Homogeneous Residual Variance Research Articles

Comparison of the Symmetric and Asymmetric Generalized Autoregressive Conditional Heteroscedasticity (GARCH) Models in Forecasting the 2018-2023 Jakarta Composite Index

The Autoregressive Integrated Moving Average with Exogenous Variables (ARIMAX) method assumes a homogeneous residual variance, but data with high volatility can cause violations of this assumption. Hence, it is interesting to compare the forecasting accuracy of symmetric and asymmetric Autoregressive Conditional Heteroskedasticity (ARCH) models in various data conditions. The research aimed to compare the accuracy of the symmetric ARCH/ Generalized Autoregressive Conditional Heteroscedasticity (GARCH) and asymmetric TGARCH models in forecasting weekly Jakarta Composite Index (JCI) data on January 1st, 2018, to April 24th, 2023, by involving the influence of COVID-19 as a covariate variable and applying several validation scenario models to training and testing data. Based on the best-selected model, forecasting was carried out from May 1st, 2023, to July 3rd, 2023. The data used were weekly JCI opening data from January 1st, 2018, to April 24th, 2023, with the COVID-19 period as a covariate variable. The analysis results show that symmetric and asymmetric methods can handle violations of the heteroscedasticity assumption in the ARIMAX model. The best model produced based on four data validation scenarios is the asymmetric ARIMAX(3,1,3)-TGARCH(1,2) model with an average MAPE value of 3.158%. In this model, the COVID-19 variable significantly influences the JCI movement. Forecasting is done with forecasting results that are stable with confidence intervals that widen in each period.

Methodologies to determine the ripening stage of coffee fruits

The asynchronous flowering of coffee trees results in uneven ripening, which makes harvesting ripe fruit crops difficult. To determine the ideal time for harvesting, representative sampling is required. However, existing sampling processes are labor-intensive. Therefore, this study aimed to determine the optimal number of plants and most efficient methodology to evaluate coffee fruit ripening. The experiment was conducted in Inconfidentes, state of Minas Gerais, with trials carried out on Catuaí Vermelho and Icatu Vermelho cultivars. The results of the fruit quantities were subjected to analysis of variance. An individual statistical analysis was performed and, if the homogeneity of residual variances was lower than seven, a joint analysis of variance was performed on the two crops. Each experiment had four treatments and six repetitions. We then performed a joint analysis of the data and used simulation to determine the optimal number of plants to be evaluated. The interaction between treatments and cultivars showed a significant difference in the green and cherry stages, but not in unripe, ripe, and dry fruits, and the optimal number of plants to sample varied by cultivar and treatment. It was found that the optimal number of plants to sample is 13 for the Icatu cultivar and 14 for the Catuaí cultivar using the central rosette harvesting treatment

Optimization of test day for milk yield recording and sire evaluation in Murrah buffaloes.

In the present study, random regression models (RRM) were used to estimate genetic parameters for test-day milk yield in Murrah buffaloes using Legendre polynomial function (LP), with the objective to find the best combination of "minimum test-day model," which would be essential and sufficient to evaluate the trait successfully. Data included for analysis were 10,615 first lactation monthly test-day milk yield records (5th, 35th, 65th, …, 305th) from 965 Murrah buffaloes for the period 1975-2018. Cubic to octic-order orthogonal polynomials with homogeneous residual variances were used for the estimation of genetic parameters. Random regression models with sixth-order were selected based on goodness of fit criteria like lower AIC, BIC and residual variance. Heritability estimates ranged from 0.079 (TD6) to 0.21(TD10). For both ends of lactation, the additive genetic and environmental variances were higher and ranged from 0.21 ± 0.12 (TD6) to 0.85 ± 0.35 kg2 (TD1) and 3.74 ± 0.36 (TD11) to 1.36 ± 0.14 kg2 (TD9), respectively. Between adjacent test-day records, genetic correlation estimates ranged from 0.09 ± 0.31 (TD1 and TD2) to 0.97 ± 0.03 (TD3 and TD4; TD4 and TD5), but values gradually declined as the distance between test days increased. Negative genetic correlations were also obtained between TD1 with TD3 to TD9, TD2 with TD9 and TD10, and TD3 with TD10. On the basis of genetic correlations, models with 5 and/or 6 test-days combination were able to account for 86.1%-98.7% of variation along the lactation. Models with fourth and fifth-order LP functions were considered to account for variance with combinations of 5 and/or 6 test-day milk yields. The model with 6 test-day combinations had a higher rank correlation (0.93) with model using 11 monthly test-day milk yield records. On the basis of relative efficiency, the model with 6 monthly test day combinations with fifth-order was more efficient (maximum 99%) than the model using 11 monthly test-day milk yield records. Looking into the similar accuracy with the 11TD model, and the low resources requirement, we recommend the use of the "6 test-day combination model" for sire evaluation. These models may help in reducing the cost and time for data recording of milk yield.

