scholarly journals USE OF SELECTION INDICES IN BREEDING WORK

2019 ◽  
pp. 114-120
Author(s):  
Игорь Ефимов ◽  
Igor' Efimov ◽  
Татьяна Усова ◽  
Tatiana Usova ◽  
Ольга Юдина ◽  
...  
Keyword(s):  
Selection Index ◽  
Animal Husbandry ◽  
Breeding Value ◽  
Federal State ◽  
Breeding Work ◽  
Selection Indices ◽  
On Line ◽  
The Difference ◽  
Livestock Products ◽  
Line Analysis

The aim of the study is to develop an effective selective index adapted to the conditions of the country, taking into account the national priorities of consumption of livestock products. Selection indices, which are widely used in de-veloped countries with high scientific and production potential in the field of animal husbandry, have been ana-lyzed. They can be divided into three groups. The first group includes five countries where a simplified version of the calculation of selective indices is used. The second group includes nine countries, which are rather complex for on-line analysis of the selection indices. The third group included countries in the breeding work of which com-bined breeding indices are used. The rating of the most valuable bulls of the Federal state unitary enterprise «Mos-kovskoye» on breeding work made with application of the most widespread selection indexes is given. Selection indices, with the similarity of the methods of their calculation, need some adjustment to be used in our country. A selection index is developed and presented under the name UI (combined index). The developed index UI in 83% of cases coincides with the index ASI (Australia), in the remaining 17% of cases the difference does not exceed 0.5 rank. With the index PIN (England) differences are more significant, in 34% of cases there is a complete coinci-dence of estimates, in 50% – the differences are only 1 rank and only in one case the discrepancy is significant enough. The index of INET (the most frequently used by breeders in Europe) coincides with the evaluation of breeding value of animals in 100% of cases. The new index is supposed to be used in the work with dairy breeds.

scholarly journals Regularized selection indices for breeding value prediction using hyper-spectral image data

2019 ◽  
Author(s):  
Marco Lopez-Cruz ◽  
Eric Olson ◽  
Gabriel Rovere ◽  
Jose Crossa ◽  
Susanne Dreisigacker ◽  
...  
Keyword(s):  
Agronomic Traits ◽  
Expression Profiles ◽  
Selection Index ◽  
Gene Expression Profiles ◽  
Wheat Breeding ◽  
Breeding Value ◽  
Model Organisms ◽  
High Dimensional ◽  
Hyper Spectral ◽  
Selection Indices

AbstractHigh-throughput phenotyping (HTP) technologies can produce data on thousands of phenotypes per unit being monitored. These data can be used to breed for economically and environmentally relevant traits (e.g., drought tolerance); however, incorporating high-dimensional phenotypes in genetic analyses and in breeding schemes poses important statistical and computational challenges. To address this problem, we developed regularized selection indices; the methodology integrates techniques commonly used in high-dimensional phenotypic regressions (including penalization and rank-reduction approaches) into the selection index (SI) framework. Using extensive data from CIMMYT’s (International Maize and Wheat Improvement Center) wheat breeding program we show that regularized SIs derived from hyper-spectral data offer consistently higher accuracy for grain yield than those achieved by canonical SIs, and by vegetation indices commonly used to predict agronomic traits. Regularized SIs offer an effective approach to leverage HTP data that is routinely generated in agriculture; the methodology can also be used to conduct genetic studies using high-dimensional phenotypes that are often collected in humans and model organisms including body images and whole-genome gene expression profiles.

scholarly journals RESULTS OF BREEDING VALUE EVALUATION OF SOWS WITH THE USE OF TRADITIONAL AND INNOVATIVE METHODS

2020 ◽  
pp. 212-224
Author(s):  
V.I. Khalak ◽  
V.S. Kozir ◽  
Yevhen Rudenko
Keyword(s):  
Economic Efficiency ◽  
Selection Index ◽  
Pair Correlation ◽  
Live Weight ◽  
Animal Husbandry ◽  
Organic Production ◽  
Breeding Value ◽  
High Fertility ◽  
Industrial Complex ◽  
Research Results

The article presents the results of research on the reproductive qualities of sows of different breeding value, as well as determines the economic efficiency of their use in the industrial complex. The study was conducted in agricultural formations of the Dnipropetrovsk region (LLC "Druzhba-Kaznacheyivka", LLC "Vidrodzhennia") and the laboratory of animal husbandry of the State Institution Institute of Grain Crops NAAS. The work was performed according to the research program of NAAS №30 "Innovative technologies of breeding, industrial and organic production of pig products" ("Pig breeding"). Evaluation of sows on the grounds of reproductive qualities was carried out taking into account the following indicators: fertility, head; high fertility, kg, number of piglets at weaning, head, nest weight at the time of weaning at the age of 28 days, kg, nest weight at the time of weaning at the age of 60 days (estimated), kg, safety, %. The breeding value of animals was determined by the multiplicity and weight of the nest at the time of weaning (according to Annex 7 of the Instructions for grading pigs) and the selection index of reproductive qualities of sows (SIVYAS). The index of alignment (homogeneity) of the sow's nest by live weight of piglets at the time of their birth (ІВГ0) was calculated according to the method of V.I. Khalak (2012), the economic efficiency of research results - according to conventional methods. Biometric processing of research results was performed according to the methods of G.F. Lakin (1990). It was found that sows of the "elite" class were superior to peers of the "extracurricular" class in terms of multiplicity, several piglets at weaning, nest weight at weaning at 28 days and nest weight at weaning at 60 days (estimated) by an average of 28, 76%. The difference between sows of classes M + and M- (distribution class for SIVYAS) in terms of multiplicity, number of piglets at the time of weaning, the weight of the nest at the time of weaning at the age of 28 days and weight of the nest at the time of weaning at the age of 60 days (estimated) is 34.32. 34.78, 27.60 and 28.30 %, respectively. The coefficients of pair correlation between the absolute indicators of reproductive qualities of sows, the index "alignment (homogeneity) of the sow's nest by live weight of piglets at birth" and the selection index of reproductive qualities of sows (SIVYAS) at 83.33-100.0 % are reliable from -0.446 to +0.989. The criterion for the selection of highly productive animals according to the Instructions for grading pigs is the class "elite", according to the selection index of reproductive qualities of sows (SIVYAS) - 97.85-123.99 and more points. The use of sows of the class "elite" and М+ (according to SIVYAS) provides additional products at the level of +11.84 - 16.49 %.

