Introduction
India is a rapidly developing nation. Population growth, urbanization and
industrial development have led to increasing emissions and have resulted in
a statistically significant increase in the tropospheric ozone mixing ratios
over the Indian subcontinent in the past decades .
Tropospheric ozone mixing ratios are expected to increase further in the
years to come .
Tropospheric ozone causes damage to crops at elevated levels, and crop yields
are extremely important to the Indian economy: 17 % of India's GDP
directly depends on agriculture and allied activities , and
54 % of the total and 72 % of the rural working population of India
still relies on agriculture as their main source of income
. As rural demand for a large range of consumer products
and cement depends directly on the year's crop yield, crop yields have a much
larger overall effect on the economy. Consequently, every 1 % decrease in
crop yields causes a 0.36 % decrease of India's GDP .
Moreover, India has to meet the challenge of feeding 17 % of the world's
human population with just 2.4 % of the world's geographical area and
4 % of its freshwater resources . Wheat and rice are the
most important food crops. In 2010 India produced 20.5 % of the world's
rice and 12.4 % of the world's wheat. India is also a major producer of
fibre crops (26 % of the world's fibre crops; ), which
provide raw material to the domestic textile industry. Punjab, with an average
cropping intensity of 190 %, is considered to be the bread basket of
India. It contributes 17.4 % to India's wheat and 10.9 % to India's
rice production and produces 60 % of the wheat and 30 % of the rice
procured and redistributed by the Department of Food and Public Distribution .
Therefore, it is extremely important to quantify crop losses due to ozone in
the north-west Indo-Gangetic Plain (NW-IGP) accurately.
Ozone effects on plants
Extensive plant damage due to tropospheric ozone was first observed during
the Los Angeles smog episodes. In the early 1950s,
reported that such plant damage could
be reproduced in the laboratory by the reaction of organic trace gases or car
exhaust with nitrogen oxides (NOx) in the presence of sunlight .
The influence of ozone on vegetation is dependent on the ozone dose and plant
phenotype . Ozone enters leaves
through plant stomata during normal gas exchange in the daylight hours and
impairs plant metabolism, leading to yield reduction in agricultural crops
.
In certain phenotypes, ozone exposure interferes with the hormone levels in
plants and has been shown to lead to the accumulation of ethylene in the leaves.
The presence of ethylene in the leaves interferes with the functioning of the
hormone abscisic acid (ABA). ABA is a hormone which normally controls stomata
closure and reduces water loss under drought conditions
. Consequently, such plant phenotypes, when exposed to
both drought and O3 stress, will continue to lose water despite the
potential for dehydration. Ozone-related crop yield losses in such phenotypes
may be enhanced in rain-fed regions, where kharif cops are frequently exposed
to mid-season drought during the monsoon season. On the other hand, the yield of
rice cultivars that show a healthy response to drought stress (i.e. close
their stomatal aperture under water stress) could substantially benefit from
the system of rice intensification (SRI) cultivation practice
in areas with high ozone mixing ratios. Paddy fields under
SRI cultivation are irrigated only when rice plots dry too much and the crop
starts withering. A healthy response of rice plants to soil drying would
reduce the ozone uptake. This could explain the higher yields frequently
observed for SRI plots during field trials as well as the spatial variability
in the yield difference between SRI plots and control treatments.
In phenotypes that are unable to control their stomata opening under ozone
stress, O3 enters the leaf. It acts as a strong oxidant causing
reactive oxygen stress (ROS) through hydrogen peroxide, superoxide, and
hydroxyl radicals that alter the basic metabolic processes in plants
. Ozone has been shown to destroy the
structure and function of biological membranes leading to electrolyte
leakage. This causes accelerated leaf senescence .
Moreover, ozone can cause pollen sterility or induce flower, ovule, or grain
injury and abortion . In such phenotypes ozone causes
visible leaf injury, senescence, and abscission . By
reducing the amount of healthy green leaf area available for photosynthesis,
the accumulated damage eventually reduces crop yield, even if the exposure
occurred at early vegetative stages of crop growth. Symptoms of ozone-associated leaf injury have been reported for 27 agricultural crops .
Certain other phenotypes respond to ozone stress by reducing their stomatal
aperture . While
this mechanism reduces the amount of ozone taken up by the plant and hence
the oxidative stress inside the leaves, it also decreases CO2 uptake,
leading to a reduction in photosynthesis. This affects the carbon transport
to the roots, reduces nutrient and water uptake and, as a result of this, limits
the storage of carbohydrates in the grains. Plants of this phenotype may show
little to no visible leaf damage and often allocate significant resources to defences induced following ROS, but crop yields might be very sensitive to
O3 stress during the grain filling stage. reported
that for different wheat cultivars, the phenotypes with the least visible leaf
damage were often the ones showing a maximum reduction in crop yield due to ozone.
The ozone-induced physiological damage such as lower yields and inferior crop
quality lead to large economic losses .
Metrics to assess the impact of ozone on crop yields
Several large-scale programs targeted at assessing the impact of ozone on
crop yields have resulted in a variety of different exposure metrics . The
National Crop Loss Assessment Network (NCLAN) of the USA was the first
systematic and large-scale study to assess the impact of O3 on crops
in the world. It relied mainly on open-top field fumigation chambers (OTC)
and used seasonal mean daytime
exposure metrics (M7 and M12) to relate crop yield losses to ozone mixing
ratios .
European researchers and policy makers focused on the critical-level concept
as a tool to identify areas where the critical ozone levels are exceeded. The
accumulated exposure over a threshold of 40 nmol mol-1 (AOT40)
was adopted as a metric during a workshop in Kuopio, Finland, in 1996, and
a set of critical-level values based on this index has been adopted for
crops, forest trees, and semi-natural vegetation . AOT40 is
the most widely used exposure plant response index. It is used by the United
Nations Economic Commission for Europe (UNECE), the United States
Environmental Protection Agency (USEPA), the World Meteorological
Organization (WMO) and the World Health Organization (WHO) and is most
frequently used in modelling studies targeted at assessing crop yield losses
.
Recently stomatal-flux-based critical levels were proposed. These address
concerns that the AOT40-based critical levels are based on the concentration
of ozone in the atmosphere, whilst the ozone-related damage depends on the
amount of the pollutant reaching the sites of damage within the leaf. Models
using stomatal uptake of O3 (flux; F) or its cumulative value (dose; D)
have significantly improved the prediction of plant injury. In particular,
they have addressed the asynchronicity of maximum stomatal conductance
(gsto) and peak ozone in plants that close their stomata when
temperatures or the water vapour pressure deficit around the leaves are too
high .
The stomatal flux of ozone is modelled using a multiplicative algorithm adapted
from . This algorithm incorporates the effects of air
temperature, vapour pressure deficit of the air surrounding the leaves,
light, soil water potential, plant phenology and ozone concentration on the
maximum stomatal conductance, i.e. the stomatal conductance under optimal
conditions. The exposure–yield relationships based on this algorithm consider
the accumulated stomatal flux over a specified time interval as
PODY (the phytotoxic ozone dose over a threshold flux of Y nmol O3 m-2 projected leaf area (PLA) s-1, with Y
ranging from 0 to 9 nmol O3 m-2 PLA s-1;
). Studies evaluating the PODY-based exposure–yield relationship for a wide range of climate zones have emphasized the need
for a local parametrization of the stomatal-flux model
. To the
best of our knowledge, no parametrization for southern Asian wheat and rice has
been reported in the peer-reviewed literature. The wheat parametrization has
been developed using European cultivars , and for rice the
parametrization has been developed using only one Japanese rice cultivar,
Koshihikari , which is known for its ozone resistance
. Despite the fact that the stomatal-flux-based model is
recommended by the UNECE CLRTAP (Convention on Long-range Transboundary Air Pollution) for ozone risk
assessment in Europe , exposure–yield relationships have so
far been internationally agreed upon only for a limited number of crops .
This study
In the present study, we present new ozone exposure crop yield relationships
for Indian rice, wheat and maize cultivars derived through a review of the
peer-reviewed literature of open-top chamber studies on southern Asian cultivars.
We verify these new relationships using ozone monitoring data from the
atmospheric chemistry facility in Mohali and yield data from a number of
relay seeding experiments conducted in Punjab and Haryana. In these
experiments crops were unintentionally exposed to different ozone levels by
virtue of their sowing date being shifted, but the relevant studies were not
conducted to investigate the effect of ozone on yields and consequently they
did not include on-site ozone monitoring or clean-air control treatments.
