John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008)...

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Climate change impacts and opportunities for agriculture John Lucas Rothamsted Research

Transcript of John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008)...

Page 1: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change

Climate change impacts and opportunities for

agriculture

John Lucas

Rothamsted Research

Page 2: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change
Page 3: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change

Plan of talk

• Potential impacts on agriculture

– Direct impacts

– Indirect effects

• Opportunities

– New science

– New markets and products?

• Examples from current Rothamsted

programme

Page 4: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change

Wheat production Australia 1998-2007

0

5

10

15

20

25

30

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Source Grain Market Report

International Grains Council

Mill

ion m

etr

ic t

ons

Forecast

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Can we predict likely impacts?Setting target traits for breeders

Changes in precipitation in 2050s

-0.35

-0.3

-0.25

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

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

Drier summersCanges in max temperature in 2050s

1.5

1.8

2.1

2.4

2.7

3

3.3

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

Hotter summers

Breeding cultivars for

drought or heat stress?

Page 6: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change

Computational framework for climate change impact

assessment (Semenov, 2008)

HadCM3

High resolution

scenarios

Impact assessment

under climate change

Model(incl. crop, pests, weeds

diseases)

Soil

Climate

Management

Cultivars/Genetics

Weather Generator

Page 7: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change

Drought stress decreased

1960-1990 2050s

Yield losses due to drought expected once every 20 years for cv. Avalon

Cultivar 1960-90 2050HI

Avalon Maturity 8 Aug 18 July

Changes in precipitation in 2050s

-0.35

-0.3

-0.25

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

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

Page 8: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change

Heat stress at flowering increased

1960-1990 2050s

The probability of heat stress at flowering resulting in substantial yield

losses for cv. Mercia

Cultivar 1960-90 2050HI

Mercia Flowering 19 June 5 June

Tmax at flowering, C 19.36 20.42

Tmax at 19 June 21.70

Page 9: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change

Conclusions

• Despite higher temperature and predicted

lower summer rainfall, impact of drought on

yield is expected to decrease, because

wheat will mature earlier in a warmer climate

and avoid severe drought.

• the probability of heat stress around

flowering is predicted to increase, leading to

yield loss.

• Breeding strategies for future climate need

to focus on wheat varieties tolerant to high

temperature, rather than to drought.

Semenov, 2008. Journal of the Royal Society, Interface

Page 10: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change

Region 1

Region 2

Region 3

Region 4

Region 5

Region 6

Tomato growing climatic regions in Brazil

1. SC, PR and

SP

2. RJ, ES and

SE MG

3. North of RS

4. Central MG,

GO and N SP

5. PE

6. CE

Information courtesy of Ricardo Gioria

Page 11: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change

• 15ºC - 25ºC to germinate (below 8ºC or above 40ºC -

problems);

• 20ºC - 25ºC for good seedling development;

• 18ºC - 24ºC to bloom;

• 14ºC - 17ºC during the night and 19ºC - 24ºC during the

day for a good fruit set;

• 20ºC - 24ºC lycopene synthesis – good fruit color.

Recommended temperature for the tomato crop

Page 12: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change

Green- possible based on the technology available;

yellow- some restriction concerning precipitation and/ or temperature;

red- uneconomic or high damage risk

Meses** Ano

J F M A M J J A S O N D

2005 Plantio 2080

2005

Região 1

Colheita 2080

2005 Plantio 2080

2005

Região 2

Colheita 2080

2005 Plantio 2080

2005

Região 3

Colheita 2080

2005 Plantio 2080

2005

Região 4

Colheita 2080

2005 Plantio 2080

2005

Região 5

Colheita 2080

2005 Plantio 2080

2005

Região 6

Colheita 2080

Comparison between 2005 and 2080: possibilities of sowing and

harvesting tomato fruits

MonthsYear

Region 1

Region 2

Region 3

Region 4

Region 5

Region 6

Sowing

Harvest

Sowing

Harvest

Sowing

Harvest

Sowing

Harvest

Sowing

Harvest

Sowing

Harvest

Page 13: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change

Indirect effects on crop yield and quality

• Focus on potential effects on pests and

diseases

– Changes in distribution and severity

– Changes in range and behaviour of disease

vectors

– Emergence of new pests and diseases

– Pests and diseases on broadacre bioenergy

crops?

