Quantifying the negative impact of clinical diseases on productive and reproductive performance of dairy cows in central Argentina

Masía, Fernando; Molina, Gabriela; Vissio, Claudina; Balzarini, Mónica; Sota, Rodolfo Luzbel de la; Piccardi, Mónica Belén

doi:10.1016/j.livsci.2022.104894

Cited by 9 publications

(4 citation statements)

References 45 publications

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“…According to Fernandes et al (2021), the chance of pregnancy during the first 300 days of lactation is not affected in dairy cows of different breeds presenting with subclinical mastitis during the first month of lactation. However, according to Masia et al (2022), clinical mastitis in cows with more than two lactations reduces fertility and prolongs the estrous cycle by up to two-fold compared with healthy cows, thereby increasing the NofAI and CCI.…”

Section: Resultsmentioning

confidence: 99%

“…Mastitis is one of the most important inflammatory diseases in dairy farming as it causes reduced milk production, premature discarding of animals, increased medication costs, and losses in the quality of milk and its derivatives, leading to significant economic losses within this industry (Carvalho-Sombra et al, 2021). There is a growing global concern regarding increasing health problems in dairy farming (Masia et al, 2022) and declining milk quality (Stocco et al, 2020), with the somatic cell count (SCC) being internationally recognized as a standard for monitoring mammary gland health and milk quality (Bobbo et al, 2017;Rienesl et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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Subclinical mastitis in Jersey dairy cows and its effects on productivity and inflammatory markers

Reitz,

Pelegrini,

Molinari

et al. 2024

Sem. Ci. Agr.

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This study assessed the effect of subclinical mastitis on the productivity and inflammatory markers in Jersey dairy cows. Blood, milk, and milk yield data were collected from 59 Jersey dairy cows reared under a semi-extensive system. Milk samples were collected from individual collectors and evaluated for their somatic cell count (SCC), lactose (Lact), protein, fat, total and defatted dry extracts (DDE), casein, freezing point, and milk urea nitrogen (MUN) levels. After milking, blood was collected by puncturing the coccygeal arteriovenous complex. In the serum samples, the inflammatory biomarkers paraoxonase-1, albumin, and total plasma protein levels were analyzed using colorimetric methods. Samples of the entire diet provided and pastures were submitted for bromatological analysis. Additionally, the body condition score, number of lactations, milk yield, days of lactation, calving interval, number of inseminations until conception, calving-conception interval, and days of pregnancy were analyzed. The cows were categorized based on their SCC into a subclinical mastitis group (SubG), with SCC levels greater than or equal to 200,000 cells/mL, and a control group (CG), with SCC levels below 200,000 cells/mL. Blood metabolic variables, milk quality, and milk production data were analyzed using multivariate regression, analysis of variance, and logistic regression using the SAS® program. The multivariate regression analysis revealed that several markers, such as Lact, total protein, and casein, influenced milk composition, predicting over 97% of the data. The SubG showed lower concentrations of Lact (4.37 vs. 4.47%; P = 0.0002), MUN (20.55 vs. 23.85 mg/dL; P = 0.02), and DDE (9.24 vs. 9.50%; P = 0.02) compared with the CG; moreover, animals in the SubG had a higher number of lactations compared with those in the CG (4.30 vs. 2.69; P = 0.0039). Cows with a Lact content lower than 4.265% (quartile 25%), compared with those with Lact content greater than 4.565% (quartile 100%), were 52 times more likely to have subclinical mastitis. Jersey dairy cows with subclinical mastitis have lower Lact, urea, and defatted dry extract levels in their milk as well as a higher number of lactations. Therefore, Lact levels in milk serve as a good diagnostic marker of subclinical mastitis in Jersey cows. Subclinical mastitis in Jersey dairy cows did not decrease milk yield nor effect reproductive performance.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Subclinical mastitis in Jersey dairy cows and its effects on productivity and inflammatory markers

Reitz,

Pelegrini,

Molinari

et al. 2024

Sem. Ci. Agr.

