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Animal Nutritional Physiological Phenomena

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Effects of post-weaning supplementation of immunomodulatory feed ingredient on body weight and cortisol concentrations in program-fed beef heifers.

Domestic animal endocrinology
The objective of this study was to determine the effects of an immunomodulatory feed ingredient during post-weaning on growth and cortisol concentrations of beef heifers. Commercial Angus heifers (n = 72) from 2 AI sires were blocked (n = 9) by BW an...

Dietary polyunsaturated fatty acid supplementation of young post-pubertal dairy bulls alters the fatty acid composition of seminal plasma and spermatozoa but has no effect on semen volume or sperm quality.

Theriogenology
The aim of this study was to examine the effects of dietary supplementation with rumen protected n-6 or n-3 polyunsaturated fatty acids (PUFA) on the quantity and quality of semen from young post-pubertal dairy bulls. Pubertal Holstein-Friesian (n = ...

Effect of energy balance profiles on metabolic and reproductive response in Holstein and Swedish Red cows.

Theriogenology
This study examined the effect of two feeding levels during the antepartum and postpartum period on reproductive performance and blood metabolites (glucose, non-esterified fatty acids (NEFA), insulin) in primiparous Holstein and Swedish Red (SRB) cow...

Influence of post-insemination nutrition on embryonic development in beef heifers.

Theriogenology
Previous studies have demonstrated that a decrease in nutrition immediately following AI reduces pregnancy success in beef heifers. The objective of this experiment was to determine if nutrient restriction following AI impacted early embryonic develo...

Broiler responses to digestible threonine at different ages: a neural networks approach.

Journal of animal physiology and animal nutrition
Three experiments were conducted with broiler chickens to evaluate the effects of digestible threonine (DThr) and crude protein (CP) on their performance at three different phases of age: 1-14, 15-28 and 29-42 days. The measured traits included the f...

Semi-supervised learning for genomic prediction of novel traits with small reference populations: an application to residual feed intake in dairy cattle.

Genetics, selection, evolution : GSE
BACKGROUND: Genomic prediction for novel traits, which can be costly and labor-intensive to measure, is often hampered by low accuracy due to the limited size of the reference population. As an option to improve prediction accuracy, we introduced a s...

Machine learning applied to transcriptomic data to identify genes associated with feed efficiency in pigs.

Genetics, selection, evolution : GSE
BACKGROUND: To date, the molecular mechanisms that underlie residual feed intake (RFI) in pigs are unknown. Results from different genome-wide association studies and gene expression analyses are not always consistent. The aim of this research was to...

ASN-ASAS SYMPOSIUM: FUTURE OF DATA ANALYTICS IN NUTRITION: Mathematical modeling in ruminant nutrition: approaches and paradigms, extant models, and thoughts for upcoming predictive analytics1,2.

Journal of animal science
This paper outlines typical terminology for modeling and highlights key historical and forthcoming aspects of mathematical modeling. Mathematical models (MM) are mental conceptualizations, enclosed in a virtual domain, whose purpose is to translate r...

Modeling energy partition patterns of growing pigs fed diets with different net energy levels based on machine learning.

Journal of animal science
The objectives of this study were to evaluate the energy partition patterns of growing pigs fed diets with different net energy (NE) levels based on machine learning methods, and to develop prediction models for the NE requirement of growing pigs. Tw...

Prediction of dry matter intake in growing Black Bengal goats using artificial neural networks.

Tropical animal health and production
Dry matter intake (DMI) determination is essential for effective management of meat goats, especially in optimizing feed utilization and production efficiency. Unfortunately, farmers often face challenges in accurately predicting DMI which leads to w...