동해안지대 도작의 냉조풍피해와 피해경감대책 (Cultural Practices for Reducing Cold Wind Damage of Rice Plant in Eastern Coastal Area of Korea)
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- 한국작물학회지
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- 제36권5호
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- pp.407-428
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- 1991
우리나라 동해안지대는 태백산맥이 동서로 뻗쳐있고 해안을 끼고있어 기후의 변화가 다양한 동시에 풍해를 입기 쉬운 환경에 놓여있다. 이지대에 풍해를 일으키는 바람의 종류는 태백산맥을 넘어오면서 휀(Fohn) 현상에 의해 상승기류된 고온건조한 편서풍에 의해 백수, 경업의 절상, 찰과상, 탈수해, 변색립, 탈립, 도복 등의 수분장해형풍해와 한냉다습한 오호츠크기단이 발달하면 냉조풍이 심하여 하계 저온현상이 일어나서 생육지연, 지경 및 영화의 퇴화, 등숙장해 등이 발생되어 동해안지대를 중심으로 전국에서 84,532M/T의 수량감소를 가져오는 큰 문제지역으로 대두되어 있다. 본논문은 우리나라 동해안지대의 냉조풍피해상습지 6,160ha에 대한 풍해경감대책을 수립코자 1982년부터 1989년까지 8개년간 경북 영덕, 울진지방에서 경북농촌진흥원과 영남작물시험장 영덕출장소에서 실시된 품종선발, 재배시기, 시비법개선, 농토배양, 방풍강설치 등의 시험성적들을 검토한 결과 몇가지 결과를 얻었기에 금후 이지대의 풍해경감대책 자료로 제공코자 한다. 1. 동해안냉조지대의 1954년부터 1989년까지 36년동안 강풍발생빈도는 8월 10일부터 9월 l0일 사이에 높아 이지역의 수도안전출수한계기는 8월10일 이전이 안전하다고 생각된다. 2. 이지대에 주로 풍해를 유발시키는 바람의 종류는 태백산맥을 넘어오면서 휀(Fohn) 현상에 의한 고온건조한 편서풍과 해양에서 내륙으로 부는 한냉다습한 냉조풍이었으며 도작기간중 발생 빈도는 각각 25%였다 3. 태풍내습의 위험시기(8월 10일～9월 10일)를 회피할수 있도록 출수기를 달리하는 3～4품종을 필지별로 접배하거나 유사시에 피해를 분산토록 하는 것이 제 1차적인 대책이 될 것이다. 4 동해안지대에서 수량생산기간(40일간)의 최적등숙온도(22.2
The objective of this research is to test the feasibility of developing a statewide truck traffic forecasting methodology for Wisconsin by using Origin-Destination surveys, traffic counts, classification counts, and other data that are routinely collected by the Wisconsin Department of Transportation (WisDOT). Development of a feasible model will permit estimation of future truck traffic for every major link in the network. This will provide the basis for improved estimation of future pavement deterioration. Pavement damage rises exponentially as axle weight increases, and trucks are responsible for most of the traffic-induced damage to pavement. Consequently, forecasts of truck traffic are critical to pavement management systems. The pavement Management Decision Supporting System (PMDSS) prepared by WisDOT in May 1990 combines pavement inventory and performance data with a knowledge base consisting of rules for evaluation, problem identification and rehabilitation recommendation. Without a r.easonable truck traffic forecasting methodology, PMDSS is not able to project pavement performance trends in order to make assessment and recommendations in the future years. However, none of WisDOT's existing forecasting methodologies has been designed specifically for predicting truck movements on a statewide highway network. For this research, the Origin-Destination survey data avaiiable from WisDOT, including two stateline areas, one county, and five cities, are analyzed and the zone-to'||'&'||'not;zone truck trip tables are developed. The resulting Origin-Destination Trip Length Frequency (00 TLF) distributions by trip type are applied to the Gravity Model (GM) for comparison with comparable TLFs from the GM. The gravity model is calibrated to obtain friction factor curves for the three trip types, Internal-Internal (I-I), Internal-External (I-E), and External-External (E-E). ~oth "macro-scale" calibration and "micro-scale" calibration are performed. The comparison of the statewide GM TLF with the 00 TLF for the macro-scale calibration does not provide suitable results because the available 00 survey data do not represent an unbiased sample of statewide truck trips. For the "micro-scale" calibration, "partial" GM trip tables that correspond to the 00 survey trip tables are extracted from the full statewide GM trip table. These "partial" GM trip tables are then merged and a partial GM TLF is created. The GM friction factor curves are adjusted until the partial GM TLF matches the 00 TLF. Three friction factor curves, one for each trip type, resulting from the micro-scale calibration produce a reasonable GM truck trip model. A key methodological issue for GM. calibration involves the use of multiple friction factor curves versus a single friction factor curve for each trip type in order to estimate truck trips with reasonable accuracy. A single friction factor curve for each of the three trip types was found to reproduce the 00 TLFs from the calibration data base. Given the very limited trip generation data available for this research, additional refinement of the gravity model using multiple mction factor curves for each trip type was not warranted. In the traditional urban transportation planning studies, the zonal trip productions and attractions and region-wide OD TLFs are available. However, for this research, the information available for the development .of the