which using equations (5) and (6) and measurement of air temperature and vapor pressure gradients by ventilated psychrometers near the soil surface, Bowen ratio at soil surface was determined. By solving equation (7) and (9) simultaneously, latent heat flux from the soil surface was determined by equation (10).

$$
\lambda \mathbf{E}\_{\mathbf{s}} = \frac{\mathbf{R}\_{\mathbf{ns}} - \mathbf{G}}{1 + \mathfrak{B}} \tag{10}
$$

#### **2.3 Energy balance theory at crop canopy**

Energy balance at maize canopy can be expressed as below (Zeggaf et al., 2008, Ham et al., 1991):

$$\mathbf{R\_{nc}} = \lambda \mathbf{E\_c} + \mathbf{H\_c} \tag{11}$$

where Rnc is net radiation absorbed by crop canopy, λEc is crop canopy latent heat flux and Hc is crop canopy sensible heat flux (all units of W/m2). Applying the principle of continuity and the definition of Rn, it can be shown that Rnc, equation (12), is the difference between Rn measured above and that below the maize canopy (Ham et al., 1991).

$$\mathbf{R\_{n\_{\mathcal{C}}}} = \mathbf{R\_n} - \mathbf{R\_{ns}} \tag{12}$$

Canopy latent heat flux was calculated from equation (13):

$$
\lambda \mathbf{E\_c = \lambda E - \lambda E\_s} \tag{13}
$$

Then Hc was calculated as a residual from equation (11).

#### **3. Methodology development**

The research was conducted in summer 2009 at experimental station of agricultural engineering research institute (AERI), Karaj-Iran (35 21′ N, 51 38′ E, 1312.5 m above sea

where, Rns is the net radiation reaching the soil surface, λEs is the soil surface latent heat flux, Hs is sensible heat flux from the soil surface (all units of W/m2). Rns was determined by the empirical equation (8) with Rn and LAI which has been used previously by some other

Bowen ratio at soil surface was calculated similar to the energy balance computation at field

*S*

 *S*

*H E*

which using equations (5) and (6) and measurement of air temperature and vapor pressure gradients by ventilated psychrometers near the soil surface, Bowen ratio at soil surface was determined. By solving equation (7) and (9) simultaneously, latent heat flux from the soil

1

Energy balance at maize canopy can be expressed as below (Zeggaf et al., 2008, Ham et al.,

where Rnc is net radiation absorbed by crop canopy, λEc is crop canopy latent heat flux and Hc is crop canopy sensible heat flux (all units of W/m2). Applying the principle of continuity and the definition of Rn, it can be shown that Rnc, equation (12), is the difference between Rn

The research was conducted in summer 2009 at experimental station of agricultural engineering research institute (AERI), Karaj-Iran (35 21′ N, 51 38′ E, 1312.5 m above sea

measured above and that below the maize canopy (Ham et al., 1991).

Canopy latent heat flux was calculated from equation (13):

Then Hc was calculated as a residual from equation (11).

**3. Methodology development**

*S*

R E H G 0 ns s s (7)

R R exp( 0.622LAI 0.055LAI ) 2 ns n (8)

(9)

R G E ns s (10)

Rnc Ec Hc (11)

Rnc Rn Rns (12)

Ec E Es (13)

**2.2 Energy balance theory at soil surface**  Energy balance at soil surface can be expressed as:

authors (Gardiol et al., 2003; Kato et al., 2004).

level with the following equation:

surface was determined by equation (10).

**2.3 Energy balance theory at crop canopy**

1991):

level). The field soil was prepared for planting in spring. Results from soil experiments up to 80 cm below surface showed the soil type was loam texture (47 % sand, 44 % silt, 9 % clay) with ECe=1.7. Irrigation water were supplied from underground well with an quality which had no negative impact for maize (EC=0.8 dS/m and pH=7.8).

The experimental field was defined in an area of 40×60 m2 in selected site (Fig. 1). A day before planting 50 kg potash fertilizer was added to soil and maize (Double Cross 370) was planted on 15 June 2009. The crop was planted with 0.75 m row width and north-south orientation. The field was bordered by irrigated maize field except in western side which was unplanted.

Fig. 1. Schematic diagram showing field position and location of energy balance measurement systems.

Irrigation water was supplied from the well and chemical quality analysis showed water in this region has good quality. A subsurface drip-tape irrigation system with 0.30 m dripper distance was used to apply irrigation water. Drip tapes were placed 15 cm below the soil surface in nearest place to the plant rows. Special attention was paid while positioning drip tapes to transfer water correctly. Crop water requirement was estimated based on long time meteorological data (averaging from 1988 to 2008) and calculation of crop ET by method recommended in FAO 56, Penman Monteith Method (P-M) (Allen et al., 1998). The P-M method is recommended for the Karaj area by Dehghanisanij et al. (2004). From the early crop growth period 20% over irrigation based on 3 day intervals was applied to prevent water stress. Recommended nitrate fertilizer (according to soil experiments it was 400 kg/ha) was distributed during the crop growth period and closely to crop establishment place by irrigation system (fertigation). At the period of this experiment 41-44 and 59-62 day after emergence (DAE), leaf area index (LAI) was measured in 41, 44, 59, 62 DAE. Each time 3-5 plants were selected randomly and the whole leaf area of a plant was measured with leaf area meter (Area Measurement system, DELA-T Devices, ENGLAND) in the laboratory. Then LAI was calculated from multiplying the average plant leaf area by plant density. LAI values for the days between the days of measurement obtained by linear interpolation (Gardiol et al., 2003). Automatic weather station was established in the field simultaneously with start of experiment period and hourly average values of solar radiation (Rs), air temperature, relative humidity and wind speed were measured and logged continuously. From 41-44 and 59-62 DAE, ET and Es were determined simultaneously by measuring all energy fluxes at maize field and soil surface using two independent measurement systems.

Evapotranspiration Partitioning in Surface and Subsurface Drip Irrigation Systems 217

Diurnal trend of evapotranspiration measurement by BREB method compare to evapotranspiration estimation by P-M method for 60 and 61 DAE are shown in Fig. 2. These days were selected because they are representative of cloudy and clear sky condition respectively which is believed they show all sky condition during measurement period.

**Time (Hour)**

As it is shown in Fig. 2 both methods have the same trend and a good correlation (R2=0.92 and 0.95 for DAE 60 and 61, respectively). Therefore evapotranspiration by BREB showed 9% variations compare to P-M method which can be acceptable. Positive value of MBE parameter shows overestimation of P-M method compare to BREB. These differences can be caused by different measurements of effective and required parameters in both methods. In other word, BREB evapotranspiration was obtained by direct measurement of required parameters at field level and soil surface while in P-M method maize evapotranspiration was obtained by estimation of reference evapotranspiration and crop coefficient. Ortega et al. (1995) found a good correlation between refernce evapotranspiration by BREB and

Daytime average of energy balance measurements in terms of (W/m2) at Maize field, soil surface and crop canopy for DI and SDI are presented in tables 2 to 4. In the measurement period net radiation values ranged from 304 to 333 (W/m2) resulted by the minimum and maximum solar radiation in corresponding days respectively (Table 2). Latent heat flux (λE) From the Maize field ranged from 207 to 267 (W/m2) for SDI and 197 to 296 (W/m2) for DI. According to the results λE ranging from 62 to 83 % of Rn under SDI and 61 to 94 % for DI, which shows maize cropping system is under non-stressed conditions (Ham et al., 1991). Herein G/Rn was ranging 6 to 8 % for SDI and 8-15 % for DI, which is close to 10 % reported

Soil surface energy balance measurements showed Rns values decreased with crop growth and canopy cover increment. Furthermore it showed Rns partitioned primarily between soil heat flux and latent heat flux from the soil surface and there was very little sensible heat flux during experiment period. The G variation under SDI was 17 to 25, and it was 25 to 47

Fig. 2. Diurnal trend of evapotranspiration by BREB and P-M method for 60 and 61 DAE.

7 8 9 10 11 12 13 14 15 16 17 18 19 20

**(DAE 60)**

**5. Evapotranspiration analysis**

7 8 9 10 11 12 13 14 15 16 17 18 19 20

Penman method on irrigated grass.

**6. Energy balance at farm level**

by Yunusa et al. (2004).

FAO56 **(DAE 61)** BREB

**ET (mm/hr)**

Then by subtracting the latent heat flux at soil surface from the latent heat flux at maize field (ET), transpiration was obtained. Energy balance equipments consisted of a net radiometer (CNR1, Kipp & Zonen), two soil heat flux plates (MF-180M, EKO Japan) and four hand made thermocouple ventilated psychrometers for Bowen ratio measurement in both field level and soil surface. The details of constructed psychrometers have been described in Kosari (2010). Two independent measuring systems separated by 5 m distance, were placed 9 m from the east edge of the field as the system number 2 was positioned 5 m from the east edge to maximize fetch to height ratio when prevailing wind (north-western to southeastern) were present (Fig. 1). That was greater than minimum adequate ratio reported by Heilman et al. (1989) for measuring Bowen ratio during our experiment period.

Measurement equipments in each measurement system were installed on a tall rod. Two ventilated psychrometers used for measuring temperature and water vapor gradients at field level above the crop. These two psychrometers were installed 1 m apart as the lowest one was positioned 0.2 m above the crop canopy (Ham et al., 1991; Jara et al., 1998). The remaining two psychrometers used for measuring temperature and water vapor gradients at soil surface. These two psychrometers were fixed 0.1 m apart on the rod as the lowest one was positioned 0.05 m above the soil surface (Ashktorab et al., 1989). Net radiation at field level was measured with net radiometer installed 1 m above crop canopy. Soil heat flux was calculated as an average of two soil heat flux plates positioned 0.02 m below the soil surface. All data were measured every minute by a CR23X data logger connected to an AM16/32 multiplexer (Campbell Scientific, Inc., UT) and averaged 30 min intervals.

## **4. Meteorological parameters variation**

Daytime average values of meteorological parameters measured by the automatic weather station in the experiment period are shown in Table 1. Plant in days 41- 44 DAE was in developing stage and in days 59-62 DAE was in mid-season stage. Irrigation has been done on 41, 44, 59 and 63 DAE, which exceptionally because of some problems the last one irrigated with 4 days interval. In the experiment period the 42 and 60 DAE received maximum and minimum solar radiation respectively.

Table 1. Daytime average values of meteorological values in experiment period.

## **5. Evapotranspiration analysis**

216 Evapotranspiration – From Measurements to Agricultural and Environmental Applications

Then by subtracting the latent heat flux at soil surface from the latent heat flux at maize field (ET), transpiration was obtained. Energy balance equipments consisted of a net radiometer (CNR1, Kipp & Zonen), two soil heat flux plates (MF-180M, EKO Japan) and four hand made thermocouple ventilated psychrometers for Bowen ratio measurement in both field level and soil surface. The details of constructed psychrometers have been described in Kosari (2010). Two independent measuring systems separated by 5 m distance, were placed 9 m from the east edge of the field as the system number 2 was positioned 5 m from the east edge to maximize fetch to height ratio when prevailing wind (north-western to southeastern) were present (Fig. 1). That was greater than minimum adequate ratio reported by

Measurement equipments in each measurement system were installed on a tall rod. Two ventilated psychrometers used for measuring temperature and water vapor gradients at field level above the crop. These two psychrometers were installed 1 m apart as the lowest one was positioned 0.2 m above the crop canopy (Ham et al., 1991; Jara et al., 1998). The remaining two psychrometers used for measuring temperature and water vapor gradients at soil surface. These two psychrometers were fixed 0.1 m apart on the rod as the lowest one was positioned 0.05 m above the soil surface (Ashktorab et al., 1989). Net radiation at field level was measured with net radiometer installed 1 m above crop canopy. Soil heat flux was calculated as an average of two soil heat flux plates positioned 0.02 m below the soil surface. All data were measured every minute by a CR23X data logger connected to an AM16/32

Daytime average values of meteorological parameters measured by the automatic weather station in the experiment period are shown in Table 1. Plant in days 41- 44 DAE was in developing stage and in days 59-62 DAE was in mid-season stage. Irrigation has been done on 41, 44, 59 and 63 DAE, which exceptionally because of some problems the last one irrigated with 4 days interval. In the experiment period the 42 and 60 DAE received

Air Temperature (°C) Relative Humidity (%) Wind

41 16.67 34.44 24.10 18.92 78.16 53.02 2.90 45.17 42 16.51 34.07 24.12 15.64 83.07 52.64 3.38 46.82 43 16.10 33.30 23.39 26.80 77.82 54.09 2.95 46.21 44 16.56 32.89 23.79 28.66 74.72 52.16 2.92 44.31

59 19.91 37.38 28.30 12.42 65.51 36.40 1.48 40.97 60 19.96 35.64 27.42 17.78 65.98 41.67 2.33 40.14 61 19.09 35.57 27.00 13.29 72.40 41.68 2.05 45.18 62 16.00 34.45 24.24 13.06 59.69 38.43 1.90 45.07

Table 1. Daytime average values of meteorological values in experiment period.

MJ/m2day Min Max Ave Min Max Ave

Speed (m/s)

Solar Radiation

Heilman et al. (1989) for measuring Bowen ratio during our experiment period.

multiplexer (Campbell Scientific, Inc., UT) and averaged 30 min intervals.

