‘transforming data to knowledge for the livestock...

B R S S E M I N A R S E R I E S P R E S E N T S :

Friday 4 February(Scroll down to view details of BRS Seminar - Friday 11 February)

‘Transforming Data to Knowledgefor the Livestock Industry’

Greg Laughlin - BRS

This seminar is a demonstration of how to ‘transform data into knowledge’ and disseminate it to a wideaudience. Our integrated toolset for assessing current rainfall and pasture conditions as well as their near-future prospects, will improve climate risk management in Australia’s southern livestock industry. These toolswill be widely accessible to farmers via the internet, and will improve their ability to understand climatevariability and risk. Farmers will be able to convert this knowledge into better within-season managementdecisions.

11.00am - 12:00noon (morning tea at 10:45am)Edmund Barton Conference Centre (in the courtyard)

Edmund Barton BuildingKings Avenue, Canberra

Bookings not required.For further details, please call the BRS Seminar Coordinator on 6272 3440.

For further information on BRS Seminars or to obtain papers/presentations supplied by previous seminarpresenters, please visit our website at: www.brs.gov.au/brsseminars

S C I E N C E F O R D E C I S I O N M A K E R SD E P A R T M E N T O F A G R I C U L T U R E , F I S H E R I E S A N D F O R E S T R Y

S C I E N C E F O R D E C I S I O N M A K E R S

B u r e a u o fR u r a l S c i e n c e s

Transforming data into knowledge for the Livestock Industry

Greg Laughlin, Simon Knapp and Simon Barry


• Meat and Livestock Australia

Hutton Oddy and Peter Speck

• BRS

Tim Brinkley, Kema Ranatunga, Ian McNaught, Robert Smart, Stuart Row,

John Sims, Brett Cullen, Antti Roppola, bound to be more!

• BoM

David Jones, Mike Coughlan, Mike Manton

• CRES ANU

Mike Hutchinson, Anthony Clark

• The MLA Reference Group

Barry White (MCVP), Ian Johnson, John Black, Peter Horwood, Terrey

Johnson, Tom Ellis

• CSIRO PI

John Donnelly, Libbey Salmon

Acknowledgements


Outline

• Feel free to ‘interrupt’

• An overview of the project (development of an electronic

toolset for livestock producers)

• Examples of how the toolset has evolved as a

result of meaningful dialog with farmers- e.g. 1 mode of presenting dynamic outputs

- e.g. 2 choice of pasture growth model

• Progress on seasonal forecasting and the

pasture growth model

• Concluding remarks


Overall mission

Develop and deliver via the web, an integrated toolset for

assessing current rainfall and pasture conditions…to

improve climate risk management in Australia’s southern

livestock industry

To incorporate climate forecasts into the toolset if they add

‘value’ to decision making

Provide outputs that are ‘farmer-friendly’ and ‘farmer

relevant’


In Plain English please

Web deliver the following for many sites in southern

Australia

• Useful information about the sitese.g. Growing season, livestock numbers, soils, climate variability

• Relevant information about how this season is trackingRainfall, pasture

Placed in historical context

Up-to-date (i.e. automatically updated weekly)

• Useful insights (if available) about how the season might

unfold

STATIC REPORT

DYNAMIC REPORT


In Plain English please

So why climate variability and not climate change as the

emphasis?

For most Australian agricultural industries, within-season variability remains the

most important feature of climate that affects profitability. The ways in which

underlying, long-term trends in climate affect within-season variability is the

most important feature of climate change for rural industries.

Source: Farming Profitably in a Changing Climate: Workshop

Summary 2004 (Will Steffen, former ED International Geosphere Biosphere Program; BRS fellow)


Geographic coverage

• Contract says south of28 degrees

• Sheep density

• Temperate climate


Daily meteorological data 1886 to now

• SILO PPD is a subset of all availablemet. sites

• In-filled missing data• 3000+ sites to support toolset• a lot of data!• Simon has written a fair bit of code


Selecting one of the SILO PPD sites

• Log onto the Internet

• Select one or more SILO PPD sites (previous figure) froma simple map interface

• Toolset ‘remembers’ users’ selections


An

over

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An

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Based on 100 years ofsimulation

Shows expected level andreliability of growth


Examples of how the toolset has evolved as a

result of meaningful dialog with farmers

• Example 1: Dynamic outputs (updated weekly)

- year-to-date rainfall and pasture

- future prospects


Examples of how the toolset has changed as a result ofmeaningful dialog with farmers, using the dynamic reports as an

example

• Mostly the year-to-date and future prospects are shown separatelyand using different modes of output (format)

mm Prob.

