Use of saline water for irrigation in monsoon climate and deep water table regions: Simulation modeling with SWAP

Agricultural Water Management 115 (2012) 186–193

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Use of saline water for irrigation in monsoon climate and deep water table regions: Simulation modeling with SWAP A.K. Verma a,∗ , S.K. Gupta b , R.K. Isaac a a b

Sam Higginbottom Institute of Agriculture, Technology & Sciences (SHIATS), Allahabad 211 007, India Central Soil Salinity Research Institute, Karnal 132 001, India

a r t i c l e

i n f o

Article history: Received 19 December 2011 Accepted 11 September 2012 Keywords: Simulation SWAP Saline water Relative yield Salinity profile Wheat

a b s t r a c t SWAP (Soil-Water-Atmosphere-Plant) version 2.0 was evaluated for its capability to simulate crop growth and salinity profiles under various combinations of fresh and saline water use for irrigation at Agra (India), located in a semiarid monsoon climatic region having a deep water table. Best available water (BAW, EC 3.6 dS/m) was used for pre-sowing irrigation to wheat crop and thereafter, twelve treatment combinations were imposed with four replications to supplement missed BAW water irrigations with saline water (EC 6/8 and 12 dS/m). The model was calibrated and validated using measurements made in field trial during 2000–2003. A close agreement was observed between the measured data and simulated values. SWAP simulated and observed values for the relative yield ranged within absolute deviations of 4.2–9.7%. The validated model was later used to illustrate the consequences of long-term use of saline water on crop growth and salinity profiles. Simulated results confirmed that a yield potential exceeding 80% could be maintained by substituting saline waters up to 8 dS/m in the absence of fresh water following a pre-sowing irrigation with BAW. This strategy helps to overcome the build-up of salts particularly in years when the monsoon rainfall is below average. It could be shown and concluded that seasonal buildup of salts due to use of saline water in winter season (November–April) crops is leached during the monsoon season (June–September) when rainfall at least during the months of July and August exceeds the potential evapotranspiration. On the whole, short-term field observations could be confirmed with application of SWAP that long-term use of saline water in monsoon climate under deep water table conditions is sustainable. © 2012 Elsevier B.V. All rights reserved.

1. Introduction In many arid and semi-arid regions, irrigation water supplies through the canal systems are limited; more so at the middle and tail ends of the system. As such, the farmers get water for 1–3 irrigations against the total requirement of 4–5 irrigations for wheat crop depending upon the seasonal climatic conditions prevailing in a particular year. As such, farmers at the middle and tail of the canal systems end up in lower yields than their counterparts at the head ends. The groundwater quality is also unfavorable for them as the groundwater quality deteriorates towards the tail ends of the system and poses a threat to the sustainability of irrigated agriculture. On the other hand, use of poor quality groundwater could

∗ Corresponding author at: Central Institute of Fisheries Education, Panch Marg, Yari Road, Versova, Andheri (W), Mumbai 400 061, India. Tel.: +91 2226310657; fax: +91 2226361573. E-mail address: [email protected] (A.K. Verma). 0378-3774/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.agwat.2012.09.005

meet the shortfall in supplies and even to release high quality water for other sectors of economy to meet their increasing demand. Field experiments on short-term basis have proved the potential of saline water use for crop production (Dinar et al., 1986; Pasternak et al., 1986; Grattan and Rhoades, 1990; Singh et al., 1992; Naresh et al., 1993; Sharma et al., 1994). It has also emerged that saline water use could be a practical solution to meet the shortfall in good quality water supply in areas where non-saline water is available during the early growing season but is limited to fully meet the crop water requirement of the entire growing season (Chauhan et al., 2008). A common argument against all such experimental evidence on use of saline water has been how fairly the results could be up scaled in a short to long-term perspective? Due to the complexity of the system and the time and funds required, site specific long-term testing through field experiments would be a daunting exercise. Moreover, the duration of a long-term experiment has been a matter of concern among the scientific community (Kaur, 2008). Under such situations, simulation/computer models offer excellent support to reduce the time and effort on repetitive tests