**4. Meteorological parameters variation**

maximum and minimum solar radiation respectively.

Growth

Developing

Mid-season

stage

stage

stage DAE

Diurnal trend of evapotranspiration measurement by BREB method compare to evapotranspiration estimation by P-M method for 60 and 61 DAE are shown in Fig. 2. These days were selected because they are representative of cloudy and clear sky condition respectively which is believed they show all sky condition during measurement period.

Fig. 2. Diurnal trend of evapotranspiration by BREB and P-M method for 60 and 61 DAE.

As it is shown in Fig. 2 both methods have the same trend and a good correlation (R2=0.92 and 0.95 for DAE 60 and 61, respectively). Therefore evapotranspiration by BREB showed 9% variations compare to P-M method which can be acceptable. Positive value of MBE parameter shows overestimation of P-M method compare to BREB. These differences can be caused by different measurements of effective and required parameters in both methods. In other word, BREB evapotranspiration was obtained by direct measurement of required parameters at field level and soil surface while in P-M method maize evapotranspiration was obtained by estimation of reference evapotranspiration and crop coefficient. Ortega et al. (1995) found a good correlation between refernce evapotranspiration by BREB and Penman method on irrigated grass.

## **6. Energy balance at farm level**

Daytime average of energy balance measurements in terms of (W/m2) at Maize field, soil surface and crop canopy for DI and SDI are presented in tables 2 to 4. In the measurement period net radiation values ranged from 304 to 333 (W/m2) resulted by the minimum and maximum solar radiation in corresponding days respectively (Table 2). Latent heat flux (λE) From the Maize field ranged from 207 to 267 (W/m2) for SDI and 197 to 296 (W/m2) for DI. According to the results λE ranging from 62 to 83 % of Rn under SDI and 61 to 94 % for DI, which shows maize cropping system is under non-stressed conditions (Ham et al., 1991). Herein G/Rn was ranging 6 to 8 % for SDI and 8-15 % for DI, which is close to 10 % reported by Yunusa et al. (2004).

Soil surface energy balance measurements showed Rns values decreased with crop growth and canopy cover increment. Furthermore it showed Rns partitioned primarily between soil heat flux and latent heat flux from the soil surface and there was very little sensible heat flux during experiment period. The G variation under SDI was 17 to 25, and it was 25 to 47

Evapotranspiration Partitioning in Surface and Subsurface Drip Irrigation Systems 219

sky. Average air temperature and relative humidity was 27.4 C and 41.6 % respectively. Daytime average of net radiation available at maize field was 304 W/m2, which was the

41 215 - - 142 66 42 230 134 58 205 89 43 233 158 68 216 93 44 232 184 79 204 88 59 245 197 80 264 108 60 237 192 81 218 92 61 255 199 78 229 90 62 252 227 90 201 80

During the day Maximum Rn and G were 674 and 70 W/m2, which occurred about 13:00 and 14:00 h respectively. As it is shown in Fig. 3 variation of Rn values is not symmetrically

Most of (Rn-G) was used to drive λE and β values ranged between 0 to 0.7 while β values reported by Zeggaf et al., (2008), in maize field was lower than 0.25. It can be due to different crop growth stage in two experiments (in this research during the 60th DAE, Maize crop covered the ground relatively complete, LAI=3.53) and various climate, soil type and/or irrigation system. Sensible heat flux accounted for about 18% of available energy (Rn-G). Similar results were reported by Steduto and Hsiao, (1998), Ham et al., (1991) and Ritchie, (1971). Soil heat flux was about 17 (W/m2) which was lower than 10% of net radiation as it is found a recommended value for daytime average of soil heat flux in

During the daytime of 60th DAE only 22% of net radiation reached the soil surface. Most of the energy was split between λEs and G. The value of Hs was small in this balance. The λEs was less than available energy except in the afternoon suggesting that the soil surface was absorbing energy from within-canopy (air stream) which provided energy for λEs. Similar signification was reported in Ham et al., (1991) for soil surface energy balance relationships. Daytime average of λEs was about 86% of Rns-G and only about 14% of Rns-G was used as sensible heat. During this day βs from soil surface, ranged from -0.5 to 0.5. Positive Hs values at soil surface indicates convective transport of heat away from the soil surface (Zeggaf et al., 2008), but the results from this research showed small amounts of available energy used as sensible heat flux. Field observations from subsurface drip irrigation system showed installation depth of drip tapes caused during irrigation, water rises up to the surface and makes the ground wet. Therefore the large proportion of soil surface latent heat flux from available energy can be due to the ground wetness. It can be concluded when drip tapes install in lower depth and the soil surface remains dry larger proportion of sensible

as a bell shape curve signifies that there were some cloud cover at sky during the day.

Subsurface drip irrigation (SDI) Surface drip irrigation (DI) λEc λEc/Rnc λEc λEc/Rnc

W/m2 % W/m2 %

smallest value in the measurement period.

Table 4. Daytime average energy fluxes at crop canopy.

Rnc

literature (Allen et al., 1998).

heat will result.

DAE

W/m2 in under DI. The λEs accounted for about 40 to 73 (W/m2) in SDI, where maximum value of λEs was 60 W/m2 for surface DI. Accordingly, λEs/Rns and G/Rns ratios ranged between 56 to 71 % and 18 to 31 % respectively (Table 3). The G under SDI was much less compared to that under DI during crop developing stage than mid-season stage. It is while, λEs was larger under SDI compared to that for DI. These results contributed to higher possible potential for T under SDI during crop developing stage (Table 3).

Table 2. Daytime average energy fluxes at maize field.

Table 3. Daytime average energy fluxes at soil surface.

Daytime average of energy balance measurements at Maize canopy showed λEc increased by crop development from 134 to 227 (W/m2) which resulted λEc/Rnc between 58 to 90% in SDI. These ratios showed canopy latent heat fluxes were often lower than available energy which resulted some sensible heat flux conducted away from canopy level. Under DI, λEc increased by crop development from 142 to 264, which was less in average compare to that for SDI (Table 4).

## **7. Diurnal energy balance pattern**

Diurnal trends of the energy balance components for the soil surface of maize field at 60th DAE are shown in Fig. 3. This day was representative of a day with some cloud cover in the

W/m2 in under DI. The λEs accounted for about 40 to 73 (W/m2) in SDI, where maximum value of λEs was 60 W/m2 for surface DI. Accordingly, λEs/Rns and G/Rns ratios ranged between 56 to 71 % and 18 to 31 % respectively (Table 3). The G under SDI was much less compared to that under DI during crop developing stage than mid-season stage. It is while, λEs was larger under SDI compared to that for DI. These results contributed to higher

> Subsurface drip irrigation (SDI) Surface drip irrigation (DI) G λE λE/Rn G/Rn G λE λE/Rn G/Rn

> > (SDI) Surface drip irrigation (DI)

G λEs λEs/Rns G/Rns G λEs λEs/Rns G/Rns

W/m2 % W/m2 %

41 2.20 107 23 63 59 21 47 55 51 44 42 2.38 104 19 73 71 18 44 60 58 43 43 2.56 96 25 54 56 26 46 39 41 48 44 2.74 88 23 58 66 26 47 39 44 53 59 3.50 70 20 50 71 29 34 32 46 49 60 3.53 67 17 43 64 25 30 35 52 45 61 3.57 71 20 49 69 28 28 42 59 39 62 3.60 70 22 40 57 31 25 44 63 36

Daytime average of energy balance measurements at Maize canopy showed λEc increased by crop development from 134 to 227 (W/m2) which resulted λEc/Rnc between 58 to 90% in SDI. These ratios showed canopy latent heat fluxes were often lower than available energy which resulted some sensible heat flux conducted away from canopy level. Under DI, λEc increased by crop development from 142 to 264, which was less in average compare to that

Diurnal trends of the energy balance components for the soil surface of maize field at 60th DAE are shown in Fig. 3. This day was representative of a day with some cloud cover in the

W/m2 % W/m2 %

41 322 23 - - 7 47 197 61 14 42 333 19 207 62 6 44 266 80 13 43 329 25 212 64 8 46 255 78 14 44 320 23 242 76 7 47 243 76 15 59 315 20 247 78 6 34 296 94 11 60 304 17 235 77 6 38 253 83 10 61 326 20 248 76 6 28 271 83 9 62 322 22 267 83 7 25 245 76 8

possible potential for T under SDI during crop developing stage (Table 3).

Subsurface drip irrigation

Table 2. Daytime average energy fluxes at maize field.

Table 3. Daytime average energy fluxes at soil surface.

**7. Diurnal energy balance pattern**

DAE Rn

DAE LAI Rns

for SDI (Table 4).

sky. Average air temperature and relative humidity was 27.4 C and 41.6 % respectively. Daytime average of net radiation available at maize field was 304 W/m2, which was the smallest value in the measurement period.

Table 4. Daytime average energy fluxes at crop canopy.

During the day Maximum Rn and G were 674 and 70 W/m2, which occurred about 13:00 and 14:00 h respectively. As it is shown in Fig. 3 variation of Rn values is not symmetrically as a bell shape curve signifies that there were some cloud cover at sky during the day.

Most of (Rn-G) was used to drive λE and β values ranged between 0 to 0.7 while β values reported by Zeggaf et al., (2008), in maize field was lower than 0.25. It can be due to different crop growth stage in two experiments (in this research during the 60th DAE, Maize crop covered the ground relatively complete, LAI=3.53) and various climate, soil type and/or irrigation system. Sensible heat flux accounted for about 18% of available energy (Rn-G). Similar results were reported by Steduto and Hsiao, (1998), Ham et al., (1991) and Ritchie, (1971). Soil heat flux was about 17 (W/m2) which was lower than 10% of net radiation as it is found a recommended value for daytime average of soil heat flux in literature (Allen et al., 1998).

During the daytime of 60th DAE only 22% of net radiation reached the soil surface. Most of the energy was split between λEs and G. The value of Hs was small in this balance. The λEs was less than available energy except in the afternoon suggesting that the soil surface was absorbing energy from within-canopy (air stream) which provided energy for λEs. Similar signification was reported in Ham et al., (1991) for soil surface energy balance relationships. Daytime average of λEs was about 86% of Rns-G and only about 14% of Rns-G was used as sensible heat. During this day βs from soil surface, ranged from -0.5 to 0.5. Positive Hs values at soil surface indicates convective transport of heat away from the soil surface (Zeggaf et al., 2008), but the results from this research showed small amounts of available energy used as sensible heat flux. Field observations from subsurface drip irrigation system showed installation depth of drip tapes caused during irrigation, water rises up to the surface and makes the ground wet. Therefore the large proportion of soil surface latent heat flux from available energy can be due to the ground wetness. It can be concluded when drip tapes install in lower depth and the soil surface remains dry larger proportion of sensible heat will result.

Evapotranspiration Partitioning in Surface and Subsurface Drip Irrigation Systems 221

wide scattering for available energy under DI which might be because soil surface is not uniformly wet under DI. However, the condition under SDI was uniformly and a linear

**Rns-G (W/m2)** -100

Fig. 4. Available energy (Rn-G) and latent heat flux (λE) from maize field and soil (Rns-G and

Fig. 5. Available energy (Rns-G) and latent heat flux (λEs) from soil surface in 41th DAE for

The Bowen ratio method could be used for partitioning ET under surface (DI) and subsurface drip irrigation system (SDI). Partitioning ET for advance irrigation system could provide us useful information for better irrigation management during crop growth stages and development of new irrigation technique. Partitioning ET and measurement of energy balance over maize field, canopy and soil by Bowen ratio showed that soil had major impact on the energy balance between the soil and canopy when soil surface is not covered fully by crop canopy. In crop developing stage, energy balance of maize field was different under DI and SDI. This result could be contributed to more difference between the systems in early

crop development stage, when soil surface is not covered fully by crop canopy.

regression lines between λEs and Rns-G were obtained.

surface (DI) and subsurface (SDI) drip irrigation.

**Rn-G (w/m2)**

λEs) in 60th DAE for surface (DI) and subsurface (SDI) drip irrigation.

**8. Conclusion**

SDI DI

**λE (W/m2)**

Fig. 3. Diurnal trend of energy balance components at soil surface of maize field in 60th DAE under surface and subsurface drip irrigation.

Available energy (Rn-G) and λE from maize field for the 60th DAE are shown in Fig. 4. The linear regression lines between λE and Rn-G were obtained with high values of r2=0.99 for both SDI and DI. Based on the slope of the trade lines in Fig. 4, there was no a significant reduction in available energy to the maize field and soil surface between two irrigation system. Accordingly, SDI aimed at reducing soil evaporation compared to DI, is not effective when soil surface covered by canopy (LAI=3.5).

Available energy (Rns-G) and λEs from soil surface for 41th DAE are shown in Fig. 5. The 41th DAE presenting crop developing stage, when LAI was about 2.20. Accordingly, there was a wide scattering for available energy under DI which might be because soil surface is not uniformly wet under DI. However, the condition under SDI was uniformly and a linear regression lines between λEs and Rns-G were obtained.

Fig. 4. Available energy (Rn-G) and latent heat flux (λE) from maize field and soil (Rns-G and λEs) in 60th DAE for surface (DI) and subsurface (SDI) drip irrigation.

