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Articles by M.A. Edriss
Total Records ( 7 ) for M.A. Edriss
  M.A. Edriss , P. Hosseinnia , M. Edrisi , H.R. Rahmani and M.A. Nilforooshan
  A mathematical model for prediction of second parity milk yield and fat percentage, with the use of first parity information seems to be helpful in order to predict the performance of prospective productive cows. As a tool for this prediction, back propagation neural network and multiple linear regression methods were compared based on their prediction differences with observed values. While, multiple linear regressions are based on linear relationships between variables, artificial neural network system also considers non-linear relationships between parameters. Data was collected from 4 medium sized dairy herds in Isfahan, Iran, which was divided into three parts in order to train, verify and test the artificial neutral network system and estimation of regression coefficients, verify and test the multiple linear regression method. The results of the simulation showed that evaluations from both multiple linear regression and artificial neural network methods are good predictors for second parity production estimated from first parity information. However, artificial neural network predictions showed lower differences with the observed values and better quality parameters than multiple linear regression predictions, which made this assumption that artificial neural network system is more accurate in prediction.
  G.R. Shadnoush , M. Alikhani , H.R. Rahmani , M.A. Edriss , A. Kamalzadeh and M. Zahedifar
  About 48 Lori-Bakhtiari lambs were used to measure the effects of restricted feeding and re-feeding on intake, body weight and development of body organs. The feeding management was divided to Feed Restriction Period (FRP) and Re-alimentation Period (RAP). During FRP, the 18 Control (C) animals were fed a low-quality roughage, ad libitum and 40 g kg BW-0.75 day-1of concentrate and the 30 animals were only fed low-quality roughage as the Restricted (R) group. At the end of FRP and RAP, six lambs of each group were slaughtered. In the RAP, the 24 remaining lambs from restricted treatment were divided into two groups of R1 and R2 and received low-quality roughage plus 40 and 48 g kg BW-0.75 day-1, of concentrate, respectively. During FRP, Dry Matter (DMI), Metabolizable Energy (MEI) and Crud Protein Intake (CPI), Daily Gain (ADG), Final Body Weight (FBW), pelt, liver and kidneys of C group were higher (p<0.05) than R group. In the RAP all groups had similar FBW but feed conversion ratio, DMI, MEI, CPI and weights of all body organs of C group were higher (p<0.05), however ADG was lower (p<0.05) than R1 and R2 groups. In general, restricted feeding following re-feeding lambs caused more efficiency of performance which was associated with lower maintenance requirements.
  P. Hosseinia , M. Edrisi , M.A. Edriss and M.A. Nilforooshan
  Neural network system can be used as a decision making support system in dairy industry as well as other industries. It can help breeders to predict future yield of dairy cows based on uncorrelated and orthogonalized available information and making selection decisions. Data from 4 medium to large sized dairy farms in Isfahan, Iran, were used. From 1880 available records of first and second parities, 1850 records were used for training a back propagation artificial neural network system and 30 randomly chosen records (not used in the system training step) were introduced to the trained neural network system for its evaluation. The results of the simulation showed that there was no significant difference between the observed and the predicted second parity milk yield and fat percentage (p>0.05). The major use of this predictive process is to make accurate selection decisions which are based on prior knowledge of the outcomes.
  H. Mohammadi and M.A. Edriss
  Genetic parameters were estimated for birth weight (BWT), weaning weight (WWT) and pre-weaning Average Daily Gain (ADG) using Restricted Maximum Likelihood (REML) procedures. Six different animal models were fitted, differentiated by including or excluding maternal effects. The direct heritability estimates (h2) ranged from 0.26 to 0.53, 0.18 to 0.32 and 0.15 to 0.33 for BWT, WWT and ADG, respectively. The estimates were substantially higher when maternal effects, either genetic or environmental, were ignored from the model. The maternal heritability (m2) for BWT was the highest (0.25) when maternal genetic effect alone was fitted in the basic model. It was decreased to 0.14 when the maternal permanent environmental effect (c2) was employed.
