soil quality evaluation in salt-affected soils with yield of major...

62

Soil quality evaluation in salt-affected soils with yield of major crops inMula command area of Ahmednagar district, Maharashtra

K.D. KALE, A.L. PHARANDE, C.A. NIMBALKAR AND V.K. KHARACHE1

Department of Soil Science and Agricultural Chemistry, Mahatma Phule Krishi Vidyapeeth,Rahuri-413722, India

1Department of Soil Science and Agricultural Chemistry, Dr. P.D.K.V., Akola-444 104, India

Introduction

Soil salinity/sodicity is one of the most seriousforms of soil quality degradation affecting approximately

10 per cent of the total land surface of the globe. Theproblem occurs in varying intensities in more than 120countries and is more prominently witnessed in the arid

and semi- arid areas (Yadav 1993). In semi-arid and arid

regions, irrigation induced problems of water- logging,secondary salinization and sodication goes hand to hand.These problems pose a great threat to the sustainable

productivity. Nearly 10-12 % of shrink-swell soils un-der different command areas of Maharashtra have turnedinto saline and sodic Vertisols (Varade et al. 1985). In

India, 9.55 m ha of soils are reported to be salt affected

Agropedology 2015, 25 (01), 62-78

Abstract: The present investigation was carried out during the year 2007-08 in the rightbank canal of Mula command distributory No. 2 in Rahuri tahsil, Ahmednagar district,Maharashtra for the assessment of soil quality degradation due to salinity and sodicityand its relation with crop productivity. The soils in Mula command area are mainlydeveloped due to the fluvial action from the basaltic alluvium and the major soils areInceptisol and Vertisols. The physical and chemical properties of soils varied greatlyand the soils in general were very high in clay (52.0 to 66.0 %), high in bulk density(1.38 to 1.74 Mg m-3) and low in the hydraulic conductivity (0.08 to 0.58 cm hr-1). Thesoils were moderately to strongly alkaline (pHs 8.10 to 9.20) with electrical conductiv-ity of saturation extract (ECe) varying from 1.69 to 8.40 dS m-1, ESP from 6.58 to 32.71and highly calcareous in nature. The data of soil physical and chemical properties fromthe command area were subjected to principle component analysis (PCA) for identifica-tion of sensitive indicators. Correlation analysis amongst the highly weighted variableswas done to remove the remaining parameters. In order to develop the soil quality indexand assess the degradation status, the sensitive indicators were normalized to 0-1 scaleby different scoring functions and finally an index was developed by using an additivemodel. The soil quality index was high (0.91) for soils of midland region, while it waslow (0.62) in the tail region. The minimum dataset soil parameters were negatively cor-related with ESP, ECe and pHs indicating soil quality degradation due to salinity andsodicity hazards. The Ca : Mg ratio was found to be the most predominant soil qualityindicator followed by organic carbon, ESP, CEC/clay and exchangeable magnesiumpercentage (EMP) in determination of soil quality. Thus, the soil quality in Mula com-mand area was severely degraded in tail region as compared to head and mid region dueto salinity and sodicity. The SQI developed for assessment of degradation due to salinityand sodicity could be used as a decision making tool for formulation of polices leadingto land restoration and reclamation of sodic soils in the command areas.

Key words: Command area, PCA, soil quality, salinity, sodicity, yield of major crops

63

(Kanwar 1994). However, in Maharashtra about 0.54 mha of black soils initially reported to be salt affected has

increased to 1.06 million ha and is about 3.4% of geo-graphical area of the state (Gaikwad and Challa 1996).

