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A topical collection in Animals (ISSN 2076-2615). This collection belongs to the section "Cattle".

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Editors

Dr. Mélissa Duplessis

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Collection Editor

Sherbrooke Research and Development Centre, Agriculture and Agri-Food Canada, Sherbrooke, QC, Canada
Interests: dairy cow; nutrition and metabolism; smart farming; integrating knowledge; vitamins and minerals; precision feeding

Dr. Liliana Fadul-Pacheco

E-Mail Website
Collection Editor

Department of Animal and Dairy Sciences, University of Wisconsin-Madison, Madison, WI, USA
Interests: dairy cows; data analysis; continuous data integration; decision support tools; smart farming; machine learning and optimization

Topical Collection Information

Dear Colleagues,

Worldwide dairy production has drastically evolved during the last decades; from hand milking to automatic milking system; from feeding hay and pasture to total mixed ration formulated using a specialized software according to cow requirements. Here are only few examples that allowed more than doubling yearly milk production of cows. Over the last few years, the concept of smart farming has emerged and there has been a tremendous increase of cow sensor use that, by generating large dataset, helps monitoring cow health, reproduction, productivity, and welfare. The technology can also be used to better meet cow nutrient requirements. We are nowadays aware that optimizing cow productivity can be done by integrating knowledge on precision feeding (i.e. better suit nutrient requirements at a given stage), cow health and welfare. Moreover, the impact of any recommendations at the farm level on the ecosystem should not be discarded from the integration.

We are seeking original research papers about how technology can be used to better meet nutrient requirements of dairy cows, better monitor cow health disorders related to nutrition or better monitor cow nutrient status. Moreover, topics may be related to precision feeding, smart farming, and integration of the ecosystem by studying the impact of nutrient supply and excretion in manure relating to dairy production.

Dr. Mélissa Duplessis
Dr. Liliana Fadul-Pacheco
Collection Editors

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Keywords

dairy cow
nutrient requirements
technology use
precision feeding
health
ecosystem integration
smart farming

Published Papers (20 papers)

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2023

Jump to: 2021, 2020

17 pages, 381 KiB

Open AccessArticle

Profitability Optimization of Dairy Farms: The Effect of Pregnancy Rate and Culling Decision

by Violetta Tóth, Emília Heinc, Edit Mikó, Tibor Csendes and Balázs Bánhelyi

Animals 2024, 14(1), 18; https://doi.org/10.3390/ani14010018 - 20 Dec 2023

Viewed by 823

Abstract

One of the most important decisions in dairy cattle production today is the correct choice of culling time for cows. In the culling decision process, the farmer has to take into account a number of factors, the complexity of which makes the decision-making task difficult. A crucial factor is the evolution of reproductive indicators. The aim of the research was to develop a microsimulation method that can be used to easily investigate the impact on profitability of increasing pregnancy rates and when the culling decision is made. In the microsimulation, the stock was examined without changing any other conditions. A microsimulation method has been developed to determine with high accuracy the effect of the pregnancy rate and the increase in culling days on the economic indicators of individual dairy farms. By microsimulation, the effect of changing these two parameters on the expected milk production of cows, the most important economic indicator for cattle farms, was investigated. The other parameters of economic importance were simulated using a cattle farm database. The purpose of microsimulation is to assist in producing certain managerial decisions in order to achieve better profitability and economic efficiency. In summary, the results showed that increasing the pregnancy rate can successfully reduce the length of the calving interval, but the improved pregnancy rate did not show a significant increase in milk production. In order to obtain results that can be used by farms, the authors intend to further develop the model in the future, adapting it to farms and taking into account their specificities. Full article

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16 pages, 2347 KiB

Open AccessArticle

In-Line Registered Milk Fat-to-Protein Ratio for the Assessment of Metabolic Status in Dairy Cows

by Ramūnas Antanaitis, Karina Džermeikaitė, Vytautas Januškevičius, Ieva Šimonytė and Walter Baumgartner

Animals 2023, 13(20), 3293; https://doi.org/10.3390/ani13203293 - 21 Oct 2023

Cited by 2 | Viewed by 2983

Abstract

This study endeavors to ascertain alterations in the in-line registered milk fat-to-protein ratio as a potential indicator for evaluating the metabolic status of dairy cows. Over the study period, farm visits occurred biweekly on consistent days, during which milk composition (specifically fat and protein) was measured using a BROLIS HerdLine in-line milk analyzer (Brolis Sensor Technology, Vilnius, Lithuania). Clinical examinations were performed at the same time as the farm visits. Blood was drawn into anticoagulant-free evacuated tubes to measure the activities of GGT and AST and albumin concentrations. NEFA levels were assessed using a wet chemistry analyzer. Using the MediSense and FreeStyle Optium H systems, blood samples from the ear were used to measure the levels of BHBA and glucose in plasma. Daily blood samples were collected for BHBA concentration assessment. All samples were procured during the clinical evaluations. The cows were categorized into distinct groups: subclinical ketosis (SCK; n = 62), exhibiting elevated milk F/P ratios without concurrent clinical signs of other post-calving diseases; subclinical acidosis (SCA; n = 14), characterized by low F/P ratios (<1.2), severe diarrhea, and nondigestive food remnants in feces, while being free of other post-calving ailments; and a healthy group (H; n = 20), comprising cows with no clinical indications of illness and an average milk F/P ratio of 1.2. The milk fat-to-protein ratios were notably higher in SCK cows, averaging 1.66 (±0.29; p < 0.01), compared to SCA cows (0.93 ± 0.1; p < 0.01) and healthy cows (1.22). A 36% increase in milk fat-to-protein ratio was observed in SCK cows, while SCA cows displayed a 23.77% decrease. Significant differences emerged in AST activity, with SCA cows presenting a 26.66% elevation (p < 0.05) compared to healthy cows. Moreover, SCK cows exhibited a 40.38% higher NEFA concentration (p < 0.001). A positive correlation was identified between blood BHBA and NEFA levels (r = 0.321, p < 0.01), as well as a negative association between BHBA and glucose concentrations (r = −0.330, p < 0.01). Notably, AST displayed a robust positive correlation with GGT (r = 0.623, p < 0.01). In light of these findings, this study posits that milk fat-to-protein ratio comparisons could serve as a non-invasive indicator of metabolic health in cows. The connections between milk characteristics and blood biochemical markers of lipolysis and ketogenesis suggest that these markers can be used to check the metabolic status of dairy cows on a regular basis. Full article

