**Silicon the Non-Essential Beneficial Plant Nutrient to Enhanced Drought Tolerance in Wheat**

Mukhtar Ahmed1, Muhammad Asif2,\* and Aakash Goyal3 *1Department of Agronomy, PMAS Arid Agriculture University Rawalpindi, 2Agricultural, Food and Nutritional Science, 4-10 Agriculture/Forestry Centre, Univ. of Alberta, Edmonton, AB, 3Bayer Crop Science, Saskatoon, 1Pakistan 2,3Canada* 

#### **1. Introduction**

30 Crop Plant

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Present water scarcity is a severe problem and cause of deterioration in quality and productivity of crops to reduce crop yield in arid and semi-arid regions. Silicon is known to better the deleterious effects of drought on plant growth and development. Silicon (Si) found to be an agronomically important fertilizer element that enhances plant tolerance to abiotic stresses (Liang et al., 2005). Silicon also known to increase drought tolerance in plants by maintaining plant water balance, photosynthetic efficiency, erectness of leaves and structure of xylem vessels under high transpiration rates due to higher temperature and moisture stress (Hattori et al., 2005). Similarly, Gong et al., (2003 and 2005) observed improved water economy and dry matter yield of water under application of silicon. A number of possible mechanisms were proposed through which Si may increase salinity tolerance in plants, especially improving water status of plants, increased photosynthetic activity and ultra-structure of leaf organelles. The stimulation of antioxidant system and alleviation of specific ion effect by reducing Na uptake were also drought tolerance mechanisms in plants exposed to silicon application (Liang et al., 2005).

#### **2. Silicon accumulation and its uptake in plants**

Silicon (Si) is most abundant in soil next to oxygen and comprises 31% of its weight. It is taken up directly as silicic acid (Ma et al., 2001). It primarily accumulated in leaves because it is distributed with the transpiration stream. In dried plant parts the silica bodies are located in silica cells below the epidermis and in epidermal appendices (Dagmar et al., 2003). Being a dominant component of soil minerals the silicon has many important functions in environment. Many studies have suggested the positive growth effects of silicon, including increased dry mass and yield, enhanced pollination and most commonly

<sup>\*</sup> Corresponding author

Silicon the Non-Essential Beneficial Plant Nutrient to Enhanced Drought Tolerance in Wheat 33

Agriculture University, Rawalpindi. Pakistan. Seeds of two varieties Chakwal-50, GA-2002 and two lines NR- 333 and NR- 372 were taken from National Agricultural Research Center (NARC). The experiment was laid out in glass oven sterilized Petri dishes lined with two layers of Whattman filter paper and one layer of toilet roll. The filter paper and toilet rolls were irrigated with respective solutions at their saturation point and excess solution was discarded. Ten seeds of each variety were sown in total 60 Petri dishes which were set in a complete randomized block design. The lid covered Petri dishes were placed in a germinator under constant darkness at a temperature of 20oC and 30-40 % relative humidity. The solution treatments were applied as T1= control (water only), T2= 5 % Potassium silicate, T3= 10 %, Potassium silicate without irrigation, T4= 5% Potassium silicate T5= 10% Potassium silicate with irrigation. In the experiment II earthen pots of dimensions (25cm length 20cm diameter) with an area of 500cm2 covered with aluminum foils to prevent an increase in soil temperature caused by solar radiation. Pots were irrigated before adding soil. Each pot was filled with 10 kg of well pulverized soil. Fertilizer was added on the basis of soil weight in the pots. Two wheat cultivars and two wheat advanced lines with three replications were used as plant material in the present study. Ten seeds of each cultivars and advanced lines were sown per pot. At three leaf stage, all the treatments were applied and potassium chloride was applied to the control pots to yield the same total potassium as in Si treatment. The pH in both solutions was adjusted to pH 5.5 with HCl prior to application. Plastic sheets coated with aluminum film were placed on the soil surface to prevent evaporation from the pots. The treatments of the study were:T1: Control, T2: 5 % level of Potassium silicate (5: 95ml) without irrigation, T3: 10 % level of Potassium silicate (10: 90ml) without irrigation, T4: 5 % level of Potassium silicate (5: 95ml) with irrigation. (300mm) and T5: 10 % level of Potassium silicate (10: 90ml) with irrigation. (300mm). Data was collected about crop growth rate (CGR), relative growth rate (RGR), net assimilation rate (NAR) and leaf area index (LAI) using the formula by Gardner et al. (1985) while leaf area was measured with the help of CI-202 area meter by averaging the value taken from three plant samples. However, leaf area duration (LAD) was calculated by formula proposed by Power et al., (1967). Similarly, physiological parameters like photosynthesis rate (A) (μ mole/m2/second), transpiration rate (E) (mole/m2/s), stomatal coductance (gs) (mol m2 s-1) were measured by Infrared Gas Analyzer (IRGA) at flag leaf stage (Long & Bernacchi, 2003). Leaf membrane stability index (LMSI) was determined according to method described by Chandrasekar et al., (2000). Meanwhile leaf succulence (mg/m²) was measured by leaves taken randomly from each plant. Fresh leaf weight was taken and their area was measured and leaves were dried at 70°C for one week and dry weight was taken. Succulence was calculated by using formula (Succulence = fresh weight-dry weight/leaf area). Relative water content (RWC) was measured from fully expended leaves. The leaves were excised and fresh weight (FW) was immediately recorded, then leaves were soaked for 4 hours in distill water at room temperature, and turgid weight (TW) was recorded. After drying for 24 hours at 80 °C total dry weight (DW) was recorded. Relative water content was measured according to that formula (Barrs & Weatherly, 1962). Meanwhile epicuticular Wax (mg m-2) were measured from leaves (0.5 g) randomly taken from the plant and their area was measured. Three leaf samples were washed three times in 10 ml carbon tetrachloride for 30 sec per wash. The extract was filtered, evaporated to dryness and the remaining wax was weighed. Wax content was expressed on the basis of leaf area only, i.e. wax content mg cm-2

increased disease resistance (Rodrigues, et al., 2004). Silicon can also alleviate imbalances between zinc and phosphorus supply. Gypsum is known to improve the productivity of dispersive soils and Sodium silicate has shown to maintain root activity under waterlogged conditions (Ma et al., 1989). Water stress is common problem in the rainfed regions of the world now a day, which have caused deviation of plant functions from normal to abnormal. Therefore, it's necessary to provide plants such type of nutrition which can maintain water balance in the plants. Silicon is considered to be important element under stress because it increased drought tolerance in plants by maintaining leaf water potential, assimilation of CO2 and reduction in transpiration rates by adjusting plant leaf area (Hattori et al., 2005). Maintenance of higher leaf water potential under stress is one of remarkable feature which silicon nutrition does for plants as reported by Lux et al., (2002). Silicon was reported to enhance growth of many plants particularly under biotic and abiotic stresses (Epstein, 1999). A number of possible mechanisms have been proposed by which Si would increase resistance of plants against salinity stress which is a major yield limiting factor in arid and semiarid areas. (Al-aghabary et al., 2004).

#### **3. How silicon can coup biotic and abiotic stresses**

Silicon (Si) is reported to have beneficial effects on the growth, development and yield of plants through protection against biotic and abiotic stresses. Silicon has not yet been considered a generally essential element for higher plants, partly because its roles in plant biology are poorly understood and our knowledge of silicon metabolism in higher plants lags behind that in other organisms (such as diatoms). However, numerous studies have demonstrated that silicon is one of the important elements of plants, and plays an important role in tolerance of plants to drought stress. Agarie et al., (1998) reported that silicon could decrease the transpiration rate and membrane permeability of wheat (*Triticum aestivum* L.) under water deficit induced by polyethylene glycerol. In Pakistan, little work has been done on silicon applications mostly on wheat crop being staple food crop of the country, cultivated under a wide range of climatic conditions. Its contribution towards value added in agriculture and GDP is 13.1 % and 2.7 % respectively. Wheat was cultivated on an area of 8.81 million hectares with the production of 24.2 m. tons and yield of 2750 Kg ha-1 (Economic Survey of Pakistan, 2010-11). It is estimated that rainfed area contributes only about 12 percent of the total wheat production. The Punjab province contributes over 71 percent of the national wheat production while the Punjab barani tract contributes 25 percent of the wheat production in the province. The yield of the crop can be increased at least two times with proper management of production factors. The crop also suffers from severe moisture stress that plays a major role in lowering the yield under rainfed areas. So, present study was conducted to evaluate the response of two wheat varieties and two lines under different levels of potassium silicate which will be source of silicon in this study. The specific objectives of proposed study were to evaluate the performance of different wheat varieties and lines under silicon enhanced drought tolerance and to verify that silicon may be useful to enhance the drought tolerance.

#### **3.1 Methodology**

The experiment relating to the study of Silicon on wheat growth, development and drought resistance index was conducted in Department of Agronomy, Pir Mehr Ali Shah, Arid

increased disease resistance (Rodrigues, et al., 2004). Silicon can also alleviate imbalances between zinc and phosphorus supply. Gypsum is known to improve the productivity of dispersive soils and Sodium silicate has shown to maintain root activity under waterlogged conditions (Ma et al., 1989). Water stress is common problem in the rainfed regions of the world now a day, which have caused deviation of plant functions from normal to abnormal. Therefore, it's necessary to provide plants such type of nutrition which can maintain water balance in the plants. Silicon is considered to be important element under stress because it increased drought tolerance in plants by maintaining leaf water potential, assimilation of CO2 and reduction in transpiration rates by adjusting plant leaf area (Hattori et al., 2005). Maintenance of higher leaf water potential under stress is one of remarkable feature which silicon nutrition does for plants as reported by Lux et al., (2002). Silicon was reported to enhance growth of many plants particularly under biotic and abiotic stresses (Epstein, 1999). A number of possible mechanisms have been proposed by which Si would increase resistance of plants against salinity stress which is a major yield limiting factor in arid and

Silicon (Si) is reported to have beneficial effects on the growth, development and yield of plants through protection against biotic and abiotic stresses. Silicon has not yet been considered a generally essential element for higher plants, partly because its roles in plant biology are poorly understood and our knowledge of silicon metabolism in higher plants lags behind that in other organisms (such as diatoms). However, numerous studies have demonstrated that silicon is one of the important elements of plants, and plays an important role in tolerance of plants to drought stress. Agarie et al., (1998) reported that silicon could decrease the transpiration rate and membrane permeability of wheat (*Triticum aestivum* L.) under water deficit induced by polyethylene glycerol. In Pakistan, little work has been done on silicon applications mostly on wheat crop being staple food crop of the country, cultivated under a wide range of climatic conditions. Its contribution towards value added in agriculture and GDP is 13.1 % and 2.7 % respectively. Wheat was cultivated on an area of 8.81 million hectares with the production of 24.2 m. tons and yield of 2750 Kg ha-1 (Economic Survey of Pakistan, 2010-11). It is estimated that rainfed area contributes only about 12 percent of the total wheat production. The Punjab province contributes over 71 percent of the national wheat production while the Punjab barani tract contributes 25 percent of the wheat production in the province. The yield of the crop can be increased at least two times with proper management of production factors. The crop also suffers from severe moisture stress that plays a major role in lowering the yield under rainfed areas. So, present study was conducted to evaluate the response of two wheat varieties and two lines under different levels of potassium silicate which will be source of silicon in this study. The specific objectives of proposed study were to evaluate the performance of different wheat varieties and lines under silicon enhanced drought tolerance and to verify that silicon may be useful

The experiment relating to the study of Silicon on wheat growth, development and drought resistance index was conducted in Department of Agronomy, Pir Mehr Ali Shah, Arid

semiarid areas. (Al-aghabary et al., 2004).

to enhance the drought tolerance.

**3.1 Methodology** 

**3. How silicon can coup biotic and abiotic stresses** 

Agriculture University, Rawalpindi. Pakistan. Seeds of two varieties Chakwal-50, GA-2002 and two lines NR- 333 and NR- 372 were taken from National Agricultural Research Center (NARC). The experiment was laid out in glass oven sterilized Petri dishes lined with two layers of Whattman filter paper and one layer of toilet roll. The filter paper and toilet rolls were irrigated with respective solutions at their saturation point and excess solution was discarded. Ten seeds of each variety were sown in total 60 Petri dishes which were set in a complete randomized block design. The lid covered Petri dishes were placed in a germinator under constant darkness at a temperature of 20oC and 30-40 % relative humidity. The solution treatments were applied as T1= control (water only), T2= 5 % Potassium silicate, T3= 10 %, Potassium silicate without irrigation, T4= 5% Potassium silicate T5= 10% Potassium silicate with irrigation. In the experiment II earthen pots of dimensions (25cm length 20cm diameter) with an area of 500cm2 covered with aluminum foils to prevent an increase in soil temperature caused by solar radiation. Pots were irrigated before adding soil. Each pot was filled with 10 kg of well pulverized soil. Fertilizer was added on the basis of soil weight in the pots. Two wheat cultivars and two wheat advanced lines with three replications were used as plant material in the present study. Ten seeds of each cultivars and advanced lines were sown per pot. At three leaf stage, all the treatments were applied and potassium chloride was applied to the control pots to yield the same total potassium as in Si treatment. The pH in both solutions was adjusted to pH 5.5 with HCl prior to application. Plastic sheets coated with aluminum film were placed on the soil surface to prevent evaporation from the pots. The treatments of the study were:T1: Control, T2: 5 % level of Potassium silicate (5: 95ml) without irrigation, T3: 10 % level of Potassium silicate (10: 90ml) without irrigation, T4: 5 % level of Potassium silicate (5: 95ml) with irrigation. (300mm) and T5: 10 % level of Potassium silicate (10: 90ml) with irrigation. (300mm). Data was collected about crop growth rate (CGR), relative growth rate (RGR), net assimilation rate (NAR) and leaf area index (LAI) using the formula by Gardner et al. (1985) while leaf area was measured with the help of CI-202 area meter by averaging the value taken from three plant samples. However, leaf area duration (LAD) was calculated by formula proposed by Power et al., (1967). Similarly, physiological parameters like photosynthesis rate (A) (μ mole/m2/second), transpiration rate (E) (mole/m2/s), stomatal coductance (gs) (mol m2 s-1) were measured by Infrared Gas Analyzer (IRGA) at flag leaf stage (Long & Bernacchi, 2003). Leaf membrane stability index (LMSI) was determined according to method described by Chandrasekar et al., (2000). Meanwhile leaf succulence (mg/m²) was measured by leaves taken randomly from each plant. Fresh leaf weight was taken and their area was measured and leaves were dried at 70°C for one week and dry weight was taken. Succulence was calculated by using formula (Succulence = fresh weight-dry weight/leaf area). Relative water content (RWC) was measured from fully expended leaves. The leaves were excised and fresh weight (FW) was immediately recorded, then leaves were soaked for 4 hours in distill water at room temperature, and turgid weight (TW) was recorded. After drying for 24 hours at 80 °C total dry weight (DW) was recorded. Relative water content was measured according to that formula (Barrs & Weatherly, 1962). Meanwhile epicuticular Wax (mg m-2) were measured from leaves (0.5 g) randomly taken from the plant and their area was measured. Three leaf samples were washed three times in 10 ml carbon tetrachloride for 30 sec per wash. The extract was filtered, evaporated to dryness and the remaining wax was weighed. Wax content was expressed on the basis of leaf area only, i.e. wax content mg cm-2

Silicon the Non-Essential Beneficial Plant Nutrient to Enhanced Drought Tolerance in Wheat 35

for NR-333 which further confirmed the significant effect of silicon application on NAR (Table 3). Drought tolerant genotypes have maximum NAR under silicon nutrition as compared to other genotypes. The effect of silicon enhanced treatments to further elucidate effect of silicon

Leaf area might be an important index in determining crop growth as it directly involved in many plant processes. Photosynthesis, transpiration and stomatal aperture take place in leaves of plants. Leaf area attributed toward good uptake of water and translocation of photoassimilates from source to sink. Results demonstrated significant variation for leaf area among different cultivars under various silicon treatments (Table 4). The results demonstrated maximum leaf area for Chakwal-50 **(**201.67 cm2**)** under 10 % silicon application with irrigation. While, minimum leaf area was observed for NR-333(173.00 cm2) under controlled condition. Leaf area significantly contributed toward physiological indices, which boosted up crop growth and accumulation of more photoassimilates from source to sink and consequently, it led to higher grain yield (Fig.1). Our results were in the line with the findings of Ahmed et al., (2011a) who reported significant impact of stress conditions

Leaf area index (LAI) at flag leaf stage among four wheat cultivars under five different concentrations of silicon application revealed that silicon nutrition has depicted significant effect upon crop growth. However, this effect was more significant for Chakwal-50 as compared to other genotypes which are drought resistance exhibiting maximum leaf area and ultimately higher leaf area index as compared to other genotype (Table. 5). While, minimum leaf area index was noted down for GA-2002 under controlled treatment. The

Leaf area duration for all four genotypes under different silicon application was shown in Table.6. Leaf area duration increased from tillering to flag leaf stage and reached maximum at flag leaf stage as it is the critical stage of wheat. Maximum physiological attributes take place at this stage which determines crop productivity. In current study, Chakwal-50 showed maximum leaf area duration (116.00) under 10% application of silicon, whereas,

increment in LAI due to silicon nutrition was considerable (Ahmed et al., 2011a).

