Assessment of crop yield losses in Punjab and Haryana using two years of continuous in-situ ozone measurements

Introduction Conclusions References

The total economic cost losses in Punjab and Haryana amounted to USD 6.5 billion in the fiscal year 2012-2013 and USD 3.7 billion in the fiscal year 2013-2014.This economic loss estimate represents a very conservative lower limit based on the minimum support price of the crop, which is lower than the actual production costs.The upper limit for ozone related crop yield losses in entire India currently amounts to 3.5-20 % of India's GDP.
Mitigation of high surface ozone would require relatively little investment in comparison to economic losses incurred presently.Therefore, ozone mitigation can yield mas-

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 (Lal et al., 2012).Tropospheric ozone mixing ratios are expected to increase further in the years to come (Giles, 2005).
Tropospheric ozone causes damage to crop at elevated levels.Extensive plant damage due to tropospheric ozone was first observed during the Los Angeles Smog episodes.In the early 1950's, Arie Haagen-Smit and co-workers reported that such plant damage could be reproduced in the laboratory by the reaction of organic trace gases or car exhaust with nitrogen oxides (NO x ) in presence of sunlight (Haagen-Smit, 1952;Haagen-Smit and Fox, 1954).
The influence of ozone on vegetation is dependent on the ozone dose and plant phenotype (Pleijel et al., 1991).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 (Wilkinson et al., 2012).
In certain phenotypes, ozone exposure interferes with the hormone levels in plants In phenotypes that are unable to control their stomata opening under ozone stress, O 3 enters the leaf and 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 causing accelerated leaf senescence and reduced photosynthesis (Calatayud et al., 2004) and can cause pollen sterility or induce flower, ovule, or grain injury and abortion (Black et al., 2000).In such phenotypes ozone causes visible leaf injury, senescence, and abscission (Kangasjaärvi et al., 2005) and can eventually reduce crop yield, even if the damage occurs at early vegetative stages of crop growth by reducing the amount of healthy green leaf area available for photosynthesis.Symptoms of ozone associated leaf injury have been reported for 27 agricultural crops (Mills et al., 2011a).
Certain other phenotypes respond to ozone stress by reducing their stomata aperture (Torsethaugen et al., 1999).While this mechanism reduces the amount of ozone taken up by the plant and hence the oxidative stress inside the leaves, it also decreases CO 2 uptake, leading to a reduction in photosynthesis.This affects the carbon transport to 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, but crop yields might be very sensitive to O 3 stress during the grain filling stage.Picchi et al. (2010) reported that for different wheat cultivars the phenotypes with least visible leaf damage were often the ones showing 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 (Avnery et al., 2011a, b;van Dingenen et al., 2009;Wilkinson et al., 2012;Giles, 2005).
Crop yields are extremely important to the Indian economy, as 17 % of India's GDP directly depends on agriculture and allied activities (RBI , 2013).However, since 54 % of the total and 72 % of the rural working population of India still relies on agriculture as their main source of income (Census, 2011), crop yields have a much larger overall effect on the economy.Rural demand for a large range of consumer products and Figures

Back Close
Full cement depends directly on the year's crop yield.Consequently every 1 % decrease in crop yields causes a 0.36 % decrease of India's GDP (Gadgil and Gadgil, 2006).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 (FAO, 2013).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, FAO, 2013), which provide raw material to the domestic textile industry.Punjab with an average cropping intensity of 190 %, 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 by the Department of Food and Public Distribution (Agricultural Statistics, 2013).
In this study we use a high quality dataset of in-situ ozone measurements at a regionally representative suburban site called Mohali 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 inter-compared for the two major crop growing seasons of Kharif (June-October) and Rabi (November-April).We also derive a new ozone exposure crop yield relationship for Indian rice and wheat cultivars by combining field data from relay seeding experiments and data from open top chamber studies reported in the peer reviewed literature.

Site description and analytical details
All ozone measurements were performed at the IISER Mohali atmospheric chemistry measurement facility (30.67 • N-76.73 • E, 310 m a.s.l., Fig. 1).The measurement site is regionally representative (Sinha et al., 2014) and located in the north-west Indo-Gangetic Plain (NW IGP).Ozone measurements from several other sites located in the Introduction