Soil quality evaluation is one of the frontier ar-

eas of research. The principle soil properties most af-fected by soil degradation process are the key attributesfor soil quality evaluation. Soil quality is defined as the

capacity of soil to function with ecosystem boundaries

to sustain biological productivity, maintaining environ-

mental quality and promote plant and animal health

(Doran and Warner 1990). The soil quality assessmentby systematic approach of soil resource studies in thecommand areas is useful to identify the soil related con-

straints. The soil quality indicators identified would re-flect the degree and kind of limitations for soil degrada-tion. Granatstein and Bezdicek (1992) reported that the

principal soil properties most affected by soil degrada-tion processes could form key attributes for soil qualityevaluation. Quantitatively, soil quality can be assessed

by developing an integrated or relative soil quality in-dex. A valid soil quality index would help to interpretdata from soil measurements and show whether manage-

ment and land use are having the desired results for pro-ductivity, environmental protection and health. Soil qual-ity assessment provides a basic means to evaluate the

sustainability of quality that is basically defined by stable,natural and inherent features related to soil forming fac-tors and dynamic changes induced by soil management

(Pierce and Larson 1993). Larson and Pierce (1994) as-sessed soil quality and proposed that the basic soil indi-cators which meet the suitability are soil texture, depth

of soil, bulk density, infiltration, water holding capacity,water retention characteristics, water content, organiccarbon and pH, EC, N, P, K and soil respiration biomass

C, total organic C, respiration biomass ratio are impor-tant soil quality indicators. The soil organic matter con-tent, infiltration rate and bulk density are included as basic

indicators. Soil quality is the key to agricultural produc-tivity, soil organic matter and nutrient levels are the primeindicators of soil quality which readily decline when soils

are taken under cultivation.

The soils of Mula command area became highlysaline and then turned sodic after the introduction of ca-

nal irrigation since 1972. Therefore, it is necessary todevelop soil quality index in salt affected soils in Mulacommand area after introduction of cannal irrigation. It

was thought worthwhile to collect the precise informa-tion on physico-chemical properties of soil for assess-ment of soil quality.

Materials and Methods

Description of site

The study area is a representative part of Mula

command area in Rahuri Tahsil of Ahmednagar districtof Maharashtra state. The Mula irrigation project wasstarted in 1972 and supplies irrigation water to four

Tahsils of Ahmednagar districts viz., Rahuri, Newasa,Shevgaon and Pathardi. The total capacity of project is26,000 million cubic feet and live storage is 21,500 mil-

lion cubic feet. The Mula project has three branch canalscovering an area of 69,534 ha. These are (1) Mula rightbank canal (2) Mula left bank canal and (3) Pathardi

branch. The Mula right bank canal with its two branches(1) Branch-I and (2) Branch-II (Ghodegaon sub-division)covers an area of 19,264 ha. The Branch-I supplies irri-

gation to Rahuri tahsil through 5 distributories coveringan area of 11,400 ha. The study area comprises of KendalBk, Kendal Kd and Chandkapur villages in Rahuri tah-

sil. Study area located between 19o51' to 19o54' N lati-tude and 74o21' to 74o25' E longitude covers total area of688.53 ha. Its elevation is 502 m above mean sea level

and is located about 15 km east to Rahuri town on bothsides of distributory (Dy) No.2 of Mula right bank canal.The soils of study area are slight to severely salt affected,

nearly leveled to very gentle slopping midlands of lowlying area of lower and upper piedmont plains (basinshape topography). The climate of study area is semi-

arid tropical and characterized by hot summer (March toMay) and general dryness in other months except in rainymonths (June to September). The average rainfall of study

area is 535.4 mm.

Soil quality evaluation in salt-affected soils

64

Table 1. Details of the soil mapping units with brief description

Sr. No.