Fig. 5. Available energy (Rns-G) and latent heat flux (λEs) from soil surface in 41th DAE for surface (DI) and subsurface (SDI) drip irrigation.

### **8. Conclusion**

220 Evapotranspiration – From Measurements to Agricultural and Environmental Applications

Fig. 3. Diurnal trend of energy balance components at soil surface of maize field in 60th

Available energy (Rn-G) and λE from maize field for the 60th DAE are shown in Fig. 4. The linear regression lines between λE and Rn-G were obtained with high values of r2=0.99 for both SDI and DI. Based on the slope of the trade lines in Fig. 4, there was no a significant reduction in available energy to the maize field and soil surface between two irrigation system. Accordingly, SDI aimed at reducing soil evaporation compared to DI, is not

Available energy (Rns-G) and λEs from soil surface for 41th DAE are shown in Fig. 5. The 41th DAE presenting crop developing stage, when LAI was about 2.20. Accordingly, there was a

DAE under surface and subsurface drip irrigation.

effective when soil surface covered by canopy (LAI=3.5).

The Bowen ratio method could be used for partitioning ET under surface (DI) and subsurface drip irrigation system (SDI). Partitioning ET for advance irrigation system could provide us useful information for better irrigation management during crop growth stages and development of new irrigation technique. Partitioning ET and measurement of energy balance over maize field, canopy and soil by Bowen ratio showed that soil had major impact on the energy balance between the soil and canopy when soil surface is not covered fully by crop canopy. In crop developing stage, energy balance of maize field was different under DI and SDI. This result could be contributed to more difference between the systems in early crop development stage, when soil surface is not covered fully by crop canopy.

Evapotranspiration Partitioning in Surface and Subsurface Drip Irrigation Systems 223

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Vol.23, pp.174-178.

pp.261-273.

crop at partial cover. Agron. J. Vol.83, pp.744-753.

water. Agric. Water Manag. Vol.33, pp.139-157.

Agric. Water Manage. Vol.65, pp.173-191.

Drainage, ASCE, Vol.121, No.6. pp.369-373.

of sweet corn. Irri. Sci. Vol. 12. pp.135-140.

influences. Agron. J. Vol.63, pp.51-55.

Agric. For. Meteorol. Vol59, pp.287-301.

No.2-3, pp.85-91.

field corn. Soil Sci. Soc. Am. J., Vol.23, pp.170-173.

transpiration from energy balance and stem flow measurements. Agric. For.

furrow, surface drip, and subsurface drip irrigation on lettuce yield and applied

evaluation of corn grown on a plastic-covered lysimeter. Soil Sci. Soc. Of Am. Proc.

measurements of latent and sensible heat fluxes. Agric. For. Meteorol. Vol.44,

ratio and water use efficiency from a sparse canopy using a compartment model.

partitioning using the Bowen Ratio Energy Balance method in a sub-surface drip irrigation. Journal of Agricultural Engineering Research. Vol.11, No.3, pp.71-86. Ortega, F.; Samuel, O.; Richard, H.; Cuenca, M. & English. (1995). Hourly grass

evapotranspiration in modified maritime environment, Journal of Irrigation. and

Effect of high frequency surface and subsurface drip irrigation on root distribution

increase of sugar beet with subsurface drip irrigation. Global Nest: The Int. J. Vol.4,

transpiration and evaporation from a soybean field without water shortage. In: Ham, J. M., Heilman, J. L. and Lascano, R. J. 1990. Determination of soil water evaporation and transpiration from energy balance and stem flow measurements.

s, vegetable growers. In Soil 2000: New Horizons for a new century. Australian and New Zealand Joint Soils Conference. Volume 2: Oral papers. Eds. Adams J. A., and

As it was shown daytime soil heat flux values were greater under DI (25-47 W/m2) compared to that under SDI (17-25 W/m2). It may caused by heat convection in DI while moving down the water from the surface and higher temperature of water when drip tapes were positioned on the ground. Therefore available energy for soil evaporation, Rns-G, was lower in DI. As it was shown λEs accounted for about 41 to 63% of Rns in DI while it was about 56 to 71% in SDI. It was observed the ground in both DI and SDI became wet but reverse direction of moving water in subsurface system, as may contributed to more evaporation in SDI. According to the results, more consideration should be applied using SDI systems on depth of lateral line which carrying the emitters, canopy size, crop type, and plant water stress affect soil and canopy energy balances. Those data will be useful for validation of ET models.

## **9. Acknowledgment**

This Study was supported by Agricultural Engineering Research Institute (AERI), Iran. The authors wish to render heartfelt gratitude to Dr. Majid Liaghat and Dr. Farhad Mirzaei, Department of Irrigation and Reclamation Engineering, Agricultural and Natural Resources Campus, University of Tehran, Iran for their useful advice during the research.

## **10. References**

As it was shown daytime soil heat flux values were greater under DI (25-47 W/m2) compared to that under SDI (17-25 W/m2). It may caused by heat convection in DI while moving down the water from the surface and higher temperature of water when drip tapes were positioned on the ground. Therefore available energy for soil evaporation, Rns-G, was lower in DI. As it was shown λEs accounted for about 41 to 63% of Rns in DI while it was about 56 to 71% in SDI. It was observed the ground in both DI and SDI became wet but reverse direction of moving water in subsurface system, as may contributed to more evaporation in SDI. According to the results, more consideration should be applied using SDI systems on depth of lateral line which carrying the emitters, canopy size, crop type, and plant water stress affect soil and canopy energy balances. Those data will be useful for

This Study was supported by Agricultural Engineering Research Institute (AERI), Iran. The authors wish to render heartfelt gratitude to Dr. Majid Liaghat and Dr. Farhad Mirzaei, Department of Irrigation and Reclamation Engineering, Agricultural and Natural Resources

Allen, R.G.; Pereira, L.S.; Raes, D. & Smith, M. (1998). Crop evapotranspiration guidelines

Ashktorab, H.; Pruitt, W.O.; Paw, U.K.T. & George, W.V. (1989). Energy balance

Ashktorab, H.; Pruitt, W.O. & Paw, U.K.T. (1994). Partitioning of evapotranspiration using

Boast, C.W. & Robertson T.M. (1982). A micro-lysimeter method for determining

Bowen, I. S. (1926). The ratio of heat losses by conduction and by evaporation from any

De Tar, W. R. (2004). Using a subsurface drip irrigation system to measure crop water use.

Dehghanisanij, H.; Yamamoto, T. & Rasiah, V. (2004). Assessment of evapotranspiration

Fritschen, L.J. & Shaw, R.H. (1961). Transpiration and evaporation of corn as related to

Gardiol, J.M.; Serio, L.A. & Della Maggiora, A.I. (2003). Modelling evapotranspiration of

Agricultural and Forest Meteorology Journal, Vol.46, pp.259-274.

for computing crop requirements. Irrigation and drainage paper 56. FAO. Rome,

determination close to the soil surface using a Micro-Bowen Ratio system.

lysimeter and Micro-Bowen Ratio system. ASCE J. of Irrigation and Drainage.

evaporation from a bare soil: Description and laboratory evaluation. Soil Sci. Soc.

water surface: In Rosenberg, N. J., Blad, B. L. and Verma, S. B., (1983). Microclimate:

estimation models for use in semi-arid environment. Agricultural Water

corn (Zea mays) under different plant densities. Journal of Hydrology, Vol.271,

Campus, University of Tehran, Iran for their useful advice during the research.

Italy. pp.300. ISBN 92-5-104219-5.

Am. J., Vol.46, pp.689-696.

Irri. Sci. Vol.3. pp.111-122.

pp.188-196.

Vol.120, No.2, (March/April), pp.450-464.

Management Journal, Vol.64, pp.91-106.

meteorological factors. Agron. J. Vol.53, pp.71-74.

The biological environment. Wiley, New York, 495 PP.

validation of ET models.

**9. Acknowledgment**

**10. References**

**12**

*1Egypt 2Japan 3Germany*

**Saving Water in Arid and Semi-Arid**

 **Countries as a Result of Optimising**

*1Faculty of Agriculture, Suez Canal University, 41522 Ismailia,*

*3Department of Plant Sciences, Technische Universität München,*

*2Japan International Research Center for Agricultural Sciences, Tsukuba,*

Although water covers about 71 percent of our planet surface, 98 percent of it has too high salt content to be used for drinking water, for irrigation, or even for most industrial purposes. Fresh water represents one percent of all the water on the earth and is distributed unevenly on the earth surface. As a result of the dramatic increase in population, in economic activities, and a subsequent increase in water usage, the world fresh water resources became scarce during the past decades (Postel et al., 1996; Hoekstra & Chapagain,

Shortage of water currently plagues almost every country in North Africa and the Middle East (MENA) to the extent that hampers economic growth and threats social stability. Most importantly, the scenarios for global environmental change suggest a future increase in aridity and in the frequency of extreme events in many areas of the earth. Economists believe that water problem cannot be solved until water is considered as economic good.

Evapotranspiration (ET) is defined simply as the sum of the amount of water returned to the atmosphere through the processes of evaporation (moisture loss from the soil) and transpiration (biological use and release of water by vegetation). Crops are different in their response to water stress at a given growth stage. Therefore, estimating ET is an important tool to calculate the actual crop water requirements in given conditions. As a result, optimizing ET may contribute in solving water shortage problems at two levels, the farm

Firstly, at the farm level, the process of water irrigation losses has two main components: one due to evapotranspiration losses, and the other including the losses resulting from the percolation of water beneath the root zone in excess of any required leaching for salinity management. Therefore, saving water on the farm level can be achieved by using deficit

There is an urgent need to develop appropriate concepts and tools to do so.

**1. Introduction**

level and the national/global level.

2007).

**Crop Evapotranspiration**

Salah El-Hendawy1, Mohamed Alboghdady1, Jun-Ichi Sakagami2 and Urs Schmidhalter3

Metherell, A. K. 3-8 December, Lincoln University. New Zealand Society of Soil Science. Pp: 263-264.

## **Saving Water in Arid and Semi-Arid Countries as a Result of Optimising Crop Evapotranspiration**

Salah El-Hendawy1, Mohamed Alboghdady1, Jun-Ichi Sakagami2 and Urs Schmidhalter3 *1Faculty of Agriculture, Suez Canal University, 41522 Ismailia, 2Japan International Research Center for Agricultural Sciences, Tsukuba, 3Department of Plant Sciences, Technische Universität München, 1Egypt 2Japan 3Germany*

## **1. Introduction**

224 Evapotranspiration – From Measurements to Agricultural and Environmental Applications

Sepaskhah A.R. & Ilampour S. (1995). Effects of soil moisture on evapotranspiration

Shaw, R. H. (1959). Water use from plastic-covered and uncovered corn plots. Agron .J.

Shawcroft, R.W. & Gardner, M.H. (1983). Direct evaporation from soil under a row crop

Steduto, P. & Hsiao, T.C. (1998). Maize canopies under two soil water regimes: I. Diurnal

Yunusa, I.A.M.; Walker, R.R., & Lu, P. (2004). Evapotranspiration components from energy

Zeggaf, T.A.; Takeuchi, S.; Dehghanisanij, H.; Anyoji, H. & Yano, T. (2008). A Bowen Ratio

patterns of energy balance, carbon dioxide flux, and canopy conductance. Agric.

balance, sap flow and micro-lysimetry techniques for an irrigated vineyard in

Technique for partitioning energy fluxes between Maize transpiration and soil

partitioning. Agric. Water Manage. Vol.28, pp.311-323.

canopy. Agric. Meteorol. Vol.28, pp.229-238.

inland Australia. Agric. For. Meteorol. 127, 93-107.

surface evaporation, Agron. J. Vol.100, pp.1-9.

For. Meteorol. Vol.89, pp.169-184.

Science. Pp: 263-264.

Vol.51, pp.172-173

Metherell, A. K. 3-8 December, Lincoln University. New Zealand Society of Soil

Although water covers about 71 percent of our planet surface, 98 percent of it has too high salt content to be used for drinking water, for irrigation, or even for most industrial purposes. Fresh water represents one percent of all the water on the earth and is distributed unevenly on the earth surface. As a result of the dramatic increase in population, in economic activities, and a subsequent increase in water usage, the world fresh water resources became scarce during the past decades (Postel et al., 1996; Hoekstra & Chapagain, 2007).

Shortage of water currently plagues almost every country in North Africa and the Middle East (MENA) to the extent that hampers economic growth and threats social stability. Most importantly, the scenarios for global environmental change suggest a future increase in aridity and in the frequency of extreme events in many areas of the earth. Economists believe that water problem cannot be solved until water is considered as economic good. There is an urgent need to develop appropriate concepts and tools to do so.

Evapotranspiration (ET) is defined simply as the sum of the amount of water returned to the atmosphere through the processes of evaporation (moisture loss from the soil) and transpiration (biological use and release of water by vegetation). Crops are different in their response to water stress at a given growth stage. Therefore, estimating ET is an important tool to calculate the actual crop water requirements in given conditions. As a result, optimizing ET may contribute in solving water shortage problems at two levels, the farm level and the national/global level.