We subsequently use a high-quality data set of in situ ozone measurements at a
regionally representative suburban site called Mohali and the newly derived
exposure–yield functions to assess ozone-related crop yield losses for wheat,
rice, cotton and maize for Punjab and the neighbouring state Haryana for the
years 2011–2013. Crop yield loss estimates calculated using two different
exposure metrics, AOT40 and M7, are intercompared for a number of sowing
dates and exposure–yield functions for the two major crop growing seasons of
kharif (June–October) and rabi (November–April).
Materials and methods
Site description and analytical details
All ozone measurements were performed at the IISER (Indian Institute of Science Education and Research) Mohali atmospheric
chemistry measurement facility (30.67∘ N, 76.73∘ E;
310 m a.s.l.; Fig. ). The measurement site is
regionally representative and located in the north-west
Indo-Gangetic Plain (NW IGP). Ozone measurements from several other sites
located in the IGP and the adjoining mountain regions (Fig. )
will be discussed in detail in Sect. to demonstrate that the
measurements obtained at the facility are, indeed, regionally representative.
Location of our site and surrounding sites for which ozone
measurements have been reported, superimposed on a land classification map
(courtesy ESA GlobCover 2009 Project).
The measurement site is located inside a residential campus of around
1.25 km2 with 800–1000 residents. Local influence is expected to be
significant only at low wind speeds (< 1 m s-1), which occur
only rarely . The predominant daytime wind
direction is west to north-west during winter, summer and in the post-monsoon season
and south to south-east during the monsoon season. The “fetch” region of air
masses arriving at the site is dominated by irrigated cropland (marked in
light blue in Fig. in the state of Punjab, north-west of the
site). During the monsoon season, south-easterly winds bring air masses from
a fetch region covering irrigated cropland in the state of Haryana,
south-east of the site.
At the measurement site, inlets and meteorological measurements are
co-located atop the ambient air quality station (AAQS) about 20 m
above ground. A comprehensive description of the site and its
representativeness for the north-west Indo-Gangetic Plain can be found in
, and a thorough description of the meteorology of the site
for all seasons can be found in .
Ozone was measured using UV absorption photometry at a time resolution of
one measurement every minute, with an accuracy that is better than 3 % and
an overall uncertainty of less than 6 %. Quality assurance of the large
data set was accomplished by regular calibrations using a NIST traceable ozone
primary standard generator and frequent zero drift calibrations. Over the
time span reported in this paper, zero drift always remained below
±0.5 nmol mol-1 between two subsequent zero drift
calibrations. The drift of the calibration factor during span calibrations
was usually less than ±3 % and always below ±8 %, even after
preventive maintenance. A detailed description of the ozone measurements and
the supporting meteorological measurements can be found in .
Calculation of ozone exposure metrics
We use two metrics to investigate the ozone exposure for crops in Punjab and
Haryana and derive southern-Asia-specific exposure–yield relationships for
wheat, maize and rice. These are the mean daytime surface ozone (M7) and
accumulated exposure over a threshold of 40 nmol mol-1 (AOT40).
The Mx metric is defined as the mean daytime 7 (M7) and 12 h (M12) surface
ozone concentrations during daylight hours, i.e. 09:00–15:59 and
08:00–19:59 LT respectively, in the crop growing season .
M7=1n∑i=1nO3ifor09:00–15:59LT
AOT40 is defined as the sum of differences between the hourly ozone
concentrations and 40 nmol mol-1 during the crop growing season
for [O3] > 40 nmol mol-1.
AOT40=∑i=1nO3i-40forO3>40nmolmol-1
Of these parameters M7 gives equal importance to all measurements and
accounts for the yield losses due to ozone concentrations of less than
40 nmol mol-1, while AOT40 gives a higher weight to high ozone
mixing ratios . Hence, the former will perform better
while evaluating plant damage and yield losses at low ozone concentration,
while the latter will capture the effect of events with very high O3
mixing ratios on plant physiology and yields better .
Missing data
For any long-term data set, gaps in the data are inevitable due to preventive
maintenance, calibrations and technical problems that arise from time to
time. The total number and percentage of missing hourly average ambient data
for each month from October 2011 to November 2013 are listed in
Table . For calculating AOT40 and M7, continuous and complete
daytime data are required, since any missing value can potentially lead to an
underestimation of the real ozone exposure. Hence, missing values need to be
filled in. For short data gaps of ≤ 3 h arising due to zero drift
calibration or span calibrations we interpolated the measurements before and
after the gap for filling in the missing values. Most gaps in the time series
are due to calibrations. For longer data gaps we calculated the average diel
ozone profile for the respective month and for each missing hour filled in
the monthly average ozone value of the respective hour. In most months less
than 5 % of the total hours were filled in. Only during the monsoon
season does the requirement to occasionally purge the system with dry zero air
lead to longer data gaps, and up to 21 % of the hourly averages had to be
filled using the method described above.
Cropping seasons and major crops in Punjab and Haryana
Rabi (winter season) and kharif (summer monsoon) are the two main
crop-growing seasons in northern India. In Punjab, kharif crops include rice,
cotton, maize, sugarcane and vegetables . During rabi
season wheat is grown in almost all of Punjab (> 90 % of the area).
In Haryana, kharif crops include rice, cotton and sugarcane and, in most of the
unirrigated areas of Haryana, pearl millet and sorghum .
Major rabi crops in Haryana include wheat, gram, sugarcane and mustard .
The most popular crop rotation systems in Punjab include rice–wheat
(> 70 %) and cotton–wheat (∼ 20 %) as well as maize–wheat crop rotation systems. In
Haryana rice–wheat (∼ 40 %) and cotton–wheat (∼ 20 %) rotation is popular
in the north, but in the dryer parts of Haryana, pearl-millet–mustard and
pearl-millet–wheat rotations are preferred . Maize is
currently not very popular but heavily promoted as an alternative to rice
when a deficient monsoon is anticipated.
Total number (N) of missing hourly average ambient data (mh), total number of hours per month (th), percentage (%) of missing hourly
average ambient data for each month and number of short (≤ 3 h) and
long (> 3 h) data gaps.
Month
mh/th
Missing
Short
Long
(N/N)
values
gaps
gaps
(%)
(N)
(N)
October 2011
2/672
0.3
2
0
November 2011
2/720
0.3
1
0
December 2011
4/744
0.5
2
0
January 2012
3/744
0.4
1
0
February 2012
1/696
0.1
1
0
March 2012
4/744
0.5
0
1
April 2012
45/720
6.3
2
1
May 2012
13/744
1.7
5
1
June 2012
3/720
0.4
2
0
July 2012
153/744
20.6
1
1
August 2012
57/744
7.7
2
1
September 2012
92/720
12.8
2
1
October 2012
8/744
1.1
2
1
November 2012
4/720
0.6
4
0
December 2012
33/744
4.3
2
2
January 2013
1/744
0.1
1
0
February 2013
1/672
0.1
1
0
March 2013
25/744
3.4
1
1
April 2013
5/720
0.7
2
0
May 2013
3/744
0.4
1
0
June 2013
108/720
15.0
1
3
July 2013
63/744
8.5
1
2
August 2013
73/744
9.8
1
1
September 2013
33/720
4.6
1
3
October 2013
42/744
5.6
1
1
November 2013
49/720
6.8
2
2
December 2013
2/672
0.3
2
0
January 2014
2/720
0.3
1
0
February 2014
4/744
0.5
2
0
The present study investigates crop yield losses for wheat and maize (rabi)
and rice, maize and cotton (kharif). In Supplement S1, we discuss the
growth stages during which these crops are potentially sensitive to ozone-related yield losses, as well as the time periods during which the plants
reach those growth stages in the northern Indo-Gangetic Plain. To summarize
briefly, different rice cultivars take between 90 to 140 days to reach
harvest maturity after the ∼ 20–30-day-old seedlings have been transplanted
into the fields. In this study we calculate the accumulated and average ozone
exposure (AOT40/M7) for a 4-month period (120 days), which is typical of
cultivars popular in the NW-IGP. We investigate the following five periods:
Period 1: 16 May (emergence) to 15 September (maturity);
Period 2: 1 June (emergence) to 30 September (maturity);
Period 3: 16 June (emergence) to 15 October (maturity);
Period 4: 15 April (emergence) to 15 August (maturity);
Period 5: 1 May (emergence) to 1 September (maturity).