Abstract PD12-06: PD12-06 Longitudinal analysis of breast density change assessed by digital mammogram is associated with breast cancer

Abstract Mammographic breast density is a well-established and strong risk factor for breast cancer. Widespread use of digital mammography has opened new potential for assessment of density changes over time. The underlying premise is that changes in breast tissue due to evolving structures that support cancer development should translate to quantifiable differences between the two breasts over time. To address this hypothesis, we draw on extensive digital mammography data and bring repeated measures over up to 10 years to evaluate the association between change in mammographic breast density and risk of breast cancer. Women were recruited from November 2008 to April 2012 through the mammography service at the Joanne Knight Breast Health Center at Washington University in St Louis. Baseline questionnaire risk factors and screening mammograms were collected from 12,153 women. Of these, 1,672 were excluded for prior history of any cancer (except non-melanoma skin) or diagnosis of breast cancer within 6 months of registration for the study, for a total of 10,481 women. Follow-up is through linking to electronic health records, tumor registry and death register. Routine screening mammograms are collected every 1 to 2 years. Follow-up of cohort participants through December 2020 was: 78% seen in 2019 or 2020; a further 4.4% seen most recently in 2018 and a further 2.4% in 2017 giving over 80% active follow-up for women seen within the last 36 months. The median number of mammograms is 5 (min = 1, max = 10; sd = 2.43). The average person-years of follow-up through most recent contact is 9.2 person-years. We have excluded women who are diagnosed within the first 6 month of baseline mammogram date in all analyses, leaving 259 cases and 695 controls with a total number of 8,966 crandiocaudal (CC) view mammograms for analysis. For these 954 women, the mean number of years between mammograms is 1.3 (10th percentile: 1.0, 90th percentile: 2.0). For the cases, the mean number of years from last mammogram date to diagnosis date was 2.0 years (10th percentile: 1.0, 90th percentile: 3.9) after excluding mammograms that are within 6 months of diagnosis. The percentage volumetric density (MD) within each digital CC-view mammogram is estimated with an automated pixel-thresholding algorithm ADAPT implemented within the Division of Public Health at WashU. The skin around the breast is automatically removed using a boundary detection algorithm prior to estimating the dense areas. The percent density (MD) is then estimated using the dense area divided by the total breast area which normalizes the difference in breast size across women. The correlation between average MD generated from our automated algorithm with Volpara is 0.82. Initial analyses use linear mixed effects with average density between breasts (fitted with R package lmer). We performed test of assumptions for all linear mixed effects models, including the normality of residuals, linearity, and homogeneity of residual variance. Assessing the averaging density between 2 breasts controlling for age, BMI, histology confirmed benign breast disease, family history, parity, and alcohol, MD decreases significantly over follow-up (time in years) (P < 0.01). At baseline, postmenopausal women had lower density than premenopausal women after controlling for the same set of risk factors. The average MD over all time points was significantly different for the case vs. control women (P < 0.01). For overweight women, the trajectory of MD over time was significantly different for the case vs. control women (P < 0.01). Drawing on over 10 years of follow-up we observe, for the first time, a dynamic effect for breast density such that divergence in density over time is related to risk for breast cancer. Citation Format: Graham A. Colditz, Shu Jiang. PD12-06 Longitudinal analysis of breast density change assessed by digital mammogram is associated with breast cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr PD12-06.

Random Regression Model for Genetic Evaluation and Early Selection in the Iranian Holstein Population.

