Search

The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes. The continued efficient use of whey as a fermentable substrate to produce alcoholic beverages will benefit from improving the measurement of key components relevant to the fermentability of whey. One component of fermentation that is not well studied in whey is the concentration of fermentable nitrogen, either as free amino nitrogen (FAN) or yeast-assimilable nitrogen (YAN). Fermentable nitrogen in media is essential for yeast cells to replicate. Insufficient concentrations of FAN or YAN to support the growing yeast population can result in sluggish or "stuck" fermentations. We evaluated 3 common methods of fermentable nitrogen determination and compared them for use in whey fermentation systems, based on ninhydrin, nitrogen by o- phthalaldehyde (NOPA), and formaldehyde pH (Sørensen titration) values. Each measurement method was evaluated independently using standard addition curves and compared for overall accuracy using paired t -tests (α = 0.05). Although the formaldehyde pH method showed high precision, it did not measure nitrogen accurately, as it overestimated YAN in whey by up to 6 times relative to other tests. The ninhydrin and NOPA methods both showed accuracy for fermentable nitrogen determination in whey based on analysis of standardized nitrogen sources. We concluded that either the ninhydrin FAN or NOPA YAN method may be used to determine the fermentable nitrogen content in whey. This is expected to improve the ability of producers to use whey as a fermentation medium. [ABSTRACT FROM AUTHOR]

The aims of this study were (1) to identify genomic regions associated with a N efficiency index (NEI) and its composition traits and (2) to analyze the functional annotation of identified genomic regions. The NEI included N intake (NINT1), milk true protein N (MTPN1), milk urea N yield (MUNY1) in primiparous cattle, and N intake (NINT2+), milk true protein N (MTPN2+), and milk urea N yield (MUNY2+) in multiparous cattle (2 to 5 parities). The edited data included 1,043,171 records on 342,847 cows distributed in 1,931 herds. The pedigree consisted of 505,125 animals (17,797 males). Data of 565,049 SNPs were available for 6,998 animals included in the pedigree (5,251 females and 1,747 males). The SNP effects were estimated using a single-step genomic BLUP approach. The proportion of the total additive genetic variance explained by windows of 50 consecutive SNPs (with an average size of about 240 kb) was calculated. The top 3 genomic regions explaining the largest rate of the total additive genetic variance of the NEI and its composition traits were selected for candidate gene identification and quantitative trait loci (QTL) annotation. The selected genomic regions explained from 0.17% (MTPN2+) to 0.58% (NEI) of the total additive genetic variance. The largest explanatory genomic regions of NEI, NINT1, NINT2+, MTPN1, MTPN2+, MUNY1, and MUNY2+ were Bos taurus autosome 14 (1.52–2.09 Mb), 26 (9.24–9.66 Mb), 16 (75.41–75.51 Mb), 6 (8.73–88.92 Mb), 6 (8.73–88.92 Mb), 11 (103.26–103.41 Mb), 11 (103.26–103.41 Mb). Based on the literature, gene ontology, Kyoto Encyclopedia of Genes and Genomes, and protein-protein interaction, 16 key candidate genes were identified for NEI and its composition traits, which are mainly expressed in the milk cell, mammary, and liver tissues. The number of enriched QTL related to NEI, NINT1, NINT2+, MTPN1, and MTPN2+ were 41, 6, 4, 11, 36, 32, and 32, respectively, and most of them were related to the milk, health, and production classes. In conclusion, this study identified genomic regions associated with NEI and its composition traits, and identified key candidate genes describing the genetic mechanisms of N use efficiency-related traits. Furthermore, the NEI reflects not only its composition traits but also the interactions among them. [ABSTRACT FROM AUTHOR]

