The Effect of Age at First Calving and Calving Interval on Productive Life and Lifetime Profit in Korean Holsteins

This study was performed to estimate the effect of age at first calving and first two calving intervals on productive life and life time profit in Korean Holsteins. Reproduction data of Korean Holsteins born from 1998 to 2004 and lactation data from 276,573 cows with birth and last dry date that calved between 2000 and 2010 were used for the analysis. Lifetime profit increased with the days of life span. Regression of Life Span on Lifetime profit indicated that there was an increase of 3,800 Won (approximately $3.45) of lifetime profit per day increase in life span. This is evidence that care of each cow is necessary to improve net return and important for farms maintaining profitable cows. The estimates of heritability of age at first calving, first two calving intervals, days in milk for lifetime, lifespan, milk income and lifetime profit were 0.111, 0.088, 0.142, 0.140, 0.143, 0.123, and 0.102, respectively. The low heritabilities indicated that the productive life and economical traits include reproductive and productive characteristics. Age at first calving and interval between first and second calving had negative genetic correlation with lifetime profit (−0.080 and −0.265, respectively). Reducing age at first calving and first calving interval had a positive effect on lifetime profit. Lifetime profit increased to approximately 2,600,000 (2,363.6) from 800,000 Won ($727.3) when age at first calving decreased to (22.3 month) from (32.8 month). Results suggested that reproductive traits such as age at first calving and calving interval might affect various economical traits and consequently influenced productive life and profitability of cows. In conclusion, regard of the age at first calving must be taken with the optimum age at first calving for maximum lifetime profit being 22.5 to 23.5 months. Moreover, considering the negative genetic correlation of first calving interval with lifetime profit, it should be reduced against the present trend of increase

Genomic Prediction Using Alternative Strategies of Weighted Single-Step Genomic BLUP for Yearling Weight and Carcass Traits in Hanwoo beef Cattle

[EN] The weighted single-step genomic best linear unbiased prediction (GBLUP) method has been proposed to exploit information from genotyped and non-genotyped relatives, allowing the use of weights for single-nucleotide polymorphism in the construction of the genomic relationship matrix. The purpose of this study was to investigate the accuracy of genetic prediction using the following single-trait best linear unbiased prediction methods in Hanwoo beef cattle: pedigree-based (PBLUP), un-weighted (ssGBLUP), and weighted (WssGBLUP) single-step genomic methods. We also assessed the impact of alternative single and window weighting methods according to their effects on the traits of interest. The data was comprised of 15,796 phenotypic records for yearling weight (YW) and 5622 records for carcass traits (backfat thickness: BFT, carcass weight: CW, eye muscle area: EMA, and marbling score: MS). Also, the genotypic data included 6616 animals for YW and 5134 for carcass traits on the 43,950 single-nucleotide polymorphisms. The ssGBLUP showed significant improvement in genomic prediction accuracy for carcass traits (71%) and yearling weight (99%) compared to the pedigree-based method. The window weighting procedures performed better than single SNP weighting for CW (11%), EMA (11%), MS (3%), and YW (6%), whereas no gain in accuracy was observed for BFT. Besides, the improvement in accuracy between window WssGBLUP and the un-weighted method was low for BFT and MS, while for CW, EMA, and YW resulted in a gain of 22%, 15%, and 20%, respectively, which indicates the presence of relevant quantitative trait loci for these traits. These findings indicate that WssGBLUP is an appropriate method for traits with a large quantitative trait loci effect.This study was carried out with the support of the Cooperative Research Program for Agriculture Science and Technology Development (Project No. PJ01260601) Rural Development Administration, RDA, Republic of Korea.Mehrban, H.; Naserkheil, M.; Lee, DH.; Cho, C.; Choi, T.; Park, M.; Ibáñez-Escriche, N. (2021). Genomic Prediction Using Alternative Strategies of Weighted Single-Step Genomic BLUP for Yearling Weight and Carcass Traits in Hanwoo beef Cattle. Genes. 12(2):1-17. https://doi.org/10.3390/genes12020266S11712

