Introduction
Population growth is intensifying stress on global food production.
Simultaneously, anthropogenic activities are changing many aspects of the
earth system. This reinforces the need to better understand how crop
production may be affected by changes to the water, air, light, and soil
required for efficient growth. For example, Challinor et al. (2014) suggest a global decline
in crop yield due to climate change of more than 10 % is likely by 2050.
However, this is uncertain and the projected sign and magnitude varies by
crop and region due to localized changes in factors such as temperature and
precipitation combined with global carbon dioxide (CO2) enhancement
(IPCC, 2014). Many studies have explored the impacts of climate and
air quality on crop production, both globally and regionally, with various
results depending on the tools, methods, and processes used by each (e.g.,
Burney and Ramanathan, 2014; Lobell and Burke, 2010; Shindell et al., 2011;
Tai et al., 2014). Investigations of the impacts of air quality on crops, in
particular, have focused mainly on the negative impact of ozone pollution
(Avnery et al., 2011; Mills et al., 2011; Van Dingenen et al., 2009). In comparison,
only limited work has been conducted to assess how atmospheric particulate
matter (PM) impacts crop production (Greenwald
et al., 2006; Schiferl and Heald, 2018), and this has been done without
considering physiological limitations (e.g., rate and magnitude of carbon
pool allocation) and other environmental stresses (e.g., water and nutrients).
Emitted from combustion and natural sources and formed through chemical
oxidation in the atmosphere, PM is the leading cause of air quality issues
globally and is responsible for over 4 million premature deaths per year
(Cohen et al., 2017). PM also
impacts crop production by modifying shortwave (SW) radiation reaching the
surface. Through the scattering of light, PM decreases the total SW
radiation at the earth's surface, which is made up of direct and
diffuse light (SW = direct + diffuse). PM also increases the diffuse
fraction (DF) of this SW radiation (DF = diffuseSW). Increased DF more evenly
distributes light throughout the canopy of a plant, redirecting light away
from (at times over-saturated) leaves in direct sunlight and onto shaded
leaves. In this way, plants can more efficiently make use of incoming solar
radiation (Kanniah et al., 2012).
Previous studies of the impact of PM on plant productivity have largely
focused on natural ecosystems, and forests in particular. Using network
observations, Niyogi et al. (2004) showed
that the CO2 sink, a measure of plant productivity, increases with PM,
indicated by aerosol optical depth (AOD), over forests, but decreases for
grasslands. More recent work related satellite AOD measurements to
observations from CO2 flux towers to quantify the impact of diffuse
light on plant productivity. In the Amazon forest, enhancements in net
ecosystem exchange (NEE) of up to 29 % are observed when the DF reaches
approximately 0.5 (Cirino et al., 2014). Strada et al. (2015) find
an increase in midday gross primary productivity (GPP) of ∼ 13 % in
US deciduous forests when DF is 0.4–0.6. Advanced canopy or
leaf-scale process modeling has been used to further examine how PM impacts
natural vegetation and the carbon cycle. The model framework of
Strada and Unger (2016) shows little
sensitivity in global total GPP (∼ 1–2 %) to PM pollution,
with regional enhancements of ∼ 5–8 % in North America and
Eurasia and ∼ 2 % in the Amazon, where forested canopies
dominate. In China, Yue and Unger (2017) use AOD thresholds along with satellite observations and
vegetation modeling to find the impact of PM pollution on net primary
production (NPP) varies spatially from -3 to +6 %. When
accounting for the direct impacts of PM on light, temperatures, and
hydrology, Yue et al. (2017) find a net increase in NPP of 5 %.
The impact of PM on managed vegetation (crops) is less well studied than for
natural vegetation. PM can increase growth and production of crops when the
increase in efficiency outweighs the loss of SW radiation. This depends on
the local light conditions (changes in SW vs. DF) and crop type (C3
vs. C4). C4 crops such as maize are less likely to be light saturated
than C3 crops such as wheat. Niyogi et al. (2004)
find that the CO2 sink increases over croplands with an
increase in AOD. In contrast with their forest sites, Strada et al. (2015) find a decrease
in midday GPP of ∼ 17 % associated with high observed AOD
for a combination of US cropland and grassland sites. They attribute this
difference to canopy architecture which minimizes leaf shading, and thus the
impact of diffuse light, when the sun is overhead. Greenwald et
al. (2006) use relationships between DF (determined by climatological AOD)
and a crop's radiative use efficiency (RUE), a measure of how effective a
plant converts light into carbon, from Sinclair et al. (1992)
along with varying meteorology and a crop model to estimate the impact of PM
on crop yield. Assuming no restrictions on growth due to stresses at several
locations, they find a large variation in impacts based on the DF-to-ΔRUE
relationship chosen. Under the maximum response relationship, maize
increases by 0–10 %, wheat increases by 0–5 % and rice increases by
0–40 % under varying cloud conditions (Greenwald
et al., 2006). Using this approach, but with a combined atmospheric
chemistry and radiative transfer model to better simulate spatial and
temporal variability of PM impacts on radiation, Schiferl and Heald (2018) estimate a
global positive impact of PM of 12, 16, and 9 % on maize,
wheat, and rice production, respectively, for the year 2010. While this
study uses a simple representation of the PM impacts on crop productivity,
the approach isolates the impact of PM on crop production, which is not
easily estimated based on previous observational analyses or mechanistic
models. Observed AOD impacts on radiation are convolved with the influence
of clouds, and as it is difficult to isolate only PM impacts, we cannot
easily translate the observed AOD-to-carbon flux relationships to the
impacts of DF on RUE. Mechanistic model studies account for all land
biomass; however, such models do not differentiate between individual crop
characteristics (e.g., canopies, growing seasons). Furthermore, the observed
and simulated changes in NEE and GPP in these studies do not correspond
directly to crop production (the harvested biomass), but rather on carbon
uptake or release.