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provided adequate data for calibration and validation of the models are available (Workman and Skaggs, 1989; Gupta et al., 1993; Breve et al., 1995, 1998). A number of models have been used for short and long-term description of salt and water transport under different climatic, drainage and crop conditions (Martin et al., 1984; Majeed et al., 1994; Prendergast et al., 1994; Singh and Singh, 1996; Lamsal et al., 1999). Currently, SWAP has been widely used as it has an edge over other models due to its many distinctive features as it can simulate physical, chemical and biological processes at field scale level and can accommodate long-term simulations with multiple crops per year (Sarwar and Bastiaanssen, 2001; Sarwar et al., 2001; Tedeschi and Menenti, 2002; Vazifedoust et al., 2008). Besides, it has the capability to predict relative yield and salinity profiles. Contrarily, the model has been tested on a limited scale especially for arid and semi-arid monsoon climatic conditions (Singh, 2004; Mostafazadeh-Fard et al., 2008; Verma et al., 2010). Since, soil resource health and crop yield are two parameters of great consequence in the use of saline water for crop production, it was decided to test the applicability of this model under saline water irrigation for monsoon climate in a region in India where the water table remained deeper than 5.5 m. Besides testing the performance of the SWAP model, the purpose of this paper is also to confirm the shortterm field observations on a long-term basis through constructing practical scenarios and forecasting the results using SWAP. 2. Brief description of the SWAP model SWAP is a deterministic model that describes water, solute and heat transport in the saturated–unsaturated zone. In the model, soil water flow in the soil matrix in the unsaturated–saturated zone, is described by the Richards’ equation: ∂ ∂ ∂h = C(h) = ∂t ∂z ∂z





K(h)



∂h +1 ∂z

− S(h)

(1)

where,  denotes the volumetric water content (cm3 cm−3 ), t the time (days), C the differential water capacity (cm−1 ), h the soil water pressure head (cm), z the vertical coordinate positive in the upward direction (cm), K the hydraulic conductivity (cm day−1 ) and S represents the soil water extraction rate by plant roots (cm3 cm−3 day−1 ). SWAP provides three different sub-model for the purpose of simulating crop growth i.e. (a) detailed crop growth, (b) detailed grass growth and (c) simple crop growth. Due to limitation of the data required for simulations with detailed crop growth model, simple crop growth model was used in this study. In this model, for each development stage, the actual yield Ya,k (kg ha−1 ) relative to the potential yield Yp,k (kg ha−1 ) is calculated by:



1−

Ya,k Yp,k





= Ky,k

1 − Ta,k Tp,k



(2)

where Ky,k is the yield response factor of growing stage k and Tp,k (cm) and Ta,k (cm) are the potential and actual transpiration, respectively during period k. The relative yield of whole growing season is calculated as product of relative yield of each growing stage.



Ya Yp

 =

n  Ya,k K=1

Yp,k

(3)

where Ya is the cumulative actual yield (kg ha−1 ) of whole growing season, Yp is the cumulative potential yield (kg ha−1 ) of whole growing season, index k is the growing stage and n is the number of defined growing stages. Although, in SWAP irrigation could be prescribed at fixed times or scheduled according to a number of criteria, fixed time approach was used in the simulations as per

187

the experimental conditions. The convection, dispersion and diffusion are the three main process of solute transport embedded in the SWAP model. The total solute flux is calculated according to equation: J = qc − (Ddif + Ddis )

∂c ∂z

(4)

where J is the total solute flux density (g cm−2 day−1 ), Ddif is the solute diffusion coefficient (cm2 day−1 ), Ddis is the solute dispersion coefficient (cm2 day−1 ) and ∂c/∂z is the solute concentration gradient. The effect of salinity on crop yield is taken into account and is defined both by a critical saturated paste EC level below which no salt stress occurs and by the decline of root water uptake above this EC and the maximum level in percentage crop yield reduction per dS/m. Although threshold and slope of the piecewise linear model for the wheat crop were available (Oosterbaan et al., 1990), it was decided to develop the piecewise linear function using the relative yield and mean root zone salinity (ECe ) of the top 90 cm of the soil profile. The data from several other experiments conducted at this site during previous years with variety Raj. 3077 were compiled and SALT programme of the USSL (van Genuchten, 1983) was used to develop the following function: RY = 100 − 4.2(EC − 5.9)

(5)

