Precision agriculture
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Precision agriculture

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This is the second presentation I was invited to give at the CAVI conference held in Galway, Ireland on October 12. it deals with precision dairy farming. A field that is coming up and growing in ...

This is the second presentation I was invited to give at the CAVI conference held in Galway, Ireland on October 12. it deals with precision dairy farming. A field that is coming up and growing in importance in modern dairy farming

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  • Twosimulationsoneforvisualdetectionby the farmer andoneforestrusdetectionwithanactivity meter. So a 30 percent point increase in SN and a 5 percent point decrease in SP.
  • Simulationresultswith 5 and 95% percentilesbetween the brackets. Sowithincreased SN for oestrus detection, milkrevenueincreasesandcullingcostdecreases, extra costfor rest. Take total cash flow andcalculatedifferencebetweenvisualand sensor.
  • Sodifference in cash flow per yearlittle over 3100 per year. Thisusedforeach of the ten years as average cash flow toapprahaise investment. Internalrate of return, return on investedcapital 11%. DiscountedPaybackPeriod, 7 years. Accounts for time value of money.

Precision agriculture Precision agriculture Presentation Transcript

  • Precision agriculture Henk Hogeveen
  • What can you expect from me  (The need for) Precision dairy farming  Some examples ● Automatic milking ● Mastitis detection ● Estrus detection  Lessons to learn
  • Trend worldwide  Less farms  Farm seize increase  Milk production increases ● Per cow ● Per labour unit ● Per farm  Increasing need for efficiency  Cows are managed in groups ..... .........becomes a disposable product?
  • Current demands to dairy industry  Animal well-being  Consumer demands  Environment  Labor  Economics
  • Current demands to dairy industry  Animal well-being  Consumer demands  Environment  Labor  Economics  We have to reduce the use of scarce resources  So: explore the full potential of each individual dairy cow
  • Is individual cow management possible? Easy (too) difficult Don’t even think about it
  • Precision dairy farming  Technology, measuring: ● Physiology ● Behaviour ● Production  Algorithms that transform data to information Is this information useful?  Integration with other data sources This can improve performance Problems: Integration of various systems, co-operation between companies.  Decision support With or without interference of the farmer This is the ultimate of precision dairy farming
  • Cow as individual animal  Enables management adjusted to the cow’s production level ● Milking management (times per day) ● Disease management (treatment or not) ● Reproduction management (insemination or not, intervention or not ● Feeding management ● Management by exception
  • Examples of technologies  Milk yield recording systems  Milk component monitors  Activity monitors  Lying and rumination behavior monitors  Milk conductivity indicators  Heat monitors
  • Review of sensor systems until now Rutten et al., 2013
  • What can you expect from me  (The need for) Precision dairy farming  Some examples ● Automatic milking ● Mastitis detection ● Estrus detection  Lessons to learn
  • Benefits  Labor ● No more milking ● Reduction milking time 50 % - 80 %  Milk production ● Increased milking frequency  Udder health ● Less overmilking ● Separated quarters ● Increased milking frequency ● ….
  • Disadvantages  More control tasks  Replacement value (investment)  Depreciation time  Maintenance  Energy and water  Udder health ● More cows per cluster ● Milking intervals ● …….
  • Previous studies  Normative (what-if calculations)  Automatic milking is not cost effective
  • Farm comparison using real data Bijl et al., 2007 AMS Total land use, ha Milk quota, kg No. of dairy cows Milk/cow, kg CMS 60.0 61.7 828,761 853,620 105 110 8,011 7,894
  • Farm comparison Bijl et al., 2007 AMS CMS 60.0 61.7 828,761 853,620 105 110 Milk/cow, kg 8,011 7,894 Total labor FTE 1.45 1.87 Family labor FTE 1.26 1.69 Employee labor FTE 0.19 0.18 Total land use, ha Milk quota, kg No. of dairy cows Source: Bijl et al., 2007