Industrial agriculture, driven by the need to produce food for a growing
human population, has modified the global nitrogen (N) cycle (e.g.,
Bouwman et al., 2013; Smil, 1999). By artificially fixing inert nitrogen gas
into reactive forms, humans have increased the fluxes of nitrogen throughout
the environment, including into the atmosphere, onto land, and into the
water (Galloway and Cowling, 2002). Nitrogen species
in the atmosphere, both reduced and oxidized, return to the surface through
deposition processes after being transported away from source regions.
Anthropogenic influences on this deposition change the nitrogen balance in
land and water ecosystems. In natural systems, this can cause acidification
and eutrophication, which negatively impacts the biosphere (Beem
et al., 2010; Erisman et al., 2007). Nitrogen accumulation into ecosystems
from deposition reduces biodiversity; secondary factors such as direct
toxicity, soil acidification, and increased susceptibility to stress can be
dominant locally (Bobbink et al.,
2010). While remaining substantially higher than during preindustrial time,
current rates of nitrogen deposition have recently declined over the US and
Europe but are expected to increase in developing countries in the future (Lamarque
et al., 2013). This will contribute to projected (for 2050) nitrogen
surpluses in Africa and Latin America corresponding with increases in crop
and livestock production (Bouwman et al., 2013).
By including the coupling of carbon and nitrogen in a land-surface model,
Thornton et al. (2007) show that GPP is limited by
the supply of nitrogen to the biosphere and simulate over 40 % less GPP
than a case that does not include this limitation. This carbon–nitrogen
coupling dampens the response of vegetation to CO2 concentration
increases by over 70 %. The addition of atmospheric nitrogen deposition
in the coupled system increases global GPP by ∼ 2 %. When
integrated into a fully coupled earth system model, there is a decrease in
carbon uptake from CO2 fertilization and an increase in carbon uptake
from climate warming from the interactions between carbon and nitrogen. This
increase in carbon uptake is due to enhanced nitrogen mineralization in the
soil from a higher rate of decomposition (Thornton et al., 2009). Thomas et al. (2013) show that
these simulated carbon–nitrogen responses for forests are smaller than those
observed. Their model modifications result in greater retention of nitrogen
deposition in biomass and a tighter coupling between nitrogen deposition and
rising atmospheric CO2 concentration. The model better represents
observations by increasing the aboveground carbon storage response to nitrogen deposition.
Nitrogen deposition can also impact crop production, by providing additional
fertilization, increasing yields in areas which are nitrogen limited
(Goulding et al., 1998). These areas include portions of Africa, South America,
India, and eastern Europe (J. Liu et al., 2010; Mueller et al., 2012).
Liu et al. (2013) show that in China, nitrogen deposition leads to increased nitrogen uptake in
non-fertilized croplands, resulting in a small increase in yield (1 t ha-1)
derived from a nitrogen uptake to yield ratio. Lassaletta et al. (2014) develop relationships
between observed total nitrogen input and crop yield on a countrywide basis,
but they do not disaggregate the impacts of deposition saying only that the
input from deposition is small, but not negligible. While
Ladha et al. (2016) estimate that 6 % of
nitrogen contained in global maize, wheat, and rice comes from deposited
nitrogen, to date, there has been no global study of the change of yield
associated with nitrogen deposition, with most studies concentrating on the
impacts of nitrogen deposition on interactions with atmospheric CO2 and
carbon storage. Folberth et al. (2016) neglect
nitrogen deposition in their study of soil and meteorological data
uncertainties in crop models due to the lack of available deposition data in
a form suitable for global crop models.
Finally, PM and nitrogen deposition are connected: the release of excess
nitrogen from fertilizer application and livestock production in the form of
ammonia (NH3) contributes to PM formation in the atmosphere under
acidic conditions (Seinfeld and Pandis, 2006). Nitric acid (HNO3),
an oxidized form of nitrogen oxides (NOx) emissions from
mobile and industrial sources, contributes both to the nitrogen burden and
these acidic conditions. Nitrogen can also be incorporated into PM as organic
nitrates when biogenic volatile organic compounds (BVOCs) react with
NOx (Mao et al., 2013).