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23 pages, 1249 KiB

Open AccessSystematic Review

Applications of Technology to Record Locomotion Measurements in Dairy Cows: A Systematic Review

by Anna Bradtmueller, Amir Nejati, Elise Shepley and Elsa Vasseur

Animals 2023, 13(6), 1121; https://doi.org/10.3390/ani13061121 - 22 Mar 2023

Cited by 1 | Viewed by 1565

Abstract

Lameness within the dairy industry is a concern because of its associated costs and welfare implications. Visual locomotion scoring has been commonly used for assessing cows’ locomotion quality, but it can have low reliability and is relatively subjective compared to automated methods of assessing locomotion. Kinematic, kinetic, and accelerometric technologies can provide a greater number of more detailed outcome measurements than visual scoring. The objective of this systematic review was to determine outcome measurements, and the relationships between them, that have been recorded using kinematic, kinetic, and accelerometric technologies, as well as other approaches to evaluating cow locomotion. Following PRISMA guidelines, two databases were searched for studies published from January 2000 to June 2022. Thirty-seven articles were retained after undergoing a screening process involving a title and abstract evaluation, followed by a full-text assessment. Locomotion measurements recorded using these technologies often overlapped, but inconsistencies in the types of technology, the arrangement of equipment, the terminology, and the measurement-recording approaches made it difficult to compare locomotion measurements across studies. Additional research would contribute to a better understanding of how factors regarding the health, environment, and management of dairy cows affect aspects of locomotion, as recorded through the detailed, objective outcome measurements provided by these technologies. Full article

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2021

Jump to: 2023, 2020

17 pages, 944 KiB

Open AccessReview

Opportunities to Harness High-Throughput and Novel Sensing Phenotypes to Improve Feed Efficiency in Dairy Cattle

by Cori J. Siberski-Cooper and James E. Koltes

Animals 2022, 12(1), 15; https://doi.org/10.3390/ani12010015 - 22 Dec 2021

Cited by 5 | Viewed by 3912

Abstract

Feed for dairy cattle has a major impact on profitability and the environmental impact of farms. Sustainable dairy production relies on continued improvement in feed efficiency as a way to reduce costs and nutrient loss from feed. Advances in breeding, feeding and management have led to the dilution of maintenance energy and thus more efficient dairy cattle. Still, many additional opportunities are available to improve individual animal feed efficiency. Sensing technologies such as wearable sensors, image-based and high-throughput phenotyping technologies (e.g., milk testing) are becoming more available on commercial farm. The application of these technologies as indicator traits for feed intake and efficiency related traits would be advantageous to provide additional information to predict and manage feed efficiency. This review focuses on precision livestock technologies and high-throughput phenotyping in use today as well as those that could be developed in the future as possible indicators of feed intake. Several technologies such as milk spectral data, activity, rumen measures, and image-based phenotypes have been associated with feed intake. Future applications will depend on the ability to repeatably measure and calibrate these data across locations, so that they can be integrated for use in predicting and managing feed intake and efficiency on farm. Full article

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16 pages, 749 KiB

Open AccessArticle

Adoption of Precision Technologies by Brazilian Dairy Farms: The Farmer’s Perception

by Rebeca Silvi, Luiz Gustavo R. Pereira, Claudio Antônio V. Paiva, Thierry R. Tomich, Vanessa A. Teixeira, João Paulo Sacramento, Rafael E. P. Ferreira, Sandra G. Coelho, Fernanda S. Machado, Mariana M. Campos and João Ricardo. R. Dórea

Animals 2021, 11(12), 3488; https://doi.org/10.3390/ani11123488 - 07 Dec 2021

Cited by 13 | Viewed by 3801

Abstract

The use of precision farming technologies, such as milking robots, automated calf feeders, wearable sensors, and others, has significantly increased in dairy operations over the last few years. The growing interest in farming technologies to reduce labor, maximize productivity, and increase profitability is becoming noticeable in several countries, including Brazil. Information regarding technology adoption, perception, and effectiveness in dairy farms could shed light on challenges that need to be addressed by scientific research and extension programs. The objective of this study was to characterize Brazilian dairy farms based on technology usage. Factors such as willingness to invest in precision technologies, adoption of sensor systems, farmer profile, farm characteristics, and production indexes were investigated in 378 dairy farms located in Brazil. A survey with 22 questions was developed and distributed via Google Forms from July 2018 to July 2020. The farms were then classified into seven clusters: (1) top yield farms; (2) medium–high yield, medium-tech; (3) medium yield and top high-tech; (4) medium yield and medium-tech; (5) young medium–low yield and low-tech; (6) elderly medium–low yield and low-tech; and (7) low-tech grazing. The most frequent technologies adopted by producers were milk meters systems (31.7%), milking parlor smart gate (14.5%), sensor systems to detect mastitis (8.4%), cow activity meter (7.1%), and body temperature (7.9%). Based on a scale containing numerical values (1–5), producers indicated “available technical support” (mean; σ²) (4.55; 0.80) as the most important decision criterion involved in adopting technology, followed by “return on investment—ROI” (4.48; 0.80), “user-friendliness” (4.39; 0.88), “upfront investment cost” (4.36; 0.81), and “compatibility with farm management software” (4.2; 1.02). The most important factors precluding investment in precision dairy technologies were the need for investment in other sectors of the farm (36%), the uncertainty of ROI (24%), and lack of integration with other farm systems and software (11%). Farmers indicated that the most useful technologies were automatic milk meters systems (mean; σ²) (4.05; 1.66), sensor systems for mastitis detection (4.00; 1.57), automatic feeding systems (3.50; 2.05), cow activity meter (3.45; 1.95), and in-line milk analyzers (3.45; 1.95). Overall, the concerns related to data integration, ROI, and user-friendliness of technologies are similar to those of dairy farms located in other countries. Increasing available technical support for sensing technology can have a positive impact on technology adoption. Full article