The values of the threshold and slope for this site are higher than the earlier reported values from Sampla and Gohana sites (Anon., 2002). It is attributed to the fact that the sites investigated by Anon. (2002) have shallow water table and soils are saline in nature. In spite of the fact that a subsurface drainage system is provided, the salinity develops both due to capillary rise and saline water irrigations. Contrarily, the Agra site has a deep water table and soil is non-saline in nature. Soil salinity at this site develops only due to use of the saline water for irrigation during the winter season, which gets leached during the monsoon season making the soil salt free at the time of wheat sowing in November. Moreover, the variety Raj 3007 used in the study is a salt tolerant variety compared to the varieties used at the Sampla and Gohana sites (Minhas and Gupta, 1992). Since, the initial and boundary conditions of the model takes care of such differences in the site conditions, we are of the view that the higher values used in the model would more realistically represent the field situation at the Agra site. The SWAP assumes that the effect of water and salinity stresses is multiplicative in nature. SWAP provides a wide range of upper and lower boundary conditions. Potential evapotranspiration, irrigation and precipitation describe upper boundary conditions of the system. The bottom boundary conditions can also be described through various options. Groundwater level as a function of time, flux to/from semi confined aquifer, flux to/from open surface drains, an exponential relationship between bottom flux and groundwater table or zero flux, free drainage or free outflow at the bottom of the profile. Measured groundwater levels as a function of time were considered to describe the bottom boundary condition. The SWAP is user friendly with clear instructions on the operation of the model provided in the manual written by Kroes et al. (1999). Model input and other details are briefly described in Section 4. 3. Materials and methods The study site, RBS College, Bichpuri, Agra is located at 27.9◦ E longitude and 27.2◦ N latitude and 163 m above mean sea level in the state of Uttar Pradesh, India. Agra is located in a semiarid region and has a monsoon climate with an average annual rainfall of 665 mm. Around 80% of the total rainfall occurs during June–October.

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Table 1 The details of experimental work. Operations

2000–2001

2001–2002

2002–2003

Crop and variety Date of sowing Depth of each irrigation Number of irrigations Rainfall during crop period Date of harvesting

Wheat (Raj. 3077) 23.11.2000 7 cm 5 27.6 mm 30.3.2001

Wheat (Raj. 3077) 09.11.2001 7 cm 4 67.0 mm 29.3.2002

Wheat (Raj. 3077) 15.11.2002 7 cm 4 48.7 mm 31.3.2003

Source: Chauhan and Singh (2003). Note: Presowing irrigation was given with best available water commonly in all the treatments.

The soils of the Agra region are predominantly alluvial in origin. The soils of experimental site are light in texture and varied from sandy loam at the soil surface to sandy clay loam in the subsurface. The soils were non-saline and alkaline in reaction. During the study period (April 2000 to June 2003), the water table fluctuated between 5.6 m (October) and 7.7 m (April) below the soil surface. Groundwater contribution to crop water use was neglected due to relatively large depth of the water table. The experiment was conducted in the plot size of 2.5 m × 2.5 m (Chauhan and Singh, 2003). To check seepage, polythene sheets were used up to 0.90 cm depth for separating each plot. The saline water of 6/8 and 12 dS/m was prepared synthetically by using CaCl2 , MgCl2 , MgSO4 , NaCl and Na2 SO4 commercial grade salts with best available water (BAW; ECiw = 3.6 dS/m). Table 1 shows the details of experimental work for three years (2000–2003). Twelve treatment combinations were set up (Table 2) with four replications to test the effect of saline water as supplement to the best available water application on wheat growth. A pre-sowing irrigation of 7 cm with best available water (BAW) was common to all the treatments. Depending upon the rainfall and climatic conditions, the number of post-sown irrigations varied during the three years of experimentation. A total of five irrigations during 2000–2001 and four irrigations during 2001–2002 and

T1 (All BAW) T2 (BAW at CRI, ECiw = 3.6 dS/m) T3 (BAW at CRI and milking) T4 (BAW at CRI, jointing and milking) T5 = T2 + saline water, ECiw = 6/8 dS/m T6 = T2 + saline water, ECiw = 12 dS/m T7 = T3 + saline water, ECiw = 6/8 dS/m T8 = T3 + saline water, ECiw = 6/8 dS/m T9 = T4 + saline water, ECiw = 6/8 dS/m T10 = T4 + saline water ECiw = 12 dS/m T11 = all irrigations with saline water ECiw = 6/8 dS/m T12 = all irrigations with saline water ECiw = 12 dS/m

4. Model inputs and outputs 4.1. Climate data The model uses meteorological data on daily basis. Data on daily rainfall, maximum and minimum temperature, wind velocity, relative humidity, sunshine hours and radiation were used to prepare input file. Since the climate conditions in the region do not very much, except the rainfall, which was obtained from Agra gauging station, all the data sets were obtained from meteorological observatory at Karnal. 4.2. Soil hydraulic properties The parameters required to describe soil hydraulic properties were adjusted as suggested in the model SWAP and are summarized in Table 3.