Wheat cultivars take between 4 to 4.5 months from emergence to maturity. High
temperatures and water stress during the grain filling stage result in a
shorter growth period. Therefore, accumulated and average ozone exposure
(AOT40/M7) was calculated for a 4.5-month period for timely sowings and for a
4-month period for late sowings. We investigate the following five periods:
Period 1: 1 November (emergence) to 15 March (maturity);
Period 2: 16 November (emergence) to 31 March (maturity);
Period 3: 1 December (emergence) to 15 April (maturity);
Period 4: 16 December (emergence) to 15 April (maturity);
Period 5: 1 January (emergence) to 30 April (maturity).
For maize we investigate two periods for each of the growing seasons.
kharif:
Period 1: 15 June (emergence) to 15 September (maturity);
Period 2: 1 July (emergence) to 1 October (maturity).
Rabi:
Period 3: 1 January (emergence) to 31 March (maturity);
Period 4: 1 February (emergence) to 30 April (maturity).
For cotton, to cover the entire range of potential ozone damage, three
time windows are investigated:
Period 1: 1 May–15 December; three pickings;
Period 2: 31 May–15 December; three pickings;
Period 3: 1 May–31 December; four pickings.
It should be noted, however, that these time windows do not correspond to the
same number of pickings and more pickings will result both in higher yields
and a longer time window in which plants can accumulate damage.
Relationships between ozone dose exposure and yield
We derive specific exposure–yield relationships for Indian wheat and rice
cultivars using a two-pronged approach.
Firstly, we use our ozone measurements conducted at a suburban site in Punjab
and a number of field studies conducted in the region that reported
variations in the sowing date of crops
,
which lead to an unintentional change in ozone exposure, and one study that
reported co-located yield and ozone measurements to
derive an empirical exposure–yield relationship for rice and wheat. The
empirical field data support the need to revise the exposure–yield
relationship for Indian cultivars and demonstrate that for rice optimizing,
the sowing date can be a suitable strategy to minimize ozone exposure and
maximize crop yields.
Exposure–relative-yield (RY) relationships established in the
literature and comparison with our own exposure–relative-yield
relationships. RY stands for relative yield.
Crop
Index
Exposure–RY relationship
References
Rice
M7
RY = e-(M7/202)2.47/e-(25/202)2.47
AOT40
RY = -0.0000039 × AOT40 + 0.94
POD10
RY = 0.996 - 0.487 × POD10
; ozone-resistant rice
M7
RY = e-(M7/86)2.5/e-(25/86)2.5
this study;Indian rice cultivars
AOT40
RY = -0.00001 × AOT40 + 0.95
this study;Indian rice cultivars
Wheat
M7
RY = e-(M7/137)2.34/e-(25/137)2.34
; winter wheat
M7
RY = e-(M7/114)1.8/e-(25/114)1.8
; winter wheat
M7
RY = e-(M7/186)3.2/e-(25/186)3.2
; spring wheat
AOT40
RY = -0.0000161 × AOT40 + 0.99
POD6
RY = 1 - 0.038 × POD6
M7
RY = e-(M7/62)4.5/e-(25/62)4.5
this study;Indian wheat cultivars
AOT40
RY = -0.000026 × AOT40 + 1.01
this study;Indian wheat cultivars
Maize
M7
RY = e-(M7/158)3.69/e-(25/158)3.69
AOT40
RY = -0.0000036 × AOT40 + 1.02
AOT40
RY = -0.0000067 × AOT40 + 1.03
Indian maize;
Cotton
AOT40
RY = -0.000016 × AOT40 + 1.07
M7
RY = e-(M7/152)2.2/e-(25/152)2.2
Secondly, we derive India-specific exposure–yield relationships by plotting
relative yields (RY) and ozone exposure for all OTC studies on Indian
cultivars reported in the peer-reviewed literature and fitting the data to
obtain an exposure–yield relationship
. For
maize, only one OTC study on two Indian cultivars has been conducted, and we
use the fit of these data to obtain an exposure–yield relationship
. We compare these exposure–yield relationships for rice and
wheat with RY observed for cultivars commonly grown in Pakistan and
Bangladesh
to investigate to what extent the results can be extrapolated to all of southern Asia. We refrain from including cultivars popular in south-east Asia
in our study, as they have been reported to show a very different
sensitivity to ozone exposure . We provide an upper and
lower limit for RY and crop yield losses for a set of five different sowing
dates for rice and wheat, of three for cotton and of two for rabi and kharif maize, using both exposure-dose–response relationships established in several studies in
the west (Table ) to provide a lower limit and our new India-specific functions to provide an upper limit to the possible loss.
We use both the old AOT40-based exposure–yield function and our revised AOT40-based relationship to calculate crop production
losses and economic cost losses and contrast the two.
Yield loss and economic loss calculations
Table summarizes the ozone exposure-dose–response
relationships for relative yield loss (RYL) for wheat, rice, maize and cotton
based on AOT40 and M7 values collected from the peer-reviewed literature.
All the ozone exposure-dose–response relationships previously reported in
the literature are based on field studies conducted in the USA or in Europe.
Relative yield loss is defined as the crop yield reduction from the
theoretical yield that would have resulted without O3-induced damages
, calculated using Eqs. (3) and (4):
RYLi=1-RYi,CPLi=RYLi1-RYLi×CPi,
where RYi stands for relative yield in the year i,
CPLi stands for crop production loss in the year i and
CPi stands for the crop production of the same year. The crop
production per fiscal year was taken from the database of the .
Economic cost loss (ECL) for any crop is defined as the financial loss due to O3-induced damage in a given financial
year. The minimum ECL is calculated for different crops based on
corresponding minimum support prices (MSPs) of the same fiscal year using the following equation:
ECLi=CPLi×MSPi.
The MSPs are recommended by Commission for Agriculture Costs and Prices
and are announced by the Government of India
at the beginning of each season for each year. These prices are defined as
the fixed price at which government purchases crops from the farmers. All our
crops of interest come under the MSP valuation process. It should be noted,
however, that the MSP is typically approximately 50 % less than the
market value of the crop and often lower than the production costs. The upper
limit for the ECL is calculated using the relationship between CPL due to
deficient monsoon rains and the Indian GDP established by
using the following equation:
ECLi[%GDP]=RYLi[%]×0.36.
Results and discussions
Ozone seasonal cycle and monthly ozone exposure indices
Figure shows the seasonal box-and-whisker plot of the daytime
(08:00–19:59 LT) 1 h average ozone mixing ratios for the period from
October 2011 to January 2014. The highest ozone levels are observed in the summer
season in April, May and June, with median ozone mixing ratios of
60–80 nmol mol-1 and peak ozone mixing ratios of approximately
130 nmol mol-1. This is expected, as conditions such
as high temperature, low humidity and high solar radiation favour the
photochemical production of O3 regionally.
Seasonal box-and-whisker plot of the 1 h average daytime
(08:00–19:59 LT) ozone mixing ratios. Whiskers denote the monthly minimum
and maximum value, the box represents the upper and lower quarter value and the
horizontal line within the box represents the median.
After summer, the next highest ozone levels are observed during the post-monsoon
season (October and November), with median ozone mixing ratios of
50–60 nmol mol-1. The post-monsoon season is characterized by
lower levels of solar radiation (range of daytime maxima
∼ 480–720 W m-2) compared to the summer season (range of daytime
maxima ∼ 600–920 W m-2), but the occurrence of large-scale
agricultural burning emissions of ozone precursors and a lower boundary layer
still result in comparably high ozone levels.
The lowest median daytime ozone mixing ratios of approximately
30 nmol mol-1 are observed in August, during the peak monsoon season,
when cloudiness and wet scavenging of ozone precursors limits the
photochemical ozone production, and during peak winter (December and January).
During winter, a reduction in the solar radiation, low temperatures and fog
result in less photochemical production of O3.
Table shows the monthly increment in AOT40 and the monthly M7
for the period October 2011 to January 2014. The yearly maximum and minimum
monthly values for all indices correspond to the same months, May and August, in both years. All indices show maxima during summer (May and
June) and post-monsoon (October and November) and minima during the monsoon (July
to September) and winter (December to February); however, the difference
between the cumulative metric (AOT40), which gives higher weight to high
values and low or no weight to low values, and the average-based metric (M7)
comes out very clearly. For AOT40 the amplitude between peaks
(∼ 14 000 nmol mol-1 h) and minima
(∼ 500 nmol mol-1 h) is very high. The annual peak values are
30 times higher for AOT40 compared to the annual minima. For M7 peaks are only
2–3 times higher compared to the minima.
