scholarly journals Economic Selection Indices: The Best Tool for Dairy Cattle Selection

EDIS ◽  
2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Francisco Peñagaricano
Keyword(s):  
Dairy Cattle ◽  
Selection Index ◽  
Multiple Traits ◽  
Selection Indices ◽  
The Us ◽  
Animal Sciences ◽  
Economic Selection

An economic selection index combines multiple traits into a single value, facilitating the identification of the best animals. This new 3-page document discusses economic selection indices, their changes in the US, and a 2018 update. Written by Francisco Peñagaricano, and published by the UF/IFAS Department of Animal Sciences, March 2019.  http://edis.ifas.ufl.edu/an353

Economic selection index in small rural dairy farms

2019 ◽  
Vol 86 (1) ◽  
pp. 25-33
Author(s):  
Marcos Jun-Iti Yokoo ◽  
Leonardo de Oliveira Seno ◽  
Luiza Corrêa Oliveira ◽  
Pedro U N da Costa ◽  
Gustavo M da Silva ◽  
...  
Keyword(s):  
Milk Production ◽  
Selection Criteria ◽  
Production Systems ◽  
Selection Index ◽  
Live Weight ◽  
Productive Performance ◽  
Selection Indices ◽  
The Difference ◽  
Economic Selection ◽  
Bioeconomic Models

AbstractThis study aimed to calculate economic values (EVs) and economic selection indices for milk production systems in small rural properties. The traits 305-d milk yield in kg (MY), fat (FP) and protein (PP) percentage, daily fat (FY) and protein (PY) yield, cow live weight in kg (LW), calving interval (CI), and logarithm of daily somatic cell count (SCC) in milk were considered the goals and selection criteria. The production systems were identified from 29 commercial properties based on the inventory of revenues and costs and of zootechnical field data. Later, bioeconomic models were developed to calculate the productive performance, revenues, and costs concerning milk production to estimate EVs, which were calculated as the difference in annual profit with dairy production resulting from a change in one unit of the trait while keeping the others constant and dividing the value by the number of cows. After the EVs were known, ten economic selection indices were estimated for each system so they could be compared by modifying the selection criteria and calculating the relative importance of each selection criteria, the accuracy of the economic selection index, and response expected to the selection in USD, among other parameters. One of the systems detected was called less intensive (LS) and was characterized by having ten cows in lactation that produced 13·5 l/d and consumed 1·8 kg of concentrate/d. The second system detected was called more intensive (IS) and had 22 cows in lactation that produced 17·5 l/d and consumed 3·4 kg of concentrate/d. Monthly profits per cows in lactation of USD 2·60 and USD 68·77 were recorded for LS and IS, respectively. The EVs of the traits MY, FP, and PP were all positive, while for the other traits they were all negative in all situations. The best economic selection indices were those featuring selection criteria MY, LW, and CI, while the trait LW had the greatest importance in both systems. These results indicate that animal frame must be controlled in order to maximize the system's profit.

scholarly journals 90 Effectiveness of an Educational Program on Beef Bull Selection that Incorporates Classroom Instruction and a Mock Auction

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 73-73
Author(s):  
Darrh Bullock ◽  
Katherine VanValin ◽  
Jeffery Lehmkuhler ◽  
Leslie Anderson ◽  
Benjamin Crites ◽  
...  
Keyword(s):  
Educational Program ◽  
Classroom Instruction ◽  
Selection Index ◽  
Extension Agents ◽  
Purchasing Decisions ◽  
Management Scenarios ◽  
Best Value ◽  
Beef Producers ◽  
The Individual ◽  
Economic Selection

Abstract An educational program was developed to assist beef producers with making informed bull purchasing decisions. There are two core pieces to this decision: targeting the bull’s genetics to the producer’s management and resources, and paying a price that maximizes the return on investment. This was a two-part educational program; the first session was classroom instruction with topics related to proper bull selection. At the conclusion of this session producers were assigned one of five management scenarios and received a sale catalogue with 60 bulls. Videos of all bulls were made available, along with all production information, including adjusted measurements, EPD and indices. The producers were tasked with returning the next week to attend the mock auction and purchase the best valued bull for their assigned scenario. At the conclusion of the auction, each scenario was discussed and the individual that purchased the best value bull in each scenario was recognized. Value was determined as the price paid for the bull compared to a price determined through an “economic selection index” equation. Beef producers (n = 322) participated in the program over 9 locations; in locations with less attendance, a reduced sale catalogue was used. Of the post-program survey respondents (n = 155), 71% were commercial beef producers, 27% were seedstock producers and 5% were Extension agents (n = 10, were cross classified). When asked how much time they spent reviewing the materials before the mock sale 8% said they made their decision at the sale, 16% spent 30 minutes or less, 58% spent 30 minutes to 2 hours and 18% spent over 2 hours preparing. When asked if the program would help with their next bull purchase, 88% said it would probably or definitely help. The conclusion was that this was a valuable educational program.