  • Farm comparison Bijl et al., 2007 AMS CMS 60.0 61.7 828,761 853,620 105 110 Milk/cow, kg 8,011 7,894 Total labor FTE 1.45 1.87 Family labor FTE 1.26 1.69 Employee labor FTE 0.19 0.18 Dairy cows/total FTE 74 59 586,241 459,117 Total land use, ha Milk quota, kg No. of dairy cows Milk/total FTE, kg Source: Bijl et al., 2007
  • No difference in margin Bijl et al., 2007 AMS CMS Milk revenues 31.53 32.27 Miscellaneous revenues 2.82 2.27 34.35 34.54 4.67 4.83 Total feed costs 6.47 6.33 Health costs 0.84 0.93 2.01 2.25 1.28 1.46 Total costs 9.76 10.04 Margin on dairy production 24.60 24.50 Total revenues Concentrate costs Total livestock costs Land use costs Source: Bijl et al., 2007
  • Other costs higher for AMS Bijl et al., 2007 AMS CMS Margin on dairy production 24.60 24.50 Gross margin 26.51 26.34 Contractor costs 2.55 1.81 Gas, water, electricity 1.24 1.01 - machinery and equipment 3.15 2.72 - land, buildings, installations 0.88 0.60 9.29 7.46 Maintenance/insurance of: Total non-accountable costs Available for rent, depreciation, interest, 17.22 labor and profit Excluding € 14,000 higher depreciation and interest for AMS 18.87 € 15,500/farm Source: Bijl et al., 2007
  • Economic results second study Cows (number) Total number of cows Land (ha) Total land use Capital costs (€/100 kg milk) Expenses on buildings Depreciation on buildings Expenses on machinery and equipment Depreciation on machinery and equipment Miscellaneous depreciation Total capital Labor costs (€/100 kg milk) Customer work Paid labor Own labor1 Total labor Steeneveld et al., 2012) AMS (n=63) 71 110 1.56 2.69 4.57 3.88 0.01 12.71 CMS (n=337) 70 113 1.54 2.51 3.48 2.53 0.04 10.10 2.89 0.46 6.95 10.30 2.96 0.70 7.06 10.72 Materials costs (€/100 kg milk) Revenues (€/100 kg milk) Total materials 17.17 16.99 Total revenues 44.87 45.33 Net output (€/100 kg milk) Total revenues – total materials 27.70 28.34
  • Study focusing on grazing (1,017 farms) Grazing (yes/no) 21,6280 0,001 Grazing time* farm seize -0,0674 0,000 -16,1506 0,004 Grazing * AMS Van de Pol-van Dasselaar et al, 2013
  • Study on motivations to invest in AMS Cows Hectares Quotum (kg) Milk/ ha Milk/ cow No grazing AM-system 87 51 752,000 15.671 8.682 33 CM-system 91 55 738,000 13.867 8.118 8 Hogeveen et al., 2003
  • Personal circumstances Age farmer Married Children No successor No need for replacement old system AM-system 44.1 55 2.6 12 CM-system 41.3 47 2.4 2 25 11
  • Motivations automatic milking Motivation Less (heavy) labour Flexibility Milking more than twice Less labour available Need new milking system Improved udder health Higher milk production Building new stable Future Other Total Reason 1 18 7 7 7 9 0 0 2 3 7 60 Reason 2 Reason 3 10 5 10 4 6 5 5 6 2 4 4 5 6 3 4 1 2 1 10 7 59 41 % 21 13 11 11 9 6 6 4 4 15 100
  • Motivations conventional milking Motivation Costs AM-system too high Dependency AM-system Uncertainty AM-system Inflexible with growing 2nd AM-system expensive Position in barn Other Total Reason 1 18 7 7 7 9 0 7 60 Reason 2 Reason 3 10 5 10 4 6 5 5 6 2 4 4 5 10 7 59 41 % 21 13 11 11 9 6 15 100
  • What can you expect from me  (The need for) Precision dairy farming  Some examples ● Automatic milking ● Mastitis detection ● Estrus detection  Lessons to learn
  • Mastitis detection  Developed in 1980’s  Sensors did not provide useful information ● Clinical mastitis, why automated detection ● Subclinical mastitis, no associated management  Never a success until automatic milking (need) ● Good enough (but far from perfect)  High capacity milking parlors: selection of cows to check
  • Problem: needle in a haystack  Every miling is a test  60 cows, 2,6 milkings per cow per day -> 57,000 milkings per year  20 mastitis cases -> 0.1 % of all milkings
  • What’s found in the past                Sensitivity Specificity Cavero et al., 2006 81 94 De Mol & Ouweltjes, 2001 100 96 De Mol & Woldt, 2001 100 99 De Mol et al., 1997 59 98 De Mol et al., 2001 71 97 Kamphuis et al., 2008 80 92 Kamphuis et al., 2008 50 99 Maatje et al., 1992 100 ? Maatje et al., 1997 90 98 Mottram et al., 2007 56 82 Nielen et al., 1995 77 69 Nielen et al., 1995 84 97 Norberg et al., 2006 43 93 Sheldrake & Hoare, 1981 49 79