Minimizing pollution from the dairy sector is paramount; one potential cause of such pollution is excess nitrogen. Nitrogen pollution contributes to a deterioration in water quality as well as an increase in both eutrophication and greenhouse gases. It is therefore essential to minimize the loss of nitrogen from the sector, including excretion from the cow. Breeding programs are one potential strategy to improve the efficiency with which nitrogen is used by dairy cows, but they rely on routine access to individual cow information on how efficiently each cow uses the nitrogen it ingests. A total of 3,497 test-day records for individual-cow nitrogen efficiency metrics along with milk yield and the associated milk spectra were used to investigate the ability of milk infrared spectral data to predict these nitrogen traits; both traditional partial least squares regression and neural networks were used in the prediction process. The data originated from 4 farms across 11 yr. The nitrogen traits investigated were nitrogen intake, nitrogen use efficiency, and nitrogen balance. Both nitrogen use efficiency and nitrogen balance were calculated considering nitrogen intake, nitrogen in milk, nitrogen in the conceptus, nitrogen used for the growth, nitrogen stored in body reserves, and nitrogen mobilized from body reserves. Irrespective of the nitrogen-related trait being investigated, the best predictions from 4-fold cross validation were achieved using neural networks that considered both the morning and evening milk spectra along with milk yield, parity, and DIM in the prediction process. The coefficient of determination in the cross validation was 0.61, 0.74, and 0.58 for nitrogen intake, nitrogen use efficiency, and nitrogen balance, respectively. In a separate series of validation approaches, the calibration and validation was stratified by herd (n = 4) and separately by year. For these scenarios, partial least squares regression generated more accurate predictions compared with neural networks; the coefficient of determination was always lower than 0.29 and 0.60 when validation was stratified by herd and year, respectively. Therefore, if the variability of the data being predicted in the validation datasets is similar to that in the data used to develop the predictions, then nitrogen-related traits can be predicted with reasonable accuracy. In contrast, where the variability of the data that exists in the validation dataset is poorly represented in the calibration dataset, then poor predictions will ensue., (© 2024, The Authors. Published by Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Increasing the nitrogen-utilization efficiency (NUE) of dairy cows by breeding selection would offer advantages from nutritional, environmental, and economic perspectives. Because data collection of NUE phenotypes is not feasible in large cow cohorts, the cow individual milk urea concentration (MU) has been suggested as an indicator trait. Considering the symbiotic interplay between dairy cows and their rumen microbiome, individual MU was thought to be influenced by host genetics and by the rumen microbiome, the latter in turn being partly attributed to host genetics. To enhance our knowledge of MU as an indicator trait for NUE, we aimed to identify differential abundant rumen microbial genera between Holstein cows with divergent genomic breeding values for MU (GBVMU; GBV HMU vs. GBV LMU , where H and L indicate high and low MU phenotypes, respectively). The microbial genera identified were further investigated for their correlations with MU and 7 additional NUE-associated traits in urine, milk, and feces in 358 lactating Holsteins. Statistical analysis of microbial 16S rRNA amplicon sequencing data revealed significantly higher abundances of the ureolytic genus Succinivibrionaceae UCG-002 in GBV LMU cows, whereas GBV HMU animals hosted higher abundances of Clostridia unclassified and Desulfovibrio. The entire discriminating ruminal signature of 24 microbial taxa included a further 3 genera of the Lachnospiraceae family that revealed significant correlations to MU values and were therefore proposed as considerable players in the GBVMU-microbiome-MU axis. The significant correlations of Prevotellaceae UCG-003, Anaerovibrio, Blautia, and Butyrivibrio abundances with MU measurements, milk nitrogen, and N content in feces suggested their contribution to genetically determined N-utilization in Holstein cows. The microbial genera identified might be considered for future breeding programs to enhance NUE in dairy herds., (The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Efficient management of N and P on dairy farms is critical for farm profitability and environmental stewardship. Annual farm-gate nutrient mass balance (NMB) assessments can be used to determine the nutrient-use efficiency of farms, set efficiency targets, and monitor the effect of management changes with minimal inputs required. In New York, feasible N and P balances have been developed as benchmarks for dairy farm NMB, alongside key performance indicators (KPI) that serve as predictors for high NMB. Here, 3 yr of NMB data from 47 farms were used to evaluate the main drivers of N and P balances and identify additional KPI. From the 141 farm records, 26% met both the feasible N balances per hectare and per megagram of milk produced. For P, 53% of the records met both benchmarks. Imports, rather than exports, drove NMB primarily by feed and fertilizer purchases, consistent with earlier findings. Linear regression analysis showed that a selection of KPI currently used, particularly animal density, nutrient-use efficiency, and the amount of home-grown feed, explained a large portion of variation in NMB. Heifer-to-cow ratio and the relative proportion of various forage crops may provide further insight into the drivers of feed and fertilizer imports and ultimately farm-gate NMB. This study provides avenues toward a better assessment of whole-farm nutrient management and means for farms to communicate progress to stakeholders and consumers., (The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