Recent global modeling studies incorporate more complex nitrogen
transformations and cycling, such as the implementation of bi-directional
ammonia fluxes into atmospheric chemistry models (Zhu et al., 2015) and
climate-dependant agricultural nitrogen pathways into earth system models
(Riddick et al., 2016).
Schiferl and Heald (2018) quantify the
impact that air quality (ozone and PM) has on current and future global crop
production. Their analysis, while consistent with the approach generally
applied to estimate air quality impacts on crops in previous studies
mentioned above, fails to account for the set of physical and biological
restrictions placed on crop growth and production. In particular, they
consider crop production enhancement due to the diffuse effect of PM to be
unlimited. However, water and nitrogen stresses and physiological caps
placed on crop production may dampen these responses. This study is a direct
follow-up to Schiferl and Heald (2018),
where we employ a crop model to simulate the enhancements in crop production
associated with PM and nitrogen deposition simulated by an atmospheric
chemistry and radiative transfer model and explore the potential impact of
resource and physiological constraints on this production.
GEOS-Chem atmospheric chemistry model
The GEOS-Chem model (http://www.geos-chem.org, last access: June 2015) simulates
the global concentration of gases and particles in three dimensions.
Simulated PM concentrations are read into the Rapid Radiative Transfer Model
for GCMs (RRTMG) to estimate the impact of PM on radiation throughout the
atmosphere (Heald et al., 2014). Together these models are referred to as GC-RT. The model
version and setup used here is the same as for the standard 2010 emissions
scenario described by Schiferl and
Heald (2018). In brief: v10-01 of GC-RT is run at 2∘ × 2.5∘
horizontal resolution using GEOS-5 meteorology for the years 2009
and 2010 from the NASA Global Modeling and Assimilation Office (GMAO).
In this study, PM refers to the sum of all simulated aerosol species:
sulfate (SO42-), nitrate (NO3-), ammonium (NH4+),
black carbon (BC), organic carbon (OC), sea salt, and
dust. Inorganic aerosol thermodynamics are coupled to an
ozone–VOC–NOx–oxidant chemical mechanism, where ISORROPIA II
(Fountoukis and Nenes, 2007) handles the gas–particle phase
partitioning of ammonium nitrate. GC-RT simulates wet and dry deposition of
both aerosols and gases (Amos
et al., 2012; Liu et al., 2001; Wang et al., 1998; Zhang et al., 2001).
Major global anthropogenic gas emissions come from the Emission Database for
Global Atmospheric Research (EDGAR) v4.2 (NOx, carbon monoxide – CO,
sulfur dioxide – SO2), the Reanalysis of the TROpospheric chemical
composition (RETRO) inventory (non-methane VOCs; Hu et al., 2015), and the Global
Emission Inventory Activity (GEIA) inventory (NH3). These are overlaid
by regional inventories where available (see Schiferl and Heald, 2018, for details).
Additional NOx emissions are from lightning and soil, described by
Murray et al. (2012) and Hudman
et al. (2012), respectively. Directly emitted aerosol sources include
anthropogenic BC and OC (Bond
et al., 2007; Leibensperger et al., 2012), dust (Fairlie et al., 2007), and sea salt
(Jaeglé et al., 2011).
GC-RT uses a bulk aerosol scheme, where each aerosol species is described by
a fixed log-normal size distribution, the physical and optical properties of
which are described in Heald et al. (2014) and
are accounted for in the radiative transfer scheme. PM sizes in GC-RT span
several orders of magnitude, with mean diameters that range, for example,
from BC, 0.04 µm, to sulfate, 0.14 µm, to dust, 8 µm.
In this study, we use the hourly output of surface SW radiation and the diffuse
and direct portions of this SW radiation from GC-RT both with and without PM
under all-sky (real time variation in cloudiness) conditions. These are used
to calculate the DF of the SW radiation. While SW and DF respond differently
to the differing properties of each PM type, here we consider the net effect
of all PM. The impacts of PM described in this study account only for the
direct radiation changes through light absorption and scattering, and do not
consider secondary feedbacks of aerosol on clouds, meteorology, and
hydrology. We also use daily output of nitrogen deposition flux from the
atmosphere, including the wet and dry deposition simulated for all nitrogen
species. Nitrogen mass deposited from five species, ammonia, ammonium,
nitric acid, nitrate, and nitrogen dioxide (NO2), make up 98 % of
the total simulated nitrogen deposition for 2010. Both the PM impacts on
surface radiation and the nitrogen deposition flux from the atmosphere are
derived from the same GC-RT simulation, providing consistency over the
emissions, chemistry, and deposition schemes described above.
