Monitoring of the dairy cow for optimizing health and production - energy and protein status

Transcription

1 Monitoring of the dairy cow for optimizing health and production - energy and protein status Department of Animal Science, Aarhus University, Tjele, Denmark AU-FOULUM KLAUS LØNNE INGVARTSEN HEAD OF DEPARTMENT 25 AUGUST

2 Outline Introduction Physiological imbalance what is it? Need for surveillance and automated precision management systems Should we focus on herd, group or individual cow level? Biomarkers and sensors for energy status Biomarkers and sensors for energy protein / AA status Conclusion 2

3 Average herd milk yield close to 10,000 kg / cow Consequences of a 1.5% increase in milk yield / year? IMPROVED: Breeding Feeding Management Environment/ Housing OUTPUT: Milk yield Efficiency - Health? Reproduction 3

4 Disease incidence relative to days from calving (Ingvartsen et al., 2003)

5 Production diseases are multifactorial Risk of disease Clinical Subclinical Nutrition Management Genotype Prod. system Stress Rumen/ organ Status Physiological Immunological Production and reproduction Better to prevent than to treat! 5

6 Physiological immunological interactions and risk of infections (mod. from Ingvartsen et al. 2003; Ingvartsen & Moyes, 2012, 2015) Genotype Immune system hematopoesis chemotaxis migration immune proteins opsonisation phagocytosis oxidative kill Ig production Changes around calving GH NEFA IGF-I ketone bodies insulin glucose leptin glutamine cortisol other AA progesterone immune estrogen proteins Peripheral tissue milk yield mobilisation of body tissue Risk of infections Liver fat infiltration collectin secretion acute phase proteins synthesis of other proteins Environment nutrition management stress infection pressure

7 From dry to late pregnancy and early lactation Day 250 of pregnancy: Foetus weight = 35; energy req. = 2.3 Mcal NE l /day or 1.2 FE/day (35-40% glucose, 55% amino acids, 5-10% acetate) + development of mammary tissue etc. Early lactation, nutrients for 50 kg milk: 2000 g milk fat 1600 g milk protein 2500 g lactose 65 g Ca, 50 g P, 8 g Mg

8 Is it milk yield or acceleration in milk yield that is a risk factor (Ingvartsen et al., 2003, Hansen et al Cause of increased disease risk: Probably not yield per se. Rate of increase in daily milk yield (acceleration) Adaptational problems. Physiological imbalance? 8

9 Physiological Imbalance (PI) Hypothesis: Immune function and health can be improved by reducing the PI in cows, and at the same time it will improve production and reproduction (Ingvartsen et al., 2003, 2006; Ingvartsen and Moyes, 2012) Definition of PI: cows whose parameters (e.g. glucose, BHBA, NEFA) deviate from the normal, and who consequently have an increased risk of developing diseases (clinical or subclinical) and reduced reproduction and/or production (Ingvartsen, 2006) 9

10 Surveillance is essential for prevention Manual surveillance is important but has its limitations Surveillance at feeding and milking has changed Large herds Subclinical problems 10

11 Early identification is key to reduced disease incidence and secures optimal production Biomarkers in relation to state: Health: Production: 11

12 Efficient management calls for: Early identification of risk cows Manage animal status & risk by changing input to risk cows Proactive management Calls for real-time on-farm solutions based on: Efficient biomarkers Automated sampling / analysis (sensors) Biological and biometric models Ability to describe animal status Methods to describe risk (e.g. for a disease) (Autom.) change of input for prevention Optimization at cow and herd level Cost effective

13 Need for automated precision management systems There is a need for cost effective automated precision management systems where equipment combines advanced sensors, technologies and biological knowledge to obtain: low disease incidence and severity, animal welfare, low impact on the environment, requested product quality, optimal production and reproduction, profitability for the producer. Individual cow monitoring cow as its own control optimization

14 Herd/group vs. individual Effect of parity and TMR energy density on plasma [BOHB] mm 1, MJ DE pr. kg DM 1, MJ DE pr. kg DM 1,00 0,82 0,67 0,55 Weeks around calving Weeks around calving Parity: = 1.; ౦ ౦ = 2.; = 3. AU UNIVERSITY

15 Large between cow differences Weeks around calving Total var. Genetic var. Between cows var. Residual 0 AU Days around calving UNIVERSITY 210 Days around calving

16 Periparturient changes in NEFA, BHBA and glucose - the text book cow, high yielding and healthy Glucose NEFA BHBA

17 Periparturient changes in NEFA, BHBA and glucose the mobilizing healthy but low performance cow Glucose NEFA BHBA

18 Periparturient changes in NEFA, BHBA and glucose the mobilizing high yielding risk cow Glucose NEFA BHBA

19 Problem and challenge EB, blod/urine variables and PI based on blood are not possible/cost effective at commercial settings! Our objectives are therefore: Identify potential biomarkers in milk for degree of PI that allow automation Automated system (like we did in e.g. the Herd Navigator system) 19

