Assessment and management of poor quality waters for crop production: A simulation model (SWAM)

Agt4CUltlll-al

watermanagement ELSEVIER

Agricultural

Water Management

30 (1996) 25-40

Assessment and management of poor quality waters for crop production: A simulation model ( SWAM) C.S. Singh a, SK. Gupta b,*, Sewa Ram a a G.B. Pant Uniuersity of Agriculture and Technology, Pantnagar, India b Central Soil Salinity Research Institute, Kamal, India Accepted 3 August

I995

Abstract model has been prepared for assessing water quality to judge its suitability for A simulation irrigation. When water is classified as poor quality water (saline/sodic/saline-sodic) utilizing standard norms for Indian agro-climatic conditions, the model determines the potential of the water for direct application. Further, it also evaluates management strategies based on conjunctive use of fresh and saline waters. For this purpose, the model requires water quality data, crop data, soil data and rules established in the expert system rule-base. Data are compiled in data files which can be updated. For conjunctive use of saline and fresh waters, an irrigation scheduling sub-model has been modified to include a soil salinization-desalinization module based on layer-wise equilibrium theory. The module was independently tested using field data. The model SWAM has been successfully tested using data from a number of field experiments. Sodic waters of 16.2 meq I-’ residual sodium carbonate would require 2.73 t ha-’ of gypsum for each 20 cm of water applied to the soil. Field observations usually attest to this requirement. Likewise, saline water of 16 dS m- ‘, when applied to a wheat crop in conjunction with fresh water of 0.5 dS m- I, would yield optimally in case two saline water irrigations are followed by one fresh water irrigation in a normal rainfall year with an initial soil salinity of 2.98 dS m- ‘. Some more useful data sets are analyzed and compared with results from field experiments. In our opinion, the model which is based upon recent guidelines can be applied to the classification of waters and their management. The minor changes necessary to apply the model to other conditions can be easily carried out. Keywords:

Water quality; Salinization;

* Corresponding

Desalinization;

Conjunctive

use; Expert system

author.

037%3774/96/$15X0 0 1996 Elsevier Science B.V. All rights reserved SSDI 0378-3774(95)01212-5

26

C.S. Singh

et d/Agricultural

Water

Management

30 (1996)

25-40

1. Introduction Poor quality ground waters are a common feature of arid and semi-arid regions. As a result of inter-sectoral competition for fresh water supplies, there is every likelihood that fresh water supplies for agriculture will shrink further. Therefore, management of poor quality ground waters for crop production will continue to be an area of research and planning for some time. Assessment of data from past experiences has revealed that very conservative rules of thumb are being used for assessing water quality. For this reason only a small fraction of the potential of poor quality ground waters is being tapped. In order to realistically assess water quality and potential for crop production, a simulation model is prepared which utilizes the most recent rules of thumb for the assessment and management of irrigation water quality. The objectives of the present paper are to describe the model, to present calibration and test results and to discuss the potential of the model for practical applications.

2. Model description 2.1. Classijkation

of irrigation

water

To classify irrigation water, total salt concentration given by the electrical conductivity (EC) value and data on major cations and anions are used. These data have been used to calculate two parameters namely the sodium adsorption ratio (SARI and residual sodium carbonate (RSC) as follows Na (1)

SAR = \‘( Ca + Mg)/2 RSC = (CO, + HCO,)

- (Ca + Mg)

(2)

where Na, Ca, Mg, CO, and HCO, are concentrations of these ions (meq 1-l 1 in water. EC, SAR and RSC formed the basis for classifying waters into various groups. In addition to chemical constituents, factors such as (i> soil texture (clay percentage), (ii) average annual rainfall (mm) and (iii) salt tolerance of the crop (qualitatively tolerant, semi-tolerant and sensitive) have been taken into account while classifying the waters for a particular agro-climatic region (see the classification rules in Tables Al and A2 in Appendix A. On the basis of the guidelines presented in Appendix A, waters would be classified into good quality water, saline water, sodic water and saline-sodic water. In view of the fact that for sodic waters, problems could arise either on account of a high SAR or high RSC, sodic and saline-sodic waters have been further subdivided into two classes each for a given agro-climatic zone making six different classes. For example, sodic water may be classified as either sodic water (high SAR) or sodic water (high RSC). In some cases, both SAR and RSC may be high, requiring waters to be classified as sodic waters (high SAR and high RSC) and saline-sodic waters (high EC, high SAR and high RSC). To resolve this problem, the calcium requirement of the water is calculated so as to