Firstly, at the farm level, the process of water irrigation losses has two main components: one due to evapotranspiration losses, and the other including the losses resulting from the percolation of water beneath the root zone in excess of any required leaching for salinity management. Therefore, saving water on the farm level can be achieved by using deficit

Saving Water in Arid and Semi-Arid Countries

which is another objective of this chapter.

and where water savings could be achieved.

objectives of this chapter.

as a Result of Optimising Crop Evapotranspiration 227

yield and IWUE simultaneously under deficit irrigation strategy which is one of the

Drip irrigation is the most effective method in terms of both maximizing yield and water conservation and also providing efficient use of limited water (Cetin & Bilgel, 2002). At the same time, several authors have shown that the water use efficiency and yield of dripirrigated crops could be improved under limited water applications by decreasing the amount of water that moves beneath the root zone (Bergez et al., 2002; El-Hendawy et al., 2008). Thus, optimizing the coupling between irrigation frequency and water application rate could help to achieve maximum yield and water use efficiency (WUE) by exerting positive or negative effects on the amount of water percolating under the root zone. For instance, coupling very high irrigation frequency and rate will avoid stress situations, but at the cost of reduced drip irrigation efficiency and WUE as a result of the increased amount of water moving beneath the root zone because the amount of water being applied can exceed the amount extracted by roots. Coupling very low irrigation frequency and rate, by contrast, can cause water stress between successive irrigation events (especially in sandy soils) because the amount of water applied at each event is insufficient to meet the water requirement of the plants as time proceeds. Finally, coupling very low irrigation frequency and very high water application rate, particularly in sandy soils, may result in a decreased efficiency of the drip irrigation system and finally water use, because the amount of water applied at each irrigation event may be higher, and possibly excessively so, than the soil– water storage capacity, thereby increasing the amount of water that moves below the root zone so as to reduce their availability to plants as time proceeds. Therefore, it is important to know the optimum coupling combinations between irrigation frequency and water application rate, to seek maximize yield and IWUE simultaneously for drip irrigated crops

Secondly, at the national/global level, while trade of real water between water-rich and water-poor regions is generally impossible due to the large distances and associated costs, it is argued that international trade moves "virtual water" from a comparatively more favorable region, where there is a surplus of soil water in soil profiles, to comparatively disadvantaged regions such as the MENA region. The virtual water is defined as the volume of water used in producing a unit of commodity, or service (Allan, 1998). Agricultural trade

The virtual water concept has two types of practical uses (Hoekstra, 2003). Firstly, virtual water can be seen as an alternative source of water assisting to achieve the regional water security. Secondly, the virtual water content of a product tells something about the environmental impact of consuming this product. Raising the awareness of virtual water content of products thus providing an idea of which goods impact most on the water system

The idea of actively promoting the import of virtual water in water-scarce countries is based on the idea that a nation can save its domestic water resources by importing a waterintensive product rather than produce it domestically. Import of virtual water therefore leads to a "national water saving" (Hoekstra & Chapagain, 2007). In a widely scope, Oki & Kanae (2004) argued that the virtual water trade produces "global water saving" when the agricultural product a traded from a country in which the unit requirement of water to produce a commodity (UW) is low to the country in which the UW is high. Conversely, the

is by far the largest transporter to move water virtually around the world.

irrigation strategy and irrigation scheduling. Deficit irrigation strategy, defined as the application of water below full crop-water requirement (evapotranspiration, ET), is an important tool to achieve the goal of increasing water use efficiency through their affecting on the amount of water losses by evapotranspiration (Fereres & Soriano, 2007). Irrigation scheduling, defined as determining when to irrigate and how much to apply, is another important element in improving water use efficiency through their exerting positive or negative effects on the amount of water percolating under the root zone (Bergez et al., 2002; El-Hendawy & Schmidhalter, 2010).

Full irrigation is the amount needed to achieve maximum yield; however, when irrigation water is insufficient to meet crop demand, limited irrigation management strategies should be considered. Use of limited irrigation strategies could save large amounts of water and might alleviate the issue of increasing food shortages found around the world. However, it is difficult to plan for reducing ET without a penalty in crop production, because evaporation from canopies is tightly coupled with the assimilation of carbon (Monteith, 1990; Steduto et al., 2006). For instance, El-Hendawy et al. (2008) found that average maize yield decreases under drip irrigation for 0.80 and 0.60 ET relative to 1.00 ET were 32 and 63%, respectively. Al-Kaisi & Yin (2003) also reported that average yield decreases for maize under sprinkler irrigation were 43% with 0.60 ET and 25% for 0.80 ET relative to 1.00ET. This indicates that application of water below the ET requirements requires further effective management strategies. These strategies should aim to achieve the highest possible economic return per unit of water applied. Recently, rapid and economically feasible approaches have been proposed to counteract yield losses and water use efficiency (WUE) decreases under deficit irrigation strategies. Several studies have emphasized that osmotic adjustment, which is achieved by exogenous application of osmoprotectants, is a useful approach for improving crop and water productivity under deficit irrigation (Agboma et al., 1997; Hussain et al., 2009). Of the different compatible solutes known, glycinebetaine (GB) is relatively more important as it is capable of promoting yields and WUE under water deficit. The reason for that may lie in the nature of GB as it exerts positive influence on the photosynthetic machinery (Xing & Rajashekar, 1999), and it does not inhibit enzymes even at high concentrations (Ashraf & Foolad, 2007). Therefore, it can be accumulated in the cytoplasm of plant cells to contribute to the osmotic balance between the cytoplasm and vacuole without causing any damages. This unique nature of GB has led to that foliar-applied GB is widely used for crop production throughout the world for different purposes. Exogenous application of GB to nonaccumulators of GB has been taken as an alternative to improve the stress tolerance. For example, Ma et al. (2006) reported that GB-treated plants maintained a higher net photosynthetic rate during drought stress than non-GB treated.

Most importantly, foliar application of GB may be a simple and cost effective methodology to increase the net benefit per unit of water applied under limited water application. Brand et al. (2007) reported that foliar application of GB can be adapted as a management strategy to alleviate water deficit at a cost less than a US \$ 2.5 per hectare. Under furrow irrigation application, Hussain et al. (2009) also found that foliar application of GB reduced water consumption by 25%, increased cost by 6% and increased net income by about 9% when the sunflower plants were exposed to water stress and treated by exogenous applications of GB simultaneously at vegetative and flowering stages. Therefore, it is important to know the optimum coupling combinations between GB levels and irrigation rate, to seek maximize

irrigation strategy and irrigation scheduling. Deficit irrigation strategy, defined as the application of water below full crop-water requirement (evapotranspiration, ET), is an important tool to achieve the goal of increasing water use efficiency through their affecting on the amount of water losses by evapotranspiration (Fereres & Soriano, 2007). Irrigation scheduling, defined as determining when to irrigate and how much to apply, is another important element in improving water use efficiency through their exerting positive or negative effects on the amount of water percolating under the root zone (Bergez et al., 2002;

Full irrigation is the amount needed to achieve maximum yield; however, when irrigation water is insufficient to meet crop demand, limited irrigation management strategies should be considered. Use of limited irrigation strategies could save large amounts of water and might alleviate the issue of increasing food shortages found around the world. However, it is difficult to plan for reducing ET without a penalty in crop production, because evaporation from canopies is tightly coupled with the assimilation of carbon (Monteith, 1990; Steduto et al., 2006). For instance, El-Hendawy et al. (2008) found that average maize yield decreases under drip irrigation for 0.80 and 0.60 ET relative to 1.00 ET were 32 and 63%, respectively. Al-Kaisi & Yin (2003) also reported that average yield decreases for maize under sprinkler irrigation were 43% with 0.60 ET and 25% for 0.80 ET relative to 1.00ET. This indicates that application of water below the ET requirements requires further effective management strategies. These strategies should aim to achieve the highest possible economic return per unit of water applied. Recently, rapid and economically feasible approaches have been proposed to counteract yield losses and water use efficiency (WUE) decreases under deficit irrigation strategies. Several studies have emphasized that osmotic adjustment, which is achieved by exogenous application of osmoprotectants, is a useful approach for improving crop and water productivity under deficit irrigation (Agboma et al., 1997; Hussain et al., 2009). Of the different compatible solutes known, glycinebetaine (GB) is relatively more important as it is capable of promoting yields and WUE under water deficit. The reason for that may lie in the nature of GB as it exerts positive influence on the photosynthetic machinery (Xing & Rajashekar, 1999), and it does not inhibit enzymes even at high concentrations (Ashraf & Foolad, 2007). Therefore, it can be accumulated in the cytoplasm of plant cells to contribute to the osmotic balance between the cytoplasm and vacuole without causing any damages. This unique nature of GB has led to that foliar-applied GB is widely used for crop production throughout the world for different purposes. Exogenous application of GB to nonaccumulators of GB has been taken as an alternative to improve the stress tolerance. For example, Ma et al. (2006) reported that GB-treated plants maintained a higher net

photosynthetic rate during drought stress than non-GB treated.

Most importantly, foliar application of GB may be a simple and cost effective methodology to increase the net benefit per unit of water applied under limited water application. Brand et al. (2007) reported that foliar application of GB can be adapted as a management strategy to alleviate water deficit at a cost less than a US \$ 2.5 per hectare. Under furrow irrigation application, Hussain et al. (2009) also found that foliar application of GB reduced water consumption by 25%, increased cost by 6% and increased net income by about 9% when the sunflower plants were exposed to water stress and treated by exogenous applications of GB simultaneously at vegetative and flowering stages. Therefore, it is important to know the optimum coupling combinations between GB levels and irrigation rate, to seek maximize

El-Hendawy & Schmidhalter, 2010).

yield and IWUE simultaneously under deficit irrigation strategy which is one of the objectives of this chapter.

Drip irrigation is the most effective method in terms of both maximizing yield and water conservation and also providing efficient use of limited water (Cetin & Bilgel, 2002). At the same time, several authors have shown that the water use efficiency and yield of dripirrigated crops could be improved under limited water applications by decreasing the amount of water that moves beneath the root zone (Bergez et al., 2002; El-Hendawy et al., 2008). Thus, optimizing the coupling between irrigation frequency and water application rate could help to achieve maximum yield and water use efficiency (WUE) by exerting positive or negative effects on the amount of water percolating under the root zone. For instance, coupling very high irrigation frequency and rate will avoid stress situations, but at the cost of reduced drip irrigation efficiency and WUE as a result of the increased amount of water moving beneath the root zone because the amount of water being applied can exceed the amount extracted by roots. Coupling very low irrigation frequency and rate, by contrast, can cause water stress between successive irrigation events (especially in sandy soils) because the amount of water applied at each event is insufficient to meet the water requirement of the plants as time proceeds. Finally, coupling very low irrigation frequency and very high water application rate, particularly in sandy soils, may result in a decreased efficiency of the drip irrigation system and finally water use, because the amount of water applied at each irrigation event may be higher, and possibly excessively so, than the soil– water storage capacity, thereby increasing the amount of water that moves below the root zone so as to reduce their availability to plants as time proceeds. Therefore, it is important to know the optimum coupling combinations between irrigation frequency and water application rate, to seek maximize yield and IWUE simultaneously for drip irrigated crops which is another objective of this chapter.

Secondly, at the national/global level, while trade of real water between water-rich and water-poor regions is generally impossible due to the large distances and associated costs, it is argued that international trade moves "virtual water" from a comparatively more favorable region, where there is a surplus of soil water in soil profiles, to comparatively disadvantaged regions such as the MENA region. The virtual water is defined as the volume of water used in producing a unit of commodity, or service (Allan, 1998). Agricultural trade is by far the largest transporter to move water virtually around the world.

The virtual water concept has two types of practical uses (Hoekstra, 2003). Firstly, virtual water can be seen as an alternative source of water assisting to achieve the regional water security. Secondly, the virtual water content of a product tells something about the environmental impact of consuming this product. Raising the awareness of virtual water content of products thus providing an idea of which goods impact most on the water system and where water savings could be achieved.

The idea of actively promoting the import of virtual water in water-scarce countries is based on the idea that a nation can save its domestic water resources by importing a waterintensive product rather than produce it domestically. Import of virtual water therefore leads to a "national water saving" (Hoekstra & Chapagain, 2007). In a widely scope, Oki & Kanae (2004) argued that the virtual water trade produces "global water saving" when the agricultural product a traded from a country in which the unit requirement of water to produce a commodity (UW) is low to the country in which the UW is high. Conversely, the

Saving Water in Arid and Semi-Arid Countries

evapotranspiration deficit (1 − ���

Doorenbos & Kassam (1979):

**2.4 Water production function**

Y can be described as follows:

to one percent change in ET. Thus,

**2.5 National water saving**

domestically.

marginal product of the input (Hexem & Heady, 1978).

EP = �� � ÷ ��� �� = ��

EP=1 if MP = WUE; that occurs at the maximum point of WUE.

EP<0 when the increase in ET leads to negative MP or decrease in Y.

The national water saving NWS�� as a result of trade of crop i in country j is:

EP=0 if MP = 0; that occurs at the maximum point of Y. EP>1, when the increase in ET leads to increase in WUE. EP<1 when the increase in ET leads to decrease in WUE.