scholarly journals Genome-wide fine-mapping identifies pleiotropic and functional variants that predict many traits across global cattle populations

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ruidong Xiang ◽  
Iona M. MacLeod ◽  
Hans D. Daetwyler ◽  
Gerben de Jong ◽  
Erin O’Connor ◽  
...  
Keyword(s):  
Dairy Cattle ◽  
Chromosome Segment ◽  
Multiple Traits ◽  
Functional Variants ◽  
Genome Wide ◽  
The Usa ◽  
Pleiotropic Qtl ◽  
Causal Variants ◽  
Genetic Value ◽  
Bayesian Mixture Models

AbstractThe difficulty in finding causative mutations has hampered their use in genomic prediction. Here, we present a methodology to fine-map potentially causal variants genome-wide by integrating the functional, evolutionary and pleiotropic information of variants using GWAS, variant clustering and Bayesian mixture models. Our analysis of 17 million sequence variants in 44,000+ Australian dairy cattle for 34 traits suggests, on average, one pleiotropic QTL existing in each 50 kb chromosome-segment. We selected a set of 80k variants representing potentially causal variants within each chromosome segment to develop a bovine XT-50K genotyping array. The custom array contains many pleiotropic variants with biological functions, including splicing QTLs and variants at conserved sites across 100 vertebrate species. This biology-informed custom array outperformed the standard array in predicting genetic value of multiple traits across populations in independent datasets of 90,000+ dairy cattle from the USA, Australia and New Zealand.

Multi-trait selection index and cluster analyses in Angus cattle

2021 ◽  
pp. 1-8
Author(s):  
G. M. Fernandes ◽  
R. P. Savegnago ◽  
L. A. Freitas ◽  
L. El Faro ◽  
V. M. Roso ◽  
...  
Keyword(s):  
Production System ◽  
Selection Process ◽  
Selection Index ◽  
Genetic Selection ◽  
Genetic Profile ◽  
Breeding Values ◽  
Cluster Analyses ◽  
Scrotal Circumference ◽  
Breeding Programmes ◽  
Economic Selection

Abstract In breeding programmes, the genetic selection process is based on the prediction of animal breeding values, and its results may vary according to the employed selection method. The current study developed an economic selection index for animals of the Angus breed; performed cluster analyses using the breeding values in order to evaluate the genetic profile of the animals candidates to selection, and compared the obtained results between the economic selection index and the cluster analyses. The evaluated traits included weaning weight, 18-month weight, scrotal circumference, fat thickness and ribeye area. Economic values were obtained using bioeconomic modelling, simulating a complete cycle production system of beef cattle breeds in Brazil, and the selection objective were the weaning rate and slaughter weight. The chosen selection index was composed of all of the traits used as selection criteria for the simulated production system. During the cluster analyses, the population was divided into two to four groups, in which the groupings containing potential animals were assessed. The animals of the grouping which was used for comparison with the selection index were identified, and most of the bulls that were included in the index were among the best in the analysed group. These results suggest that the cluster analyses can be used as a tool for the selection of animals to be used as parents for future generations.

scholarly journals Yield traits as selection indices in seedling populations of cassava

2010 ◽  
Vol 10 (3) ◽  
pp. 191-196 ◽  
Author(s):  
Henry Fred Ojulong ◽  
Maryke Tine Labuschagne ◽  
Liezel Herselman ◽  
Martin Fregene
Keyword(s):  
Dry Matter ◽  
Tuber Yield ◽  
Selection Index ◽  
Matter Content ◽  
Root Weight ◽  
Dry Matter Content ◽  
Root Yield ◽  
Breeding Scheme ◽  
Selection Indices ◽  
Root Quality

The cassava breeding scheme currently used is long, because initial stages concentrate mainly on improving yield, with root quality selection following later. To shorten the scheme, yield and root quality should be selected simultaneously, starting at the seedling nursery. In this study, a nursery comprising of eight cassava families and 1885 seedlings developed from parents adapted to three major agro-ecologies, were evaluated for yield related traits in Colombia. Percentage dry matter content (DMC) and harvest index produced similar ranking of the parents. Tuber yield, weight, and number showed potential of increasing yield through conventional breeding. A selection index including fresh root yield, percentage DMC, root weight and roots per plant, with heavier weights being assigned to root weight and roots per plant, should be used.