GM model is limited to Ground Counts (GC) and a limited set ofOD TLFs. The GM is calibrated using the limited OD data, but the OD data are not adequate to obtain good estimates of truck trip productions and attractions .. Consequently, zonal productions and attractions are estimated using zonal population as a first approximation. Then, Selected Link based (SELINK) analyses are used to adjust the productions and attractions and possibly recalibrate the GM. The SELINK adjustment process involves identifying the origins and destinations of all truck trips that are assigned to a specified "selected link" as the result of a standard traffic assignment. A link adjustment factor is computed as the ratio of the actual volume for the link (ground count) to the total assigned volume. This link adjustment factor is then applied to all of the origin and destination zones of the trips using that "selected link". Selected link based analyses are conducted by using both 16 selected links and 32 selected links. The result of SELINK analysis by u~ing 32 selected links provides the least %RMSE in the screenline volume analysis. In addition, the stability of the GM truck estimating model is preserved by using 32 selected links with three SELINK adjustments, that is, the GM remains calibrated despite substantial changes in the input productions and attractions. The coverage of zones provided by 32 selected links is satisfactory. Increasing the number of repetitions beyond four is not reasonable because the stability of GM model in reproducing the OD TLF reaches its limits. The total volume of truck traffic captured by 32 selected links is 107% of total trip productions. But more importantly, ~ELINK adjustment factors for all of the zones can be computed. Evaluation of the travel demand model resulting from the SELINK adjustments is conducted by using screenline volume analysis, functional class and route specific volume analysis, area specific volume analysis, production and attraction analysis, and Vehicle Miles of Travel (VMT) analysis. Screenline volume analysis by using four screenlines with 28 check points are used for evaluation of the adequacy of the overall model. The total trucks crossing the screenlines are compared to the ground count totals. L V/GC ratios of 0.958 by using 32 selected links and 1.001 by using 16 selected links are obtained. The %RM:SE for the four screenlines is inversely proportional to the average ground count totals by screenline .. The magnitude of %RM:SE for the four screenlines resulting from the fourth and last GM run by using 32 and 16 selected links is 22% and 31 % respectively. These results are similar to the overall %RMSE achieved for the 32 and 16 selected links themselves of 19% and 33% respectively. This implies that the SELINICanalysis results are reasonable for all sections of the state.Functional class and route specific volume analysis is possible by using the available 154 classification count check points. The truck traffic crossing the Interstate highways (ISH) with 37 check points, the US highways (USH) with 50 check points, and the State highways (STH) with 67 check points is compared to the actual ground count totals. The magnitude of the overall link volume to ground count ratio by route does not provide any specific pattern of over or underestimate. However, the %R11SE for the ISH shows the least value while that for the STH shows the largest value. This pattern is consistent with the screenline analysis and the overall relationship between %RMSE and ground count volume groups. Area specific volume analysis provides another broad statewide measure of the performance of the overall model. The truck traffic in the North area with 26 check points, the West area with 36 check points, the East area with 29 check points, and the South area with 64 check points are compared to the actual ground count totals. The four areas show similar results. No specific patterns in the L V/GC ratio by area are found. In addition, the %RMSE is computed for each of the four areas. The %RMSEs for the North, West, East, and South areas are 92%, 49%, 27%, and 35% respectively, whereas, the average ground counts are 481, 1383, 1532, and 3154 respectively. As for the screenline and volume range analyses, the %RMSE is inversely related to average link volume. 