Accuracy of genomic breeding value prediction for intramuscular fat using different genomic relationship matrices in Hanwoo (Korean cattle)

Objective Intramuscular fat is one of the meat quality traits that is considered in the selection strategies for Hanwoo (Korean cattle). Different methods are used to estimate the breeding value of selection candidates. In the present work we focused on accuracy of different genotype relationship matrices as described by forni and pedigree based relationship matrix. Methods The data set included a total of 778 animals that were genotyped for BovineSNP50 BeadChip. Among these 778 animals, 72 animals were sires for 706 reference animals and were used as a validation dataset. Single trait animal model (best linear unbiased prediction and genomic best linear unbiased prediction) was used to estimate the breeding values from genomic and pedigree information. Results The diagonal elements for the pedigree based coefficients were slightly higher for the genomic relationship matrices (GRM) based coefficients while off diagonal elements were considerably low for GRM based coefficients. The accuracy of breeding value for the pedigree based relationship matrix (A) was 13% while for GRM (GOF, G05, and Yang) it was 0.37, 0.45, and 0.38, respectively. Conclusion Accuracy of GRM was 1.5 times higher than A in this study. Therefore, genomic information will be more beneficial than pedigree information in the Hanwoo breeding program

Genomic Analysis Using Bayesian Methods under Different Genotyping Platforms in Korean Duroc Pigs

Genomic evaluation has been widely applied to several species using commercial single nucleotide polymorphism (SNP) genotyping platforms. This study investigated the informative genomic regions and the efficiency of genomic prediction by using two Bayesian approaches (BayesB and BayesC) under two moderate-density SNP genotyping panels in Korean Duroc pigs. Growth and production records of 1026 individuals were genotyped using two medium-density, SNP genotyping platforms: Illumina60K and GeneSeek80K. These platforms consisted of 61,565 and 68,528 SNP markers, respectively. The deregressed estimated breeding values (DEBVs) derived from estimated breeding values (EBVs) and their reliabilities were taken as response variables. Two Bayesian approaches were implemented to perform the genome-wide association study (GWAS) and genomic prediction. Multiple significant regions for days to 90 kg (DAYS), lean muscle area (LMA), and lean percent (PCL) were detected. The most significant SNP marker, located near the MC4R gene, was detected using GeneSeek80K. Accuracy of genomic predictions was higher using the GeneSeek80K SNP panel for DAYS (Δ2%) and LMA (Δ2–3%) with two response variables, with no gains in accuracy by the Bayesian approaches in four growth and production-related traits. Genomic prediction is best derived from DEBVs including parental information as a response variable between two DEBVs regardless of the genotyping platform and the Bayesian method for genomic prediction accuracy in Korean Duroc pig breeding

Digestibility of phosphorous in cereals and co-products for animal feed

Feed ingredients used in swine diets contain various levels and availabilities of nutrients. Nutritional precision evaluation of each ingredient is necessary for formulating diets of pigs. Especially, phosphorous (P) is one of important nutrients for metabolism. However, current data of P digestibility were most apparent digestibility. Therefore, this study was aimed to estimate the coefficient of total tract standardized digestibility (CTTSD) of P in cereals and various co-products used in pig diet. Twelve barrows (initial BW ± SD, 46.70 ± 3.21 kg) were used in this experiment. The experimental design was a 12 × 8 incomplete Latin square with 12 diets and 8 periods. Experimental diets were consisted of barley, wheat, lupine kernel (LK), soybean meal (SBM), almond meal (AM), corn gluten meal (CGM), corn gluten feed from China (CGF-C), corn gluten feed from Korea (CGF-C), wheat bran (WB), rice bran (RB), lupine hull (LH) and P-free diet. The CTTAD of Ca was higher in AM than RB and CGF-K. The LK and CGM showed greater CTTSD of P than RB and LH. In conclusion, our results indicated that the cereals and co-products as P sources were the ideally used as an ingredient in mixed diets of the growing-finishing pigs. Keywords: CTTAD, CTTSD, Feed ingredients, Nutrients, Growing-finishing pig