pDSSAT crop model
Model description
We use the Decision Support System for Agrotechnology Transfer (DSSAT) v4.6
crop system model (Hoogenboom et al., 2015), along with the parallel System
for Integrating Impact Models and Sectors (pSIMS) v2.0
(Elliott et al., 2014), together called
pDSSAT, to simulate the global production of maize, wheat, and rice. DSSAT
provides a unified interface which combines various crop simulation models
(Jones et al., 2003). Inherently a
point model, DSSAT uses daily meteorological data (minimum temperature,
maximum temperature, precipitation, solar radiation, wind speed, and
relative humidity) along with soil and management information at a given
location. The model then calculates a crop yield at harvest taking into
account soil–plant–atmosphere dynamics throughout the growing season. Plant
growth, in our case, is determined by the Crop Environment Resource
Synthesis (CERES) model module for each crop. CERES modules, developed
separately for maize, wheat, and rice, simulate the carbon and nitrogen
pools, among other parameters, associated with the various plant parts
(e.g., leaves, stems, roots, grain) throughout the growth stages of each
crop type. Potential dry matter (carbon) production is determined as a
function of the solar radiation, SW (see Eq. 1). The actual dry matter
production at each time step is limited by the effects of non-optimal
temperature, water stress, and/or nitrogen stress, if applicable. Water and
nitrogen stresses are determined by comparing the requirements of each crop
with the amount of each resource available to the plant. Dry matter produced
is then distributed into the plant parts based on those associated with the
growth stage at that time. The sensitivity of growth rates and physical
limitations for each plant part during each growth stage is determined by
the physiology of that crop and cultivar (Jones et al., 1986; Ritchie et
al., 1998; Ritchie and Otter, 1985). The simulation of these individual
plant parts, rather than only total carbon, is of critical importance for
this study as we are concerned with the production of grain to address
impacts on food security. A recent review of CERES performances for maize,
wheat, and rice finds that the models reproduce observed grain yield well,
with relative errors of ∼ 10, ∼ 20, and
∼ 10 %, respectively (Basso et al., 2016).
They also find that secondary parameters such as soil temperature and
nitrogen cycling were much less well represented.
pSIMS allows for the globally gridded simulation of crop yield by running
DSSAT in parallel at various grid boxes using consistent data and setting
input methods (Elliott et al., 2014). In our
study, we set pDSSAT to run at 0.5∘ × 0.5∘
horizontal resolution. This is only limited by the availability of suitable
global input data. pDSSAT uses daily meteorological information from AgMERRA
(Ruane et al., 2015), a version of the NASA
Modern-Era Retrospective Analysis for Research and Applications (MERRA)
product developed for use in the Agricultural Model Intercomparison and
Improvement Project (AgMIP; Rosenzweig et
al., 2013). We note that this meteorological product is closely related to
the GEOS-5 product which drives the GC-RT simulations. Soil inputs come from
the Global Soil Dataset for Earth System Modeling (GSDE; Shangguan et al., 2014). Additional
required information includes the range of planting dates (Portmann
et al., 2010; Sacks et al., 2010), distribution of cultivars (based on local
growing degree days – GDD), and fertilizer application amounts at each grid
box. We use fertilizer information from the
Spatial Production Allocation Model (SPAM; You et al., 2012). We highlight that
direct fertilizer application is the only source of nitrogen supplied to
crops in the pDSSAT model in addition to the baseline nitrogen content in
each soil layer given by GSDE. Except for the soil inputs, which are
modified in pSIMS v2.0, the pDSSAT input data listed above are consistent
with those used by the Global Gridded Crop Model Intercomparison (GGCMI)
portion of AgMIP (Rosenzweig et al., 2014).
Integration of GEOS-Chem with pDSSAT
Using the hourly SW, diffuse, and direct radiation output from GC-RT, we
calculate the daily mean daytime (SW > 0) SW and DF for each
GEOS-Chem grid box (2∘ × 2.5∘ horizontal
resolution) for all of 2009 and 2010. We group the nitrogen deposition
fluxes of individual species into two groups, reduced nitrogen (NHx)
and oxidized nitrogen (NOy), and calculate the daily total flux for
each group for the same time period. The daily SW and DF values, along with
the daily NHx and NOy deposition flux values, are regridded to the
pDSSAT grid and resolution using area-weighted regridding and integrated
into the input meteorology.
For the PM simulations, the daily SW and DF are used in the pDSSAT
crop-specific plant growth modules to modify the potential carbon
production. Following each crop-specific CERES growth module, Eq. (1) is used
for maize and wheat, and Eq. (2) is used for rice:
Pcarb∝0.5×SW×RUEs,DF,Pcarb∝(0.5×SW)0.65×RUEs,DF,
where Pcarb is the potential carbon production, SW is the daily mean
shortwave radiation from GC-RT, and RUEs is crop-specific radiation use
efficiency (Ritchie et al., 1998). For simulations
with PM affecting SW and DF, SW modified by PM from GC-RT is used as input
for the relationships in Eqs. (1) and (2) only and is not used in other
functions dependent on solar radiation, such as evaporation (i.e., the GC-RT
SW without PM remains applied to these processes). In this study, we apply
only the maximum DF-to-ΔRUE relationship discussed in Schiferl and Heald (2018) to modify the
RUEs based on the DF, where max ΔRUE = 100 % at DF = 0.8
(Greenwald et al., 2006). This represents the upper limit of potential PM impacts on
crop production. We note that additional processes which impact plant
productivity, such as evapotranspiration and water use efficiency, have also
been shown in both observations and simulations to be affected by changes in
DF (Lu et al., 2017; Wang et al., 2008). These second-order effects may dominate
the crop response under certain conditions and therefore should be included in
any assessment of the overall environmental impacts on crop growth. However,
the goal of this work is to explore only the direct impact of radiation
changes (due to PM) on crop productivity, enabling a comparison with
Schiferl and Heald (2018).