C.S. Singh et al./Agricultural

Water Management 30 (1996) 25-40

27

bring the SAR and RSC of the water to the desired limits (as per rules of thumb). The calculated neutralization needed in the two cases is compared and water is classified as high SAR or high RSC depending upon whether the calcium requirement is more to lower the SAR or the RSC. An index of the calcium requirement of water, Ca, (meq 1-l ) for lowering its SAR or RSC is given as follows

(Ca),

= 2(Na)*

(Ca),

= (RSC)i

1 (SAR)2, ---

1

1

(SAR)T

1

- (RSC),

(3) (4)

the subscripts i and d with SAR and RSC represent the values of these parameters for the irrigation water and the desired values, respectively. Based on the above criteria, all the waters could be grouped into six classes: (1) good quality water; (2) saline water; (3) sodic water (high SARI; (4) sodic water (high RSC); (5) saline-sodic water (high SAR); (6) saline-sodic water (high RSC). 2.2. Yield test for salinity In order to quantify yield reduction in cases where water is classified as saline or saline-sodic, an additional test is conducted. For this purpose, crop production functions for 20 crops were selected (Gupta, 1992) and incorporated in the model. The crop yield reduction is calculated using a piecewise linear model adopted from the model of Maas and Hoffman (1977) as follows Y,=O,

O
Y,=S(EC,-EC,), Y, = 100,

(54

EC, < EC,

(5b) (SC)

where Y, is the relative yield reduction (%); EC, is the salinity of the irrigation water (dS m-’ >; EC, is the threshold salinity of the irrigation water (dS m-’ ); EC, is the salinity beyond which yield is zero (dS m-’ ) and S is the value of the slope of the response function between EC, and EC,. Where values of EC, and slope for any crop other than the 20 crops built in the model are known, the yield reduction for that crop can also b,e calculated using an option provided in the model. 2.3. Management

strategies

A number of management strategies are available for useful application of poor quality waters in agriculture. For saline waters, conjunctive use of saline and fresh waters may be achieved by either of the two common practices of blending or cyclic application. For sodic waters, blending or application of chemical amendments to neutralize SAR or the RSC are the two practices in common use. These management strategies are discussed briefly as follows.

C.S. Singh et al./Agriculturd

28

2.3.1. Blending

Water Management

30 (1996) 25-40

of water supplies

Mixing of low quality water with good quality water dilutes the poor quality water to acceptable quality. The ratio of mixing is worked out on the basis of salinity of saline water, salinity of fresh water and the desired salinity of the mix. The following equation forms the basis of the calculations (Ayers and Westcot, 1976) EC, = [EC, X FWF] + [EC, X SWF]

(6)

where FWF is the fresh water fraction; SWF is the saline water fraction and is equal to (1 - FWF); the subscripts f, s and m denote the fresh water, saline water and mixed water, respectively.EC, is calculated on the basis of Eq. (5b) as follows

(7)

EC,=;+EC,

where Y, is the desired yield reduction. Usually 10% reduction in the yield is allowed while working with poor quality waters. In the case of mixing sodic water with good quality water, the model utilizes a trial and error procedure. The following equations are used

(S~)nl= (RSC),

Pahn

(8)

J(Ca+ WJ2

= (CO, -t HCO,),

- (Ca + Mg),

where (Na),

= (Na),

(Ca + Mg),

X SARWF + (Na), X FWF

= (Ca + Mg), X SARWF + (Ca + Mg)r X FWF

(CO, + HCO,),

= (CO, + HCO,), +(CO,

(10) (11)

X RSCWF

-t HCO,),X

FWF

(12)

SARWF is the fraction of the water with high SAR and RSCWF is the fraction of the water with high RSC. In each case, calculations are carried out for all three parameters, salinity (EC), SAR and RSC. In order to stay below the limits indicated in Tables Al and A2, a blending ratio which requires maximum proportion of the fresh water is then recommended for mixing. Once the mixed water is prepared on this basis, all three parameters (EC, SAR and RSC) will be within the permissible limit. These values for the mixed water are calculated and given in the output. 2.3.2. Cyclic use