From equation (5) the EP-ET-MP-WUE relations can be described as follows:

as a Result of Optimising Crop Evapotranspiration 229

�=��(1 − ���

Where ETa and ETm are the actual and maximum seasonal crop evapotranspirations (mm), respectively, and Ya and Ym are the corresponding actual and maximum yields (kg ha-1).

One of the more useful and widely accepted production function forms is based on the consumptive use, or evapotranspiration (ET). Grimes et al. (1969) and Gulati & Murty (1979) with wheat, barley, and sugarcane reported that the yield-evapotranspiration (Y–ET) relations for these crops were best described by quadratic functions. Many functions were tested to fit the data of the experiment. The quadratic form was the best fitting function.

As the function is obtained from a given set of yield and ET data, it is only appropriate and reliable for the given range of the observations. In the case of the quadratic function, both α and β� have negative signs when the function is derived from regression analysis. The ratio Y to ET reflects the water use efficiency (WUE) for a given amount of seasonal ET. The first derivative of Y, that is, dY/dET, describes the change in Y per unit change in ET that is called marginal product of ET (MP). Corresponding to the microeconomic theory, the Y in Eq. (4) represents the total product, Y/ET the average product of the input, and dY/dET the

The elasticity in the production function (EP) is defined as the percentage changes of Y due

where VWX�� and VWM�� are the virtual water exported and imported, respectively, contained in crop i by country j. We can calculate them by multiplying the specific water demand of crop i with exported/imported quantities of the same crop in a given year. Both exported and imported virtual waters are calculated as if the mentioned crop is produced

��� � �

�� � = ��

����

), were determined using the formula given by

Y=α+β�ET + β�ET� (4)

NWS�� = VWM�� − VWX�� (6)

) (3)

��� (5)

����

�1 − �� ��

global water loss occurs when the trade is from a high UW country to a low UW country for a particular crop.

## **2. Method**

## **2.1 Experimental design, treatments**

Two field experiments of drip-irrigated maize were conducted to establish the optimal coupling combinations between ET deficit and irrigation frequency or glycinebetaine concentrations. Each experiment was conducted using a randomized complete block splitplot design with three drip irrigation rates (1.00, 0.80, and 0.60 of the estimated evapotranspiration, ET) as the main plot and four irrigation frequencies (F1, F2, F3 and F4, irrigation events once every 1, 2, 3 or 4 days, respectively) in the first experiment and five exogenous application concentrations of GB (0, 25, 50, 75 and 100 mM) in the second experiment as the split plots. The drip irrigation system was divided into four main sectors in the first experiment and three main sectors in the second experiment with the irrigation frequency treatments and irrigation rates being assigned to the four sectors and three sectors, respectively. The water application rate treatments (1.00 ET, 0.80 ET and 0.60 ET) in the first experiment and GB concentrations (0, 25, 50, 75 and 100 mM) in the second experiment were randomly nested within each main sector as a subplot, with each subplot having three replicates for subplots treatments.

## **2.2 Calculation of evapotranspiration**

The crop evapotranspiration ETc is given by multiplying the reference crop evapotranspiration ET0 with the crop coefficient Kc.

$$\rm ET\_c = K\_c \times ET\_0 \tag{1}$$

The concept of the reference evapotranspiration was introduced to study the evaporative demand of the atmosphere independently of crop type, crop development and management practices. ET0 is a climatic parameter and can be computed from weather data. ET0 expresses the evaporating power of the atmosphere at a specific location and time of the year and does not consider the crop characteristics and soil factors (Allen et al., 1998).

With reference to Hoekstra & Hung (2002), crop evapotranspiration is calculated on the basis of the FAO Penman-Monteith equation:

$$\rm ET\_0 = \frac{0.408(R\_\text{n} - G) + \chi900/(T + 273)\mathbb{U}\_2(\mathbf{e\_a} - \mathbf{e\_d})}{\Delta + \chi(1 + 0.34\,\text{U}\_2)} \tag{2}$$

where ET0 is the reference evapotranspiration (mm day-1), Rn the net radiation at the crop surface (MJ m-2 day-1), G the soil heat flux density (MJ m-2 day-1), T the mean daily air temperature at 2 m height (ºC ), u� the wind speed at 2 m height (m s-1), e� the saturation vapor pressure (kPa), e�the actual vapor pressure (kPa), e� − e� the saturation vapor pressure deficit (kPa), D the slope of the saturation vapor pressure curve (kPa ºC-1), and g is the psychrometric constant (kPa ºC-1).

## **2.3 Yield response factor (ky)**

Seasonal values of the yield response factor (ky) for different combinations, which represent the relationship between the relative maize yield reduction �� − �� �� ��and the relative evapotranspiration deficit (1 − ��� ���� ), were determined using the formula given by Doorenbos & Kassam (1979):

$$\chi\left(1-\frac{\mathbf{y}\_{\rm a}}{\mathbf{y}\_{\rm m}}\right) = \mathbf{k}\_{\rm Y}(1-\frac{\mathbf{E}\mathbf{T}\_{\rm a}}{\mathbf{E}\mathbf{T}\_{\rm m}})\tag{3}$$

Where ETa and ETm are the actual and maximum seasonal crop evapotranspirations (mm), respectively, and Ya and Ym are the corresponding actual and maximum yields (kg ha-1).

#### **2.4 Water production function**

228 Evapotranspiration – From Measurements to Agricultural and Environmental Applications

global water loss occurs when the trade is from a high UW country to a low UW country for

Two field experiments of drip-irrigated maize were conducted to establish the optimal coupling combinations between ET deficit and irrigation frequency or glycinebetaine concentrations. Each experiment was conducted using a randomized complete block splitplot design with three drip irrigation rates (1.00, 0.80, and 0.60 of the estimated evapotranspiration, ET) as the main plot and four irrigation frequencies (F1, F2, F3 and F4, irrigation events once every 1, 2, 3 or 4 days, respectively) in the first experiment and five exogenous application concentrations of GB (0, 25, 50, 75 and 100 mM) in the second experiment as the split plots. The drip irrigation system was divided into four main sectors in the first experiment and three main sectors in the second experiment with the irrigation frequency treatments and irrigation rates being assigned to the four sectors and three sectors, respectively. The water application rate treatments (1.00 ET, 0.80 ET and 0.60 ET) in the first experiment and GB concentrations (0, 25, 50, 75 and 100 mM) in the second experiment were randomly nested within each main sector as a subplot, with each subplot

The crop evapotranspiration ETc is given by multiplying the reference crop

The concept of the reference evapotranspiration was introduced to study the evaporative demand of the atmosphere independently of crop type, crop development and management practices. ET0 is a climatic parameter and can be computed from weather data. ET0 expresses the evaporating power of the atmosphere at a specific location and time of the year and does

With reference to Hoekstra & Hung (2002), crop evapotranspiration is calculated on the

ET� = �����(����)������(�����)��(�����)

where ET0 is the reference evapotranspiration (mm day-1), Rn the net radiation at the crop surface (MJ m-2 day-1), G the soil heat flux density (MJ m-2 day-1), T the mean daily air temperature at 2 m height (ºC ), u� the wind speed at 2 m height (m s-1), e� the saturation vapor pressure (kPa), e�the actual vapor pressure (kPa), e� − e� the saturation vapor pressure deficit (kPa), D the slope of the saturation vapor pressure curve (kPa ºC-1), and g is

Seasonal values of the yield response factor (ky) for different combinations, which represent

not consider the crop characteristics and soil factors (Allen et al., 1998).

the relationship between the relative maize yield reduction �� − ��

ET� = K� × ET� (1)

���(��������) (2)

��

��and the relative

a particular crop.

**2.1 Experimental design, treatments**

having three replicates for subplots treatments.

evapotranspiration ET0 with the crop coefficient Kc.

basis of the FAO Penman-Monteith equation:

the psychrometric constant (kPa ºC-1).

**2.3 Yield response factor (ky)**

**2.2 Calculation of evapotranspiration**

**2. Method**

One of the more useful and widely accepted production function forms is based on the consumptive use, or evapotranspiration (ET). Grimes et al. (1969) and Gulati & Murty (1979) with wheat, barley, and sugarcane reported that the yield-evapotranspiration (Y–ET) relations for these crops were best described by quadratic functions. Many functions were tested to fit the data of the experiment. The quadratic form was the best fitting function. Y can be described as follows:

$$\text{Y} = \alpha + \beta\_1 \text{ET} + \beta\_2 \text{ET}^2 \tag{4}$$

As the function is obtained from a given set of yield and ET data, it is only appropriate and reliable for the given range of the observations. In the case of the quadratic function, both α and β� have negative signs when the function is derived from regression analysis. The ratio Y to ET reflects the water use efficiency (WUE) for a given amount of seasonal ET. The first derivative of Y, that is, dY/dET, describes the change in Y per unit change in ET that is called marginal product of ET (MP). Corresponding to the microeconomic theory, the Y in Eq. (4) represents the total product, Y/ET the average product of the input, and dY/dET the marginal product of the input (Hexem & Heady, 1978).

The elasticity in the production function (EP) is defined as the percentage changes of Y due to one percent change in ET. Thus,

$$\text{LP} = \frac{\text{dY}}{\text{Y}} \div \frac{\text{dET}}{\text{ET}} = \frac{\text{dY}\_{\text{/dET}}}{\text{Y}\_{\text{/ET}}} = \frac{\text{MP}}{\text{WUE}}\tag{5}$$

From equation (5) the EP-ET-MP-WUE relations can be described as follows:

EP=1 if MP = WUE; that occurs at the maximum point of WUE.

EP=0 if MP = 0; that occurs at the maximum point of Y.

EP>1, when the increase in ET leads to increase in WUE.

EP<1 when the increase in ET leads to decrease in WUE.

EP<0 when the increase in ET leads to negative MP or decrease in Y.

#### **2.5 National water saving**

The national water saving NWS�� as a result of trade of crop i in country j is:

$$\text{VWS}\_{\text{lj}} = \text{VWM}\_{\text{li}} - \text{VWM}\_{\text{li}} \tag{6}$$

where VWX�� and VWM�� are the virtual water exported and imported, respectively, contained in crop i by country j. We can calculate them by multiplying the specific water demand of crop i with exported/imported quantities of the same crop in a given year. Both exported and imported virtual waters are calculated as if the mentioned crop is produced domestically.

$$\mathbf{VWX\_{lj}} = \mathbf{SWD\_{lj}} \times \mathbf{X\_{lj}} \tag{7}$$

Saving Water in Arid and Semi-Arid Countries

Corporation, Los Angeles, CA, USA).

**3. Results and discussion 3.1 Yield response factor (ky)**

**2.7 Statistical analysis**

world average specific water demand for each crop.

as a Result of Optimising Crop Evapotranspiration 231

given period, we compensate each specific water demand of the exporting country by the

Analysis of variance (ANOVA) appropriate for a randomized complete block split plot design was employed to test the overall significance of the data, while the least significant difference test (at P = 0.05) was used to compare the differences among treatment means. Graphical presentation of data was carried out using Microsoft Excel program (Microsoft

The response of yield to water deficit is quantified through the yield response factor (ky), which represents the decrease in yield caused by decreases in evapotranspiration (Doorenbos & Kassam, 1979). Generally, higher ky values indicate that the crop will have a greater yield loss when the crop evapotranspiration is not met. One important finding of this study was the strong response of ky values to the combination of evapotranspiration deficit with irrigation frequencies or glycinebetaine (GB) concentrations (Table 1). Regarding the combination between ET deficit and irrigation frequency, it is interesting to note that the lowest ky values were recorded for the combination of 0.80 ET with irrigation frequency once every one day (ky = 0.12) followed by 0.60 ET with the same irrigation frequency (ky = 1.15). Both values were lower than those reported by Doorenbos & Kassam (1979; 1.25). The ky value of 1.25 obtained by Doorenbos & Kassam (1979) represents water deficit's effect on maize crop yield for the total growing period, which means that the reduction in yield for both combinations was proportionately less than the reduction in ET. By contrast, the combination of 1.00 ET with the same irrigation frequency or 1.00, 0.80 and 0.60 ET with the irrigation frequency once every 4 days resulted in significant increases in ky values (Table 1). These results indicate that optimal coupling combinations of ET deficit with irrigation frequency are useful for maximizing the net income per unit of water applied. Therefore, under the imposed water regime we recommended to switch from irrigation every other day to much smaller irrigations several times. This may be explained by the application of water at low irrigation frequency exceeding the soil–water storage capacity leading to excessive water percolation under the effective root zone. Furthermore, a portion of the water application was not used by the plant and the remaining available water will not meet

the long term water requirements of the plants till the next irrigation event.

For the combination of ET deficit with GB concentrations, the maximum maize yield during this study was obtained from a combination between 1.00 ET and 25 mM GB. The value of evapotranspiration at 1.00 ET was assumed as maximum evapotranspiration, and evapotranspiration and grain yield at different combination of 0.80 and 0.60 ET with different GB concentrations were assumed as actual evapotranspiration and actual grain yield. According to the result in Table 1, the ky value for the combination of 0.80 ET with 25 and 50 mM GB or 0.60 ET with 50 and 75mM GB were less than 1, which means that the reduction in yield for former combinations was proportionately less than the reduction in ET, despite the amount of water applied for 0.80 and 0.60 ET treatments was 20 and 40% lower than those for the 1.00 ET treatment, respectively. These results provided important clues to the physiological role of optimum doses of GB in possibly improving water use

$$\text{VWM}\_{\text{lij}} = \text{SWD}\_{\text{li}} \times \text{M}\_{\text{li}} \tag{8}$$

where Xij refers to the exported quantities (ton) of crop i in a given year by country j, and Mij is the imported quantities (ton) of crop i by country j. SWD�� refers to the specific water demand (m3 ton-1) of crop i in country j.