A growing number of companies within the cheese-making industry are now using high-protein (e.g., 4-5%) milks to increase cheese yield. Previous studies have suggested that cheeses made from high-protein (both casein and whey protein; WP) milks may ripen more slowly; one suggested explanation is inhibition of residual rennet activity due to elevated WP levels. We explored the use of microfiltration (MF) to concentrate milk for cheese-making, as that would allow us to concentrate the casein while varying the WP content. Our objective was to determine if reducing the level of WP in concentrated cheese milk had any impact on cheese characteristics, including ripening, texture, and nutritional profile. Three types of 5% casein standardized and pasteurized cheese milks were prepared that had various casein:true protein (CN:TP) ratios: (a) control with CN:TP 83:100, (b) 35% WP reduced, 89:100 CN:TP, and (c) 70% WP reduced, 95:100 CN:TP. Standardized milks were preacidified to pH 6.2 with dilute lactic acid during cheese-making. Composition, proteolysis, textural, rheological, and sensory properties of cheeses were monitored over a 9-mo ripening period. The lactose, total solids, total protein, and WP contents in the 5% casein concentrated milks were reduced with increasing levels of WP removal. All milks had similar casein and total calcium levels. Cheeses had similar compositions, but, as expected, lower WP levels were observed in the cheeses where WP depletion by MF was performed on the cheese milks. Cheese yield and nitrogen recoveries were highest in cheese made with the 95:100 CN:TP milk. These enhanced recoveries were due to the higher fraction of nitrogen being casein-based solids. Microfiltration depletion of WP did not affect pH, sensory attributes, or insoluble calcium content of cheese. Proteolysis (the amount of pH 4.6 soluble nitrogen) was lower in control cheeses compared with WP-reduced cheeses. During ripening, the hardness values and the temperature of the crossover point, an indicator of the melting point of the cheese, were higher in the control cheese. It was thus likely that the higher residual WP content in the control cheese inhibited proteolysis during ripening, and the lower breakdown rate resulted in its higher hardness and melting point. There were no major differences in the concentrations of key nutrients with this WP depletion method. Cheese milk concentration by MF provides the benefit of more typical ripening rates., (The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).)

Our objective was to determine the within and between laboratory performance of an enzymatic spectrophotometric method for milk urea nitrogen (MUN) determination. This method first uses urease to hydrolyze urea into ammonia and carbon dioxide. Next, ammonia (as ammonium ions) reacts with 2-oxoglutarate, in the presence of reduced nicotinamide-adenine dinucleotide phosphate (NADPH) and the enzyme glutamate dehydrogenase (GlDH), to form l-glutamic acid, water, and NADP + . The change in light absorption at 340 nm due to the conversion of NADPH to NADP+ is stoichiometrically a function of the MUN content of a milk sample. The relative within (RSD r ) and between (RSD R ) laboratory method performance values for the MUN enzymatic spectrophotometric method were 0.57% and 0.85%, respectively, when testing individual farm milks. The spectrophotometric MUN method demonstrated better within and between laboratory performance than the International Dairy Federation differential pH MUN method with a much lower RSD r (0.57 vs. 1.40%) and RSD R (0.85 vs. 4.64%). The spectrophotometric MUN method also had similar method performance statistics as other AOAC International official validated chemical methods for primary milk component determinations, with the average of all RSD r and RSD R values being <1%. An official collaborative study of the enzymatic spectrophotometric MUN method is needed to achieve International Dairy Federation, AOAC International, and International Organization for Standardization official method status., (© 2021, The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).)