Conclusions References
Tables Figures

Back Close
Full IGP and the adjoining mountain regions (Fig. 1) will be discussed in detail in Sect.3.1 to demonstrate that the measurements obtained at the facility are, indeed regionally representative.
The measurement site is located inside a residential campus of around 1.25 km 2 with 800-1000 residents.Local influence is expected to be significant only at low wind speeds (< 1 m s −1 ), which occur only rarely (Sinha et al., 2014;Pawar et al., 2015).The predominant daytime wind direction is west to northwest during winter, summer and post monsoon season and south to southeast 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. 1 in the state of Punjab, north-west of the site).During 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 N.W. Indo Gangetic Plain can be found in Sinha et al. (2014) and a thorough description of the meteorology of the site for all seasons can be found in Pawar et al. (2015).
Ozone was measured using UV absorption photometry at a time resolution of 1 measurement every minute with an accuracy that is better than 3 %, and overall uncertainty of less than 6 %.Quality assurance of the large dataset 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 Sinha et al. (2014).Introduction

Conclusions References
Tables Figures

Back Close
Full

Calculation of ozone exposure metrics
The potential of ozone to damage the vegetation has been known for over 50 years, but only in the early 1980s ozone related crop yield losses became a major topic of concern in the environmental science communities all over the world (Fuhrer et al., 1997).
In 1979, US EPA recognized the importance of O 3 -dose-plant-response relationships for assessing the crop yield loss. Crop yield was chosen as parameter to assess the response of agricultural crops to ozone damage (Heck et al., 1984a).In the 1980's, National Crop Loss Assessment Network (NCLAN) of the USA was the first systematic and large scale study to assess the impact of O 3 on crops in the world.It relied mainly on Open -Top field fumigation Chambers (OTC) (Heck et al., 1984b;Adams et al., 1989;Lesser et al., 1990) and used seasonal mean and peak concentration values to relate crop yield losses to ozone mixing ratios (Lefohn et al., 1988).Subsequently data use was restricted to daytime data due to the fact that leaf stomata are open and gas exchange is maximized in daylight hours (Lee and Hogsett, 1999).
The Mx metric is defined as the mean daytime 7 (M7) and 12 h (M12) surface ozone concentrations during the daylight hours 09:00-15:59 and 08:00-19:59 LT respectively in the crop growing season (Hollaway et al., 2012).has been adopted for crops, forest trees, and semi-natural vegetation (Fuhrer et al., 1997).The AOT40 is defined as the sum of differences between the hourly ozone concentrations and 40 nmol mol −1 during the crop growing season (Fuhrer et al., 1997) for AOT40 is the most widely used exposure plant response index and 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) (Hollaway et al., 2012).The W126 metric was proposed and adopted in United States by United States Environmental Protection Agency (USEPA) to assess potential vegetation damage from ozone exposure.The W126 metric is defined as the sum of hourly ozone concentrations (weighted by a sigmoidal function) during daylight hours (07:00-18:59 or 08:00-19:59 depending upon location of site) during the crop growing season (Eq.1).The W126 due to its sigmoidal weighting function gives more weight to higher ozone mixing ratios and is less sensitive to ozone mixing rations between 40 and 50 nmol mol −1 (Tong et al., 2009).
Recently stomatal flux-based critical levels were proposed to address concerns that the AOT40-based critical level 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.Stomatal flux of ozone is modelled using a multiplicative algorithm adapted from Emberson et al. (2000) (5) Introduction

Conclusions References
Tables Figures

Back Close
Full that incorporates the effects of air temperature (f temp ), vapour pressure deficit of the air surrounding the leaves (f VDP ), light (f light ), soil water potential (f SWP ), plant phenology (f phen ) and ozone concentration (f O 3 ) on the maximum stomatal conductance (gmax, mmol O 3 , m −2 , PLA, s −1 ), 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 POD Y (the Phytotoxic Ozone Dose over a threshold flux of Y nmol O 3 , m −2 , PLA, s −1 with Y ranging from 0 to 9 nmol O 3 , m −2 , PLA, s −1 (Mills et al., 2011b).While M7 and M12 give equal importance to all measurements and account for the yield losses due to ozone concentrations of less than 40 nmol mol −1 , AOT40 and W126 give a higher weight to high ozone mixing ratios (Tuovinen, 2000).Hence, the former two are the preferred metrics for evaluating plant damage and yield losses at low ozone concentration while the latter will capture the effect of events with very high O 3 mixing ratios on plant physiology and yields better (Hollaway et al., 2012).The POD Y based exposure yield relationship considers the stromata uptake specifically and have been evaluated using data from a wide range of climate zones across Europe, but exposure yield relationships have so far been agreed upon only for a limited number of crops (Mills et al., 2011b).