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12 pages, 266 KiB

Open AccessArticle

Effects of Milk Urea Nitrogen (MUN) and Climatological Factors on Reproduction Efficiency of Holstein Friesian and Jersey Cows in the Subtropics

by Edward Cottington Webb and Elandri de Bruyn

Animals 2021, 11(11), 3068; https://doi.org/10.3390/ani11113068 - 27 Oct 2021

Cited by 2 | Viewed by 1558

Abstract

This study investigated the effects of MUN and climatological factors on the inter calving period (ICP), reproductive performance (RP%), and reproductive index (RI) in Holstein Friesian (n = 1177) and Jersey cows (n = 3305) in different seasons in the subtropics. Threshold values for MUN on the reproduction of dairy cows in the subtropics remain controversial due to complex environmental interactions, especially with high environmental temperatures. A retrospective analysis was conducted of data obtained from the National Milk Recording scheme of the Agricultural Research Council (ARC) in South Africa. The results confirm that MUN influences the reproduction of dairy cows in the subtropics. MUN concentrations exceeding 18.1 ± 4.28 mg/dL in Holstein Friesian cows and 13.0 ± 4.70 mg/dL in Jersey cows extended the inter calving period (ICP), and decreased RP% and RI. Jersey cows have a lower threshold MUN concentration compared to Holstein Friesian cows, but they are not adversely affected by high humidity or temperatures, while Holstein Friesian cows are. Full article

11 pages, 247 KiB

Open AccessCommentary

Data Governance in the Dairy Industry

by Roger Cue, Mark Doornink, Regi George, Benjamin Griffiths, Matthew W. Jorgensen, Ronald Rogers, Amit Saha, Kyle Taysom, Victor E. Cabrera, Steven R. Wangen and Liliana Fadul-Pacheco

Animals 2021, 11(10), 2981; https://doi.org/10.3390/ani11102981 - 15 Oct 2021

Cited by 2 | Viewed by 2706

Abstract

Data governance is a growing concern in the dairy farm industry because of the lack of legal regulation. In this commentary paper, we discuss the status quo of the available legislation and codes, as well as some possible solutions. To our knowledge, there are currently four codes of practice that address agriculture data worldwide, and their objectives are similar: (1) raise awareness of diverse data challenges such as data sharing and data privacy, (2) provide data security, and (3) illustrate the importance of the transparency of terms and conditions of data sharing contracts. However, all these codes are voluntary, which limits their adoption. We propose a Farmers Bill of Rights for the dairy data ecosystem to address some key components around data ownership and transparency in data sharing. Our hope is to start the discussion to create a balanced environment to promote equity within the data economy, encourage proper data stewardship, and to foster trust and harmony between the industry companies and the farmers when it comes to sharing data. Full article

12 pages, 314 KiB

Open AccessArticle

Modelling Extended Lactations in Polish Holstein–Friesian Cows

by Agnieszka Otwinowska-Mindur, Ewa Ptak, Joanna Makulska and Olga Jarnecka

Animals 2021, 11(8), 2176; https://doi.org/10.3390/ani11082176 - 22 Jul 2021

Cited by 1 | Viewed by 3110

Abstract

The objectives of this study were (1) to examine different shapes of lactation curves for milk, fat, protein and lactose yields and urea content in milk fitted by the Wilmink function using the test-day (TD) records and (2) to find the function that best describes test-day records beyond 305 days in milk (DIM) for Polish Holstein–Friesian cows. The data were 6,955,768 TD records from the 702,830 first six lactations of 284,193 Polish Holstein–Friesian cows. Cows calved in 19,102 herds between 2001 and 2018. The following functions were fitted to TD data from each lactation: (1) Wilmink model fitted to the whole lactation, (2) Wilmink model fitted to TD records from 5 to 305 DIM and linear function fitted to TD records from 306 to 400 DIM, (3) Wilmink model fitted to TD records from 5 to 305 DIM and squared function fitted to TD records from 306 to 400 DIM. The present study showed that urea content in milk was modelled slightly worse than other milk traits. The results suggested that the course of lactation could be successfully modelled by a nonlinear model, for example, the Wilmink function, for up to 305 DIM, and by the linear or squared function afterwards. Full article

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17 pages, 248 KiB

Open AccessCommentary

Integrated Decision Support Systems (IDSS) for Dairy Farming: A Discussion on How to Improve Their Sustained Adoption

by Michel Baldin, Tom Breunig, Roger Cue, Albert De Vries, Mark Doornink, Jan Drevenak, Robert Fourdraine, Regi George, Robert Goodling, Randall Greenfield, Matthew W. Jorgensen, Andy Lenkaitis, Doug Reinemann, Amit Saha, Chakra Sankaraiah, Saleh Shahinfar, Cori Siberski, Kevin M. Wade, Fan Zhang, Liliana Fadul-Pacheco, Steven Wangen, Tadeu E. da Silva and Victor E. Cabrera Show full author list Hide full author list

Animals 2021, 11(7), 2025; https://doi.org/10.3390/ani11072025 - 07 Jul 2021

Cited by 11 | Viewed by 5850

Abstract

Dairy farm decision support systems (DSS) are tools which help dairy farmers to solve complex problems by improving the decision-making processes. In this paper, we are interested in newer generation, integrated DSS (IDSS), which additionally and concurrently: (1) receive continuous data feed from on-farm and off-farm data collection systems and (2) integrate more than one data stream to produce insightful outcomes. The scientific community and the allied dairy community have not been successful in developing, disseminating, and promoting a sustained adoption of IDSS. Thus, this paper identifies barriers to adoption as well as factors that would promote the sustained adoption of IDSS. The main barriers to adoption discussed include perceived lack of a good value proposition, complexities of practical application, and ease of use; and IDSS challenges related to data collection, data standards, data integration, and data shareability. Success in the sustainable adoption of IDSS depends on solving these problems and also addressing intrinsic issues related to the development, maintenance, and functioning of IDSS. There is a need for coordinated action by all the main stakeholders in the dairy sector to realize the potential benefits of IDSS, including all important players in the dairy industry production and distribution chain. Full article