Page 14: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change

Stem canker of oilseed rape – Leptosphaeria

maculans

Mapping disease

Page 15: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change

Predicted yield losses from phoma stem canker

(susceptible cultivars)

•Combined disease and crop model to predict yield loss

•Yield losses (susceptible cultivars) in England will double

•Priority to breed cultivars with better resistance

2020HI1980s 2050HI

Page 16: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change

Predicted date of first canker at Rothamsted (spring)

0 100 200 300

05

00

10

00

15

00

20

00

25

00

30

00

35

00

Hi2020 vs current date

Days since harvest

Degre

e-d

ays

Canker starts after

1200 degree-days

Page 17: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change

Mapping predicted changes in disease

parameters

• Pathogen life cycles

• Risk factors for disease

• Focus on key crops in Brazil

– Two examples – coffee and banana

Raquel Ghini, Emilia Hamada et.al. Embrapa Meio Ambiente

Page 18: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change

Incubation period (days) – coffee rust (Hemileia vastatrix)

Page 19: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change

Black Sigatoka of banana

Mycosphaerella fijiiensis

Page 20: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change

Black Sigatoka (Mycosphaerella fijiensis) – banana

Page 21: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change

Black Sigatoka (Mycosphaerella fijiensis) – banana

Page 22: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change

Plant disease vectors

Airborne aphids sampled

by suction trap

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-1 0 1 2 3 4 5 6 7

Peach – potato aphid at Rothamsted 1965 - 2006

Jan - Feb mean temperature °C

April

May

June

July

Fir

st

su

cti

on

tra

p r

ec

ord

r 2 = 0.786

P < 0.001

1960s black

1970s blue

1980s green

1990s gold

2000s red

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Advance in aphid flight

dates

EXAMINE consortium

Page 25: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change

Invasive diseases – Bluetongue disease of ruminants

2006-7 bluetongue outbreak in N Europe

Culicoides disease vector

Page 26: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change

BTV-4

BTV-16

BTV-9

BTV-1

BTV-4

BTV-2

&

BTV-4

BTV-8

BTV-1

BTV- type 8?

BTV-15BTV-4

BTV-16

BTV-9

BTV-1

BTV-4

BTV-2

&

BTV-4

BTV-8

BTV-1

BTV- type 8?

BTV-15

Genetic analysis of bluetongue viruses isolated in Europe has shown that

six types of the virus (1, 2, 4, 8, 9 and 16) have entered the region since

1998. There are at least four distinct routes by which these viruses have

arrived. Further information is available from the dsRNA virus web site at :

www.reoviridae.org/dsRNA_virus_proteins/

Page 27: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change
Page 28: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change

Pandemic disease expansion

Asian soybean rust Phakopsora pachyrhizi

•First detected in S. America in 2001- Brazil and

Paraguay

•USA in 2004

Page 29: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change

Extreme events may contribute to disease dispersal

Hurricane Katrina's winds helped push soybean rust further north inUSA (Alan Blaine, Mississippi State University soybean specialist).

Increase in

extreme

weather

events?

Page 30: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change

Factors increasing uncertainty

• Climate change effects on host

development and physiology

• Effects on host resistance

• Pathogen adaptation

• Change will not be linear or uniform –

chaotic system

Page 31: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change

Uncertainty v RISK

What can be done?

• Improved surveillance and monitoring

• Improved high throughput diagnostics

• Improved information systems

• Better networks to pre-empt problems

Page 32: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change

Opportunities for physical sciences in a changing climate : new technologies

for detection of emerging pests and diseases in agriculture

Rapid, non-invasive detection of (biomarkers of) pests and diseases

(UK Foresight DIID project, 2006; RSC report, 2009)

• Semiochemical (pheromone)-based trapping systems for pest monitoring

• Biosensors based on insect olfaction

• Physical sensors based on mass spectrometry

• Sentinel plants/animals based on activation of plant/animal defence

Honeybees can be “trained” to respond to

new odours, including disease markers

Developing sentinel plants/animals

for detecting infestation/infection

Inscentinel Ltd“Naturally inspired sensing solutions”

Cross section of

antennal sensillum

V

V

V

BP

BP - V

R

O = Odour

BP = Binding protein

= Receptor

Page 33: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change

Soybean rust

• First detected in S. America in 2001- Brazil and Paraguay

• USA in 2004

• USDA coordinated network – sentinel plots

• 75,000 extension presentations!

