Big Data in de melkveehouderij

Roel Veerkamp & Claudia Kamphuis

What is Big Data ?

1.79 billion 317 millionmonthly active users

Big Data and the 6Vs

Capability to acquire, understand, and interpret data real-time

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Forms of (un)structured data (spreadsheets, text, tweets, video, drone images)

Reliability and quality of data

Data whose meaning is constantly changing

Expectations are huge if analysis of Big Data delivers insights and information

Volume

Velocity

Variety

Veracity

Variability

Value

Big data ........

Challenge application Big Data

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Domain knowledge

Data analytics

ICT skills

Big Data & Wageningen Livestock Research

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Management tools

Sensor technologies

Food chain

Three examples of Big Data projects

Predict dairy cow’s longevity

Resilience and efficiency of animal and farms

More specific manure application (norms): Bemestwijs

Big Data & Wageningen Livestock Research

Example project 1

Predict dairy cow’s longevity

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Predicting cows longevity

Longevity of a cow important: economics, management and society.

Predict expected longevity of an animal?

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Predicting cows longevity

DNA van 6847 calves on 463 farms used to predict phenotype: breeding value for 50 traits

72 additional phenotypic records; Pedigree, dam, own birth and calving records, test milk days, movement (transport), inseminations, viability & vitality of calves, survival status at various points, farm...

Statistical methods: Machine learning

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Predicting cow longevity

Breeding value of lifespan, and one or more from;

● fertility

● udder health’, conformation

● feet and legs (foot angle)

● body conformation (size)

Important phenotypic traits are;

● Season of birth and calving

● Fertility traits (insemination#, NR status)

● Age at first calving

● Milk production (kg)

● Udder health (cell score)

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Predicting cows longevity

top 50% heifer calves are selected:

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Big Data & Wageningen Livestock Research

Example project 2 Gentore:

Efficiency and resilience at cow and farm level

Claudia Kamphuis, Wijbrand Ouweltjes, Yvette de Haas

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WP3 On-farm phenotyping

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Near or far-off market technologies

Big Data across farms

At-market technologies

Resilience

Resilience through the theory of critical transitions

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Scheffer et al., 2012

Stable

state 1

Stable

state 2

Perturbation

Stable

state 1

Stable

state 2

Perturbation

Heat stress: resilant farm systems

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July 2014 June 2015

How to measure resilience

using existing data

Resilient

Not resilient

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Disturbance

Disturbance

MY

MY

Variance in deviations

Lag-1 autocorrelation of

deviations

Skewness of deviations

Big Data & Wageningen Livestock Research

Example project 3

Field specific phosphate application norms

Erwin Mollenhorst, Claudia Kamphuis, Gerard Migchels

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Hackatons

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(Be)MestWijs won de aanmoedigingsprijs

voor meest marktrijpe resultaat. vlnr Job de

Pater (NMI), Reinier Wieringa (EZ-Dictu),

Erwin Mollenhorst (WUR), Justin Steenhuis

(VAA ICT), Herbert Meuleman (CRV),

Claudia Kamphuis en Gerard Migchels

(beide WUR). Niet op de foto: Roel

Veerman (Akkerweb)

2017

2018

Winnaars van de #Bodemhack

op de Marke, samen werken

aan een data- en IT-instrumentarium

om ecosysteemdiensten en resultaten

zichtbaar en meetbaar te maken

(Be)MestWijs stapsgewijs naar kunstmestvrij

Bedrijf -

KringloopwijzerPercelen -

Akkerweb

Percelen –

Precisie bemesting

Nu Korte Termijn Middellange & Lange Termijn

First trials:

Currently:

● Fixed phosphate application norms for crops / grassland

● 3 classes, based on P status of field

● For crops: 50/60/75 kg P2O5

Can we predict future maize yields (= P) based on farm data and open source weather data?

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Dataset from “KTC De Marke”

162 records of maize yields

24 different fields

Years 1996 – 2014

On average 7 times maize

Information on:

● N and P input and output

● Irrigation, P status of field

● Weather data (own weather station and open source)

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Variable importance (average 5 years)

4 most important variables

● Crop in previous year (grass/maize) (0.99)

● Phosphate status field (0.55)

● Maximum temperature in July (0.36)

● Average Pyield maize on same field in past 7 years (0.32)

Machine learning is marginally better in predicting P yield than a generic norm (similar RMSE)

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Summary

More and more big data will come available (6xVs)

Technology will allow us to use data in management, better use sensors and connection in food production chain

Replace some of the classic ways of working

Technology is not the silver bullet!

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Take home

Success Big Data is not about

technical tools, but

connecting the tools with

people and domain expertise

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