• Korean Journal of Animal Reproduction
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• v.24 no.2
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• pp.171-178
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• 2000

To improve the pig farm management and efficiency of swine industry by inducing the farrowing to day-time from night, In the first experiment, reproduction records of purebred and crossbred pigs were collected and analyzed to estimate the $\varepsilon$ ffec 잉 of factors affecting day and night farrowing. The general linear model was used to estimate the least square means of the factors affecting various reproductive characteristics. And also, chi-square tests were used to examine the independence of the reproductive traits and environmental factors using the SAS (1992). The comparisons between pure and crossbred pigs for total number born, percentage of number born alive, gestation length, time length for farrowing were determined. The results indicated that the gestation length of crossbred (115.11 d) was slightly longer than that of purebred (114.89 d, p＜0.05). For the seasonal effects on total number born, the largest was found in spring and no differences were found among summer, fall and winter. The average gestation length was 1 day longer in spring and winter than in summer and fall. The total number born and number born alive were smaller in first, second, and greater or equal to sixth parity than other parities. And also, the percentage of no. born alive was least in greater or equal to sixth parity. For the effect of mating methods, natural and artificial insemination, on total number born and number born alive, no differences between the two methods were found. However, the percentage of number born alive for natural mating was 98.06% and was higher than artificial insemination(93.75%). The time length for farrowing was I hour were found for the 6 hrs of farrowing time. In general situation of pig farms, day-time farrowing was 34.8% and night farrowing was 65.2%, indicating that night farrowing was almost double of the night farrowing.

• Journal of Korean Society of Forest Science
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• v.94 no.6
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• pp.488-495
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• 2005

This study was conducted to understand the influences of forest structure on throughfall, stemflow and interception loss. The study plots included the natural old-growth deciduous, Pinus koraiensis and Abies holophylla forests in Gwangneung and the rehabilitated young mixed forest in Yangju, Gyeonggido. The Pinus koraiensis and Abies hotophylla had been planted in 1976. The rehabilitated young mixed forest had been established to control erosion in 1974. Total and net rainfall were monitored from March, 2003 to October, 2004. Tipping bucket rain gauge recorded total rainfall. Throughfall and stemflow were measured by custom-made tipping bucket and CR10X data logger at each $10m{\times}10m$ plots at intervals of 30 minutes. Interception loss in the Pinus koraiensis plot were most as 37.2% of total rainfall and least as 22.6% in the rehabilitated young mixed forest. Stemflow in the rehabilitated young mixed forest was 10.7% of total rainfall and stemflow in the Pinus koraiensis plot was 2.4%. The average throughfall ratio ranged from 66% to 77% depending on the canopy coverage. The relationship of stemflow and total rainfall represented in a linear regression equation though the variation of data was large. The ratio of stemflow-conversion was 2% of total rainfall in the Pinus koraiensis plot and 12% in the rehabilitated young mixed forest, respectively. The stem storage of the natural old-growth deciduous was the largest of 0.21 mm whereas that of the Pinus koraiensis plot was the least of 0.003 mm. A deciduous forest produced stemflow more than a coniferous forest due to a smooth bark and steeply angled branches. Interception loss of all study plots increased linearly as total rainfall increased. The distribution of interception loss data related in total rainfall became wider in a deciduous forest than a coniferous. It resulted from seasonality of leaf area index in a deciduous forest. As considered above results, it was confirmed that there were great differences of throughfall, stemflow and interception loss depending on forest stand structures. The simulation model for predicting interception loss must have parameters such as forest stand characteristics and LAI in order to describe the influence of forest structure on interception loss.