  • Specificities re-arranged Specificity (%) 100 95 90 85 80 0 5 10 15 20 Total time window (days) 25 30
  • Sensitivities added Specificity/sensitivity (%) 100 80 60 40 0 5 10 15 20 Total time window (days) 25 30
  • Mastitis is not a black-and-white situatiom Severe clinical mastitis Healthy
  • Detection of mastitis by farmer Check report Interpret report •Conductivity •Colour •Milk production deviation •Total number of alerts •SCC (optional) •Check history alert and/or check alert in the barn Check history alert Interpret history alert Check alert in the barn •Milkquality •Milk visits •Conductivity chart •Check alert in the barn •Check cow •Check udder •Spurt and check milk •CMT Interpret check Take action •Mastitis? •Take action! •Take milk sample •Treat mastitis
  • Study on 7 farms Check report Interpret report •Conductivity •Colour •Milk production deviation •Total number of alerts •SCC (optional) •Check history alert and/or check alert in the barn Check history alert Interpret history alert Check alert in the barn •Milkquality •Milk visits •Conductivity chart •Check alert in the barn •Check cow •Check udder •Spurt and check milk •CMTsTUDY ON Quick glance 10 times a day – 2 times a week Interpret check Take action •Mastitis? •Take action! •Take milk sample •Treat mastitis
  • Check and interpret history alert Check report Interpret report •Conductivity •Colour •Milk production deviation •Total number of alerts •SCC (optional) •Check history alert and/or check alert in the barn Check history alert Interpret history alert Check alert in the barn •Milkquality •Milk visits •Conductivity chart •Check alert in the barn •Check cow •Check udder •Spurt and check milk •CMT Only when alarming Definition of alarming varies between farmers Interpret check Take action •Mastitis? Take action! •Take milk sample •Treat mastitis
  • Check alert in the barn  Only 3,5% of the alerts are checked by the farmer!
  • Alerts checked by farmer (n=15) 20% Clinical Subclinical 13% 67% Negative CMT
  • Alerts checked by researcher Overview of the checked mastitis alerts Clinical mastitis Subclinical mastitis Negative CMT Total Number of individual quarter alerts 30 47 150 227 Number of repeated quarter alerts 9 81 104 194 46% Total 39 10% 128 254 60% 421
  • Checked clinical mastitis 26% Unchecked clinical 74% mastitis Subclinical mastitis 100%
  • Question?  How bad is this?
  • What can you expect from me  (The need for) Precision dairy farming  Some examples ● Automatic milking ● Mastitis detection ● Estrus detection  Lessons to learn
  • Oestrus detection  Advantages twofold ● Labour savings ● Better estrus detection rates -> preg rates  Clear management (decision support) associated with information  Adoption rate: ± 15 % in US and Netherlands (personal communication Knijn and Bewley)
  • Two simulations Titelstijl van model bewerken Visual SN 50%, SP 100% Sensor SN 80%, SP 95% • Klik om de tekststijl van het model te bewerken – Tweede niveau • Derde niveau – Vierde niveau » Vijfde niveau
  • Financial results (*1000 €/herd/year) Titelstijl van model Milk bewerken 330 334 • Klik om de tekststijl van het model Feed -128 -129 te bewerken Calves -7 -8 Inseminations niveau -7 • Derde -7 – Tweede niveau Culling – Vierde niveau -7 » Vijfde niveau Labour -1 -6 -0.7
  • Investment analysis Titelstijl van model bewerken Cash flow (€/year) Internal Rate of Return (%) Pay back period (Years) 11% 7 • Average om 3,151 tekststijl van het model Klik de te bewerken – Tweede niveau • Derde niveau – Vierde niveau » Vijfde niveau
  • What can you expect from me  (The need for) Precision dairy farming  Some examples ● Automatic milking ● Mastitis detection ● Estrus detection  Final words
  • Go back to the individual cow  One size does not fit all!!  We are throwing away a part of the potential of our dairy cows!!!!
  • Precision dairy farming is going to increase  What is the vet going to do?  Use data from sensor systems  Adapt herd health programs  …….
  • Thank you for your attention @henkhogeveen animal-health-management.blogspot.com On-line courses on Veterinary Economics on: www.elevatehealth.eu