Season and reproductive status rather than genetics factors influence change in ewe weight and fat over time. 3. Analysis of Merino ewes

2014 ◽  
Vol 54 (6) ◽  
pp. 821 ◽  
Author(s):  
S. F. Walkom ◽  
F. D. Brien ◽  
M. L. Hebart ◽  
S. I. Mortimer ◽  
W. S. Pitchford
Keyword(s):  
Genetic Variation ◽  
Body Condition ◽  
Production Systems ◽  
Genetic Effect ◽  
Additive Genetic Effect ◽  
Production Cycle ◽  
Production Cycles ◽  
Australian Sheep ◽  
Sheep Production ◽  
Stocking Rates

The profitability of southern Australian sheep production systems depends on the optimisation of stocking rates by meeting the nutritional demands of the breeding ewe while effectively utilising grown pasture. The aim of the study was to evaluate the genetic variation in liveweight and body condition of Merino ewes across their breeding life within a wool-based enterprise. The results were consistent with findings in crossbred ewes and showed that the genetic component of weight and body condition remained constant across the production cycle and age. The overall additive genetic effect accounted for 92% of the genetic variation in weight of Merino ewes bred across five production cycles. A genetic correlation of 0.85 suggested that ewes that were superior at maintaining their condition when rearing a single lamb would maintain this superiority when rearing multiple lambs. To improve weight and condition of Merino ewes during the ‘tough’ times, when nutrient requirements are not met by the pasture, selection can be made at any time and this will result in increased genetic condition at all times.

scholarly journals Assessment of Meat-Type Sheep Welfare Using Animal-Based Measures

Animals ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 2120
Author(s):  
Naceur M’Hamdi ◽  
Cyrine Darej ◽  
Khaoula Attia ◽  
Hajer Guesmi ◽  
Ibrahim El Akram Znaïdi ◽  
...  
Keyword(s):  
Body Condition ◽  
Production Systems ◽  
Body Condition Score ◽  
Increased Risk ◽  
Condition Score ◽  
Welfare Problem ◽  
Welfare Measures ◽  
Human Animal ◽  
Sheep Production ◽  
Global Mean

This study aimed to assess the welfare of Tunisian sheep in extensive sheep production systems using animal-based measures of ewe welfare. This study encompasses the first national survey of sheep welfare in which animal-based outcomes were tested. Animal-based welfare measures were derived from previous welfare protocols. Fifty-two Tunisian farms were studied and a number from 20 to 100 animals by flock were examinated. The whole flock was also observed to detect clinical diseases, lameness, and coughing. The human-animal relationship was selected as welfare indicators. It was evaluated through the avoidance distance test. The average avoidance distance was 10.47 ± 1.23 and 8.12 ± 0.97 m for a novel person and farmer, respectively. The global mean of body condition score (BCS) was 2.4 with 47% of ewes having a BCS of two, which may be associated with an increased risk of nutritional stress, disease, and low productivity. Ten farms had more than 7% of lambs with a low body condition score, which may be an indication of a welfare problem. The results obtained in the present study suggest that the used animal-based measures were the most reliable indicators that can be included in welfare protocols for extensive sheep production systems.

Season and reproductive status rather than genetics factors influence change in ewe weight and fat over time. 1. Analysis of crossbred ewes

2014 ◽  
Vol 54 (6) ◽  
pp. 802 ◽  
Author(s):  
S. F. Walkom ◽  
F. D. Brien ◽  
M. L. Hebart ◽  
N. M. Fogarty ◽  
S. Hatcher ◽  
...  
Keyword(s):  
Genetic Correlations ◽  
Adult Life ◽  
Production Cycle ◽  
Strong Correlations ◽  
Multiple Production ◽  
Fat Reserves ◽  
Production Cycles ◽  
Australian Sheep ◽  
Maternal Performance ◽  
Fat Score