For the nitrogen deposition simulations, NHx and NOy fluxes are
applied daily as an additional source of fertilizer to the surface layer of
the soil as NH4+ and NO3-, respectively, due to their
similar behaviors in soils (Ladha et al., 2016).
We apply these deposition fluxes beginning 30 days prior to the planting
date at each location. The timing of this initiation is uncertain, as the
fate of deposited nitrogen is not well constrained, and the impacts of
nitrogen deposition can be assessed over a single growing season to
multi-year timescales (Goulding et al., 1998). Our
selection of 30 days is therefore somewhat arbitrary. We discuss the impact
of this assumption in Sect. 4.2.
Base simulation
We configure pDSSAT to run for 2009 and 2010 with water and nitrogen stress
turned off. Our modification for potential carbon production using input
from GC-RT is applied to SW only (with SW values from GC-RT without PM).
Maize, wheat, and rice are simulated independently. We sample the results
for each crop for the growing season ending in 2010. For example, crops
planted in Northern Hemisphere spring and harvested in fall are grown
entirely within 2010, while winter crops are planted in fall 2009 and
harvested in spring 2010. These planting and harvest dates are determined
within pDSSAT by the life cycle characteristics of each crop and vary based
on the location-specific meteorological (e.g., GDD, timing of rainfall) and
resource (e.g., fertilizer amount, irrigated vs. rainfed) inputs for that
simulation. For a consistent comparison, we determine crop production by
multiplying the pDSSAT crop yield by the crop area from the Global
Agro-Ecological Zones (GAEZ) assessment for 2000 (FAO, 2016) scaled to 2010
as in Schiferl and Heald (2018), rather
than by using the internal pDSSAT harvested area parameter. The results from
this simulation, our base simulation, are shown in Fig. 1. Also as in
Schiferl and Heald (2018), our
figures focus on the industrialized areas of the Northern Hemisphere, which rely
heavily on maize, wheat, and rice, though all numbers presented are global.
Since our base simulation has no restrictions on water and nitrogen (both
the nitrogen supply and irrigation are unlimited), the simulated crop
production vastly surpasses that from GAEZ. For maize, this is 2062 Tg from
pDSSAT compared to only 871 Tg from GAEZ. Simulated wheat production is
2591 Tg, and simulated rice production is 1250 Tg compared to GAEZ values of
667 and 705 Tg, respectively.
We rerun the crop model with water stress only, nitrogen stress only, and
both stresses together to characterize the sensitivity of the base
simulation to these resources (Fig. 1). Water stress occurs when the amount
of soil water available is below the potential transpiration rate of the
plant. For maize, the negative effect of water stress on production is most
evident in the US Plains and northern China and produces a
29 % production reduction globally. The effect of water stress is globally larger
on wheat (40 % reduction), and is largest in the southern US plains,
northern China, and throughout western Asia. Rice production is
impacted the least by water stress, with only a 14 % reduction in
production when imposing water stress, mostly in northern India. Water
stress is dependent on the precipitation prescribed from the meteorology of
that growing season, so these results will vary from year to year.
Top row panels: crop production from base pDSSAT scenario (GC-RT SW
only, no PM) with no stress applied for growing season ending in 2010.
Difference in crop production due to water stress (second row panels),
nitrogen (N) stress (third row panels), and both water and N stresses (bottom row panels).
For each row, maize (left column panels), wheat (middle column panels), and
rice (right column panels) are shown. Filtered for GAEZ base crop production greater than
0.01 Mg km-2. Global production (top row panels) or relative production
change (second row–bottom row panels) shown in upper right of each map.
![]()
Nitrogen stress occurs when the plant tissue nitrogen concentration is less
than the critical nitrogen concentration determined to provide optimal
growth. In our base simulation, nitrogen stress follows different patterns
compared to water stress for many regions and crops, although the global
magnitudes in production reduction are similar. This response to
carbon–nitrogen coupling is similar in sign and magnitude as that found for
global GPP by Thornton et al. (2007). Maize
production is affected by nitrogen stress primarily in the US plains and
the American Midwest. Nitrogen stress for wheat is distributed into all regions, while
the effect on rice production is again lowest globally, it is largest in
Southeast (SE) Asia. We note that the apparent impact of nitrogen stress on
maize in Midwestern US is magnified by the large crop area in this region.