Missing data
For any long term dataset 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 1.For calculating AOT40, W126, M7 and M12 continuous and complete daytime data is 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 Introduction

Conclusions References
Tables Figures

Back Close
Full 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 the requirement to occasionally purge the system with dry zero air leads 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, however, in some districts crops are also planted in summer zaid/zayad season (April-June).In Punjab, kharif crops include rice, cotton, maize, sugarcane and vegetables (Sharma and Sood, 2003).During rabi season wheat is grown in almost entire Punjab (> 90 % of the area).Minor rabi crops are potato, rabi maize, sugarcane, rabi pulses and oilseeds (Sharma and Sood, 2003).Punjab has an average cropping intensity of about 190 %.This means each piece of agricultural land is sown 1.9 times in one year on an average.In recent times there is a tendency to increase the cropping intensity further, in particular in the vicinity of urban centres.In between kharif and rabi season farmers plant potato (sowing: September/October; harvest: November/December) and during zaid/zayad season (April-June) farmers plant fodder crops (sorghum), pulses (moong dal) or seasonal fruits and vegetables (musk melon, water melon, gourds and cucumber).
In Haryana kharif crops include rice, cotton, sugarcane and in most of the unirrigated areas of Haryana pearlmillet and sorghum (Panigrahy et al., 2010).Mayor Rabi crops in Haryana include wheat, gram, sugarcane and mustard (Panigrahy et al., 2010).Zayad season crops include moong and vegetables (Saroj et al., 2014).
The most popular crop rotation systems in Punjab include rice-wheat and cottonwheat as well as maize based crop rotation systems.Sorghum-wheat rotation is popular in the Shivalik mountains.In Haryana rice-wheat, cotton-wheat, rice-mustard and Introduction

Conclusions References
Tables Figures

Back Close
Full rice-gram rotation is popular in the north but in the dryer parts of Haryana pearlmilletmustard and pearlmillet-wheat rotations are preferred (Panigrahy et al., 2010).The present study investigates crop yield losses for wheat and maize (Rabi) and rice, maize and cotton (Kharif).In the 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.

Ozone dose exposure yield relationships
Till date, only a limited number of field experiments to establish ozone related crop yield losses have been carried out in South Asia.Despite the fact that open top chamber studies in particular those conducted in Pakistani Punjab (Wahid et al., 1995a, b;Maggs et al., 1995;Maggs and Ashmore, 1998;Wahid, 2006;Wahid et al., 2011) and India (Rai et al., 2007(Rai et al., , 2010;;Rai and Agrawal, 2008;Singh et al., 2009;Singh and Agrawal, 2010;Sarkar andAgrawal, 2010, 2012) suggest that Asian cultivars are more sensitive to ozone related crop yield losses, no ozone exposure dose response relationship specific to the Indian subcontinent has been established in the literature so far.
In this study we use exposure dose-response relationships established in several studies in the West (Table 2) to provide a lower limit to the estimated crop yield losses, but also derive independent exposure dose-response relationships for South Asian cultivars.We obtain a yield exposure relationship by relating the crop yields obtained during several regional relay seeding trials to the ozone exposure of the crop and comparing the results with the relative yields (RY) for OTC studies reported in the peer reviewed literature.This provides an upper limit to the possible loss and helps to establish whether optimizing the sowing date can be a suitable strategy to minimize ozone exposure and maximise crop yields.Introduction

Conclusions References
Tables Figures

Back Close
Full

Yield loss and economic loss calculations
Table 2 summarises the ozone exposure dose-response relationships for relative yield loss (RYL) for wheat, rice, maize and cotton based on the AOT40, W126, M7, and M12 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 O 3 -induced damages (Avnery et al., 2011a) calculated using the Eqs.( 5) and ( 6) wherein RY i stands for relative yield in the year i , CPL i stands for crop production loss in the year i and CP i stands for the crop production of the same year.The crop production per fiscal year was taken from the database of the Directorate of Economics and Statistics, Department of Agriculture and Cooperation 2013.
Economic cost loss (ECL) for any crop is defined as the amount of loss in terms of money due to O 3 -induced damages for particular financial year.The minimum ECL is calculated for different crops based on corresponding Minimum Support Prices (MSP) of the same fiscal year using the equation: The MSP are recommended by Commission for Agriculture Costs and Prices (Directorate of Economics and Statistics, 2013) 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 MSP valuation process.It should be noted, however, that the MSP Introduction