• First plant disease where information has been managed entirely via Internet

Page 34: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change

Opportunities

• New crops, crop products and

technologies to mitigate climate change

Page 35: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change

• £27m investment to build research capacity in the UK

• Virtual Centre with academic and industrial research partners

• Six integrated programmes

BIOMASS

GROWTH

BIOMASS

COMPOSITION

BIOMASS

DECONSTRUCTION FERMENTATION

Page 36: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change

Six main themes

•Improving perennial biomass crops

•Manipulating lignin to optimise sugar release

•Improving release of sugars from plant cell walls

•Discovering new enzymes for sugar release

•Developing yeast strains to ferment sugars

•Bacterial fermentation of sugars to butanol

Page 37: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change

•Short rotation coppice Willow Salix species

•UK National Willow collection

• c.1500 genotypes – 100 species

• Exploring genetic diversity and productivity

Page 38: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change

New crops – Bioenergy grasses at Rothamsted

Page 39: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change

Ligno-cellulosics, energy crops

SRC willow Miscanthus

wood chip Switchgrass

Feedstocks

Page 40: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change

DRAX

Co-fired power station

900 MW of new dedicated

biomass fired generation

Significant contribution to

UK renewables target

Page 41: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change

Biomass for energy genetic improvement

network (BEGIN)

B

EG

I

N

To deliver the breeding programme and plant materials

that will allow further improvement of willow as a biomass

crop

• Improve yield and resistances to insects pests,

diseases and other stress (drought)

• Widen genetic base of the varieties available for

growers

• Ensure continuous delivery of new genotypes to

meet future needs of the industry

Page 42: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change

Composite picture shows (top left, SRC willow; top middle, willow

catkins; top right, SRC poplar; lower left, microarray; lower right,

microsatellites). The Populus cDNA microarray was created as

part of the Swedish Genome Project and was provided by Stefan

Jansson (Umea Plant Science Centre).

Page 43: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change

0

2

4

6

8

10

12

14

16

Q83

Orm

Rapp

Jorr

Tora

Sven

Olo

f

Gudru

n

Dis

covery

Resolu

tion

Yield ODT/ha/yr

1993 1995-7 2000-2002

Two EWBP

varieties

Biomass yield increased from 8-15 ODT/ha/yr

Swedish

varieties

Swedish

varieties

Early

varieties

2005-2006

Increasing yield

Target >20 ODT/ha/yr

Page 44: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change

Data set for empirical model (Miscanthus)

0

2

4

6

8

10

12

14

16

18

20

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005

Yie

ld (

dry

ma

tte

r -

t h

a-1

)

RES 408

RES 480

0

5

10

15

20

Arth

ur R

ickw

ood

Box

wor

th

Brid

gets

Bro

oms Bar

n TG

Buc

kfas

t Abbe

y

Gleadt

horpe

Hig

h M

owth

orpe

Rose

mau

nd

Rose

war

ne

Roth

amsted

408

Roth

amsted

480

Roth

amsted

TG

SCRI

Wobu

rn m

ain

TG

Wobu

rn m

icro

TG

Yie

ld (

dry

matt

er

- t

ha

-1 )

Rothamsted

Around the UK

Courtesy Richter, Daily & Riche. Rothamsted Research

Page 45: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change

GIS Yield map for England and

Wales using soils and climate

data sets as input to empirical

Miscanthus yield model.

Richter, G.M., Riche, A.B., Dailey, A.G., Gezan S.A., & Powlson D.S. (2008) Is

UK biofuel supply from Miscanthus water-limited? Soil Use and Management,

24, 235–245

Page 46: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change

Short rotation coppice – Willow rust Melampsora spp

Page 47: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change

Acknowledgements

UK

Mikhail Semenov

Bruce Fitt

Mike Birkett

James Logan

Ian Shield

Angela Karp

Andy Whitmore

Brazil

Ricardo Gioria

Raquel Ghini

Page 48: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change
Page 49: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change

Soybean rust Brazil 2007(source Neto, Godoy, Toledo)

Page 50: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change

Source: Royal Society Report Sustainable biofuels:

prospects and challenges 2008

Page 51: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change

Energy balanceCNG 0.86

Petrol 0.91

Diesel 0.94

Sugar beet to Ethanol 1.4 – 2.1

Wheat grain to Ethanol 0.7 – 2.7

Wheat straw to Ethanol 0.8 – >20a

Oilseed rape to diesel 0.7 – 4.4

SRC willow to Fischer Tropsch >13.9

a Extremely sensitive to the allocation of growing “costs” of straw,

Miscanthus and switchgrass give similar values >20.

Compiled from the work of; Woods and Bauen (2003), Elsayed et al. (2003)

and Saynor et al. (2003)

Page 52: John Lucas - Fapesp · Computational framework for climate change impact assessment (Semenov, 2008) HadCM3 diseases) High resolution scenarios Impact assessment under climate change

Miscanthus x giganteus

A perennial rhizomatous C4 grass originating in E. Asia

A natural hybrid between M. sinensis and M.

sacchariflorus, brought to Europe in early C20 as an

ornamental, M. sinensis still common in garden centres.

Common name also miscanthus