NR-333 exhibited minimum leaf area duration (87.00) under control conditions.

Mean 13.61AB 12.73C 13.12B 13.88A LSD for Genotypes= 0.64, Treatments=0.72 and Genotypes x Treatments= 1.43

Treatments Chakwal-50 GA-2002 NR-333 NR-372 Mean T1 10.10ijkl 10.00jkl 9.23l 9.56l 9.72E T2 11.53ghi 9.83kl 10.10ijkl 11.23hijk 10.68D T3 12.10fgh 11.36hij 12.85efg 13.50ef 12.45C T4 16.23bc 13.90de 15.20cd 16.93ab 15.57B T5 18.10a 16.77ab 18.20a 18.17a 17.80A

Table 1. Crop growth rate (g m-2 day-1) of wheat genotypes under different silicon treatments

on NAR of crop revealed that silicon application has positive effect on crop NAR.

**4.4 Leaf area (cm2**

upon leaf area of crop.

**4.5 Leaf area index** 

**4.6 Leaf area duration** 

**)** 

(Silva Fernandus et al., 1964). SPAD chlorophyll meter was used to measure chlorophyll contents at flag leaf. Drought resistance index is define as DC multiplied by variety minimum yield and then divided by average minimum yield of the varieties used in experiments (DRI =DC× (Ya / Ya) where DC =Drought resistance coefficient=Ya /Ym, Whereas Ya is the average yield of all the varieties with no irrigation, Ya is yield of the variety without irrigation and Ym is maximum yield of variety under irrigation). Proline content (µg g-1) was estimated spectrophotometerically following the ninhydrine method (Bates et al. 1973). The silicon concentration in leaves (mg) were measured at flag leaf stage according to Lux et al., (2002) The dried powdered plant sample was ashed in a muffle oven at 500 0C for 5 h. The plant ash was dissolved in diluted HCl (1: 1; 10 ml) at 100 0C. The process of dissolving in HCl and evaporation to dryness was repeated three times. Then, diluted HCl (1: 1; 15ml) was added and sample was heated at 100 0C, filtered placed into a ceramic crucible and ashed again in the oven at 540 0C for 5 h. After cooling, the weight of silicon was determined gravimetrically. At the end observations collected were analyzed for variance by STATISTIX 8.1.

#### **4. Effect of Silicon on growth kinetics**

#### **4.1 Crop Growth Rate (CGR) (g m-2 day-1)**

Crop growth rate is unit increase in drymatter of crop on daily basis. Temperature and moisture are the main determinant factors which affects growth and many other physiological processes of plants. Significant findings were observed for CGR at flag leaf stage under present study. The results revealed that maximum CGR was observed for NR-333 line under 10 % silicon application with irrigation followed by Chakwal-50 and NR-372 (Table 1). Whereas, NR-333 under controlled conditions exhibited minimum crop growth rate. Application of silicon enhanced crop growth rate under stressed conditions. Our results were in the line with the findings of Hattori et al., (2005)) who reported an increase in drymatter and growth rate of crop by silicon application under drought stress conditions.

#### **4.2 Relative Growth Rate (RGR) (g g-1 day-1)**

The pattern of relative growth rate for different wheat genotypes under various silicon treatments presented in table 2 .The results of current study depicted that maximum relative growth rate was observed for Chakwal-50 under 10 % silicon application with irrigation, followed by NR-333 under same treatment. While, minimum relative growth rate was exhibited by NR-333 under controlled conditions (without silicon application). This variation in relative growth rate might be due to difference in genetic potential and adaptability measures to cope stress using silicon enhanced treatments. Our results were similar to the findings that RGR decreased under stress conditions and negatively related to plant age.

#### **4.3 Net Assimilation Rate (NAR) (g cm-2 day-1)**

Remarkable increase in net assimilation rate (NAR) was recorded due to silicon treatments. The maximum NAR was recorded for Chakwal-50 (6.54 g cm-2 day-1**)** with 10% of silicon application which might increase water conversion capacity toward assimilates by boosting photosynthesizing machinery. However, under control conditions, NAR was 1.99 g cm-2 day-1

for NR-333 which further confirmed the significant effect of silicon application on NAR (Table 3). Drought tolerant genotypes have maximum NAR under silicon nutrition as compared to other genotypes. The effect of silicon enhanced treatments to further elucidate effect of silicon on NAR of crop revealed that silicon application has positive effect on crop NAR.

#### **4.4 Leaf area (cm2 )**

34 Crop Plant

(Silva Fernandus et al., 1964). SPAD chlorophyll meter was used to measure chlorophyll contents at flag leaf. Drought resistance index is define as DC multiplied by variety minimum yield and then divided by average minimum yield of the varieties used in experiments (DRI =DC× (Ya / Ya) where DC =Drought resistance coefficient=Ya /Ym, Whereas Ya is the average yield of all the varieties with no irrigation, Ya is yield of the variety without irrigation and Ym is maximum yield of variety under irrigation). Proline content (µg g-1) was estimated spectrophotometerically following the ninhydrine method (Bates et al. 1973). The silicon concentration in leaves (mg) were measured at flag leaf stage according to Lux et al., (2002) The dried powdered plant sample was ashed in a muffle oven at 500 0C for 5 h. The plant ash was dissolved in diluted HCl (1: 1; 10 ml) at 100 0C. The process of dissolving in HCl and evaporation to dryness was repeated three times. Then, diluted HCl (1: 1; 15ml) was added and sample was heated at 100 0C, filtered placed into a ceramic crucible and ashed again in the oven at 540 0C for 5 h. After cooling, the weight of silicon was determined gravimetrically. At the end observations collected were analyzed for

Crop growth rate is unit increase in drymatter of crop on daily basis. Temperature and moisture are the main determinant factors which affects growth and many other physiological processes of plants. Significant findings were observed for CGR at flag leaf stage under present study. The results revealed that maximum CGR was observed for NR-333 line under 10 % silicon application with irrigation followed by Chakwal-50 and NR-372 (Table 1). Whereas, NR-333 under controlled conditions exhibited minimum crop growth rate. Application of silicon enhanced crop growth rate under stressed conditions. Our results were in the line with the findings of Hattori et al., (2005)) who reported an increase in drymatter and growth rate of crop by silicon application under drought stress conditions.

The pattern of relative growth rate for different wheat genotypes under various silicon treatments presented in table 2 .The results of current study depicted that maximum relative growth rate was observed for Chakwal-50 under 10 % silicon application with irrigation, followed by NR-333 under same treatment. While, minimum relative growth rate was exhibited by NR-333 under controlled conditions (without silicon application). This variation in relative growth rate might be due to difference in genetic potential and adaptability measures to cope stress using silicon enhanced treatments. Our results were similar to the findings that RGR decreased under stress conditions and negatively related to plant age.

Remarkable increase in net assimilation rate (NAR) was recorded due to silicon treatments. The maximum NAR was recorded for Chakwal-50 (6.54 g cm-2 day-1**)** with 10% of silicon application which might increase water conversion capacity toward assimilates by boosting photosynthesizing machinery. However, under control conditions, NAR was 1.99 g cm-2 day-1

variance by STATISTIX 8.1.

**4. Effect of Silicon on growth kinetics 4.1 Crop Growth Rate (CGR) (g m-2 day-1)** 

**4.2 Relative Growth Rate (RGR) (g g-1 day-1)** 

**4.3 Net Assimilation Rate (NAR) (g cm-2 day-1)** 

Leaf area might be an important index in determining crop growth as it directly involved in many plant processes. Photosynthesis, transpiration and stomatal aperture take place in leaves of plants. Leaf area attributed toward good uptake of water and translocation of photoassimilates from source to sink. Results demonstrated significant variation for leaf area among different cultivars under various silicon treatments (Table 4). The results demonstrated maximum leaf area for Chakwal-50 **(**201.67 cm2**)** under 10 % silicon application with irrigation. While, minimum leaf area was observed for NR-333(173.00 cm2) under controlled condition. Leaf area significantly contributed toward physiological indices, which boosted up crop growth and accumulation of more photoassimilates from source to sink and consequently, it led to higher grain yield (Fig.1). Our results were in the line with the findings of Ahmed et al., (2011a) who reported significant impact of stress conditions upon leaf area of crop.

#### **4.5 Leaf area index**

Leaf area index (LAI) at flag leaf stage among four wheat cultivars under five different concentrations of silicon application revealed that silicon nutrition has depicted significant effect upon crop growth. However, this effect was more significant for Chakwal-50 as compared to other genotypes which are drought resistance exhibiting maximum leaf area and ultimately higher leaf area index as compared to other genotype (Table. 5). While, minimum leaf area index was noted down for GA-2002 under controlled treatment. The increment in LAI due to silicon nutrition was considerable (Ahmed et al., 2011a).

#### **4.6 Leaf area duration**

Leaf area duration for all four genotypes under different silicon application was shown in Table.6. Leaf area duration increased from tillering to flag leaf stage and reached maximum at flag leaf stage as it is the critical stage of wheat. Maximum physiological attributes take place at this stage which determines crop productivity. In current study, Chakwal-50 showed maximum leaf area duration (116.00) under 10% application of silicon, whereas, NR-333 exhibited minimum leaf area duration (87.00) under control conditions.


LSD for Genotypes= 0.64, Treatments=0.72 and Genotypes x Treatments= 1.43

Table 1. Crop growth rate (g m-2 day-1) of wheat genotypes under different silicon treatments

Silicon the Non-Essential Beneficial Plant Nutrient to Enhanced Drought Tolerance in Wheat 37

Treatments Chakwal-50 GA-2002 NR-333 NR-372 Mean T1 101.67h 98.00i 87.00k 96.00i 95.67E T2 108.67de 103.00gh 91.00j 101.00h 100.92D T3 114.00ab 107.33ef 97.67i 105.67fg 106.17C T4 115.00a 111.67bc 101.00h 110.33cd 109.50B T5 114.00ab 116.00a 105.00fg 115.00a 112.50A

Mean 110.67A 107.20B 96.33D 105.60C

Table 6. Leaf area duration (LAD) of wheat genotypes under different silicon treatments

Fig. 1. Crop growth rate and grain yield of wheat genotypes under different silicon treatments

Fig. 2. Leaf area and grain yield of wheat genotypes under different silicon treatments

LSD for Genotypes= 1.23, Treatments= 1.37 and Genotypes x Treatments= 2.74


LSD for Genotypes= 1.18, Treatments= 1.32 and Genotypes x Treatments= 2.64

Table 2. Relative growth rate (g m-2 day-1) of wheat genotypes under different silicon treatments


LSD for Genotypes= 0.41, Treatments= 0.46 and Genotypes x Treatments= 0.91

Table 3. Net assimilation rate (g m-2 day-1) of wheat genotypes under different silicon treatments


LSD for Genotypes= 2.86, Treatments= 3.19 and Genotypes x Treatments= 6.39

Table 4. Leaf area (cm2) of wheat genotypes under different silicon treatments


LSD for Genotypes= 0.03, Treatments= 0.04 and Genotypes x Treatments= 0.07

Table 5. Leaf area index (LAI) of wheat genotypes under different silicon treatments


LSD for Genotypes= 1.23, Treatments= 1.37 and Genotypes x Treatments= 2.74

36 Crop Plant

Treatments Chakwal-50 GA-2002 NR-333 NR-372 Mean T1 87.37fg 81,13k 73.69l 84.00hij 81.55D T2 92.23e 86.23gh 83.33jk 86.07ghi 86.97C T3 93.73de 89.30f 96.53bc 89.03f 92.15B T4 85.37ghi 83.50ijk 83.17jk 94.90bcd 86.73C T5 103.00a 93.93cde 96.80b 94.27bcde 97.00A

Mean 92.34A 86.82C 86.70C 89.65B LSD for Genotypes= 1.18, Treatments= 1.32 and Genotypes x Treatments= 2.64

Mean 6.06A 4.54B 3.47C 3.18C

Table 3. Net assimilation rate (g m-2 day-1) of wheat genotypes under different silicon

Treatments Chakwal-50 GA-2002 NR-333 NR-372 Mean T1 184.33def 184.00ef 173.00g 183.00ef 181.08D T2 193.33bc 186.00de 179.33fg 186.00de 186.17C T3 197.67ab 187.67cde 183.00ef 189.00cde 189.33BC T4 198.00ab 187.00cde 187.00cde 193.33bc 191.33AB T5 201.67a 183.00ef 190.67cd 198.67ab 193.50A

LSD for Genotypes= 0.41, Treatments= 0.46 and Genotypes x Treatments= 0.91

Mean 195.00A 185.53C 182.60D 190.00B LSD for Genotypes= 2.86, Treatments= 3.19 and Genotypes x Treatments= 6.39

Table 4. Leaf area (cm2) of wheat genotypes under different silicon treatments

Mean 1.68A .85C .89B 0.79D LSD for Genotypes= 0.03, Treatments= 0.04 and Genotypes x Treatments= 0.07

Table 5. Leaf area index (LAI) of wheat genotypes under different silicon treatments

Treatments Chakwal-50 GA-2002 NR-333 NR-372 Mean T1 1.16d 0.55k 0.85g 0.68j 0.815D T2 1.13d 0.72ij 0.81gh 0.72ij 0.845D T3 1.93b 0.94e 0.92ef 0.85g 1.16D T4 1.83c 0.85fg 0.77hi 0.78ghi 1.06C T5 2.32a 1.14d 1.11d 0.95e 1.38A

treatments

treatments

Table 2. Relative growth rate (g m-2 day-1) of wheat genotypes under different silicon

Treatments Chakwal-50 GA-2002 NR-333 NR-372 Mean T1 5.46bc 3.90de 1.99g 2.14fg 3.37C T2 5.78ab 4.30d 2.75fg 2.91f 3.93B T3 6.58a 4.70cd 2.88fg 3.02ef 4.30B T4 5.59ab 4.33d 2.87fg 3.87de 4.25B T5 6.54a 5.48bc 5.42bc 5.39bc 5.71A Table 6. Leaf area duration (LAD) of wheat genotypes under different silicon treatments