Simple SummaryThe objective of this study was to use a model to predict breeding values for sires and cows at an early stage of the first lactation of cows and progeny groups in the Iranian Holstein population to support the early selection of sires. Our results show that we can select sires according to their daughters’ early lactation performance before they finish first lactation. Cross-validation results show that early selection accuracy can be high, and such an early selection can decrease the generation interval and lead to an increased genetic gain in the Iranian Holstein population.The objective of this study was to use a model to predict breeding values for sires and cows at an early stage of the first lactation of cows and progeny groups in the Iranian Holstein population to enable the early selection of sires. An additional objective was to estimate genetic and phenotypic parameters associated with this model. The accuracy of predicted breeding values was investigated using cross-validation based on sequential genetic evaluations emulating yearly evaluation runs. The data consisted of 2,166,925 test-day records from 456,712 cows calving between 1990 and 2015. (Co)-variance components and breeding values were estimated using a random regression test-day model and the average information (AI) restricted maximum likelihood method (REML). Legendre polynomial functions of order three were chosen to fit the additive genetic and permanent environmental effects, and a homogeneous residual variance was assumed throughout lactation. The lowest heritability of daily milk yield was estimated to be just under 0.14 in early lactation, and the highest heritability of daily milk yield was estimated to be 0.18 in mid-lactation. Cross-validation showed a highly positive correlation of predicted breeding values between consecutive yearly evaluations for both cows and sires. Correlation between predicted breeding values based only on records of early lactation (5–90 days) and records including late lactation (181–305 days) were 0.77–0.87 for cows and 0.81–0.94 for sires. These results show that we can select sires according to their daughters’ early lactation information before they finish the first lactation. This can be used to decrease generation interval and to increase genetic gain in the Iranian Holstein population.

Genotype-environment interaction effects on weight gain in cattle using reaction norms

The existence of genotype-environment interaction (GEI) using reaction norm models and their impact on the genetic evaluation of Nellore sires for body weight at 120, 210, 365 and 450 days of age was verified. Three models were used: animal model (AM) that disregards GEI and the one-step reaction norm model with homogeneous and heterogeneous residual variance (1SRNMH_het). Bayes Inference via Gibbs Sampling was used to estimate the variance components. The AM model better fits to weights at 120 and 210 days of age, while 1SRNMH_het was more adequate for body weights at 365 and 450 days of age, suggesting the existence of GEI. The posterior means of direct heritability were 0.33±0.01 and 0.36±0.01 and maternal heritability of 0.21±0.01 and 0.19±0.01 for body weights at 120 and 210 days of age, respectively. For body weights at 365 and 450 days of age, posterior means of heritability varied along the environmental gradient, but the ranking of sires based on breeding values was not changed by different environmental gradients. All rank correlations were greater than 0.80, strongly suggesting a scale effect of GEI. Despite the evidence of GEI on post-weaning weight gain, it did not change the ranking of sires. Therefore, it did not have a relevant impact on the genetic evaluation of sires because they are robust to environmental changes.

Biometric approach applied to soybean genotypes cultivated in Rio Grande do Sul, Brazil

The study had the purpose to evidence the agronomic performance, inter-relations of characters and the multivariate differentiation of soybean genotypes cultivated in the preferential season, in the state of Rio Grande do Sul, Brazil. In the crops season of 2017/2018, The experimental design was the completely randomized blocks, being 25 genotypes with three replicates. The data obtained was submitted to presuppositions based on normality and homogeneity of residual variances, variance analysis, Tocher method, Euclidian algorithm, linear correlations, relative contribution of characters by Singh and artificial neural networks. The agronomic performance of the genotypes presents superior seeds yield per plant through the elevated magnitude of reproductive nodes, legumes and seeds per plant. The plant height of the soybean is positively associated with the number of total nodes and reproductive nodes in the main stem and branches, where they are directly linked with the soybean productive potential. The most polymorphic characters correspond to the number and mass of thousand seeds, being possible to differentiate in a multivariate way the soybean genotypes though the similarity profiles.

Models for optimizing selection based on adaptability and stability of cotton genotypes

ABSTRACT: In multi-environment trials (MET), large networks are assessed for results improvement. However, genotype by environment interaction plays an important role in the selection of the most adaptable and stable genotypes in MET framework. In this study, we tested different residual variances and measure the selection gain of cotton genotypes accounting for adaptability and stability, simultaneously. Twelve genotypes of cotton were bred in 10 environments, and fiber length (FL), fiber strength (FS), micronaire (MIC), and fiber yield (FY) were determined. Model selection for different residual variance structures (homogeneous and heterogeneous) was tested using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The variance components were estimated through restricted maximum likelihood and genotypic values were predicted through best linear unbiased prediction. The harmonic mean of relative performance of genetic values (HMRPGV) were applied for simultaneous selection for adaptability, stability, and yield. According to BIC heterogeneous residual variance was the best model fit for FY, whereas homogeneous residual variance was the best model fit for FL, FS, and MIC traits. The selective accuracy was high, indicating reliability of the prediction. The HMRPGV was capable to select for stability, adaptability and yield simultaneously, with remarkable selection gain for each trait.