Intuitively, the virtual water exported (VWXφj) or imported (VWMφj) contained in a group of crops φ by country j is calculated by the summation of multiplying the specific water demand of each crop i in the group φ with the exported/ imported quantities of the same crop in a given year.

$$\text{VWX}\_{\text{op}\rangle} = \Sigma\_{\text{i=1}}^{\text{n}} (\text{SWD}\_{\text{ij}} \times X\_{\text{ij}}) \tag{9}$$

$$\text{VWM}\_{\text{op}\downarrow} = \Sigma\_{\text{l=1}}^{\text{n}} \text{(SWD}\_{\text{l\downarrow}} \times \text{M}\_{\text{li}\downarrow}) \tag{10}$$

For crop i in country j, the specific water demand can be calculated by Hoekstra & Hung (2005)

$$\text{SWD}\_{\text{lj}} = \frac{\text{CWR}\_{\text{lj}}}{\text{CY}\_{\text{lj}}} \tag{11}$$

CY is the crop yield (ton ha-1). Although the values for crop evapotranspiration and crop water requirement are identical, crop water requirement refers to the amount of water that needs to be supplied, while crop evapotranspiration refers to the amount of water that is lost through evapotranspiration under the standard conditions (Allen et al., 1998).

It is sensible to mention that a country may not produce a specific crop and imports all the needed quantities. In this case, we have no data that are required for calculating the specific water demand such as crop yield and some factors required for calculating the crop water requirements. Thus, we compensate the domestic specific water demand of crop j by the weighted average of the regional specific water demand of the same cereal crop.

Crop water requirements are calculated by accumulation of data on daily crop evapotranspiration ETc (mm day-1) over the complete growing period as follows:

$$\text{CWR}\_{\text{ij}} = \mathbf{10} \,\Sigma\_{\text{d}=1}^{\text{L}} \,\text{ET}\_{\text{c(ij)}} \tag{12}$$

where, the factor 10 is meant to convert (mm ha-1) into (m3 ha-1). The summation is done over the period from day 1 to the last day of the growing period (L) (Sallam and Abd El Nasser, 2006).

## **2.6 Global water saving** �������

The global water saving GWS���� through trade of crop i from an exporting country e to an importing country j is:

$$\rm{VWS}\_{\rm l,e} = \rm{VWM}\_{\rm{ij}} - \rm{VWX}\_{\rm{le}} \tag{13}$$

Where, VWM�� and VWX�� are the virtual water content of the importing and exporting quantities of crop i in favor of the actual specific water demand and the actual crop yield of the import and export countries.

Due to the difficulties and complications of calculating specific water demand in each exporting country as each importing country imports from many variable sources over the given period, we compensate each specific water demand of the exporting country by the world average specific water demand for each crop.

## **2.7 Statistical analysis**

230 Evapotranspiration – From Measurements to Agricultural and Environmental Applications

where Xij refers to the exported quantities (ton) of crop i in a given year by country j, and Mij is the imported quantities (ton) of crop i by country j. SWD�� refers to the specific water

Intuitively, the virtual water exported (VWXφj) or imported (VWMφj) contained in a group of crops φ by country j is calculated by the summation of multiplying the specific water demand of each crop i in the group φ with the exported/ imported quantities of the same

For crop i in country j, the specific water demand can be calculated by Hoekstra & Hung (2005)

SWD�� = ����� ����

CY is the crop yield (ton ha-1). Although the values for crop evapotranspiration and crop water requirement are identical, crop water requirement refers to the amount of water that needs to be supplied, while crop evapotranspiration refers to the amount of water that is lost

It is sensible to mention that a country may not produce a specific crop and imports all the needed quantities. In this case, we have no data that are required for calculating the specific water demand such as crop yield and some factors required for calculating the crop water requirements. Thus, we compensate the domestic specific water demand of crop j by the

Crop water requirements are calculated by accumulation of data on daily crop

CWR�� = 10 ∑ ET�(��) �

where, the factor 10 is meant to convert (mm ha-1) into (m3 ha-1). The summation is done over the period from day 1 to the last day of the growing period (L) (Sallam and Abd El

The global water saving GWS���� through trade of crop i from an exporting country e to an

Where, VWM�� and VWX�� are the virtual water content of the importing and exporting quantities of crop i in favor of the actual specific water demand and the actual crop yield of

Due to the difficulties and complications of calculating specific water demand in each exporting country as each importing country imports from many variable sources over the

VWX�� = ∑ (SWD�� �

VWM�� = ∑ (SWD�� �

through evapotranspiration under the standard conditions (Allen et al., 1998).

weighted average of the regional specific water demand of the same cereal crop.

evapotranspiration ETc (mm day-1) over the complete growing period as follows:

demand (m3 ton-1) of crop i in country j.

crop in a given year.

Nasser, 2006).

**2.6 Global water saving** �������

the import and export countries.

importing country j is:

VWX�� = SWD�� × X�� (7)

VWM�� = SWD�� × M�� (8)

��� × X��) (9)

��� × M��) (10)

��� (12)

GWS���� = VWM�� − VWX�� (13)

(11)

Analysis of variance (ANOVA) appropriate for a randomized complete block split plot design was employed to test the overall significance of the data, while the least significant difference test (at P = 0.05) was used to compare the differences among treatment means. Graphical presentation of data was carried out using Microsoft Excel program (Microsoft Corporation, Los Angeles, CA, USA).

## **3. Results and discussion**

## **3.1 Yield response factor (ky)**

The response of yield to water deficit is quantified through the yield response factor (ky), which represents the decrease in yield caused by decreases in evapotranspiration (Doorenbos & Kassam, 1979). Generally, higher ky values indicate that the crop will have a greater yield loss when the crop evapotranspiration is not met. One important finding of this study was the strong response of ky values to the combination of evapotranspiration deficit with irrigation frequencies or glycinebetaine (GB) concentrations (Table 1). Regarding the combination between ET deficit and irrigation frequency, it is interesting to note that the lowest ky values were recorded for the combination of 0.80 ET with irrigation frequency once every one day (ky = 0.12) followed by 0.60 ET with the same irrigation frequency (ky = 1.15). Both values were lower than those reported by Doorenbos & Kassam (1979; 1.25). The ky value of 1.25 obtained by Doorenbos & Kassam (1979) represents water deficit's effect on maize crop yield for the total growing period, which means that the reduction in yield for both combinations was proportionately less than the reduction in ET. By contrast, the combination of 1.00 ET with the same irrigation frequency or 1.00, 0.80 and 0.60 ET with the irrigation frequency once every 4 days resulted in significant increases in ky values (Table 1). These results indicate that optimal coupling combinations of ET deficit with irrigation frequency are useful for maximizing the net income per unit of water applied. Therefore, under the imposed water regime we recommended to switch from irrigation every other day to much smaller irrigations several times. This may be explained by the application of water at low irrigation frequency exceeding the soil–water storage capacity leading to excessive water percolation under the effective root zone. Furthermore, a portion of the water application was not used by the plant and the remaining available water will not meet the long term water requirements of the plants till the next irrigation event.

For the combination of ET deficit with GB concentrations, the maximum maize yield during this study was obtained from a combination between 1.00 ET and 25 mM GB. The value of evapotranspiration at 1.00 ET was assumed as maximum evapotranspiration, and evapotranspiration and grain yield at different combination of 0.80 and 0.60 ET with different GB concentrations were assumed as actual evapotranspiration and actual grain yield. According to the result in Table 1, the ky value for the combination of 0.80 ET with 25 and 50 mM GB or 0.60 ET with 50 and 75mM GB were less than 1, which means that the reduction in yield for former combinations was proportionately less than the reduction in ET, despite the amount of water applied for 0.80 and 0.60 ET treatments was 20 and 40% lower than those for the 1.00 ET treatment, respectively. These results provided important clues to the physiological role of optimum doses of GB in possibly improving water use

Saving Water in Arid and Semi-Arid Countries

as a Result of Optimising Crop Evapotranspiration 233

frequency once every two days and was higher to those obtained for the 1.00 ET treatment with either irrigation frequency once every three or four days (Fig. 1). The combined effects of ET treatments and GB concentration on IWUE were also significant. The highest IWUE was obtained under an irrigation rate of 0.80 ET at 50 mM GB, but it was at par with an irrigation rate of 0.60 ET at 75 mM GB. By contrast, the lowest IWUE was recorded for either the combination of 1.00 ET with 75 and 100 mM GB, and 0.80 or 0.60 ET with 100 mM GB. Because of the combination effects between ET treatments and irrigation frequency on the amount of water that is percolated under the root zone or absorbed by the roots (Assouline, 2002; Wang et al., 2006; Uçan et al., 2007) and the combination effects between ET treatments and GB concentration on the amount of water that are transported from canopy (Agboma et al., 1997), the optimal combinations between ET deficit and irrigation frequency or GB

concentrations were instead often crucial for maximizing net income per unit water.

Fig. 1. Irrigation water use efficiency (IWUE) for the combination of evapotranspiration

A linear relationship has been reported between maize grain yield and seasonal crop ET (Payero et al., 2006) and was also indicated here for the combination of ET treatment with irrigation frequency or GB concentration (Fig. 2 A and B). However, when broken down

deficit (ET) with irrigation frequency (F) or with glycinebetaine concentration.

**3.3 Yield-seasonal crop evapotranspiration relationship**

efficiency under ET deficit. The decreases of ky value under ET deficit with the application of optimum dose of GB indicate that more grain yield was produced from less water use.

\*Not determined

Table 1. Relative yield response factors (ky) shown as the relationships between the relative yield decrease (1- Ya/Ym) and the corresponding relative evapotranspiration deficit (1- ETa/ETm) for combination of evapotranspiration deficit with irrigation frequencies or glycinebetaine concentrations.

This may be due to the effects of GB on transpiration rate. Agboma et al. (1997) reported that the transpiration rate of GB treated plants was decreased to 85% of untreated plants, strongly indicating an anti-transpirant effect of GB treatment. The slower transpiration rate allows the plant to access water for a longer period and exhibit greater photosynthesis, where it would contribute to improved yield and water use efficiency under ET deficit.

#### **3.2 The relation of irrigation water use efficiency with evapotranspiration deficit**

In recent years, several researchers have reported about "site specific management", which means applying the right amount of input in the right place at the right time to get maximum profit from per unit input. Based on this basic definition, the profitability of irrigation water applied can be maximized by determining the optimal combination of ET treatments with irrigation frequency or GB concentrations. In this study, the irrigation water use efficiency (IWUE) was significantly affected by the different combination of ET treatments with irrigation frequency or with GB concentrations (Fig. 1). It is interesting to note that the combination of 0.60 ET with irrigation frequency once every one day had IWUE values similar to those obtained for the corresponding 0.80 or 1.00 ET with irrigation

efficiency under ET deficit. The decreases of ky value under ET deficit with the application of optimum dose of GB indicate that more grain yield was produced from less water use.

Irrigation frequency treatments (F) Once in 1 day (F1) 1.15 0.12 2.34 Once in 2 days (F2) 1.42 1.24 0.00 Once in 3 days (F3) 2.35 2.19 1.50 Once in 4 days (F4) 2.33 3.01 3.74

Glycinebetaine concentration treatments (GB)

Table 1. Relative yield response factors (ky) shown as the relationships between the relative yield decrease (1- Ya/Ym) and the corresponding relative evapotranspiration deficit (1- ETa/ETm) for combination of evapotranspiration deficit with irrigation frequencies or

This may be due to the effects of GB on transpiration rate. Agboma et al. (1997) reported that the transpiration rate of GB treated plants was decreased to 85% of untreated plants, strongly indicating an anti-transpirant effect of GB treatment. The slower transpiration rate allows the plant to access water for a longer period and exhibit greater photosynthesis, where it would contribute to improved yield and water use efficiency under ET deficit.

