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Advances in Innovative Dairy Production

Advances in Innovative Dairy Production. “Research and Technology as Vital Component of Dairy Production”. Maristela Rovai & Gerardo Caja Ruminant Research Group (G2R). EMFOL MSc Program Veterinary School (UAB) Bellaterra – August 14 th 2014. PRESENTATION HIGHLIGHTS. Sustainability.

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Advances in Innovative Dairy Production

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  1. Advances in Innovative Dairy Production “Research and Technology as Vital Component of Dairy Production” MaristelaRovai & Gerardo Caja Ruminant Research Group (G2R) EMFOL MScProgram Veterinary School (UAB) Bellaterra – August 14th2014

  2. PRESENTATION HIGHLIGHTS Sustainability Technology Dissemination Introduction 2/42

  3. Animal health Animal wellbeing Animal performance Traceability Food borne diseases Product quality End-users feedback Animal welfare Food Safety Dairy Sustainability Environment Carbon footprint Water footprint Soil erosion Socioeconomic Profitability Societal impact Generational renewal Introduction 3/40

  4. Dairy Farming Cycle Feed Composition Nutritional Feeding Values Harvest & Storage Physical Chemical Environmental Fodder Resources Milk Grow Urine & Manure Plant Physical Chemical Environmental Production Cycle 4/40

  5. GOOD MILK?

  6. Same milk quality?

  7. Farmer Dairy Industry Animals & gland level Bulk tank Is there any difference between “good” milk? Industry results: different cheese yield!!! What "good” milk means? 7/40 Leitneret al., 2007, JDS 18:109-113

  8. No infection: healthy udders • Clinicalinfections (with visible symptoms) • - Dramatic changes in milk appearance • - Changes in milk composition • Subclinicalmammary infections (without visual symptoms) • - Absenceof changes in “basic” milk components Mammary gland inflamation 8/40

  9. Day 2 Leitneret al., 2007, JDS 18:109-113 Cheese production 9/40

  10. Cheese yield (g/L) Strep. dysgalactiae Healthy Subclinical IMI Merinet al., 2008, Dairy Sci. Technol., 88: 407-419 Cheese quality 10/40

  11. Presentation Outline 1 – Sampling 2 – Near Infrared Spectroscopy 1 – Sampling A – On-line Milk Assessment B – Feed Sampling 3 – Biosignals & Labeling A – Ultrassonography B – Infrared Thermography C – Video Camera 4– Education and Training A –Dissemination Channels B –Other Strategies

  12. 1 – Sampling Variation in diet composition • Potential costs of diet composition variation: • 1) Higher feed costs (over-formulation) • 2) Lower production (nutrient deficiency) • 3) Health problems (under/over) • 4) Environmental costs (over-formulation) • Sources of variation in silage composition: Variation = Effect of farm + True day to day differences + Sampling variation + Analytical variation Variation in diet composition 13/40

  13. Partitioning Variance within farm DM NDF B. Weiss, 2013. IAMZ Master course, Spain Sampling 14/40

  14. 31 36 27 29 29 35 30% Silo sampling Face Mon Tue Wed Thu Fri Sat Sun How many samples would be ideal? Right answer = <5% from mean 35 36% 30 31 27 Mean 31% SD = 3.3 29 29 B. Weiss, 2013. IAMZ Master course, Spain Silage variation 15/40

  15. Presentation Outline 1 – Sampling 2 – Near infrared spectroscopy 2 – Near Infrared Spectroscopy A – On-line Milk Assessment B – Feed Sampling 3 – Biosignals & Labeling A – Ultrassonography B – Infrared Thermography C – Video Camera 4–Education and Training A –Dissemination Channels B –Other Strategies

  16. 2 – Near Infrared Spectroscopy • Near infrared reflectance spectroscopy(NIRS) is a spectroscopic method that uses the near-infrared region of the electromagnetic spectrum (from about 800 nm to 2500 nm). Real-time measuring device On-site measurement instrument Wave length (nm) NIR 17/40

  17. A – On-line Milk Assessment • Online milk TM analyzers (Afilab; Afikim, Israel) provide real-time online analysis of fat, protein, lactose and milk coagulating properties during milking, and milk separation at the individual cow level • A commercial light emitting diode (LED) based milk spectrometer in the visible (vis)–NIR regime Drinking milk Best product Cheese Best product Leitneret al., 2011. JDS 94: 2923-2932 On-line milk assessment 18/46

  18. B – Feed sampling • Innovative system for feed sample quality control • NIRS can predict the ingredient composition with: • 1) High accuracy identifying animal feed ingredients • 2) Daily adjustment of feed (e.g. DM and other nutrients) • 3) On-farm analysis and low cost Portable NIR 19/40

  19. Presentation Outline 1 – Sampling 2 – Near Infrared Spectroscopy A – On-line Milk Assessment B – Feed Sampling 3 – Biosignals & Labeling 3 – Biosignals & Labeling A – Ultrassonography B – Infrared Thermography C – Video Camera 4–Education and Training A –Dissemination Channels B –Other Strategies

  20. 3 – Biosignals & Labeling • What “biosignals” refer to? • - Diseases - Feed intake • - Discomfort and stress - Body weight • - Production capacity • Research: real-time signals, manual labeling, statistic validation, automatic labeling validation and verification • Commercial farms: on-line and automatic labeling of selected signals (as numbers) and warning (e.g. sensors) Biosignals 21/40

  21. A – Ultrassonography Scanner Ayadiet al., 2003, JDS, 70: 1-7 Udder scan 22/40

  22. Changes in cisternal area during milking Dairy cows milking 23/40

  23. Application • To use in selection programs • Detection of milk storage capacity (e.g. relevant for simplifying milking routines) • Detection of teat canal problems Molenaaret al., 2013, NZ Society of Animal Production, 73:114-116 Rovai et al., 2007, JDS, 90(2):682-90 Ultrasound Application 24/40

