Showing 4 results for Ewe
A. Zare Shahneh, H. Sadeghi Panah,
Volume 8, Issue 4 (1-2005)
Abstract
In order to determine the effects of fetal growth rate on plasma concentrations of glucose, triglyceride, total protein, and urea in ewes, this experiment was conducted at Animal Research Station of Tehran University Agricultural College. Sixteen pregnant Varamini ewes were allocated to 4 groups (n=4). During the breeding season, ewes of group 1 were mated by Varamini ram, animals in groups 2, 3 and 4 were mated by Moghani, Afshari and Shall rams, respectively. Because of the different genetic capacities of sires, fetal growth rate was expected to be different in various groups. Ewes in all groups were fed the same ration, so it was expected that differences in fetal growth rates would affect the mobilization of maternal fat and protein storage and blood levels of their metabolites. Blood samples were collected from jugular veins of ewes during the last 8 weeks of pregnancy and first week after lambing.
Plasma levels of glucose, cholestrol, and triglycerid were not different among 4 groups. Total plasma protein in ewes carrying heavier fetuses was lower than in ewes with lighter fetuses (p<0.05). Conversely, plasma urea concentrations in ewes with heavier fetuses were higher than in ewes with lighter fetuses.
M. Vatankhah, M. A. Talebi, M. A. Edris,
Volume 11, Issue 41 (10-2007)
Abstract
In this study 5025 records from the Lori-Bakhtiari sheep stud were used to predict phenotypic, genetic and environmental change in ewe traits from 1989 to 2004. Best linear unbiased prediction (BLUP) of breeding values were estimated by Drivative Free Restricted Maximum Likelihood (DFREML) procedure using single and multi-trait animal model. Phenotypic, genetic and environmental trends were calculated by regressing of the average phenotypic values, predicted breeding values and environmental values in the year of ewe birth respectively. The estimated phenotypic trends were –0.1223 kg for ewe body weight, -0.0415 kg for greasy fleece weight, 0.6639% for conception rate, 0.0003 for number of lambs born per ewe lambing, 0.0094 for number of lambs weaned per ewe lambing, 0.0380 kg for total birth weight per ewe exposed and 0.4227 kg for total weaning weight per ewe exposed. The estimated genetic trends were 0.0603 kg, -0.0004 kg, 0.0183%, -0.0012, -0.0007, 0.0030 kg and 0.0211 kg from single trait analysis and 0.0549 kg, -0.0006 kg, 0.0089%, -0.0008, -0.0008, 0.0030 kg and 0.0230 kg respectively from multi-trait analysis. The estimated phenotypic and environmental trends were significant but genetic trends were not significant (P<0.05) for often traits.
M.a. Abdollahi, J. Abedi Koupai, M.m Matinzadeh,
Volume 28, Issue 3 (10-2024)
Abstract
Today, the problems related to floods and inundation have increased, particularly in urban areas due to climate change, global warming, and the change in precipitation from snow to rain. Therefore, there has also been an increasing focus on rainfall-runoff simulation models to manage, reduce, and solve these problems. This research utilized SewerGEMS software to explore different scenarios to evaluate the model's performance based on the number of sub-basins (2 and 8) and return periods (2 and 5 years). Additionally, four methods of calculating concentration time (SCSlag, Kirpich, Bransby Williams, and Carter) were compared to simulate flood hydrographs in Shahrekord city. The results indicated that increasing the return period from 2 to 5 years leads to an increase in peak discharge in all scenarios. Furthermore, based on the calculated continuity error, the Kirpich method is preferred to estimate the concentration-time in scenarios with more sub-basins and smaller areas. For the 2-year return period, a continuity error of 4% was calculated for the scenario with 2 sub-basins, while for the 5-year return period, the continuity error was 19%. On the other hand, the SCSlag method is preferred to estimate the concentration-time in scenarios with fewer sub-basins and larger areas. For the scenario with 8 sub-basins, a continuity error of 16% was calculated for the 2-year return period, and 11% for the 5-year return period.
M. Ranjbari Hajiabadi, J. Abedi Koupai, M.m. Matinzadeh,
Volume 28, Issue 4 (12-2024)
Abstract
Urban runoff is a serious issue due to urbanization and climate change. Therefore, paying attention to rainfall-runoff simulation models is important to manage and reduce adverse consequences. In this research, the performance of the SewerGEMS software was examined by studying different modes based on the number and area of sub-basins. Two modes, consisting of nine and seventeen sub-basins, were evaluated with varying durations of rainfall of 6 and 12 hours. Additionally, the performance of three methods for calculating concentration time (Kerpich, Brnsby-Williams, Carter) was compared to simulate flood hydrographs in Minab City. The results showed that the total volume of produced runoff in the nine sub-basins was 4% higher than in the seventeen sub-basins. The maximum runoff peak flow in the nine sub-basins was also 20% higher than in the seventeen sub-basins. Furthermore, the Brnsby-Williams method exhibited the least software continuity error among the three calculation methods for concentration time. On the other hand, the Carter method had the highest continuity error. The concentration time calculated by this method in some sub-basins exceeded the 6-hour duration of rain. A t-test was performed to compare the peak discharge data obtained from the Kerpich and Barnesby-Williams methods. The results indicated a significant difference between the data from the two methods at a 95% confidence level (p<0.05). Considering that the Kerpich method is suitable for calculating concentration time in small basins, it was used to compare the nine and seventeen sub-basins. Based on the findings, it was observed that merging the sub-basins and reducing their number from seventeen to nine resulted in an increase in the total volume of produced runoff from approximately 123,839 cubic meters to 128,446 cubic meters, as well as an increase in the maximum peak flow of runoff from about 2.400 m3/s to 2.884 m3/s. This demonstrates an increase in both the total volume and maximum peak discharge of the runoff.