Mapping unit* Brief description Soil series

1 Cdp-1 mA1 S2 Clay,0-1% slope, slight erosion, moderately saline 2 Cdp-1 m B1 S2 Clay,1-3 % slope, slight erosion, moderately saline

3 Cdp-1 mB S1 Clay,1-3% slope,slightly saline 4 Kkd-1 mA SS1 Clay,0-1% slope, slightly saline-sodic

Chandkapur-1, Typic Haplustepts

5 Cdp-2 mA S1 Clay,0-1% slope, slightly saline 6 Cdp-2 mB1 S2 Clay,1-3% slope, slight erosion, moderately saline 7 Cdp-2 mA S2 Clay,0-1% slope, moderately saline 8 Cdp-2 mB S3 Clay,1-3% slope, strongly saline

Chandkapur-2,Vertic Haplustepts

9 Kkd-1 mA SS1 Clay,0-1% slope, moderately saline-sodic 10 Kkd-1 mB1 SS2 Clay,1-3% slope, slight erosion, moderately saline-sodic 11 Kkd-1 mB SS2 Clay,1-3% slope, moderately saline-sodic 12 Kkd-1 mA SS1 Clay,0-1% slope, slightly saline-sodic

Kendal Kd-1, Typic Haplustepts

13 Kkd-2 mA1 SS1 Clay,0-1% slope, slight erosion, slightly saline-sodic 14 Kkd-2 mA3 SS1 Clay,1-3% slope, severe erosion, slightly saline-sodic 15 Kkd-2 mC1 SS1 Clay,3-5% slope slight erosion, slightly saline-sodic 16 Kkd-2 mA2 SS1 Clay,0-1% slope, moderate erosion, slightly saline-sodic 17 Kkd-2 mB1 SS1 Clay,1-3% slope, slight erosion, slightly saline-sodic

Kendal Kd-2, Vertic Haplustepts

18 Cdp-3 mA3 SS1 Clay,0-1% slope, severe erosion, slightly saline-sodic 19 Cdp-3 mC1 SS1 Clay,3-5% slope, slight erosion, slightly saline-sodic 20 Cdp-3 mB2 SS1 Clay,1-3 % slope, moderate erosion, slightly saline-sodic 21 Cdp-3 mC2 SS1 Clay, 3-5% slope, moderate erosion, slightly saline-sodic 22 Cdp-3 mB SS2 Clay,1-3% slope, moderately saline-sodic

Chandkapur-3, Vertic Haplustepts

23 Kbk-1 mA1 SD1 Clay,0-1% slope, slight erosion, slightly sodic 24 Kbk-1 mB SD2 Clay,1-3% slope, moderately sodic 25 Kbk-1 mA1 SD2 Clay,0-1% slope, slight erosion, moderately sodic 26 Kbk-1 mB1 SD2 Clay,1-3% slope, slight erosion, moderately sodic

Kendal Bk-1, Sodic Haplusterts

27 Kkd-3 mA1 SD2 Clay,0-1% slope, slight erosion, moderately sodic 28 Kkd-3 mB2 SD1 Clay,1-3% slope, moderate erosion, slightly sodic 29 Kkd-3 mA1 SD2 Clay,0-1% slope, slight erosion, moderately sodic 30 Kkd-3 mB SD2 Clay,1-3% slope, moderately sodic 31 Kkd-3 mB2 SD2 Clay,1-3% slope, moderate erosion, moderately sodic

Kendal Kd-3, Sodic Haplusterts

32 Kkd-4 mA1 SD1 Clay,0-1% slope, slight erosion, slightly sodic 33 Kkd-4 mB SD2 Clay,1-3% slope, moderately sodic 34 Kkd-4 mB1 SD1 Clay,1-3% slope, slight erosion, slightly sodic 35 Kkd-4 mA2 SD1 Clay,0-1% slope, moderate erosion, slightly sodic 36 Kkd-4 mB2 SD1 Clay,1-3% slope, moderate erosion, slightly sodic

Kendal Kd-4, Sodic Calciusterts

37 Kbk-2 mA SD2 Clay,0-1% slope, moderately sodic 38 Kbk-2 mA SD1 Clay,0-1% slope, slightly sodic 39 Kbk-2 mB SD1 Clay,1-3% slope, slightly sodic 40 Kbk-2 mB1 SD1 Clay,1-3% slope, slight erosion, slightly sodic

