What aspects of climate change really matter for agriculture, and vice versa?

David B. Lobell

Associate Professor, Department of Environmental Earth System Science

Associate Director, Center on Food Security and the Environment

dlobell@stanford.edu

Six common misperceptions

First, a quick overview of expected impacts

multiple climate model

projections

Adaptation scenario(s)

1 Crop model Estimated impacts

Downscaled T & P

projections

CO2

Flow of a typical crop impact assessment

Still considerable uncertainty for individual countries

Higher latitudes unhurt by warming, benefit from CO2

Major current producers likely to see losses (high input rainfed systems, or irrigated wheat in hot areas)

-55 -20 0 20 50 100

Muller et al., World Bank Dev. Report 2010

First, a quick overview of expected impacts

Six common misperceptions 1. Crop modeling is the easy part

www.xkcd.com

For impacts, the uncertainties from crop modeling can often be bigger than those from climate models

Variation in impact from crop models

Variation in impact from climate models

Asseng et al. 2013, Nature Clim Change

• New AgMIP studies are a big step toward catching up with climate MIPs

•AgMIP is producing “response emulators” that may be useful for Earth System Models (see http://www.agmip.org/c3mp/)

•But still very focused on process-based crop models that are >20 years old

In reality, modeling crops and farmers can be just as tricky as modeling clouds and oceans. The ag community has just been slower to quantify uncertainty

Six common misperceptions 1. Crop modeling is the easy part 2. “Process-based” crop models are inherently more

suitable for extrapolation into the future

Approaches to understanding climate impacts on crops

(1) “Process-based” models (usually with daily-time step)

Overview of APSIM model (Hemmer et al. 2010)

Approaches to understanding climate impacts on crops

(1) “Process-based” models (usually with daily-time step)

• Originally designed to assist field-scale management decisions

• 3 Big Challenges when using for climate impacts -lack of good input data (e.g. soil properties, management) -difficult to parameterize (many parameters, few data) -missing processes relevant to climate change

Approaches to understanding climate impacts on crops

(2) “Empirical” models

Thompson (1969, 1975)

Approaches to understanding climate impacts on crops

(2) “Empirical” models

Schlenker and Roberts, PNAS (2009)

Approaches to understanding climate impacts on crops

(2) “Empirical” models

These have the advantage of being developed at the spatial scale of interest, and have generally been more tested “out of sample” than process-based models 3 main challenges: -correlation ≠ causation, leading to concerns about projecting forward -relationships reflect current practices and technologies, not potential future ones -hard to capture effect of variables that vary mainly in time (CO2)

Do crop models capture relevant processes?

White et al, 2011, Field Crops Research

•White et al. recently reviewed 211 papers that used “process-based” simulation models to estimate climate change impacts

What processes respond to warming? Process

1. Crop growth – photosynthesis, respiration

2. Crop development – duration of key stages (e.g., grain filling)

3. Water stress – greater vapor pressure deficits drive more water loss

4. Cell damage – very low or high temperatures can cause irreversible damage

5. Biotic stress – pests and diseases develop more quickly

What processes respond to warming? Process Do Models

Consider? How well do models treat process (0-10)

1. Crop growth – photosynthesis, respiration

2. Crop development – duration of key stages (e.g., grain filling)

3. Water stress – greater vapor pressure deficits drive more water loss

4. Cell damage – very low or high temperatures can cause irreversible damage

5. Biotic stress – pests and diseases develop more quickly

Yes Yes Usually Rarely No

9 7 5 n/a n/a

Exposure to “critical” heat for cell damage

(Gourdj, Sibley, Lobell 2013, ERL)

Global fraction of crop area exposed to 5 days above critical temperature near flowering stage

CMIP5 RCP8.5

Six common misperceptions 1. Crop modeling is the easy part 2. “Process-based” crop models are inherently more

suitable for extrapolation into the future 3. “Statistical” crop models are much more pessimistic

than “process-based” models

The few existing comparisons show fairly good agreement

E.g. Lobell et al. (2013) compared APSIM and statistical crop models for 45 years at 3 sites

