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This chapter presents common aspects applying in all EU Member States and in the UK (since Directive 2010/63/EU was transposed into British law before Brexit), and gives the specifications that apply in each research institute of the SmartCow project., Chapter of the Living Handbook Methods in cattle physiology and behaviour – Recommendations from the SmartCow consortium

Animal welfare is a value of the European Union (EU) enshrined in Article 13 of the Treaty on the Functioning of the European Union (TFEU) [1]: "In formulating and implementing the Union's agriculture, fisheries, transport, internal market, research and technological development and space policies, the Union and the Member States shall, since animals are sentient beings, pay full regard to the welfare requirements of animals, while respecting the legislative or administrative provisions and customs of the Member States relating in particular to religious rites, cultural traditions and regional heritage." In order to eliminate disparities between laws and regulations of the Member States regarding the protection of animals used for scientific purposes, the EU passed the Directive 86/609/EEC in 1986. This directive aimed at ensuring that animals used for experimental or other scientific purposes are adequately cared for and no unnecessary pain or suffering is inflicted to them. These rules were refined with the adoption of Directive 2010/63/EU [2] in 2010. Directive 2010/63/EU harmonises animal research legislation across EU Member States and ensures high standards of animal welfare in scientific research. Directive 2010/63/EU was transposed into national law in each Member State by 2013 or 2014. Member States, which enacted more rigorous measures to protect experimental animals prior to the introduction of Directive 2010/63/EU, can maintain them as long as they do not hinder EU-wide scientific cooperation and trade. So national legislations to protect animals used for experimental or scientific purposes include EU standards as defined in Directive 2010/63/EU and possibly additional national standards. This chapter presents common aspects applying in all EU Member States and in the UK (since Directive 2010/63/EU was transposed into British law before Brexit), and gives the specifications that apply in each research institute of the SmartCow project. Methods in cattle physiology and behaviour research – Recommendations from the SmartCow consortium 10.5680/ info:eu-repo/semantics/publishedVersion

Simple Summary Plant extracts containing isoquinoline alkaloids (IQ) have been demonstrated to have anti-inflammatory properties. In pigs, IQ supplementation has been shown to downregulate the stress response and improve the digestibility of nutrients. The present experiment was conducted to test the hypothesis that supplementing sows’ diets with IQ during gestation would decrease stress at farrowing and improve colostrum quality, positively affecting the piglets’ health and performance. Abstract Isoquinoline alkaloids (IQ) exert beneficial antimicrobial and anti-inflammatory effects in livestock. Therefore, we hypothesized that supplementing sows’ diets with IQ during gestation would decrease farrowing stress, affecting the piglets’ development and performance. Sows were divided into: IQ1, supplemented with IQ from gestation day 80 (G80) to weaning; IQ2, supplemented from gestation day 110 (G110) to weaning, and a non-supplemented (NC) group. Sow body weight (BW), feed intake, back-fat thickness and back-muscle thickness were monitored. Cortisol, glucose and insulin were measured in sows’ blood collected 5 d before, during, and after 7 d farrowing. Protein, fat, IgA and IgG were analyzed in the colostrum and milk. Piglets were monitored for weight and diarrhea score, and for ileum histology and gene expression 5 d post-weaning. IQ-supplemented sows lost less BW during lactation. Glucose and insulin levels were lower in the IQ groups compared to NC-sows 5 d before farrowing and had higher levels of protein and IgG in their colostrum. No other differences were observed in sows, nor in the measured parameters in piglets. In conclusion, IQ supplementation affected sows’ metabolism, reducing body weight loss during lactation. Providing IQ to sows from their entrance into the maternity barn might be sufficient to induce these effects. IQ improved colostrum quality, increasing the protein and IgG content, improving passive immunity for piglets.

Simple Summary Dry matter intake, related to the number of nutrients available to an animal to meet its production and health needs, is crucial for the economic, environmental, and welfare management of dairy herds. Because the equipment required to weigh the ingested food at an individual level is not broadly available, we propose some new ways to approach the actual dry matter consumed by a dairy cow for a given day. To do so, we used regression models using parity (number of lactations), week of lactation, milk yield, milk mid-infrared spectrum, and prediction of bodyweight, fat, protein, lactose, and fatty acids content in milk. We chose these elements to predict individual dry matter intake because they are either easily accessible or routinely provided by regional dairy organizations (often called “dairy herd improvement” associations). We succeeded in producing a model whose dry matter intake predictions were moderately related to the actual values. 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 (RMSECV) 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 RMSECIV 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.

