Greetings,
loyal G-FEED Readers,
My
colleague (and former postdoctoral mentor!), V. "Ram" Ramanathan and
I have a paper out this week in PNAS (here) on the impacts of short-lived
climate pollutants (SLCPs) on Indian crop yields over the past 30 years
(1980-2010). Anticipating the publication, the G-FEED crew invited me to guest post
this week. Thanks, guys! It's a fantastic opportunity to talk about the paper
itself as well as what we know more generally about air pollution and
agricultural impacts. I'm biased, but I think this topic is going to be
increasingly important in the coming years. And it feels fitting to discuss it
here, as G-FEEDers have been active contributors to understanding the
relationship between air quality and crop yields: this work builds on some
papers by Ram and Max (here and here), speaks to current work by Wolfram
and co. on ozone impacts in the US, and benefitted from feedback from Marshall
and David at various points.
First,
some background. When we think about anthropogenic climate change, we've
usually got carbon dioxide (CO2), and maybe methane (CH4),
nitrous oxide (N2O), and CFCs/HCFCs on our minds. These greenhouse
gases are basically homogeneously mixed in the atmosphere, and they have very
long atmospheric lifetimes (with the exception of methane, centuries). They're
the reason that, even if human emissions ceased instantaneously tomorrow, the
earth would still see another degree or so of warming. However, the big boon in
climate science in the past decade or so has been our increasing understanding
of the role that short-lived species play in the climate system. These
compounds are more conventionally considered pollutants, and include things
like aerosol particulates, ozone precursor compounds, and short-lived greenhouse
gases (e.g., tropospheric [surface] ozone and HFCs). The one thing they have in
common is their relatively short atmospheric lifetimes, but -- as their physical
and chemical properties vary widely -- their mechanisms of impact on our
climate system differ. In some cases we don't yet have a full handle on their
impacts: for example, uncertainty in cloud-aerosol interactions represents one
of the biggest uncertainties remaining in GCMs. (Take a look at the radiative
forcing attribution figure from the IPCC AR5 for fun.)
What
we do know is this: black carbon is the second or third largest contributor to
present warming (after CO2 and perhaps methane, depending on how you
partition its impact – it is part LLGHG and part SLCP, since it’s also an ozone
precursor). And the four main warming SLCPs combined (BC, tropospheric ozone,
methane, and HFCs) have contributed around half of present warming (again, it
depends on how you partition methane’s impacts). Moreover, two of these – ozone
and BC – have some clear pathways of impact on crops beyond their impacts
through temperature and precipitation. Ozone is directly toxic to plants, and
BC cuts down on surface radiation, which should negatively impact
photosynthesis. And both ozone and BC are very spatially heterogeneous – they
are not well-mixed, and so local emissions could very well be expected to have
local impacts.
So…given
these realities we took the unabashedly empirical approach and threw available
data at the problem. We ran a panel regression analysis that included growing
season and crop-area averaged temperature, precipitation, and pollutant
emissions on the RHS, along with various time controls. Our unit of analysis
was major rice- and wheat- producing states in India, and we looked at data from
1980-2010. We find that relative yield loss (RYL) for climate and air pollution
(weighted average for India) is 36% for wheat. That is, yields in 2010 would
have been 36% higher absent climate and pollution trends over the past 3
decades. Moreover, most of that RYL is due to pollution (90%). (Our estimate
for rice RYL is 20% but is not statistically significant.) This suggests a few
things: first, cost benefit analyses for air pollution mitigation programs will
likely look a lot better in a place like India if agricultural benefits are
included. Second, in the near term, cleaning up the air could help offset T-
and P-related yield losses.
How should
we think about these results in comparison to previous work? First, what we
find in terms of climate (T & P) impacts is slightly smaller than the
coarser-scale findings for S. Asia by David and Wolfram et al (here). (They find -5-6%, we have -3.5%.)
These are probably not statistically different, but one way to think about this
is that, absent pollution variables on the RHS, we’d expect the coefficients
for T and P to include some of the impacts of SLCPs. The same holds for the
time controls. Wolfram and others have made the case that these panel analyses
should include quadratic time trends of one form or another to account for an
empirical leveling off of yields. Biophysical limits alone make this a totally
sensible proposition, but it’s also likely that the “leveling off” trend that
has been observed (more on that by David, here)
includes the previously unexplored impact of pollution.
Some
previous studies have explored the impact of individual pollutants on yields.