Kendal Bk-2, Sodic Haplusterts

*Mapping unit designations:Name of soil series: Cdp 1; Texture : m- clay, Slope: A 0-1%, B 1-3% C 3-5%; Erosion: 1 slight, 2 moderate, 3 severe; Salinity- S1 slight (ECe <4 dSm-1), S2 moderate (ECe 4-8 dSm-1), S3 strong (ECe > 8 dSm-1) ; Saline-sodic - SS1slight( ESP > 15 & ECe >4 dSm-1), SS2 moderate ( ESP 15-30 & ECe 4-8 dSm-1), SS3 strong ( ESP >30 & ECe >8dSm-1); Sodicity- SD1 slight( ESP > 15), SS2 moderate ( ESP 15-30), SS3 strong ( ESP > 30).

K.D. Kale et al.

65

Soil sampling and analysis

The standard methodology of detailed soil survey wasfollowed. The survey of India (SOI) topographical sheetsin 1:50,000 scale (47 I/11) was used to collect topographic

information. The toposheets were used for location ofsample areas, ground truth sites and planning for traverseroutes in the field and cultural details. A cadastral map of

the area on the scale of 1:8000 was used as a base mapfor delineating boundaries and number of soil sample

spots of scale 1:250 m were marked to collect 155 sur-

face soil samples. In each class one representative pedonwas dug and examined morphologically (Soil Survey Staff2006). Nine representative typifying pedons were selected

for this study (Table 2). The soil samples were analysedfor physical and chemical properties by using standardprocedures (Page et al. 1982). The yield data of major

crops viz.; sugarcane, wheat, cotton and soybean in thecommand area were recorded.

Table 2. Yield of major crops in study area

Yield of major crops Location, soil series and taxonomic classification

Pedon No. Soil

Quality Index

Sugarcane CO-86032 (t/ha)

Cotton Rashi-2 (q/ha)

Wheat HD-2189

(q/ha)

Soybean JS-335 (q/ha)

Normal soil 160.00 40.00 40.00 32.00 Head region Chandkapur-2 Vertic Haplustepts

P2 0.89 140.00 (12.50)

34.80 (18.00)

32.25 (19.38)

27.50 (14.06)

(Kendal Kd-2, Vertic Haplusterts

P4 0.83 133.00 (16.88)

32.80 (18.00)

32.25 (19.38)

24.63 (23.03)

Kendal Bk- 2, Sodic Haplusterts

P9 0.71 122.00 (23.75)

30.00 (25.00)

29.25 (26.88)

21.80 (31.19)

Mid region Kendal Bk-1, Sodic Haplusterts

P6 0.75 120.00 (25.00)

28.80 (28.00)

28.20 (29.50)

21.50 (32.80)

Kendal Kd-1, Typic Haplustepts

P3 0.84 128.00 (20.00)

32.20 (19.50)

31.75 (20.63)

25.00 (21.87)

Chandkapur-1, Typic Haplustepts

P1 0.90 138.00 (13.75)

35.2 (12.00)

35.25 (11.88)

28.60 (10.63)

Tail region Kendal Kd-3, Sodic Haplusterts

P7 0.73 120.00 (25.00)

29.60 (26.00)

29.50 (26.25)

22.00 (31.25)

Kendal Kd-4, Sodic Calciusterts

P8 0.70 112.00 (30.00)

27.60 (31.00)

26.25 (34.38)

20.50 (35.94)

Chandakapur-3, Vertic Haplusterts

P5 0.77 124.75 (22.03)

30.80 (23.00)

31.12 (22.20)

24.90 (22.19)

*Crop yields are the mean values of four farmers. *Figures in parenthesis indicate per cent yield reduction over normal soil.