Fig. 1. Crop growth rate and grain yield of wheat genotypes under different silicon treatments

Fig. 2. Leaf area and grain yield of wheat genotypes under different silicon treatments

Silicon the Non-Essential Beneficial Plant Nutrient to Enhanced Drought Tolerance in Wheat 39

through stomata. Optimum conductance led to maximum uptake of water and CO2 for photosynthetic efficiency and ultimately more yield. Stomatal conductance depends upon favourable climatic conditions. Drought resistant genotypes have good conductance of stomata and adaptation strategies to cope stress. The results revealed maximum stomatal conductance for Chakwal-50 (0.42 mol m2 s-1) with 10% application of silicon as compared to other genotypes and silicon applications. Whereas, NR-333 showed minimum stomatal conductance (0.17 mol m2 s-1) under controlled conditions (Table 9). Silicon application boosted up physiological attributes under stressed conditions and resistant cultivars showed better response under enhanced silicon concentrations. A positive and linear relationship was observed between stomatal conductance and grain yield which support

Results regarding leaf membrane stability index (Table 10) showed significant variation. It was observed that in control treatment, all the varieties showed little variation in LMSI but all varieties had greater membrane stability as compared to stress conditions. Chakwal-50 showed maximum value of LMSI (84.2%) followed by NR-333 (81.9%) under the 10% application of silicon. On the other hand NR-333 showed minimum value (60.33%) Perhaps this was the distinction which made some varieties able to perform better under water stress condition and others not. Similarly Plants having more LMSI value show less membrane injury by accumulating more saturated fatty acids, which is very important mechanism of plants to resist drought. So the selection of wheat varieties like Chakwal-50 with high LMSI

The outcomes of the current study demonstrated that in controlled condition, Chakwal-50 (15.80 mg/m²) showed maximum leaf succulence followed by NR-372 (14.70 mg/m²) (Table 11). On the other hand NR-333 showed minimum value of 6.48 mg/m² preceded by GA-2002 (7.42 mg/m²). It was noted that with increase in water stress, value of leaf succulence was also reduced. Different varieties showed different behaviors, Chakwal-50 and NR-372 showed maximum leaf succulence under enhanced silicon application followed by GA-2002 and NR-333. Some varieties showed less reduction in leaf succulence and some varieties showed much reduction. Here the distinction was developed among drought resistant and drought susceptible varieties. Leaf succulence is important adaptive mechanism of plants to resist drought. Varieties with high LS are considered to be more drought resistant. It has also been observed by many scientists that increased level of leaf succulence under drought conditions is a key adaptive mechanism to resist the drought. Ahmed et al., (2011a) also found that decreasing water potential cause reduction in leaf succulence value. So selection of wheat varieties, having more LS under water stress is very important for water deficit

The maximum relative water contents were recorded for drought tolerant genotype due to silicon nutrition. This elaborate that silicon entered inside the plants and might followed an

value under drought condition is very essential to increase production.

our results (Fig.4).

**5.5 Leaf succulence** 

conditions.

**5.6 Relative water content** 

**5.4 Leaf Membrane Stability Index (LMSI)** 

#### **5. Effect of silicon on physiological parameters**

#### **5.1 Photosynthesis (A) (μ mole/m2 /second)**

Photosynthesis is a determinant factor for crop growth and development as maximum photosynthesis contributes toward more yield and production. Results demonstrated significant variation for photosynthetic efficiency for various genotypes. Genotypes from diverse climatic regions behaved differently for photosynthetic efficiency. Chakwal-50 (17.47μ mole/m2/second) performed well over all other genotypes for photosynthesis followed by NR-372 (15.33 μ mole/m2/second) under 10 % silicon application with irrigation. While, minimum photosynthesis observed in NR-333 (8.23 μ mole/m2/second) which ultimately led to reduced yield (Table 7). Chakwal-50 genotype has some adaptability characteristics which promoted physiological attributes and ultimately better production due to silicon application. Our results were in the line with the findings of Ahmed et al., (2011b) who reported significant impact of drought variation upon photosynthetic efficiency of wheat crop. Flag leaf stage is crucial stage in crop growth and development as crop produces maximum photosynthate using all available resources and it can only be achieved if suitable genotype sown at optimum time. Increased temperature and reduction in moisture promote photosynthetic efficiency up to an optimum. A linear relationship between photosynthetic rate and grain yield was observed (Fig. 3) which depicted that more photosynthesis led to maximum accumulation of photoassimilates from source to sink and ultimately maximum yield. Chakwal-50 genotypes performed very well due to efficient translocation of photoassimilates from source to sink using available thermal units under drought stress conditions.

#### **5.2 Transpiration rate (E) (mole/m<sup>2</sup> /s)**

Transpiration is the removal of moisture from plant parts and it has significant impact on yield of crops and a major constituent to measure water use efficiency of agricultural crops. Crop yield is inversely related to transpiration rate. It depends upon climatic variants like solar radiation, temperature, water vapor pressure deficit, wind speed and the water status of the plants (Ahmed et al., 2011a). Present study depicted significant variation among different genotypes under changing silicon concentration which enhanced transpiration rate (Table 8). Resistant cultivars have defensive mechanism to cope with stress and gaining yield potential under limited available resources. The results of present study described maximum transpiration rate for GA-2002 under control and 10% application of silicon, while, minimum transpiration was showed by NR-333 with 5% application of silicon followed by NR-372 under the same treatment. This variation in transpiration might be due to genetic potential of genotypes and various silicon treatments impact. Stomatal aperture plays a vital role in leaves as water evaporates through them. Stomatal conductance and transpiration were positively correlated and stomatal closure led to reduce transpirational losses and ultimately good production. Our results were at par with the outcomes of Ahmed et al., (2011b).

#### **5.3 Stomatal conductance (mol m2 s-1)**

Stomata plays important role in plant physiological indices, as water and nutrients enter in plant through stomata. Stomatal conductance is the speed of passage of water and nutrients through stomata. Optimum conductance led to maximum uptake of water and CO2 for photosynthetic efficiency and ultimately more yield. Stomatal conductance depends upon favourable climatic conditions. Drought resistant genotypes have good conductance of stomata and adaptation strategies to cope stress. The results revealed maximum stomatal conductance for Chakwal-50 (0.42 mol m2 s-1) with 10% application of silicon as compared to other genotypes and silicon applications. Whereas, NR-333 showed minimum stomatal conductance (0.17 mol m2 s-1) under controlled conditions (Table 9). Silicon application boosted up physiological attributes under stressed conditions and resistant cultivars showed better response under enhanced silicon concentrations. A positive and linear relationship was observed between stomatal conductance and grain yield which support our results (Fig.4).

#### **5.4 Leaf Membrane Stability Index (LMSI)**

Results regarding leaf membrane stability index (Table 10) showed significant variation. It was observed that in control treatment, all the varieties showed little variation in LMSI but all varieties had greater membrane stability as compared to stress conditions. Chakwal-50 showed maximum value of LMSI (84.2%) followed by NR-333 (81.9%) under the 10% application of silicon. On the other hand NR-333 showed minimum value (60.33%) Perhaps this was the distinction which made some varieties able to perform better under water stress condition and others not. Similarly Plants having more LMSI value show less membrane injury by accumulating more saturated fatty acids, which is very important mechanism of plants to resist drought. So the selection of wheat varieties like Chakwal-50 with high LMSI value under drought condition is very essential to increase production.

#### **5.5 Leaf succulence**

38 Crop Plant

Photosynthesis is a determinant factor for crop growth and development as maximum photosynthesis contributes toward more yield and production. Results demonstrated significant variation for photosynthetic efficiency for various genotypes. Genotypes from diverse climatic regions behaved differently for photosynthetic efficiency. Chakwal-50 (17.47μ mole/m2/second) performed well over all other genotypes for photosynthesis followed by NR-372 (15.33 μ mole/m2/second) under 10 % silicon application with irrigation. While, minimum photosynthesis observed in NR-333 (8.23 μ mole/m2/second) which ultimately led to reduced yield (Table 7). Chakwal-50 genotype has some adaptability characteristics which promoted physiological attributes and ultimately better production due to silicon application. Our results were in the line with the findings of Ahmed et al., (2011b) who reported significant impact of drought variation upon photosynthetic efficiency of wheat crop. Flag leaf stage is crucial stage in crop growth and development as crop produces maximum photosynthate using all available resources and it can only be achieved if suitable genotype sown at optimum time. Increased temperature and reduction in moisture promote photosynthetic efficiency up to an optimum. A linear relationship between photosynthetic rate and grain yield was observed (Fig. 3) which depicted that more photosynthesis led to maximum accumulation of photoassimilates from source to sink and ultimately maximum yield. Chakwal-50 genotypes performed very well due to efficient translocation of photoassimilates from source to sink using available thermal units under

**/second)** 

**/s)** 

 **s-1)** 

Stomata plays important role in plant physiological indices, as water and nutrients enter in plant through stomata. Stomatal conductance is the speed of passage of water and nutrients

Transpiration is the removal of moisture from plant parts and it has significant impact on yield of crops and a major constituent to measure water use efficiency of agricultural crops. Crop yield is inversely related to transpiration rate. It depends upon climatic variants like solar radiation, temperature, water vapor pressure deficit, wind speed and the water status of the plants (Ahmed et al., 2011a). Present study depicted significant variation among different genotypes under changing silicon concentration which enhanced transpiration rate (Table 8). Resistant cultivars have defensive mechanism to cope with stress and gaining yield potential under limited available resources. The results of present study described maximum transpiration rate for GA-2002 under control and 10% application of silicon, while, minimum transpiration was showed by NR-333 with 5% application of silicon followed by NR-372 under the same treatment. This variation in transpiration might be due to genetic potential of genotypes and various silicon treatments impact. Stomatal aperture plays a vital role in leaves as water evaporates through them. Stomatal conductance and transpiration were positively correlated and stomatal closure led to reduce transpirational losses and ultimately good production. Our results were at par with the outcomes of Ahmed

**5. Effect of silicon on physiological parameters** 

**5.1 Photosynthesis (A) (μ mole/m2**

drought stress conditions.

et al., (2011b).

**5.2 Transpiration rate (E) (mole/m<sup>2</sup>**

**5.3 Stomatal conductance (mol m2**

The outcomes of the current study demonstrated that in controlled condition, Chakwal-50 (15.80 mg/m²) showed maximum leaf succulence followed by NR-372 (14.70 mg/m²) (Table 11). On the other hand NR-333 showed minimum value of 6.48 mg/m² preceded by GA-2002 (7.42 mg/m²). It was noted that with increase in water stress, value of leaf succulence was also reduced. Different varieties showed different behaviors, Chakwal-50 and NR-372 showed maximum leaf succulence under enhanced silicon application followed by GA-2002 and NR-333. Some varieties showed less reduction in leaf succulence and some varieties showed much reduction. Here the distinction was developed among drought resistant and drought susceptible varieties. Leaf succulence is important adaptive mechanism of plants to resist drought. Varieties with high LS are considered to be more drought resistant. It has also been observed by many scientists that increased level of leaf succulence under drought conditions is a key adaptive mechanism to resist the drought. Ahmed et al., (2011a) also found that decreasing water potential cause reduction in leaf succulence value. So selection of wheat varieties, having more LS under water stress is very important for water deficit conditions.

#### **5.6 Relative water content**

The maximum relative water contents were recorded for drought tolerant genotype due to silicon nutrition. This elaborate that silicon entered inside the plants and might followed an

Silicon the Non-Essential Beneficial Plant Nutrient to Enhanced Drought Tolerance in Wheat 41

Proline, accumulates in plants under environmental stresses is proteinogenic amino acid with an exceptional conformational rigidity, and is essential for primary metabolism. It acts as a signal to triggers specific gene action which may be essential for crop recovery from stress.In the present study, proline contents differed significantly among wheat genotypes. Chakwal-50 being a drought resistance genotype, exhibited maximum proline accumulation (52.23 µg g-1) followed by NR-372 (51.70µg g-1). Whereas, minimum proline accumulation observed for GA-2002 (42.70 µg g-1) (Table 16). Similar findings have been documented that proline contents reduced under increased temperature and moisture stress; however, it increased in resistant cultivars which led to higher yield. Grain yield of wheat found to be

positively related to proline contents under different silicon regimes (Fig.6).

**5.11 Correlation coefficients among physiological attributes with grain yield** 

resistivity of some genotypes which had adaptability measures to cope stress.

Mean 14.54A 12.55B 12.06B 12.8B LSD for Genotypes= 0.81, Treatments= 0.90 and Genotypes x Treatments= 1.81

Mean 5.82B 6.06A 5.55C 5.51C

Table 8. Transpiration rate (E) of wheat genotypes under different silicon treatments

LSD for Genotypes= 0.12, Treatments= 0.13 and Genotypes x Treatments= 0.27

Treatments Chakwal-50 GA-2002 NR-333 NR-372 Mean T1 11.74fgh 8.56jk 8.23k 9.20ijk 9.42E T2 13.35def 10.34hij 10.53hi 11.23gh 11.36D T3 14.34cd 12.49efg 12.5efg 13.47def 13.21C T4 17.47a 15.15bcd 14.27cde 14.73bcd 14.99B T5 15.79abc 16.23ab 14.77bcd 15.33bc 15.95A

Table 7. Net photosynthesis (An) of wheat genotypes under different silicon treatments

Treatments Chakwal-50 GA-2002 NR-333 NR-372 Mean T1 5.95bc 6.67a 5.87bcde 5.73cdef 6.05A T2 5.90bcd 6.13b 6.00bc 5.70def 5.93A T3 5.90bcd 6.67a 5.73cdef 5.73cdef 6.00A T4 5.87bcde 5.22gh 4.87i 5.23gh 5.30B T5 5.47fg 5.60ef 5.30gh 5.17h 5.39B

Correlation coefficients among physiological attributes with grain yield of various wheat genotypes under silicon enhanced treatments showed variable response. Grain yield of wheat genotypes under stressed conditions and silicon application found to be positively correlated with photosynthesis (A), stomatal conductance (gs), chlorophyll contents (cc), proline contents (PC), relative water contents (RWC) and drought resistance index (DRI). However, significant negative correlation was observed between transpiration rate (E), leaf membrane stability (LMS) and grain yield. Significant and positive relationship led to conclusion that grain yield increase with increase in these physiological indices and application of silicon in this regard holds a vital place. Silicon concentration enhanced

**5.10 Proline contents (µg g-1)** 

active transport pathway and through xylem it reached inside the leaves in order to maintain water potential under water stress. The results of present study investigated that Chakwal-50 being drought resistant genotype, exhibited maximum relative water contents under enhanced silicon application (91.83%). While, minimum RWC was noted down for NR-372 (71.43) (Table 12). Control treatment (without silicon application) depicted less water contents as compared to silicon enhanced treatments. The findings of Ma et al., (2001) supported our results as they were of the opinion that silicon improved crop relative water potential.

#### **5.7 Epicuticular wax**

Epicuticular wax might be an important attribute in drought tolerant genotypes. Maximum epicuticular wax (8.62 mg) was observed in Chakwal-50 (Table 13). Whereas, minimum epicuticular wax was measured for NR-333 (4.36 mg). Drought resistant genotypes develop epicuticular wax on leaves which prohibited loss of water from plant leaves which then be used by plants under stress conditions. Silicon application played a crucial role in development of waxy layer on resistant varieties. The similar result reported by earlier researcher who documented that cuticle wax accumulation increase the drought tolerance in plants and silicon holds a vital place under such circumstances.