The Australian sheep industry has historically made rapid advances in the quality and quantity of meat and wool through genetic improvement, but unfortunately, maternal performance, i.e. number of lambs weaned, is well below desired levels. The aim of the present paper is to investigate the potential to select for increased weight and fat across the production cycle to improve maternal performance. The analysis explores the potential to improve the weight and fat score of breeding ewes during ‘tough’ periods (i.e. when nutrient requirements are not met by the pasture), preparing the breeding ewe for the upcoming mating without an increase in overall ewe size. The 2846 ewes within the maternal central progeny test were weighed and scored for fatness 12 times across three production cycles. Low to moderate heritability estimates for weight (0.04–0.23) and fat (0.02–0.06) changes across the production cycle provide little hope for selection against weight loss during tough periods. The analysis showed very strong genetic correlations between time-points across multiple production cycles for both weight (0.99–0.93) and fat score (0.88–0.98). The very strong correlations between measurements suggest that weight and fat score are genetically the same trait throughout the ewe’s adult life. With 74% and 77% of the genetic variation in weight and fat, respectively, constant across the production cycle, there is little opportunity to select against the natural fluctuations in weight and fat reserves. In conclusion, selection for increased fat can be made at any time and it will result in more fat during tough times.

Genetic evaluation of adult ewe bodyweight and condition: relationship with lamb growth, reproduction, carcass and wool production

2017 ◽  
Vol 57 (1) ◽  
pp. 20 ◽  
Author(s):  
S. F. Walkom ◽  
D. J. Brown
Keyword(s):  
Genetic Variation ◽  
Body Condition ◽  
Current Practice ◽  
Genetic Relationships ◽  
Physiological Status ◽  
Production Cycle ◽  
Production Traits ◽  
Wool Production ◽  
The Impact ◽  
Lamb Growth

This paper reports on genetic variation in the growth, wool production, carcass, reproduction and the bodyweight and body condition of ewes managed in the Information Nucleus Flock (INF), with a focus on evaluating the potential value of including adult ewe bodyweight and condition change traits in the Australian national sheep genetic evaluations provided by Sheep Genetics. Data were collected over a 7-year period (2007–2013) at eight research sites across southern Australia. Approximately 13 700 ewes were weighed and condition scored with ewes on average mated four times during the study. Adult ewe weight and body condition were recorded across the production cycle and the impact of the physiological status and change in status of the ewe on the genetic relationships with lamb growth, carcass and wool production traits was evaluated. Strong genetic correlations between measurements across the production cycle for adult ewe bodyweight and condition, low heritability of change traits, along with weak genetic relationships between change traits and key production traits suggest that in production systems where nutritional challenges can be managed, change traits provide no improvement to the current practice of using static bodyweight and condition records. The genetic variation in weight and body condition and their genetic relationships with production traits were highly consistent across ages and the production cycle. As a result, the current practice by Sheep Genetics to treat adult weight as a single trait with repeat records is most likely sufficient. However, the inclusion of body condition within the Sheep Genetics evaluation has potential to assist in improving maternal performance, and the feed costs associated with maintaining ewe body condition.

Prediction of body fat from body condition score and live body weight in adult churra ewes

1994 ◽  
Vol 1994 ◽  
pp. 209-209
Author(s):  
P. Frutos ◽  
A.R. Mantecón ◽  
P.R. Revesado ◽  
J.S. González
Keyword(s):  
Body Weight ◽  
Body Fat ◽  
Body Condition ◽  
Production Systems ◽  
Body Condition Score ◽  
Fat Depots ◽  
Live Body Weight ◽  
Condition Score ◽  
Sheep Production ◽  
Semiarid Conditions

Most sheep production systems under arid or semiarid conditions are dependent on the ability of animal to retain and movilize body fat.The sucess of body condition score (BCS) method (Russel et al., 1969) and live body weight (LBW) as mesures of body reserves is in function of body fat depots distribution, and the sheep genotype could determine this distribution (Taylor et al., 1989).The aim of this work is to predict body fat depots of adult Churra ewes, from BCS and/or LBW.A total of 36 adult Churra ewes, with a range of body condition score between 1.25 and 4.00 and live body weight between 30.3 and 52.6 kg were used.At slaugther, internal fat depots (IF; omental:OF, mesenteric:MF and perirenal:PF) were removed and weighed. Chemical fat of carcass (CF) and non-carcass (NCF) were also estimated.