'The SELINK adjustments of productions and attractions resulted in a very substantial reduction in the total in-state zonal productions and attractions. The initial in-state zonal trip generation model can now be revised with a new trip production's trip rate (total adjusted productions/total population) and a new trip attraction's trip rate. Revised zonal production and attraction adjustment factors can then be developed that only reflect the impact of the SELINK adjustments that cause mcreases or , decreases from the revised zonal estimate of productions and attractions. Analysis of the revised production adjustment factors is conducted by plotting the factors on the state map. The east area of the state including the counties of Brown, Outagamie, Shawano, Wmnebago, Fond du Lac, Marathon shows comparatively large values of the revised adjustment factors. Overall, both small and large values of the revised adjustment factors are scattered around Wisconsin. This suggests that more independent variables beyond just 226; population are needed for the development of the heavy truck trip generation model. More independent variables including zonal employment data (office employees and manufacturing employees) by industry type, zonal private trucks 226; owned and zonal income data which are not available currently should be considered. A plot of frequency distribution of the in-state zones as a function of the revised production and attraction adjustment factors shows the overall " adjustment resulting from the SELINK analysis process. Overall, the revised SELINK adjustments show that the productions for many zones are reduced by, a factor of 0.5 to 0.8 while the productions for ~ relatively few zones are increased by factors from 1.1 to 4 with most of the factors in the 3.0 range. No obvious explanation for the frequency distribution could be found. The revised SELINK adjustments overall appear to be reasonable. The heavy truck VMT analysis is conducted by comparing the 1990 heavy truck VMT that is forecasted by the GM truck forecasting model, 2.975 billions, with the WisDOT computed data. This gives an estimate that is 18.3% less than the WisDOT computation of 3.642 billions of VMT. The WisDOT estimates are based on the sampling the link volumes for USH, 8TH, and CTH. This implies potential error in sampling the average link volume. The WisDOT estimate of heavy truck VMT cannot be tabulated by the three trip types, I-I, I-E ('||'&'||'pound;-I), and E-E. In contrast, the GM forecasting model shows that the proportion ofE-E VMT out of total VMT is 21.24%. In addition, tabulation of heavy truck VMT by route functional class shows that the proportion of truck traffic traversing the freeways and expressways is 76.5%. Only 14.1% of total freeway truck traffic is I-I trips, while 80% of total collector truck traffic is I-I trips. This implies that freeways are traversed mainly by I-E and E-E truck traffic while collectors are used mainly by I-I truck traffic. Other tabulations such as average heavy truck speed by trip type, average travel distance by trip type and the VMT distribution by trip type, route functional class and travel speed are useful information for highway planners to understand the characteristics of statewide heavy truck trip patternS. Heavy truck volumes for the target year 2010 are forecasted by using the GM truck forecasting model. Four scenarios are used. Fo~ better forecasting, ground count- based segment adjustment factors are developed and applied. ISH 90 '||'&'||' 94 and USH 41 are used as example routes. The forecasting results by using the ground count-based segment adjustment factors are satisfactory for long range planning purposes, but additional ground counts would be useful for USH 41. Sensitivity analysis provides estimates of the impacts of the alternative growth rates including information about changes in the trip types using key routes. The network'||'&'||'not;based GMcan easily model scenarios with different rates of growth in rural versus . . urban areas, small versus large cities, and in-state zones versus external stations. cities, and in-state zones versus external stations.
합성배지(合成培地)에서 느타리 버섯균(菌)의 균사생육(菌絲生育)과 자실체형성(子實體形成)에 대한 영양적(營養的) 특성(特性)과 생리화학적(生理化學的) 제성질(諸性質)을 구명(究明)하고 볏짚과 톱밥 양(兩) 배지(培地)에서 느타리 버섯의 대량(大量) 생산(生産)을 위한 배양조건(培養條件)을 밝히고, 느타리 버섯 재배기간(栽培期間) 중 배지(培地)와 버섯중의 각종(各種) 성분(成分)의 추이(推移)를 알고자 실험을 수행하여 다음과 같은 결과를 얻었다. 1. 탄소원(炭素源) 중 mannitol과 서은 균사생육(菌絲生育)과 자실체(子實體) 형성(形成)이 빠르고 자실체(子實體)의 수량(收量)이 많았으나 lactose와 rhamnose는 균사(菌絲) 조차도 생육하지 못하였다. 또한 구연산, 호박산, ethyl alcohol 및 glycerol에서는 자실체(子實體) 형성(形成)이 매우 빈약(貧弱)하였고, 식초산, 개미산, 푸마르산, n-butyl alcohol, iso-butyl alcohol 및 n-propyl alcohol은 균사생육(菌絲生育)을 저해(阻害)하였다. 2. 질소원(窒素源)중 peptone은 균사생육(菌絲生育)과 자실체(子實體) 형성(形成)이 빠르고 자실체(子實體)의 수량(收量)이 많았으나 DL-alanine, asparagine, L-aspartic acid, glycine및 serine은 자실체형성(子實體形成)이 매우 빈약(貧弱)하였으며 아질산태질소(亞窒酸態窒素), L-tryptophan 및 L-tyrosine은 균(菌)의 생육을 저해(沮害)하였다. 또한 peptone에 무기태질소(無機態窒素)와 아미노산(酸)을 혼용(混用)한 결과