15 pages, 647 KiB

Open AccessArticle

Assessment of the Relationship between Postpartum Health and Mid-Lactation Performance, Behavior, and Feed Efficiency in Holstein Dairy Cows

by Malia J. Martin, Kent A. Weigel and Heather M. White

Animals 2021, 11(5), 1385; https://doi.org/10.3390/ani11051385 - 13 May 2021

Cited by 7 | Viewed by 2301

Abstract

The objective of this study was to investigate the relationships between postpartum health disorders and mid-lactation performance, feed efficiency, and sensor-derived behavioral traits. Multiparous cows (n = 179) were monitored for health disorders for 21 days postpartum and enrolled in a 45-day trial between 50 to 200 days in milk, wherein feed intake, milk yield and components, body weight, body condition score, and activity, lying, and feeding behaviors were recorded. Feed efficiency was measured as residual feed intake and the ratio of fat- or energy-corrected milk to dry matter intake. Cows were classified as either having hyperketonemia (HYK; n = 72) or not (n = 107) and grouped by frequency of postpartum health disorders: none (HLT; n = 94), one (DIS; n = 63), or ≥2 (DIS+; n = 22). Cows that were diagnosed with HYK had higher mid-lactation yields of fat- and energy-corrected milk. No differences in feed efficiency were detected between HYK or health status groups. Highly active mid-lactation time was higher in healthy animals, and rumination time was lower in ≥4th lactation cows compared with HYK or DIS and DIS+ cows. Differences in mid-lactation behaviors between HYK and health status groups may reflect the long-term impacts of health disorders. The lack of a relationship between postpartum health and mid-lactation feed efficiency indicates that health disorders do not have long-lasting impacts on feed efficiency. Full article

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18 pages, 1901 KiB

Open AccessArticle

The Ruminant Farm Systems Animal Module: A Biophysical Description of Animal Management

by Tayler L. Hansen, Manfei Li, Jinghui Li, Chris J. Vankerhove, Militsa A. Sotirova, Juan M. Tricarico, Victor E. Cabrera, Ermias Kebreab and Kristan F. Reed

Animals 2021, 11(5), 1373; https://doi.org/10.3390/ani11051373 - 12 May 2021

Cited by 9 | Viewed by 4667

Abstract

Dairy production is an important source of nutrients in the global food supply, but environmental impacts are increasingly a concern of consumers, scientists, and policy-makers. Many decisions must be integrated to support sustainable production—which can be achieved using a simulation model. We provide an example of the Ruminant Farm Systems (RuFaS) model to assess changes in the dairy system related to altered animal feed efficiency. RuFaS is a whole-system farm simulation model that simulates the individual animal life cycle, production, and environmental impacts. We added a stochastic animal-level parameter to represent individual animal feed efficiency as a result of reduced residual feed intake and compared High (intake = 94% of expected) and Very High (intake = 88% of expected) efficiency levels with a Baseline scenario (intake = 100% of expected). As expected, the simulated total feed intake was reduced by 6 and 12% for the High and Very High efficiency scenarios, and the expected impact of these improved efficiencies on the greenhouse gas emissions from enteric methane and manure storage was a decrease of 4.6 and 9.3%, respectively. Full article

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16 pages, 321 KiB

Open AccessFeature PaperArticle

Toward Precision Feeding Regarding Minerals: What Is the Current Practice in Commercial Dairy Herds in Québec, Canada?

by Mélissa Duplessis, Liliana Fadul-Pacheco, Débora E. Santschi and Doris Pellerin

Animals 2021, 11(5), 1320; https://doi.org/10.3390/ani11051320 - 05 May 2021

Cited by 6 | Viewed by 1721

Abstract

This analysis is performed to obtain information on the current situation regarding phosphorus (P), cobalt (Co), copper (Cu), iron (Fe), manganese (Mn), and zinc (Zn) concentrations in cow diets of commercial dairy herds in Québec, Canada, and to compare them with National Research Council recommendations. Data are collected on 100 Holstein dairy herds in Québec, Canada, and 4430 cows were involved. Rations are analyzed for selected minerals and cow requirements relative to the recommendations were calculated. Median percentages of mineral recommendations fulfilled by forage were 55%, 196%, 54%, 776%, 181%, and 44% for P, Co, Cu, Fe, Mn, and Zn, respectively. Daily dietary concentrations of P, Cu, Mn, and Zn decreased as lactation progressed, whereas Co and Fe were stable throughout lactation. Phosphorus was the mineral fed the closest to the requirements, cows below 21 days in milk were even underfed by 11%. All studied trace minerals were fed in excess for the majority of cows. Cobalt was fed on average 480% above requirements regardless of the stage of lactation. For Cu, Fe, Mn, and Zn, rations for cows below 21 days in milk were fed 23% (95% confidence interval: 15–32), 930% (849–1019), 281% (251–314), and 35% (22–47) above the recommendations, respectively, and were closer to the requirements than after 21 days in milk. These results show that most nutritionists are aware that precision feeding regarding P is important to minimize detrimental environmental impacts of dairy production. However, some efforts should be made to limit trace mineral overfeeding to ensure environmental resiliency. Full article

19 pages, 3880 KiB

Open AccessArticle

Multiple Country Approach to Improve the Test-Day Prediction of Dairy Cows’ Dry Matter Intake

by Anthony Tedde, Clément Grelet, Phuong N. Ho, Jennie E. Pryce, Dagnachew Hailemariam, Zhiquan Wang, Graham Plastow, Nicolas Gengler, Eric Froidmont, Frédéric Dehareng, Carlo Bertozzi, Mark A. Crowe, Hélène Soyeurt and on behalf of the GplusE Consortium