Conclusions References
Tables Figures

Back Close
Full is typically approximately 50 % less than the market value of the crop and often lower than the production costs.The upler limit for the ECL is calculated using the relationship between CPL due to deficient monsoon rains and the Indian GDP established by Gadgil and Gadgil (2006) using the equation.Table 3 shows the monthly values of ozone exposure indices (AOT40, W126, M7 and M12) for the period October 2011 to January 2014.The yearly maximum and minimum monthly value for all indices correspond to the same months, May and August respectively in both years.All indices show maxima during summer (May and June) and post monsoon (October and November) and minima during monsoon (July to September) and winter (December to Febuary), however, the difference between the cumulative metrics (AOT40 and W126), that give higher weight to high values and low or no weight to low values and the average based metrics (M7 and M12) comes out very clearly.For AOT40 and W126 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 and 50 times higher for AOT40 and W126 respectively compared to the annual minima.For M7 and M12 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 (Jain et al., 2005;Kumar et al., 2010;Sharma et al., 2013) or a time series of average daytime ozone for their site (Maggs et al., 1995;Wahid, 2006;Wahid et al., 2011;Singla et al., 2011).
Table 4 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 earlier onset of the monsoon in the eastern part of the IGP.The only site further to the west for which ozone measurement have been reported is located close to the centre of the summertime "heat low" (Das, 1962) over the NW IGP and reports summertime and monsoon season M7 that are higher than those observed at our site and a strong anticorrelation of the observed ozone during monsoon season with the intensity of the monsoon rainfall.
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 Introduction

Conclusions References
Tables Figures

Back Close
Full values.Debaje (2014) did so using a linear relationship.When applied to our data presented in Table 3 the relationship estimates reasonable AOT40 values (slope AOT40 predicted vs. AOT40 observed: 0.93, R 2 = 0.87) but performs poorly, while reproducing peak AOT40 values.We find that at our site the actual data follows an exponential curve AOT40 = 0.0201 × M7 3.0765 R 2 = 0.94 (10) and AOT40 values predicted using this curve match peak AOT40 observations better (slope AOT40 predicted vs. AOT40 observed: 1.03, R 2 = 0.97).
Several studies attempted to model ozone levels and exposure metrics over the IGP.Deb Roy et al. (2009)

Ozone exposure yield relationships
Crop yield losses and associated economic losses due to ozone are well constraint for USA and Europe (Avnery et al., 2011).In Asia, even today, O 3 -dose-plant-response relationships developed in the United States and Europe are used to assess the crop yield loss (Deb Roy et al., 2009;Ghude et al., 2014), despite the fact that several studies revealed that Asian wheat and rice cultivars are more susceptible to O 3 induced damage than North American their counterparts (Emberson et al., 2009;Oksanen et al., 2013).The analyses of crop production losses so far made for India are based on model derived O 3 mixing ratios (Deb Roy et al., 2009;Ghude et al., 2014;van Dingenden et al., 2009, Avnery et al., 2011a) and apply O 3 -dose-plant-response metrics and formulae developed in the US (Adams et al., 1889;Lesser, 1990;Heck et al., 1984b;Wang and Mauzerall, 2004) or in Europe (Mills et al., 2007).Such predictions may underestimate crop yield losses.It has already been pointed out above that for some models, the model predicted daytime O 3 mixing ratios or AOT40 values tend to be lower than the observed O 3 mixing ratios or AOT40 in particular for Zayad and Kharif season.Hence model predictions need to be validated and improved using in-situ ozone measurements.
O 3 -dose-plant-response metrics used in the modelling studies conducted so far also underestimate crop production losses due to the fact that South Asian wheat and rice cultivars are more sensitive to ozone.Emberson et al. (2009)  of Asian open top chamber (OTC) and plant chamber studies, but refrained from deriving an Asia specific dose response curves for wheat and rice due to the large spread in the observational data.Emberson et al. (2009) 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 Sawada and Kohno (2009) 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).This suggests that the spread in the data is indeed caused by differences in the sensitivity of different cultivars.
Consequently 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 (Chahal et al., 2007;Jalota et al., 2008Jalota et al., , 2009;;Mahajan et al., 2009;Brar et al., 2012;Buttar et al., 2013;Ram et al., 2013) which lead to coincidental change in ozone exposure and one study that reported collocated yield and ozone measurements (Agrawal et al., 2003) to derive an empirical exposure-yield relationship for rice and wheat.Secondly, we derive a relationship from a series of OTC studies conducted in India (Rai et al., 2007;Rai and Agrawal, 2008;Singh et al., 2009;Rai et al., 2010;Singh and Agrawal, 2010;Sarkar andAgrawal, 2010, 2012) and compare with cultivars commonly grown in Pakistan and Bangladesh (Wahid et al., 1995b;Maggs et al., 1995;Maggs and Ashmore, 1998;Wahid, 2006;Akhtar et al., 2010a, b;Wahid et al., 2011) to investigate to which extent the results can be extrapolated to entire South Asia.We refrain from including cultivars popular in South East Asia that may show a very different sensitivity to ozone exposure.sowed on different sowing dates has been calculated using our data (Table S2).Yield data for rice has been taken from the peer reviewed literature (Chahal et al., 2007;Jalota et al., 2009;Mahajan et al., 2009;Brar et al., 2012).There is a significant trend of the reported crop yields as a function of ozone exposure indices (Fig. 3, R 2 = 0.58 for M7 and R 2 = 0.57 for AOT40).