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“…Previous studies have shown both the combined, long-term effects of these diseases (Carvalho et al 2019) and the economic impact of individual diseases, including lameness (Robcis et al 2023) and postpartum diseases on the herd size (Dubuc and Denis-Robichaud 2017). Research efforts have also been made to classify production diseases into 5 categories and to assess their effects on production and reproduction (Masia et al 2022).…”

mentioning

confidence: 99%

A decision support system based on disease scoring enables dairy farmers to proactively improve herd health

Saro,

Stádník,

Bláhová

et al. 2024

Czech J. Anim. Sci.

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Decision support systems (DSSs) enable dairy farmers to make informed and timely decisions on herd health management. However, the lack of a disease scoring system by category and severity limits the application of this approach. In this study, we developed an innovative approach to dairy herd health management by establishing a novel scoring system for dairy herd health management aimed at providing a more nuanced understanding of disease impact. For this purpose, we retrieved 5-year data from 2 558 disease diary records of 798 primiparous and multiparous cows housed on a Czech farm and classified 125 production diseases into six categories, namely lameness, mastitis, postpartum diseases, digestive system, reproductive diseases and other diseases. Based on this metric, we developed a data-driven DSS for farm management. Using this DSS, we identified markers of disease categories for efficient veterinary monitoring on dairy farms. This DSS highlighted a decreasing trend of average monthly disease scores, yet the prevalence of postpartum and other diseases increased during the same period, due to changes in reproduction management within the herd. These findings underscore the need for data-driven targeted interventions for promoting the herd health. Therefore, our scoring model not only provides a comprehensive framework for dairy herd health monitoring and improvement but also advances dairy farming by providing a decision support system easily applicable to dairy farms based on available data recorded in disease diaries.

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“…Additionally, LCC can characterize perturbations of milk and offer insights about how cows respond to challenges during the lactation (Abdelkrim et al, 2021;Adriaens et al, 2021;Wang et al, 2022). For disease detection, lactation curve analysis can significantly contribute to the assessment of both short-and longterm effects of metabolic diseases on milk production (Yamazaki et al, 2009;Hostens et al, 2012;Masia et al, 2022). For calculation of economic disease impacts, lactation curve analysis are used to determine milk production losses due to disease (Steeneveld et al, 2007(Steeneveld et al, , 2011Andersen et al, 2011).…”

Section: Difference Between Modelsmentioning

confidence: 99%

Lactation curve modelling in dairy production

Chen

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Various metrics have been proposed to evaluate milk production of dairy cows, like cumulative milk production over a specific period (e.g., 305 days, 365 days, or an entire lactation) and milk yield per day within a certain period. These metrics are based on simple calculations on raw data. However, they provide an overview of milk production performance without capturing changes in milk production over the entire lactation period. These changes, or patterns in milk production offer more information (e.g., peak yield, peak time, persistency) about the lactation, which can be useful for breeding and selection, health monitoring, and other applications. Lactation curve models can extrapolate and quantify lactation curves and estimate actual production from incomplete data sets, generating various lactation curve characteristics (LCC) to describe the curve in different ways. LCC can serve as a metric to evaluate milk production performance at the cow level and have diverse applications in various dairy research fields. However, some important research topics have received insufficient attention. This thesis was conducted to explore the application of lactation curve modelling based on farm data collected on commercial dairy farms in the Netherlands and Belgium. Applications in cow reproduction performance and herd economic performance were developed. Four objectives were formulated: (1) to predict lactation persistency for DIM 305 at different insemination moments, (2) to investigate the association between days post conception and persistency, (3) to summarize cow lactation curves into herd lactation curve characteristics (HLCC) and illustrate a field application of HLCC, and (4) to compare whether HLCC or the herd’s average 305-day milk production (HM305) is better able to explain herd economic performance.

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Quantifying the negative impact of clinical diseases on productive and reproductive performance of dairy cows in central Argentina

Cited by 9 publications

References 45 publications

Subclinical mastitis in Jersey dairy cows and its effects on productivity and inflammatory markers

Subclinical mastitis in Jersey dairy cows and its effects on productivity and inflammatory markers

A decision support system based on disease scoring enables dairy farmers to proactively improve herd health

Lactation curve modelling in dairy production

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