Animals 2021, 11(5), 1316; https://doi.org/10.3390/ani11051316 - 04 May 2021

Cited by 9 | Viewed by 2642

Abstract

We predicted dry matter intake of dairy cows using parity, week of lactation, milk yield, milk mid-infrared (MIR) spectrum, and MIR-based predictions of bodyweight, fat, protein, lactose, and fatty acids content in milk. The dataset comprised 10,711 samples of 534 dairy cows with a geographical diversity (Australia, Canada, Denmark, and Ireland). We set up partial least square (PLS) regressions with different constructs and a one-hidden-layer artificial neural network (ANN) using the highest contribution variables. In the ANN, we replaced the spectra with their projections to the 25 first PLS factors explaining 99% of the spectral variability to reduce the model complexity. Cow-independent 10 × 10-fold cross-validation (CV) achieved the best performance with root mean square errors (RMSE_CV) of 3.27 ± 0.08 kg for the PLS regression and 3.25 ± 0.13 kg for ANN. Although the available data were significantly different, we also performed a country-independent validation (CIV) to measure the models’ performance fairly. We found RMSE_CIV varying from 3.73 to 6.03 kg for PLS and 3.69 to 5.08 kg for ANN. Ultimately, based on the country-independent validation, we discussed the developed models’ performance with those achieved by the National Research Council’s equation. Full article

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17 pages, 3457 KiB

Open AccessArticle

Rapid and Non-Destructive Monitoring of Moisture Content in Livestock Feed Using a Global Hyperspectral Model

by Daniel Dooyum Uyeh, Juntae Kim, Santosh Lohumi, Tusan Park, Byoung-Kwan Cho, Seungmin Woo, Won Suk Lee and Yushin Ha

Animals 2021, 11(5), 1299; https://doi.org/10.3390/ani11051299 - 30 Apr 2021

Cited by 3 | Viewed by 2399

Abstract

The dry matter (DM) content of feed is vital in cattle nutrition and is inversely correlated with moisture content. The established ranges of moisture content serve as a marker for factors such as safe storage limit and DM intake. Rapid changes in moisture content necessitate rapid measurements. A rapid and non-destructive global model for the measurement of moisture content in total mixed ration feed and feed materials was developed. To achieve this, we varied and measured the moisture content in the feed and feed materials using standard methods and captured their images using a hyperspectral imaging (HSI) system in the spectral range of 1000–2500 nm. The spectral data from the samples were extracted and preprocessed using seven techniques and were used to develop a global model using partial least squares regression (PLSR) analysis. The range preprocessing technique had the best prediction accuracy (R² = 0.98) and standard error of prediction (2.59%). Furthermore, the visual assessment of distribution in moisture content made possible by the generated PLSR-based moisture content mapped images could facilitate precise formulation. These applications of HSI, when used in commercial feed production, could help prevent feed spoilage and resultant health complications as well as underperformance of the animals from improper DM intake. Full article

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23 pages, 1979 KiB

Open AccessArticle

Hyperketonemia Predictions Provide an On-Farm Management Tool with Epidemiological Insights

by Ryan S. Pralle, Joel D. Amdall, Robert H. Fourdraine, Garrett R. Oetzel and Heather M. White

Animals 2021, 11(5), 1291; https://doi.org/10.3390/ani11051291 - 30 Apr 2021

Cited by 6 | Viewed by 2499

Abstract

Prediction of hyperketonemia (HYK), a postpartum metabolic disorder in dairy cows, through use of cow and milk data has allowed for high-throughput detection and monitoring during monthly milk sampling. The objective of this study was to determine associations between predicted HYK (pHYK) and production parameters in a dataset generated from routine milk analysis samples. Data from 240,714 lactations across 335 farms were analyzed with multiple linear regression models to determine HYK status. Data on HYK or disease treatment was not solicited. Consistent with past research, pHYK cows had greater previous lactation dry period length, somatic cell count, and dystocia. Cows identified as pHYK had lower milk yield and protein percent but greater milk fat, specifically greater mixed and preformed fatty acids (FA), and greater somatic cell count (SCC). Differential somatic cell count was greater in second and fourth parity pHYK cows. Culling (60d), days open, and number of artificial inseminations were greater in pHYK cows. Hyperketonemia prevalence decreased linearly in herds with greater rolling herd average milk yield. This research confirms previously identified risk factors and negative outcomes associated with pHYK and highlights novel associations with differential SCC, mixed FA, and preformed FA across farm sizes and production levels. Full article

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15 pages, 747 KiB

Open AccessArticle

Validation of Dairy Cow Bodyweight Prediction Using Traits Easily Recorded by Dairy Herd Improvement Organizations and Its Potential Improvement Using Feature Selection Algorithms

by Anthony Tedde, Clément Grelet, Phuong N. Ho, Jennie E. Pryce, Dagnachew Hailemariam, Zhiquan Wang, Graham Plastow, Nicolas Gengler, Yves Brostaux, Eric Froidmont, Frédéric Dehareng, Carlo Bertozzi, Mark A. Crowe, Isabelle Dufrasne, GplusE Consortium Group and Hélène Soyeurt

Animals 2021, 11(5), 1288; https://doi.org/10.3390/ani11051288 - 30 Apr 2021

Cited by 8 | Viewed by 2558

Abstract

Knowing the body weight (BW) of a cow at a specific moment or measuring its changes through time is of interest for management purposes. The current work aimed to validate the feasibility of predicting BW using the day in milk, parity, milk yield, and milk mid-infrared (MIR) spectrum from a multiple-country dataset and reduce the number of predictors to limit the risk of over-fitting and potentially improve its accuracy. The BW modeling procedure involved feature selections and herd-independent validation in identifying the most interesting subsets of predictors and then external validation of the models. From 1849 records collected in 9 herds from 360 Holstein cows, the best performing models achieved a root mean square error (RMSE) for the herd-independent validation between 52 ± 2.34 kg to 56 ± 3.16 kg, including from 5 to 62 predictors. Among these models, three performed remarkably well in external validation using an independent dataset (N = 4067), resulting in RMSE ranging from 52 to 56 kg. The results suggest that multiple optimal BW predictive models coexist due to the high correlations between adjacent spectral points. Full article