Impact of the Order of Legendre Polynomials in Random Regression Model on Genetic Evaluation for Milk Yield in Dairy Cattle Population.

The random regression test-day model has become the most commonly adopted model for routine genetic evaluations in dairy populations, which allows accurately accounting for genetic and environmental effects over lactation. The objective of this study was to explore appropriate random regression test-day models for genetic evaluation of milk yield in a Holstein population with a relatively small size, which is the common situation in regional or independent breeding companies to preform genetic evaluation. Data included 419,567 test-day records from 54,417 cows from the first lactation. Variance components and breeding values were estimated using a random regression test-day model with different orders (from first to fifth) of Legendre polynomials (LP) and accounted for homogeneous or heterogeneous residual variance across the lactation. Models were compared based on Akaike information criterion (AIC), Bayesian information criterion (BIC), and predictive ability. In general, models with a higher order of LP showed better goodness of fit based on AIC and BIC values. However, models with third, fourth, and fifth order of LP led to similar estimates of genetic parameters and predictive ability. Models with assumption of heterogeneous residual variances achieved better goodness of fit but yielded similar predictive ability compared with those with assumption of homogeneous residual variances. Therefore, a random regression model with third order of LP is suggested to be an appropriate model for genetic evaluation of milk yield in local Chinese Holstein populations.

Genetic parameters of somatic cell scores using random regression test-day models with Legendre polynomials in Tunisian dairy cattle

The aim of this study was to analyse the genetic parameters of test-day somatic cell scores (TDSCS) in Tunisian dairy cattle using random regression models (RRM) with homogeneous residual variances. Data included 43,647 test-day somatic cell count (TDSCC) records collected on 4825 Holstein-Friesian cows with parities up to the fifth, and calving dates between 2000 and 2014. Records of the same animal belonging to different lactations were treated as repeated records. Additive genetic (AG) and permanent environmental (PE) random effects were fitted using Legendre polynomials functions (LP) up to the fifth-order. The models included fixed regressions on days-in-milk (DIM), modelling the average phenotypic curve using Ali-Schaeffer's lactation function. Overall, three models (LP3, LP4 and LP5) where compared using the -2log marginal density for Bayes factor (-2logp) and the deviance information criterion (DIC). Models were also compared by similarities between the estimated breeding values and genetic trends. For all models, the total variances tended to decrease during the first month of the lactation, then they maintained a nearly constant trend. All models showed the largest AG and PE variances at the beginning and at the end of lactation with an oscillating pattern in the middle. The heritability estimates of from the three models ranged from 0.01 to 0.08 with the lowest heritability estimates being in the mid-lactation period. Genetic correlations between adjacent DIM were high and then decreased as the intervals widened. All the best statistical criteria were obtained with the LP5 model, however, no differences were observed between the estimated 305 average breeding values (EBV305) from the three models. This assumes that the LP3 model can be selected as a parsimonious model since it contains the smallest number of parameters. This RRM study provided a good insight into the changes in genetic merit of the somatic cell score trait in Tunisian dairy cattle and that an EBV for the SCS should be included in the selection programs. It also showed the benefits given of using RRMs to perform national genetic evaluations of dairy cattle for milk production traits in Tunisia.

Evaluation of body weight and hip height in Nellore cows in a tropical environment

The aim of this study was to evaluate random regression models to estimate genetic parameters for body weight and hip height in Nellore females and identify the best time to select these characteristics for adult size control. The dataset comprised 33,569 body weight and 31,804 height records from 3,860 and 3,487 females, respectively. Contemporary group and physiological state (for body weight analysis) were modeled as fixed effects, whereas age at birth (both linear and quadratic effects) and Legendre polynomials of age at (k = 4) as covariates. Random effects, including the additive genetic effects, and the animal permanent environment effect and the dam (for body weight analysis), were also included in the model. Random effects were modeled by Legendre polynomials of degree k in [3, 6], while residues were considered to be homogeneous or heterogeneous. The best model for body weight included heterogeneous residual variance with 21 classes and Legendre polynomials of degree k = 3, k = 6 and k = 1 for the additive genetic effects, animal and maternal permanent environment effects, respectively. The best model for hip height included homogeneous residual variance and Legendre polynomials of degree k = 4 and k = 3 for the additive genetic effects and animal permanent environment effect, respectively. Heritability estimates were moderate to high and suggest the studied characteristics could be used for maintaining cow size, being the selection at 3 years sufficient to get the desired size.