Rice
Figure 4 compares the empirical ozone exposure response curve derived from the field data presented in Fig. 3 (solid line) with RY values determined in open top chamber studies (OTC) conducted in India (squares, dash and dot line fit) and Pakistani Punjab (diamonds).Large diamonds indicate studies 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, Mills et al., 2007) and American (M7, Adams et al., 1989) dose response relationship.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 as 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 2) is derived by fitting all relative yields for Indian cultivars form OTC studies (Fig. 4).We calculate relative yields for all 5 reference periods defined in Supplement S1 both using the old (Mills et al., 2007;Adams et al., 1989) and the revised RYL relationships.
It is clear from Fig. 4 and Table S2 that the RY curve derived by Adams et al. (1989) overestimates the RY of Oryza sativa L. Indica cultivars planted in the IGP significantly and interesting to note that there seems to be a East-West gradient in the sensitivity of local cultivars to ozone exposure.Bangladeshi cultivars showed the lowest sensitivity Introduction

Conclusions References
Tables Figures

Back Close
Full and highest relative yields and 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 (Adams et al., 1989) underestimates crop production losses in South Asia by approximately 20-30 % (Table S2).For AOT 40 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 South 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 obtained in the current steeper than the slope reported by Mills et al. (2007) 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 underestimates crop production losses in South Asia by approximately 5-15 % (Table S2).Table S2 summarises relative yields for the five reference periods (which correspond to different sowing dates) and inter-compares RY obtained by our calculation 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 Mills et al. (2007) and the exposure yield relationship for Indian cultivars (Table S2) 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 Adams et al. (1989) and the exposure yield relationship for Indian cultivars (Table S2).The difference between the two is ∼ 20 %.After the revision relative yields calculated using the M7 and AOT40 metrics agree within 5-6 % 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 O 3 concentration.Introduction

Conclusions References
Tables Figures

Back Close
Full

Wheat
Figure 5 shows the empirical correlation of rice yields and ozone exposure indices for field studies with variations in sowing in Punjab and Haryana.Ozone exposure for wheat sowed on different sowing dates has been calculated using our data (Table S3).Yield data for wheat have been taken from the peer reviewed literature (Agrawal et al., 2003;Chahal et al., 2007;Jalota et al., 2008;Coventry et al., 2011;Buttar et al., 2013;Ram et al., 2013).Agrawal et al. (2003) 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 date.The yield data has been positioned in conformation to the emergence dates (Period 1 to 5) defined in Supplement S1.There is a significant decrease in yield as a function of increasing ozone exposure (Fig. 5) for both ozone exposure indices (R 2 = 0.55 of M7 and R 2 = 0.7 for AOT40).Based on the values of slopes and y-intercept, we determined our own values of the relative yield relationship (solid line, Fig. 6) by calculating the relative yield at the observed M7 compared to the yield that would be obtained for background concentrations of 25 nmol mol −1 ozone.For AOT 40 the relative yield is determined with respect to the yield that would have been obtained for AOT40 = 0).Figure 6 compares the empirical ozone exposure response curve derived from field data (solid line) with RYL relationships reported in the literature (Mills et al., 2007;Heck et al., 1984b;Lesser et al., 1990;Adams et al., 1989) with open top chamber studies (OTC) conducted in India (squares, dash and dot line) and Pakistani Punjab (diamonds).Circles show plant chamber studies on Bangladeshi wheat cultivars conducted in Japan.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 func-Introduction