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13 pages, 3234 KiB

Open AccessArticle

A Preliminary Investigation of Social Network Analysis Applied to Dairy Cow Behavior in Automatic Milking System Environments

by Liliana Fadul-Pacheco, Michael Liou, Douglas J. Reinemann and Victor E. Cabrera

Animals 2021, 11(5), 1229; https://doi.org/10.3390/ani11051229 - 24 Apr 2021

Cited by 4 | Viewed by 3273

Abstract

We have applied social network analysis (SNA) to data on voluntary cow movement through a sort gate in an automatic milking system to identify pairs of cows that repeatedly passed through a sort gate in close succession (affinity pairs). The SNA was applied to social groups defined by four pens on a dairy farm, each served by an automatic milking system (AMS). Each pen was equipped with an automatic sorting gate that identified when cows voluntarily moved from the resting area to either milking or feeding areas. The aim of this study was two-fold: to determine if SNA could identify affinity pairs and to determine if milk production was affected when affinity pairs where broken. Cow traffic and milking performance data from a commercial guided-flow AMS dairy farm were used. Average number of milked cows was 214 ± 34, distributed in four AMS over 1 year. The SNA was able to identify clear affinity pairs and showed when these pairings were formed and broken as cows entered and left the social group (pen). The trend in all four pens was toward higher-than-expected milk production during periods of affinity. Moreover, we found that when affinities were broken (separation of cow pairs) the day-to-day variability in milk production was three times higher than for cows in an affinity pair. The results of this exploratory study suggest that SNA could be potentially used as a tool to reduce milk yield variation and better understand the social dynamics of dairy cows supporting management and welfare decisions. Full article

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11 pages, 2122 KiB

Open AccessArticle

Predicting Daily Dry Matter Intake Using Feed Intake of First Two Hours after Feeding in Mid and Late Lactation Dairy Cows with Fed Ration Three Times Per Day

by Shulin Liang, Chaoqun Wu, Wenchao Peng, Jian-Xin Liu and Hui-Zeng Sun

Animals 2021, 11(1), 104; https://doi.org/10.3390/ani11010104 - 06 Jan 2021

Cited by 5 | Viewed by 2128

Abstract

The objective of this study was to evaluate the feasibility of using the dry matter intake of first 2 h after feeding (DMI-2h), body weight (BW), and milk yield to estimate daily DMI in mid and late lactating dairy cows with fed ration three times per day. Our dataset included 2840 individual observations from 76 cows enrolled in two studies, of which 2259 observations served as development dataset (DDS) from 54 cows and 581 observations acted as the validation dataset (VDS) from 22 cows. The descriptive statistics of these variables were 26.0 ± 2.77 kg/day (mean ± standard deviation) of DMI, 14.9 ± 3.68 kg/day of DMI-2h, 35.0 ± 5.48 kg/day of milk yield, and 636 ± 82.6 kg/day of BW in DDS and 23.2 ± 4.72 kg/day of DMI, 12.6 ± 4.08 kg/day of DMI-2h, 30.4 ± 5.85 kg/day of milk yield, and 597 ± 63.7 kg/day of BW in VDS, respectively. A multiple regression analysis was conducted using the REG procedure of SAS to develop the forecasting models for DMI. The proposed prediction equation was: DMI (kg/day) = 8.499 + 0.2725 × DMI-2h (kg/day) + 0.2132 × Milk yield (kg/day) + 0.0095 × BW (kg/day) (R² = 0.46, mean bias = 0 kg/day, RMSPE = 1.26 kg/day). Moreover, when compared with the prediction equation for DMI in Nutrient Requirements of Dairy Cattle (2001) using the independent dataset (VDS), our proposed model shows higher R² (0.22 vs. 0.07) and smaller mean bias (−0.10 vs. 1.52 kg/day) and RMSPE (1.77 vs. 2.34 kg/day). Overall, we constructed a feasible forecasting model with better precision and accuracy in predicting daily DMI of dairy cows in mid and late lactation when fed ration three times per day. Full article

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2020

Jump to: 2023, 2021

16 pages, 2782 KiB

Open AccessArticle

Fatty Acid Profiles from Routine Milk Recording as a Decision Tool for Body Weight Change of Dairy Cows after Calving

by Franziska Dettmann, Daniel Warner, Bart Buitenhuis, Morten Kargo, Anne Mette Hostrup Kjeldsen, Niels Henning Nielsen, Daniel M. Lefebvre and Debora E. Santschi

Animals 2020, 10(11), 1958; https://doi.org/10.3390/ani10111958 - 23 Oct 2020

Cited by 4 | Viewed by 2243

Abstract

Cows mobilize body reserves during early lactation, which is reflected in the milk fatty acid (FA) profile. Milk FA can be routinely predicted by Fourier-transform infrared (FTIR) spectroscopy, and be, thus, used to develop an early indicator for bodyweight change (BWC) in early lactating cows in commercial dairy farms. Cow records from 165 herds in Denmark between 2015 and 2017 were used with bodyweight (BW) records at each milking from floor scales in automatic milking systems. Milk FA in monthly test-day samples was predicted by FTIR. Predictions of BWC were based on a random forest model and included parity, stage of lactation, and test day milk production and components (fat, protein, and FA). Bodyweight loss was mainly explained by decreased short-chain FA (C4:0–C10:0) and increased C18:0 FA. The root mean square error (RMSE) of prediction after cross-validation was 1.79 g/kg of BW (R² of 0.94). Model evaluation with previously unseen BWC records resulted in reduced prediction performance (RMSE of 2.33 g/kg of BW; R² of 0.31). An early warning system may be implemented for cows with a large BW loss during early lactation based on milk FA profiles, but model performance should be improved, ideally by using the full FTIR milk spectra. Full article

