a ssociate p rofessoryvette everin gham jam escook un...

Un d erstan d in g clim ate variablesto im prove resilien ce

A ssociate P rofessorYvette Everin gh am

Jam esCook Un iversity,A ustralia &

W orld M eteorologicalOrgan isation ,Com m ission forA griculturalM eteorology.

CCRSP I CONFERENCE,A pril27 -28 ,2016

Session :Clim ate resilien ce in prim ary in d ustries.

GlobalP opulation

“In ord erto feed th islarger,m ore urban an d rich er

population ,food prod uctionm ustin crease by 7 0% ”FA O (2009):H ow to feed th e

w orld in 2050

Challenge

How tofeedanincreasingpopulationw ithfew erinputsatatim ew henyieldsofm ajorcropshaveflatlined w ithavariableandchangingclim ate.

Yearof P rod uction

Yie

ld

Research Investments

En h an c in g th e sustain ability of th eA ustralian sugarin d ustry

Background to AustralianSugar industry

T heAustralianS ugarIndustry

T heAustralianS ugarIndustry

3rd supplierof raw sugar

7 th ag exporter

24sugarm ills

4000 farm s

30 M tcan e

4-5M tsugar

$2b A UD

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Slid e A d apted from D.Skocaj

Clim ateIm pactsFarm ing,Harvesting,M illingandM arketingS trategies

Methods

Building climate resilience

1. In tegrate available tech n ologies

• C roppin g system ssim ulators

• C lim ate scien ce tech n ologies

• B ig d ata tech n ologies

2. P artn erin g w ith in d ustry

3. A ction Learn in g A pproach

P lan

Act

L earn

R eflect

Rain fall

Rad iation

Tem perature

SugarYield

N itrogen FertiliserRequirem en ts

IrrigationRequirem en ts

Cropping Systems Simulation

M an agem en t

Decision s

Climate Science

W eath erforecasts

M JO

ENSO

Decad alvariability

C lim ate projection s

Extrem e even ts

Volum e,variety & velocity d ata capture

Ourability to filter,an alyse,d iscover

New d ata iscon tin uously bein g prod uced

2.5x 101 8 bytes(exabytes)perd ay

Big Data

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BIO

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A ccum ulation

GCM FORECA STS/P ROJECTIONS

M axim umTem perature

Rad iation

Rain fallM in im umTem perature

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IRR

IGA

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CCSA ccum ulation

Key ch allen ge:projectstod eveloplin kagesbetw een fullycoupledclim ate m od elsan d cropm od els

R unAP S IM foreachensem blem em berfor30 years

Methodsof

tomorrow

Gartner’s 2015 Hype Cycle forEmerging Technologies


1.

new technology sparks media interest,some proof of concept stories,

usually no useable products at this stage


2. Some success stories

Some companies take action

Gartner’s 2015 Hype Cycle forEmerging Technologies 3. “Make or Break”

Need to attract early adopters


4. More examples of how the technologycan help enterprises.


5. Mainstream adoption andbenefits


What technologies mightWhat technologies mightclimate science mergewith in the future anduseful framework for

wider adoption ofclimate and Agtechnologies.

Results

Dual(CropandClim ate)Ensem bleM odellingApproach

Everin gh am etal(2015)A d ualen sem ble agroclim ate m od ellin g proced ure to assessclim ate

ch an ge im pactson sugarcan e prod uction in A ustralia.A griculturalScien ces,6.pp.8 7 0-8 8 8 .

P lausible to plan for in crease in yield (t/h a)un d er B 1 Scen ario

H igh ly P lausible ......................................... A 2 Scen ario

Sexton ,Justin ,Everin gh am ,Yvette,an d Tim bal,B ertran d (2015)H arvestd isruption projection sforth e

A ustralian sugarin d ustry.In tern ation alJourn alof C lim ate C h an ge Strategiesan d M an agem en t,7 (1).pp.

41-57 .

ChangeinunharvestabledaysN S W

L im ited evid en ce of ch an ge in un h arvestable d ays

When to start the harvest?