Table 2 Detail description of the treatments. Treatment

2002–2003, each of 7 cm depth, were applied in all the treatments except T2 to T4 (Table 2). The duration of each irrigation was set on the basis of tube well discharge and the plot size to apply 7 cm of irrigation. The salinity of the soil saturation extract at harvest was measured from two locations in each plot at depths of 0–15, 15–30, 30–60 and 60–90 cm over a period of 3 years. The average salinity of the soil (ECe ) for each treatment was determined by averaging the soil salinity at 8 points (number of points times number of replications).

Number of irrigationsa 2000–2001

2001–2002

2002–2003

5 1

4 1

4 1

2

2

2

3

3

3

5 (4)

4 (3)

4 (3)

5 (4)

4 (3)

4 (3)

5 (3)

4 (2)

4 (2)

5 (3)

4 (2)

4 (2)

5 (2)

4 (1)

4 (1)

5 (2)

4 (1)

4 (1)

5

4

4

5

4

4

BAW, best available water; CRI, crown root initiation; water salinity was 6 and 12 dS/m during 2000–2001 and 8 and 12 dS/m during 2001–2002 and 2002–2003. a Values in parentheses indicate number of saline water irrigations. No additional water was applied in T2, T3 and T4.

4.3. Crop and irrigation data A summary of the SWAP input data with regard to crop and irrigation plan is given in Table 4. While pre-sowing irrigation was common to all the treatments, Irrigations were scheduled at crown root initiation, tillering, jointing, flowering and milking growth Table 3 Parameters describing the soil hydraulic properties. Soil

Topsoil

Subsoil

Depth of layer (m) Soil texture Residual water content,  res (cm3 cm−3 ) Saturated water content,  sat (cm3 cm−3 ) Shape parameter, ˛ (per cm) Shape parameter, n Shape parameter,  Horizontal saturated hydraulic conductivity, Kx (m day−1 ) Vertical saturated hydraulic conductivity, Kzz (m day−1 )

0–1.2 Sandy loam 0.02

1.2–1.8 Sand loam/loam 0.01

0.36

0.36

0.017

0.015

1.45 0.5 0.16

2.0 0.5 1.1

0.25

0.75

Source: Hirekhan et al. (2007) and value selected from the proposed range suggested in the model SWAP.

A.K. Verma et al. / Agricultural Water Management 115 (2012) 186–193 Table 4 Crop and irrigation data.

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Table 5 Parameters for the calculation of solute transport.

General Simulation period Crop Boesten parameter, ˇp (cm1/2 ) Irrigation water quantity (mm) EC of pre-sowing irrigation water (dS/m)

Agra July 2000–June2004 Wheat 0.54 70 (pre-irrigation); 70 (growing period) 3.6

Crop specific

Wheat

Length of crop cycle (days) Maximum crop height (cm) Leaf Area Index, LAI (–) Maximum rooting depth (cm) h1 , h2 , h3h b h3l , h4 (cm)b Threshold ECe (dS/m) Slope (% per dS/m) Minimum canopy resistance (S/m) Precipitation interception coefficient (cm)

128, 141, 137a 90 0.05–0.87–5.83–2.9 100 −0.1, −1.0, −500 −900, −2000 5.9 4.2 0.70 0.25

a

128 days in year 2000–01, 141 days in year 2001–02, 137 days in year 2002–03. b Parameters of Feddes functions for simulating the effect of matric stress on plant growth (adjusted as per values suggested by Wesseling et al. (1991) and Taylor and Ashcroft (1972)).