scholarly journals Economic weights of current and new breeding objective traits in Aberdeen Angus

2020 ◽  
Vol 65 (No. 3) ◽  
pp. 77-85 ◽  
Author(s):  
Zuzana Krupová ◽  
Emil Krupa ◽  
Marie Wolfová
Keyword(s):  
Marketing Strategy ◽  
Feed Efficiency ◽  
Growth Traits ◽  
Selection Index ◽  
High Growth ◽  
Breeding Value ◽  
Economic Weights ◽  
Economic Weight ◽  
The Absolute ◽  
The Difference

Breeding values estimated for growth, calving performance, and exterior traits are currently combined into simple selection indices for bulls, cows, and heifers of the Aberdeen Angus breed. To establish a comprehensive economic index for this breed, the absolute and relative economic weights (EW) for a complex of 16 production, functional, carcass, and feed efficiency traits were calculated. The absolute EW of a trait expressed the difference in the present values of profit that will be obtained from the descendants of a bull with the average breeding value for this trait, and descendants of a bull with the breeding value one unit higher than the average one. The relative EW of a trait was defined as the standardised EW of a trait (i.e. EW per genetic standard deviation) expressed as percentage of the sum of standardised EWs of all evaluated traits. Sensitivity analysis was conducted to explore the EW of traits under variable production and economic conditions. Variability in the marketing strategy, in product prices and costs, and in trait means was considered in this analysis. Relative EW of the feed efficiency of breeding heifers and of cows reached 4%. The highest relative EW was obtained in three growth traits: weight gains of calves from birth to 120, from 120 to 210, and from 210 to 365 days of age (66% combined). The survival rate of calves until weaning and cow productive lifetime reached 11% and 8% of the total economic importance of traits, respectively. These growth and functional traits accounted for 84% (in marketing strategy involving selling breeding animals) to 90% (in populations with high growth intensity) of the total economic weight of all 16 evaluated traits. Therefore, these traits should be considered as new selection criteria when constructing a comprehensive selection index for the Czech Aberdeen Angus population in future.

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 Optimal breeding-value prediction using a Sparse Selection Index

Genetics ◽  
2021 ◽  
Author(s):  
Marco Lopez-Cruz ◽  
Gustavo de los Campos
Keyword(s):  
Sample Size ◽  
Dna Sequences ◽  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Regularization Parameter ◽  
Selection Index ◽  
Prediction Method ◽  
Training Data ◽  
Breeding Value ◽  
Data Set

Abstract Genomic prediction uses DNA sequences and phenotypes to predict genetic values. In homogeneous populations, theory indicates that the accuracy of genomic prediction increases with sample size. However, differences in allele frequencies and in linkage disequilibrium patterns can lead to heterogeneity in SNP effects. In this context, calibrating genomic predictions using a large, potentially heterogeneous, training data set may not lead to optimal prediction accuracy. Some studies tried to address this sample size/homogeneity trade-off using training set optimization algorithms; however, this approach assumes that a single training data set is optimum for all individuals in the prediction set. Here, we propose an approach that identifies, for each individual in the prediction set, a subset from the training data (i.e., a set of support points) from which predictions are derived. The methodology that we propose is a Sparse Selection Index (SSI) that integrates Selection Index methodology with sparsity-inducing techniques commonly used for high-dimensional regression. The sparsity of the resulting index is controlled by a regularization parameter (λ); the G-BLUP (the prediction method most commonly used in plant and animal breeding) appears as a special case which happens when λ = 0. In this study, we present the methodology and demonstrate (using two wheat data sets with phenotypes collected in ten different environments) that the SSI can achieve significant (anywhere between 5-10%) gains in prediction accuracy relative to the G-BLUP.

First on-line analysis of petroleum from single inclusion using ultrafast laser ablation

2010 ◽  
Vol 41 (2) ◽  
pp. 74-77 ◽  
Author(s):  
Herbert Volk ◽  
David Fuentes ◽  
Alexander Fuerbach ◽  
Christopher Miese ◽  
Wolfgang Koehler ◽  
...  
Keyword(s):  
Laser Ablation ◽  
Ultrafast Laser ◽  
Single Inclusion ◽  
On Line ◽  
Line Analysis ◽  
Ultrafast Laser Ablation
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