#### **5.8 Chlorophyll contents (SPAD)**

Crop growth could be related to rate of photosynthesis which is directly proportional to chlorophyll contents in leaves. Plants use chlorophyll to trap light from sun for photosynthesis and green colour of plants is due to absorption of all visible colours instead green by photosynthesizing pigments. In this experiment maximum chlorophyll contents were measured for Chakwal-50 (51.26) followed by GA-2002 (45.60), while, minmum for NR-372(21.33) under control conditions. Our findings were in close agreement with Paknejad et al., (2007) who found that chlorophyll contents in different wheat cultivars could be reduced more than 25% due to drought stress. A linear relationship was observed between chlorophyll contents and grain yield (Fig. 5) which described that increase in chlorophyll contents led to increased photosynthesis and consequently grain yield. So it can be concluded that selection of wheat varieties having more chlorophyll contents under drought stress is very important to increase production.

#### **5.9 Drought resistance index for selected wheat cultivars**

Drought resistance index is define as DC multiplied by variety's minimum yield, and then divided by average minimum yield of the varieties used in experiment. Cultivars showing greater value of DRI are considered to have more resistance against drought. However, the cultivars having less value of DRI were considered to be less resistant to drought. The findings of current study highlighted that Chakwal-50 showed maximum Drought Resistance Index (0.58) whereas GA-2002 showed minimum value (0.34) for drought resistance index. Our results were at par with the findings of previous researcher who calculated the DRI values for seven wheat cultivars grown under irrigated and nonirrigated conditions and found that cultivars having more DRI values were more resistant to drought.

#### **5.10 Proline contents (µg g-1)**

40 Crop Plant

active transport pathway and through xylem it reached inside the leaves in order to maintain water potential under water stress. The results of present study investigated that Chakwal-50 being drought resistant genotype, exhibited maximum relative water contents under enhanced silicon application (91.83%). While, minimum RWC was noted down for NR-372 (71.43) (Table 12). Control treatment (without silicon application) depicted less water contents as compared to silicon enhanced treatments. The findings of Ma et al., (2001) supported our results as they were of the opinion that silicon improved crop relative water

Epicuticular wax might be an important attribute in drought tolerant genotypes. Maximum epicuticular wax (8.62 mg) was observed in Chakwal-50 (Table 13). Whereas, minimum epicuticular wax was measured for NR-333 (4.36 mg). Drought resistant genotypes develop epicuticular wax on leaves which prohibited loss of water from plant leaves which then be used by plants under stress conditions. Silicon application played a crucial role in development of waxy layer on resistant varieties. The similar result reported by earlier researcher who documented that cuticle wax accumulation increase the drought tolerance in

Crop growth could be related to rate of photosynthesis which is directly proportional to chlorophyll contents in leaves. Plants use chlorophyll to trap light from sun for photosynthesis and green colour of plants is due to absorption of all visible colours instead green by photosynthesizing pigments. In this experiment maximum chlorophyll contents were measured for Chakwal-50 (51.26) followed by GA-2002 (45.60), while, minmum for NR-372(21.33) under control conditions. Our findings were in close agreement with Paknejad et al., (2007) who found that chlorophyll contents in different wheat cultivars could be reduced more than 25% due to drought stress. A linear relationship was observed between chlorophyll contents and grain yield (Fig. 5) which described that increase in chlorophyll contents led to increased photosynthesis and consequently grain yield. So it can be concluded that selection of wheat varieties having more chlorophyll contents under

Drought resistance index is define as DC multiplied by variety's minimum yield, and then divided by average minimum yield of the varieties used in experiment. Cultivars showing greater value of DRI are considered to have more resistance against drought. However, the cultivars having less value of DRI were considered to be less resistant to drought. The findings of current study highlighted that Chakwal-50 showed maximum Drought Resistance Index (0.58) whereas GA-2002 showed minimum value (0.34) for drought resistance index. Our results were at par with the findings of previous researcher who calculated the DRI values for seven wheat cultivars grown under irrigated and nonirrigated conditions and found that cultivars having more DRI values were more resistant

plants and silicon holds a vital place under such circumstances.

drought stress is very important to increase production.

**5.9 Drought resistance index for selected wheat cultivars** 

potential.

to drought.

**5.7 Epicuticular wax** 

**5.8 Chlorophyll contents (SPAD)** 

Proline, accumulates in plants under environmental stresses is proteinogenic amino acid with an exceptional conformational rigidity, and is essential for primary metabolism. It acts as a signal to triggers specific gene action which may be essential for crop recovery from stress.In the present study, proline contents differed significantly among wheat genotypes. Chakwal-50 being a drought resistance genotype, exhibited maximum proline accumulation (52.23 µg g-1) followed by NR-372 (51.70µg g-1). Whereas, minimum proline accumulation observed for GA-2002 (42.70 µg g-1) (Table 16). Similar findings have been documented that proline contents reduced under increased temperature and moisture stress; however, it increased in resistant cultivars which led to higher yield. Grain yield of wheat found to be positively related to proline contents under different silicon regimes (Fig.6).

#### **5.11 Correlation coefficients among physiological attributes with grain yield**

Correlation coefficients among physiological attributes with grain yield of various wheat genotypes under silicon enhanced treatments showed variable response. Grain yield of wheat genotypes under stressed conditions and silicon application found to be positively correlated with photosynthesis (A), stomatal conductance (gs), chlorophyll contents (cc), proline contents (PC), relative water contents (RWC) and drought resistance index (DRI). However, significant negative correlation was observed between transpiration rate (E), leaf membrane stability (LMS) and grain yield. Significant and positive relationship led to conclusion that grain yield increase with increase in these physiological indices and application of silicon in this regard holds a vital place. Silicon concentration enhanced resistivity of some genotypes which had adaptability measures to cope stress.


LSD for Genotypes= 0.81, Treatments= 0.90 and Genotypes x Treatments= 1.81



LSD for Genotypes= 0.12, Treatments= 0.13 and Genotypes x Treatments= 0.27

Table 8. Transpiration rate (E) of wheat genotypes under different silicon treatments

Silicon the Non-Essential Beneficial Plant Nutrient to Enhanced Drought Tolerance in Wheat 43

Treatments Chakwal-50 GA-2002 NR-333 NR-372 Mean T1 7.90abc 8.20abc 7.10cdef 8.13abc 7.83A T2 7.83abc 7.60abcd 6.10fghi 8.16abc 7.42AB T3 8.26ab 7.33bcde 5.30hij 6.33def 6.88B T4 5.90ghi 6.36efgh 5.10ij 6.56defg 5.98C T5 8.66a 5.80ghi 4.36j 5.53ghi 6.09C

Table 13. Epicuticular wax (EW) of wheat genotypes under different silicon treatments

Treatments Chakwal-50 GA-2002 NR-333 NR-372 Mean T1 29.83hi 25.60kl 22.50m 21.33m 24.18E T2 35.91de 29.67hi 26.63jk 23.10lm 28.82D T3 45.06b 40.50c 34.13ef 33.06ef 38.19B T4 39.30c 34.86ef 31.41gh 28.50ij 33.52C T5 51.26a 45.60b 38.26cd 35.30ef 42.60A

Table 14. Chlorophyll contents (cc) of wheat genotypes under different silicon treatments

Treatments Chakwal-50 GA-2002 NR-333 NR-372 Mean T1 0.46hi 0.36k 0.38k 0.37k 0.39D T2 0.52cd 0.48fgh 0.43j 0.49efg 0.48C T3 0.54bc 0.50def 0.45ij 0.52cde 0.50B T4 0.5800a 0.525bcd 0.47ghi 0.54bc 0.53A T5 0.55b 0.54bc 0.49fg 0.55b 0.53A

Mean 7.71A 7.06B 7.00B 5.60C LSD for Genotypes= 0.51 Treatments= 0.57 and Genotypes x Treatments= 1.15

Mean 40.27A 35.24B 30.59C 28.60D

Mean 0.53A 0.48B 0.44C 0.49B

Mean 49.94A 47.90B 48.47B 49.82A LSD for Genotypes=0.66 Treatments= 0.74 and Genotypes x Treatments= 1.4886

Table 15. Drought Resistance Index (DRI) of wheat genotypes under different silicon

Treatments Chakwal-50 GA-2002 NR-333 NR-372 Mean T1 50.16def 51.53bcd 50.50cde 51.23bc 51.05B T2 48.23gh 49.56efg 41.83k 48.00h 46.90D T3 51.20bcd 44.20i 43.96ij 49.43efgh 47.20CD T4 48.90fgh 42.70jk 52.00b 48.00h 47.90C T5 52.23b 51.50bcd 54.06a 51.70bc 52.12A

Table 16. Proline contents (pc) of wheat genotypes under different silicon treatments

LSD for Genotypes= 0.13 Treatments= 0.14 and Genotypes x Treatments= 0.24

treatments

LSD for Genotypes=0.95 Treatments=0.10 and Genotypes x Treatments= 0.21


LSD for Genotypes= 0.02, Treatments= 0.02 and Genotypes x Treatments= 0.03

Table 9. Stomatal conductance (Gs) of wheat genotypes under different silicon treatments


LSD for Genotypes= 1.58, Treatments= 1.77 and Genotypes x Treatments= 3.54

Table 10. Leaf membrane stability index (LMSI) of wheat genotypes under different silicon treatments


LSD for Genotypes= 0.91, Treatments= 1.02 and Genotypes x Treatments= 2.03

Table 11. Leaf succulence of wheat genotypes under different silicon treatments


LSD for Genotypes= 1.02, Treatments= 1.14 and Genotypes x Treatments= 2.29

Table 12. Relative water content (RWC) of wheat genotypes under different silicon treatments


LSD for Genotypes= 0.51 Treatments= 0.57 and Genotypes x Treatments= 1.15

42 Crop Plant

Treatments Chakwal-50 GA-2002 NR-333 NR-372 Mean T1 0.26i 0.24ij 0.37cd 0.24ij 0.28D T2 0.32fg 0.26i 0.17k 0.28hi 0.26E T3 0.35def 0.31gh 0.21j 0.33efg 0.30C T4 0.38bcd 0.37cd 0.26i 0.36cde 0.34B T5 0.42a 0.39abc 0.30gh 0.41ab 0.38A

Table 9. Stomatal conductance (Gs) of wheat genotypes under different silicon treatments

Treatments Chakwal-50 GA-2002 NR-333 NR-372 Mean T1 80.57bc 76.7efg 80.67abc 60.80i 74.68B T2 80.03bcde 75.67fgh 77.90c-g 61.27i 73.71BC T3 80.17bcde 76.90defg 77.93c-g 63.00i 74.5B T4 78.83b-f 74.80gh 74.83gh 60.37i 72.20C T5 84.2a 80.33bcd 81.9ab 73.06h 79.88A

Table 10. Leaf membrane stability index (LMSI) of wheat genotypes under different silicon

Treatments Chakwal-50 GA-2002 NR-333 NR-372 Mean T1 15.80a 14.33abc 12.81b-f 14.70ab 14.41A T2 15.40a 12.59cdef 10.40ghi 12.60cdef 12.74B T3 14.36abc 11.39defg 9.20hij 12.90bcde 11.96B T4 13.26bcd 9.24hij 8.53ij 10.80fgh 10.46C T5 10.86efgh 7.42jk 6.48k 10.40ghi 8.79D

Mean 0.35A 0.31B 0.26C 0.32B LSD for Genotypes= 0.02, Treatments= 0.02 and Genotypes x Treatments= 0.03

Mean 80.70A 76.80C 78.64B 63.70D

Mean 13.94A 10.99C 9.48D 12.28B LSD for Genotypes= 0.91, Treatments= 1.02 and Genotypes x Treatments= 2.03

Table 11. Leaf succulence of wheat genotypes under different silicon treatments

Mean 81.74A 77.60C 79.14B 78.40BC

Table 12. Relative water content (RWC) of wheat genotypes under different silicon

LSD for Genotypes= 1.02, Treatments= 1.14 and Genotypes x Treatments= 2.29

Treatments Chakwal-50 GA-2002 NR-333 NR-372 Mean T1 73.00hij 77.66k 74.76ghi 71.93jk 72.59E T2 77.03g 72.66ijk 74.80ghi 71.50jk 74.00D T3 81.66f 76.67g 74.96gh 71.43jk 76.18C T4 85.20de 81.34f 82.96ef 87.76bc 84.31B T5 91.83a 86.66cd 88.20bc 89.40b 89.05A

LSD for Genotypes= 1.58, Treatments= 1.77 and Genotypes x Treatments= 3.54

treatments

treatments

Table 13. Epicuticular wax (EW) of wheat genotypes under different silicon treatments


LSD for Genotypes=0.95 Treatments=0.10 and Genotypes x Treatments= 0.21

Table 14. Chlorophyll contents (cc) of wheat genotypes under different silicon treatments


LSD for Genotypes= 0.13 Treatments= 0.14 and Genotypes x Treatments= 0.24

Table 15. Drought Resistance Index (DRI) of wheat genotypes under different silicon treatments


LSD for Genotypes=0.66 Treatments= 0.74 and Genotypes x Treatments= 1.4886

Table 16. Proline contents (pc) of wheat genotypes under different silicon treatments

Silicon the Non-Essential Beneficial Plant Nutrient to Enhanced Drought Tolerance in Wheat 45

Fig. 5. Chlorophyll contents and grain yield of wheat genotypes under different silicon

Fig. 6. Proline contents and grain yield of wheat genotypes under different silicon

treatments

treatments

Fig. 3. Photosynthesis and grain yield of wheat genotypes under different silicon treatments

Fig. 4. Stomatal conductance and grain yield of wheat genotypes under different silicon treatments

Fig. 3. Photosynthesis and grain yield of wheat genotypes under different silicon treatments

Fig. 4. Stomatal conductance and grain yield of wheat genotypes under different silicon

treatments

Fig. 5. Chlorophyll contents and grain yield of wheat genotypes under different silicon treatments

Fig. 6. Proline contents and grain yield of wheat genotypes under different silicon treatments

Silicon the Non-Essential Beneficial Plant Nutrient to Enhanced Drought Tolerance in Wheat 47

Agarie, S., Hanaoka, N., Ueno, O., Miyazaki, A., Kubota, F., Agata W. & Kaufman P.B.

Ahmed, M., Hassan, F. U.., & Y. Khurshid. (2011b). Does silicon and irrigation have impact

Al-aghabary, K., Zhu Z.J. & Q.H. Shi. (2004) Influence of silicon supply on chlorophyll

Barrs, H. D. & P. E. Weatherley. (1962). Are-examination of relative turgidity technique for

Bates, L.S., Waldren R.P., & I.D. Teare. (1973). Rapid determination of free proline for water

Chandrasekar, V., Sarium R.k. & G.C.Srivastava. (2000). Physiology and biological response of hexapoloid and tetraploid wheat to stress. *J. Agron. Crop Sci*., 18: 219-227. Dagmar, D., Simone, H., Wolfgang, B., Rüdiger, F., Bäucker, E., Rühle, G., Otto, W. & M.