Conclusions References
Tables Figures

Back Close
Full tion.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 2) is derived by fitting all relative yields for Indian cultivars form OTC studies (Fig. 6).We calculate relative yields for all 5 reference periods defined in Supplement S1 both using the old (Mills et al., 2007;Adams et al., 1989) and the revised RYL relationships.It is clear from Fig. 6 that the RY curves for winter wheat derived by Lesser et al. (1990) and Heck et al. (1984b) 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 (Lesser et al., 1990;Heck et al., 1984b) underestimates crop production losses in South Asia by approximately 10 and 20 % for the equation of Heck et al. (1984b) and Lesser et al. (1990) respectively (Table S3).
For AOT 40 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 Mills et al. (2007), although a limited number of cultivars planted in the IGP show an exposure RY relationship similar to that reported by Mills et al. (2007).Cultivars with lower sensitivity to ozone include Bijoy (Akhtar et al., 2010a), Inqilab-91, Punjab-96 andPasban-90 (Wahid, 2006), HUW234, PBW343 and Sonalika (Singh et al., 2009;Sarkar and Agrawal, 2010).For HUW468 the sensitivity obtained by Singh et al. (2009) and Singh and Agrawal (2010) differ.However, for most cultivars crop production losses calculated using the equation derived based on European studies underestimates crop production losses in South Asia.Table S3 summarises relative yield that are obtained by our calculation.For AOT40 the exposure yield relationship of Mills et al. (2007) and the exposure yield relationship for Indian cultivars (Table S3) differ by ∼ 15 %.For M7 the exposure yield relationship of Lesser et al. (1990) overestimates the yields by ∼ 20 % and the exposure yield relationship of Heck et al. (1984b) by ∼ 10 % (Table S3).Introduction

Conclusions References
Tables Figures

Back Close
Full After the revision relative yields calculated using the M7 and AOT40 metrics still show 15-20 % discrepancy.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 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. 2) 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.Picchi et al. (2010) reported high sensitivity of wheat cultivars to ozone exposure during the grain filling stage and our observations agree well with their finding.Therefore, for South 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 previously used exposure response relationships (−15 % for AOT40).This can be attributed to the variety of cultivars.Most Indian cultivars are more sensitive to high O 3 concentration, though few individual cultivars show higher resistance.

Cotton
Cotton yield data for this region has only been reported in two studies (Jalota et al., 2008;Buttar et al., 2013) and OTC studies on cotton in India have not been conducted till date.Buttar et al. (2013) reported yields for different number of pickings (period 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 shows very high variability and no significant influence of ozone on yields, when the results are averaged over 2 years (Jalota et al., 2008).However, there is a significant intra and inter-annual variability of yields as a function of rainfall reported from the site on which the crop was grown (Jalota et al., 2008).Since the crop was irrigated sufficiently, this 2376 Introduction

Conclusions References
Tables Figures

Back Close
Full yield dependence on rain should not be related to drought stress.Ozone levels in Punjab during monsoon season are strongly influence by 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 days after sowing 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 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 from 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 Mills et al. (2007) and Heck et al. (1984b) to calculate relative yields (Table S4).
For cotton there are extreme differences of 30-60 % between the relative yields calculated using AOT40 (Mills et al., 2007) and M7 (Heck et al., 1984b).Ozone fumigation studies on Indian cultivars are urgently required to constrain relative yields and crop production losses due to ozone more accurately.Introduction

Conclusions References
Tables Figures

Back Close
Full

Maize
Maize is planted both as Rabi and Kharif crop, however, cultivation occurs only on a limited area.We could not find any study reporting crop yields for maize planted in Punjab or Haryana in the peer reviewed literature.Neither could be identify any study investigating ozone related crop yield losses for Indian maize cultivars, In the absence of suitable data we were unable to derive a ozone exposure RY relationship for Indian maize cultivars and use the relationship of Mills et al. (2007) and Heck et al. (1984b) to calculate relative yields (Table S5).

Yield loss and economic loss in Punjab and Haryana
Table 5 summarises 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 1970's and 1980's (Heck et al., 1984b;Adams et al., 1989;Lesser et al., 1990) 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 (Tuovinen, 2000;Hollaway et al., 2012).
The old AOT40 exposure-response-relationship by Mills et al. (2007) does not capture the sensitivity of most South 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 2, Figs. 4 and 6) 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 2) predicts that RYL for Indian cultivars are 1.5-2 times the RYL predicted based on the equation by Mills et al. (2007).
A recent modeling studies for the year 2005 predicted RYL of 1 and 1.2 % for Punjab and Haryana respectively for wheat and 8.1 % for Punjab for rice (Ghude et al., 2014).Introduction