For cyclic application a normal irrigation scheduling module was modified to include salt transport in the root zone. Crop reference evapotranspiration is computed with either of the three procedures, namely (i) the irrigation water to potential evapotranspiration ratio commonly known as the IW/PE ratio method, (ii) Doorenbos-Pruitt-Penman equation or (iii) on the basis of available moisture utilizing open pan evaporation values. These methods are in common use and are described in the literature. Without going into

C.S. Singh et al./Agricultural

the details of the irrigation model.

scheduling

Water Management 30 (1996) 25-40

module,

discussion

29

is limited to the salt transport

2.3.3. Salinization-desalinization in cyclic use Steady state equations based on single layer theory have been used for predicting salinization or desalinization of the crop root zone. Such applications are common and have been found to perform well under field conditions (van Der Molen, 1956; Bresler, 1967; Bums, 1974; Pandey and Gupta, 1978; Khosla et al., 1979; van Hoom, 1981; Gupta and Pandey, 1983; Gupta, 1985; Kapoor and Pal, 1986). The simplicity in the calculations of the solute transport using this theory adds further to its widespread application. Here we apply an equation proposed by van Der Molen (1983). The equation treats the soil root zone as a single layer. Under steady state conditions and assuming no dissolution or precipitation of salts, the change in salt concentration of the root zone can be given by the following relation (van Der Molen, 1983) sz=

(ZCi-(l-f)R’Ci-(~*/W,)Z,)

(1 +JR*/2wfc)

(13)

where 6Z is the change in root zone salt content in meq m-*, R * = R - G is the net deep percolation (mm), I is the depth of irrigation (mm), Ci is the salt concentration of irrigation water (meq I-‘), f is the leaching fraction, Z, is the initial salt content in the root zone (meq m-* ) and W, is the soil moisture content at field capacity. This equation is applied after each irrigation or rainfall event to calculate the root zone salinity. If the salinity of the root zone exceeds the prescribed salinity, a switch over is made from poor quality water to fresh water. The soil salinity is recalculated and is used as an input during the next irrigation. To take advantage of the fact that crops are more tolerant during later growth stages compared with germination or initial establishment stages, it is possible to change the prescribed salinity for each new irrigation. 2.3.4. Application of chemical amendments Chemicals containing calcium, e.g. gypsum, calcium chloride and calcium polysulfide, have been used for sodic water management. Gypsum requirement, G,, is calculated on the basis of the following equation 0.86(Ca),NiDi G, =

(14) Ps

G, is the amount of gypsum required (t ha-‘); Di is the depth of each irrigation (cm); Ni is the number of irrigations and Pg is the purity of gypsum (%). In Eq. 14 the higher of the two values of Ca, as calculated from Eq. 3 or Eq. 4 is used. 2.4. Flow chart A flow chart indicating various steps in the calculations is shown in Fig. 1. Some details in the flow chart have been withheld for the sake of minimizing space requirement without compromising on clarity.

30

C.S. Singh et al. /Agricultural

Water Management

30 (1996) 2.5-40

2.5. About the model The computer simulation model (SWAM) is written in FORTRAN 77. Because many decisions need to be taken by the user during assessment and management, the model has been prepared as a fully interactive model where each value is keyed in by the user (except the climatological data for calculating evapotranspiration). However, to keep the

Y*lOO,

ompemanl

0<

EC I

EC)

Procliies

lRRl XC I-+F)DP

Fig. 1. Flow chart of model.

XCI-=!$$

C.S. Singh et ul./Agriculturul

Table I Chemical

composition

of irrigation

Water Management 30 (1996) 25-40

31

waters

Sample No.

EC (dSm_‘)

Ions (meq 1-l) Na

Ca+ Mg

K

co, + HCO,

cl+ SO,

1

4.0 18.2 2.1 6.6 2.2 1.6 0.8 1.1 3.6

36.0 140.0 8.1 46.9 12.7 10.7 4.1 4.9 32.0

3.8 45.2 12.2 19.6 2.9 4.3 5.3 4.7 6.5

ND 0.8 1.0 1.1 0.2 0.1 0.3 0.2 ND

18.8 6.1 1.1 1.1 10.4 11.0 7.0 9.0 4.5

21.5 175.2 20.2 66.6 5.4 1.7 1.7 2.4 33.9

PH

RSC (meq 1- ‘)

6.9 ND 7.3 7.5 9.1 9.0 ND ND 7.7

15.0

7.5 6.7 1.7 4.3

SAR (mm01 ,- 1)1/Z 26.1 29.4 3.3 15.0 10.6 7.3 2.5 3.2 17.7

ND, not determined.

time requirement to a minimum, it is useful to apply the model as a semi-interactive model where several inputs can be supplied via the files while others could be provided by the user. Details for the application of the simulation model are provided in a user’s manual.

