Data Driven Innovations to Improve Dairy Farm Management. Kees Lokhorst

Transcription

1 Data Driven Innovations to Improve Dairy Farm Management Kees Lokhorst Symposium The cost effective analysis research of Dutch Dairy Farming th DairyCare Conference in Lisbon

2 Content Share some insights Food for inspiration

3 Personal drive

4 Kees Lokhorst Expert in Precision Livestock Farming Every 4 to 6 six years I focused my work on a different sector (dairy, pigs, poultry, goats/sheep, arable farming) or in a different role. A broad network and expertise in precision agriculture, precision livestock farming and system innovation in national and international projects. Two jobs

5 Agro Tech Intensification Major trend Animal welfare Full in discussion Technological improvement Innovation and knowledge economy Intersectoral and international Cows go wireless Mar 11th 2004 From The Economist print edition Animal health From curative to preventive Ecological footprint LCA and sustainability

6 No progress without variation! Every animal is unique! Every herd is unique! Every livestock farmer is unique! Every farm is unique! Every chain/network is unique! Appreciate or eliminate?

7 Connecting ICT, livestock farmer, and cow ICT - tools/aids Looking at and understanding the cow(s) Action SOP

8 Transition in attitudes and actions As dairy farmer I will guarantee that every cow/calf gets the care it requires at the right moment, in the right place, and to the right extent, and I want to be transparent in this. Care = drinking water, food, milking, foot bath, brushing, insemination, pasture access,... The choice then relates to how this is to be organised. Data Driven Management Support Choice? Work harder oneself Ask more family members/ friends to help Take on employees Contract work out Automate work

9 Innovation

10 Innovation I = A * T New market Existing Product Service Process System Existing market New

11 Some examples from the past Electronic ID Milk meters I&R registration Breeding stimulus Concentrate feeding ICAR and ISO certified

12 Some examples from the past Concrete floors New housing systems

13 Some examples from the past From cow card to management information systems

14 Some examples from the past

15 Some examples from the past Grass, hay and roughage

16 Some examples from the past and present Sensor technology Temperature Conductivity Cell count Activity Weight.. Location Rumination Progesteron.. Wireless Interval Accuray Zooming

17 Insight? Reasons for cow replacement: %Fertility %Mastitis %Lameness.. ICT related Innovations What I see: Increase in complexity farming systems (beneficial for cow) Increase in farm size and production Farmers implemented this in their farming systems Figure from Roel Veerkamp: effect of broader breeder goals

18 Intermezzo Plaats hier uw voettekst Pagina 19

19 Key on-farm socio-economic indicators Social indicators: 1. Labour conditions 2. Number of working hours 3. Pride about animals and facilities 4. Availability of advisory systems 5. Successor for farm business Economic indicators: 1. Feed conversion 2. Growth 3. Health costs 4. Delivery weight 5. Energy costs

20 Value is more than Tangible Cost benefit Semi Tangible Break even Intangible Conjoint analysis 21

21 VHL practical implementation

22 Dairy Farming Complexity

23 Complex (inter)national food production chains Plaats hier uw voettekst Pagina 24

24 Tjeerd de Groot, NZO, Summercourse Dutch Dairy Chain The international market for Dutch dairy is growing, and demands quality and sustainability, but nationally the sector s image is under pressure meanwhile governments are setting limits to this growth and is calling for the dairy sector to take action

25 Additional challenges from future developments Structural developments in the dairy sector up to with sustainability challenges such as Quota eliminated in 2015 Upscaling Increasing milk production per cow Greenhouse gas emissions Animal welfare Grazing Antibiotics Soy Manure production Ammonia emissions Tjeerd de Groot, NZO, Summercourse Dutch Dairy Chain 27

26 Mainstream disruptive trends in technology (Leo den Hartog) (Gen)omics: Radical change Micro systemand Nanotechnology: Radical change Informatie and Communication Technology: Continuous change Implementation in livestock will have same trend

27 ICT developments Wireless Location awareness Sensor networks Internet of Things (connectivity, interoperability) BIG Data Social Media Remote access and control Security Lab on a chip Blockchain..

28 IoF2020 & BigData

29 IoF2020 Large Scale Pilot

30 Overall concept for 4 year, 30M project

31 Plaats hier uw voettekst Pagina 38

32 BigData strategy WUR

33 TO2 project Big data Analysis for Smart Farming User(s): Roel and dairy farmer Question: interested in genotypic and phenotypic information for estimation overall feed efficiency of cows Why how to maximize milk per ha land. Big unknown is feed intake form grass and silage and animal feeding behaviour.

34 Orientation on Literature Dairy Case and Dairy Campus as example for connection with genotype database Semantics and Linked Open Data (Ontology) Machine learning (ANN) for roughage intake prediction Remote observations (satellite and drone) for grass growth

35 Domain knowledge Focus on decisions for processes & farms, based on real time big data and knowledge from the production chain and network? Cloud Based Event Management Contribute to a common language through an Ontology? Does IoT leads us to more standardisation and cooperation? Are we able to pose the right questions?

36 Take home message

37 Farmer What will be the farmers role in these developments? Cow/Farm Data and Information have Value! How to participate in Innovation? As Individual, in Cooperative or..? Choice? Work harder oneself Ask more family members/ friends to help Take on employees Servitization Automate work

38 Take home message Farmers will Innovate: Social innovation As part of a network Data driven Co-innovator Complexity for farmers increases: Be connected Be yourself Be awake Be aware

39 The End ;)