Genetic variability and linear relationships between plant architecture and maize grain yield

ABSTRACT: The objectives of this study were to analyze whether there is genetic variability and assess the linear relationships between plant architecture and maize grain yield. Three experiments were carried out in a complete randomized block design. A group of 51 cultivars was assessed in relation to 22 traits: number of leaves, plant height, ear height, leaf angle, leaf length, leaf width, leaf area, and grain yield. Individual analyses of variance were performed, the assumptions of normality of errors and homogeneity of residual variances were tested, and means were grouped by the Scott-Knott test. The phenotypic correlation matrix was constructed using the 22 traits of the 51 cultivars. Results showed that there is genetic variability among cultivars for number of leaves, plant height, ear height, leaf angle, leaf length, leaf width, leaf area, and grain yield. Leaves close to the ear have smaller leaf angle and larger length, width, and area of the leaves. Leaf angle gradually increases towards the lower and upper ends of the plant. Length, width, and area gradually decreases in leaves towards the lower and upper ends of the plant. Cultivars with higher number of leaves and larger leaf area are associated with higher grain yield.

Lactation and test day random regression models for genetic evaluation of Murrah buffaloes

In this study the variance components, genetic parameters and breeding values for 305 day or less milk yield were estimated using lactation model and random regression models for the first three lactations in Murrah buffaloes. Random regression model were taken both as random regression model with homogeneous residual variance (RRMHOM) and heterogeneous residual variance (RRM-HET). The estimates of additive genetic variance using RRM were higher than lactation model in all the three lactations. RRM-HET gave higher estimates of additive genetic variance than RRM-HOM in first lactation while RRM-HOM gave higher estimates in second and third lactation. From RRM, it was possible to account for permanent environmental variance arising due to individual milk yield variations during lactation. The heritability estimates were comparable in all the three models. However, in first lactation, the heritability estimates from lactation model RRM-HOM and RRM-HET were 0.319, 0.296 and 0.305, respectively. Likewise in second and third lactations these estimates were 0.004, 0.137, 0.135 and 0.520, 0.315, 0.264, respectively. The breeding value rank correlation was high in all the lactations. More sires were common for each model among the top 10 ranked sires in all three lactations. In conclusion, RRM can be an alternative to lactation model owing to high accuracy, early evaluation, high additive genetic variance, comparable heritability and high rank correlation for breeding values.

Heritability and genotype-environment interactions for growth curve parameters in meat-type quail fed different threonine:lysine ratios from hatching to 21 d of age

The aim of the present study was to estimate heritabilities and evaluate the existence of genotype-environment interactions for growth curve parameters in quail fed diets containing different threonine:lysine ratios. A total of 4,441 body weight information from two genetic quail groups (LF1 and LF2) fed diets containing 0.66%, 0.71%, 0.76%, 0.81%, and 0.86% threonine:lysine ratios from hatching to 21 d of age were evaluated. From 22 to 35 d of age, quail received a single diet. The Gompertz model was used to estimate growth curve parameters. Genetic analyses were performed using random regression models, by Legendre polynomials of the second kind, considering homogeneity of residual variances. The following characteristics were evaluated: asymptotic weight, asymptotic growth rate, and inflection point. Increases in threonine:lysine ratios promoted higher heritability estimates for these variables in the LF1 genetic group compared to LF2, which indicates that the additive genetic variation was modified due to the environmental variation influenced by the evaluated amino acid ratios, with differences between both genetic groups. Thus, it is recommended that quail be selected in the 0.86% ratio in genetic group LF1 and 0.66% in genetic group LF2, where greater heritabilities were observed. Dispersion of individual breeding values along the environmental gradient was observed for all evaluated characteristics, in both genetic groups, suggesting the existence of genotype-environment interactions for these variables. The evaluated amino acid ratios should be considered in quail breeding programs, since breeding value predictions for a determined threonine:lysine ratio are not valid for other ratios.