**3.2 The relation of irrigation water use efficiency with evapotranspiration deficit**  In recent years, several researchers have reported about "site specific management", which means applying the right amount of input in the right place at the right time to get maximum profit from per unit input. Based on this basic definition, the profitability of irrigation water applied can be maximized by determining the optimal combination of ET treatments with irrigation frequency or GB concentrations. In this study, the irrigation water use efficiency (IWUE) was significantly affected by the different combination of ET treatments with irrigation frequency or with GB concentrations (Fig. 1). It is interesting to note that the combination of 0.60 ET with irrigation frequency once every one day had IWUE values similar to those obtained for the corresponding 0.80 or 1.00 ET with irrigation

0 mM 1.53 1.53 nd\* 25 mM 1.32 0.87 nd 50 mM 0.97 0.51 nd 75 mM 0.93 1.49 nd 100 mM 1.50 2.07 nd

Evapotranspiration deficit treatments

0.60 ET 0.80 ET 1.00 ET

Treatments

\*Not determined

glycinebetaine concentrations.

frequency once every two days and was higher to those obtained for the 1.00 ET treatment with either irrigation frequency once every three or four days (Fig. 1). The combined effects of ET treatments and GB concentration on IWUE were also significant. The highest IWUE was obtained under an irrigation rate of 0.80 ET at 50 mM GB, but it was at par with an irrigation rate of 0.60 ET at 75 mM GB. By contrast, the lowest IWUE was recorded for either the combination of 1.00 ET with 75 and 100 mM GB, and 0.80 or 0.60 ET with 100 mM GB. Because of the combination effects between ET treatments and irrigation frequency on the amount of water that is percolated under the root zone or absorbed by the roots (Assouline, 2002; Wang et al., 2006; Uçan et al., 2007) and the combination effects between ET treatments and GB concentration on the amount of water that are transported from canopy (Agboma et al., 1997), the optimal combinations between ET deficit and irrigation frequency or GB concentrations were instead often crucial for maximizing net income per unit water.

Fig. 1. Irrigation water use efficiency (IWUE) for the combination of evapotranspiration deficit (ET) with irrigation frequency (F) or with glycinebetaine concentration.

## **3.3 Yield-seasonal crop evapotranspiration relationship**

A linear relationship has been reported between maize grain yield and seasonal crop ET (Payero et al., 2006) and was also indicated here for the combination of ET treatment with irrigation frequency or GB concentration (Fig. 2 A and B). However, when broken down

Saving Water in Arid and Semi-Arid Countries

yield (Paolo & Rinaldi, 2008; Farré & Faci, 2009).

as a Result of Optimising Crop Evapotranspiration 235

according to the irrigation frequency treatments or GB concentration treatments, the relationships between grain yield and seasonal ET deviated from linear relationships to second order relationship for irrigation frequency once every one day or 50 mM GB concentrations (Fig. 3 A and B). Therefore, the regression of Fig. 2A and B shows that about 51 and 39% of the grain yield variation is not explained by seasonal ET under the combinations of ET treatment with irrigation frequency or with GB concentrations, respectively. The second order relationship also indicates that the increase in maize grain yield was not proportional with the increment in the amount of irrigation water. This phenomenon was especially obvious when irrigation frequency once every one day or 50 mM GB was combined with 1.00 ET because a portion of the water applied does not contribute to ET. At the same time, both former combinations resulted in significant reductions in grain yield due to the combination of 1.00 ET with the irrigation frequency once every one day resulting in a very humid region in the root zone and the combination of 1.00 ET with 50 mM GB effects on stomatal conductance and transpiration rates, which in turn both affects the crop photosynthesis. Therefore, it is suggested that the ET treatments should be matched well with other variables which directly or indirectly affects on grain

Fig. 4. Relationship between grain yield and irrigation water use efficiency (IWUE). Power regression equations; \*\*\*, \* indicate significances at 0.001 and 0.05 P level, respectively.

Fig. 2. Relationship between seasonal crop evapotranspiration (ET) and grain yield for the combination of ET treatment with irrigation frequency (A) or with GB concentration (B).

Fig. 3. Relationship between seasonal crop evapotranspiration (ET) and grain yield for irrigation frequency once every one day (A) and for 50 mM GB concentration (B).

Fig. 2. Relationship between seasonal crop evapotranspiration (ET) and grain yield for the combination of ET treatment with irrigation frequency (A) or with GB concentration (B).

Fig. 3. Relationship between seasonal crop evapotranspiration (ET) and grain yield for irrigation frequency once every one day (A) and for 50 mM GB concentration (B).

according to the irrigation frequency treatments or GB concentration treatments, the relationships between grain yield and seasonal ET deviated from linear relationships to second order relationship for irrigation frequency once every one day or 50 mM GB concentrations (Fig. 3 A and B). Therefore, the regression of Fig. 2A and B shows that about 51 and 39% of the grain yield variation is not explained by seasonal ET under the combinations of ET treatment with irrigation frequency or with GB concentrations, respectively. The second order relationship also indicates that the increase in maize grain yield was not proportional with the increment in the amount of irrigation water. This phenomenon was especially obvious when irrigation frequency once every one day or 50 mM GB was combined with 1.00 ET because a portion of the water applied does not contribute to ET. At the same time, both former combinations resulted in significant reductions in grain yield due to the combination of 1.00 ET with the irrigation frequency once every one day resulting in a very humid region in the root zone and the combination of 1.00 ET with 50 mM GB effects on stomatal conductance and transpiration rates, which in turn both affects the crop photosynthesis. Therefore, it is suggested that the ET treatments should be matched well with other variables which directly or indirectly affects on grain yield (Paolo & Rinaldi, 2008; Farré & Faci, 2009).

Fig. 4. Relationship between grain yield and irrigation water use efficiency (IWUE). Power regression equations; \*\*\*, \* indicate significances at 0.001 and 0.05 P level, respectively.

Saving Water in Arid and Semi-Arid Countries

sorghum crops.

during 2007 -2008 in Egypt.

0

2000

4000

6000

Specific water demand

8000

10000

12000

2003-2007

as a Result of Optimising Crop Evapotranspiration 237

Figure 6 shows the notable variations among the specific water demand for the cereal crops production in North Africa countries and on the world average. Such highly significant variations derive mainly from two reasons. One reason is that in the North Africa region the evaporative demand is relatively high. The second reason is the low yield of the cereal crops in most NA countries. The water requirements to produce one ton of cereals in NA are equal to five times those in the world average. Highest differences were witnessed in maize and

Fig. 5. Relations of EP, WUE, MP, Y, and ET for a quadratic production function for maize

Fig. 6. Specific water demand for cereal crops in the North African and world (m3/ton)

Crops

Barley Maize Rice Wheat Sorghum cereals

NA world

#### **3.4 The relation of irrigation water use efficiency with grain yield**

Chen et al. (2003) mentioned that the relationship between WUE and grain yield is often used for determining the optimal irrigation strategy for arid and semiarid regions. Based on R2 values, the power model was found to be the best fit model to describe the IWUE-yield relationship of our data, with a coefficient of determination value of 0.93 and 0.41 for the combination of ET treatments with irrigation frequency or GB concentrations, respectively (Fig. 4). In the power model, the elasticity of y with respect to x is the percentage change in y for each percentage change in x. Based on this definition, when grain yield is increased by 10%, the IWUE was increased by 8.0 and 3.9% in the combination of ET with irrigation frequency or with GB concentrations, respectively (equations in Fig 4). These equations indicated that high irrigation water use efficiency is obtained for high yield values. Therefore, it is important to know the optimum coupling combinations between ET and irrigation frequency or GB to seek maximize yield and IWUE simultaneously with saving water on the farm level.

#### **3.5 Marginal analysis of water production function**

The Y-ET function in a quadratic form is described as:

$$Y = -5921.93 + 38.6ET - 0.028ET^2 \quad \text{R}^2 = 0.78 \tag{14}$$

The relationships for EP, WUE, MP, Y, and ET based on the function of Eq. (5) are shown in Fig. 5 under a limited water supply. One of two goals can be achieved by ET applications. The first goal is maximizing the WUE, point A. Such goal can be achieved, in terms of the economic sight of the production function, at the point of which the first derivative of WUE is equal to zero, MP is equal to WUE, and EP is equal to one. The maximum WUE of 12.9 kg mm can be attained when ET was equal to 459.9 mm. The second goal is maximizing crop production, point B. At this point, the MP and EP are equal to zero as the first derivative of Y is equal to zero. ET maximizing yield is 689.3 mm at which the total yield is 7381.3 kg. Consequently, the WUE is with 1.07 kgm-3 less than the maximum WUE by 17.1%. It is sensible to mention that the EP is a reliable indication to recognize the range of desirable or economic production stages which fall in the range 1≥ EP≥ 0. With other words, the rational economic productivity must fall between the maximum WUE and maximum yield.

#### **3.6 Virtual water as instrument to achieve water security in North Africa**

The aim of this section is to analyze the consequences of North African international virtual water flows associated with cereals trade on national and global water budgets. With this aim, it quantifies and assesses national and global water savings and losses per cereal crop in the North Africa region.

Virtual water trade has a positive direct effect on water saving for the importing countries. This effect has been intensively discussed in virtual water studies since the concept of virtual water raised by Allan in the nineties. In this study, we examine the water saving at two levels. Firstly, national water saving that concentrates on the benefit gained by importing countries. Secondly the global water saving, the global net effect of virtual water trade between two nations will depend on the actual water volume that would have been required to produce a commodity in the importing and exporting countries. (Chapagain et al., 2006).

Chen et al. (2003) mentioned that the relationship between WUE and grain yield is often used for determining the optimal irrigation strategy for arid and semiarid regions. Based on R2 values, the power model was found to be the best fit model to describe the IWUE-yield relationship of our data, with a coefficient of determination value of 0.93 and 0.41 for the combination of ET treatments with irrigation frequency or GB concentrations, respectively (Fig. 4). In the power model, the elasticity of y with respect to x is the percentage change in y for each percentage change in x. Based on this definition, when grain yield is increased by 10%, the IWUE was increased by 8.0 and 3.9% in the combination of ET with irrigation frequency or with GB concentrations, respectively (equations in Fig 4). These equations indicated that high irrigation water use efficiency is obtained for high yield values. Therefore, it is important to know the optimum coupling combinations between ET and irrigation frequency or GB to seek maximize yield and IWUE simultaneously with saving

The relationships for EP, WUE, MP, Y, and ET based on the function of Eq. (5) are shown in Fig. 5 under a limited water supply. One of two goals can be achieved by ET applications. The first goal is maximizing the WUE, point A. Such goal can be achieved, in terms of the economic sight of the production function, at the point of which the first derivative of WUE is equal to zero, MP is equal to WUE, and EP is equal to one. The maximum WUE of 12.9 kg mm can be attained when ET was equal to 459.9 mm. The second goal is maximizing crop production, point B. At this point, the MP and EP are equal to zero as the first derivative of Y is equal to zero. ET maximizing yield is 689.3 mm at which the total yield is 7381.3 kg. Consequently, the WUE is with 1.07 kgm-3 less than the maximum WUE by 17.1%. It is sensible to mention that the EP is a reliable indication to recognize the range of desirable or economic production stages which fall in the range 1≥ EP≥ 0. With other words, the rational

economic productivity must fall between the maximum WUE and maximum yield.

The aim of this section is to analyze the consequences of North African international virtual water flows associated with cereals trade on national and global water budgets. With this aim, it quantifies and assesses national and global water savings and losses per cereal crop

Virtual water trade has a positive direct effect on water saving for the importing countries. This effect has been intensively discussed in virtual water studies since the concept of virtual water raised by Allan in the nineties. In this study, we examine the water saving at two levels. Firstly, national water saving that concentrates on the benefit gained by importing countries. Secondly the global water saving, the global net effect of virtual water trade between two nations will depend on the actual water volume that would have been required to produce a commodity in the importing and exporting countries. (Chapagain et

**3.6 Virtual water as instrument to achieve water security in North Africa**

ܻ ൌ െͷͻʹͳǤͻ͵ ͵ͺǤܧܶ െ ͲǤͲʹͺܧܶଶ R2 = 0.78 (14)

**3.4 The relation of irrigation water use efficiency with grain yield**

water on the farm level.

in the North Africa region.

al., 2006).

**3.5 Marginal analysis of water production function**  The Y-ET function in a quadratic form is described as:

Figure 6 shows the notable variations among the specific water demand for the cereal crops production in North Africa countries and on the world average. Such highly significant variations derive mainly from two reasons. One reason is that in the North Africa region the evaporative demand is relatively high. The second reason is the low yield of the cereal crops in most NA countries. The water requirements to produce one ton of cereals in NA are equal to five times those in the world average. Highest differences were witnessed in maize and sorghum crops.

Fig. 5. Relations of EP, WUE, MP, Y, and ET for a quadratic production function for maize during 2007 -2008 in Egypt.

Fig. 6. Specific water demand for cereal crops in the North African and world (m3/ton) 2003-2007

Saving Water in Arid and Semi-Arid Countries

African countries, average 2003-2007

Net virtual water trade (%)

loss.

as a Result of Optimising Crop Evapotranspiration 239

Other cereals Sorghum Rice Maize Wheat Barley

Fig. 7. Percentages of net virtual water trade embedded in each cereal crop, in the North

Countries

The international trade theory confirms that the global food import is approximately equal to the global food exports to achieve the global food market equilibrium over a given period. On the other hand, such equilibrium does not take place in the global virtual water trade associated with global food trade system. This imbalance is a result of the inequality of water used for producing a given amount of food between importing and exporting countries (Yang et al., 2006). Seeing that the global saving is obtained as the difference between the water productivities of the trading partners (Hoekstra & Chapagain, 2007), the positive sign on the difference, in the direction of exporting to importing countries, intuitively indicates global water saving. In contrast, the negative sign refers to global water

Table 3 shows that the global saving resulting from international cereals trade between NA and the world countries is 77.2 billion cubic meter per year as on average of the period 2003 – 2007 . Approximately half of the amount of saved water originated only from Algeria. In addition, more than one quarter of the referred global water saving originated from Morocco. Thus, about 75% of the global water saving through the international trade of cereals crops in NA is derived only from Algeria and Morocco. One can see that there is notable global water saving achieved by cereal trade in all NA countries except in Egypt. The global water losing achieved by Egypt reflects a relatively high water productivity of cereal crops (Table 4). Furthermore, Egypt is the only country in the NA region with complete irrigation coverage. Consequently, the 100% irrigation coverage, adopting modern technology, and agricultural policy reforms explain its stable and significant increase of cereals production (Yang & Zehnder, 2002; El-Sadek, 2009). Considering the crop water requirements and yield, it has been noted that Egypt showed the highest cereal yield (7.5 ton ha-1) among the NA countries. This yield is more than two times of the world average yield. On the other hand, the crop water requirements for

**3.6.2 Global water saving through North Africa cereals trade.**

## **3.6.1 National water saving in NA countries through cereals trade**

Table 2 gives estimates of the national water saving of the North Africa countries expressed in volumes of virtual water embedded in net cereals trade. Seeing that the cereals trade is varying significantly from year to year, the figure presents the annual average for 2003-2007.