3. Some computational

experiments

Simulations were made using chemical quality data of water samples collected from various places in Haryana State (India). Water quality data for nine water samples are presented in Table 1. For management options, climatic data and other field data of the Sampla station of the Central Soil Salinity Research Institute, Karnal were used. 3.1. Assessment

of water quality

Four water samples (Nos. 1, 3,4 and 9) out of nine water samples reported in Table 1 have been assessed and classified into one of the six classes (Table 2). In the assessment of each water sample, the parameters soil texture, rainfall and crop sensitivity have been varied to illustrate the role of these factors in assessing the suitability or otherwise of an irrigation water. A water designated as poor quality for soils with heavy texture could be designated as good quality for light textured soils (Table 2). For example, the results for water sample No. 3 reveal that for sensitive crops the water is assessed as saline for all three rainfall groups if used on a soil with clay content exceeding 30%. If the soil has a clay content of 20-30%, the water is assessed as good provided rainfall is in the 550-750 mm range. If the clay content of the soil is less than 10% then the water is assessed as good for all the three rainfall regions. Similar differences in assessment could be noticed for three different groups of the crops. The data for water sample No. 9 in Table 2 reveal that for the same rainfall region and the same soil texture group (clay content less than lo%), the water is assessed as saline for sensitive crops, whereas it is assessed as good for semi-tolerant and tolerant crops.

32

C.S. Singh et al./Agricultural

Table 2 Assessment

of irrigation

Water Munugement

30 (1996) 25-40

water quality a

Water sample

Soil texture (% clay)

Sensitive crop <350b

350-550

550-750

<3.50

350-550

550-750

<350

350-550

550-750

1

>30 20-30 lo-20
5 5 6 6

5 5 6 6

5 5 6 6

5 5 4 4

5 5 4 4

5 3 4 4

5 3 4 4

5 3 4 4

3 3 4 4

3

>30 20-30 10-20
2 2 2 1

2 2 1 1

2 1 1

2

2 1 1 1

I

1 1

1 1 1 1

1 1 1

I

2 2 1 1

I

1 1 1

4

>30 20-30 10-20
5 5 2 2

5 5 2 2

5 5 2 2

5 5 2 2

5 5 2 1

5 5 1 1

5 5 2 1

5 5 1 1

5 3 1 1

9

>30 20-30 10-20
5 5 5 2

5 5 5 2

5 5 5 2

5 5 3 1

5 5 3 1

5 3 3 1

5 3 3 1

5 3 3 1

3 3 3 1

Semi-tolerant

I

crop

Tolerant crop

a 1, good quality water; 2, saline water; 3, sodic water (high SAR); 4, sodic water (high RSC); 5, saline-sodic water (high SAR); 6, saline-sodic water (high RSC). b Rainfall region (mm).

3.2. Potential for direct application Yield reductions for different crops with the four water samples (Nos. 2, 3, 4 and 9) are reported in Table 3. In the preparation of this table, adverse effects of salinity only have been considered, irrespective of the water quality. The potential of water sample

Table 3 Percentage

reduction

in yield on direct application

crop

Barley Brinjal Lady’s Finger Mustard Pearlmillet Potato Rice Safflower Sunflower Wheat a Values in parentheses

EC,(dS m- ‘)

Slope

8.3 9.8 0.6 6.6 4.6 1.7 1.3 3.8 4.9 7.0

2.2 10.6 4.2 7.4 2.0 6.3 7.6 3.3 7.8 4.5

are the electrical

Water sample 2c18.2)

3f2.1)

21.7 89.0 74.4 85.2 27.6 100.0 100.0 47.6 100.0 50.0

0.0 0.0 6.4 0.0 0.0 2.6 6.2 0.0 0.0 0.0

conductivity

46.7)

N3.6)

0.0

0.0

0.0

0.0

25.5 0.3 4.1 30.9 40.5 9.4 13.6 0.0

12.6 0.0 0.0 11.8 17.4 0.0 0.0 0.0

of the water samples (EC,, dS m-

’).