  M.A. Edriss , G. Dashab , A.A. Ghareh Aghaji , M.A. Nilforooshan and H. Movassagh
  In order to evaluate some physical and qualitative characteristics of Naeini sheep wool for using in textile industry, herds from 6 different regions of Isfahan province were chosen. Staple length from 3 different body parts (shoulder, side and britch) was measured prior to shearing. Wool samples were taken from a 100 cm2 surface on the mid-side of the Animals. Then the samples were transferred to the Laboratory of Animal Sciences for evaluations of some quality characteristics and to the Fiber Physics Laboratory for measuring tenacity, breaking strength and elongation at break of fibers. The means and standard deviations of staple length were estimated (10.8 ±2.36), (9.71 ±3.14) and (10.99 ±2.49) cm for shoulder, side and britch parts, respectively. The total average staple length of Naeini sheep (10.5 cm) is suitable for using in textile industry. Wool fibers of Naeini sheep have desirable tenacity and breaking strength (1.22 cN/dtex and 13.76 centi-Newton) to resist against mechanical tensions of the spinning step. However, Naeini sheep wool fibers have an adequate, but not a desirable %elongation at break (28.6%). Based on the fiber diameter mean of Naeini sheep and its variation (28.51 ± 4.33 μm) the fleece grade of Naeini sheep was predicted 54`s, which is an intermediate grade. However, by conducting breeding programs toward enhancing fleece grade, Naeini sheep wool will be simply applicable in textile industry.
  G. Dashab , M.A. Edriss , A.A. Ghare Aghaji , H. Movasagh and M.A. Nilforooshan
  The objective of this study was to evaluate fiber quality characteristics of Naeini sheep. An experiment was carried out on six flocks from different regions of Isfahan province. Two hundred and twenty seven Naeini sheep (191 ewes and 36 rams) were sampled from a 100 cm2 surface on the mid-side. Fleeces shorn in 1999 within two sampling seasons (June and December) were sent to the laboratory of Animal Sciences at Isfahan University of Technology in order to study some wool quality traits including: fiber diameter mean, variability in fiber diameter, percent of true, modulated and heterotype fibers, accumulation of scales in 100 μm fiber lengths and diameter mean of modulated and heterotype fibers on Naeini sheep. Fiber diameter mean, within sample variability of fiber diameter and diameter mean of heterotype and modulated fibers were estimated 28.51, 11.19, 46.67 and 65.78 μm, respectively. The percentages of true, modulated and heterotype fibers were 89.53, 5.93 and 4.38, respectively and the average number of scales was 6.2 per 100 μm length of fiber. Herd and season had significant effects on all of the studied measures. Herd effects may be due to genetic, environment and pasture (nutritional) differences between herds. Also, the results of this study showed that there would be finer wool type at June than December shearing times for Naeini sheep. Age had a significant effect on % of heterotype fibers only and sex effect was only significant on the scales accumulation. On average, Naeini rams had finer fibers than ewes, which indicate higher selection intensities on rams. The results of this study revealed that Naeini sheep has great qualifications for carpet industry. However, further breeding programs are needed to meet textile industries qualifications.
  M.A. Edriss , M.A. Nilforooshan and J.M. Sadeghi
  (Co)variance components and genetic parameters were estimated using Derivative-free Restricted Maximum Likelihood (DFREML) approach for milk yield and fat yield of Iranian Holsteins of Isfahan province. Data was consisted of first lactation records on milk and fat yields of 12,047 Holsteins from 45 herds, which were calved from 1995 to the end of 2001. Records were pre-adjusted to mature equivalent yields (ME-2X-305d). Six different animal models were fitted, which were differentiated by including or excluding maternal additive genetic effects, maternal permanent environmental effects and direct-maternal genetic covariance. Animal Models included Herd-Year-Season and direct additive genetic effects as fixed and random effects, respectively. Direct heritability estimates (h2) ranged from 0.157 to 0.229 and 0.203 to 0.243 for milk yield and fat yield, respectively. The estimates were substantially higher when maternal effects were ignored from the model. Mean estimates of the maternal genetic and maternal permanent environment variances and direct-maternal genetic covariance as fractions to the phenotypic variances were 0.07, 0.02 and 0.025 for milk yield and 0.01, 0.05 and 0.016 for fat yield, respectively, while, positive direct-maternal relationships (Covam) were obtained. The results of this study showed that maternal additive genetic and maternal permanent environmental effects had not any important influence on the yield of dairy cows. However, they can improve genetic evaluations. Also, there is no need for the inclusion of direct-maternal genetic covariance in the animal models for dairy cows.
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