~2 M AU D benefitifdelay harvestinElN iñoyears

Low erCCS

HARVEST

DECISION

W h en to starth arvestin g?

Forecast

Ja Fe DeNoOcSeAuMa Ap Ma Ju Ju

H igh erCCS

Low erCCS

Everin gh am ,Yvette L.,Stoeckl,Natalie E.,C usack,Justin ,an d Osborn e,Joh n A .(2012)Q uan tifyin g th e ben efits

of a lon g-lead ENSO pred iction m od elto en h an ce h arvestm an agem en t:a case stud y forth e H erbertsugarcan e

grow in g region ,A ustralia.In tern ation alJourn alof C lim atology,32 (7 ).pp.1069-107 6.

Everin gh am ,Sexton ,In m an -B am ber,Skocaj(2016)A ccurate yield pred iction of sugarcan e usin g a ran d om

forestalgorith m

A gron om y forSustain able Developm en tDOI 10.1007 /s13593-016-0364-z

CropForecasts

B um percrop forecasted forTully

80

82

84

86

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90

92

94Tu

llyY

ield

fore

cast

20

16

(t/h

a)

Yie

ldAnom

aly

(tonnes

cane

per

hect

are

)

Everin gh am ,Y.L.,M uch ow ,R.C .,Ston e,R.C .,an d C oom an s,D.H .(2003)Usin g south ern oscillation in d ex ph asesto

forecastsugarcan e yield s:a case stud y forn orth eastern A ustralia.In tern ation alJourn alof C lim atology,23(10).pp.

1211-121 8 .

M arketing

Sugarpricesstren gth en ed from 12.5c/lb to 16.5c/lb in Q 1 2016

P ricesd riven by a globalsugard efic item ergin g after5yearsofprod uction surpluses

W eath er(actualan d forecast)h aslow ered prod uction estim atesinB razil,Th ailan d ,In d ia an d Europe

Th ailan d ’sd rough tim pacted crop d ow n 10%

W etfin ish to B razil’s2015/16crop h as40 m illion m tton n e can e leftasstan d over

W orld con sum ption forecasted to exceed w orld prod uction

M arketing

• W h atisth e size of th e crop?

• H ow m uch sugarto forw ard sell?

• W h atare th e optim alsh ippin g an d storage

requirem en tsso th atsugarcan be sh ipped to

custom erson tim e an d pen altiesavoid ed ?

A sim ulationstudy –How much N isneeded forBU L GAN S oils?

(P hD T hesisDanielleS kocaj)

El Niño

NeutralLa Niña

20% Less Nin La Niña

B iggsetal(2013)In teraction sbetw een clim ate ch an ge an d sugarcan e m an agem en tsystem sforim provin g w aterqualityleavin g farm sin th e M ackay W h itsun d ay region ,A ustralia.A griculture,Ecosystem san d En viron m en t

T hefrequency ofyearsthatannualNlossesexceededtheavoidablethresholdsintheW etT ropics

Everin gh am ,B aillie,In m an -B am ber,an d B aillie (2008 )Forecastin g w aterallocation sforB un d aberg

sugarcan e farm ers.C lim ate Research ,36(3).pp.231-239.z

IrrigationS cheduling

Stream flow s-> W aterA llocation M od el-> Optim alTim e to Irrigate

Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun

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Recommendations

R ecom m endations

In crease in d ustry

prepared n essto ENSO

an d extrem e even ts

Recommendations

C apacity build in g:

In vestin n extgen eration

research ersto prod uce

in terd isciplin ary talen ted grad uates

espec ially in m ath em aticsan d

agriculture

R ecom m endations

B etterin tegration tech n iquesof GC M san dC rop M od elsan d prom ote successstories

Exploitn ew tech n ologiesto ad van ce th eGreen Data Revolution

Em bed C lim ate Sm artA griculture w ith in Big Data

Recommendations

R ecom m endations

Risk Risk Risk ! ! !

Thankyou

Thankyou

Knowledge will never be as complete as we mightlike, yet risk must be managed.

Source:G.P earm an Green h ouse con feren ce 201 1

a ssociate p rofessoryvette everin gham jam escook un...

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