Max et al looked at black carbon indirectly, by examining the impact of surface
radiation on yields (here and here). They see the signature of
atmospheric brown clouds on precipitation and thus rice yields, but no direct
radiative impact. My best guess is that: (1) their metric of total radiation contains
a combination of two effects – a reduction in direct radiation from black
carbon and an increase in diffuse radiation from scattering aerosols like
sulfates. Plants can often use diffuse radiation more effectively for
photosynthesis. (2) More important, rainy-season rice is probably the hardest
place to see this signal, as precipitation clears particulates out of the air.
We put both BC and sulfur dioxide (the main precursor of sulfate aerosols,
which are scattering, or net cooling) in our model to account for these two
effects independently, and we see significance for BC’s impact on wheat but not
rice, as would be expected based on seasonal concentrations.
On the
ozone side of things, a handful of papers have used atmospheric chemistry
models to estimate surface ozone concentrations at a given point in time. Those
concentrations have then been used in conjunction with exposure-response (E-R)
relationships from test plots to estimate yield losses. In theory, if you
really do have a good handle on surface concentrations and a valid E-R
function, this would be the way to go. The preferred method is AOT40 –
accumulated hours of exposure over 40ppbv. But this number is extremely
sensitive to crop cultivar, estimated concentrations, when/how long you count
exposure, and other management factors. So these kinds of estimates have very
large error bars / uncertainties. (See here, here, here, and here.) A few of those studies suggest
RYL in 2000 to be 15%-28% for India for ozone though – a comforting similarity
in magnitude.
For me, the
nice part of this analysis was that we really took a physical approach – we put
stuff on the right hand side in forms that made physical sense, and it turned
out to hold up to all the (sometimes nonsensical) things reviewers asked us to
do. Best of all, the results agree at least in magnitude with some other
approaches.
In terms of
impact, I hope this paper helps bring agricultural benefits into the
conversation about air pollution mitigation. On the methods side, we did a lot
of work to think about how to detangle air pollution and climate impacts based
on what we can actually measure – an exercise fraught with nonlinearity. I hope
some of our work can help guide future efforts to estimate pollution (and
pollution policy) impacts. Readers of this blog are well familiar with
threshold-y temperature and precipitation effects, but the pollution impacts
and mitigation landscape is worse. A real heterogeneous treatment effects
nightmare, where everything is co-emitted with other stuff that all acts in
different directions with different thresholds. (See FAQ 8.2, Figure 1 from the
IPCC AR5 below for a hilarious, if depressing, rendition.)
I mentioned the competing
impacts on radiation from absorbing and scattering aerosols above, but another
interesting example is in ozone formation. Ozone depends on both the absolute
and relative concentrations of NOx and VOCs. We had evidence (satellite and
ground) that we had both types of ozone regimes (NOx-sensitive or
NOx-saturated) in our study area. We used the VOC/NOx ratio to account for
that; it should probably be standard practice in these kinds of econometric
analyses if you’re doing anything involving NOx or ozone.
All that
horn-tooting aside, our analysis was limited in a few ways, and I’m excited to
push it in some other directions as a result. A few thoughts:
First, we
use emissions, and not concentrations, of pollutants. The main reason is that
there aren’t any long-run records of pollutant concentrations in India (or most
places, for that matter), and we need better satellite-aided algorithms for
extrapolating station data to get these kind of reliable exposure maps (the
main task consuming my time right now). So we use emissions inventories of
aerosols and ozone precursors as proxies for concentrations. Of course, these
inventories are also just estimates, and in some cases (e.g., here) have been shown to be wayyy off
(particularly low for black carbon). So I’m looking forward to using better
exposure proxies. Ideally, one would look at both emissions estimates (the
policy-relevant variable), and concentrations (the physically-relevant
variable) together.
Second,
we’re also just statistically limited. Emissions of all pollutants have been
going up fairly monotonically, and there’s not a ton of signal there for
estimation. Going to smaller scales doesn’t make sense from a physical
perspective (then you have to worry about transport). So the best thing to do
would be to run this same kind of analysis in different countries. I’m
particularly excited to look at different pollution regimes – think biomass
dominated versus coal dominated, etc. Hopefully I can convince some G-FEEDers
that this would be an awesome collaboration idea (ahem, Sol).
I’ll leave
off there, though I’m always happy to discuss this stuff if there are any
questions. Thanks to G-FEED for having me, and to all of you for reading this
tome.
Don’t
forget to vote!
- Jen
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