stages in the year 2001. When four irrigations were scheduled in years 2002 and 2003, only one instead of two irrigations was provided at the jointing/flowering stage depending upon the rainfall occurrence. Wherever irrigations with BAW were not given at these growth stages (missed irrigation) in treatments T2, T3 and T4, these were supplemented by saline water in treatments T5 to T10. The parameters, maximum crop height, Leaf Area Index (LAI) and maximum rooting depth as a function of the crop development stages were selected from the literature. The LAI values for each crop relate to the start of the initial stage (emergence), the end of the initial stage, the mid-season stage and the end of the season stage, respectively. The soil water extraction by the plants besides osmotic stress is also governed by the matric stress experienced by the crop. The effect of matric stress is simulated by a sink term proposed by Feddes et al. (1978). Critical pressure head values required for simulation were selected from the data proposed by Wesseling et al. (1991) and Taylor and Ashcroft (1972) and are given in Table 4. SWAP has built-in alternatives to calculate actual crop evapotranspiration. In our case, the potential crop evapotranspiration is calculated by Penman-Monteith equation for a reference crop and is separated into potential crop transpiration and potential soil evaporation. Precipitation interception coefficient and LAI as a function of crop growth stage are used to calculate the precipitation interception by the crop canopy using Braden (1985) formula. Till such time this intercepted water is evaporated from the crop canopy, transpiration component is treated as zero. The potential evaporation rate of a soil under a standing crop is derived from the Penman Monteith equation by neglecting the aerodynamic term, assuming that the net radiation inside the canopy decreases according to an exponential function, and that the soil heat flux can be neglected (Goudriaan, 1977; Belmans et al., 1983; van Dam et al., 1997).

Initial soil ECe (dS/m) Initial EC groundwater ECe (dS/m) Dispersion length, ˛L (cm) Diffusion coeff. in water, Dw (cm2 day−1 )

1.0 at soil surface 4.5 at 180 cm depth 3.6 20.0 0.72

Source: Adjusted within the range suggested by SWAP.

4.5. Model calibration and validation The recorded data for the years 2000–01 were used to calibrate the model while those of 2001–02 and 2002–03 were used to validate the model. 4.6. Scenario building A scenario was constructed wherein pearl millet (rain fed during monsoon season) and wheat (irrigated with fresh, saline and their conjunctive use as per treatments in Table 2 during winter season) yield was simulated for a period of 10 years. All the data for the year 2000–03 were repeated during 2003–06 and 2006–09 in the same sequence while for the last year data was assumed to be the same as that of the year 2000–01 making a total set of 10 years. The relative yields under various treatments were obtained to see the yield variations over a period of 10 years. Herein the relative yields were calculated by dividing the yield in a particular treatment and year with the highest yield obtained irrespective of the year and the treatment. The same exercise was also carried out for salt build-up in the soil profile. 5. Model performance evaluation The model performance was evaluated by comparing the measured relative yield (yield in a given treatment divided by yield in the treatment T1 for a given year) and salinity profile (measured and simulated salinity at a given depth) with the simulated results. The agreement between the measured and predicted values was statistically quantified by calculating the standard error and absolute deviation. Statistically, the average absolute deviation and standard error are indicators of quantitative dispersion between the measured and predicted values. The standard error was calculated as:



Standard error (SE) =

(Ym − Yp )2 n

(6)

where SE = standard error, n = number of observations considered in the test, Ym = measured value, Yp = predicted value. The average absolute deviation (AD) was computed for each test period as follows:



Absolute deviation (AD) =

|Ym − Yp | n

(7)

The variables in this equation have the same meaning as for standard error (Eq. (6)). 6. Results and discussion 6.1. Calibration

4.4. Solute transport parameters Since the parameters related to solute transport affect the salinity profile, the crop growth was adjusted by varying the parameters within the range suggested in the SWAP (Table 5).

The calibration exercises for the year 2000–01 revealed a close match between the simulated and the observed results for the relative yield of wheat crop (Fig. 1). Apparently, the model simulated little high relative yield for treatments T2, T3 and T4 where

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Fig. 4. Measured and simulated relative yield during 2001–02. Fig. 1. Measured and simulated relative yield during 2000–01.

Electrical Conductivity (dS/m) 0

0

2

4

6

8

10

12

14

16

18

20

-10

Depth (cm)

-20 -30 -40 -50 -60 -70 -80 2000-01

-90

6.2. Validation

T1(S)

T2(S)

T3(S)

T4(S)

T5 (S)

T6(S)

T1(O)

T2(O)

T3(O)

T4(O)

T5 (O)

T6(O)

Fig. 2. Measured and simulated salinity profile in treatments T1 to T6 at harvest of wheat crop (saline water 6 dS/m).