Nitrogen stress is more similar from year to year in the model as fertilizer
application, which provides nitrogen to the soil, and inherent soil nitrogen
content is identical for all simulation years. Small variations do exist as
variable temperatures and radiation impact the onset of crop growth stages
and use of nitrogen. Folberth et al. (2016) find
that uncertainty in soil data can impact simulated crop yield variability
more than meteorological variability, especially for no water stress
(irrigated) and high nitrogen stress areas. In contrast, they find that
irrigated areas with high nitrogen inputs show little difference between
yield due to soil and meteorological input variability. Total production
change due to both water and nitrogen stress does not combine linearly. This
illustrates the interconnected system simulated by the crop model. Overall,
these environmental and management constraints greatly reduce global crop
production from its unstressed potential. They are important to consider
when analyzing the impact of PM and nitrogen deposition on crop production.
Mean change in daytime (SW > 0) downward SW
radiation (top row panels) and DF of the SW radiation (bottom row panels) at the surface due to PM
from GC-RT. For pDSSAT growing season (determined by the base simulation)
ending in 2010 for maize (left column panels), wheat (middle column panels), and
rice (right column panels). Filtered for GAEZ base crop production greater than
0.01 Mg km-2.
![]()
Change in pDSSAT crop production due to PM with max ΔRUE = 100 %
with no stress (top row panels) and water and nitrogen (N) stresses (bottom row panels)
applied. For growing season ending in 2010 for maize (left column panels),
wheat (middle column panels), and rice (right column panels). Filtered for GAEZ base crop
production greater than 0.01 Mg km-2. Global relative production change
shown in upper right of each map.
![]()
Regional relative change in crop production due to PM with
maxΔRUE = 100 %: offline analysis from Schiferl and Heald (2018)
(blue bars with hatching), pDSSAT simulation with no stress (dark blue bars),
and pDSSAT simulation with water and nitrogen stresses (light blue bars). Change
due to nitrogen (N) deposition in orange. For growing season ending in 2010 for
(a) maize, (b) wheat, and (c) rice. Regions with a
base production lower than 5 % of the global total are not shown.
![]()
Results
Impact of particulate matter on crop growth
To simulate the effect of PM on crop production, we run pDSSAT as above
(for 2009 and 2010, sampling to the growing season ending 2010) with SW and DF
input from GC-RT with and without PM. The differences in SW and DF due to PM
over the pDSSAT growing season (determined by the base simulation) are shown
in Fig. 2. PM has a negative effect on SW everywhere and positive effect on
DF. The largest influence of PM is over China for all three crops. The
influence is especially noticeable for wheat, where a growing season over
the winter corresponds with higher PM concentrations. The difference between
the simulations with and without PM is the change in production due to PM,
and this is shown in Fig. 3. We perform this procedure first with no stress
factors applied in order to compare to the results found in Schiferl and Heald (2018), referred to
here as the “offline analysis”. The offline analysis uses a relativistic
methodology which allows for unlimited growth enhancement (or loss) and is
determined by the accumulated PM impacts throughout the growing season. In
this pDSSAT simulation with no stress applied, global maize production
increases by 1.7 %, wheat increases by 17.0 %, and rice increases by
6.2 %. Wheat production in the Indian and China + SE Asia regions is most
affected by PM, and the regional proportional change is shown in Fig. 4. For
wheat and rice, the proportional enhancement in crop production due to PM
simulated with pDSSAT is very similar to that found in the offline analysis
(Fig. 4). This is true globally and
within each region. The pDSSAT scenario with no stress is closely related to
the offline analysis, which was unrestricted in production enhancement, so
this good comparison is expected.
Unlike for wheat and rice, the proportional increase in maize production due
to PM simulated by the pDSSAT model is much lower than that from the offline
analysis. This can be explained by a physiological restriction within the
model which limits the maximum number of kernels per maize plant based on
its genetic potential. Within pDSSAT, hybrid cultivars are limited to about
900 kernels per plant, while open-pollinated cultivars are limited to about
550 kernels per plant. When the maximum number of kernels per plant is
reached, biomass is redistributed to other parts of the plant, such as the
roots and stems. In the scenario above with no stress, PM only produces a
1.2 % increase in maize production over the US (Fig. 5), a region of
substantial, high-intensity maize production. For most locations in this
domain, pDSSAT simulates the maximum maize production dictated by the kernel
number both with and without PM. When we artificially increase the limit by
500 kernels per plant, an arbitrary amount chosen for illustrative purposes,
the maize production increases, as expected. Production without PM increases
by 25 %, and production with PM increases by 34 %. This results in an
8.4 % increase in maize production due to PM over the US under no stress,
which is similar to the approximately 10 % increase found in the offline
analysis. This dependence on a kernel limit demonstrates the importance of
including physiological limitations to growth as represented in a crop
production model when addressing the air quality impacts on crops.