The value of genetic fatness in Merino ewes differs with production system and environment

2010 ◽  
Vol 50 (12) ◽  
pp. 1011 ◽  
Author(s):  
M. B. Ferguson ◽  
J. M. Young ◽  
G. A. Kearney ◽  
G. E. Gardner ◽  
I. R. D. Robertson ◽  
...  
Keyword(s):  
Production System ◽  
Selection Pressure ◽  
Production Systems ◽  
High Response ◽  
Economic Modelling ◽  
Sheep Industry ◽  
True Value ◽  
Australian Sheep ◽  
Sheep Production ◽  
Small Change

Selection against fatness in the Australian sheep industry has been a priority, but defining the true value of fat requires an understanding of the effects it has on both the value of lamb carcasses and on sheep productivity. A Merino flock with 10 years of reproduction data was used to analyse the correlation between breeding values for fatness at yearling age (YFAT) and the number of lambs born per ewe mated (NLB). In 2 production years, NLB was related (P < 0.01) to YFAT resulting in an extra 14 or 24.5 lambs born per 100 ewes mated per mm of YFAT. Based on these relationships, bio-economic modelling was used to assess the whole-farm value of YFAT for different sheep production systems and for years representing a low, medium and high response of NLB to YFAT. The changes in whole-farm profitability for a 1-mm increase in YFAT varied from $1000 (2%) for a wool enterprise with a low response up to $44 000 (25%) for a lamb enterprise with a high response. Appropriate carcass value discounts for higher YFAT were investigated but were not evident because of the small change in GR fat depth associated with the range of YFAT investigated. In most years there is no impact of YFAT on NLB and therefore profitability, yet in years where Merino ewes with higher YFAT produce higher NLB, ewes with an extra 1 mm of YFAT will be up to 25% more profitable. Therefore, care is required in determining the appropriate selection pressure to be placed on YFAT in Merino selection.

Personal Goals of Older Female Twins

2009 ◽  
Vol 14 (2) ◽  
pp. 160-167 ◽  
Author(s):  
Katariina Salmela-Aro ◽  
Sanna Read ◽  
Jari-Erik Nurmi ◽  
Markku Koskenvuo ◽  
Jaakko Kaprio ◽  
...  
Keyword(s):  
Physical Exercise ◽  
Independent Living ◽  
Environmental Effect ◽  
Close Relationships ◽  
Environmental Influence ◽  
Genetic Effect ◽  
Additive Genetic Effect ◽  
Personal Goals ◽  
Cultural Activities ◽  
The Individual

This study examined genetic and environmental influences on older women’s personal goals by using data from the Finnish Twin Study on Aging. The interview for the personal goals was completed by 67 monozygotic (MZ) pairs and 75 dizygotic (DZ) pairs. The tetrachoric correlations for personal goals related to health and functioning, close relationships, and independent living were higher in MZ than DZ twins, indicating possible genetic influence. The pattern of tetrachoric correlations for personal goals related to cultural activities, care of others, and physical exercise indicated environmental influence. For goals concerning health and functioning, independent living, and close relationships, additive genetic effect accounted for about half of the individual variation. The rest was the result of a unique environmental effect. Goals concerning physical exercise and care of others showed moderate common environmental effect, while the rest of the variance was the result of a unique environmental effect. Personal goals concerning cultural activities showed unique environmental effects only.

scholarly journals 395 Impact of pig mortality on U.S. pig producers

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 184-185
Author(s):  
Caleb M Shull
Keyword(s):  
Feed Efficiency ◽  
Management Practices ◽  
Production Systems ◽  
Research Priorities ◽  
Functional Approach ◽  
Production Cycle ◽  
Limiting Factor ◽  
Significant Challenge ◽  
The Relationship ◽  
Positive Step

Abstract Swine producers in the U.S. face a significant challenge. On top of the ever-changing market dynamics that lead to wide swings in profitability or loss, is an underlying issue of pig mortality that the industry must address. While significant improvements in total piglets born per litter have been achieved over the last 10 years, pig mortality has seen no improvement or has worsened (Figure 1). When expressed as a percentage of piglets born (excluding mummies), a total of 7.9% were recorded as stillborn and 13.4% died prior to weaning in 2019. Assuming a typical mortality range of 7–10% from weaning to harvest, a typical U.S. producer could expect to lose around 27–30% of all piglets born. In addition, the average producer had around 12% annual sow mortality (Figure 1). Litter size and post-weaning growth rate and feed efficiency will always factor heavily into research priorities due to the economic impact associated with those traits; however, the opportunity to drive value through reduction in pig losses across the production cycle is staggering. In defense of the industry, improving pig survival is not an easy task for a number of reasons. The sample size (i.e., number of pigs) required to do mortality research correctly is often a limiting factor for many production systems. Furthermore, a cross-functional approach is likely required to make significant improvements in mortality. Specifically, the relationship between genetics, health, and management practices warrant consideration. Recent collaboration across the industry to improve mortality is a positive step forward and this collaboration should continue moving forward.