Furthermore all respective countries are net virtual water importers, there is a wide range of national water saving from 4.93 billion m3 in Egypt to 44.23 billion m3 in Algeria. The percentages of the national water saving through cereals net trade, to the total fresh water resources availability in each NA countries do extremely vary from around 9 in Egypt to 2136 in Libya. One can intuitively explain such a wide span in these percentages due to the fact that Egypt and Sudan are mainly representing irrigated agriculture countries while the rest countries are mainly rain fed agricultures. In addition, Libya is one of the driest countries in the world. The annual rainfall is very low with more than 95% of the country receiving less than 100 mm y-1 (Wheida & Verhoeven, 2007).

% is the percentage national water saving achieved by each country to the total water resources in the same country.

Table 2. National water saving (106 m3) achieved by the North Africa net trade of cereals crops, average 2003-2007

Figure 7 show that maize and wheat are the prominent cereal crops in terms of net virtual water imports. Wheat representations vary from around 23% in Tunisia to around 60% in Sudan. In summation, wheat represents about 44% of the national water saving in cereal net trade in NA region. On the other hand, maize representations vary from 7% in Sudan to 71% in Morocco. On the region level, maize is the highest cereal crop in terms of virtual water imports by around 50%. From what has been said, one can conclude that maize and wheat are representing around 91% of the national water saving in the North Africa region. In addition, all crops in the region and in each individual country attain national water saving except rice in Egypt. That because Egypt is a rice net exporter.

Table 2 gives estimates of the national water saving of the North Africa countries expressed in volumes of virtual water embedded in net cereals trade. Seeing that the cereals trade is varying significantly from year to year, the figure presents the annual

Furthermore all respective countries are net virtual water importers, there is a wide range of national water saving from 4.93 billion m3 in Egypt to 44.23 billion m3 in Algeria. The percentages of the national water saving through cereals net trade, to the total fresh water resources availability in each NA countries do extremely vary from around 9 in Egypt to 2136 in Libya. One can intuitively explain such a wide span in these percentages due to the fact that Egypt and Sudan are mainly representing irrigated agriculture countries while the rest countries are mainly rain fed agricultures. In addition, Libya is one of the driest countries in the world. The annual rainfall is very low with more than 95% of the country

Barley Wheat Maize Rice Sorghum Others Total %

Algeria 134.5 24205.7 19489.6 370.4 2.3 32.5 44234.9 378.1

Egypt 1.5 2755.5 3465.0 -1307.0 NR 15.0 4930.0 8.6

Libya 921.7 5787.0 3668.0 912.9 NR 1524.6 12814.1 2135.7

Morocco 1361.4 5597.5 17375.9 5.5 NR 101.2 24441.4 84.3

Sudan NR 4557.4 520.6 334.4 2160.1 15.5 7588.1 11.8

Tunisia 1581.5 2506.6 6368.4 30.2 326.9 7.3 10820.7 235.2

Region 4000.5 45409.6 50887.4 346.4 2489.3 1696.0 104829.2 62.5

% is the percentage national water saving achieved by each country to the total water resources in the

Figure 7 show that maize and wheat are the prominent cereal crops in terms of net virtual water imports. Wheat representations vary from around 23% in Tunisia to around 60% in Sudan. In summation, wheat represents about 44% of the national water saving in cereal net trade in NA region. On the other hand, maize representations vary from 7% in Sudan to 71% in Morocco. On the region level, maize is the highest cereal crop in terms of virtual water imports by around 50%. From what has been said, one can conclude that maize and wheat are representing around 91% of the national water saving in the North Africa region. In addition, all crops in the region and in each individual country attain national water saving

Table 2. National water saving (106 m3) achieved by the North Africa net trade of cereals

except rice in Egypt. That because Egypt is a rice net exporter.

**3.6.1 National water saving in NA countries through cereals trade**

receiving less than 100 mm y-1 (Wheida & Verhoeven, 2007).

average for 2003-2007.

same country.

crops, average 2003-2007

Fig. 7. Percentages of net virtual water trade embedded in each cereal crop, in the North African countries, average 2003-2007

### **3.6.2 Global water saving through North Africa cereals trade.**

The international trade theory confirms that the global food import is approximately equal to the global food exports to achieve the global food market equilibrium over a given period. On the other hand, such equilibrium does not take place in the global virtual water trade associated with global food trade system. This imbalance is a result of the inequality of water used for producing a given amount of food between importing and exporting countries (Yang et al., 2006). Seeing that the global saving is obtained as the difference between the water productivities of the trading partners (Hoekstra & Chapagain, 2007), the positive sign on the difference, in the direction of exporting to importing countries, intuitively indicates global water saving. In contrast, the negative sign refers to global water loss.

Table 3 shows that the global saving resulting from international cereals trade between NA and the world countries is 77.2 billion cubic meter per year as on average of the period 2003 – 2007 . Approximately half of the amount of saved water originated only from Algeria. In addition, more than one quarter of the referred global water saving originated from Morocco. Thus, about 75% of the global water saving through the international trade of cereals crops in NA is derived only from Algeria and Morocco. One can see that there is notable global water saving achieved by cereal trade in all NA countries except in Egypt. The global water losing achieved by Egypt reflects a relatively high water productivity of cereal crops (Table 4). Furthermore, Egypt is the only country in the NA region with complete irrigation coverage. Consequently, the 100% irrigation coverage, adopting modern technology, and agricultural policy reforms explain its stable and significant increase of cereals production (Yang & Zehnder, 2002; El-Sadek, 2009). Considering the crop water requirements and yield, it has been noted that Egypt showed the highest cereal yield (7.5 ton ha-1) among the NA countries. This yield is more than two times of the world average yield. On the other hand, the crop water requirements for

Saving Water in Arid and Semi-Arid Countries

cereal crops trade.

**4. Conclusion**

cereal crops.

**5. References**

51-64.

Ground Water 36:545-546.

drainage paper 56.

trading are representing 90.18% of all total global water saving.

relatively high water productivity of cereal crops.

as a Result of Optimising Crop Evapotranspiration 241

With respect to the global water saving achieved by individual cereal crop trade in NA, it is obvious that maize trading produces 44.1 billion m3 y-1 representing about 57.2% of the global water saving achieved by the region cereals trade. In the same context, wheat trading achieves 25.5 billion m3 y-1 representing about 33.1%. Therefore, only maize and wheat

Although water scarcity is not the main driver of cereals trade on the global level, it plays an essential role in cereal trade between North Africa and the other trade partner countries in the world. At the national level, all North Africa countries achieve water saving to the extent that exceed the total fresh water resources in some cases. Trading of maize and wheat is the most important player of the national water saving among the

At the global level, reductions in global water use occur if production by the exporter is more water efficient than by the importer. All countries in the North Africa region achieve global water saving except Egypt. The global water losing achieved by Egypt reveals a

The results of this chapter contribute to a better understanding of the role of evapotranspiration in saving water on the farm level and on the national and global levels through their different applications. On the farm level, the optimal coupling combinations of water application rate as ET deficit with irrigation frequency or with GB concentrations play important roles to seek maximum yield and efficient irrigation water used simultaneously under deficit irrigation conditions. At the national level, all North Africa countries achieve water savings to the extent that exceed the total fresh water resources in some cases. Trading of maize and wheat is the most important player of the national water saving among the cereal crops trade. At the global level, reductions in global water use occur if production by the exporter is more water efficient than by the importer. All countries in the North Africa region achieve a global water saving except Egypt. The global water losing achieved by Egypt reveals a relatively high water productivity of

Agboma, P. C., Sinclair, T. R., Jokinen, K., Peltonen-Sainio, P. & Pehu, E., (1997). An

Al-Kaisi, M.M. & Yin, X., (2003). Effects of nitrogen rate, irrigation rate, and plant population on corn yield and water use efficiency. Agron. J. 95, 1475–1482. Allan, J.A. (1998) Virtual water: A strategic resource global solutions to regional deficits.

Allen, R.G., Pereira L.S., Raes D. & Smith M. (1998) Crop evapotranspiration -

evaluation of the effect of exogenous glycinebetaine on the growth and yield of soybean: timing of application, watering regimes and cultivars. Field Crops Res. 54,

Guidelines for computing crop water requirements. FAO Irrigation and

cereal production in Egypt is more than the cereals water requirements of Libya, Morocco, and the world average (Table 4).

% is the percentage of global water saving achieved by each country to this achieved by the NA region

Table 3. Global water saving (106 m3) achieved by North Africa net trade of cereals crops, average 2003-2007

Table 4. Crop water requirements, crop yields and the specific water demand of cereals in North Africa countries and in the world. Period 2003-2007.

With respect to the global water saving achieved by individual cereal crop trade in NA, it is obvious that maize trading produces 44.1 billion m3 y-1 representing about 57.2% of the global water saving achieved by the region cereals trade. In the same context, wheat trading achieves 25.5 billion m3 y-1 representing about 33.1%. Therefore, only maize and wheat trading are representing 90.18% of all total global water saving.

Although water scarcity is not the main driver of cereals trade on the global level, it plays an essential role in cereal trade between North Africa and the other trade partner countries in the world. At the national level, all North Africa countries achieve water saving to the extent that exceed the total fresh water resources in some cases. Trading of maize and wheat is the most important player of the national water saving among the cereal crops trade.

At the global level, reductions in global water use occur if production by the exporter is more water efficient than by the importer. All countries in the North Africa region achieve global water saving except Egypt. The global water losing achieved by Egypt reveals a relatively high water productivity of cereal crops.

## **4. Conclusion**

240 Evapotranspiration – From Measurements to Agricultural and Environmental Applications

cereal production in Egypt is more than the cereals water requirements of Libya, Morocco,

Algeria 24.1 18016.2 17853.8 286.9 2.1 16.7 36223.7 47.4

Egypt -0.8 -3461.7 296.5 29.4 NR 26.5 -4003.8 -5.2

Libya 3360.1 707.2 NR 1251.5 10537.5 13.8

Morocco 897.3 2597.5 16206.0 -0.9 NR 85.7 19706.3 25.8

Sudan NR 2990.0 485.3 239.3 2033.8 12.9 5859.1 7.7

Tunisia 913.4 975.9 5857.7 22.1 292.6 40.0 8133.2 10.6

% is the percentage of global water saving achieved by each country to this achieved by the NA region

Table 3. Global water saving (106 m3) achieved by North Africa net trade of cereals crops,

Yield

Specific water demand (m3/ton)

(ton/ha)

Region 2554.3 25520.3 44059.5 1284.0 2328.5 1433.3 76455.9

Algeria 8498 1.4 6007

Egypt 4495 7.5 598

Libya 3808 0.6 6123

Morocco 6617 1.2 5611

Sudan 3036 0.6 4775

Tunisia 6034 1.5 4094

World 3574 3.3 1088

North Africa countries and in the world. Period 2003-2007.

Table 4. Crop water requirements, crop yields and the specific water demand of cereals in

Crop water requirement (m3/ha)

Barley Wheat Maize Rice Sorghum Others Total %

and the world average (Table 4).

average 2003-2007

The results of this chapter contribute to a better understanding of the role of evapotranspiration in saving water on the farm level and on the national and global levels through their different applications. On the farm level, the optimal coupling combinations of water application rate as ET deficit with irrigation frequency or with GB concentrations play important roles to seek maximum yield and efficient irrigation water used simultaneously under deficit irrigation conditions. At the national level, all North Africa countries achieve water savings to the extent that exceed the total fresh water resources in some cases. Trading of maize and wheat is the most important player of the national water saving among the cereal crops trade. At the global level, reductions in global water use occur if production by the exporter is more water efficient than by the importer. All countries in the North Africa region achieve a global water saving except Egypt. The global water losing achieved by Egypt reveals a relatively high water productivity of cereal crops.

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**13**

*1Oman 2Japan*

**The Impact of Seawater Salinity on**

**Different Meteorological Conditions**

*2Arid Land Research Center, Tottori University 1390 Hamasaka,*

Ahmed Al-Busaidi1 and Tahei Yamamoto2

*Engineering, Sultan Qaboos University, Muscat,*

**Evapotranspiration and Plant Growth Under**