Un derst an d in g cl im at e var iablestoimprove res il ien ce A ssociat e ProfessorYvett e Ever in gh am Jam esCook Univers it y,Austral ia & W orl d M et eorologicalOrganisat ion,Com m iss ion forA gr icult uralM et eorology. C C RSP I C O N FEREN C E,A pr il27 -28 ,2016 Sess ion :Cl im at eres il ien ce in pr im aryindustr ies.
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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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J J A S O N D J F M A M J
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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FE
RT
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
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
Gartner’s 2015 Hype Cycle forEmerging Technologies
2. Some success stories
Some companies take action
Gartner’s 2015 Hype Cycle forEmerging Technologies 3. “Make or Break”
Need to attract early adopters
Gartner’s 2015 Hype Cycle forEmerging Technologies
4. More examples of how the technologycan help enterprises.
Gartner’s 2015 Hype Cycle forEmerging Technologies
5. Mainstream adoption andbenefits
Gartner’s 2015 Hype Cycle forEmerging Technologies
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
88
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
01
02
03
04
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Month
Pro
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rtio
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ota
lWa
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Allo
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nU
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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
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