To investigate the more realistic effect of PM on crop production, we impose
both water and nitrogen stress on our pDSSAT simulations. The results for
this scenario (Figs. 3 and 4) indicate an 11 % increase in global wheat
production due to PM and a 3.4 % increase in rice. These proportional
enhancements are about one-third lower with stresses for wheat compared to
without and about one-half for rice. While similar declines occur on a
regional basis, these stresses have a larger impact on India for wheat,
where nearly one-half of additional simulated wheat production is lost. For
maize, including stress factors under the standard kernel restriction lowers
the total production with and without PM, but allows for a larger
proportional change due to PM in most areas (i.e., 1.7 % global
production increase without stress, but 2.3 % with stresses) as more
areas are producing below the production limit. When additional kernels are
allowed with stresses turned on, production due to PM also increases, but to
a lesser percentage compared to without stress (not shown).
For pDSSAT base production (top row panels) and production
with increased maximum kernels per plant (bottom row panels) under no stress: maize
production with no PM (left column panels), maize production with PM (with
maxΔRUE = 100 %) (middle column panels), and maize production due to PM
(right column panels). Filtered for GAEZ base crop production greater than 0.01 Mg km-2.
Global production (left and middle column panels) or relative production change
(right column panels) shown in upper right of each map.
![]()
Total nitrogen (N) deposition from GEOS-Chem (top row panels) and
reduced nitrogen (NHx) fraction of this total (bottom row panels).
For pDSSAT growing season (determined by the base simulation) ending in 2010
for maize (left column panels), wheat (middle column panels), and rice (right column panels).
Filtered for GAEZ base crop production greater than 0.01 Mg km-2.
![]()
Change in pDSSAT crop production due to nitrogen (N) deposition with
N stress (top row panels) and water and N stresses (bottom row panels) applied. For
growing season ending in 2010 for maize (left column panels), wheat (middle column panels),
and rice (right column panels). Filtered for GAEZ base crop production greater than
0.01 Mg km-2. Global relative production change shown in upper right of each map.
![]()
Impact of nitrogen deposition on crop growth
To quantify the impact of nitrogen deposition on crop production, we run
pDSSAT as above (for 2009 and 2010, sampling to the growing season ending 2010)
with NHx and NOy atmospheric deposition fluxes from
GEOS-Chem and compare the results to the base simulation, which contains no
atmospheric nitrogen deposition (only direct fertilizer application). In
this case, we do not consider PM impacts on radiation. The total nitrogen
deposition flux for each crop over the base simulation growing season is
shown in Fig. 6. There is high nitrogen deposition in India and China for
all three crops, but especially wheat in China. The magnitude of nitrogen
deposition from GEOS-Chem is generally lower than that applied as fertilizer
in pDSSAT. For example, two fertilizer applications for maize span roughly
50–100 kg ha-1 each over the US, Europe, and China, whereas nitrogen
deposition during the growing season rarely exceeds 20 kg ha-1 in China
and India. However, nitrogen deposition is continuous, while fertilizer
application is sporadic and limited temporally. We also plot the fraction of
total nitrogen deposition made up of NHx in Fig. 6. This fraction is
slightly higher in agricultural areas of the US, Europe, and China, where
reduced species from agriculture mix with oxidized species from industry. In
India, the NHx fraction is very high, as there is little industrial
emission to offset the large agricultural emissions. While we do apply
reduced and oxidized nitrogen deposition (NHx vs. NOy) separately
in our simulations, this separation has little impact on our results as soil
nitrification quickly converts all soil NH4+ into NO3-
in the pDSSAT model.
When accounting for both nitrogen and water stress, crop production
increases globally by 1.9 % for maize, 1.8 % for wheat, and 1.9 %
for rice due to atmospheric nitrogen deposition applied beginning 30 days
before the planting date (Fig. 7). The largest impact of nitrogen deposition
is for wheat in China, which receives large amounts of nitrogen through
deposition and is highly sensitive to nitrogen stress (Fig. 1). Nitrogen
deposited to the surface accumulates in the soil throughout the growing
season, moving quickly to lower levels of the soil profile. When fertilizer
applied toward the beginning of the growing season runs out, this additional
nitrogen reservoir from deposition allows for a mitigation of nitrogen
stress and furthers plant growth. The fate of nitrogen in soil is not well
constrained, and the length of time nitrogen is retained in the soil and
useful to the plant is uncertain. The regional impacts of nitrogen
deposition on crop production for this scenario are shown in Fig. 4. In all
cases, except for Indian rice, the nitrogen deposition effect is proportionally smaller
than the enhancing effect of PM (disregarding the European
maize simulation, which is restricted by kernel density).
When we apply nitrogen deposition to pDSSAT at the onset of the growing
season, rather than 30 days prior to planting, we find that the impact of
nitrogen deposition is dampened somewhat (production enhancement due to
nitrogen deposition is then 1.6 % for maize, 1.5 % for wheat, and 1.5 %
for rice). Conversely, applying nitrogen deposition in the crop model
earlier enhances the increase in crop production. pDSSAT could be configured
to run in series over numerous years, as done by H. L. Liu et al. (2010),
to simulate the long-term impacts on nitrogen cycling, but the uncertainty
regarding the timing and retention of nitrogen deposited onto soils would
remain, especially if not evaluated against observations.