20 Off feed challenge Cows, design, sampling 29 healthy Holstein cows: early lactation (n = 14; DIM) mid-lactation (n = 15; DIM) Nutrient Restriction: 40% NE L requirements Hours Daily registrations: Feed intake, milk yield and components Blood collection for analysis of NEFA, BHBA, and glucose Milk for detailed analysis Liver samples collected for: 1. Chemical analysis 2. DEPARTMENT itraq-based OF ANIMAL SCIENCE quantitative profiling using LC-MS/MS (proteomics)

21 Change in energy density caused marked changes in DMI and milk yield DMI, Kg/d Milk Yield, kg/d Time relative to restriction, h Time relative to restriction, h Bjerre-Hapøth et al.,

22 EB was reduced by reduced energy density EB, Mcal/d Time relative to restriction, h Bjerre-Hapøth et al.,

23 Changes in milk parameters during nutrient restriction early and mid-lactation Early lact. Mid lact. Time relative to nutrient restriction (0-96 h) 23

24 Are requirements for metabolizable protein fulfilled in early lactation? Metabolisable protein supply Casein Control 2000 g/d Bell et al., F677-1; n = Days relative to calving Larsen et al

25 Are control animals experiencing imbalance? Milk yield Arterial essential AA Milk yield, kg/d P trt = < 0.01; P DIM < 0.01, P trt x DIM = Casein Control Days relative to calving F677-1; n = 4 µmol/l P trans x trt < 0.01 P trt = 0.22, P DIM = 0.20, P trt x DIM = 0.02 Control Casein Days relative to calving F677-1; n = 4 Mogens Larsen, pers. comm. 25

26 Treatments abomasal infusion of protein or water Foulum exp., 4 cows Canadian exp., 5 cows Infusion af kasein Infusion of casein Infusion af frie aminosyrer Infusion of amino acids Kasein, g/d Aminosyrer, g/d Larsen et al Larsen et al n = 4 n = Days Dage efter after kælvning calving Days Dage after efter calving kælvning Basal ration: 15.9 % crude prot. Basal ration: 16.4 % crude prot. 100

27 Milk yield increased by 7 to 8 kg/d/cow Are control animals experiencing imbalance? Foulum experiment Canadian experiment Milk yield Mælkeydelse Milk yield Mælkeydelse P trt < 0.01; P DIM < 0.01 AAT Kontrol P trt < 0.01, P DIM < ± ± 0.9 kg/d kg/d AAT Kontrol Days Dage efter around kælvning calving Days Dage around efter kælvning calving 10 Larsen et al Larsen et al. 2015

28 High utilization of extra protein Foulum experiment Canadian experiment g/d Utilization Udnyttelse af of ekstra extra AAT til mælkeprotein for milk protein 1800 Mælkeprotein CTRL Mælkeprotein AAT 1600 Infunderet protein 65% udnyttelse 43% udnyttelse Days Dage efter around kælvning calving 60% udnyttelse n = 4 g/d Udnyttelse af ekstra AAT til mælkeprotein 1800 Mælkeprotein CTRL Mælkeprotein AAT 1600 Infunderet protein 58% udnyttelse Larsen et al Larsen et al Utilization of extra AAT for milk protein 50% udnyttelse Days Dage efter around kælvning calving 57% udnyttelse n = 5

29 Catabolism of amino acids? Foulum experiment Canadian experiment mmol/l Urea i in blodet blood P trans x beh = 0.01 P beh = 0.03, P dag = 0.37 Kontrol AAT Larsen et al n = 4 Larsen et al Days Dage around efter kælvning calving mmol/l Urea in i blodet blood P beh = 0.01, P dag = 0.50 Kontrol AAT n = Days Dage around efter kælvning calving

30 Dry matter intake did not change significantly Foulum experiment Canadian experiment Tørstofoptagelse Dry matter intake Dry Urea matter in blood intake Tørstofoptagelse P trt = 0.36, P DIM < 0.01 AAT Kontrol P trt = 0.02, P DIM < 0.01 AAT Kontrol 20.4 ± 0.5 kg/d kg/d ± Larsen et al Larsen et al n = 4 n = Dage efter kælvning Days around calving 5 Days Dage around efter kælvning calving

31 Only increased fat mobilization in week one Foulum experiment Canadian experiment Plasma NEFA Langkædede fedtsyrer i blodet Plasma NEFA Langkædede fedtsyrer i blodet P trans x trt = 0.97 P trt x DIM = 0.07 Kontrol AAT P trt x DIM = 0.05 Kontrol AAT mol/l mol/l n = Days Dage around efter kælvning calving Days around calving Dage efter kælvning n = 5

32 Animal behavior as indicators Diseases: E.g. abnormal walking, reduced eating time, increased lying -> indicator of lameness. Basic needs: Easy access to food and water, milking, resting. Effect of increasing milk yield Hours/day ,3 8,8 4,7 5,4 10,9 9,8 Standing/walking Eating Lying 0 20kg 40kg 32

33 In conclusion - future challenges To better understand the physiology and immunology of the dairy cow, particularly in the periparturient period To understand the biological basis of individual differences To improve phenotyping by: Making better use of existing data Developing new biomarkers for common use in management and genomic selection (e.g. physiological imbalance) To further develop sensors and technology for future automatic proactive management strategies (incl. optimization) To find the local truth To optimization at both individual cow and herd level production, reproduction, risk of disease, environmental impact, animal welfare, 33

34 Thank you for your attention 34