Conclusions References
Tables Figures

Back Close
Full These relative yield losses are a factor of 15-30 lower compared to the RYL calculated using the same equation (Mills et al., 2007) but employing in-situ measurements for calculating AOT40 for wheat and a factor of 1.5 to 1.8 lower for rice (Table 5 Column RYL AOT40 , Mills et al., 2007).Debaje (2014) 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 both based on the M7 exposure response relationship for winter wheat proposed by Lesser et al. (1990) of 10.8 % and for the AOT40 based exposure response relationship by Mills et al. (2007) 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 S3) for the years 2011-2014 (Table 5 Column RYL AOT40 , Mills et al., 2007).This indicates that the underestimation of RYL by Ghude et al. (2014) is due to an underestimation of the AOT40 values during the wheat growing season in the north west IGP caused by the fact that the Ghude and co-workers 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 (Picchi et al., 2010).Avnery et al. (2011a) used the Mozart-2 model to predict national average RYL of 25-30 % for wheat using the AOT40 based equation, which agrees well with our observations.van Dingenen et al. ( 2009) using TM5 model predicted RYL ranging from 20-30 % for wheat, 10-15 % for rice and 5-10 % for maize for the year 2000, which agrees well with the observations.Table 6 shows the crop production, crop production loss and MSP for the fiscal year Haryana was obtained from Department of Agriculture Haryana (2014).The table also presents economic cost losses calculated for wheat, rice, maize and cotton using the old (Mills et al., 2007) Ghude et al. (2014) predicted crop production losses of 0.85 million t only 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 shorer ozone sensitive growth period (factor 1.5-1.8)and the revision of the exposure yield relationship (Table 2) to account for the sensitivity of Indian rice cultivars (factor 1.9).Economic losses amount to INR 67.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 like Basmati.
Secondly, we do not account for the losses in the food processing sector and other allied industries.The value gain from MSP to 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.78 % of the rural population depends on agriculture as primary source of income.Hence, rural income is affected strongly by crop yields.
Previous studies investigating the relationship between monsoon rainfall, food grain production and the Nations GDP for the years 1951-2003 (Gadgil and Gadgil, 2006) found that one percent decrease in food grain production due to deficient monsoon lead to a 0.36 % decrease in Indias 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 (Avnery et al., 2011a;van Dingenen et al., 2009), the real economic burden of current ozone levels in terms of the Indias GDP is likely to fall into the range from 3.6 to 20 % (Eq.8).Introduction

Conclusions References
Tables Figures

Back Close
Full ing the water productivity of the crop and preserving precious ground water.It will also increase the profit margin, as farmers often run tubewells 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.New tillage practises that facilitate timely sowing such as relay seeding into cotton and zero or low tillage regimes that incorporates rice straw or machinery to rapidly clear rice residues from the fields are urgently required to facilitate timely sowings.However, screening a large number of cultivars to identify and promote those cultivars that are less susceptible to ozone damage also offers a way forward.
Mitigating the increasing tropospheric ozone levels in India remains a challenging task for policy makers and the regulatory authorities.Enforcing existing legislation aimed at reducing the emission of ozone precursors remains a challenge even in metropolitan cities.Most area sources of ozone precursors, which include domestic cooking and heating, emissions from cottage industries, waste burning and crop residue burning are either not within the purview or not within the reach of the regulatory authorities.Low cost indigenous solutions which are attractive alternatives to the existing technologies are urgently required to curb precursor emissions and should be a major research focus.Developing suitable solutions requires interdisciplinary efforts, as technical feasibility, costs and social acceptability of the proposed solutions needs to be assessed in order to ensure widespread implementation.Sci. Total Environ., 371, 304-313, 2006. 2365, 2368, 2371, 2375, 2396 Wahid, A., Ahmad, S. S., Butt, Z. A., and Ahmad, A. M.: Exploring the hidden tread of gaseous pollutants using rice (Oryza sativa L.) plants in Pakistan, Pak. J. Bot., 43, 365-382, 2011. 2365, 2368, 2371, 2396 Wang, X. and Mauzerall, D. L.: Characterizing distributions of surface ozone and its impact on grain production in China, Japan and South Korea: 1990and 2020, Atmos. Environ., 38, 4383-4402, 2004. 2394 Wilkinson, S., Mills, G., Illidge, R., and Davies, W. J.: How is ozone pollution reducing our food Introduction

Conclusions References
Tables Figures

Back Close
Full and has been shown to lead to accumulation of ethylene in the leaves.The presence of ethylene in the leaves interferes with the functioning of the hormone abscisic acid (ABA), a hormone which normally controls stomata closure and reduces water loss under drought conditions (Wilkinson et al., 2012).Consequently ozone related crop yield losses in such phenotypes may be enhanced in regions where plants are frequently exposed to temperature or water stress.Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 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 metric during a workshop in Kuopio, Finland in 1996 and a set of critical level values based on this index Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

