Showing 3 results for Regression.
A. Dehdari, M. Mobli, A. Rezai,
Volume 5, Issue 4 (1-2002)
Abstract
In order to determine the relationships among the different traits of onion and to study the direct and indirect effects of these traits on bulb and seed yield, an experiment was conducted in 1998 at the research farm of Isfahan University of Technology. Results showed that phenotypic and genotypic correlations were similar and bulb weight showed the highest and lowest coefficients of correlation with bulb diameter and number of days to emergence, respectively. Results of stepwise regression analysis showed that leaf width at 25% of its length from the neck, leaf length, and leaf dry weight were the best estimators for leaf area bulb diameter, bulb length, plant height and number of days to maturity were the most important determining characters for bulb yield variation. Number of fertilized florets was the best determinator of seed yield and bulb weight, while diameter and volume were the best describing characters for the number of meristems on the basal plate. Path-coefficient analysis revealed that bulb diameter showed the highest direct positive effect on bulb yield and the indirect effect of plant height through bulb diameter on it was of prime importance. Number of fertilized florets per plant and number of inflorescence per plant through the number of fertilized florets showed the highest direct and indirect effects on seed weight, respectively.
H. Farhangfar, H. Naeemipour , R. Lotfi,
Volume 12, Issue 43 (4-2008)
Abstract
This study was undertaken to estimate genetic trend and parameters of Holstein cattle in Khorasan province for milk yield using a spline random regression test day animal model. A total of 32854 monthly test day milk records (twice and thrice a day milking) obtained from 3842 Holstein heifers (progeny of 466 sires) distributed in 125 herds and calved from 2001 to 2005 was used to predict breeding value of individual animals. In the model, fixed effects of herd including year-month of recording, milking times, age at calving (linear and quadratic covariables), Holstein gene percentage (linear covariable) as well as random effects of additive genetic and permanent environment were studied. To take account of the shape of the lactation curve at genetic and environmental levels, cubic spline polynomials were also included in the test day model. Bayesian method by applying Gibbs sampling technique (100000 chains applying RRGIBBS software) was utilized to obtain posterior means of predicted breeding value of animals for milk yield at individual month of lactation. The results showed that mean of breeding value for 305-day milk yield was 52.90 kg (p<0.05). Spearman rank correlations between predicted breeding values at different months of lactation decreased as the interval between them increased. The highest and lowest rank correlations were found between months 8 and 9 (0.998) and between months 1 and 10 (0.312), respectively. Predicted breeding value of 305-day milk had the lowest and highest rank correlations with predicted breeding value at months 1 (0.553) and 6 (0.990), respectively. Regression analysis of average predicted breeding value of progenies in their birth year showed that the amount of genetic trend for 305 day milk yield was 17.75 kg per year, statistically no different from zero (p value=0.165).
E. Tavakoli, B. Ghahraman, K. Davari, H. Ansari,
Volume 17, Issue 65 (12-2013)
Abstract
Quantitative evaluation of evapotranspiration on a regional scale is necessary for water resources management, crop production and environmental assessments in irrigated lands. In this study, in order to estimate ETo and because of few synoptic stations and also little recorded meteorological data in North Khorasan Province, Iran, with arid and semi-arid climate, 7 stations from neighboring provinces were used. Reference evapotranspiration was calculated using 6 different methods which required a small amount of input data, including Class A pan, Hargreaves-Samani, Priestly-Tailor, Turc, Makkink and the method proposed by Allen et al (1998) to estimate ETo with missing climate data. Besides, the standard FAO-Penman-Monteith was used (because there was no Lysimetric data in the region) to evaluate the applied formulas. Since there was no agreement over the appropriate method to calculate ETo in the selected stations, by using significance test of regression lines, a linear regression equation was computed for each month, in order to convert the best calculating method to FAO-Penman-Monteith formula. Evaluations of these equations showed their acceptable accuracy, in comparison with the previous researches, specifically for cold months (MAE values ranged from 0.3 to 1.4 mm/day).