Selection of indicators

The method described by Andrews et al. (2002)was followed for development of soil quality index (SQI),

which consisted of following four main steps (i) definegoal (ii) select a minimum data set (MDS) of indicatorsthat best represent soil function (iii) score the MDS indi-

cators based on their performance of soil function and

(iv) integrate the indicator scores into a comparative in-dex of soil quality. The significant variables were chosenfor the next step in MDS formation through principle

component analysis (PCA) (Andrews et al. 2002a andb). The principle components receiving high Eigen val-ues and variables with high factor loading were assumed

to be variables that best represent the system attributes.


66

Therefore, only the PCs with Eigen > 1 (Brejda et al.

2000) and those that explained at least 5 per cent of thevariation in the data were examined. Within each PC,only highly weighted factors were retained for MDS.

Highly weighted factor loadings were defined as havingabsolute values within 15 per cent of highest factor load-ing. When more than one factor was retained under a

single PC, multivariate correlation coefficients wereemployed to determine if the variables could be consid-ered redundant and therefore eliminated from the MDS

(Andrews et al. 2002a).

Development of soil quality Index

After determining the MDS indicators, every

observation of each MDS indicator was transformed us-ing a linear scoring method (Andrews et al. 2002b). In-dicators were arranged in order depending on whether a

higher value was considered ‘good’ or ‘bad’ in terms ofsoil function. For ‘more is better’ indicators, each obser-vation was divided by the highest observed value such

that the highest observed value received a score of 1. For‘less is better’ indicators, the lowest observed value (inthe numerator) was divided by each observation (in the

denominator) such that the lowest observed value re-ceived a score of 1. Once transformed, the MDS vari-ables for each observation were weighted using the PCA

results. Each PC explained a certain amount (%) of thevariation in the total data set. This percentage, dividedby the total percentage of variation explained by all PCs

with Eigen vectors > 1, provided the weighted factor forvariable chosen under a given PC. The weighted MDSvariable scores were then summed up for each observa-

tions using the following equation (Sharma et al. 2005,2008).

n

SQI = Σ WiSi

i=1

where,

Si is the score for the subscripted variable andWi is the weighing factor derived from the PCA. Herethe assumption is that higher index scores means better

soil quality or greater performance of soil function. Fur-

ther, the per cent contribution of each final key indicatorwas also calculated. The SQI values so obtained weretested for their level of significance at P = 0.05.The prin-

ciple component analysis (PCA) was performed on stan-dardized measured soil attributes with a mean of 0 andvariance of 1; therefore, the total variance was 40 (i.e.

the number of measured soil attributes). The PCs withEigen values < 1 indicates the PC could explain less vari-ance than an individual attribute and therefore, it was

rejected.

Statistical analysis

The analysis of variance (ANOVA) was per-

formed to determine the effect of soil properties on soilquality attributes. The statistical analysis of data (PCA,correlations) was carried out by using SPSS window ver-

sion as per the procedure given by Kshirsagar (1972).Soil quality indicators were tested for their level of sig-nificance at p= 0.05.

Results and Discussion

Soil properties in Mula command areas

The entire database of soil properties are presented in

Table 3. The particle size distribution showed that ma-jority of the soils had fairly high amount of clay. Theclay content varied from 52.0 to 66.1 per cent. Basalt

being the parent material of these soils is known to pro-duce higher amount of clay. The bulk density of soilsranged from 1.32 to 1.74 Mg m-3. The higher bulk den-

sity in surface soils may be due to high clay content re-sulting in greater compaction of swelling clay soils (Ahujaet al. 1988). The hydraulic conductivity was low (0.06

to 0.68 cm hr-1) and it was drastically reduced in the soilsof tail region of the command. The lowest values of hy-draulic conductivity in sodic soil could be due to increase

in sodiumized clay and high dispersion index (Table 3)indicating impairment in physical condition of soil. It isgenerally observed that the soils having high ESP have

lower hydraulic conductivity value indicating poor inter-nal drainage condition. The overall pHs values of thestudied soils ranged from 7.9 to 9.20 suggesting that soils

are moderately to strongly alkaline (Table 3).