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17 pages, 1059 KiB

Open AccessArticle

Effect of Feeding Improved Grass Hays and Eragrostis tef Straw Silage on Milk Yield, Nitrogen Utilization, and Methane Emission of Lactating Fogera Dairy Cows in Ethiopia

by Shigdaf Mekuriaw, Atsushi Tsunekawa, Toshiyoshi Ichinohe, Firew Tegegne, Nigussie Haregeweyn, Nobuyuki Kobayashi, Asaminew Tassew, Yeshambel Mekuriaw, Misganaw Walie, Mitsuru Tsubo, Toshiya Okuro, Derege Tsegaye Meshesha, Mulugeta Meseret, Laiju Sam and Veerle Fievez

Animals 2020, 10(6), 1021; https://doi.org/10.3390/ani10061021 - 11 Jun 2020

Cited by 10 | Viewed by 4127

Abstract

The nutritionally imbalanced poor-quality diet feeding is the major constraint of dairy production in tropical regions. Hence, alternative high-quality roughage-based diets are required to improve milk yield and reduce methane emission (CH4). Thus, we tested the effects of feeding natural pasture hay, improved forage grass hays (Napier and Brachiaria Hybrid), and treated crop residues (Eragrostis tef straw) on nutrient digestibility, milk yield, nitrogen balance, and methane emission. The eight lactating Fogera cows selected for the experiment were assigned randomly to a 4 × 4 Latin square design. Cows were housed in well-ventilated individual pens and fed a total mixed ration (TMR) comprising 70% roughage and 30% concentrate. The four roughage-based basal dietary treatments supplemented with formulated concentrate were: Control (natural pasture hay (NPH)); treated teff straw silage (TTS); Napier grass hay (NGH); and Brachiaria hybrid grass hay (BhH). Compared with the control diet, the daily milk yield increased (p < 0.01) by 31.9%, 52.9%, and 71.6% with TTS, NGH, and BhH diets, respectively. Cows fed BhH had the highest dry matter intake (8.84 kg/d), followed by NGH (8.10 kg/d) and TTS (7.71 kg/d); all of these intakes were greater (p = 0.01) than that of NPH (6.21 kg/d). Nitrogen digestibility increased (p < 0.01) from the NPH diet to TTS (by 27.7%), NGH (21.7%), and BhH (39.5%). The concentration of ruminal ammonia nitrogen was higher for cows fed NGH than other diets (p = 0.01) and positively correlated with plasma urea nitrogen concentration (R² = 0.45). Feeding TTS, NGH, and BhH hay as a basal diet changed the nitrogen excretion pathway from urine to feces, which can help protect against environmental pollution. Estimated methane yields per dry matter intake and milk yield were decreased in dairy cows fed BhH, NGH, and TTS diets when compared to cows fed an NPH diet (p < 0.05). In conclusion, feeding of TTS, NGH, and BhH roughages as a basal diet to lactating dairy cows in tropical regions improved nutrient intake and digestibility, milk yield, nitrogen utilization efficiency, and reduced enteric methane emission. Full article

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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

1. Review

Opportunities to harness data from precision technologies to manage feed and improve feed efficiency in dairy cattle.

Cori J Siberski* and James E Koltes*

Department of Animal Science, Iowa State University

Abstract: Feed use and management plays a large role in the sustainability of the dairy industry, with economic, environmental, and social implications. Unfortunately, collection of feed intake data is limited to the research sector because of the costs associated with the technology and labor required to measure individual cow phenotypes. Improvement of feed efficiency in the industry will require routinely collected data from commercial farms. Therefore, research is needed to identify affordable and portable technologies to collect feed intake and related measurements. Interest has grown in the use of precision technologies (ex: wearable sensors, image data, global positioning systems, and milk monitoring systems) as possible indicators of feed intake, due to their practicality and increasing use in commercial farms. Additional research is needed to investigate new high-throughput phenotypes, genomics data, and prediction methods to improve the accuracy of feed intake prediction. Critical consideration needs to be given to how feed intake or efficiency is defined, changes in efficiency at different stages of life, health, management systems, and environmental factors such as feed quality and climate when developing future precision feeding tool. The ability of the dairy industry to utilize diverse data has facilitated major improvements in production efficiency. Capitalizing on precision technologies and high-throughput data could impel the next advance in precision feed management.

2. Prediction of dry matter intake ingested by dairy cows from lactation stage, parity, milk yield and milk mid-infrared spectrum using different machine learning techniques

TEDDE A., GRELET C., HO P.N., PRYCE J.E., GENGLER N., DEHARENG F., CONSORTIUM G.P.L.U.S.E, SOYEURT H.

3. Multiple breeds and countries predictions of mineral contents in milk from milk mid-infrared spectrometry.

Christophe O.¹, Grelet C.¹, Reuter V.¹, Bertozzi C.², Veselko D.³, Lecomte C.⁴, Höckels P.⁵, Werner A.⁶, Auer F.J.⁷, Gengler N.⁸, Dehareng F.¹, Soyeurt H.⁸

4. The use of fatty acid profiles from milk recording samples to predict body weight change of dairy cows in early lactation in commercial dairy farms

Dettmann,*† D. Warner,* A. J. Buitenhuis,‡ M. Kargo,‡§ A. M. Hostrup Kjeldsen,§ N.H. Nielsen,# D. M. Lefebvre,* and D. E. Santschi*

*Lactanet, Sainte-Anne-de-Bellevue, QC, H9X3R4, Canada

†LKV Niedersachsen e.V., 26789 Leer, Germany

‡Aarhus University, Center for Quantitative Genetics and Genomics, 8830 Tjele, Denmark