International audience; Greenhouse gases (GHG) emissions from livestock and more especially methane (CH4) emissions from cattle related to ruminal fermentations remain the most important source of GHG within the agricultural sector. Main levers to reduce those emissions are the diet and the genetic selection. To study the impact of those levers and how reduce CH4 emissions, a large amount of reference measurements are needed. However, existing techniques to measure CH4 emissions from dairy cows are expensive, time consuming and difficult to apply on a large amount of animals. This is why the availability of a robust proxy to estimate individual daily CH4 emissions from dairy cows would be valuable. Estimate CH4 emissions from milk mid infrared (MIR) spectrum present potential to meet this aim as it can be obtained routinely at reasonable cost through milk recording process. Develop this equation is particularly challenging as the CH4 prediction equation from milk MIR spectra as CH4 is not a direct milk component but an indirect phenotype linked to milk composition through ruminal fermentations which theoretically influence both. To increase the variability of the calibration set, two datasets (CH4 measurements and milk MIR spectra) have been merged: A) 532 data from 156 cows of Ireland and Belgium using the SF6 tracer technique; B) 584 data from 147 cows of Switzerland, United Kingdom, France, Denmark and Germany collected in respiration chambers. In addition of the calibration using the raw reference values, a second calibration was performed with a reduction of 8 % to CH4 values from chambers evaluate the need to correct the potential method bias in accordance with literature. A 5-groups cross-validation was performed to test the robustness of the models. Those equations showed a R² and a standard error of cross-validation of 0.63 and 62 g/day respectively for the calibration on raw values and 0.65 and 59 g/day when respiration chamber values are adjusted. The slight improvement due to adjustment of chamber measurement is not significant. With errors around 60 g/day, the current equations does not permit to distinguish slight variation of CH4 emissions as it is often required in nutritional context. However, more variability is included (cows, breeds, diets, and country specific information), marked trends can be differentiated and statistics confirming its potential as proxy especially for genetic evaluations or life cycle analyses.

National audience

Evaluation and mitigation of enteric methane (CH4) emissions from ruminant livestock, in particular from dairy cows, have acquired global importance for sustainable, climate-smart cattle production. Based on CH4 reference measurements obtained with the SF6 tracer technique to determine ruminal CH4 production, a current equation permits evaluation of individual daily CH4 emissions of dairy cows based on milk Fourier transform mid-infrared (FT-MIR) spectra. However, the respiration chamber (RC) technique is considered to be more accurate than SF6 to measure CH4 production from cattle. This study aimed to develop an equation that allows estimating CH4 emissions of lactating cows recorded in an RC from corresponding milk FT-MIR spectra and to challenge its robustness and relevance through validation processes and its application on a milk spectral database. This would permit confirming the conclusions drawn with the existing equation based on SF6 reference measurements regarding the potential to estimate daily CH4 emissions of dairy cows from milk FT-MIR spectra. A total of 584 RC reference CH4 measurements (mean ± standard deviation of 400 ± 72 g of CH4/d) and corresponding standardized milk mid-infrared spectra were obtained from 148 individual lactating cows between 7 and 321 d in milk in 5 European countries (Germany, Switzerland, Denmark, France, and Northern Ireland). The developed equation based on RC measurements showed calibration and cross-validation coefficients of determination of 0.65 and 0.57, respectively, which is lower than those obtained earlier by the equation based on 532 SF6 measurements (0.74 and 0.70, respectively). This means that the RC-based model is unable to explain the variability observed in the corresponding reference data as well as the SF6-based model. The standard errors of calibration and cross-validation were lower for the RC model (43 and 47 g/d vs. 66 and 70 g/d for the SF6 version, respectively), indicating that the model based on RC data was closer to actual values. The root mean squared error (RMSE) of calibration of 42 g/d represents only 10% of the overall daily CH4 production, which is 23 g/d lower than the RMSE for the SF6-based equation. During the external validation step an RMSE of 62 g/d was observed. When the RC equation was applied to a standardized spectral database of milk recordings collected in the Walloon region of Belgium between January 2012 and December 2017 (1,515,137 spectra from 132,658 lactating cows in 1,176 different herds), an average ± standard deviation of 446 ± 51 g of CH4/d was estimated, which is consistent with the range of the values measured using both RC and SF6 techniques. This study confirmed that milk FT-MIR spectra could be used as a potential proxy to estimate daily CH4 emissions from dairy cows provided that the variability to predict is covered by the model., Journal of Dairy Science, 101 (8), ISSN:0022-0302, ISSN:1525-3198