C.S. Singh et al./Agricultural

33

Water Management 30 (1996) 25-40

No. 2 with direct application is minimal as it cannot be applied to any of the crops as yield decline is more than 20% for each crop. Water sample No. 3 is the best quality water and can be used for almost all the crops with yield reduction of less than 10%. The other two samples fall between these two extremes. They can be used for some crops while for others they cannot be used without substantial yield reductions. 3.3. Blending of poor quality water with good quality water Conjunctive use of saline and fresh waters through blending is a commonly recommended practice. Through blending, the water quality of the mix can be brought to a desirable level. Values for commonly available fresh water (EC = 0.8 dS m-t, SAR = 2.5, RX = 1.7 meq 1-l) were used to work out blending ratios. The criterion used for blending was that none of the three parameters (i.e. EC, SAR and RSC) of the blended water exceeds the limits given in Table Al and Table A2. Recommended percentages of poor quality water and EC, SAR and RSC values of the blended water are reported in Table 4. It may be noted that water samples 3 and 7 needed no blending as these are suitable for direct use on the crop. The percentage of poor quality water in the blended water varied from 6 to 35. This means that with blending, l/16 to nearly l/3 of the fresh water can be saved when waters assessed as poor quality are used to grow wheat. 3.4. Calibration

and testing of salinization-desalinization

module

Before the module (Eq. 13) was utilized in the main model for cyclic application, the module was calibrated and tested for its applicability in predicting salinization-desalinization. For this purpose, data on soil salinization-desalinization relating to the &year period 1986-1992 was obtained from the Sampla farm of the Institute (Sharma et al., 1991). The data consisted of various parameters listed in groups I and II of Table 5. In addition, soil salinity values recorded at various times of the year (before cropping, in

Table 4 Parameters Water sample

1 2 3 4 5 6 7 8 9

of mixed water with recommended Recommended percentage of poor quality water 6 9 25 14 16 31 35

mixing ratios

EC (dS m-‘1

SAR (mmol l- ‘)‘/*

RX

(meq l- ‘)

Original

Mixed

Original

Mixed

original

Mixed

4.0 18.2 2.1 6.6 2.2 1.6 0.8 1.1 3.6

1.0 2.3 2.1 2.3 1.0 1.0 0.8 0.9 1.8

26.1 29.4 3.3 15.0 10.6 7.3 2.5 3.2 17.7

3.7 7.7 3.2 7.0 3.3 3.2 2.5 2.7 8.1

15.0 -39.1 - 11.1 - 18.5 7.5 6.7 1.7 4.3 - 2.0

2.5 - 1.9 - 11.1 -3.4 2.5 2.5 1.7 2.5 0.4

34 Table 5 Group-wise

C.S. Singh et d/Agricultural

Water Manqement

listing of the model parameters

Group

Parameter

Value

I

Water content at field capacity (W,) Bulk density (BD)

133.4-266.8

No. of irrigations (N,) No. of irrigations with saline and fresh water (N,, N, respectively) Depth of irrigation ( Di) Depth of soil (D) Precipitation (P) Initial salt content in the root zone (2,) Salt concentration of irrigation water (CJ Salt concentration of rain water (EC, 1

3-4 Case dependent

II

III

30 (1996) 25-40

Application

efficiency

(AE) Precipitation contribution to leaching (PE) Leaching fraction (f)

Remarks mm

1.52 Mg m-3

Variable with soil depth _ _

55 mm 4-90 cm _ _

Variable Variable Variable

_

Month-wise

variable 0.1 dS m-

’

0.50-0.70 0.10-0.50 0.20-0.60

_ Variable with soil depth Highly variable Variable with soil texture

Fitted values of AE, PE and f are 0.70, 0.30 and 0.50, respectively.

