• KOREAN JOURNAL OF CROP SCIENCE
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• v.17
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• pp.1-44
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• 1974

These studies were made at Suwon in 1972 and at Suwon, Iri, and Kwangju in 1973 to investigate grain filling process and variation of grain quality of NB 68513 and Caprock as hard red winter wheat, Suke ＃169 as soft red winter wheat variety and Yungkwang as semi-hard winter variety, grown under-three different fertilizer levels and seeding dates. Other experiments were conducted to find the effects of temperature, humidity and light intensity on the grain filling process and grain quality of Yungkwang and NB 68513 wheat varieties. These, experiments were conducted at Suwon in 1973 and 1974. 1. Grain filling process of wheat cultivars: 1) The frequency distribution of a grain weight shows that wider distribution of grain weight was associated with large grain groups rather than small grain group. In the large grain groups, the frequency was mostly concentrated near mean value, while the frequency was dispersed over the values in the small grain group. 2) The grain weight was more affected by the grain thickness and width than by grain length. 3) The grain weight during the ripening period was rapidly increased from 14 days after flowering to 35 days in Yungkwang and from 14 days after flowering to 28 days in NB 68513. The large grain group, Yungkwang was rather slowly increased and took a longer period in increase of endosperm ratio of grain than the small grain group, NB 68513. 4) In general, the 1, 000 grain weight was reduced under high temperature, low humidity, while it was increased under low temperature and high humidity condition, and under high temperature and humidity condition. The effect of shading on grain weight was greater in high temperature than in low temperature condition and no definite tendency was found in high humidity condition. 5) The effects of temperature, humidity and shading on 1, 000 grain weight were greater in large-grain group, Yungkwang than in small grain group, NB 68513. Highly significant positive correlation was found between 1, 000 grain weight and days to ripening. 6) The 1, 000 grain weight and test weight were increased more or less as the fertilizer levels applied were increased. However, the rate of increasing 1, 000 grain weight was low when fertilizer levels were increased from standard to double. The 1, 000 grain weight was high when planted early. Such tendency was greater in Suwon than in Kwangju or Iri area. 2. Milling quality: 7) The milling rate in a same group of varieties was higher under the condition of low temperature, high humidity and early maturing culture which were responsible for increasing 1, 000 grain weight. No definite relations were found along with locations. 8) In the varieties tested, the higher milling rate was found in large grain variety, Yungkwang, and the lowest milling rate was obtained from Suke ＃ 169, the small grain variety. But the small grained hard wheat variety such as Caprock and NB 68513 showed higher milling rate compared with the soft wheat variety, Suke ＃ 169. 9) There were no great differences of ash content due to location, fertilizer level and seeding date while remarkable differences due to variety were found. The ash content was high in the hard wheat varieties such as NB 68513, Caprock and low in soft wheat varieties such as Yungkwang and Suke ＃ 169. 3. Protein content: 10) The protein content was increased under the condition of high temperature, low humidity and shading, which were responsible for reduction of 1, 000 grain weight. The varietal differences of protein content due to high temperature, low humidity and shading conditions were greater in Yungkwang than in NB 68513. 11) The high content of protein in grain within one to two weeks after flowering might be due to the high ratio of pericarp and embryo to endosperm. As grains ripen, the effects of embryo and pericarp on protein content were decreased, reducing protein content. However, the protein content was getting increased from three or four weeks after flowering, and maximized at seven weeks after flowering. The protein content of grain at three to four weeks after flowering increased as the increase of 1, 000 grain weight. But the protein content of matured grain appeared to be affected by daily temperature on calender rather than by duration of ripening period. 12) Highly significant positive correlation value was found between the grain protein content and flour protein content. 13) The protein content was increased under the high level of fertilizers and late seeding. The local differences of protein content were greater in Suwon than in Kwangju and Iri. 14) Protein content in the varieties tested were high in Yungkwang, NB 68513 and Caprock, and low in Suke ＃ 169. However, variation in protein content due to the cultural methods was low in Suke ＃ 169. 15) Protein yield per unit area was increased in accordance with increase of fertilizer levels and early maturing culture. However, nitrogen fertilizer was utilized rather effectively in early maturing culture and Yungkwang was the highest in protein yield per unit area. 4. Physio-chemical properties of wheat flour: 16) Sedimentation value was higher under the conditions of high temperature, low humidity and high levels of fertilizers than under the conditions of low temperature, high moisture and low levels of fertilizers. Such differences of sedimentation values were more apparent in NB 68513 and Caprock than Yungkwang and Suke ＃ 169. The local difference of sedimentation value was greater in Suwon than in Kwangju and Iri. Even though the sedimentation value was highly correlated with protein content of grain, the high humidity was considered one of the factors affecting sedimentation value. 17) Changes of Pelshenke values due to the differences of cultural practices and locations were generally coincident with sedimentation values. 18) The mixing time required for mixogram was four to six minutes in NB 68513, five to seven minutes in Cap rock. The great variation of mixing time for Yungkwang and Suke ＃ 169 due to location and planting conditions was found. The mixing height and area were high in hard wheat than in soft wheat. Variation of protein content due to cultural methods were inconsistent. However, the pattern of mixogram were very much same regardless the treatments applied. With this regard, it could be concluded that the mixogram is a kind of method expressing the specific character of the variety. 19) Even though the milling property of NB 68513 and Caprock was deteriorated under either high temperature and low humidity of high fertilizer levels and late seeding conditions, baking quality was better due to improved physio-chemical properties of flour. In contrast, early maturing culture deteriorated physio-chemical properties, milling property of grain and grain protein yield per unit area was increased. However, it might be concluded that the hard wheat production of NB 68513 and Caprock for baking purpose could be done better in Suwon than in Iri or Kwangju area. 5. Interrelationships between the physio-chemical characters of wheat flour: 20) Physio-chemical properties of flour didn't have direct relationship with milling rate and ash content. Low grain weight produced high protein content and better physio-chemical flour properties. 21) In hard wheat varieties like NB 68513 and Caprock, protein content was significantly correlated with sedimentation value, Pelshenke value and mixing height. However, gluten strength and baking quality were improved by the increased protein content. In Yungkwang and Suk ＃ 169, protein content was correlated with sedimentation value, but no correlations were found with Pelshenke value and mixing height. Consequently, increase of protein content didn't improve the gluten strength in soft wheat. 22) The highly significant relationships between protein content and gluten strength and sedimentation . value, and between Pelshenke value, mixogram and gluten strength indicated that the determination of mixogram and Pelshenke value are useful for de terming soft and hard type of varieties. Determination of sedimentation value is considered useful method for quality evaluation of wheat grain under different cultural practices.

• Proceedings of the KOR-KST Conference
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• 1995.02a
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• pp.101-113
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• 1995

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.