limited fresh water irrigations were applied. It appears that the model underestimated the matric stress since the excellent match was obtained in other treatments where supplemental irrigations with saline water were provided to these treatments. Low values of deviations between the predicted and the measured relative yield as revealed through AD (6.6%) and SE (11.0%) respectively also attest to this observation. These values of AD and SE for relative yield are within the acceptable range as reported by Wahba et al. (2002), and Verma et al. (2010). The salinity profiles for the year 2002 (crop season 2001–02) were used to calibrate the model. The data obtained for various treatments were grouped into two to facilitate easy graphics and interpretation. The observed and simulated results showed that the model simulations captured both the values of soil salinity and variations in soil salinity with depth quite well (Figs. 2 and 3). Both the simulated and observed results show the lowest EC in treatment T2 Electrical Conductivity (dS/m)) 0

while maximum in treatment T12. The deviations observed at various points and times could be attributed to large spatial variability in the values of soil parameters reported in many Indian studies (Sharma et al., 2003; Gupta, 2002; Verma et al., 2010). Moreover, considering the spatial averages of various model inputs and the comparison with the results obtained in previous studies, it could be conclude that the agreement between the simulated and the observed results for salinity profiles is good to excellent. The deviation between predicted and measured data revealed that AD was 1.35 dS/m and SE was 1.58 dS/m for salinity profile which are on the lower side of the range reported by Hirekhan et al. (2007) for this parameter using model WaSim.

0

2

4

6

8

10

12

14

16

18

20

The calibrated model parameters were used as such to validate the model. The simulated and the observed data for relative yield for the two years are essentially similar but with a clearly visible improvement in treatments T2 and T3 for the validation period (Figs. 4 and 5). This further attests to the observation made during the calibration that model underestimated the matric stress. Since during these two years the rainfall was more than twice the rainfall for the year 2000–01, the matric stress reduced considerably and therefore, clearly visible close match could be obtained in treatments T2 and T3. The differences in treatment T2 continued because the matric stress continued to affect the yield in this treatment. The values of AD and SE for relative yield ranged from 4.2 to 9.7% and 7.9 to 10.9% respectively for the years 2001–03. Comparing these values with those of Wahba et al. (2002) and Verma et al. (2010), it emerged that the present simulation yielded good agreement between simulated and observed yields except in cases where matric stress predominates. A plot of simulated and observed relative crop yields is reasonably close to the 1:1 line. It also indicated a good agreement between the model predicted and observed relative crop yield (Fig. 6). The simulated and the observed salinity profiles for the years 2003 and 2004 for the crop seasons (2001–02 and 2002–03) revealed almost similar results as for the calibration period

-10

Depth (cm)

-20 -30 -40 -50 -60 -70 -80

2000-01

-90 T7 (S) T7 (O)

T8(S) T8(O)

T9(S) T9(O)

T10(S) T10(O)

T11(S) T11(O)

T12(S) T12(O)

Fig. 3. Measured and simulated salinity profile in treatments T7 to T12 at harvest of wheat crop (saline water 6 dS/m).

Fig. 5. Measured and simulated relative yield during 2002–03.

A.K. Verma et al. / Agricultural Water Management 115 (2012) 186–193

1.00

6.3. Scenario building to assess the management options

Simulated

0.80 0.60 0.40 0.20 0.00 0.00

0.20

0.40

0.60

0.80

1.00

Observed Fig. 6. Relation between simulated and observed relative crop yield for all different treatments. Electrical Conductivity (dS/m) 0

2

4

6

8

10

12

14

16

18

-10

Depth (cm)

-20 -30 -40 -50 -60 -70 -80

2001-02

-90 T1(S) T1(O)

T2(S) T2(O)

T3(S) T3(O)

T4(S) T4(O)

T5 (S) T5 (O)

T6(S) T6(O)

Fig. 7. Measured and simulated salinity profile in treatments T1 to T6 at harvest of wheat crop (saline water 8 dS/m).

(Figs. 7 and 8). A close match with some deviation here and there proves the capability of the model in simulating salinity profiles under situations where fresh and saline waters are used in conjunctive mode. The values of AD (1.12 dS/m) and SE (1.54 dS/m) for salinity profile are even much lower side of range reported by Hirekhan et al. (2007).