Few studies have so far reported ozone exposure indices over the IGP;
however, a number of studies have reported average diel profiles for each
month of the year or a time series of
average daytime ozone for their site .
Table shows the M7 or average daytime ozone calculated from
the data in those studies. The seasonality and monthly average daytime ozone
levels are similar for all urban and suburban sites in the IGP and the
adjoining mountain valleys. However, sites located further to the east report
lower M7 values during May and June, due to the higher frequency of summer
rain, lower temperatures and the earlier onset of the monsoon in the eastern part
of the IGP. The only site further to the west for which ozone measurements
have been reported is located close to the centre of the summertime “heat
low” over the NW IGP; it reports summertime and monsoon
season M7 that are higher than those observed at our site and also a strong
anticorrelation of the observed ozone during monsoon season with the
intensity of the monsoon rainfall.
Monthly values of M7 and increments in AOT40 for the period October 2011 to January 2014.
Month
AOT40
M7
October 2011
7770
71
November 2011
6150
63
December 2011
2879
46
January 2012
1705
39
February 2012
2729
47
March 2012
5391
57
April 2012
7286
64
May 2012
14 783
83
June 2012
12 544
77
July 2012
4005
49
August 2012
478
32
September 2012
2760
46
October 2012
6951
63
November 2012
5041
57
December 2012
1820
42
January 2013
1372
32
February 2013
1133
37
March 2013
3714
51
April 2013
7608
64
May 2013
13 381
80
June 2013
8123
63
July 2013
3014
46
August 2013
883
37
September 2013
3310
49
October 2013
4968
55
November 2013
4730
56
December 2013
2617
43
January 2014
1370
36
Given the fact that the most reliable crop-yield–exposure indices are based
on AOT40 and not M7 values, there is urgent need to relate the available
observations to AOT40 values. did so using a linear
relationship. When applied to our data presented in Table , the
relationship estimates reasonable AOT40 values (slope AOT40 predicted
vs. AOT40 observed: 0.93; R2 = 0.87) but performs poorly, while reproducing peak
AOT40 values. We find that at our site the actual data follow an exponential
curve,
AOT40=0.0201×M73.0765R2=0.94,
and AOT40 values predicted using this curve match peak AOT40 observations
better (slope AOT40 predicted vs. AOT40 observed: 1.03; R2 = 0.97).
Several studies attempted to model ozone levels and exposure metrics over the
IGP. modelled AOT40 over the Indian region for the year
2003 using the model REMO-CTM (REgional MOdel chemistry transport model). For the north-western part of the IGP, close to
the foothills, REMO-CTM models 5000–6000 nmol mol-1 h in May,
1500–2000 nmol mol-1 h in July and
6000–7000 nmol mol-1 h in October. We find that the model
underestimates the observed AOT40 in the north-west IGP by a factor of 2 to 3
during May and July and reproduces the observations well during October.
Consequently, the model would be able to predict crop production losses during
rabi season better and would underestimate crop production losses during
zayad and kharif seasons.
Comparison of the average monthly ozone exposure indices observed
at a suburban site in Mohali with measurements at other
urban (superscript letters a–h) and suburban (superscript i and j) sites in
the IGP and nearby remote mountain (superscript l) and suburban
valley (superscript k) sites indicated in Fig. 1.
Site
Mohalia
Mohalia
Lahoreb
Lahorec, d
New
New
Agrag
Agrah
Varanarsii, j
Kulluk
Nainitall
Delhie
Delhif
Years
2011–
2011–
1992–
2003–
2001
1997–
2000–
2008–
2003–
2010
2006–
2014
2014
1993
2004;
2004
2002
2009
2005
2008
2007
Index
M7
M12
10:00–
08:00–
M7
11:00–
09:00–
09:00–
M12
M7
M7
16:00
16:00
18:00
18:00
17:00
January
36
32
40
66
35
32
56
28
35
46
38
February
42
37
48
80
57
46
11
45
41
53
42
March
54
48
47
92
60
50
45
52
48
70
43
April
64
58
52
96
62
55
19
60
53
65
61
May
82
74
–
–
50
55
19
61
56
77
63
June
70
66
61
95
41
41
27
46
51
62
41
July
48
45
43
93
51
30
16
22
34
48
27
August
35
31
48
84
30
24
11
12
25
–
23
September
48
42
55
69
45
30
25
29
29
–
27
October
63
51
58
60
56
40
36
42
42
58
40
November
59
46
33
53
53
41
53
51
41
53
43
December
44
38
36
57
56
34
30
34
37
53
39
a this study; b ;
c ; d ;
e ; f ;
g ; h ;
i ; j ;
k ; l . Except in the case of values from this study, from and from , values in the table were
calculated from the available diel profiles or time series
plots.
In a more recent study conducted using WRF-Chem (weather research and forecasting chemistry model), predicted
ozone daytime concentrations of ∼ 50 nmol mol-1 for kharif
season and ∼ 40 nmol mol-1 for rabi season for the
Chandigarh UT. However, the authors considered only the time windows of 15 June
to 15 September and of December to February for kharif and rabi seasons
respectively. For these time windows, predicted ozone daytime concentrations
agree well with the measured M12.
intercompared model-predicted ozone with surface
observation for the HANK model. The model could not resolve the daytime ozone
peak in Delhi and, hence, will perform poorly in predicting AOT40. Comparing
the reported values for Chandigarh with our measurements, we find that the
model has equal difficulty in resolving the seasonality, in particular the
high ozone levels in summer.
compared MATCH (Model of Atmospheric Transport and Chemistry)-modelled M7 values with measured surface
ozone for Varanarsi and Lahore and found good agreement between model and
observations for both cropping seasons. For our site, too, there is excellent agreement between modelled and observed M7 values (model:
40–50 nmol mol-1 for rabi season and
50–70 nmol mol-1 for kharif season; observations:
40–52 nmol mol-1 for rabi season and
47–64 nmol mol-1 for kharif season).
used a global model (TM5 – TM stands for transport model) to predict surface ozone
over India, and the model reproduces surface observations for our site equally well.
Ozone-exposure–yield relationships
Crop yield losses and associated economic losses due to ozone are well
constrained for the USA and Europe (Avnery et al., 2011a). The analyses of crop
production losses made so far for India are based on model-derived O3
mixing ratios and apply O3-dose–plant-response
metrics and formulae developed in the US or in Europe
. Such predictions may underestimate crop yield losses. It has already
been pointed out above that for some models, the model predicted daytime
O3 mixing ratios or AOT40 values tend to be lower than the observed
O3 mixing ratios or AOT40 in particular for zayad and kharif seasons.
Hence, model predictions need to be validated and improved using in situ ozone measurements.
Empirical correlation of rice yields and ozone exposure indices for
field studies with variations in sowing date. Ozone exposure for rice sown
on different dates has been calculated using our data
(Table ). Yield data for rice have been taken from the peer-reviewed literature . Error
bars on the x axis show the variance in the ozone exposure metrics for the
same growth period (see Supplement S1 for definition) for different years.
Error bars on the y axis show the variance in the yield obtained. Variance
is introduced by replicating the study on several test plots (in different
districts; plots with different soil properties using different cultivars) and in several years or by transplanting seedlings with a
different age at the time of transplanting.
The O3-dose–plant-response metrics used in the modelling studies
conducted so far also underestimate crop production losses due to the fact
that southern Asian wheat and rice cultivars are more sensitive to ozone .
reviewed a large number of Asian OTC
and plant chamber studies but refrained from deriving Asia-specific dose
response curves for wheat and rice due to the large spread in the
observational data. suggested that the spread could be
due to the large variety of different cultivars studied or due to the
diversity of experimental conditions. In the same year
compared 20 different rice cultivars under identical conditions in a plant
chamber and showed that most Oryza sativa L. Japonica
cultivars were resistant to ozone damage (11 out of 12), while most
Oryza sativa L. Indica cultivars showed significant yield
losses (5 out of 8). A follow-up metabolomic analysis of selected cultivars
by the same authors, , showed that the only japonica cultivar
with high yield losses, Kirara 397, down-regulated proteins associated with
photosynthetic electron transport as a response to ROS induced by ozone. One
of the indica cultivars with high yield losses, Takanari, showed no
noteworthy changes in the metabolic pathway of photosynthesis resulting from
ozone exposure, but its yields were equally sensitive to ozone, and most
down-regulated proteins were associated with protein destination and storage
and unknown functions. In one of the japonica cultivars (Koshihikari), which did not suffer
yield losses, ozone stress up-regulated the expression of
certain proteins in the Calvin cycle of the energy metabolism.
reported the expression of the RuBisCO, and several energy-metabolism-related proteins were adversely affected by ozone exposure in the two
indica cultivars Malviyadhan 36 and Shivani. These results seem to indicate
that the responses to ozone are indeed cultivar-specific. More studies are
required to understand the damage mechanisms in different cultivars at a
fundamental level and identify high-yielding cultivars that are resistant to
ozone stress, which can be promoted by the relevant government agencies in
affected areas.
