Advances in methodology for utilizing sequential records

1999 ◽  
Vol 24 ◽  
pp. 55-61 ◽  
Author(s):  
W. G. Hill ◽  
S. Brotherstone
Keyword(s):  
Genetic Analysis ◽  
Milk Yield ◽  
Functional Data ◽  
Random Regression ◽  
Covariance Functions ◽  
Sire Family ◽  
The Uk ◽  
Over Time

AbstractThere have been substantial advances in recent years in methods for genetic analysis of traits that are expressed repeatedly over time, for example milk yield on successive test days during lactation. The background to the methods, notably random regression, covariance functions and splines, are outlined. The utility of these methods for analysing functional data on which individual records on cows are few but sire family records that span the lactation, is reviewed. Methods of analyses for measures of herd life are discussed and that being adopted in the UK is outlined.

Importance of genotype by environment interaction on genetic analysis of milk yield in Iranian Holstein cows using a random regression model

2019 ◽  
Vol 59 (8) ◽  
pp. 1438
Author(s):  
Y. Fazel ◽  
A. Esmailizadeh ◽  
M. Momen ◽  
M. Asadi Fozi
Keyword(s):  
Genetic Analysis ◽  
Regression Model ◽  
Milk Yield ◽  
Genetic Parameter ◽  
Genotype By Environment Interaction ◽  
Random Regression ◽  
Holstein Cows ◽  
Environment Interaction ◽  
Genotype By Environment ◽  
Random Regression Model

Changes in the relative performance of genotypes (sires) across different environments, which are referred to as genotype–environment interactions, play an important role in dairy production systems, especially in countries that rely on imported genetic material. Importance of genotype by environment interaction on genetic analysis of milk yield was investigated in Holstein cows by using random regression model. In total, 68945 milk test-day records of first, second and third lactations of 8515 animals that originated from 100 sires and 7743 dams in 34 herds, collected by the Iranian animal breeding centre during 2007–2009, were used. The different sires were considered as different genotypes, while factors such as herd size, herd milk average (HMA), herd protein average and herd fat average were used as criteria to define the different environments. The inclusion of the environmental descriptor improved not only the log-likelihood of the model, but also the Bayesian information criterion. The results showed that defining the environment on the basis of HMA affected genetic parameter estimations more than did the other environmental descriptors. The heritability of milk yield during lactating days reduced when sire × HMA was fitted to the model as an additional random effect, while the genetic and phenotypic correlations between lactating months increased. Therefore, ignoring this interaction term can lead to the biased genetic-parameter estimates, reduced selection accuracy and, thus, different ranking of the bulls in different environments.

Genetic evaluation using random regression models with different covariance functions for test-day milk yield in an admixture population of Thailand goats

2014 ◽  
Vol 86 (7) ◽  
pp. 655-660 ◽  
Author(s):  
Mongkol Thepparat ◽  
Wuttigrai Boonkum ◽  
Monchai Duangjinda ◽  
Sornthep Tumwasorn ◽  
Sansak Nakavisut ◽  
...  
Keyword(s):  
Milk Yield ◽  
Regression Models ◽  
Genetic Evaluation ◽  
Random Regression ◽  
Covariance Functions ◽  
Admixture Population

scholarly journals Estimation of genetic parameters for test-day milk yield in Khuzestan buffalo

2016 ◽  
Vol 51 (7) ◽  
pp. 890-897 ◽  
Author(s):  
Mostafa Madad ◽  
Navid Ghavi Hossein-Zadeh ◽  
Abdol Ahad Shadparvar
Keyword(s):  
Genetic Correlation ◽  
Milk Yield ◽  
Environmental Effects ◽  
Regression Models ◽  
Genetic Parameters ◽  
Information Criterion ◽  
Step Function ◽  
Random Regression ◽  
Residual Variance ◽  
Covariance Functions

Abstract: The objective of this work was to estimate covariance functions for additive genetic and permanent environmental effects, as well as to obtain genetic parameters for buffalo test-day milk yield using random regression models on Legendre polynomials (LPs). A total of 2,538 test-day milk yield (TDMY) records from 516 first lactation records of Khuzestan buffalo, calving from 1993 to 2009 and belonging to 150 herds located in the state of Khuzestan, Iran, were analyzed. The residual variances were modeled through a step function with 1, 5, 6, 9, and 19 classes. The additive genetic and permanent environmental random effects were modeled by LPs of days in milk using quadratic to septic polynomial functions. The model with additive genetic and animal permanent environmental effects adjusted by cubic and third order LP, respectively, and with the residual variance modeled through a step function with nine classes was the most adequate one to describe the covariance structure. The model with the highest significant log-likelihood ratio test (LRT) and with the lowest Akaike information criterion (AIC) and Bayesian information criterion (BIC) was considered to be the most appropriate one. Unexpected negative genetic correlation estimates were obtained between TDMY records of the twenty-fifth and thirty-seventh week (-0.03). Genetic correlation estimates were generally higher, close to unity, between adjacent weeks during the middle of lactation. Random regression models can be used for routine genetic evaluation of milk yield in Khuzestan buffalo.