Figure 2
Figure 2 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 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 favourable conditions such as high temperature, low humidity and high solar radiation favour the photochemical production of O 3 regionally.After summer, the next highest ozone levels are observed during 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 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 results in comparably high ozone levels.The lowest median daytime ozone mixing ratios of approximately 30 nmol mol −1 are observed in August, during 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 reduction in the solar radiation, low temperatures and fog result in less photochemical production of O 3 .2367 Discussion Paper | Discussion Paper | Discussion Paper | modelled AOT40 over the Indian region for the year 2003 using the model REMO-CTM.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 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 season.In a more recent study conducted using WRF-Chem, Ghude et al. (2014) 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 15 June to 15 September and December to Febuary for kharif and rabi season respectively.For those time windows, predicted ozone daytime concentrations agree well with the measured M12.Mittal et al. (2007) inter-compared 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.Discussion Paper | Discussion Paper | Discussion Paper | Emberson et al. (2009) compared MATCH 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 an 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).van Dingenen et al. (2009) used a global model (TM5) to predict surface ozone over India and the model reproduces surface observations for our site equally well.
Discussion Paper | Discussion Paper | Discussion Paper |

Figure 3
Figure 3 shows the empirical correlation of rice yields and ozone exposure indices for field studies with variations in sowing in Punjab and Haryana.Ozone exposure for rice 2371 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 2012-2013 and 2013-2014.Data on crop production was obtained from the following sources: Directorate of Economics and Statistics (2013); Agricultural Statistics (2013), Procurement data was obtained from the Food Corporation of India (2013).For the fiscal year 2013-2014 data for Punjab are based on estimates while final data for Discussion Paper | Discussion Paper | Discussion Paper | 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.For cotton and maize economic losses amount to INR 79.15 billion and 47.50 billion (USD 1.3 and 0.8 billion) for cotton and INR 0.65 billion and INR 0.85 billion (USD 11 and 14 million) for maize in the fiscal year 2012-2013 and 2013-2014 respectively.The total economic losses for the agricultural sector in Punjab and Haryana amount to INR 390.89 billion (USD 6.5 billion) in the fiscal year 2012-2013 and INR 223.34 billion (USD 3.7 billion) in the fiscal year 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 like Basmati.Secondly, we do not account for the losses in the food processing sector and other quality dataset 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 compared 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, 9-11 % for maize and 47-58 % for cotton.Crop production losses for wheat amounted to 20.8 million t in fiscal year 2012-2013 and 10.3 million t in fiscal year 2013-2014 for Punjab and Haryana jointly.Crop production losses for rice totaled 5.4 million t in fiscal year 2012-2013 and 3.2 million t year 2013-2014 for Punjab and Haryana jointly.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/rice to ∼ 820 million of India's economically weaker sections of society.The lower limit for economic cost losses in Punjab and Haryana amounted to USD 6.5 billion in the fiscal year 2012-2013 and USD 3.7 billion in the fiscal year 2013-2014.The upper limit for the ozone related economic losses incurred at current ozone levels for entire India amount 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 of healthcare related costs and the number of workdays lost and a reduction of 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 ozone exposure of the crop.Reaching out to farmers for promoting this change in cropping practise will yield co-benefits in terms of increas-Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | 2009 Project (ESA 2010 and UCLouvain) for providing a high resolution land classification map of the region.Discussion Paper | Discussion Paper | Discussion Paper | Wahid, A.: Influence of atmospheric pollutants on agriculture in developing countries: a case study with three new wheat varieties in Pakistan, Discussion Paper | Discussion Paper | Discussion Paper |

Figure 1 .Figure 2 .Figure 3 .Figure 4 .Figure 5 .
Figure 1.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).
and revised exposure-yield relationship.The losses are present for both Haryana and Punjab separately and cumulatively.The highest crop production loss is seen for wheat: 20.8 million t in fiscal year 2012-2013 and 10.3 million t in fiscal year 2013-2014 for Punjab and Haryana jointly.Ghude et al. (2014) predicted crop production losses of 0.25 million t only for the year 2005 for both states.The discrepancy is mostly due to the fact that this study assumed the ozone sensitive growth period of wheat lasts only from December to February and, hence, did not capture the effect of the high AOT40 during the grain filling stage of the crop in March (factor ∼ 15-30) and partially due to the revision of the exposure response relationship (Table 2; factor ∼ 2).Debaje (2014) estimated crop production losses of 10.9 million t year −1 on average for both states combined.The estimate falls within the same order of magnitude as our estimate.Avnery et al. (2011a) estimated CPL of 26 million t for entire India but did not resolve losses for individual states.Eco-

Table 6 .
(Mills et al., 2007) for Punjab (PB) and Haryana (HR) and MSP for the fiscal year 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(Mills et al., 2007)a and for wheat and rice also using the revised AOT40 based exposure-response relationship b .CP and CPL for rice, wheat and maize are given in tonnes (t);CP and CPL in bales (b.)