between a crop and after the harvest of the crop) were collected. The weighted average salinity build-up (in O-45, O-60, O-7.5 and O-90 cm depth) following irrigation with saline water and desalinization following rainfall or fresh water application were calculated from the data sets. Out of the data on soil salinization-desalinization for the 6 years, data for the year 1986-1987 were chosen to calibrate the model. For calibration of the module, parameters of groups I and II were assigned values obtained from direct measurements or estimated by independent techniques (Table 5). For the parameters of group III (Table 51, values assessed as a first guess were used initially for predicting soil salinity for all four depths of the soil profile. These values were then increased in increments, keeping in view the field conditions until good correlation coefficients were obtained. Correlation coefficients decreased with depth and showed no further improvement when further changes were made in one or the other of the three parameters. The values of the parameters thus estimated were then used, without any modifications, for the test and the verification of the model. Depth-wise soil salinity was used to test and verify the model initially. For this purpose, initial salinity of the soil profile observed during October 1987 was taken as the starting point (Kaur et al., 1995). The pooled predicted values for 5 years for all depths are reported in Table 6. Considering the long period for which predictions have been made, the correlation

C.S. Singh et al./Agricultural

Table 6 Depth-wise

pooled correlation

coefficients

Water Management

30 (1996) 25-40

35

for the data for a 5 year period (1987- 1992)

Depth(cm)

r

No. of data points

45 60 75 90

0.86 0.85 0.84 0.84

55 55 55 55

coefficients appear to be quite high. This shows that the proposed model is a good and realistic tool for prediction of depth-wise soil salinization-desalinization. The observed and predicted values utilizing pooled data (220 data points) for 5 years and four depths are shown in Fig. 2. A high correlation coefficient of 0.83 confirmed the earlier finding based on depth-wise prediction of soil salinization-desalinization. On this basis, it was concluded that the model can be realistically applied to calculate cyclic applications of saline and fresh waters. The irrigation scheduling sub-model used in this model has been applied successfully by various workers (Doorenbos and Pruitt, 1975; James, 1988). The major contribution to the main model is therefore the salinization-desalinization module which has not been utilized previously.

3.5. Cyclic application

of fresh and saline water

To illustrate the potential of the model in working out and saline waters, a hypothetical case is prepared with semi-arid region of the Haryana State. Fresh canal water drainage water (EC = 12 dS m- ’) were utilized to

the cyclic use strategy for fresh climatic data representing the (EC = 0.5 dS m- ’) and saline estimate the total number of

Obrerved(dS/m)

Fig. 2. Pooled observed and predicted

salinities for four depths and 5 years ( 1987- 1992).

C.S. Singh et al./Agricultural

36

Water Management

30 (1996125-40

irrigations, number of irrigations with saline and fresh water and the resultant crop root zone salinity as a consequence of cyclic application of these waters. A wheat crop was chosen as the test crop. Three climatic conditions were assumed: low rainfall, normal rainfall and high rainfall. The results of these simulations are presented in Table 7. The results indicated that two to three irrigations need to be given to the wheat crop in addition to a fresh water pre-sowing irrigation. The third (or fourth) irrigation was predicted just a few days before the harvest. Therefore, the last irrigation was not taken into account. In these calculations, it was assumed that soil salinity at any time should not exceed 5.9 dS m-‘. Whenever salinity exceeded this value, saline water was replaced with fresh water in order to check yield loss in the wheat crop. This assumption was made on the basis of experience at the study site. Data depict that where the initial soil salinity is low (0.6 dS m-‘1, one can safely apply all irrigations with saline water, irrespective of the prevailing rainfall conditions. In contrast, when initial soil salinity is medium (1.7 dS m- ’>, out of a total of three irrigations applied, at least one irrigation needs to be of fresh water under low rainfall conditions. On the other hand, under medium and high rainfall conditions all irrigations can be given with saline water. When initial soil salinity is as high as 3.0 dS m- ‘, at least one fresh water irrigation is necessary under both low and medium rainfall conditions. Under high rainfall conditions, however, no fresh water irrigation need be applied. This is because rainfall provided water equivalent to one good quality irrigation. It is thus clear that for the study site cyclic application of saline and fresh water irrigations can be carried out under various rainfall conditions. It may, however, be noted that by doing so, in all cases, a non-saline soil turned saline and a saline soil became more saline. Thus, any attempt to utilize saline water, even with conjunction with fresh water, calls for the leaching of accumulated salts either by conserving rainfall or by applying fresh water. For the Indian conditions, sufficient leaching is carried out during the monsoon season (summer) when rainfall exceeds evapotranspiration over a period of about 3 months.

