• KOREAN JOURNAL OF CROP SCIENCE
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• v.15
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• pp.1-31
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• 1974

Introducing genes for earliness of wheat varieties is important to develop early varieties in winter wheat. In oder to obtain basic informations on the response of heading to the different day length and temperature treatments and on the inheritance of heading dates, experiments were conducted at the field and greenhouse of the Crop Experiment Station, Suwon. Varieties used in this experiments were, early variety Yecora F70, medium varieties Suke #169, Parker and Yukseung #3, and late varieties Changkwang, Bezostaia, Sturdy and Blueboy. The parents and F$_1$s of partial diallel crosses of above eight varieties were subjected the following four different treatments; 1. high temperature and long day, 2. high temperature and short day, 3. low temperature and long day, and 4. low temperature and short day. The same materials were grown also in field condition. Parents, F$_1$ and F$_2$ generation were grown also in both greenhouse under high temperature and short day and in field. The results obtained were summarized as follow: 1. No effects of temperature and daylength on the number of leaves on the main stem were found when -varieties were vernalized. The number of main stem leaves were fewer for spring type of varieties than for winter type of varieties. 2. The effects of temperature and daylength on the days to flag leaf opening were dependent on the speed of leaf emergence. The speed of leaf emergence were faster for lower leaves than for upper leaves. 3. The response to short day and long day (earliness of narrow sense) of varieties were found to be direct factor responsible to physiology of heading dates in vernalized varieties. Great difference of varieties to heading date was found in high temperature and short day treatment, but less differences were found in high temperature and long day, low temperature and long day and low temperature and short day treatments respectively. The least varietal difference for heading dates was found in the field condition. 4. Changkwang and Parker were found to be the most sensitive to short day treatment (photosensitive) and the heading of these varieties were delayed by short day treatment. No great varietal differences were found among other varieties. 5. Varietal differences of heading dates due to daylength were greater in high temperature than in low temperature. 6. Varietal differences of heading dates due to temperature were not great. but in general the heading dates of varieties were faster under high temperature than under low temperature. 7. Earliness of heading dates was due to partial dominance effect of genes involved in any condition. The degree of dominance was greater under short day than under long day treatment. 8. The varietal differences of heading date under high temperature and long day were due to earliness or narrow sense (response to long day) of varieties. The degree of dominance was greater for Yecora F70, spring type than for other winter type of varieties. No differences or less differences of degree of dominance was found among winter type of varieties. The estimated number of effective factor concerned in the earliness of narrow sense was one pair of allele with minor genes. 9. The insensitivity of varieties to short day treatment in heading dates was due to single dominant gene effect. Under the low temperature the sensitivity of varieties to short day treatment was less apparent. 10. The earliness of short day and long day (earliness of narrow sense) sensitivities of varieties appearea to be due to partial dominance of earliness over lateness. In strict sense, the degree of the dominance should be distinguished. 11. Dominant gene effects were found for the thermo-sensitivity of varieties, and the effect was less, significant than the earliness in narrow sense. 12. One pair of allele, ee and EE, for photosensitivity was responsible for the difference in the heading dates between Changkwang and Suke #169. Two pairs of alleles, ee, enen and EE, EnEn. appeared to be responsible for the difference between Changkwang and Yecora F70. The effects of EE and EnEn were, additive to the earliness and the effects of EE were greater than EnEn under short day. However, the effects of EE were not evident in long day but the effects of EnEn were observed in long day. 13. Two pairs of dominant alleles for the earliness were estimated from the analysis of F$_1$ diallels in the field but the effects of these alleles in F$_2$ were not apparent due to low temperature and short day treatment in early part of growth and high temperature and long day treatment in later part of growth. The F$_2$ population shows continuous variation due to environmental effects and due to other minor gene effects. 14. The heritabilities for heading dates were ranged from 0.51 to 0.72, indicating that the selection in early generation might be effective. The extent of heritability for heading dates varied with environments; higher magnitude of heritability was obtained in short day treatment and high temperature compared with long day and low temperature treatments. The heritabilities of heading date due to response to short day were 0.86 in high temperature and 0.76 in low temperature. The heritabilities of heading date due to temperature were not significantly high. 15. The correlation coefficients of heading dates to the number of grains per spike, weight of 1, 000 grains. and grain yield were positive and high, indicating the difficulties of selections of high yielding lines from early population. But no significant correlation coefficient was obtained between the earliness and the number of spikes, indicating the effective selection for high tillering from early varieties for high yielding.