Gardner, F. P., Pearce, R. B. & R. L. Mitchell. (1985). Physiology of Crop Plants. 2nd ed. Lowa

Gong, H. J., Chen, K. M., Chen, G. C., Wang, S. M. & C. L. Zhang. (2003). Effect of silicon on growth of wheat under drought. *Journal of Plant Nutrition*. 26(5): 1055-1063. Gong, H. J., Chen K. M., Chen G. C., Wang S. M. & C. L. Zhang. (2005). Silicon alleviates oxidative damage of wheat plants in pots under drought. *Plant.Sci*. 169: 313–321. GOP. (2011). Economic Survey of Pakistan, 2010-11. Economic advisory wing, Finance

Hattori, T., Inanaga, S., Tanimoto, E., Lux, A., Luxova M. & Y. Sugimoto. (2005). Silicon-

Hattori, T., Inanaga S., Araki H., An P., Mortia S., Luxova M. & A. Lux. (2005). Application

Liang, Y., Si, J., & V. Romheld. (2005). Silicon uptake and transport is an active process in

Long, S.P. & C.J. Bernacchi. (2003). Gas exchange measurements, what can they tell us about

Lux, A., Luxova, M., Hattori, T., Inanaga, S., & Y., Sugimoto. (2002). Silicification in sorghum

induced changes in viscoelastic properties of sorghum root cell walls. *Plant Cell* 

of silicon enhanced drought tolerance in sorghum bicolor. *Physiolgia Plantarum*. 123:

the underlying limitation to photosynthesis? Procedure and sources of error, *J. Exp.* 

(*Sorghum bicolor*) cultivars with different drought tolerance. *Physiol. Plant*, 115: 87–

607. Available from http://www.academicjournals.org/AJAR.

plants under salt stress, *Journal of Plant Nutrition* 27, pp. 1–15.

estimating water deficit in leaves. *Aust. J. Biol. Sci*., 15: 413-428.

L. *Analytical and Bioanalytical Chemistry*. 376(3): 399-404. Epstein, E., (1999). Silicon. Annl. Rev. *Plant Physiol. Plant Mol. Biol*., 50:641-664.

stress studies, *Plant and Soil*, 39, 205-207.

stat univ. press, *Ames, IA*., 200-205.

*Cucumis sativus*. *New Phytologist*. 167: 797–804.

Division, Islamabad.

*Physiol.,* 44: 743–749.

*Bot.* 54: 2393-2401.

459-466.

92.

(1998). Effects of silicon on tolerance to water deficit and heat stress in rice plants (*Oryza sativa* L.) monitored by electrolyte leakage. *Plant Production Science*. 96–103. Ahmed, M., F.U. Hassen, U. Qadeer & M.A. Aslam. (2011a). Silicon application and drought

tolerance mechanism of sorghum. *African Journal of Agricultural Research.* 6(3): 594-

on drought tolerance mechanism of sorghum? *Agricultural Water Management*.

content, chlorophyll fluorescence, and antioxidative enzyme activities in tomato

Günter.(2003). Silica accumulation in Triticum aestivum L., and Dactylis glomerata

**7. References** 

98(12): 1808-1812.


(GY= Grain yield, An= Net photosynthesis rate, E= Transpiration rate, GS= Stomatal conductance, PC= Proline contents (PC), RWC= Relative water content, DRI= drought resistance index (DRI) \*\*\*, \*\*, \* = Significant at 1 %, 5 % and 10 % respectively, ns = Non-significant)

Table 17. Correlation coefficients among physiological attributes of wheat genotypes with grain yield under different silicon applications

#### **6. Conclusion**

Drought is the major threat to agriculture in the world and Pakistan. Amongst different approaches being used to combat the drought stress, exogenous application of nutrients is much more important e.g. k+ the addition of Silicon in the growth medium is also beneficial to enhance the crop growth affected due to drought. Si is reported to accumulate in the plant body of various crops like rice that enables the plants to tolerate the drought stress. Likewise wheat an important serial in Pakistan has been designated as a Si accumulator. In this regard, the present study was undertaken in pots at PMAS, Arid Agriculture University Rawalpindi. Seeds of two varieties i.e. GA-2002, Chakwal-50 and two lines i.e. NR-333 and NR- 372 were taken from National Agricultural Research Center (NARC). The source of silicon, potassium silicate was used in the silicon applied treatments (+Si) and potassium chloride in the silicon deficient treatment (-Si). Effect of potassium silicate at 5 % and 10 % was investigated for germination and compared with control. Secondly two wheat cultivars and two advanced lines with three replications were sown per pot. At three leaf stage, 5 % and 10 % of potassium silicate solution were applied to the pots of the +Si treatments and it was compared with control. Potassium chloride solution was applied to the pots of the –Si treatments to yield the same total potassium as in the +Si treatments. Parameters like leaf membrane stability index, epicuticular wax, crop growth rate, relative water content, stomatal conductance, transpiration rate, photosynthetic rate, leaf area, leaf area index, chlorophyll contents, leaf succulence, relative leaf water contents, silicon concentration in leaves, proline contents, spikelets per spike, no. of grains per spike and weight of 100 grains were measured. The outcomes of the study highlighted maximum crop growth, physiological attributes and yield parameters for Chakwal-50 under 10 % silicon application with irrigation as compared to other genotypes under other levels of silicon concentrations. Drought resistant genotype showed responsive behaviour toward silicon and adaptability measures to cope stress condition. From all the discussion, it can be concluded that A single trait can not make a plant resistant to water stress, rather complex combinations of different traits make a plant able to survive in the drought conditions. Application of silicon in this regard would be beneficial for screening of drought resistant genotypes.

#### **7. References**

46 Crop Plant

GY An E GS CC PC RW DRI

LM -0.25**\*\*** 0.07**ns** 0.10**ns** -0.04**ns** 0.43**\*\*\*** 0.02**ns** 0.23**\*** -0.06**ns** (GY= Grain yield, An= Net photosynthesis rate, E= Transpiration rate, GS= Stomatal conductance, PC= Proline contents (PC), RWC= Relative water content, DRI= drought resistance index (DRI)

Table 17. Correlation coefficients among physiological attributes of wheat genotypes with

Drought is the major threat to agriculture in the world and Pakistan. Amongst different approaches being used to combat the drought stress, exogenous application of nutrients is much more important e.g. k+ the addition of Silicon in the growth medium is also beneficial to enhance the crop growth affected due to drought. Si is reported to accumulate in the plant body of various crops like rice that enables the plants to tolerate the drought stress. Likewise wheat an important serial in Pakistan has been designated as a Si accumulator. In this regard, the present study was undertaken in pots at PMAS, Arid Agriculture University Rawalpindi. Seeds of two varieties i.e. GA-2002, Chakwal-50 and two lines i.e. NR-333 and NR- 372 were taken from National Agricultural Research Center (NARC). The source of silicon, potassium silicate was used in the silicon applied treatments (+Si) and potassium chloride in the silicon deficient treatment (-Si). Effect of potassium silicate at 5 % and 10 % was investigated for germination and compared with control. Secondly two wheat cultivars and two advanced lines with three replications were sown per pot. At three leaf stage, 5 % and 10 % of potassium silicate solution were applied to the pots of the +Si treatments and it was compared with control. Potassium chloride solution was applied to the pots of the –Si treatments to yield the same total potassium as in the +Si treatments. Parameters like leaf membrane stability index, epicuticular wax, crop growth rate, relative water content, stomatal conductance, transpiration rate, photosynthetic rate, leaf area, leaf area index, chlorophyll contents, leaf succulence, relative leaf water contents, silicon concentration in leaves, proline contents, spikelets per spike, no. of grains per spike and weight of 100 grains were measured. The outcomes of the study highlighted maximum crop growth, physiological attributes and yield parameters for Chakwal-50 under 10 % silicon application with irrigation as compared to other genotypes under other levels of silicon concentrations. Drought resistant genotype showed responsive behaviour toward silicon and adaptability measures to cope stress condition. From all the discussion, it can be concluded that A single trait can not make a plant resistant to water stress, rather complex combinations of different traits make a plant able to survive in the drought conditions. Application of silicon in this

An 0.52**\*\*\***

\*\*\*, \*\*, \*

**6. Conclusion** 

E -0.28**\*\*** -0.34**\*\*\***

GS 0.35**\*\*\*** 0.61\*\*\* -0.23\*

CC 0.12**ns** 0.41\*\*\* 0.39**\*\*\*** 0.26**\*\***

grain yield under different silicon applications

PC 0.28**\*\*** 0.11**ns** -0.20**ns** 0.29**\*\*** 0.09**ns**

RW 0.16**ns** 0.69**\*\*\*** -0.57**\*\*\*** 0.58**\*\*\*** 0.13**ns** 0.16**ns**

= Significant at 1 %, 5 % and 10 % respectively, ns = Non-significant)

regard would be beneficial for screening of drought resistant genotypes.

DR 0.51**\*\*\*** 0.81**\*\*\*** -0.22**\*** 0.65**\*\*\*** 0.46**\*\*\*** -0.02**ns** 0.54**\*\*\***


**3** 

*1India 2Nepal 3South Korea 4,5Japan* 

**Impacts of Ozone (O3) and Carbon Dioxide (CO2)** 

*1CSIR-SRF, Laboratory of Air Pollution and Global Climate Change, Ecology Research Circle, Department of Botany, Banaras Hindu University, Varanasi, Uttar Pradesh, 2Research Laboratory for Biotechnology and Biochemistry (RLABB), Kathmandu, 3KRFC Research Fellow, Seoul Center, Korea Basic Science Institute, Seoul, 4Department of Anatomy I, School of Medicine, Showa University, Tokyo,* 

*5Graduate School of Life and Environmental Sciences, University of Tsukuba, Ibaraki,* 

The human race has evolved through centuries and civilized through many ways on their home planet Earth. Although travelling through the 'ages' – 'man' had learned the use of fire, utilized the nature and natural resources, gathered knowledge, practiced agriculture, developed industries, and gradually moved towards a superior modernized life. While climbing the steps of this 'modernized civilization', 'man' introduced a new term – 'pollution' to the world's vocabulary. By definition, pollution is the "undesirable state of the natural environment being contaminated with harmful substances as a consequence of human activities" (source - http://wordnet.princeton.edu). But, at present, this 'undesirable state of natural environment' has turned into a major concern for the survival of life on Earth. Air, water, and soil – the three major natural resources, and fundamental backbone of Earth's environment, have been found to be heavily 'contaminated with harmful substances' throughout the world. This does not imply all is contaminated and lost, but just to highlight

Though, there is no historical account on the pollution on Earth, it was found while reviewing the available literatures that the incident of 'air pollution' is not a new event to our society. In an article, published in '*Science*', John D. Spengler and Ken Saxton commented that – "….soot found on ceilings of prehistoric caves provides ample evidence of the high levels of pollution that was associated with inadequate ventilation of open fires…." (Spengler and Sexton, 1983). During the past couple of decades, rapid urbanization,

**1. Introduction** 

how precarious the situation is for us humans.

**Environmental Pollutants on Crops:** 

Kyoungwon Cho3, Junko Shibato4 and Randeep Rakwal2,4,5\*

**A Transcriptomics Update** 

Abhijit Sarkar1, Ganesh Kumar Agrawal2,


## **Impacts of Ozone (O3) and Carbon Dioxide (CO2) Environmental Pollutants on Crops: A Transcriptomics Update**

Abhijit Sarkar1, Ganesh Kumar Agrawal2, Kyoungwon Cho3, Junko Shibato4 and Randeep Rakwal2,4,5\* *1CSIR-SRF, Laboratory of Air Pollution and Global Climate Change, Ecology Research Circle, Department of Botany, Banaras Hindu University, Varanasi, Uttar Pradesh, 2Research Laboratory for Biotechnology and Biochemistry (RLABB), Kathmandu, 3KRFC Research Fellow, Seoul Center, Korea Basic Science Institute, Seoul, 4Department of Anatomy I, School of Medicine, Showa University, Tokyo, 5Graduate School of Life and Environmental Sciences, University of Tsukuba, Ibaraki, 1India 2Nepal 3South Korea 4,5Japan* 

#### **1. Introduction**

48 Crop Plant

Ma, J., Nishimura K. & E. Takahashi. (1989). Effect of silicon on the growth of rice plant at

Ma, J.F., Miyake Y. & E. Takahashi. (2001). Silicon as a beneficial element for crop plants.

Paknejad, F., Nasir M., Moghadam H. R. T., Zahidi H.,& M. J. Alahmadi. (2007). Effect of

Power, J.F., Willis W.O., Grunes D.L. & G.A. Riechman. (1967). Effect of soil, temperature, phosphorous and plant age on growth analysis of barley. *Agron. J.,* 59: 231-234. Rodrigues, F.Á., McNally, D.J., Datnoff, L.E., Jones, J. B., Labbe, C., Benhamou, N., Menzies,

Silva Fernandus, A.M., Baker E.A. & J.T. Martin. (1964). Studies of plant cuticle vi. The isolation and fractionation of cuticular waxes. *Ann. Appl. Biol*., 53: 43-58.

drought stress on cholorophyll fluorescense parameters, chlorophyll contents and grain yield of wheat cultivars. Dept. of Agric. Islamic Azad Uni. Karaj Branch Iran.

J.G., & R.R. Bélanger. (2004). Silicon enhances the accumulation of diterpenoid phytoalexins in rice: A potential mechanism for blast resistance. *Phytopathology*. 94:

different growth stages. *Soil Sci. Plant Nutr*., 35: 347-356.

Elsevier Science, Amsterdam, pp. 17-39.

*J. of Biol. Sci.* 7(6): 841-847.

177-183.

The human race has evolved through centuries and civilized through many ways on their home planet Earth. Although travelling through the 'ages' – 'man' had learned the use of fire, utilized the nature and natural resources, gathered knowledge, practiced agriculture, developed industries, and gradually moved towards a superior modernized life. While climbing the steps of this 'modernized civilization', 'man' introduced a new term – 'pollution' to the world's vocabulary. By definition, pollution is the "undesirable state of the natural environment being contaminated with harmful substances as a consequence of human activities" (source - http://wordnet.princeton.edu). But, at present, this 'undesirable state of natural environment' has turned into a major concern for the survival of life on Earth. Air, water, and soil – the three major natural resources, and fundamental backbone of Earth's environment, have been found to be heavily 'contaminated with harmful substances' throughout the world. This does not imply all is contaminated and lost, but just to highlight how precarious the situation is for us humans.

Though, there is no historical account on the pollution on Earth, it was found while reviewing the available literatures that the incident of 'air pollution' is not a new event to our society. In an article, published in '*Science*', John D. Spengler and Ken Saxton commented that – "….soot found on ceilings of prehistoric caves provides ample evidence of the high levels of pollution that was associated with inadequate ventilation of open fires…." (Spengler and Sexton, 1983). During the past couple of decades, rapid urbanization,

Impacts of Ozone (O3) and Carbon Dioxide (CO2)

climate change, the prime incidents are as follows a. Retreating mountain glaciers on all continents b. Thinning ice caps in the Arctic and Antarctic

affecting food security and safety

spatial distributions of both these pollutants.

