Genetic parameters of differential somatic cell count, milk composition, and cheese-making traits measured and predicted using spectral data in Holstein cows

Pegolo, Sara; Mota, Lúcio Flávio Macêdo; Bisutti, Vittoria; Martínez-Castillero, María; Giannuzzi, Diana; Gallo, Luigi; Schiavon, Stefano; Chion, Andrea Revello; Trevisi, Erminio; Negrini, Riccardo; Marsan, P. Ajmone; Cecchinato, Alessio

doi:10.3168/jds.2021-20395

Cited by 16 publications

(5 citation statements)

References 56 publications

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“…La disminución de la cantidad de células somáticas de la leche post ordeño, se debió a que dentro de la capacitación, se puso especial énfasis en las buenas prácticas de ordeño, específicamente en la higiene del personal ordeñador, de los utensilios y del animal, recomendando realizar un continuo lavado de las manos y brazos del ordeñador, lavado correcto de los pezones de las vacas, seguido de secado con toalla individual y de papel descartable, y finalmente un sellado post ordeño obligatorio; estas recomendaciones fueron sugeridas y monitoreadas, ya que las malas prácticas de ordeño, afectan la higiene y salud de la ubre, siendo una de las principales causas del aumento de las células somáticas en la leche (Hernández Reyes & Bedolla Cedeño, 2008), como parte de la respuesta inmunitaria, producto de amenazas de microorganismos, principalmente patógenos, a la glándula mamaria de las vacas lecheras. Esto, a su vez, permitió el aumento del volumen de leche, pues cuando existe un proceso infeccioso, el aumento de células somáticas ocasiona una disminución en la síntesis de lactosa, por un daño a los lactocitos, con lo cual el ingreso de agua se ve afectado, disminuyendo el volumen de leche (Alhussien y Dang, 2018;Pegolo et al, 2021;Reyes et al, 2017).…”

Section: Discussionunclassified

Plan piloto para el mejoramiento de la calidad nutricional y conteo de células somáticas de leche bovina, producida por pequeños productores de la provincia de Pichincha-Ecuador, aplicando un programa de capacitación

Puga-Torres,

Meneses Cunama,

Meneses Pineda

et al. 2024

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La producción láctea en Ecuador es una fuente de ingresos económicos para muchos pequeños y medianos productores; sin embargo, la falta de asesoría técnica conlleva a que tengan problemas en la calidad de la leche y se vea afectado su volumen de producción, por lo que la presente investigación tuvo como finalidad realizar un plan piloto para el mejoramiento de la calidad de leche producida por 57 pequeños productores de diferentes parroquias de los cantones Quito, Rumiñahui y San Miguel de los Bancos de la provincia de Pichincha, mediante un plan de capacitación en buenas prácticas de ordeño y estrategias de alimentación. Se realizaron 2 muestreos iniciales (entre octubre y noviembre de 2021), previo a la capacitación, y 2 muestreos finales (en junio de 2022) para medir el impacto de estas. Se recolectaron 1.273 muestras, donde solamente el 4,02 % (52/1.273) de las mismas, cumplen con los valores referenciales de la normativa nacional, mientras que el 95,98 % (1.243/1.273) no cumple con alguno de los parámetros evaluados; sin embargo, la gran mayoría de dicha leche sí es apta para el consumo humano, pero requiere ser mejorada en su calidad nutricional. Se logró disminuir considerablemente (aunque no estadísticamente) el conteo de células somáticas, de un promedio inicial de 623.530 CS ml-1 a 365.660 CS ml-1 luego de la capacitación, siendo correlacionado con el aumento significativo del porcentaje de lactosa entre el muestreo inicial y el final; también existió diferencias significativas en el porcentaje de grasa y sólidos totales, aunque en estos dos casos disminuyeron sus valores luego de la capacitación (grasa de 2,58 % en el muestreo inicial a 2,10 % en el final y sólidos totales de 11,44 % a 11,00 %). El resto de los parámetros no mostraron diferencias significativas entre los muestreos. Se concluye que la capacitación fue beneficiosa, pues la disminución considerable del conteo de células somáticas aumentó el volumen de producción de leche de los pequeños productores y, por ende, sus ingresos económicos. Para el resto de los parámetros se recomienda reforzar las capacitaciones de nutrición, alimentación, manejo de pasturas, etc., para incrementar sus valores, así como mantener constantes programas de capacitación a los productores, apoyados desde la academia, la empresa privada y entidades públicas.

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Section: Discussionunclassified

Plan piloto para el mejoramiento de la calidad nutricional y conteo de células somáticas de leche bovina, producida por pequeños productores de la provincia de Pichincha-Ecuador, aplicando un programa de capacitación

Puga-Torres,

Meneses Cunama,

Meneses Pineda

et al. 2024

SIEMBRA

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“…The dataset comes from the BENELAT project, which aims at creating strategies and approaches to mitigate stressful factors to improve animal welfare and milk components in dairy cattle production systems [ 34 ]. Cows were fed twice daily with a ration based on corn silage and sorghum, and energy-protein supplementation was formulated following the nutritional recommendations for dairy cattle [ 35 ].…”

Section: Methodsmentioning

confidence: 99%

Genomic prediction of blood biomarkers of metabolic disorders in Holstein cattle using parametric and nonparametric models