Multiple trait and random regression models using linear splines for genetic evaluation of multiple breed populations

This study aimed to verify if random regression models using linear splines (RRMLS) are suitable for identifying genetic parameters in multiple-breed populations and also to investigate whether an interaction exists between the breeding value (BV) of sires and their progeny breed group. Ten populations were simulated by crossing 2 breeds with distinct genetic variance and nonzero segregation variance. To obtain the genetic parameters, 2 models were used: a multiple-trait model (MULT), in which the trait was considered distinct when evaluated in each group (1/2P1 + 1/2P2, 5/8P1 + 3/8P2, and 3/4P1 + 1/4P2), and a RRMLS with the spline polynomial knots adjusted to these same groups. The genetic parameters estimated through MULT and RRMLS did not differ from the simulated values. The correlations between BV (simulated and estimated) of animals were high and varied from 0.74 to 0.76, which indicates the efficiency of using MULT and RRMLS for predicting BV. Using field data, the traits age at first calving (AFC), first lactation length (LL), and 305-d milk yield (MY-305) from a multiple-breed population of Holstein-Gyr cattle were analyzed. The BV of animals were modeled through RRMLS with 3, 5, and 7 knots, distributed in accordance with the fraction of Holstein breed in each progeny breed group. It was verified that RRMLS with 7 knots for adjusting mean trajectories and genetic effects, with homogeneous residual variance, best fit AFC and LL. For MY-305, the best fit for mean trajectory and genetic effects was the RRMLS with 5 knots and with homogeneous residual variance. The posterior means of heritability varied from 0.21 to 0.48, 0.21 to 0.38, and 0.10 to 0.33 for AFC, LL, and MY-305, respectively. Estimates from genetic parameters obtained by using RRMLS with field data showed that this model is a useful tool for genetic evaluations of populations formed by a great number of breed groups. An interaction occurred between the BV of sires and their progeny breed group, and the genetic parameters for AFC, LL, and MY-305 traits from a multiple-breed population depend on breed composition of the progeny from which the evaluations are based.

Models of reaction standards for analysis of effects of interaction genotype environment

The objective of this review was to evaluate the effects of environmental genotype interaction in beef cattle through the use of hierarchical models of one - step and two - step reaction standards, with assumptions of homogeneous and heterogeneous residual variances, comparing the results with the Standard animal assessment model. The hierarchical model of one - step reaction standards presented as the best fit to the data to evaluate the effects of the environmental gradient on the animal genotype, among the reviewed studies. Models of reaction norms can bring significant progress in identifying the effects of genotype environment interaction of different races. Leading to increases in the efficiency of production systems, through reported information, which can assist technicians and producers in the choice of specific genetic materials that meet the particularities of the production systems that are inserted.

Random regression test-day milk yield models as a suitable alternative to the traditional 305-day lactation model for genetic evaluation of Sahiwal cattle

In the present study, first three lactation 305-day milk yield variance components, genetic parameters and breeding value were estimated using test-day milk yield and 305-day actual milk yield data of Sahiwal cattle. The estimates were obtained using three methods viz. random regression model (RRM) with homogeneous residual variance (RRM-HOM), RRM with heterogeneous residual variance (RRM-HET) and univariate animal model. The additive genetic variance of 305-day milk yield estimated from RRM was higher compared to univariate animal model for all lactation. From RRM, it was possible to account for permanent environmental effects due to individual milk yield variations during lactation. The heritability estimates were low for first (0.072 to 0.079) and third lactation (0.087 to 0.112) 305-day milk yield from all three methods. For second lactation, low heritability estimate from univariate animal model (0.144) and moderate estimate from different RRM (0.206 to 0.219) were obtained. For all lactation, breeding value rank correlation was more than 0.78 between lactation model and random regression testday model. The same bull was identified as top ranking bull from all three methods. It can be concluded that random regression test-day models can replace conventional 305-day lactation model for genetic evaluation as it resulted in higher additive genetic variance estimates, gave similar/higher heritability value and moderate to high rank correlation estimates for breeding values.

Estimation of the effects of selection on French Large White reproductive performance using frozen semen.