**1.2.1 Tropospheric O3 trend** 

subtropics

**stop?** 

c. Rising sea level – about 6-7 inches in the 20th century

areas, which affects the survival of both animal and plant life

Environmental Pollutants on Crops: A Transcriptomics Update 51

at global level, has become an important issue and a hot debatable topic throughout the world, in both the developed and developing countries. Initially, it was only an 'issue' to be considered, then as a major 'scientific issue' to be studied, and after that as an principle 'environmental policy issue' to be endlessly debated. But in the past few years, it has been metamorphosed into a significant risk factor to be addressed by the global community, especially for those in the energy sector (Sioshansi and Oren, 2007). Although there are thousands of events that can be directly or indirectly correlated to the global warming and

d. More frequent heavy precipitation events (rainstorms, floods or snowstorms) in many

e. More intense and longer droughts over wider areas, especially in the tropics and

f. Reduction in the yield production of major agricultural crops around the world, hence

**1.2 Past and present trends in the concentrations of ambient O3 and CO2: Where we** 

Interestingly, both the above mentioned gases are present in the earth's atmosphere from ancient periods. However, according to the IPCC (2007) report, mean daily O3-concentration is estimated to have increased from around 10 ppb, prior to the industrial revolution, to a current level of approximately 60 ppb during the summer months. Projections show that the level will rise 20 to 40% more by the year 2050 in the industrializing countries of the Northern hemisphere. As per the reports and projections, it is quite apparent that this secondary air pollutant will be a far more critical crisis in the coming future than the present time. On the other hand, CO2 also increased in the atmosphere primarily since the industrial revolution, through the burning of fossil fuels in energy industries, transportation, households, and others. Both these O3 and CO2 are the principle greenhouse gases (GHGs) too. In the following sections, we will mainly discuss on the global trends in temporal and

In the ambient air, O3 precursors play an important role during long range transport downwind from the sources. Polluted air masses from urban and industrial areas can affect suburban and rural areas, even reaching to remote rural areas traveling for considerable distances. High O3 levels from one particular urban area can extend as far as 48 to 80 km (Krupa and Manning, 1988). Ozone formation also depends largely upon prevailing meteorological conditions of the area. Tiwari et al. (2008) reported positive correlation

Background O3 concentrations have more than doubled in the last century (Meehl et al., 2007). There is an increase in annual mean values of O3 ranging from 0.1 to 1 ppb per year-1 (Coyle et al., 2003). In the Northern hemisphere, O3 is also influenced by the influx from the

between mean maximum temperature/sunlight with O3 concentration.

unplanned industrialization, fast growth in vehicle use, uncontrolled fossil fuel burning, and injudicious management of natural resources have transformed this indoor problem of 'prehistoric' origin into a serious environmental hazard of the present century. The 'clean air' of today (and tomorrow) is no more as like was a few decades ago. Looking back, this may have been unavoidable as the human race grew and progressed, paying a price for modernization. In a report on the health effects of air pollution, the World Health Organization (WHO) stated that about two million people die every year because of air pollution throughout the world, while many more suffer from breathing ailments, heart diseases, lung infections, and even cancer (WHO, 2008).

In general, air pollutants can be largely divided in two major categories depending on their formation. The first category is of the primary pollutants, which are emitted directly into the atmosphere, and are mostly present at higher concentrations in urban areas and close to large point emission sources, like carbon dioxide (CO2). The second category is of the secondary pollutants, which are created by the reactions of primary pollutants under favorable environmental conditions, like tropospheric ozone (O3) formed due to a series of photochemical reactions involving nitrogen oxides (NOx) and volatile organic compounds (VOCs) under bright sunlight. However, over past decades, it has been quite clearly understood that both these above mentioned air pollutants are also two major components of much discussed 'global warming', and hence 'global climate change' phenomenon. Tropospheric O3, along with methane and black carbon, are key contributors to global warming, augmenting the radiative forcing of CO2 by 65% (Penner et al., 2010). Global agriproduction, hence food security, is under severe crisis due to the direct and indirect effects of both the pollutants. Studies have shown that O3 and CO2 generally enter plants through stomata, and subsequently affect the inter- and intra-cellular system by modifying the gene expression behavior (Fiscus et al., 2005; Bokhari et al., 2007; Cho et al., 2008; Sarkar et al., 2010). High throughput '-omics' is a combination of potential and central techniques, which can answer many key biological questions in both plants as well as animals. In the present chapter, we mainly focused our ideas towards describing the in depth and up-to-date '*transcriptomics*' analyses of major agricultural crops under the influence of O3 and CO2 rise. We hope that the overall coordinated picture of O3 and CO2 – responsive crop transcriptome might help to construct a future roadmap towards the development of next generation crops with optimized yield and other functions for future high-O3 and CO2-world.

#### **1.1 Defining 'global climate change': Is it really changing?**

According to the IPCC report, climate change refers to a statistically significant variation in either the mean/stable state of the climate and in its components/variability, persisting for an extended period (typically decades or longer). It might happen due to the natural internal processes of atmospheric components and/or external forcing and/or to persistent anthropogenic changes in the composition of the atmosphere and/or in the use and management of natural resources (source: http://www.ipcc.ch/ipccreports/tar/ wg1/518.htm). In 2007, IPCC for the first time reported to the United Nations (UN) that the earth's climate system has undoubtedly and significantly got warmer in the past years and will continue to be. According to the action groups of IPCC, the average annual temperature in the Pacific Northwest rose by 1.5° F in the 20th century and is expected to rise 0.5° F per decade in the first half of the 21st century. Now, this climate warming, hence climate change

unplanned industrialization, fast growth in vehicle use, uncontrolled fossil fuel burning, and injudicious management of natural resources have transformed this indoor problem of 'prehistoric' origin into a serious environmental hazard of the present century. The 'clean air' of today (and tomorrow) is no more as like was a few decades ago. Looking back, this may have been unavoidable as the human race grew and progressed, paying a price for modernization. In a report on the health effects of air pollution, the World Health Organization (WHO) stated that about two million people die every year because of air pollution throughout the world, while many more suffer from breathing ailments, heart

In general, air pollutants can be largely divided in two major categories depending on their formation. The first category is of the primary pollutants, which are emitted directly into the atmosphere, and are mostly present at higher concentrations in urban areas and close to large point emission sources, like carbon dioxide (CO2). The second category is of the secondary pollutants, which are created by the reactions of primary pollutants under favorable environmental conditions, like tropospheric ozone (O3) formed due to a series of photochemical reactions involving nitrogen oxides (NOx) and volatile organic compounds (VOCs) under bright sunlight. However, over past decades, it has been quite clearly understood that both these above mentioned air pollutants are also two major components of much discussed 'global warming', and hence 'global climate change' phenomenon. Tropospheric O3, along with methane and black carbon, are key contributors to global warming, augmenting the radiative forcing of CO2 by 65% (Penner et al., 2010). Global agriproduction, hence food security, is under severe crisis due to the direct and indirect effects of both the pollutants. Studies have shown that O3 and CO2 generally enter plants through stomata, and subsequently affect the inter- and intra-cellular system by modifying the gene expression behavior (Fiscus et al., 2005; Bokhari et al., 2007; Cho et al., 2008; Sarkar et al., 2010). High throughput '-omics' is a combination of potential and central techniques, which can answer many key biological questions in both plants as well as animals. In the present chapter, we mainly focused our ideas towards describing the in depth and up-to-date '*transcriptomics*' analyses of major agricultural crops under the influence of O3 and CO2 rise. We hope that the overall coordinated picture of O3 and CO2 – responsive crop transcriptome might help to construct a future roadmap towards the development of next generation crops

with optimized yield and other functions for future high-O3 and CO2-world.

According to the IPCC report, climate change refers to a statistically significant variation in either the mean/stable state of the climate and in its components/variability, persisting for an extended period (typically decades or longer). It might happen due to the natural internal processes of atmospheric components and/or external forcing and/or to persistent anthropogenic changes in the composition of the atmosphere and/or in the use and management of natural resources (source: http://www.ipcc.ch/ipccreports/tar/ wg1/518.htm). In 2007, IPCC for the first time reported to the United Nations (UN) that the earth's climate system has undoubtedly and significantly got warmer in the past years and will continue to be. According to the action groups of IPCC, the average annual temperature in the Pacific Northwest rose by 1.5° F in the 20th century and is expected to rise 0.5° F per decade in the first half of the 21st century. Now, this climate warming, hence climate change

**1.1 Defining 'global climate change': Is it really changing?** 

diseases, lung infections, and even cancer (WHO, 2008).

at global level, has become an important issue and a hot debatable topic throughout the world, in both the developed and developing countries. Initially, it was only an 'issue' to be considered, then as a major 'scientific issue' to be studied, and after that as an principle 'environmental policy issue' to be endlessly debated. But in the past few years, it has been metamorphosed into a significant risk factor to be addressed by the global community, especially for those in the energy sector (Sioshansi and Oren, 2007). Although there are thousands of events that can be directly or indirectly correlated to the global warming and climate change, the prime incidents are as follows -


#### **1.2 Past and present trends in the concentrations of ambient O3 and CO2: Where we stop?**

Interestingly, both the above mentioned gases are present in the earth's atmosphere from ancient periods. However, according to the IPCC (2007) report, mean daily O3-concentration is estimated to have increased from around 10 ppb, prior to the industrial revolution, to a current level of approximately 60 ppb during the summer months. Projections show that the level will rise 20 to 40% more by the year 2050 in the industrializing countries of the Northern hemisphere. As per the reports and projections, it is quite apparent that this secondary air pollutant will be a far more critical crisis in the coming future than the present time. On the other hand, CO2 also increased in the atmosphere primarily since the industrial revolution, through the burning of fossil fuels in energy industries, transportation, households, and others. Both these O3 and CO2 are the principle greenhouse gases (GHGs) too. In the following sections, we will mainly discuss on the global trends in temporal and spatial distributions of both these pollutants.

#### **1.2.1 Tropospheric O3 trend**

In the ambient air, O3 precursors play an important role during long range transport downwind from the sources. Polluted air masses from urban and industrial areas can affect suburban and rural areas, even reaching to remote rural areas traveling for considerable distances. High O3 levels from one particular urban area can extend as far as 48 to 80 km (Krupa and Manning, 1988). Ozone formation also depends largely upon prevailing meteorological conditions of the area. Tiwari et al. (2008) reported positive correlation between mean maximum temperature/sunlight with O3 concentration.

Background O3 concentrations have more than doubled in the last century (Meehl et al., 2007). There is an increase in annual mean values of O3 ranging from 0.1 to 1 ppb per year-1 (Coyle et al., 2003). In the Northern hemisphere, O3 is also influenced by the influx from the

Impacts of Ozone (O3) and Carbon Dioxide (CO2)

emissions (Woodward, 2002).

Environmental Pollutants on Crops: A Transcriptomics Update 53

anthropogenic activities throughout the world have caused the atmospheric CO2 to increase continuously from about 280 ppm at the beginning of the 19th century to 369 ppm at the beginning of the 21st century (Figure 1). Projections also range between about 450 and 600 ppm by the year 2050, but strongly depend on future scenarios of anthropogenic

Fig. 1. Increasing trends in atmospheric CO2 concentrations at global level. [data adopted

**2. Evaluation of O3- and CO2-effect on agricultural crops through modern** 

The effect of both O3 and CO2 on the various levels of crop's responses till the yield has been very well studied so far (for detail, see review Cho et al., 2010). However, their specific effects on the genome, hence – transcriptome and proteome, has not been evaluated to a large extent. In the next section, we have made an attempt to portray the impact of both these pollutants on the agricultural crops through modern OMICS approaches depending

We are running through the golden era of genomics (study of whole 'genome' is loosely called 'genomics'). The genomics era is also in position to use multiple parallel approaches for the functional analysis of genomes in a high-throughput manner. These parallel approaches surely results in an exceptionally swift and effective system for the analyses and deductions of gene(s) function in a wide range of plants, at the level of transcript (transcriptomics), protein (proteomics), and metabolite (metabolomics) (Figure 2). All together, these four approaches are commonly referred as the multi-parallel OMICS

from http://www.esrl.noaa.gov/gmd/ccgg/trends/]

**2.1 Multi-parallel OMICS approaches in modern biology**

**OMICS approaches: A face-to-face mêlée** 

on the available reports.

approaches in modern biology.

stratosphere (Grewe, 2007). However, O3 varies strongly with episodic peak concentrations during the warmest months in summer in the most polluted regions and maximum concentrations during spring prevailing at background sites (Vingarzan, 2004). In regions such as East Asia exposed to the summer monsoon which transports oceanic air with less O3, the seasonal patterns show a peak during pre- and post-monsoon periods (He et al., 2008). During the day, O3 concentration pattern depends on elevation and shows strong diurnal variations at lowland sites where its destruction dominates during the night and vertical mixing together with photochemical activity causes highest levels in the afternoon.

In rural agricultural areas of the USA, mean O3 concentrations reach between 50 and 60 ppb (90th percentile) (USEPA, 2006). Concentrations over the mid and high-latitude of the Eurasian and North American continents were 15 - 25 ppb in 1860, but increased to 40 - 50 ppb even in remote areas and from 10 - 15 ppb to 20 - 30 ppb over the mid- and highlatitude Pacific Ocean, respectively (Lelieveld and Dentener, 2000). Measures taken to reduce O3 precursor emissions, led to changes in O3 levels in many rural and urban areas of Europe, North America, and Japan; the frequency of the highest values shows a declining trend, while lowest values are increasing (Jenkin, 2008). The US EPA has reported that emission reduction in O3 precursors has been substantial over the past 29 years (US EPA, 2009). The percent change in emissions of NOx and VOCs were 40 and 47%, respectively, for the period 1980 - 2008.

In India, despite of the favorable climatic conditions for O3 formation, very limited data from systematic monitoring of O3 are available. O3 concentrations are continuously increasing from 1992 to 2008 with higher peaks in rural areas. In a field transect study at urban sites of Varanasi, O3 concentrations varied from 6 to 10.2 ppb during 1989-1991 (Pandey and Agrawal, 1992). During the same period, daytime O3 concentrations (9 hr mean) were reported to vary from 9.4 to 128.3 ppb at an urban site in Delhi (Varshney and Agrawal, 1992). It was observed that 10 h ground level mean O3 concentrations in Delhi varied between 34 to 126 ppb during the winter of 1993 (Singh et al., 1997). At Pune, an annual average daytime O3 concentration of 27 ppb and hourly concentration between 2 and 69 ppb were reported during August 1991 to July 1992 (Khemani et al., 1995). Lal et al*.* (2000) studied the pattern of O3 concentrations from 1991 to 1995 at an urban site of Ahmedabad (India), and reported that daytime mean O3 concentrations exceeding 80 ppb were rarely observed. The monthly average O3 concentrations ranged between 62 and 95 ppb in summer (April - June) and 50 and 82 ppb in autumn (October - November) at New Delhi (Jain et al., 2005). Emberson et al. (2009) reported that large parts of South Asia experience up to 50 - 90 ppb mean 7 h (M 7) O3 concentration. Mittal et al. (2007) using the HANK model reported O3 concentration varying from 25 to 100 ppb over the entire Indian region.