Ozone exposure according to different exposure indices and relative
yields for rice. Data for the five periods used to plot Fig. 3 are provided in
the table. Periods (P) 1–3 correspond to the periods in which rice is usually
grown in Punjab and Haryana, and the average yield loss of these three periods
is used to calculate crop production loss and economic loss for each fiscal
year.
Time
AOT40
M7
RYAOT40
RYM7
RYAOT40
RYM7
Mills et al.
Adams et al.
Indian
Indian
(2007)
(1989)
OTC
OTC
studies
studies
2012 P1
25 641
55
0.84
0.97
0.69 ± 0.05
0.75 ± 0.06
2012 P2
19 788
51
0.86
0.97
0.75 ± 0.04
0.80 ± 0.06
2012 P3
16 715
49
0.87
0.98
0.78 ± 0.04
0.82 ± 0.06
2012 P4
35 640
64
0.80
0.95
0.59 ± 0.06
0.65 ± 0.07
2012 P5
31 853
60
0.82
0.96
0.63 ± 0.05
0.70 ± 0.07
Average P1–3
20 715
52
0.86
0.97
0.74 ± 0.04
0.79 ± 0.06
2013 P1
20 839
53
0.86
0.97
0.74 ± 0.04
0.78 ± 0.06
2013 P2
15 330
49
0.88
0.98
0.80 ± 0.04
0.82 ± 0.05
2013 P3
12 623
47
0.89
0.98
0.82 ± 0.03
0.84 ± 0.05
2013 P4
29 259
60
0.83
0.96
0.66 ± 0.05
0.70 ± 0.07
2013 P5
25 498
56
0.84
0.96
0.70 ± 0.05
0.74 ± 0.06
Average P1–3
16 264
49
0.88
0.98
0.79 ± 0.04
0.81 ± 0.06
Comparison of the empirical exposure–response relationship based on
field data (solid line) with OTC studies conducted in India (squares with
dash and dot fit) and Pakistan (diamonds, not included in line fit). Large
diamonds indicate studies conducted on basmati; all other studies were
conducted on paddy. Circles show plant chamber studies on Bangladeshi rice
cultivars conducted in Japan, and the dashed line delineates the European
(AOT40; ) and American (M7; ) dose–response relationship. In all studies presented in this figure, rice plants
were exposed to elevated ozone from the date of transplantation until
harvest.
Rice
Figure shows the empirical correlation of rice yields and
ozone exposure indices for field studies with variations in sowing in Punjab
and Haryana. There is a significant trend in the reported crop yields as
a function of ozone exposure indices (Fig. ; R2 = 0.58 for M7
and R2 = 0.57 for AOT40). For rice, late sowing (1 June) and late
transplantation (1 July) leads to the lowest relative yield losses (18 %),
while early sowing (1 April) and transplantation (1 May) doubles ozone-related yield losses (35 %; Table ).
Figure compares the empirical ozone-exposure–response curve
derived from the field data presented in Fig. (solid line)
with RY values determined in OTC studies conducted in
India (squares, dash and dot line fit) and Pakistani Punjab (diamonds). For
studies that did not report AOT40 but did report monthly or seasonal M7, M8
or M12, AOT40 was calculated using the relationship between the respective
index and AOT40 at our site. For M7, all data points of OTC studies lie close
to the line derived from the empirical relationship between crop yields and
ozone exposure in Punjab. The fit for the OTC studies gives a similar slope
to the linear fit of the yield data. Since OTC studies compare yield losses
of plants exposed to ozone with those of plants grown under identical
conditions but in clean filtered air, the ozone-exposure–response curve
derived from OTC studies of Indian cultivars provides the most accurate
estimate of the RYL. A new RYL equation for Indian rice cultivars
(Table ) is derived by fitting all relative yields for Indian
cultivars from OTC studies (Fig. ). We calculate relative
yields for all five reference periods defined in Supplement S1, using both the
old and the revised RYL relationships.
Empirical correlation of wheat yields and ozone exposure indices for
field studies with variations in sowing date. Ozone exposure for wheat sown
on different dates has been calculated using our data
(Table ). Yield data for wheat have been taken from the peer-reviewed literature .
reported co-located measurements of ozone exposure and
yields for a number of urban locations that included residential areas and
kerb site locations, where NO titration leads to low wintertime ozone levels.
Other studies reported yields corresponding to different sowing dates. The
yield data have been positioned to conform with the emergence dates
(Periods 1 to 5) defined in Supplement S1. Error bars on the x axis show the
variance in the ozone exposure metrics for the same growth period (see
Supplement S1 for definition) for different years. Error bars on the y axis
show the variance in the yield obtained. Variance is introduced by
replicating the study on several test plots, in multiple years or varying
growing conditions and by the number of irrigations and or the tillage
practices.
It is clear from Fig. and Table that the RY
curve derived by significantly overestimate the RY of Oryza sativa
L. Indica cultivars planted in the IGP, and it is interesting to note that there seems to be an east–west gradient in the
sensitivity of local cultivars to ozone exposure. Bangladeshi cultivars
showed the lowest sensitivity and highest relative yields, though this could
be due to the fact that the study was conducted in the sheltered environment
of a plant chamber. Pakistani cultivars showed the highest sensitivity to
ozone exposure and the lowest relative yields.
Crop production losses calculated using the equation derived based on
American studies underestimate crop production losses in
southern Asia by approximately 20–30 % (Table ). For AOT40
both the empirical relationship between crop yields and ozone exposure and
the OTC studies conducted in India lead to line fits with similar slopes;
however, OTC studies show an intercept of 0.95 for AOT40 = 0,
indicating that in southern Asia ozone levels below 40 nmol mol-1
damage local paddy cultivars. While deriving the empirical relationship from
field data, the RY for AOT40 = 0 was defined as 1 due to the absence
of clean-air controls. The slope of the revised equation is steeper than the
slope reported by , and the intercept of the Indian OTC
studies is also lower; hence RY and crop production losses calculated using
the equation derived based on European studies underestimate crop production
losses in southern Asia by approximately 5–15 % (Table ).
Table summarizes relative yields for the five reference
periods (which correspond to different sowing dates) and intercompares RY
calculated using the new equation with RY calculated using the old
relationships. It can be noted that AOT40 shows a better degree of agreement
between the exposure–yield relationship of and the exposure–yield relationship for Indian cultivars (Table ). The
difference between the two is generally ∼ 10 %. On the other hand,
M7 shows a lower degree of agreement between the exposure–yield relationship
of and the exposure–yield relationship for Indian cultivars
(Table ). The difference between the two is ∼20 %.
Using the revised relationship, relative yields calculated using the M7 and
AOT40 metrics agree within the uncertainty, while previously the discrepancy
between the crop yield losses calculated using M7 and AOT40 metrics exceeded
10 %. Our revised ozone exposure crop yield relationships show
significantly lower relative yields than those using the previous exposure–response relationships. This can be attributed to the variety of cultivars.
The Indian cultivars are more sensitive to O3 exposure.
Wheat
Figure shows the empirical correlation of wheat yields and
ozone exposure indices for field studies with variations in sowing in Punjab
and Haryana. There is a significant decrease in yield as a function of
increasing ozone exposure (Fig. ) for both ozone exposure
indices (R2 = 0.55 of M7 and R2 = 0.7 for AOT40).
For AOT40 the relative yield is determined with respect to the
yield that would have been obtained for AOT40 = 0.
Ozone exposure according to different exposure indices and relative
yields for wheat. Data for the five periods used to plot Fig. 5 are provided
in the table. Period 2 (P2) and Period 3 (P3) correspond to the periods in
which wheat is usually grown in Punjab and Haryana in the rice–wheat cropping
cycle, while Period 4 (P4) and 5 (P5) correspond to the cotton–wheat cropping
cycle. The average yield loss of the rice–wheat cycle is used to calculate
crop production loss and economic loss for each fiscal year as most of the
area is cultivated in the rice–wheat cropping system.