Machine Learning-Based Live Weight Estimation for Hanwoo Cow

Live weight monitoring is an important step in Hanwoo (Korean cow) livestock farming. Direct and indirect methods are two available approaches for measuring live weight of cows in husbandry. Recently, thanks to the advances of sensor technology, data processing, and Machine Learning algorithms, the indirect weight measurement has been become more popular. This study was conducted to explore and evaluate the feasibility of machine learning algorithms in estimating the body live weight of Hanwoo cow using ten body measurements as input features. Various supervised Machine Learning algorithms, including Multilayer Perceptron, k-Nearest Neighbor, Light Gradient Boosting Machine, TabNet, and FT-Transformer, are employed to develop the models that estimate the body live weight using body measurement data. Data analysis is exploited to explore the correlation between the body size measurements (the features) and the weights (target values that need to be estimated) of cows. Data analysis results show that ten body measurements have a high correlation with the body live weight. High performance of all applied Machine Learning models was obtained. It can be concluded that estimating the body live weight of Hanwoo cow is feasible by utilizing Machine Learning algorithms. Among all of the tested algorithms, LightGBM regression demonstrates not only the best model in terms of performance, model complexity and development time

Machine Learning-Based Live Weight Estimation for Hanwoo Cow

Live weight monitoring is an important step in Hanwoo (Korean cow) livestock farming. Direct and indirect methods are two available approaches for measuring live weight of cows in husbandry. Recently, thanks to the advances of sensor technology, data processing, and Machine Learning algorithms, the indirect weight measurement has been become more popular. This study was conducted to explore and evaluate the feasibility of machine learning algorithms in estimating the body live weight of Hanwoo cow using ten body measurements as input features. Various supervised Machine Learning algorithms, including Multilayer Perceptron, k-Nearest Neighbor, Light Gradient Boosting Machine, TabNet, and FT-Transformer, are employed to develop the models that estimate the body live weight using body measurement data. Data analysis is exploited to explore the correlation between the body size measurements (the features) and the weights (target values that need to be estimated) of cows. Data analysis results show that ten body measurements have a high correlation with the body live weight. High performance of all applied Machine Learning models was obtained. It can be concluded that estimating the body live weight of Hanwoo cow is feasible by utilizing Machine Learning algorithms. Among all of the tested algorithms, LightGBM regression demonstrates not only the best model in terms of performance, model complexity and development time

Social behavior and group growth of finishing pigs with divergent social breeding values

