서는 A, B, C, 재배지에서 침엽과 활엽지역 간 통계적 유의성이 있는 것으로 나타났으며, D 재배지는 침엽과 활엽지역의 유의차가 거의 없는 것으로 나타났다. 진세노사이드 함유량 분석 결과에서는 Re, Rb1, Rg1의 순서로 높은 함량을 나타 내고 있어서 평창 산양삼의 주성분을 확인할 수 있었다. 연구 결과로 산양삼은 산 삼이 잘 생육할 수 있는 환경과 비슷한 곳을 재배지로 선정하여야 고품질의 제품 을 생산할 수 있다고 예상할 수 있다. 향후 본 연구 결과가 임업인 소득증대에 도 움을 줄 수 있는 자료로도 활용될 수 있기를 기대한다.

This experiment was conducted to comparison study on the productivity for certified varieties of import adaptability of silage corn in Pyeongchang area. Total eight varieties (Gangpyeongok, 31N27, 32P75, 32W86, P3156, P3394, DK 689 및 DK 729) were evaluated. The experimental design was 8 treatment of randomized block with three replications. Corn varieties were cultivated in experimental field of Pyeongchang campus, SNU from 1 May to 2 September, 2015 and plot size was 15㎡. Plant height of 32W86 and ear height of Gwanpyeongok was the highest (p<0.05). Tasseling and silking date was 27 July-3 August and silking occurred after 1-3 day of tasseling. Average day to silking was 92 days and that of 31N27 variety was short (p<0.05). The varieties of DeKalbo Company (DK 689 and DK 729) required more times to silking. Average GDD (growth degree day) of eight varieties was 1,023℃ and P3352 was the lowest GDD. In the trials of resistance evaluation, P3394 was strong in disease, P3156 was the highest in insect. All varieties did not show the lodging and variety of DK 729 showed the highest stay green score (p<0.05). Average dry matter content was 30.77%, it showed higher trend in DM. 32W86 was the highest DM content among the varieties, but there was no significant difference among varieties (p>0.05). The weight per ear was the highest in 32W86 and the lowest in Gwanpyeongok. The ration of ear to whole plant was higher in 32W86 and P3394, but it was not found the significant difference (p>0.05). Average yield of fresh and DM was 59,017 and 13,476 kg/ha, respectively. DK 689 showed higher DM and TDN yield than others, but there was not significant difference (p>0.05). According to results, the difference of productivity was not found among certified variety of import adaptability of silage corn. The varieties Gwanpyeongok, 32W86 and 32P75 would be recommendable in Pyeongchang area for stable cultivation.

In the Sosan reclamation land constructed by Hyundai Co,1984, the basic soil test and electric resistivity survey are carried out for the soft grounds around Tee and Kkot islands composed of tuffbreccia, and granite, respectively. The soil of soft grounds are classified as sandy clay(SC) and sandy silt(SM) in the Tee island, and as sandy silt(SM) in Kkot island according to the unified standard classification system. The vertical distribution of the electric resistivity is identified 4 layes with the value 30∼224 Ωm, 3∼59 Ωm, 0.15∼1.5 Ωm, 110∼1280 Ωm(Tee island:112∼122 Ωm, Kkot island:1260∼1283 Ωm), and the layers are interpreted as aeration layer, freshwater saturated layer, seaweater saturated layer, weathered layer, and basement rock respectively. The electric resistivity value decreases with increase of salinity, and pore water and clay mineral content.

In this study, a weighted ensemble method of numerical weather prediction by ensemble models is applied for PyeongChang area. The post-processing method takes into account combination and calibration of forecasts from different numerical models, assigning greater weight to ensemble models that exhibit the better performance. Three different numerical models, including European Center Medium-Range Weather Forecast, Ensemble Prediction System for Global, and Limited Area Ensemble Prediction System, were used to perform the post-processing method. We compared the model outputs from the weighed combination of ensembles with those from the Ensemble Model Output Statistics (EMOS) model for each raw ensemble model. The results showed that the weighted ensemble method can significantly improve the post-processing performance, compared to the raw ensemble method of the numerical models.

In this study, we analyzed the performance of calibrated probabilistic forecasts of surface temperature over Pyeongchang area in Gangwon province by using Bayeisan Model Averaging (BMA). BMA has been proposed as a statistical post-processing method and a way of correcting bias and underdispersion in ensemble forecasts. The BMA technique provides probabilistic forecast that take the form of a weighted average of Gaussian predictive probability density function centered on the bias-corrected forecast for continuous weather variables. The results of BMA to calibrate surface temperature forecast from 24-member Ensemble Prediction System for Global (EPSG) are obtained and compared with those of multiple regression. The forecast performances such as reliability and accuracy are evaluated by Rank Histogram (RH), Residual Quantile-Quantile (R-Q-Q) plot, Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and the Continuous Ranked Probability Score (CRPS). The results showed that BMA improves the calibration of the equal weighted ensemble and deterministic-style BMA forecasts performs better than that of the deterministic forecast using the single best member.