Time
AOT40
M7
RYAOT40
RYM7
RYM7
RYAOT40
RYM7
Mills
Lesser
Heck
Indian
Indian
et al.
et al.
et al.
OTC
OTC
(2007)
(1990)
(1984b)
studies
studies
2012 P1
15 843
49
0.73
0.93
0.85
0.60 ± 0.10
0.74 ± 0.07
2012 P2
15 807
49
0.74
0.93
0.86
0.60 ± 0.10
0.75 ± 0.07
2012 P3
16 168
49
0.73
0.93
0.86
0.59 ± 0.10
0.75 ± 0.07
2012 P4
14 754
49
0.75
0.93
0.85
0.63 ± 0.10
0.74 ± 0.07
2012 P5
17 110
52
0.71
0.92
0.84
0.57 ± 0.11
0.69 ± 0.07
Average P2–3
15 987
49
0.73
0.93
0.86
0.59 ± 0.10
0.75 ± 0.07
2013 Period-1
11 384
42
0.81
0.96
0.91
0.71 ± 0.09
0.88 ± 0.05
2013 Period-2
9887
40
0.83
0.96
0.92
0.75 ± 0.08
0.90 ± 0.05
2013 Period-3
11 375
41
0.81
0.96
0.91
0.71 ± 0.09
0.88 ± 0.05
2013 Period-4
10 012
41
0.83
0.96
0.91
0.75 ± 0.08
0.89 ± 0.05
2013 Period-5
13 817
46
0.77
0.94
0.88
0.65 ± 0.10
0.81 ± 0.06
Average P2–3
10 631
41
0.82
0.96
0.91
0.73 ± 0.08
0.89 ± 0.05
Comparison of the empirical exposure–response relationship based on
field data (solid line) with OTC studies conducted in India (squares with
line fit) and Pakistan (diamonds, not included in line fit). Circles show
plant chamber studies on Bangladeshi wheat cultivars conducted in Japan. The
exposure–response relationship based on American and European studies is
plotted in the same graph for comparison. In all studies on southern Asian
cultivars, wheat was exposed to elevated ozone levels from emergence to
harvest, while the European and American exposure–response curves include
data sets acquired on wheat crops that were exposed to elevated ozone during the
last 3 months prior to harvest.
Figure compares the empirical ozone-exposure–response curve derived from field data (solid line) with RYL relationships reported in the
literature and with OTC studies conducted in India (squares, dash and dot line) and
Pakistani Punjab (diamonds). For studies that did not report AOT40 but did
report monthly or seasonal averaged M7 or M12, AOT40 was estimated. For M7
most data points of OTC studies with Indian cultivars lie close to the line
derived from the empirical relationship between crop yields and ozone
exposure in Punjab. However, the exposure–response relationship for wheat can
only be appropriately described by fitting a Weibull function. Since OTC
studies compare yield losses of plants exposed to ozone with those of plants
grown under identical conditions but in clean filtered air, the ozone
exposure–response curve derived from OTC studies of Indian cultivars provides
the most accurate estimate of the RYL. A new RYL equation for Indian wheat
cultivars (Table ) is derived by fitting all relative yields
for Indian cultivars from OTC studies (Fig. ). We calculate
relative yields for all five reference periods defined in Supplement S1 both
using the old and the revised RYL relationships.
It is clear from Fig. that the RY curves for winter wheat
derived by and overestimates the RY of
most Triticum aestivum L. cultivars planted in the IGP. For
Triticum aestivum L. there is no significant trend between cultivars
planted in different countries. Crop production losses calculated using the
M7 index and the equation derived based on American studies
underestimates crop production losses in southern Asia by approximately 10 and 20 % for the equation of
and respectively (Table ).
For AOT40 both the empirical relationship between crop yields and ozone
exposure and the OTC studies conducted in India lead to line fits with
similar slopes and intercepts. The slope obtained in the current study is
steeper than the slope reported by , although a limited
number of cultivars planted in the IGP show an exposure–RY relationship
similar to that reported by . Cultivars with lower
sensitivity to ozone include Bijoy , Inqilab-91, Punjab-96
and Pasban-90 , HUW234, PBW343 and Sonalika
. For HUW468 the sensitivities obtained by
and differ. However, for most cultivars
crop production losses calculated using the equation derived based on
European studies underestimate crop production losses in southern Asia.
Table summarizes relative yields that are obtained by our
calculation. For AOT40 the exposure–yield relationship of
and the exposure–yield relationship for Indian cultivars
(Table ) differ by ∼ 10–15 %. For M7 the exposure–yield relationship of overestimates the yields by
∼ 20 % and the exposure–yield relationship of by
∼ 10 % (Table ). After the revision, relative yields
calculated using the M7 and AOT40 metrics still show a ∼ 15 %
discrepancy although the estimates do overlap within the combined
uncertainty. The quality of the fit for M7 is better than the fit for AOT40;
however, given the very steep slope of the M7 curve at
> 35 nmol mol-1 and the large number of points below the fit line
for higher M7 values, it is credible that cultivars with such a sensitivity to
ozone would respond very strongly to even a few days with extremely high
ozone, and such behaviour will only be captured by the AOT40 index. Daytime
peaks with ∼70–100 nmol mol-1 are observed in March and
April (Fig. ) during the grain filling stage of the plants, and
the M7 for the full growth period does not capture such extreme events. AOT40
is the better indicator to accurately reflect exposure when the variance of
the amplitude of daytime peak ozone is high. reported a high
sensitivity of wheat cultivars to ozone exposure during the grain filling
stage, and our observations agree well with their finding. Therefore, for
southern Asian wheat cultivars, the revised exposure–response curve using AOT40
will provide the best estimate of the crop production losses. Our revised
ozone-exposure–crop-yield relationships show significantly lower relative
yields than those obtained by exposure–response relationships used previously (-15 % for AOT40). This can be attributed to the variety of cultivars.
Most Indian cultivars are more sensitive to a high O3 concentration,
although a few individual cultivars show higher resistance.
Ozone exposure according to different exposure indices and relative
yields for cotton. Period 1 (P1) and Period 2 (P2) correspond to the periods
in which cotton is usually grown.
Time
AOT40
M7
RYAOT40
RYM7
Mills
Heck
et al.
et al.
(2007)
(1984b)
2012 P1
47926
57
0.30
0.91
2012 P2
33 728
53
0.53
0.91
2012 P3
48 342
56
0.30
0.92
Average P1–2
40 825
55
0.42
0.91
2013 P1
40 029
55
0.43
0.92
2013 P2
27 312
51
0.63
0.92
2013 P3
41 046
53
0.41
0.93
Average P1–2
33 670
53
0.53
0.92
Cotton
Cotton yield data for this region have only been reported in two studies
, and OTC studies on cotton in India have not
been conducted to date. reported yields for different
numbers of pickings (Periods 2 and 3), and hence his observations cannot be used
to investigate the crop response to ozone. Exposure–yield relationships
acquired abroad indicate that cotton is potentially extremely sensitive to
ozone-induced damage. The yield data from India show very high variability
and no significant influence of ozone on yields when the results are
averaged over 2 years . However, there is
a significant intra- and interannual variability in yields as a function of
rainfall reported from the site on which the crop was grown
. Since the crop was irrigated sufficiently, this yield
dependence on rain should not be related to drought stress. Ozone levels in
Punjab during the monsoon season are strongly influenced by the wet scavenging of
precursors and cloudiness; hence, rain spells can be taken as a proxy for
times of low photochemical ozone production. The lowest yields were observed
for Period 1 sowings in 2004 that were affected by a prolonged dry spell from
60 to 100 days after sowing. This corresponds to the period of maximum
square production and peak bloom in a cotton plant. In 2005 the same Period 1
sowings received regular rain (every 5–7 days) in the same time period
(total of 400 mm between 60 to 100 days after sowing) and showed the
highest yields (2.4 times the yield of the previous year on average). The
Period 2 sowings in 2005 received rain 40 to 80 days after sowing but
were subjected to a dry spell during the second half of the square production
and peak bloom period. Observed yields were 1.9 times higher compared to the
plants that were subjected to a dry spell during the entire period. Period 2
sowings in 2004 received a short (∼ 7-day) rain spell around 80 days
after sowings during the peak square production period and showed yields that
were 1.4 times the dry-spell yields. Considering the average difference
between dry-spell and rain spell M7 of approximately
10–20 nmol mol-1, the observations described above seem to
suggest a strong sensitivity of the plant to ozone levels during square
production and peak bloom (60–100 days after sowing), but it is difficult to
separate the effect of yield losses due to adverse meteorological conditions
from that due to ozone exposure. In the absence of dedicated OTC fumigation
studies conducted in India that separate the two effects, we use the
relationship of and to calculate relative
yields (Table ).