Genetic trends for male and female sexual development and components of litter size and weight at birth (LB) as well as traits related to sow lifetime productivity were estimated in a French Large White (LW) pig population using frozen semen. Two experimental groups (EXP = L77 and L98) were produced by inseminating LW sows with either stored frozen semen from 17 LW boars born in 1977 (EXP = L77) or with semen from 23 LW boars born in 1998 (EXP = L98). In each group, about 100 males were measured for testicular development and bulbo-urethral gland development, and 90 gilts were checked for puberty, among which about 50 gilts were measured for their first ovulation rate at puberty (ORP). The females were then mated to 15 randomly chosen males for each group for 6 successive parities and measured for total number born (TNB), number born alive (NBA), litter weight at birth (LWB), and interval from weaning to first estrus as well as ovulation rate at fertilization (ORF = ovulation rate of gestating sows) on a subset of litters. The data were analyzed using mixed linear models including the fixed effects of EXP, of finishing (sexual development traits) or farrowing group, and parity (LB traits); the random effects of birth litter (sexual development traits) or sow (LB traits); and, when relevant, age or BW as covariates. The homogeneity of residual variances across EXP was also tested. For each trait, the genetic trend was estimated as twice the difference between L98 and L77. Limited differences were observed for sexual development traits, except for ORF, which increased by 0.17 ± 0.06/yr. The residual SD of NBA (2.7 vs. 2.0; = 0.004) and LWB (3.1 vs. 2.5; = 0.03) were larger in L98 sows than in L77 sows. Positive trends were observed for all litter traits (0.22 ± 0.07 ova, 0.12 ± 0.04 piglet, 0.09 ± 0.04 piglet, and 0.23 ± 0.06 kg/yr for ORF, TNB, NBA, and LWB, respectively) but at the expense of a degradation of prenatal and farrowing survival. A negative trend was also observed for length of productive life (-8.9 ± 3.9 d/yr). These results suggest that gains in productivity were associated with a loss of robustness.

ANALISIS GGE BIPLOT PADA HASIL KLON-KLON UBI KAYU MENGGUNAKAN METODE RESTRICTED MAXIMUM LIKELIHOOD

The study was conducted in five locations i.e Kediri, Ponorogo, Probolinggo, Malang, and Mojokerto, from November 2010 until August 2011. The planting materials used were 15 cassava clones. The research objective was to compare analysis methods of the genotype × environment interaction, namely: a) GGE technique using REML without A matrix by assuming homogeneous residual error variance, b) GGE technique using REML with A matrix by assuming homogeneous residual error variance, c) GGE technique using REML without A matrix by assuming heterogeneous residual error variance, and d) GGE technique using REML with A matrix by assuming heterogeneous residual error variance. The results showed that GGE technique using REML without A matrix by assuming heterogeneous residual error variance was more appropriate. Clones CMM 03038-7 (G8) had a wide adaptability and high yield potential, and its clone was closest to the ideal criteria for genotype compared with other genotypes. Clones CMM 03094-4 (G10) had specific adaptability in the environments S2 (Malang) and S5 (Mojokerto), and it had higher yield potential than the control varieties UJ5, Malang 6, and Adira 4. Environment Kediri (S1) had the highest yield among other environments and Kediri was a suitable environment for the growth and selection of cassava.<p align="center"> </p>

GENOTYPE-ENVIRONMENT INTERACTIONS ON THE WEIGHT OF TABAPUA CATTLE IN THE NORTHEAST OF BRAZIL

ABSTRACT: The objective of this work was to verify the presence of genotype-environment interactions (GEI) on the weight adjusted to 365 days of age (W365) for Tabapua cattle in the Northeast of Brazil, using reaction norm (RN) models. The reaction norm model with homogeneous residual variance and 1-step provided the best fit to the data by the criteria information of deviance, deviance based on the conditional predictive ordinate and deviance based on the Bayes factors. Direct and maternal heritability estimates for the animal model were 0.17±0.04 and 0.07±0.03, respectively. The direct heritability for model reaction norm in the low and high management environment were 0.21±0.06 and 0,70±0.04, respectively. The correlations between the slope and intercept of the reaction norm were 0.68±0.10 and 0.96±0.06 for the direct and maternal effect, indicating that the animals with high genetic values respond positively to environment improvements, especially for the maternal effect. The Spearman correlations between the sire classifications ranged from 0.14 to 0.94 in the different environments and models. Genetic values of most animals were robust to environmental variation, however, considering the sires with the highest number of progeny, there is the presence of GEI.