• Journal of the Korean Wood Science and Technology
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• v.14 no.1
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• pp.3-44
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• 1986

In order to obtain the basic physical and mechanical properties of steel wire reinforced particleboard, particleboards were formed with large particles through 2.11 mm (12 meshes) and retained on 1.27mm (20 meshes) sieves and small particles through 1.27mm (20 meshes) and retained on 0.42mm (60 meshes) sieves from the plywood mill wastes of meranti (Shorea spp.) in the form of pallmanchips, applying urea-formaldehyde resin as an adhesive on the particle surface in 10 percent on the oven dried weight of particles, and arranging steel wires of 1mm in diameter 5,10,15,20, and 25mm in longitudinal and transverse direction with crossing in the mid of the board depth in single layer boards, 10mm in longitudinal or transverse direction without crossing in two layers and 10mm in longitudinal and transverse directions with and without crossing in three steel wire layers boards. The stepwise 9-minutes-multi-pressing schedule in 5 minutes at 35 kgf/$cm^2$, 2.5 minutes at 25 kgf/$cm^2$. and 1.5 minutes at 15 kgf/$cm^2$ was applied for $300{\times}200{\times}13$mm board at the temperature of 160$^{\circ}C$ in a hot press. Specific gravity, thickness swelling, bending properties of modulus of rupture (MOR), modulus of elasticity(MOE), work to proportional limit, and work to ultimate load, internal bond (IB), and screw holding power(SHP) of the reinforced boards were analyzed on the wire openings and wire layers. The results obtained are summarized as follows; 1) In specific gravity, particleboards with large particles and small particles had higher value with more steel wire placements and more steel layers composition, 2) Particleboards with large particles in accordance with more steel wire liners composition gave very poor thickness swelling. 3) The mechanical properties of particleboards formed with large or small particles were reinforced with more steel wire layers. Therefore, bending strength was improved in modulus of rupture, modulus of elasticity, and work to ultimate load. Especiallv, particleboards with two or three steel wire layers showed the tension lamination effect when the steels in lower steel wire layer were oriented parallel to the board length. 4) The modulus of rupture, modulus of elasticity, and work to ultimate load in bending varied with opening area, distance of lengthwise wires multipled by distance of transverse wires. Particleboards formed with large particles resulted in higher value in modulus of rupture with 1.5-3 $cm^2$ opening area, 1-2cm distance between transverse wires, and 1.5-2.5cm distance between lengthwise wires. Particle boards formed with small particles showed higher value with 0.5-1.5$cm^2$ or 3.75-6.25 $cm^2$ opening area, 0.5 or 2.5cm distance between transverse wires. 5) In modulus of elasticity, particleboards formed with large particles with one steel wire layer suggested higher value with 5-3$cm^2$ opening area, 1-2.5cm distance between transverse wires and also 1-2.5 cm distance between lengthwise wires. Particleboards formed with small particles showed higher value with 0.75-1.25$cm^2$ or 3-6.25$cm^2$ opening area and 0.5 or 2.5cm distance between transverse wires. 6) Particleboards formed with large particles gaved higher value in work to ultimate load with 1-3$cm^2$ opening area. Particleboards formed with small particles showed increasing tendancy with decreasing opening area. 7) In internal bond and screw holding power, particleboards formed with large particles had increasing value in two and three steel wire layers compositions, but particleboards formed with small particles showed no difference. Particleboards formed with large particles containing one steel wire layer showed no difference in internal bond and screw holding power, and particleboards formed with small panicles containing one steel wire layer resulted in increasing value in internal bond and decreasing value in screw holding power in accordance with increase in opening area.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.10
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• pp.1-43
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• 1971