For cotton there are extreme differences of 30–60 % between the relative
yields calculated using AOT40 and M7 .
Ozone fumigation studies on Indian cultivars are urgently required to
constrain relative yields and crop production losses due to ozone more accurately.
Maize
Maize is planted both as rabi and kharif crop; however, cultivation occurs
only in a limited area, but maize is heavily promoted as an alternative to
rice when a deficient monsoon is anticipated. We could not find any study
reporting crop yields for maize planted in Punjab or Haryana in the peer-reviewed literature. A recent study investigating ozone-related crop yield
losses for Indian maize cultivars found that Indian maize
cultivars are twice as sensitive to ozone as their American and
European counterparts. However, maize is 1 order of magnitude less
sensitive to ozone compared to rice and wheat and is, therefore, a suitable
alternative for drought years. We use all three ozone exposure RY
relationships to calculate relative
yields (Table ) and find that in the real world both the
differences between the revised and old relationship and the overall losses
are minor.
Yield loss and economic loss in Punjab and Haryana
Table summarizes the relative yield loss calculated according
to different exposure indices. In general, crop production losses calculated
using the M7 index exposure–response relationships based on American studies
conducted in the 1970s and 1980s
tend to underestimate the actual yield losses of Indian cultivars, as the M7
index fails to capture the effect of extreme events on plant physiology and
yields . The old AOT40
exposure–response relationship by does not capture the
sensitivity of most southern Asian cultivars. Only Bangladeshi rice cultivars
and a few select wheat cultivars follow this relationship, while most Indian
wheat and rice cultivars are far more sensitive to elevated ozone levels. We
propose a revised relationship (Table , Figs.
and ) based on a literature review of OTC studies conducted on
Indian cultivars and demonstrate that this relationship adequately describes
the empirical relationship between crop yield and AOT40 in field trials that
were not aimed at studying the effect of ozone on crops. The revised equation
(Table ) predicts that RYL for Indian cultivars are
1.5–2 times the RYL predicted based on the equation by .
Ozone exposure according to different exposure indices and relative
yields for rabi and kharif maize.
Time
AOT40
M7
RYAOT40
RYM7
RYAOT40
Mills
Heck
Indian
et al.
et al.
OTC
(2007)
(1984b)
2012 P1
11 346
46
0.98
0.97
0.95
2012 P2
7522
43
0.99
0.99
0.98
Average
9434
45
0.99
0.99
0.97
2011/2012 P3
9824
48
0.98
0.99
0.96
2011/2012 P4
15 406
56
0.96
0.98
0.93
Average
12 615
52
0.97
0.99
0.95
2013 P1
9496
46
1.00
0.99
0.97
2013 P2
7209
44
0.98
0.99
0.98
Average
8353
45
0.99
0.99
0.97
2012/2013 P3
6219
40
0.99
0.99
0.99
2012/2013 P4
12 455
51
0.99
0.99
0.95
Average
9337
46
0.99
0.99
0.97
Relative yield losses calculated according to different ozone
exposure–response relationships for rice, wheat cotton and
maize.
Time
RYLAOT40
RYLM7
RYLM7
RYLM7
RYLM7
RYLAOT40
Mills
Adams
Heck
Lesser
this
this
et al.
et al.
et al.
et al.
study
study
(2007)
(1989)
(1984b)
(1989)
Rabi 2011–2012
Wheat
0.27
0.14
0.07
0.25 ± 0.07
0.41 ± 0.10
Maize
0.03
0.01
0.05
Kharif 2012
Rice
0.14
0.03
0.21 ± 0.06
0.26 ± 0.04
Cotton
0.58
0.09
Maize
0.01
0.01
0.03
Rabi 2012–2013
Wheat
0.18
0.09
0.04
0.11 ± 0.05
0.27 ± 0.08
Maize
0.01
0.01
0.03
Kharif 2013
Rice
0.12
0.02
0.19 ± 0.06
0.21 ± 0.04
Cotton
0.47
0.08
Maize
0.01
0.01
0.03
Crop production (CP) for Punjab (PB) and Haryana (HR) and MSP for
the fiscal years of 2012–2013 and 2013–2014. Crop production loss (CPL) and
economic cost losses (ECL) are calculated for wheat, rice, maize and cotton
using the old AOT40-based exposure–yield relationship
a and for wheat and rice, also using the revised
AOT40-based exposure–response relationshipb. CP and CPL for rice,
wheat and maize are given in tonnes (t); CP and CPL are given in
bales (b).
CP
CP
CP
MSP
CPLa
CPLa
CPLa
ECLa
ECLa
ECLa
CPLb
CPLb
CPLb
ECLb
ECLb
ECLb
PB
HR
Total
PB
HR
Total
PB
HR
Total
PB
HR
Total
PB
HR
Total
2012–
106 t
106 t
106 t
INR/kg
106 t
106 t
106 t
106INR
106INR
106INR
106 t
106 t
106 t
106 INR
106 INR
106 INR
2013
Wheat
17.28
12.69
29.97
11.7
6.39
4.69
11.09
74 777
54 915
129 692
12.01
8.80
20.81
140 495
103 176
243 671
Rice
11.37
3.98
15.35
12.5
1.85
0.65
2.50
23 137
8099
31 235
4.00
1.40
5.39
49 936
17 480
67 416
Maize
0.48
0.02
0.50
11.75
0.005
0.0002
0.005
56
2
59
0.015
0.001
0.015
173
7
180
106 b
106 b
106 b
INR/b
106 b
106 b
106 b
106INR
106INR
106INR
Cotton
2
2.5
4.5
12 737
2.8
3.5
6.2
35 179
43 974
79 154
2013–
106 t
106 t
106 t
INR/kg
106 t
106 t
106 t
106INR
106INR
106INR
106 t
106 t
106 t
106 INR
106 INR
106 INR
2014
Wheat
16.11
11.80
27.91
12.85
3.54
2.44
6.13
45 442
33 285
78 727
5.93
4.36
10.32
76 567
56 082
132 649
Rice
8.16
4.00
12.16
13.1
1.11
0.55
1.66
14 577
7142
21 719
2.17
1.06
3.23
28 415
13 922
42 338
Maize
0.56
0.03
0.60
13.1
0.006
0.0003
0.006
74
4
78
0.017
0.001
0.018
228
11
239
106 b
106 b
106 b
INR/b
106 b
106 b
106 b
106INR
106INR
106INR
Cotton
2.1
2.0
4.1
13 064
1.9
1.8
3.6
24 329
23 170
47 499
A recent modelling study for the year 2005 predicted RYLs of 1 and 1.2 %
for Punjab and Haryana respectively for wheat and 8.1 % for Punjab for
rice . These relative yield losses are a factor of 15–30
lower compared to the RYL calculated using the same equation
but employing in situ measurements for calculating AOT40
for wheat and a factor of 1.5 to 1.8 lower for rice (Table
Column RYLAOT40, ).
estimated the crop production loss of winter wheat based
on a review of measured ozone mixing ratios published in the peer-reviewed
literature for the years 2000–2007. The calculated relative yield losses, based both on the M7 exposure–response relationship for winter wheat proposed
by of 10.8 % and on the AOT40-based exposure–response
relationship by of 29.8 % RYL for Punjab and Haryana,
agree well with crop yield losses calculated by applying the same equations
to our in situ observations (Table ) for the years 2011–2014
(Table Column RYLAOT40,
). This indicates that the underestimation of RYL by
is due to an underestimation of the AOT40 values during the
wheat growing season in the north-west IGP caused by the fact that Ghude and
colleagues only considered December to February as the ozone-sensitive growth
periods and excluded the months of March and April, which show the highest
AOT40 values in the growing season of wheat. However, in the NW-IGP the grain
filling stage of the crop is only reached in March, and wheat has been shown
to be extremely sensitive to high ozone during the grain filling stage
. used the MOZART-2 (Model for OZone and Related chemical Tracers, version 2) to predict
a national average RYL of 25–30 % for wheat using the AOT40-based
equation, which agrees well with our observations.