  24. B – Infrared Thermography • Non invasive imaging tool based on heat emission from objects • Thermal response (fever) of animals to local and systemic (body) inflammation • Diagnostic tool for early detection of pathologies • Infrared camera: IRI 4010 (Irisys, UK) Costa et al., 2014, JDS 97(3):1377-1387 Infrared Thermography 26/40

  25. Application • Interest for the early detection of mammary infections, BCS, hoof lesions, … • Not enough response to subclinical mastitis Hind claws Mammary gland clinical IMI lesion nonlesion Alsaaodand Büscher, 2012, JDS 95(2):735-742 Pezeshki et al., 2011, Vet Res:42(15) Infrared Thermography 27/40

  26. Images from the over head Fat cow Thin cow Halachmiet al., 2008, JDS 91:4444–4451 Infrared Thermography 28/40

  27. B – Video Camera Happy cow! Images labeling 29/40

  28. Images labeling 30/40

  29. Presentation Outline 1 – Sampling 2 – Near Infrared Spectroscopy A – On-line milk assessment B – Feed Sampling 3 – Biosignals & Labeling A – Ultrassonography B – Infrared Thermography C – Video Camera 4–Education and Training 4–Education and Training A –Dissemination Channels B –Other Strategies

  30. 4 - Education and Training Dissemination Research Final users • Use of effective communication and multiple delivery format to best inform, motivate, and service their clientele • Training of the clienteleto be able to use correctly and efficiently the information disseminated Trained staff and KEEP them trained!! Education and training 32/40

  31. A – Dissemination Channels • Face-to-face meetings and on-farm visits • Workshops: classroom and on dairies • Field activities: field days and short courses • Online: electronic surveys • Publications: newsletters, fact sheets and scientific papers • Scientific meetings: regional, national and international • Other: radio podcast, internet, email, video conferencing, webinars, telephone, mailing surveys, etc. • Communication is the seed for collaboration and create a WIN-WIN situation (team spirit) Communication channels and feedback 33/40

  32. Internet Dissemination Channels 1) Connection, interaction and exchange 2) Instructional and trainingcourses Technology has changed society tremendously, and globalization is impacting our lives on a daily basis. Dissemination tools and e-learning platforms 34/40

  33. Short Instructive Videos The Use of QR 1) Instructions 2) Video 3) Questions Video & QR 35/40

  34. Journal Survey “Milk runs without a break” • Questionnaire sent to: 56,717 farms. • Farmer’s participation: 1,116 farms (35,850 cows). Survey 36/40

  35. A Career in Dairy Age of EU (15) farmers: Age of US farmers: 33% are age 65 and older 12% are 75 and older 6% are below age 35 http://capreform.eu/the-greying-of-european-farmers/ http://newyork.farmland.org/next-generation/

  36. Farming handover: transition from one generation to another has not being a key consideration for many farming businesses, due to: • - Work schedule (e.g. free time) - Market pressures • - Long-term viability - Increased competition • ForBeginners “young farmers” • - Lack of capital for gain access to farmland • - Competing with established farmers and RE developers What can be done? • Create a regional network service to help young farmers and landowners to interact (e.g. Facebook groups) • Educating farmersabout planning for farm transfers • Research possible obstacles faced by the next generation of farmers in starting and sustaining their businesses

  37. University Extension Specialists Communication Research Collaboration Industry & Consumers Technology Trust

  38. Is this the societal farm vision ??

  39. We can help for a clear vision! Thank you! maristela.rovai@gmail.com

  40. Instructional Modes & Learning Retention National Training Laboratory What about memory retention? “Our newly learned knowledge and made memories are halved in a matter of days or weeks unless the information is reviewed” Process of learning 34/46

  41. Short Instructive Videos • Method: e-learning platform (e.g. Captivate) • Packing knowledge into tools for use by farmers • Design of programs for online user-friendly decision Aim: to create videos with approx. 5’ length with instructions and a set of 10 questions with multiple choice 1) Importance of sampling and how to sample e.g. forage 2) Pre-milking preparation … Interactive e-learning softwares 36/52

  42. B –Other Strategies • Technical trips (e.g. other farms, states, countries) • Exchange of farm people • Internet application programs: phone emit an alarm sound via twitter (e.g. according to weather conditions) Timing harvest of some crops will be a challenge! Frost damage or immature corn? Other strategies 38/40

  43. Excel application: to calculate time of harvesting and its risks http://www.dairymgt.info/tools.php Computer programs 40/46

  44. 5 – A Career in Dairy Age of U.S. farmers: 33% are age 65 and older 12% are 75 and older 6% are below age 35 http://newyork.farmland.org/next-generation/ • Farming handover: transition from one generation to another has not being a key consideration for many farming businesses, due to: • - Work schedule (e.g. free time) - Market pressures • - Long-term viability - Increased competition Dairy Farming career 42/46

  45. ForBeginners “young farmers” • - Lack of capital for gain access to farmland • - Competing with established farmers • - Competing with real estate developers over a shrinking agricultural land base What can be done? • Create a regional network service to help young farmers and landowners to interact (e.g. Facebook groups) • Educating farmersabout planning for farm transfers • Research possible obstacles faced by the next generation of farmers in starting and sustaining their businesses Adapted from America Farmland Trust (http://www.farmland.org/) Future farmers 43/46

  46. The Use of QR 1) Pre-milking preparation 2) How to sample: feed, milk, … Works as immediate on-hand reminds! QR code 37/40

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