Sometimes aspercentiles (rank)


Dialog with farmers

• From a farmer’s perspective the year-to-date and futureprospects need to be combined

• 35% probability concept is hard to understand

- how far above or below the median?

- how variable might a ‘correct’ prediction be?

• Next slides show how we tried to combine these into a singlemode of output…for a site in SA


Week number (1-52)

Overview – dynamic report



These lines aresummaries of 118 yearsof records



?

Weeks


•Grey = full range ofpossibilities•Red= chosen by forecastingsystem•Individual years convey avery different sense ofcertainty

Chosen because these yearshad the same ocean temp.patterns at the same time ofyear


Examples of how the toolset has changed as a resultof meaningful dialog with farmers

• The previous slide integrates the year-to-date with the futureprospects

• It conveys a powerful sense of variability and uncertainty

• But possibly more important, it allows farmers to use theirexperience and recollections of ‘similar’ years, if we labelledthe individual years in the previous figure

- And that can be a powerful thing!

Label the red years


Examples of how the toolset has changed as a result of meaningfuldialog with farmers

• Having arrived at such a format for rain, we assumed pasture growth wouldbe similarly handled, and so produced this

Test

Past, present and future onone figure…nice



• But, to our surprise, the farmers wanted different ways to show the past andfuture but did not want (only) accumulated curves like rainfall (why?)

• This is their alternative, actual values (weeklies)

The past looks okwith this format

But the future is difficultto interpret

Test



• Many, many iterations later …

Test


Test

The forecast years havea narrower range-past,present and future infarmer terms?


TestExamples of how the toolset has changed as a


• Example 2: Choice of pasture model

- we assumed that since the contract specified rainfalland pasture conditions, then, eventually, we would needto be able to predict pasture growth

- pasture growth is complex and requires many site-specific variables (soil, management, stocking rates etc)

- could take years to achieve this for 3,000 sites

- was our assumption correct?


Test What did the farmers say?

¸ we know what feed we have on the ground

¸ we know what soils and pastures we have in ourdifferent paddocks, as well as stocking rates etc

¸you let us worry about those (above) things and youworry about the weather

¸ in other words, you tell us about how conducive theweather has been/will be for growth and let us link that towhat’s in our paddocks




• What did we do?

GROWEST Version 2.0

M.F. Hutchinson, H.A. Nix and C. McTaggart

GI = LI x TI x MI (mostly weather-driven)

A simple 0-1 index

GROWEST PLUS – A tool for rapid assessment ofseasonal growth for environmental planning andassessment

Timothy R. Brinkley, Gregory P. Laughlin and Michael F.Hutchinson




• What did the farmers say?

- this simple little model has been able tomeaningfully characterise the average growingseason (site reports)

- it has been able to rank the various seasons

- and more important (?) … the farmers can relateto its use, advantages and especially its‘limitations’ (weather-driven)

- it is basically keeping a ‘running account’ of the weatherand farmers have always known how to manage aroundthat


TestProgress on seasonal forecasting and the pasture

growth model

• Maintaining farmers’ ‘trust’ is crucial

• We must continue to ask questions like

How good are the forecasts?

Although we use GrowEst in a particular way,how might it compare to growth that a farmer cansee (and sooner or later farmers will compare)


TestSeasonal forecasting

• Based on persistent patterns of oceantemperatures in Pacific and Indian oceans

• 9 patterns (phases)

• Red lines (forecast) are those years in the recordwhich had the ocean temp. patterns at the sametime of year

• 1 month lead, 3 month coverage


Test


Test

Same test, randomised phase table, 200 re-samples…oh dear

More work needed!


Wagga (NSW) - Annual Pasture

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Mt Barker (WA) - Annual Pasture

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Roseworthy (SA) - Annual Pasture

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Canberra (ACT) - Penerrial Pasture

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Hamilton (Vic) - Perennial Pasture

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GrassGro GrowEst


TestConcluding comments in the context of science for

agriculture

• Facts do not necessarily speak forthemselves

• Useful to ask whether the science is reallylimiting the effectiveness of the project?

• Or could it be the communication of thescience?


Test• A user reference group can really help

• If the group works well:

be prepared to have ‘your science’ questioned

be more prepared to have your outputs changed (andchanged again)

• Keep it simple if you can, but …

• Continue to develop the scientific andstatistical underpinnings

• Try not to think of statisticians as scary

They have added agreat deal to thisproject

‘transforming data to knowledge for the livestock...

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