Abstract Background: Behavioral traits of pigs have been shown to be partly under genetic control, which raises the possibility that behavior might be altered by genetic selection, resulting in pigs with better growth performance. Objective: To evaluate the behavior and growth of finishing pigs and investigate pigs selected for high or low social breeding value (SBV) in relation to social behavior and group growth. Methods: Thirty-five females and 35 boars from five positive and five negative SBV groups of finishing pigs were grown from 30 to 90 kg and housed in 10 test pens (3.0 × 3.3 m, 7 pigs/pen). Pigs were recorded with video technology for nine consecutive hours on days 1, 15, and 30 after mixing. Pigs were weighed at approximately 90 kg body weight and the number of days to reach 90 kg was then calculated. Results: The frequency and duration of behaviors were present in the positive and negative SBV groups after mixing. On day 1 after mixing, agonistic behavior was significantly higher (p=0.027) for the -SBV group compared with the +SBV group. Feeding and feeding-together behaviors were significantly higher (p<0.003) in the +SBV group on days 1 and 30 after mixing. Moreover, growth performance to reach 90 kg body weight was significantly faster (p<0.002) in the +SBV group than in the -SBV group. Conclusion: Social interactions, such as feeding-together behavior, among pen mates might affect their growth rate and feed intake. Selection for SBV could be used as an indirect technique for improving growth performance of pigs.Resumo Antecedentes: Os traços comportamentais dos porcos demonstraram estar parcialmente sob controle genético, o que aumenta a possibilidade de que o comportamento possa ser alterado pela seleção genética e resulte em porcos com melhor comportamento de crescimento. Objetivo: Avaliar o comportamento e o crescimento dos porcos de engorda e investigar os porcos selecionados para alto ou baixo valor de reprodução social (SBV) em relação ao comportamento social e crescimento do grupo. Métodos: Trinta e cinco fêmeas e 35 machos, pertencentes a cinco grupos de SBV positivos e cinco negativos de porcos de engorda, foram engordados até 90 de 30 kg e alojados em 10 currais de teste (3,0 × 3,3 m, 7 porcos/curral). Os porcos foram observados com o auxílio de tecnologia de vídeo durante nove horas consecutivas nos dias 1, 15 e 30 após a mistura. Além disso, os porcos foram sopesados em aproximadamente 90 kg de peso corporal e o número de dias para atingir 90 kg foi então calculado. Resultados: A frequência e a duração dos comportamentos dos porcos de engorda foram apresentadas com grupos de SBV positivo e negativo após a mistura. No dia 1 após a mistura, o comportamento agonístico foi significativamente maior (p=0,027) no grupo -SBV do que no grupo +SBV. Os comportamentos de alimentação e alimentação conjunta foram significativamente maiores (p<0,003) no grupo +SBV nos dias 1 e 30 após a mistura. Além disso, o comportamento de crescimento do grupo para atingir 90 kg de peso corporal foi significativamente mais rápido (p<0,002) no grupo +SBV do que no grupo -SBV. Conclusão: As interações sociais, como o comportamento de alimentação conjunta, entre companheiros de curral podem afetar a taxa de crescimento e a ingestão alimentar. A seleção para SBV pode ser uma técnica indireta para melhorar o comportamento de crescimento dos porcos.Resumen Antecedentes: Se ha demostrado que los rasgos conductuales de los cerdos están parcialmente bajo control genético, lo que plantea la posibilidad de que el comportamiento pueda ser alterado vía selección genética y resulte en cerdos con mejores rendimientos de crecimiento. Objetivo: Evaluar el comportamiento y crecimiento de los cerdos en etapa de finalización e investigar cerdos seleccionados por un valor alto o bajo de crianza social (SBV) en relación al comportamiento social y al crecimiento grupal. Métodos: Treinta y cinco hembras y 35 verracos, pertenecientes a cinco grupos positivos y cinco grupos negativos de SBV de cerdos en etapa de finalización, llevados hasta los 90, desde 30 kg de peso, alojados en 10 corrales de prueba (3,0 x 3,3 m, 7 cerdos/corral). Los cerdos fueron observados con la ayuda de tecnología de vídeo por nueve horas consecutivas en los días 1, 15 y 30 luego de ser mezclados. Además, los cerdos se pesaron a los 90 kg de peso aproximadamente y se calculó el número de días para alcanzar dicho peso. Resultados: La frecuencia y duración de los comportamientos de los cerdos en la etapa de finalización se presentaron en los grupos de SBV negativos y positivos luego de ser mezclados. El día 1 luego de la mezcla, el comportamiento agonístico fue significativamente mayor (p=0,027) en el grupo -SBV que en el grupo +SBV. Los comportamientos de consumo de alimento y de consumo en compañía fueron significativamente mayores (p<0,003) en el grupo +SBV en los días 1 y 30 luego de la mezcla. Además, el crecimiento para alcanzar 90 kg de peso corporal fue significativamente más rápido (p=0,002) en el grupo +SBV que el grupo -SBV. Conclusiones: Las interacciones sociales, tales como el comportamiento de consumo de alimento en compañía, entre los compañeros de corral, pueden afectar la tasa de crecimiento y consumo de alimento. La selección por SBV podría usarse como técnica indirecta para mejorar el rendimiento de crecimiento en cerdos