If water stress is removed, the proportional enhancement of nitrogen
deposition on crop production is slightly higher, as shown in Fig. 7. The
largest change is for wheat, which is more water stressed than maize and
rice in the model globally.
Discussion and conclusions
To our knowledge, this is the first effort to integrate atmospheric air
quality inputs into the dynamic simulation of a crop model. While ozone and
PM air pollution have been incorporated into models which examine plant
productivity, a crop model is needed to quantify the impacts on crop yield
(not total biomass), the critical factor for understanding food security.
This study takes into account crop-specific effects using the individual
characteristics and distribution of each crop and the air pollution specific
to the time frame when each crop is grown. In this way, we produce a better
constrained assessment of the impacts of PM (radiation) and nitrogen
deposition on crop production.
Using restrictions on water and nitrogen availability as well as physiological
limitations from the crop model provides a more realistic estimate of the
impact of PM on crop production than in our earlier work which considered no
such restrictions (Schiferl and Heald,
2018). Maize production increases by only 2.3 % due to PM (11.5 % in
Schiferl and Heald, 2018) using the
max ΔRUE = 100 % relationship, while wheat increases by
11.0 % (16.4 %) and rice increases by 3.4 % (8.9 %). The positive
effect of PM on crop production is lessened when considering realistic
restrictions to crop growth, but remains significant throughout the globe,
especially in northern China. While it is difficult to compare across
studies with varying approaches and metrics, our results are consistent in
sign with the change in the CO2 sink for crops due to PM found by
Niyogi et al. (2004) and the global GPP change due to PM found by Strada and
Unger (2016), noting that CO2 and GPP are not necessarily consistent
with crop yield. We also find similar enhancements on a regional scale as
Strada and Unger (2016), but for
different regions, China and India in our case, as we do not consider the
forested areas which dominate their results. For maize and wheat, the
proportional increase in production is larger than the NPP increase found
for all vegetation in China by Yue and Unger (2017). Our crop
model is generally less responsive to PM than those enhancements found in
forests in the locations studied by Cirino
et al. (2014) and Strada et al. (2015), which is consistent with the smaller
canopies of crops. However, we are inconsistent in sign with the negative
response in GPP due to PM found by Strada et al. (2015), although their
study convolves croplands with grasslands.
Given that PM is simulated using current emissions (2010), these
enhancements are already folded into present-day crop production and may
therefore be important to consider for air quality policy decisions which
would reduce PM and thereby reduce production in areas with crops sensitive
to PM. For example, the decline in PM associated with the recent decrease in
US SO2 emissions has been shown to reduce US GPP by over 1 %
since 1995 (Keppel-Aleks and Washenfelder,
2016). While this amount is small and aggregated for productivity over a
large area, the impact of future PM change may be larger and more important
to consider over a concentrated, highly polluted area. However, we note that
our results assume the maximum sensitivity of crops to PM and therefore the
impact of PM on food production may be more modest, especially when
considering secondary effects of PM (e.g., hydrological, meteorological)
which may offset such enhancements. More laboratory work is needed to
understand how different crop varietals respond to changes in radiation
throughout the growing season.
Our coupling of an atmospheric chemistry model with a crop model also
provides an opportunity to explore the impact of atmospheric nitrogen
deposition on crop production. We find that the impact of nitrogen
deposition on crop production is significant, but more modest than the
effect of PM. Our results are consistent with Thornton et al. (2007), who find
an ∼ 2 % enhancement of global GPP due to nitrogen deposition. For crop yield,
the impact of nitrogen deposition we find is also consistent in sign with
Liu et al. (2013) over China. We
underpredict the effect of nitrogen deposition on crops compared to the
metric of sourced nitrogen content used by Ladha
et al. (2016). This may be due to our relatively short assumed nitrogen
deposition time frame. The fate of nitrogen in soil in managed ecosystems is
a key uncertainty in estimating the response of crop production to changing
atmospheric nitrogen deposition.
A future with enhanced fertilizer inputs to feed growing populations will,
if applied in excess, increase nitrogen inputs through deposition as well,
potentially enhancing crop production further. At the same time, lower
future NOx emissions are likely due to regulatory efforts, which will
reduce the nitrogen deposition flux. These reductions could also reduce PM
in areas prone to ammonium nitrate formation. The future trajectory of
nitrogen deposition and PM remain uncertain, and thus the net impact on
global crop production is unclear. An increased understanding of the
implications of nitrogen deposition on crop production may also lead to
better optimization of fertilizer application in areas where this impact is substantial.
The crop model responses to DF and nitrogen deposition examined in this
study are uncertain and may vary from year to year. More work is needed,
particularly controlled laboratory studies, to understand and evaluate these
responses. It is critical to develop realistic crop models with reliable
sensitivity to environmental factors to understand the pressure on future
food security. Crop models tuned to reproduce observed yields without
accounting for PM impacts (both direct and secondary) and nitrogen
deposition may be less reliable under future levels of air pollution.