Scenario-based assessment of climate

change impacts on agriculture

Gerald C. NelsonProfessor Emeritus, University of Illinois,

Urbana-Champaign

Presentation at the AAAS session “New Scenarios for Assessing Future Climate Change”, 26 February 2014

… or how we used the SSPs (and an RCP) in a model intercomparison

exercise

… or should we do any more integrated

assessments until we fix the Lamppost problem

Policy questions that need scenario answers

What is the future of agricultural prices?

How will agricultural production evolve?

How will climate change alter …PricesLand useTradeUndernourishment

Do we have the tools to answer these questions?

Alternate perspectives on future prices with no climate change, 2000-2050 in

2011

IMPACT – big price increases (e.g. 80% increase in coarse grains price)

Why do the results differ?Differing perspectives on

Today’s unknowns that we should know Future unknowns

Economic development Population growth Climate change Natural resource availability Technological advance

Differing economic modeling approaches CGE models more ‘flexible’ (?) Functional forms determine outcomes (e.g., Armington

assumption, demand parameters; ref Bennett’s Law)

We had no idea which is most important

Scenario harmonization:Common values for key drivers

Harmonized four key drivers Population from SSP2 and 3 GDP from SSP2 and 3 Exogenous component of agricultural yield growth Climate change effects on yield growth

No harmonization on other important drivers

Three ‘orthogonal’ comparisons in 2050 Socioeconomics – SSP2 versus SSP3 Bioenergy policies – not covered here Climate change – no climate change versus RCP 8.5 with

no CO2 fertilization

SSPs: What did we use and why?What?

SSP2 and SSP3, version 0.5GDP, OECD version Population

What not?StorylinesPopulation makeupUrbanization

Why?

SSP per capita incomePer capita incomes in 2010;

SSP2 and SSP3 in 2050

Wor

ld

Develop

ing E

ast A

sia

South

Asia

Euro

pe & C

entra

l Asia

WB d

efiniti

ons

Mid

dle E

ast &

Nor

th A

frica

Sub-Sah

aran

Afri

ca

Latin

Am

erica

& C

arib

bean

High In

com

e0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

2010 SSP2 SSP3

The modeling chain:From biophysical to

socioeconomic

Nelson, et al., PNAS 2013

Results: price changes between 2005 and 2050

are smaller, but…

Maximum price changes between 2005 and 2050 reduced. Large differences remain.

General equilibrium Partial equilibrium

2050 price effects from the socioeconomic

scenarios, with no climate change

Model differ dramatically in their responses to the negative future of

SSP3

Price declines

Price increases

Price not affected

Climate change and economic responses

Quantity

Price

Demand

Supply

Climate change shifts supply to the left

Initial shortage caused by climate

change

Initial shortage =

big price increase

Final quantity

Final

pri

ce

Model choices- Supply response?

- Area- Yield

- Demand response?- Where?- How much trade?

Results from the climate change scenarios

Comparisons are differences in 2050 outcomes

Mean -17% -11% +11% -2% - 1% -3% +20%

Climate change reduces 2050 yields. Producers, consumers, & trade

partially compensate

Nelson, et al., PNAS 2013

Finalyield

Biophysicaleffect

How do the model distribute their 2050 responses to climate change?

Mostly area

Mostly yield

  Demand Area YieldDemand/Supply

Model Type

AIM 0.12 0.90 -0.02 0.14 CGE

ENVISAGE 0.17 0.32 0.51 0.2 CGE

FARM 0.04 0.67 0.29 0.04 CGE

GTEM 0.06 0.29 0.65 0.06 CGE

MAGNET 0.1 1.39 -0.49 0.11 CGE

GCAM 0.22 0.78 0 0.28 PE

GLOBIOM 0.49 0.12 0.38 0.98 PE

IMPACT 0.38 0.53 0.09 0.61 PE

MAgPIE -0.01 -0.08 1.09 -0.01 PE

AVERAGE 0.17 0.53 0.3 0.21  

Large demand

Nelson, et al., Agricultural Economics 2014

How ‘plausible’ are the ensemble results? Two

viewsThey are too pessimistic

GHG concentration pathway with the greatest forcing (RCP 8.5)

Crop models assumed constant CO2 concentrations throughout the period

They are not too badActual GHG concentrations similar to RCP 8.5 so

farField results from FACE experiments suggest CO2

fertilization effect in the field is less than in the lab

And then there is the Lamppost Problem

What is missing in our climate change results?

The models don’t include effects of Increasing pest and disease pressure Increasing extreme events Increasing ozone Effects on nutrition

Models for most crops don’t exist

These could swamp the negative effects already quantified

What to do about the Lamppost Problem?

Merge the silos!

It’s not enough for each community to work together. We

need a community of communities, old and new.

• Standard data protocols, developed together• Commonly agreed aggregation methods• ‘Centrally’ managed data storage

Open the black boxes so they aren’t reinvented

• Share code within each community• Identify ‘best’ versions• ‘Centrally’ manage code storage and

dissemination• Identify data needs and develop new sources

Develop 21st century modeling environment

Approach depends on topic

Crop modeling Code hierarchy with modular construction

Plant-level functions (e.g. photosynthesis) Species-level functions Variety-level functions

Design with modularity in mind Identify critical parameters and data needs at each

level Talk to the computational biology folks Facilitates the needed bulk development of models for

fruits and vegetables

ConclusionsSubstance

RCP8.5 results in lower yieldsAdaptation reduces some of those effects across

the supply and demand sideEconomic models allocate response differently

between supply (area and yield) and demand

ProcessExisting models/methods are underestimating the

effects of climate change on food security. We urgently need to address the Lamppost Problem before we do more assessments.