Electrical Conductivity (dS/m) 0

2

4

6

8

10

12

14

16

18

20

-10

Depth (cm)

-20 -30 -40 -50 -60 -70 -80

2001-02

-90 T7 (S) T7 (O)

T8(S) T8(O)

T9(S) T9(O)

T10(S) T10(O)

T11(S) T11(O)

Long-term use of saline water even in conjunctive mode has been commented upon adversely because of the fear of salt buildup in the long-term. Since experiments are usually conducted over a few years, it has been difficult to remove this fear. Many modelers have discounted such a build-up (Sarwar and Bastiaanssen, 2001; Sarwar et al., 2001; Tedeschi and Menenti, 2002; Singh, 2004; Verma et al., 2010). Limited studies conducted under monsoon climate conditions related to situations where shallow water table existed and a provision of subsurface drainage had been made, have discounted this fear yet questions were still raised for conditions under deep water table with no drainage provision (Hirekhan et al., 2007; Verma et al., 2010). Long-term scenario building exercises over a ten-year period revealed that the relative crop yield varied from 95 to 100% when best available water (T1) was used. On the other hand, the relative yield varied from 48 to 64, 66 to 89, 83 to 97, 95 to 99, 91 to 95, 95 to100, 92 to 96, 95 to 100, 94 to 98, 94 to 99 and 86 to 94% when irrigations were supplemented with saline water or saline water was used for all the post sown irrigations (Table 6). Clearly, over the simulation period of 10 years, the yield in treatments T5 to T11, where supplementary irrigations were given or all irrigations were given with low saline water (6–8 dS/m), are almost similar to yield obtained in treatment T1. This simulation study brings out the following important points:

20

0

0

191

T12(S) T12(O)

Fig. 8. Measured and simulated salinity profile in treatments T7 to T12 at harvest of wheat crop (saline water 8 dS/m).

• The simulated yields over a period of 10 years have remained stable in treatments where matric stress has been avoided by using saline water for irrigation as supplement or low saline water fully for post sowing irrigations. • The matric stress reduced the yield more and over a far wider range (48–64%) compared to when the matric stress was minimized through use of either fresh (T4, 83–97%) or low saline water (T5, 95–99%) or high saline water (T8, 92–96%). • The yield is sustainable with lesser year to year variations when matric stress is minimized by using low or high saline water. • Even with high saline water alone, the yield variation was in the range of 86–94% showing that over a 10 year period the yield loss in a year would not exceed 15% whereas it could be as high as 58% when one irrigation with fresh water alone is applied. Apparently, it justifies the use of high saline water (12 dS/m) for wheat production in areas with fresh water scarcity. Indirectly, it is also justified to say that there is not much buildup of salts on annual basis in the profile for a 10 years simulation period. The salt accumulation and leaching patterns are also discussed in the following text. To simulate salinity build-up in the root zone, weekly solute concentrations were simulated for three years having different annual rainfall i.e. of around 500 mm (480.8 mm in the year 2001), a dry year with annual rainfall of around 275 mm (271.9 in the year 2002) and a relative wet year with rainfall around 800 mm (792.1 in the year 2003) for the treatment T11 where all saline irrigation irrigations were given with low saline water of ECiw 6–8 dS/m. It could be seen that there has been seasonal build-up of salinity till May in all the years reaching levels of 7.30, 8.33 and 8.41 dS/m in 2000–01, 2001–02 and 2002–03 or first, second and third year (Fig. 9). Following the monsoon rainfall of 480.8 mm in first year salinity reduced to 1.15 during mid-September and there was no salt build-up till the sowing of the crop. In the subsequent year rainfall being less soil salinity during mid September was slightly high i.e. 1.81 dS/m but following the third year the salinity at the same time was 0.61 dS/m indicating that over a three years cycle there is no build-up of salts. Even the slight build-up during dry year should not be viewed seriously as this build-up might not be harmful to the semi-tolerant wheat crop usually grown in these areas. Besides pre-sowing

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Table 6 Relative crop yield with supplementing saline irrigation for missed fresh water irrigation. S. No.

Year

T1

T2

T3

T4

T5

T6

T7

T8

T9

T10

T11

T12

1 2 3 4 5 6 7 8 9 10

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

0.99 0.98 0.96 0.99 0.99 0.95 1.00 0.99 0.96 0.99

0.52 0.64 0.64 0.59 0.49 0.48 0.51 0.59 0.62 0.55

0.72 0.82 0.89 0.80 0.66 0.71 0.70 0.73 0.86 0.78

0.92 0.95 0.96 0.97 0.83 0.89 0.91 0.84 0.94 0.96

0.99 0.98 0.96 0.99 0.98 0.95 0.99 0.98 0.96 0.99

0.93 0.95 0.94 0.94 0.93 0.91 0.94 0.95 0.93 0.95

0.99 0.98 0.96 0.99 0.98 0.95 1.00 0.99 0.96 0.99

0.95 0.96 0.95 0.96 0.95 0.92 0.95 0.96 0.94 0.96

0.99 0.98 0.96 0.99 0.98 0.95 1.00 0.99 0.96 0.99

0.97 0.98 0.96 0.98 0.98 0.94 0.97 0.98 0.95 0.98

0.99 0.97 0.95 0.99 0.96 0.94 0.99 0.98 0.95 0.99

0.88 0.91 0.92 0.88 0.88 0.86 0.89 0.94 0.88 0.89

2.5 T1

2.0

EC (dS/m)