Mota,

Giannuzzi,

Pegolo

et al. 2024

Genet Sel Evol

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Background Metabolic disturbances adversely impact productive and reproductive performance of dairy cattle due to changes in endocrine status and immune function, which increase the risk of disease. This may occur in the post-partum phase, but also throughout lactation, with sub-clinical symptoms. Recently, increased attention has been directed towards improved health and resilience in dairy cattle, and genomic selection (GS) could be a helpful tool for selecting animals that are more resilient to metabolic disturbances throughout lactation. Hence, we evaluated the genomic prediction of serum biomarkers levels for metabolic distress in 1353 Holsteins genotyped with the 100K single nucleotide polymorphism (SNP) chip assay. The GS was evaluated using parametric models best linear unbiased prediction (GBLUP), Bayesian B (BayesB), elastic net (ENET), and nonparametric models, gradient boosting machine (GBM) and stacking ensemble (Stack), which combines ENET and GBM approaches. Results The results show that the Stack approach outperformed other methods with a relative difference (RD), calculated as an increment in prediction accuracy, of approximately 18.0% compared to GBLUP, 12.6% compared to BayesB, 8.7% compared to ENET, and 4.4% compared to GBM. The highest RD in prediction accuracy between other models with respect to GBLUP was observed for haptoglobin (hapto) from 17.7% for BayesB to 41.2% for Stack; for Zn from 9.8% (BayesB) to 29.3% (Stack); for ceruloplasmin (CuCp) from 9.3% (BayesB) to 27.9% (Stack); for ferric reducing antioxidant power (FRAP) from 8.0% (BayesB) to 40.0% (Stack); and for total protein (PROTt) from 5.7% (BayesB) to 22.9% (Stack). Using a subset of top SNPs (1.5k) selected from the GBM approach improved the accuracy for GBLUP from 1.8 to 76.5%. However, for the other models reductions in prediction accuracy of 4.8% for ENET (average of 10 traits), 5.9% for GBM (average of 21 traits), and 6.6% for Stack (average of 16 traits) were observed. Conclusions Our results indicate that the Stack approach was more accurate in predicting metabolic disturbances than GBLUP, BayesB, ENET, and GBM and seemed to be competitive for predicting complex phenotypes with various degrees of mode of inheritance, i.e. additive and non-additive effects. Selecting markers based on GBM improved accuracy of GBLUP.

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“…The present study was carried out as part of the BENELAT project that aimed at developing short- and long-term interventions for improving animal welfare and efficiency, and the quality of dairy cattle production [ 30 ]. Milk samples were collected during the evening milking from 1150 Holstein cows that belonged to two herds in northern Italy (Emilia-Romagna region) and were managed under similar dairy production systems.…”

Section: Methodsmentioning

confidence: 99%

Integrating on-farm and genomic information improves the predictive ability of milk infrared prediction of blood indicators of metabolic disorders in dairy cows

et al. 2023

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Background Blood metabolic profiles can be used to assess metabolic disorders and to evaluate the health status of dairy cows. Given that these analyses are time-consuming, expensive, and stressful for the cows, there has been increased interest in Fourier transform infrared (FTIR) spectroscopy of milk samples as a rapid, cost-effective alternative for predicting metabolic disturbances. The integration of FTIR data with other layers of information such as genomic and on-farm data (days in milk (DIM) and parity) has been proposed to further enhance the predictive ability of statistical methods. Here, we developed a phenotype prediction approach for a panel of blood metabolites based on a combination of milk FTIR data, on-farm data, and genomic information recorded on 1150 Holstein cows, using BayesB and gradient boosting machine (GBM) models, with tenfold, batch-out and herd-out cross-validation (CV) scenarios. Results The predictive ability of these approaches was measured by the coefficient of determination (R2). The results show that, compared to the model that includes only FTIR data, integration of both on-farm (DIM and parity) and genomic information with FTIR data improves the R2 for blood metabolites across the three CV scenarios, especially with the herd-out CV: R2 values ranged from 5.9 to 17.8% for BayesB, from 8.2 to 16.9% for GBM with the tenfold random CV, from 3.8 to 13.5% for BayesB and from 8.6 to 17.5% for GBM with the batch-out CV, and from 8.4 to 23.0% for BayesB and from 8.1 to 23.8% for GBM with the herd-out CV. Overall, with the model that includes the three sources of data, GBM was more accurate than BayesB with accuracies across the CV scenarios increasing by 7.1% for energy-related metabolites, 10.7% for liver function/hepatic damage, 9.6% for oxidative stress, 6.1% for inflammation/innate immunity, and 11.4% for mineral indicators. Conclusions Our results show that, compared to using only milk FTIR data, a model integrating milk FTIR spectra with on-farm and genomic information improves the prediction of blood metabolic traits in Holstein cattle and that GBM is more accurate in predicting blood metabolites than BayesB, especially for the batch-out CV and herd-out CV scenarios.

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Genetic parameters of differential somatic cell count, milk composition, and cheese-making traits measured and predicted using spectral data in Holstein cows

Cited by 16 publications

References 56 publications

Plan piloto para el mejoramiento de la calidad nutricional y conteo de células somáticas de leche bovina, producida por pequeños productores de la provincia de Pichincha-Ecuador, aplicando un programa de capacitación

Plan piloto para el mejoramiento de la calidad nutricional y conteo de células somáticas de leche bovina, producida por pequeños productores de la provincia de Pichincha-Ecuador, aplicando un programa de capacitación

Genomic prediction of blood biomarkers of metabolic disorders in Holstein cattle using parametric and nonparametric models

Integrating on-farm and genomic information improves the predictive ability of milk infrared prediction of blood indicators of metabolic disorders in dairy cows

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