Table 7 Irrigation

scheduling

and cycle of fresh and saline water irrigations

for wheat crop

EC, before sowing (dS m-‘)

Date of irrigation I

II

III

Low

0.6 1.7 3.0

12/12(S) 12/12(S) 12/12(S)

13/2(S) 13/2(S) 13/2(S)

8/3(S) 8/3(F) 8/3(F)

5.0 4.4 5.5

Medium

0.6 1.7 3.0

17/1x.% 17/12(S) 17/12(S)

4/2(S) 4/2(S) 4/2(S)

19/3(S) 19/3(S) 19/3(F)

4.3 5.1 4.5

High

0.6 1.7 3.0

29/l(S) 29/l(S) 29/l(S)

6/3(S) 6/3(S) 6/3(S)

_

3.7 4.7 5.8

Rainfall

S and F in parentheses denote saline and fresh water irrigation, Permissible EC, = 5.9 dS m- ’.

_ respectively.

EC, at harvest (dS m- ‘)

C.S. Singh et al./Agricultural

Water Management

30 (1996) 25-40

37

3.6. Amendment application Sodic or saline-sodic waters can be used directly on soils provided that the soil is treated to offset the adverse effect of sodicity on soil properties. For saline-sodic waters, the salinity of the irrigation water also needs to be lowered to the optimum level. Gypsum is commonly used in India to ameliorate soils irrigated with sodic/saline-sodic waters. The amount of the chemical required to lower high SAR or high RSC is calculated once the total amount of water to be applied (number of irrigations times the depth of irrigation) is known. Calculations for the three water samples Nos. 1, 2 and 9 reveal that sample No. 9 required the greatest amount of gypsum, followed by sample No. 2 and then sample No. 1. A useful application of this module is in selecting a crop that can be best grown with such waters. For example, with highly sodic waters such as sample No. 9, it may be worthwhile trying a crop with low water requirement in order to minimize the cost of amendment application.

4. Concluding

remarks

The simulation model presented in this paper has proven to be a very useful and efficient tool in classifying the waters, assessing their suitability and for evaluating management strategies for applying poor quality waters for crop production. It is found to work well on the experimental data gathered so far at various experimental sites. This model could be a useful tool to predict irrigation schedules as well as the crop root zone salinity on land irrigated with saline water.

Appendix

A

Table Al Guidelines for salinity classification of irrigation between good quality and saline water Soil texture (% clay) > 30 20-30 10-20 < 10

Sensitive crop

Semi-tolerant

water. EC values indicate the limits

crop

Tolerant crop

< 350 a

350550

550750

< 350

350550

550750

< 350

350550

550750

1.0 1.5 2.0 3.0

1.0 2.0 2.5 3.0

1.5 2.5 3.0 3.0

1.5 2.0 4.0 6.0

2.0 3.0 6.0 7.5

3.0 4.5 8.0 9.0

2.0 4.0 6.0 8.0

3.0 6.0 8.0 10.0

4.5 8.0 10.0 12.0

Source: Minhas The limits are expected that with no yield reduction Textural criteria

and Gupta (1992).a Rainfall region (mm). based on field experiences in different agro-climatic zones. It is waters of salinity equal to or lower than these limits, there would be on a long term basis and no adverse effect on the soil resource. should be applicable for all soil layers to at least 1.5 m depth.

C.S. Singh et nl./Agricultural

38

Table A2 Guidelines for sodicity classification values exceeding thelimits are classified

Water Management 30 (1996) 25-40

of irrigation water. Waters with SAR or RSC as sodic or saline-sodic, depending on EC

Soil texture (% clay)

Upper limits of SAR (mmole l- ’)‘I2

RSC (meq l- ‘)

> 30 20-30 10-20 < 10

10 10 15 20

2.5-3.5 3.5-5.0 5.0-7.5 7.5- 10.0

Source: Minhas and Gupta (1992). The limits are based on field experiences in different agro-climatic zones. It is expected that with waters of SAR or RSC equal to or lower than these limits, there would be no yield reduction on a long term basis and there would not be any increase in the ESP to adversely affect the crop. Textural criteria should be applicable for all soil layers to at least 1.5 m depth.