#### **1.2.2 Tropospheric CO2 trend**

Atmospheric CO2 is accelerating upward from decade to decade. For the past ten years, the average annual rate of increase is 2.04 ppm. This rate of increase is more than double the increase in the 1960s. However, other than being a potent GHG and the main basis of global warming, CO2 is also a key substrate for plant growth. Interestingly, scientists observed nearly similar trends in the overall concentrations of ambient CO2 throughout the globe, which means that everybody is under similar crisis. Uncontrolled

stratosphere (Grewe, 2007). However, O3 varies strongly with episodic peak concentrations during the warmest months in summer in the most polluted regions and maximum concentrations during spring prevailing at background sites (Vingarzan, 2004). In regions such as East Asia exposed to the summer monsoon which transports oceanic air with less O3, the seasonal patterns show a peak during pre- and post-monsoon periods (He et al., 2008). During the day, O3 concentration pattern depends on elevation and shows strong diurnal variations at lowland sites where its destruction dominates during the night and vertical mixing together with photochemical activity causes highest levels in the afternoon. In rural agricultural areas of the USA, mean O3 concentrations reach between 50 and 60 ppb (90th percentile) (USEPA, 2006). Concentrations over the mid and high-latitude of the Eurasian and North American continents were 15 - 25 ppb in 1860, but increased to 40 - 50 ppb even in remote areas and from 10 - 15 ppb to 20 - 30 ppb over the mid- and highlatitude Pacific Ocean, respectively (Lelieveld and Dentener, 2000). Measures taken to reduce O3 precursor emissions, led to changes in O3 levels in many rural and urban areas of Europe, North America, and Japan; the frequency of the highest values shows a declining trend, while lowest values are increasing (Jenkin, 2008). The US EPA has reported that emission reduction in O3 precursors has been substantial over the past 29 years (US EPA, 2009). The percent change in emissions of NOx and VOCs were 40 and 47%, respectively, for

In India, despite of the favorable climatic conditions for O3 formation, very limited data from systematic monitoring of O3 are available. O3 concentrations are continuously increasing from 1992 to 2008 with higher peaks in rural areas. In a field transect study at urban sites of Varanasi, O3 concentrations varied from 6 to 10.2 ppb during 1989-1991 (Pandey and Agrawal, 1992). During the same period, daytime O3 concentrations (9 hr mean) were reported to vary from 9.4 to 128.3 ppb at an urban site in Delhi (Varshney and Agrawal, 1992). It was observed that 10 h ground level mean O3 concentrations in Delhi varied between 34 to 126 ppb during the winter of 1993 (Singh et al., 1997). At Pune, an annual average daytime O3 concentration of 27 ppb and hourly concentration between 2 and 69 ppb were reported during August 1991 to July 1992 (Khemani et al., 1995). Lal et al*.* (2000) studied the pattern of O3 concentrations from 1991 to 1995 at an urban site of Ahmedabad (India), and reported that daytime mean O3 concentrations exceeding 80 ppb were rarely observed. The monthly average O3 concentrations ranged between 62 and 95 ppb in summer (April - June) and 50 and 82 ppb in autumn (October - November) at New Delhi (Jain et al., 2005). Emberson et al. (2009) reported that large parts of South Asia experience up to 50 - 90 ppb mean 7 h (M 7) O3 concentration. Mittal et al. (2007) using the HANK model reported O3 concentration varying from 25 to 100 ppb over the entire

Atmospheric CO2 is accelerating upward from decade to decade. For the past ten years, the average annual rate of increase is 2.04 ppm. This rate of increase is more than double the increase in the 1960s. However, other than being a potent GHG and the main basis of global warming, CO2 is also a key substrate for plant growth. Interestingly, scientists observed nearly similar trends in the overall concentrations of ambient CO2 throughout the globe, which means that everybody is under similar crisis. Uncontrolled

the period 1980 - 2008.

Indian region.

**1.2.2 Tropospheric CO2 trend** 

anthropogenic activities throughout the world have caused the atmospheric CO2 to increase continuously from about 280 ppm at the beginning of the 19th century to 369 ppm at the beginning of the 21st century (Figure 1). Projections also range between about 450 and 600 ppm by the year 2050, but strongly depend on future scenarios of anthropogenic emissions (Woodward, 2002).

Fig. 1. Increasing trends in atmospheric CO2 concentrations at global level. [data adopted from http://www.esrl.noaa.gov/gmd/ccgg/trends/]

#### **2. Evaluation of O3- and CO2-effect on agricultural crops through modern OMICS approaches: A face-to-face mêlée**

The effect of both O3 and CO2 on the various levels of crop's responses till the yield has been very well studied so far (for detail, see review Cho et al., 2010). However, their specific effects on the genome, hence – transcriptome and proteome, has not been evaluated to a large extent. In the next section, we have made an attempt to portray the impact of both these pollutants on the agricultural crops through modern OMICS approaches depending on the available reports.

#### **2.1 Multi-parallel OMICS approaches in modern biology**

We are running through the golden era of genomics (study of whole 'genome' is loosely called 'genomics'). The genomics era is also in position to use multiple parallel approaches for the functional analysis of genomes in a high-throughput manner. These parallel approaches surely results in an exceptionally swift and effective system for the analyses and deductions of gene(s) function in a wide range of plants, at the level of transcript (transcriptomics), protein (proteomics), and metabolite (metabolomics) (Figure 2). All together, these four approaches are commonly referred as the multi-parallel OMICS approaches in modern biology.

Impacts of Ozone (O3) and Carbon Dioxide (CO2)

related proteins were reported in the rice leaf proteome.

**2.2.1.2 Wheat (***Triticum aestivum* **L.)** 

**2.2.1.3 Maize (***Zea mays* **L.)** 

under higher O3-stress.

Environmental Pollutants on Crops: A Transcriptomics Update 55

defense/stress-related proteins (i.e., PR5 and two PR10 proteins OsPR10/PBZ1 and RSOsPR10) were reported. In another independent study, Cho et al. (2008) also checked the expression profiles of genes in leaves of two-week-old rice seedlings exposed to 200 ppb O3 for 1, 12, and 24 h using a 22K rice DNA microarray chip. A total of 1,535 genes were differentially expressed more than five-fold over the control. Their functional categories revealed genes involved in transcription, pentose phosphate pathway, and signal transduction at 1 h. Genes related to antioxidant enzymes, ribosomal protein, posttranslational modification (PTM), signal transduction, jasmonate, ethylene, and secondary metabolism at 12 and 24 h play a crucial role in O3-response (Cho et al., 2008). Recently, Frei et al. (2010) have tried to identify the possible mechanism of O3-response in rice seedlings by characterizing two important quantitative trait loci (QTL), in two different chromosome segment substitution lines (SL15 and SL41). Their findings suggest that the activity of some major antioxidant genes might contribute significantly in the response strategy of rice plant

In contrast with the above laboratory-based experimental models, Sarkar and Agrawal (2010) had applied 'field-based integrated OMICS' approach to understand the background of O3 response in two high-yielding cultivars (*Malviya dhan 36* and *Shivani*) of mature rice plants under natural conditions. They found dependable phenotypical response, in the form of foliar injury, followed by definite changes in leaf proteome. Major damage was in the photosynthetic proteins (large and small subunits of RuBisCO) and primary metabolismrelated proteins. Moreover, an induced expression of some antioxidant and defense/stress-

Wheat (*Triticum aestivum* L.) is the third most important crop around the globe, and nearly two thirds of the world population depends on this crop for their primary nutrition supplement. Sarkar et al. (2010) employed 'field-based integrated OMICS' approach to understand the background of O3 response in two wheat cultivars (cvs Sonalika and HUW 510) against elevated O3 concentrations (ambient + 10 and 20 ppb) under near natural conditions using open top chambers (OTCs). Results of their study showed drastic reductions in the abundantly present RuBisCO large and small subunits. Western blot analysis confirmed induced accumulation of antioxidative enzymes like SOD and APX protein(s) and common defense/stress-related thaumatin-like protein(s). 2-DGE analysis revealed a total of 38 differentially expressed protein spots, common in both the wheat cultivars. Among those, some major leaf photosynthetic proteins (including Rubisco and Rubisco activase) and important energy metabolism proteins (including ATP synthase, aldolase, and phosphoglycerate kinase) were drastically reduced, whereas some stress/defense-related

proteins (such as harpin-binding protein and germin-like protein) were induced.

Maize (*Zea mays* L.) is another important crop at global context. Being a C4 crop, its response to climate change has been always been a bit different from the other plants/crops. Torres et al. (2007) have performed a detailed investigation of O3 response in maize (cv. Guarare 8128) plants through gel-based proteomics approach. In that experiment, 16-day-old maize plants (grown in controlled environment at green house) were exposed to 200 ppb O3 for 72 h, and

Fig. 2. Multi-parallel OMICS approaches in modern plant/crop biology.

#### **2.2 Case studies**

#### **2.2.1 Effect of ozone (O3) on crops**

#### **2.2.1.1 Rice (***Oryza sativa* **L.)**

Among all the major crops, rice (*Oryza sativa* L.) has been studied most for its response to O3-stress (Agrawal et al., 2002; Cho et al., 2008, Feng et al., 2008; Frei et al., 2010). Agrawal et al. (2002) first reported a detailed combined trancriptomics and proteomics response of rice plants under elevated O3-exposure. Two-weeks-old rice (*cv.* Nipponbare) seedlings were exposed to 200 ppb of O3 for three days in a controlled fumigation chamber. A drastic visible necrotic damage in O3-exposed leaves and consequent increase in ascorbate peroxidase (APX) protein(s) accompanied by rapid changes in the immunoblotting analysis and two-dimensional gel electrophoresis (2-DGE) protein profiles were observed. They also reported nearly 52 differentially expressed proteins. Among which were the O3-caused drastic reductions in the major leaf photosynthetic proteins, including the abundantly present ribulose-1, 5-bisphosphate carboxylase/oxygenase (RuBisCO) and induction of various defense/stress-related proteins. Most prominent change in the rice leaves, within 24 h post-treatment with O3, was the induced accumulation of a pathogenesis related (PR) class 5 protein, three PR 10 class proteins, APX(s), superoxide dismutase (SOD), calcium-binding protein, calreticulin, a novel ATP-dependent CLP protease, and an unknown protein. Feng et al. (2008) also followed the established two-week-old rice seedlings experimental model of Agrawal and co-workers (2002) and exposed plants to 0, 40, 80, and 120 ppb O3 for nine days. A drastic damage in the photosynthetic proteins (mainly large and small subunits of RuBisCO) and primary metabolism related proteins was found, whereas an induced expression of some major antioxidant (i.e., glutathione-S-transferase and MnSOD) and

Fig. 2. Multi-parallel OMICS approaches in modern plant/crop biology.

Among all the major crops, rice (*Oryza sativa* L.) has been studied most for its response to O3-stress (Agrawal et al., 2002; Cho et al., 2008, Feng et al., 2008; Frei et al., 2010). Agrawal et al. (2002) first reported a detailed combined trancriptomics and proteomics response of rice plants under elevated O3-exposure. Two-weeks-old rice (*cv.* Nipponbare) seedlings were exposed to 200 ppb of O3 for three days in a controlled fumigation chamber. A drastic visible necrotic damage in O3-exposed leaves and consequent increase in ascorbate peroxidase (APX) protein(s) accompanied by rapid changes in the immunoblotting analysis and two-dimensional gel electrophoresis (2-DGE) protein profiles were observed. They also reported nearly 52 differentially expressed proteins. Among which were the O3-caused drastic reductions in the major leaf photosynthetic proteins, including the abundantly present ribulose-1, 5-bisphosphate carboxylase/oxygenase (RuBisCO) and induction of various defense/stress-related proteins. Most prominent change in the rice leaves, within 24 h post-treatment with O3, was the induced accumulation of a pathogenesis related (PR) class 5 protein, three PR 10 class proteins, APX(s), superoxide dismutase (SOD), calcium-binding protein, calreticulin, a novel ATP-dependent CLP protease, and an unknown protein. Feng et al. (2008) also followed the established two-week-old rice seedlings experimental model of Agrawal and co-workers (2002) and exposed plants to 0, 40, 80, and 120 ppb O3 for nine days. A drastic damage in the photosynthetic proteins (mainly large and small subunits of RuBisCO) and primary metabolism related proteins was found, whereas an induced expression of some major antioxidant (i.e., glutathione-S-transferase and MnSOD) and

**2.2 Case studies** 

**2.2.1 Effect of ozone (O3) on crops** 

**2.2.1.1 Rice (***Oryza sativa* **L.)** 

defense/stress-related proteins (i.e., PR5 and two PR10 proteins OsPR10/PBZ1 and RSOsPR10) were reported. In another independent study, Cho et al. (2008) also checked the expression profiles of genes in leaves of two-week-old rice seedlings exposed to 200 ppb O3 for 1, 12, and 24 h using a 22K rice DNA microarray chip. A total of 1,535 genes were differentially expressed more than five-fold over the control. Their functional categories revealed genes involved in transcription, pentose phosphate pathway, and signal transduction at 1 h. Genes related to antioxidant enzymes, ribosomal protein, posttranslational modification (PTM), signal transduction, jasmonate, ethylene, and secondary metabolism at 12 and 24 h play a crucial role in O3-response (Cho et al., 2008). Recently, Frei et al. (2010) have tried to identify the possible mechanism of O3-response in rice seedlings by characterizing two important quantitative trait loci (QTL), in two different chromosome segment substitution lines (SL15 and SL41). Their findings suggest that the activity of some major antioxidant genes might contribute significantly in the response strategy of rice plant under higher O3-stress.

In contrast with the above laboratory-based experimental models, Sarkar and Agrawal (2010) had applied 'field-based integrated OMICS' approach to understand the background of O3 response in two high-yielding cultivars (*Malviya dhan 36* and *Shivani*) of mature rice plants under natural conditions. They found dependable phenotypical response, in the form of foliar injury, followed by definite changes in leaf proteome. Major damage was in the photosynthetic proteins (large and small subunits of RuBisCO) and primary metabolismrelated proteins. Moreover, an induced expression of some antioxidant and defense/stressrelated proteins were reported in the rice leaf proteome.

#### **2.2.1.2 Wheat (***Triticum aestivum* **L.)**

Wheat (*Triticum aestivum* L.) is the third most important crop around the globe, and nearly two thirds of the world population depends on this crop for their primary nutrition supplement. Sarkar et al. (2010) employed 'field-based integrated OMICS' approach to understand the background of O3 response in two wheat cultivars (cvs Sonalika and HUW 510) against elevated O3 concentrations (ambient + 10 and 20 ppb) under near natural conditions using open top chambers (OTCs). Results of their study showed drastic reductions in the abundantly present RuBisCO large and small subunits. Western blot analysis confirmed induced accumulation of antioxidative enzymes like SOD and APX protein(s) and common defense/stress-related thaumatin-like protein(s). 2-DGE analysis revealed a total of 38 differentially expressed protein spots, common in both the wheat cultivars. Among those, some major leaf photosynthetic proteins (including Rubisco and Rubisco activase) and important energy metabolism proteins (including ATP synthase, aldolase, and phosphoglycerate kinase) were drastically reduced, whereas some stress/defense-related proteins (such as harpin-binding protein and germin-like protein) were induced.

#### **2.2.1.3 Maize (***Zea mays* **L.)**

Maize (*Zea mays* L.) is another important crop at global context. Being a C4 crop, its response to climate change has been always been a bit different from the other plants/crops. Torres et al. (2007) have performed a detailed investigation of O3 response in maize (cv. Guarare 8128) plants through gel-based proteomics approach. In that experiment, 16-day-old maize plants (grown in controlled environment at green house) were exposed to 200 ppb O3 for 72 h, and

Impacts of Ozone (O3) and Carbon Dioxide (CO2)

were the major changed proteins.