§ SEGES, 8200 Aarhus N, Denmark

#RYK, 8200 Aarhus N, Denmark

Abstract: Cows typically mobilize body reserves to maintain milk fat production during early lactation, which is reflected in the milk fatty acid (FA) profile. Milk FA can be routinely predicted by Fourier-transform infrared (FT-IR) spectroscopy. This rapid milk analysis offers therefore an opportunity to develop an early indicator for body weight change (BWC) based on the milk FA profile. The objective of this study was to validate if the milk FA profile can be used to predict BWC in early lactating cows in commercial dairy farms. Data originated from 16,847 Danish Holstein cows at 7-35 days in milk across 165 herds in Denmark between March 2015 and March 2017 with body weight (BW) records from floor scales in Lely automatic milking systems at each milking. Milk FA in monthly test-day milk samples were predicted by FT-IR. Data for BWC predictions included parity, stage of lactation, and test day data for milk production and components (fat, protein, and FA concentrations). Daily BWC (mean ± standard deviation) was −0.52 ± 2.65 g/kg of BW (first parity), −0.64 ± 2.82 g/kg of BW (second parity) and −0.82 ± 5.53 g/kg of BW (third parity). Predictions of BWC were based on a random forest model. Body weight loss was mainly explained by decreased short-chain FA (SCFA; C4:0–C10:0) and increased C18:0 FA. The root mean square error (RMSE) of prediction after cross-validation was 1.79 g/kg of BW (R² of 0.94). Model evaluation with previously unseen BWC records resulted in reduced prediction performance (RMSE of 2.33 g/kg of BW; R² of 0.31). An early warning system may thus be implemented for cows with a large BW loss during early lactation based on the FT-IR milk FA profiles, but model performance should be improved, ideally by using the full FT-IR milk spectra.

5. Hyperketonemia predictions provide an on-farm management tool with epidemiological insights.

Pralle, R., Fourdraine, R. and White, H. M.

University of Wisconsin Madison, Madison, WI, USA

6. Assessment of the relationship between postpartum hyperketonemia and mid-lactation residual feed intake in Holstein dairy cows

Martin, M. J., K. A. Weigel, and H. M. White

7. Data Science Tools for Smart Farming: Moving Academic Projects into Practice within Dairy Brain

Ferris, M.C., Wangen, S.R., Issaka, S.M. and Shortnacy, L.

University of Wisconsin-Madison, Madison-WI, USA

Abstract: We describe a decision support tool – the Dairy Brain – that couples together data analytics tools with a suite of applications to integrate the cow, herd and economic data in ways that inform management, operational and animal health improving practices and controls uncertainties. Specific applications are often generated from research papers in academic journals using specialized data collection and formats. We show how to transform these into decision support tools to inform improved operation of modern-day dairy farms. The paper demonstrates a particular application for feed efficiency and outlines the design of an application programming interface to allow data from multiple sources that is gathered, cleaned and organized to be disseminated using an agricultural data hub into a working tool that uses it and machine learning to provide additional value to the stakeholders or users.

8. Connecting Precision Feed Management to Environmental Outcomes with the Ruminant Farm Systems (RuFaS) Model

Reed, Kristan F.¹, Tayler L. Hansen¹, Manfei Li², Jinghui Li³, Victor E. Cabrera²

¹ Department of Animal Science, Cornell University, Ithaca, NY 14850

²Department of Dairy Science, University of Wisconsin-Madison, Madison WI 53705

³Department of Animal Science, University of California -Davis, Davis CA 95616

Abstract: Feed efficiency is a primary driver of economic and environmental sustainability for dairy producers. Metrics like total feed costs, milk production, and income over feed costs connect feed efficiency to economic outcomes, but linking environmental outcomes to improved feed efficiency is more complex as it requires an integrated system approach. The Ruminant Farm Systems (RuFaS) model simulates nutrient cycling, production, and environmental impacts under various conditions and it’s modular is customizable to represent both current and future management systems. Through this flexible structure, RuFaS can assess novel management practices that improve feed efficiency such as herd-level nutritional grouping or cow-level residual feed intake and quantify their impacts on environmental outcomes like enteric methane and manure production.

9. Adoption of precision technologies by Brazilian dairy farms: the farmer’s perception

R. Silvi*, L. G. R. Pereira¹†, C. A. V. Paiva †, T. R. Tomich†, M. M. Campos†, F. S. Machado†, V. T. Amorim‡, S. G. Coelho‡, L. C.S Gonçalves‡, J.R.R. Dórea⁺

* Universidade Estadual de Santa Cruz, Ilhéus, Bahia, Brazil, 45662-900

† Brazilian Agricultural Research Corporation – Embrapa Dairy Cattle, Juiz de Fora, Minas Gerais, Brazil, 36038-330

‡Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil, 31270-901

⁺Department of Animal and Dairy Sciences, University of Wisconsin, Madison 53706, United States

Abstract: The use of precision farming technologies, such as milking robots, automated calf feeders, wearable sensors, and others, has significantly increased in dairy operations over the last few years. The growing interest for such technologies to reduce labor, maximize productivity, and increase profitability is becoming noticeable in several countries, including Brazil. Information regarding technology adoption, perception, and effectiveness in dairy farms could shed light on challenges that need to be addressed by scientific research and extension programs. In this context, we created and applied a survey to: i) characterize Brazilian dairy farms that adopt precision technologies; ii) identify the main technologies used; and iii) investigate the motivation to invest in precision farming technologies. An online survey was distributed to 372 Brazilian farms between 2019 to 2020. The South of Brazil was the region with the larger adoption of precision technologies. The standard Brazilian farm using precision farm technologies has free-stalls or compost bedded pack systems, midline piped milking machines as milking parlor, and Holstein as the main breed. Among the technologies adopted by Brazilian producers, the most frequent were: automatic weighing systems (36%), milking parlor separation gate (17%), and cow activity meters (10%). Producers scored a list of technologies on usefulness using a 5-point scale (from 1 = not useful to 5 = useful). Producers indicated (mean ± SD) automatic weighing, milk flow detection systems (4.03 ± 1.23), automatic mastitis detectors (3.98 ± 1.22), and cow activity meters (3.53 ± 1.33) to be the most useful. Producers were asked to score (from 1 = not important to 5 = important) the reasons to purchase a precision dairy farming technology from a predetermined list of technologies. Producers indicated that the availability of technical support (4.48 ± 0.95), total investment cost (4.46 ± 0.93), and user-friendly interface (4.32 ± 0.91) are the most important factors when deciding whether to implement a technology. On the other hand, producers indicated that investment in other areas (22%), the uncertainty of return on investment (20.4%), and the lack of integration between the technologies and the main management software used in the farm are the most important factors when deciding to do not invest in precision technologies.

Keywords: cattle, sensor, livestock, smart farm, survey

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