Experiment I: The objective of this study was to know variation in some selected agronomic characters of sweet sorghum when planted in several growing seasons. The 17 different sweet sorghum varieties having various maturities, and plant, syrup and sugar types were used in this study which had been carried out for the period of two years from 1968 to 1969 at Industrial Crops Division of Crop Experiment Station in Suwon. These varieties were planted at an interval of 20 days from April 5 to August 25 both in 1968 and 1969. The experimental results could be summarized as follows: 1. As planting was made early, the number of days from sowing to germination was getting prolonged while germination took place early when planted at the later date of which air temperature was relatively higher. However, such a tendency was not observed beyond the planting on August 25. In general, a significant negative correlation was found between the number of days from sowing to germination and the average daily temperature but a positive correlation was found between the former and the total accumulated average temperature during the growth period. 2. The period from sowing to heading was generally shortened as planting was getting delayed. The average varietal difference in number of days from sowing to heading was as much as 30.2 days. All the varieties were grouped into early-, medium and late-maturing groups based upon a difference of 10 days in heading. The average number of days from sowing to heading was 78.5$\pm$4.5 days in the early-maturing varieties, 88.5$\pm$4.5 days in the medium varieties and 98.5$\pm$4.5 days in the late-maturing varieties, respectively. The early-maturing varieties had the shortest period to heading when planted from July 15 to August 5, the medium varieties did when planted before July 15 and the late-maturing varieties did when planted before June 5. 3. The relationship between the sowing date (x) and number of days from sowing to heading could be expressed in an equation of y=a+bx. A highly positive correlation was found between the coefficient of the equation(shortening rate in heading time) and the average number of days from sowing to heading. 4. The number of days from sowing to heading was shortened as the daily average temperature during the growth period was getting higher. Early-maturing varieties had the shortest period to heading at a temperature of 24.2$^{\circ}C$, medium varieties at 23.8$^{\circ}C$ and late-maturing varieties at 22.9$^{\circ}C$, respectively. In other words, the number of days from sowing to heading was shortened rapidly in case that the average temperature for 30 days before heading was 22$^{\circ}C$ to $25^{\circ}C$. It prolonged relatively when the temperature was lower than 21$^{\circ}C$. 5. There was a little difference in plant height among varieties. In case of early planting, no noticeable difference in the height was observed. The plant height shortened generally as planting season was delayed. Elongation of plant height was remarkably accelerated as planting was delayed. This tendency was more pronounced in case of early-maturing varieties rather than late-maturing varieties. As a result, the difference in plant height between the maximum and the minimum was greater in late-maturing varieties than in early-maturing varieties. 6. Diameter of the stalk was getting thicker as planted earlier in late-maturing varieties. On the other hand, medium or early-maturing varieties had he thickest diameter when they were planted on April 25. 7. In general, a higher stalk yield was obtained when planted from April 25 to May 15. However, the planting time for the maximum stalk yield varied from one variety to another depending upon maturity of variety. Ear]y-maturing varieties produced the maximum yield when planted about April 25, medium varieties from April 25 to May 15 and late-maturing varieties did when planted from April 5 to May 15 respectively. The yield decreased linearly when they were planted later than the above dates. 8. A varietal difference in Brix % was also observed. The Brix % decreased linearly when the varieties were planted later than May 15. Therefore, a highly negative relationship between planting date(x) and Brix %(y) was detected. 9. The Brix % during 40 to 45 days after leading was the highest at the 1st to the 3rd internodes from the top while it decreased gradually from the 4th internode. It increased again somewhat at the 2nd internode from the ground level. However, it showed a reverse relationship between the Brix % and position of internode before heading. 10. Sugar content in stalk decreased gradually as planting was getting delayed though one variety differed from another. It seemed that sweet sorghum which planted later than June had no value as a sugar crop at all. 11. The Brix % and sugar content in stalk increased from heading and reached the maximum 40 to 45 days after heading. The percentage of purity showed the same tendency as the mentioned characters. Accordingly, a highly positive correlation was observed between. percentage of purity and Brix % or sugar content in stalk. 12. The highest refinable sugar yield was obtained from the planting on April 25 in late-maturing varieties and from that on May 15 in early-maturing varieties. The yield rapidly decreased when planted later than those dates. Such a negative correlation between planting date(x) and refinable sugar yield(y) was highly significant at 1% level. 13. Negative correlations or linear regressions between delayed planting and the number of days from sowing to germination. accumulated temperature during germination period, number of days to heading, accumulated temperature to heading, plant height, stem diameter, stalk weight, Brix %. sugar content, refinable sugar yield or Purity % were obtained. On the other hand, highly positive correlations between the number of days from sowing to heading(x) and Brix %, sugar content, purity %, refinable sugar yield, plant height or stalk yield, between Brix %(x) and purity %, refinable sugar yield or stalk yield, between sugar content(x) and purity% or refinable sugar yield(y), between purity %(x) and refinable sugar yield and between daylength at heading(x) and Brix %. number of days from sowing to heading, sugar content, purity % or refinable sugar yield (y), were found, respectively. Experiment II: The 11 varieties were selected out of the varieties used in Experiment I from ecological and genetic viewpoints. Complete diallel cross were made among them and the heading date, stalk length, stalk yield, Brix %, syrup yield, combining ability and genetic behavior of F$_1$ plants and their parental varieties were investigated. The results could be summarized as follows: 1. In general, number of days to heading showed a partial dominance over earliness or late maturity or had a mid-value, though there were some specific combinations showing a complete dominance or transgressive segregation in maturity. Some combinations showed relatively high general or specific combining abilities in maturity. Therefore, a 50 to 50 segregation ratio in heading date could be estimated in this study and it might be positive to have a selection in early generation since heritability of the character was relatively high. 2. A vigorous hybrid vigor was observed in stalk length. A complete or partial dominant effect of long stalk was obtained. The general combining ability and specific combining ability of stalk length were generally high. Long and short stalks segregated in a ratio of 50:50 and its heritability was relatively low. 3. Except for several specific combinations, high stalk yield seemed to be partial dominant over the low yield. Some varieties demonstrated relatively high general as well as specific combining abilities. It was assumed that several recessive genes were involved in expression of this character. The interaction among regulating recessive genes was also obtained. Accordingly, the heritability of stalk yield seemed to be rather low. 4. The Brix % of hybrid plants located around mid-parental value though some of them showed much higher or lower percentage. It could be explained by the fact that such behavior might be due to partial dominance of Brix %. The varieties with, relatively higher Brix % were high both in general. and specific combining abilities. Therefore, it could be recommended to use the varieties having higher sugar content in order to develop higher-sugar varieties. 5. The syrup yield seemed to be transgressively segregated or completely dominant over low yield. Hybrid vigor of syrup yield was relatively high. No-consistent relationship between general combining ability and specific combining ability was observed. However, some cases demonstrated that the varieties with relatively higher general combining ability had relatively lower specific combining ability. It was assumed that the frequencies of dominant and recessive alleles were almost same.

• 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.