Multi-trait covariance functions to estimate genetic correlations between milk yield, dry-matter intake and live weight during lactation

1999 ◽  
Vol 24 ◽  
pp. 147-151 ◽  
Author(s):  
R. F. Veerkamp ◽  
R. Thompson
Keyword(s):  
Energy Balance ◽  
Milk Yield ◽  
Fixed Effects ◽  
Dry Matter ◽  
Genetic Correlations ◽  
Live Weight ◽  
Random Regression ◽  
Dry Matter Intake ◽  
Covariance Functions ◽  
High Level

AbstractEnergy balance is a function of dry-matter intake (DMI), live weight and milk yield over a certain time period. To investigate potential strategies to use genetic selection for the improvement of the negative energy balance, genetic co-variances were estimated among DMI, live weight and milk yield during the first 15 weeks of lactation (no.=628). Rather than estimating the full 45 by 45 matrix a random regression model was used to estimate a second order covariance functions for the additive genetic and permanent environmental effects. Fixed effects were test-day, a group effect and week in lactation. Estimates for the genetic covariance function demonstrated that a high level of milk yield is only moderately correlated with a high level of DMI (0.21) but very strongly correlated to an increase of intake (0.97) and a loss of live weight (-0.46) during the first 15 weeks of lactation. Levels of weight and intake were correlated strongly (0.81). Estimates for the genetic correlations between weeks 1 and 15 were 0.79, 0.34 and 0.83 for milk yield, DMI and live weight respectively. DMI during early lactation was negatively correlated with milk yield but DMI during the later weeks was positively correlated with milk yield. The implication is that when selection is for a linear combination of milk yield, DMI and live weight (i.e. energy balance or efficiency) the moment in lactation of measuring each trait on the cow is of importance

scholarly journals The use of a Bivariate Random Regression Model for Genetic Analysis of Milk Yield in Iranian Native Buffalo

2018 ◽  
Vol 9 (19) ◽  
pp. 102-112 ◽  
Author(s):  
Yousef Naderi ◽  
Mohsen Gholizadeh ◽  
Mostafa Madad ◽  
◽  
◽  
...  
Keyword(s):  
Genetic Analysis ◽  
Regression Model ◽  
Milk Yield ◽  
Random Regression ◽  
Random Regression Model

UK Fiscal Squeezes over a Century

2017 ◽  
Author(s):  
Christopher Hood ◽  
Rozana Himaz
Keyword(s):  
Data Sources ◽  
Fiscal Consolidation ◽  
Financial Outcomes ◽  
Financial Conditions ◽  
Deficit Reduction ◽  
Before And After ◽  
The Uk ◽  
Changes Over Time ◽  
Spending Cuts ◽  
Over Time

This chapter draws on historical statistics reporting financial outcomes for spending, taxation, debt, and deficit for the UK over a century to (a) identify quantitatively and compare the main fiscal squeeze episodes (i.e. major revenue increases, spending cuts, or both) in terms of type (soft squeezes and hard squeezes, spending squeezes, and revenue squeezes), depth, and length; (b) compare these periods of austerity against measures of fiscal consolidation in terms of deficit reduction; and (c) identify economic and financial conditions before and after the various squeezes. It explores the extent to which the identification of squeeze episodes and their classification is sensitive to which thresholds are set and what data sources are used. The chapter identifies major changes over time that emerge from this analysis over the changing depth and types of squeeze.

scholarly journals Topological and Morphological Controls on Morphodynamics of Salt Marsh Interiors

2021 ◽  
Vol 9 (3) ◽  
pp. 311
Author(s):  
Ben R. Evans ◽  
Iris Möller ◽  
Tom Spencer
Keyword(s):  
Salt Marsh ◽  
Salt Marshes ◽  
East Coast ◽  
Marsh Surface ◽  
Coastal Environments ◽  
Coastal System ◽  
Morphological Segmentation ◽  
The Usa ◽  
The Uk ◽  
Over Time

Salt marshes are important coastal environments and provide multiple benefits to society. They are considered to be declining in extent globally, including on the UK east coast. The dynamics and characteristics of interior parts of salt marsh systems are spatially variable and can fundamentally affect biotic distributions and the way in which the landscape delivers ecosystem services. It is therefore important to understand, and be able to predict, how these landscape configurations may evolve over time and where the greatest dynamism will occur. This study estimates morphodynamic changes in salt marsh areas for a regional domain over a multi-decadal timescale. We demonstrate at a landscape scale that relationships exist between the topology and morphology of a salt marsh and changes in its condition over time. We present an inherently scalable satellite-derived measure of change in marsh platform integrity that allows the monitoring of changes in marsh condition. We then demonstrate that easily derived geospatial and morphometric parameters can be used to determine the probability of marsh degradation. We draw comparisons with previous work conducted on the east coast of the USA, finding differences in marsh responses according to their position within the wider coastal system between the two regions, but relatively consistent in relation to the within-marsh situation. We describe the sub-pixel-scale marsh morphometry using a morphological segmentation algorithm applied to 25 cm-resolution maps of vegetated marsh surface. We also find strong relationships between morphometric indices and change in marsh platform integrity which allow for the inference of past dynamism but also suggest that current morphology may be predictive of future change. We thus provide insight into the factors governing marsh degradation that will assist the anticipation of adverse changes to the attributes and functions of these critical coastal environments and inform ongoing ecogeomorphic modelling developments.

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