**3. Concluding remarks**

**2.2.2.2 Wheat** 

Environmental Pollutants on Crops: A Transcriptomics Update 57

Hogy et al. (2009) have studied the effect of elevated CO2 on the grain proteome of wheat (*T. aestivum* L. cv. Triso) in a completely free-air CO2 enrichment (FACE) setup. Results of this experiment revealed a total of 32 proteins were affected. Out of them, 16 proteins were upregulated and 16 proteins were down-regulated. Among the up-regulated proteins, triticin precursor, putative avenin-like beta precursor, serpin, peroxidase 1, alpha-chain family 11 xylanase, starch synthase I, and cytosolic glyceraldehyde-3-phosphate dehydrogenase (GAPDH) were the major proteins, whereas among down-regulated proteins, globulin (Glb 1) storage protein, low-molecular weight glutenin, ATP synthase β subunit, and alpha-tubulin

The projected levels of O3 and CO2 are critically alarming, and have become a major issue of concern for food security worldwide. Scientific evidences indicate that crop plants are in

Plant resistance to O3 involves a wide array of response ranging from the molecular and cellular level to the whole plant level. Significant effects of O3 are early leaf senescence, decreased photosynthetic assimilation, altered stomatal behavior, decreased growth and productivity, and reduced carbon allocation to roots and changes in metabolic pathways. Genotype differences in response to O3 are related to stomatal behavior, anti-oxidative potential hormonal regulation, and carbon allocation during reproduction affecting the yield responses. Detailed understanding of genotypic response is crucial in predicting the long-term impacts of O3 on agriculture in global context, including the breeding of resistant cultivars. Several potential O3 biomarkers have been identified, which could be exploited to screen and develop O3-tolerant varieties in future (Figure 3). However, in case of CO2, it is an integrated compound for plant's survival. So, at the initial stages, any increment in the ambient CO2 levels showed a positive response towards plants yield, but also raised many questions. The behavior of RuBisCO, key

While reviewing the available reports on O3 and CO2 air pollutants, we found that both the stresses leave some kind of specific 'signature' on the crops response and that the 'signature' is not crop dependent. However, it must be emphasized that there are only limited OMICS studies available on crop responses to O3 and CO2. Future work in our laboratories and those around the world will help provide new and much needed insight into the nature of the plant response to air pollutants and ways and means to help circumvent their deleterious effects. It is quite clear that we will need proper engineering of crops to combat the emerging problem, and researches, analysis, and reviews on initial crop-pollutants interaction have pointed toward some important functional traits required while considering the next-generation crops: i. Crops should have efficient and effective stomatal behavior to properly maintain the balance of external gas influx. As per the present research outcomes, we can see that

crops prefer avoidance more than developing resistance towards any stress.

generate energy for combating the prevailing unfavorable atmosphere.

molecular network within the cell.

ii. Crops should possess efficient photosynthetic system with higher catalytic ability to

iii. Crops should have improved detoxification system and superior stress tolerant

general sensitive to both these air pollutants, but in different ways.

enzyme of photosynthesis, is still under debate at higher CO2 levels (Figure 3).

then the response was compared with a controlled plant (grown under filtered pollutant-free air). Results showed that nearly 12 protein spots were differentially expressed under O3 exposure, and can be exploited as marker proteins. Expression levels of catalase (increased), SOD (decreased), and APX (increased) were drastically changed by O3 depending on the leaf stage, whereas cross-reacting heat-shock proteins (HSPs; 24 and 30 kDa) and naringenin-7-*O*methyltransferase (NOMT; 41 kDa) proteins were strongly increased in O3-stressed younger leaves. The study also enumerated leaf injury as biomarker under O3 stress in maize leaves.

#### **2.2.1.4 Bean (***Phaseolus vulgaris* **L.)**

Torres et al. (2007) also conducted a study on response of cultivated bean (*Phaseolus vulgaris*  L. cv. IDIAP R-3) against O3 stress using the same experimental protocol as for maize (see above) and the effects were evaluated using gel-based proteomics followed by MS and immunoblotting. Results showed that in bean leaves two SOD proteins (19 and 20 kDa) were dramatically decreased, while APX (25 kDa), small HSP (33 kDa), and a NOMT (41 kDa) were increased after O3 fumigation.

#### **2.2.1.5 Pepper (***Capsicum annuum* **L.)**

Lee and Yun (2006) applied cDNA microarrays to monitor the transcriptome of O3 stressregulated genes in two pepper cultivars [*Capsicum annuum* cv. Dabotop (O3-sensitive) and cv. Buchon (O3-tolerant)]. Ozone stress up-/down-regulated 180 genes more than three-fold with respect to their controls. Transcripts of 84 genes increased, transcripts of 88 others diminished, and those of eight either accumulated or diminished at different time points in the two cultivars or changed in only one of the cultivars. A total of 67% (120 genes) were regulated differently in O3-sensitive and O3-tolerant pepper cultivars, most being specifically up-regulated in the O3-sensitive cultivar.

#### **2.2.1.6 Linseed (***Linum usitassimum* **L.)**

Tripathi et al. (2011) analyzed the response of linseed plants under elevated O3-stress through combined genomics and proteomics approaches. The results showed that 10 ppb elevation over ambient O3 concentration can cause 50% damage in the genome stability of linseed plants. In line to the genome response, leaf proteome was also severely affected under O3 stress, and the damages were mainly observed on the photosynthetic and primary metabolism-related proteins.

#### **2.2.2 Effect of carbon dioxide (CO2) on crops**

#### **2.2.2.1 Rice**

Bokhari et al. (2007) had exposed 10-day-old rice (*O. sativa* L. spp *Indica* cv. 93-11) seedlings to 760, 1140, and 1520 ppm of CO2 for 24 h, and assessed the response of test plants through 2-D gel-based proteomics followed by protein identification. Comparative analysis of leaf proteome revealed 57 differentially expressed proteins under elevated CO2 in the rice leaf proteome. Majority of these differentially expressed proteins belonged to photosynthesis (34%), carbon metabolism (17%), protein processing (13%), energy pathway (11%), and antioxidants (4%). Several molecular chaperones and APX were found to be up-regulated under higher CO2, whereas major photosynthetic proteins like RuBisCO and RuBisCO activase, and different proteins of Calvin cycle were down-regulated.

#### **2.2.2.2 Wheat**

56 Crop Plant

then the response was compared with a controlled plant (grown under filtered pollutant-free air). Results showed that nearly 12 protein spots were differentially expressed under O3 exposure, and can be exploited as marker proteins. Expression levels of catalase (increased), SOD (decreased), and APX (increased) were drastically changed by O3 depending on the leaf stage, whereas cross-reacting heat-shock proteins (HSPs; 24 and 30 kDa) and naringenin-7-*O*methyltransferase (NOMT; 41 kDa) proteins were strongly increased in O3-stressed younger leaves. The study also enumerated leaf injury as biomarker under O3 stress in maize leaves.

Torres et al. (2007) also conducted a study on response of cultivated bean (*Phaseolus vulgaris*  L. cv. IDIAP R-3) against O3 stress using the same experimental protocol as for maize (see above) and the effects were evaluated using gel-based proteomics followed by MS and immunoblotting. Results showed that in bean leaves two SOD proteins (19 and 20 kDa) were dramatically decreased, while APX (25 kDa), small HSP (33 kDa), and a NOMT (41

Lee and Yun (2006) applied cDNA microarrays to monitor the transcriptome of O3 stressregulated genes in two pepper cultivars [*Capsicum annuum* cv. Dabotop (O3-sensitive) and cv. Buchon (O3-tolerant)]. Ozone stress up-/down-regulated 180 genes more than three-fold with respect to their controls. Transcripts of 84 genes increased, transcripts of 88 others diminished, and those of eight either accumulated or diminished at different time points in the two cultivars or changed in only one of the cultivars. A total of 67% (120 genes) were regulated differently in O3-sensitive and O3-tolerant pepper cultivars, most being

Tripathi et al. (2011) analyzed the response of linseed plants under elevated O3-stress through combined genomics and proteomics approaches. The results showed that 10 ppb elevation over ambient O3 concentration can cause 50% damage in the genome stability of linseed plants. In line to the genome response, leaf proteome was also severely affected under O3 stress, and the damages were mainly observed on the photosynthetic and primary

Bokhari et al. (2007) had exposed 10-day-old rice (*O. sativa* L. spp *Indica* cv. 93-11) seedlings to 760, 1140, and 1520 ppm of CO2 for 24 h, and assessed the response of test plants through 2-D gel-based proteomics followed by protein identification. Comparative analysis of leaf proteome revealed 57 differentially expressed proteins under elevated CO2 in the rice leaf proteome. Majority of these differentially expressed proteins belonged to photosynthesis (34%), carbon metabolism (17%), protein processing (13%), energy pathway (11%), and antioxidants (4%). Several molecular chaperones and APX were found to be up-regulated under higher CO2, whereas major photosynthetic proteins like RuBisCO and RuBisCO

**2.2.1.4 Bean (***Phaseolus vulgaris* **L.)** 

kDa) were increased after O3 fumigation. **2.2.1.5 Pepper (***Capsicum annuum* **L.)** 

**2.2.1.6 Linseed (***Linum usitassimum* **L.)** 

metabolism-related proteins.

**2.2.2.1 Rice** 

specifically up-regulated in the O3-sensitive cultivar.

**2.2.2 Effect of carbon dioxide (CO2) on crops** 

activase, and different proteins of Calvin cycle were down-regulated.

Hogy et al. (2009) have studied the effect of elevated CO2 on the grain proteome of wheat (*T. aestivum* L. cv. Triso) in a completely free-air CO2 enrichment (FACE) setup. Results of this experiment revealed a total of 32 proteins were affected. Out of them, 16 proteins were upregulated and 16 proteins were down-regulated. Among the up-regulated proteins, triticin precursor, putative avenin-like beta precursor, serpin, peroxidase 1, alpha-chain family 11 xylanase, starch synthase I, and cytosolic glyceraldehyde-3-phosphate dehydrogenase (GAPDH) were the major proteins, whereas among down-regulated proteins, globulin (Glb 1) storage protein, low-molecular weight glutenin, ATP synthase β subunit, and alpha-tubulin were the major changed proteins.

#### **3. Concluding remarks**

The projected levels of O3 and CO2 are critically alarming, and have become a major issue of concern for food security worldwide. Scientific evidences indicate that crop plants are in general sensitive to both these air pollutants, but in different ways.

Plant resistance to O3 involves a wide array of response ranging from the molecular and cellular level to the whole plant level. Significant effects of O3 are early leaf senescence, decreased photosynthetic assimilation, altered stomatal behavior, decreased growth and productivity, and reduced carbon allocation to roots and changes in metabolic pathways. Genotype differences in response to O3 are related to stomatal behavior, anti-oxidative potential hormonal regulation, and carbon allocation during reproduction affecting the yield responses. Detailed understanding of genotypic response is crucial in predicting the long-term impacts of O3 on agriculture in global context, including the breeding of resistant cultivars. Several potential O3 biomarkers have been identified, which could be exploited to screen and develop O3-tolerant varieties in future (Figure 3). However, in case of CO2, it is an integrated compound for plant's survival. So, at the initial stages, any increment in the ambient CO2 levels showed a positive response towards plants yield, but also raised many questions. The behavior of RuBisCO, key enzyme of photosynthesis, is still under debate at higher CO2 levels (Figure 3).

While reviewing the available reports on O3 and CO2 air pollutants, we found that both the stresses leave some kind of specific 'signature' on the crops response and that the 'signature' is not crop dependent. However, it must be emphasized that there are only limited OMICS studies available on crop responses to O3 and CO2. Future work in our laboratories and those around the world will help provide new and much needed insight into the nature of the plant response to air pollutants and ways and means to help circumvent their deleterious effects. It is quite clear that we will need proper engineering of crops to combat the emerging problem, and researches, analysis, and reviews on initial crop-pollutants interaction have pointed toward some important functional traits required while considering the next-generation crops:


Impacts of Ozone (O3) and Carbon Dioxide (CO2)

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Finally, as both O3 and CO2 stress leave a specific signature on the crop plant transcriptome, inputs from genome-wide analysis could be effectively exploited for further crop improvement *in vivo*, and the objective of our on-going studies.

Fig. 3. Diagrammatic representation of O3- and/CO2-effects on plants, at cellular levels.

### **4. Acknowledgements**

AS also acknowledge the support of Prof. S. B, Agrawal for promoting collaborative work, and financial help from CSIR, New Delhi, India in the form of Senior Research Fellowship. KC appreciates the support of Dr. Akihiro Kubo for his stay and research at the National Institute for Environmental Studies (NIES) in Tsukuba, Japan, where he worked extensively on ozone responses in rice as an Eco-Frontier Fellow (09-Ba086-02). RR acknowledges the great support of Professor Seiji Shioda and Dr. Tetsuo Ogawa (Department of Anatomy I, Showa University School of Medicine), and the Provost, Professor Yoshihiro Shiraiwa of the Graduate School of Life and Environmental Sciences (University of Tsukuba) in promoting interdisciplinary research and unselfish encouragement.

### **5. References**


Finally, as both O3 and CO2 stress leave a specific signature on the crop plant transcriptome, inputs from genome-wide analysis could be effectively exploited for further crop

Fig. 3. Diagrammatic representation of O3- and/CO2-effects on plants, at cellular levels.

AS also acknowledge the support of Prof. S. B, Agrawal for promoting collaborative work, and financial help from CSIR, New Delhi, India in the form of Senior Research Fellowship. KC appreciates the support of Dr. Akihiro Kubo for his stay and research at the National Institute for Environmental Studies (NIES) in Tsukuba, Japan, where he worked extensively on ozone responses in rice as an Eco-Frontier Fellow (09-Ba086-02). RR acknowledges the great support of Professor Seiji Shioda and Dr. Tetsuo Ogawa (Department of Anatomy I, Showa University School of Medicine), and the Provost, Professor Yoshihiro Shiraiwa of the Graduate School of Life and Environmental Sciences (University of Tsukuba) in promoting

Agrawal, G.K., R. Rakwal, M. Yonekura, A. Kubo, and H. Saji. 2002. Proteome analysis of

Beig, G., Ali, K. 2006. Behaviour of boundary layer ozone and its precursors over a great alluvial plain of the world: Indo-gangetic plains. Geophysical Research Letters 33: L 24813. Bokhari, S.A., Wan, X., Yang, Y., Zhou, L., Tang, W., Liu, J. 2007. Proteomic response of rice seedling leaves to elevated CO2 levels. Journal of Proteome Research 6: 4624-4633.

differentially displayed proteins as a tool for investigating ozone stress in rice

**4. Acknowledgements**

**5. References**

interdisciplinary research and unselfish encouragement.

(Oryza sativa L.) seedlings. Proteomics 2:947-959.

improvement *in vivo*, and the objective of our on-going studies.


**1. Introduction** 

genetic activities.

Corresponding author

 \* **4** 

**Phenomenal RNA Interference:** 

**From Mechanism to Application** 

The phenomenon of dsRNA-mediated interference (RNAi), was first demonstrated in nematodes in 1998 by *Professor* Andrew Z. Fire *at Stanford University, California, USA* and *Professor* Craig C. Mello at University of Massachusetts *Medical School in Worcester, USA.* It is thought to have evolved as a type of "genetic immune system" to protect organisms from the presence of foreign or unwanted genetic material. To be more specific, RNAi probably evolved as a mechanism to block the replication of viruses and/or to suppress the movements of transposons within the genome, because both of these potentially dangerous processes typically involve the formation of dsRNA intermediates. Cells can recognize dsRNAs as "undesirable" because such molecules are not produced by the cell's normal

The RNAi was introduced to the public in mid-2001 and in just about few years it became one of the most widely used technologies in both academic and industrial research environments. In recognition of overwhelming importance of RNAi is an biological process and universally applicable tool, the leading Journal Science proclaimed it "The breakthrough of the year, 2002". During the last decade, our knowledge repertoire of RNAmediated functions has hugely increased, with the discovery of small non-coding RNAs which play a central part in a process called RNA silencing. Ironically, the very important phenomenon of co-suppression has recently been recognized as a manifestation of RNA interference (RNAi), an endogenous pathway for negative post-transcriptional regulation. RNAi has revolutionized the possibilities for creating custom "knock-downs" of gene activity. RNAi operates in both plants and animals, and uses double stranded (dsRNA) as a trigger that targets homologous mRNAs for degradation or inhibiting its transcription or

Pallavi Mittal1,\*, Rashmi Yadav2, Ruma Devi3, Shubhangini Sharma4 and Aakash Goyal5

*1ITS Paramedical College, Ghaziabad,* 

*3PAU Regional Station, Gurdaspur, 4Aptara (Techbook International), Delhi* 

*5Bayer Crop Science Saskatoon,* 

*1,2,3,4India 5Canada* 

*2All India Institute of Medical Science, Delhi,* 

fourth assessment report of IPCC on climate change. Cambridge University Press, Cambridge, UK, NY, USA.