, using the TM5 model, predicted RYL ranging from 20–30 % for wheat,
10–15 % for rice and 1–3 % for maize for the year 2000, which
agrees well with the observations.
Table shows the crop production loss and MSP for the fiscal years of 2012–2013 and 2013–2014. Data on crop production were obtained from the
following sources: the and . Procurement data
were obtained from the . For the fiscal year of 2013–2014, data
for Punjab are based on estimates, while final data for Haryana were obtained
from the . The table also presents economic cost losses
calculated for wheat, rice, maize and cotton using the old
and revised exposure–yield relationship. The losses are present for Haryana and Punjab, both separately and cumulatively.
The highest crop production loss is seen for wheat:
20.8 ± 10.4 million t in the fiscal year of 2012–2013 and
10.3 ± 4.7 million t in the fiscal year of 2013–2014 for Punjab and
Haryana taken together. predicted crop production losses of
only 0.25 million t for the year 2005 for both states. The
discrepancy is mostly due to the fact that this study assumed that the ozone-sensitive growth period of wheat lasts only from December to February, and,
hence, this study did not capture the effect of the high AOT40 during the grain filling
stage of the crop in March (factor ∼ 15–30). Thus, the discrepancy is also partially due to the
revision of the exposure–response relationship (Table ; factor
∼ 2). estimated crop production losses of
10.9 million t yr-1 on average for both states combined. The
estimate falls within the same order of magnitude as our estimate.
estimated a CPL of 26 million t for all of India
but did not resolve losses for individual states. Economic cost losses amount
to INR 244 ± 121 billion and INR 133 ± 60 billion in the fiscal years of 2012–2013 and 2013–2014 respectively. At an exchange rate of 60 INR/USD, this
amounts to USD 4.1 ± 2.0 and 2.2 ± 1.0 billion respectively.
Rice shows crop production losses of 5.4 ± 1.2 million t in the fiscal year of 2012–2013 and 3.2 ± 0.8 million t in the fiscal year of 2013–2014
for Punjab and Haryana taken together. predicted crop production
losses of only 0.85 million t for the year 2005 for both states. The
discrepancy is caused both by an underestimation of the AOT40 due to the fact
that the author considered a shorter ozone-sensitive growth period (factor 1.5–1.8)
and by the revision of the exposure–yield relationship
(Table ) to account for the sensitivity of Indian rice
cultivars (factor 1.9). Economic losses amount to INR 67 ± 15 billion and
INR 42 ± 11 billion for the fiscal years of 2012–2013 and 2013–2014
respectively. At an exchange rate of 60 INR/USD, this amounts to
USD 1.1 ± 0.2 and 0.7 ± 0.2 billion respectively.
The Indian National Food Security Ordinance entitles ∼ 820 million of
India's poor to purchase about 60 kg of rice or wheat per person
annually at subsidized rates. The scheme requires 27.6 Mt of wheat
and 33.6 Mt of rice per year. Cutting down ozone-related crop
production losses in Punjab and Haryana alone could provide > 50 % of
the wheat and 10 % of the rice required for the scheme.
Economic losses amount to INR 79.15 billion and
47.50 billion (USD 1.3 and 0.8 billion) for cotton and INR 0.18 billion
and INR 0.24 billion (USD 3 and 4 million) for maize in the fiscal years of 2012–2013 and 2013–2014 respectively.
The total economic losses for the agricultural sector in Punjab and Haryana
amount to INR 391 ± 136 billion (USD 6.5 ± 2.2 billion) in the fiscal year of 2012–2013 and INR 223 ± 71 billion (USD 3.7 ± 1.2 billion) in
the fiscal year of 2013–2014. The loss estimates presented above underestimate
the real economic losses due to ozone on several accounts.
Firstly, the crop is valued only at the MSP for common grade crops. The MSP
is often even lower than the actual production cost and the economic value of
the crop is typically much higher. This is particularly true for high-quality
rice varieties such as basmati.
Secondly, we do not account for the losses in the food processing sector and
other allied industries. The value gain from MSP to the final end consumer
product ranges from a factor of 2 to 20 for food crops to a factor of > 100 for cotton.
Thirdly, this calculation does not consider the relationship between the
rural demand for consumer products and rural income. Rural
income is affected strongly by crop yields, 78 % of the rural
population depends on agriculture as primary source of income.
Previous studies investigating the relationship between monsoon rainfall,
food grain production and the nation's GDP for the years 1951–2003
found that a 1 % decrease in food grain production
due to a deficient monsoon led to a 0.36 % decrease in India's GDP. Ozone-related crop production losses are likely subject to the same multiplication
factor. With relative yields losses currently ranging from 10 to 58 % for
the different crops (, van Dingenen et al., 2009), the
real economic burden of current ozone levels in terms of India's GDP is
likely to fall into the range from 3.6 to 20 % (Eq. 8).
Conclusions
Using a high-quality data set of in situ ozone measurements in the NW-IGP and
yield data from the two neighbouring states of Punjab and Haryana, we derived
a new crop-yield–ozone-exposure relationship for Indian rice and wheat
cultivars. Indian cultivars are a factor of 2–3 more sensitive to ozone
than to their European and south-east Asian counterparts. Relative yield
losses based on the AOT40 metrics ranged from 30–42 % for wheat,
22–26 % for rice, 3–5 % for maize to 47–58 % for cotton.
Crop production losses for wheat amounted to 20.8 ± 10.4 million t
in the fiscal year of 2012–2013 and 10.3 ± 4.7 million t in the fiscal year of
2013–2014 for Punjab and Haryana taken together. Crop production losses for rice
totaled 5.4 ± 1.2 million t in the fiscal year of 2012–2013 and
3.2 ± 0.8 million t in the year 2013–2014 for Punjab and Haryana
taken together. Cutting these ozone-related crop production losses alone could
provide 50 % of the wheat and 10 % of the rice required to provide
60 kg of subsidized wheat or rice to ∼ 820 million of India's
economically weaker members of society.
The lower limit for economic cost losses in Punjab and Haryana amounted to
USD 6.5 ± 2.2 billion in the fiscal year of 2012–2013 and
USD 3.7 ± 1.2 billion in the fiscal year of 2013–2014. The upper limit for
the ozone-related economic losses incurred at current ozone levels for all of India amounts to 3.5–20 % of India's GDP. The wealth gained by mitigating
tropospheric ozone and decreasing ozone-related economic losses would be
distributed among a large group of beneficiaries, as 54 % of the India's
population and 79 % of India's rural population still rely on agriculture
as their principle source of income. Co-benefits of ozone mitigation include
a decrease in the ozone-related mortality and morbidity, a reduction in healthcare-related costs and the number of workdays lost and a reduction
in the ozone-induced warming in the lower troposphere.
At current tropospheric ozone levels, optimizing the sowing date of rice
towards sowing at the start of June and transplantation in the first week of
July can increase crop yields substantially by reducing the ozone exposure of the
crop. Reaching out to farmers in order to promote this change in cropping practice
will yield co-benefits in terms of increasing the water productivity of the
crop and preserving precious groundwater. It will also increase the profit
margin, as farmers often run tube wells on diesel whenever grid power supply
is not available.
For wheat, too, timely sowing is crucial to minimize ozone exposure during
the grain filling stage of the crop by advancing the harvest from the normal time
(end of April to beginning of May) to an earlier time window (end of March to early April). New tillage practices that facilitate timely
sowing, such as relay seeding into cotton and zero or low tillage regimes that
incorporate rice straw, are urgently required to facilitate timely sowings.
Providing a “Happy Seeder” machine to every village in Punjab would cost
∼ USD 0.04 billion. The Happy Seeder sows through the crop residue and
leaves it as mulch on the fields. Promoting this technology would not only
reduce ambient ozone mixing ratios by curbing crop residue burning, which
contributes significantly to ozone precursor emission in the post-monsoon season
, but it would also protect the young seedlings against ozone
as the mulch acts as protective cover and reduces the dry deposition of ozone
onto the leaf surface. Co-benefits of this technology include a higher carbon
sequestration in the soil and a higher water productivity of the crop.
For all crops, screening a large number of domestic cultivars using the new
stomatal-flux-based exposure metrics to identify and promote those cultivars
that are less susceptible to ozone damage also offers a way forward.