irrigation with fresh water, a common practice of cultivation, helps in overcoming the minor adverse effects of salt build-up during such years. The redeeming feature that has emerged from this simulation exercise is that in monsoon climatic conditions, above normal rainfall years helps in maintaining the long-term salt balance by leaching the salt below the root zone. For the long-term simulation, it was assumed that the climatic conditions prevalent during these 3 years would occur in succession and normal cultivation practices as adopted in the three years of experimentation would be adopted. It was also assumed that a pearl millet crop would be grown in the monsoon season as rain fed although no detailed simulations for this crop were undertaken. It was also assumed that initially (in the month of October) soil is non-saline with a soil salinity of the root zone as 0.4 dS/m. The total rainfall during October–December 2000 was taken as 10 mm. The annual salinity build-up scenario for 10 years with cultivation of pearl millet and wheat as per various treatments listed in Table 2 revealed that salinity at the wheat harvest ranged from 2.02 to 2.58, 0.96 to 2.13, 1.51 to 2.29, 1.81 to 2.46, 4.13 to 5.69, 6.75 to 8.83, 2.99 to 3.25, 3.78 to 5.32, 2.56 to 3.37, 2.89 to 4.47, 3.78 to 5.32 and 7.14 to 9.40 dS/m in treatments T1 to T12 respectively. Clearly, there is seasonal build-up of salts varying from one year to another as anticipated. For the same treatment, the variation is due to initial salinity at the time of sowing as affected by monsoon rainfall as well as rainfall during the crop growing season. The soil salinity before sowing of wheat in the year 2010 were 0.77, 0.49, 0.66, 0.72, 1.39, 1.96, 1.07, 1.33, 0.96, 1.13, 1.47, 2.11 dS/m in treatments T1 to T12 respectively (Figs. 10 and 11). The maximum salinity was in case of T12, which is just near to 2.0 dS/m, which is the criterion to classify the soil as non-saline. The maximum build-up in salinity at this time is in year 2002, a dry year (rainfall 271.9 mm) while the lowest has been in the year 2003, a wet year (rainfall 792.1 mm) and ranged from 0.14 to 0.32 dS/m. This simulation together with the simulation for soil salinity after the wheat harvest support the statements made on the basis of three years results.

T2

T3

T4

T5

T6

1.5 1.0 0.5 0.0 2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

Year Fig. 10. Annual salt build-up within the root zone in treatments T1 to T6 with different treatment (before sowing).

Based on these results, several conclusions could be made. The first and the foremost could be that in spite of seasonal build-up of salts due to use of saline water in the winter season, the concentrated excess rainfall during the monsoon season would leach down the accumulated salts making the root zone salt free in areas with deep water table having free drainage. Such conclusions in Indian context have been drawn previously by several investigators (Minhas and Gupta, 1992; Naresh et al., 1993; Sharma and Rao, 1998; Hirekhan et al., 2007). Slightly higher salinity anticipated during dry years would be taken care of by a pre-sowing irrigation with fresh quality canal water, which is usually practiced in arid and semiarid regions of India for wheat cultivation. Thus, there seems to be no fear of sustainability of irrigated agriculture with supplemental saline waters irrigation under deep water table conditions in monsoon climatic conditions. Besides, it has also emerged that SWAP model has some limitations in predicting relative yield under moisture stress in monsoon climatic conditions.

2001 (Normal Year) 2002 (Dry Year) 2003 (Wet Year)

10 8 6 4

T7

T8

T9

T10

T11

T12

1.5 1.0 0.5

2 0

2.0

EC (dS/m)

Electrical Conductivity (dS m-1 )

2.5

O

N

D

J

F

M

A

M

J

J

A

S

Period Fig. 9. Solute concentration within the root zone of wheat for different rainfall years, due to irrigation with saline water of 6–8 dS/m.

0.0 2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

Year Fig. 11. Annual salt build-up within the root zone in treatments T7 to T12 with different treatment (before sowing).

A.K. Verma et al. / Agricultural Water Management 115 (2012) 186–193

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