Appendix B Table Bl Input data Input data for water quality assessment EC Na Ca+Mg K CO, + HCO, Cl + SO“ PH Soil texture Crop type

Rainfall

region

Input data for direct application Crop Desired yield reduction EC’ Slope

dS m-’ meq 1-l meq 1-l meq 1-l meq 1-l meq 1-l % clay (i) Sensitive (ii) Semi-tolerant (iii) Tolerant (i) < 350 mm (ii) 350-550 mm (iii) 550-750 mm

% dS m-’ %

C.S. Singh et al./Agricultural

Input

Water Management 30 (1996) 25-40

39

data for blending

EC of fresh water

Na of fresh water Ca + Mg of fresh water CO, + HCO, of fresh water Mereorological

dS m-’ meq 1-l meq 1-l meq 1-l

data on daily basis

mm mm “C “C % % h m s-’

Rainfall in 24 h Open pan evaporation Maximum temperature Minimum temperature Maximum RH Minimum RH Sunshine hours Wind speed Other parameters

for cyclic application

IW/PE

ratio Station latitude Station elevation Porosity Field capacity Max. allowable deficiency Depth of root zone Crop coefficient (K,) Beginning date of growing season Ending date of growing season Pan coefficient ( KP> Initial soil salinity Leaching efficiency Input a’ata for amendment

Depth of each irrigation Number of irrigations Purity of gypsum

degrees m % volume % volume % mm

dS m-’ fraction

application

cm %

References Ayers, R.S. and Westcot, D.W., 1976. Water Quality for Agriculture. Inig. Drain. Pap. No. 29, FAO, Rome, p. 51. Bresler, E., 1967. A model for tracing salt distribution in the soil profile and estimating the efficient combination of water quality and quantity under varying field conditions. Soil Sci., 104: 227-233. Bums, LG., 1974. A model for predicting the redistribution of salts applied to fallow soils after excess rainfall or evaporation. J. Soil Sci., 25: 165-178. Doorenbos, J. and Pruitt, W.O., 1975. Crop Water Requirements. Irrig. Drain. Pap. No. 24, FAO, Rome, p. 34. Gupta, S.K., 1985. Leaching of saline soils through rainfall. J. Indian Sot. Soil Sci., 33: 128-136. Gupta, S.K., 1992. Leaching of salt affected soils. Techn. Bull. No. 17, CSSRI, p. 71.

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Wuter Management 30 (1996) 25-40

Gupta, SK. and Pandey, R.S., 1983. A mathematical model for evaluating leaching of saline soils when saline and fresh water are used in conjunction. Indian J. Agric. Sci., 53(S): 701-706. James, L.G., 1988. Principles of Farm Irrigation System Design. Washington State University, p. 446. Kapoor, A.K. and Pal, R., 1986. Predicting salinization and sodification of a bare sandy loam soil after irrigation with poor-quality water inter spread with ram. Soil Sci., 141: 281-288. Kaur, R., Sharma, D.P., Gupta, S.K. and Singh, C.S., 1995. Predicting salinization-desalinization in saline water irrigated lands. J. Indian Sot. Soil Sci., 43: w-447. Khosla, B.K., Gupta, R.K. and Abrol, I.P., 1979. Salt leaching and the effect of gypsum application in saline-sodic soil. Agric. Water Manage., 2(3): 193. Maas, E.V. and Hoffman, G.J., 1977. Crop salt tolerance: Current assessment. J. Irrig. Drainage Div. 103(IR2): 115-134. Minhas, P.S. and Gupta, R.K., 1992. Quality of Irrigation Water-Assessment and Management. Information and Publication Section, ICAR, p. 102. Pandey, R.N. and Gupta, S.K., 1978. Equation to predict leaching of soluble salts in saline soils. J. Agric. Sci., 91: 131-133. Sharma, D.P., Singh, K.N., Rao, K.V.G.K. and Kumbhare, P.S., 1991. Irrigation of wheat with saline drainage water on a sandy loam soil. Agric. Water Manage., 19: 223-233. van Der Molen, W.H., 1956. Desalinization of saline soils as a column process. Soil Sci., 81: 19-27. van Der Molen, W.H., 1983. Salt balance and leaching requirement. In: Drainage Principles and Applications. Vol. 11, 3rd edn. ILRI, Wageningen, p. 68. van Hoom, J.W., 1981. Salt movement, leaching efficiency, and leaching requirement. Agric. Water Manage., 4: 409-428.