Big data and climate change

Post on 10-May-2015

217 views 0 download

Tags:

description

Presentation for the CCAFS Science Meeting 2014.

Transcript of Big data and climate change

1

Big data and climate change:Big words or big opportunity?Innovative approaches to disseminating CSA practices and advisories

Jacob van Etten

Theme leader Climate Change Adaptation

9th April 2014

2

1. Big data, what is it?

2. Big words only?

3. What about CCAFS?

3

4

Social data / crowdsourcing

5

6

Bioinformatics

7

8

Data journalism

9

A movie passes the Bechdel Test if women that are named have at least one conversation that is not about a man.

10

Crowdsourcing

11

12

13

In summary

• ICT generate more and more data

• Data are going social

• Big Data is about “repurposing” in many different ways

• Dumb force of big n obviates precision, long waits and big

$

14

Big words only?

15

16

17

18

19

20

21

Gartner’s hype cycle

22

Where is Big Data on the curve?

23

24

In summary

• Many caveats apply

• Big Data is on the Peak of Inflated Expectations

• There is a Plateau of Productivity ahead!

25

What about CCAFS?

26

Historical data

27

28

Big data illusions

29

30

Sensors

31

32

33

Site-specific agriculture (CIAT)

+ + =

Climate Soil Crop management Productivity

% ? + % ? + % ? = To Explain (100 %)

34

Citizen science

35

36

37

Nifty statistics: Bradley-Terry model with recursive partitioning

38

39

4. Farmers test and report back by mobile phone

2. Each farmer gets a different combination of varieties

3. Environmental data (GPS, sensors) to assess adaptation

1. A broad set of varieties is evaluated

6. Detect demand for new varieties and traits

5. Farmers receive tailored variety recommendations and can order seeds

40

Crowdsourcing Monitoring and Evaluation?

41

42

In conclusion…

43

but some work is required to get it

out of the ground

44

Big data session

Two micro-presentations

•Participatory modelling with historical crop data (Dieudonne

Harahagazwe, CIP)

•Big data to optimize agricultural systems through location-specific

advise (Daniel Jiménez, CIAT)

Group discussion

45

Thank you!