scholarly journals Pitfalls and Remedies for Cross Validation with Multi-trait Genomic Prediction Methods

2019 ◽  
Author(s):  
Daniel Runcie ◽  
Hao Cheng
Keyword(s):  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Cross Validation ◽  
Prediction Models ◽  
Selection Index ◽  
Parametric Method ◽  
Multiple Traits ◽  
Gold Standard Method ◽  
Secondary Traits ◽  
Validation Strategy

ABSTRACTIncorporating measurements on correlated traits into genomic prediction models can increase prediction accuracy and selection gain. However, multi-trait genomic prediction models are complex and prone to overfitting which may result in a loss of prediction accuracy relative to single-trait genomic prediction. Cross-validation is considered the gold standard method for selecting and tuning models for genomic prediction in both plant and animal breeding. When used appropriately, cross-validation gives an accurate estimate of the prediction accuracy of a genomic prediction model, and can effectively choose among disparate models based on their expected performance in real data. However, we show that a naive cross-validation strategy applied to the multi-trait prediction problem can be severely biased and lead to sub-optimal choices between single and multi-trait models when secondary traits are used to aid in the prediction of focal traits and these secondary traits are measured on the individuals to be tested. We use simulations to demonstrate the extent of the problem and propose three partial solutions: 1) a parametric solution from selection index theory, 2) a semi-parametric method for correcting the cross-validation estimates of prediction accuracy, and 3) a fully non-parametric method which we call CV2*: validating model predictions against focal trait measurements from genetically related individuals. The current excitement over high-throughput phenotyping suggests that more comprehensive phenotype measurements will be useful for accelerating breeding programs. Using an appropriate cross-validation strategy should more reliably determine if and when combining information across multiple traits is useful.

SELECTION FOR YIELD, KERNEL WEIGHT AND PROTEIN CONTENT IN EARLY GENERATIONS OF SIX WHEAT CROSSES

1988 ◽  
Vol 68 (3) ◽  
pp. 641-649 ◽  
Author(s):  
H. GEBRE-MARIAM ◽  
E. N. LARTER ◽  
L. E. EVANS
Keyword(s):  
Triticum Aestivum ◽  
Protein Content ◽  
Selection Index ◽  
Correlation Coefficients ◽  
Triticum Aestivum L ◽  
Kernel Weight ◽  
High Protein ◽  
Selection Indices ◽  
Different Populations ◽  
Relative Potential

Early generation data consisting of F1 heterosis, F1, F2 and F3 mean performances, parent-offspring regression, and F2–F3 intergeneration correlation were used to identify potentially promising spring wheat (Triticum aestivum L. em Thell) crosses in terms of yield, kernel weight and protein content. The F1 test identified one high-yielding cross out of six showing significant level of higher parent (38%) and mid-parent (70%) heterosis for yield, respectively. The top yielding cross, viz. Glenlea × NB505, in F1 was also the second highest yielding population in F2 and the best yielder in F3 based on two locations. Hence, although F2 single plant productivity measurements misplaced the rankings of some of the crosses, F1 and F3 performances indicated the relative potential of the different populations. Most crosses showed nonsignificant F2–F3 regression and correlation coefficients for yield but significant coefficients for kernel weight. For protein content highly significant F2–F3 regression and correlation coefficients were observed only for crosses involving the high protein parent. The use in F1 of weight-free selection indices involving yield, kernel weight and protein content ranked Glenlea × NB505 as the best of six populations whereas in F3 the same cross had the best aggregate merit when only yield and kernel weight were considered in the index. The inclusion of protein content in the index favored a high protein cross, Sinton × Glenlea.Key words: Wheat, Triticum aestivum, heterosis, parent-offspring regression, intergeneration correlation, selection index

scholarly journals Economic selection index in the genomic era

2019 ◽  
Vol 136 (3) ◽  
pp. 151-152 ◽  
Author(s):  
Hiroyuki Hirooka
Keyword(s):  
Selection Index ◽  
Economic Selection

scholarly journals Comparison of gene editing versus conventional breeding to introgress the POLLED allele into the US dairy cattle population

2019 ◽  
Vol 102 (5) ◽  
pp. 4215-4226 ◽  
Author(s):  
M.L. Mueller ◽  
J.B. Cole ◽  
T.S. Sonstegard ◽  
A.L. Van Eenennaam
Keyword(s):  
Dairy Cattle ◽  
Gene Editing ◽  
Cattle Population ◽  
Conventional Breeding ◽  
The Us
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