Improving sheep production and welfare in extensive systems through precision sheep management

2012 ◽  
Vol 52 (7) ◽  
pp. 665 ◽  
Author(s):  
Jessica E. Morris ◽  
Greg M. Cronin ◽  
Russell D. Bush
Keyword(s):  
Radio Frequency ◽  
Radio Frequency Identification ◽  
Production Efficiency ◽  
Sheep Industry ◽  
Monitoring Frequency ◽  
Frequency Identification ◽  
Australian Sheep ◽  
Sheep Production ◽  
The Cost ◽  
Improve Production Efficiency

This overview discusses how precision sheep management could be utilised in the Australian sheep industry to improve production efficiency and reduce animal welfare concerns due to low monitoring frequency by stockpeople. The concept of precision sheep management is described. This is a system in which sheep are managed as individuals or small groups rather than as a (whole) flock. Precision sheep management utilises the application of radio frequency identification technology, enabling producers to better monitor sheep in extensive situations, and contribute to improved efficiency of management and sheep welfare. Examples of combining radio frequency identification with other technologies such as walk-over-weighing and Pedigree Matchmaker are discussed. These technologies provide producers with tools to improve the cost effectiveness of, and labour efficiency associated with, collecting data on individual animals. The combined technologies should also improve consistency and reliability of information, enhancing decision-making by producers, for example, from regular monitoring of biometric variables such as liveweight, or calculating breeding values to enable superior genetic comparisons over time.

Evaluation of a new technology when applied to sheep production systems: Part I—Model description

1989 ◽  
Vol 29 (1) ◽  
pp. 35-47 ◽  
Author(s):  
P.J. Bowman ◽  
D.A. Wysel ◽  
D.G. Fowler ◽  
D.H. White
Keyword(s):  
Production Systems ◽  
New Technology ◽  
Model Description ◽  
Sheep Production

scholarly journals A review of deep learning applications for genomic selection

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Osval Antonio Montesinos-López ◽  
Abelardo Montesinos-López ◽  
Paulino Pérez-Rodríguez ◽  
José Alberto Barrón-López ◽  
Johannes W. R. Martini ◽  
...  
Keyword(s):  
Deep Learning ◽  
Plant Breeding ◽  
Genomic Selection ◽  
Genomic Prediction ◽  
Mixed Model ◽  
Prediction Models ◽  
Genetic Effect ◽  
Training Data ◽  
Additive Genetic Effect ◽  
Main Body

Abstract Background Several conventional genomic Bayesian (or no Bayesian) prediction methods have been proposed including the standard additive genetic effect model for which the variance components are estimated with mixed model equations. In recent years, deep learning (DL) methods have been considered in the context of genomic prediction. The DL methods are nonparametric models providing flexibility to adapt to complicated associations between data and output with the ability to adapt to very complex patterns. Main body We review the applications of deep learning (DL) methods in genomic selection (GS) to obtain a meta-picture of GS performance and highlight how these tools can help solve challenging plant breeding problems. We also provide general guidance for the effective use of DL methods including the fundamentals of DL and the requirements for its appropriate use. We discuss the pros and cons of this technique compared to traditional genomic prediction approaches as well as the current trends in DL applications. Conclusions The main requirement for using DL is the quality and sufficiently large training data. Although, based on current literature GS in plant and animal breeding we did not find clear superiority of DL in terms of prediction power compared to conventional genome based prediction models. Nevertheless, there are clear evidences that DL algorithms capture nonlinear patterns more efficiently than conventional genome based. Deep learning algorithms are able to integrate data from different sources as is usually needed in GS assisted breeding and it shows the ability for improving prediction accuracy for large plant breeding data. It is important to apply DL to large training-testing data sets.

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