Good agreement on maize yield response to extreme heat

Lobell et al. 2013, Nature Climate Change

EDD defined here as degree days above 30°C

Six common misperceptions 1. Crop modeling is the easy part 2. “Process-based” crop models are inherently more

suitable for extrapolation into the future 3. “Statistical” crop models are much more pessimistic

than “process-based” models 4. Resolving rainfall uncertainties are really important

for understanding impacts and adaptations

This is partly the fault of standard representation of uncertainty by climate modelers

Tebaldi et al. 2012, GRL

It also relates to the fact that most agronomists have developed all their intuition at the field scale, and from year-to-year variability

And to the fact that rainfall is highly correlated with production for most rainfed systems

Illinois yields (detrended), July T and P, 1950-2012

But some of this correlation is an artifact of being warmer when it’s drier. Better to look across space+time and control for one variable

July T vs. P for all counties & years in Illinois, Iowa, Indiana

The APSIM simulations indicate growth is not controlled much directly by heat, but by water stress

Lobell et al. 2013, Nature Climate Change

Daily Ratio of Water Supply to Demand

Daily Tmax

Water stress depends a lot on heat at the daily time scale

Lobell et al. 2013, Nature Climate Change

Daily Tmax

Daily

Rat

io o

f Wat

er S

uppl

y to

Dem

and

Water stress depends even more on heat at the monthly time scale

Lobell et al. 2013, Nature Climate Change

Water stress in key month much more sensitive to warming than drop in precipitation

Six common misperceptions 1. Crop modeling is the easy part 2. “Process-based” crop models are inherently more

suitable for extrapolation into the future 3. “Statistical” crop models are much more pessimistic

than “process-based” models 4. Resolving rainfall uncertainties are really important

for understanding impacts and adaptations 5. Humidity changes can be ignored

Relative humidity projections indicate drops in key crop regions

Mean projected 50 year trends in RH, RCP8.5, for summertime in agricultural areas

As many of you know, warmer projections tend also to have lower RH

Lobell et al., in prep

Projected changes in RH vs. Tmax for 29 models

This means that mean and range of yield impacts likely bigger than if just considering temperature changes

Lobell et al., in prep

Boxplot of projected changes in VPD in Johnston IA

Typical crop modeling approaches

This means that mean and range of yield impacts likely bigger than if just considering temperature changes

Lobell et al., in prep

Boxplot of projected changes in yield in Johnston IA, RCP8.5, 2060-2010

Typical crop modeling approaches

Six common misperceptions 1. Crop modeling is the easy part 2. “Process-based” crop models are inherently more

suitable for extrapolation into the future 3. “Statistical” crop models are much more pessimistic

than “process-based” models 4. Resolving rainfall uncertainties are really important

for understanding impacts and adaptations 5. Humidity changes can be ignored 6. Technological progress in yields is guaranteed, and

can be treated exogenously

Yield changes are the eventual outcome of sustained investments in R&D and infrastructure

•Most integrated assessment modelers realize how critical assumptions about yields are for their experiments

•For example, the impacts of climate change on food prices, whether biofuels make sense, etc. all hinge on how much we can squeeze out of existing croplands

•It is time to be more explicit about the factors that relate to yield increases, including possible biophysical limits

Yield changes are the eventual outcome of sustained investments in R&D and infrastructure

•For example, with Uris Baldos and Tom Hertel, we recently developed a model to incorporate ag investment, and look at land use and carbon effects of investments in productivity

Invest only in Africa and Latin America

Invest in all regions

Lobell et al., 2013, Env. Research Letters

Summary • There are lots of exciting developments in the crop

modeling community, including • Model intercomparisons • Much larger datasets for empirical analyses • Better understanding of mechanisms behind impacts

• At the same time, there remain some misperceptions that, in my view, lead to misallocation of effort

• As a crop modeler, I would appreciate more info from climate modelers on VPD, extreme heat exposure, and soil moisture. And…

And if I had time for a 7th, it would have been about decadal variability and “20-yr cycles” in the US

Satellite view of China January 13, 2013

Rolling 20 year trend of June-August Corn Belt Temperatures

(data from Hadley CRUTEM4.1)

If I had time for an 8th, it would have been about ozone Thanks for your attention!

Satellite view of China January 13, 2013