K.D. Kale et al.

67Ta

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3 8.

9 17

.4

30.5


68 R

ange

8.

4-8.

8 6.

2-6.

9 5.

5-8.

2 62

-88

52-6

0.2

30.2

-32.

4 53

.1-5

7.0

1.54

-1.6

8 41

.5-4

5.2

0.58

-0.6

7

Mea

n 8.

60

6.44

7.

8 75

.0

57.6

31

.66

55.0

1.

62

43.7

9 0.

62

11

Kkd

-1 m

B S

S2

CV

19

.5

19.6

22

.1

26.2

15

.2

31.5

8.

5 9.

3 16

.3

31.4

Ran

ge

8.4-

8.8

4.0-

5.2

6.5-

8.2

72-1

15

57.0

-62.

1 17

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1.5

54-5

7.3

1.5-

1.58

42

.3-4

6.2

0.51

-0.6

0

Mea

n 8.

65

4.30

7.

2 97

.5

60.4

19

.29

56.0

1.

54

44.9

1 0.

55

12

Kkd

-1 m

A S

S1

CV

23

.4

19.9

22

.5

23.8

15

.9

31.2

8.

4 8.

7 16

.9

33.2

Ran

ge

8.6-

9.0

4.5-

5.0

7.1-

8.5

76-1

12

45.2

-53.

5 16

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8.4

61.5

-65

1.42

-1.6

2 34

.5-3

8.3

0.25

-0.3

3

Mea

n 8.

9 4.

72

8.0

95.0

48

.0

17.1

8 64

.0

1.55

36

.10

0.28

13

Kkd

-2 m

A1

SS1

CV

22

.8

19.7

22

.3

27.5

15

.4

28.4

8.

7 8.

8 15

.9

33.5

Ran

ge

8.4-

8.8

4.3-

4.8

5.0-

7.1

85-1

20

47-5

5.2

16.3

-18.

8 61

-64.

2 1.

46-1

.6

39.6

-44.

2 0.

20-0

.32

Mea

n 8.

6 4.

62

5.3

110.

0 51

.80

17.5

0 63

.0

1.54

42

.00

0.24

14

Kkd

-2 m

A3

SS1

CV

21

.9

22.0

22

.5

24.6

15

.2

28.1

8.

3 8.

9 15

.3

32.8

Ran

ge

8.4-

8.7

4.9-

5.3

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7.7

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07

45-5

7.0

15.4

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5 60

-62.

8 1.

55-1

.62

44.2

-48.

1 0.

22-0

.30

Mea

n 8.

5 5.

1 6.

8 95

.0

53.8

16

.62

62.0

1.

58

46.1

2 0.

27

15

Kkd

-2 m

C1

SS1

CV

26

.4

23.1

22

.9

26.5

16

.3

27.8

8.

1 9.

2 15

.1

32.9

Ran

ge

8.6-

9.0

4.2-

6.2

6.5-

8.7

77-1

10

48-5

9.0

17.3

-19.

1 58

-61.

5 1.

6-1.

69

35.3

-39.

4 0.

31-0

.39

Mea

n 8.

9 4.

55

7.9

92.5

54

.4

18.0

9 60

.0

1.62

37

.00

0.35

16

Kkd

-2 m

A2

SS1

CV

20

.2

22.2

23

.3

27.4

16

.7

27.3

8.

2 9.

1 15

.5

33.4

Ran

ge

8.6-

9.2

4.85

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5.

8-7.

5 82

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04

51-6

1.0

15.8

-17.

9 57

-61.

0 1.

5-1.

63

36.5

-40.

2 0.

36-0

.44

Mea

n 8.

80

5.50

6.

0 90

.0

55.0

16

.64

59.0

1.

56

38.0

0 0.

39

17

Kkd

-2 m

B1

SS1

CV

23

.3

23.4

23

.6

26.5

16

.8

26.4

8.

6 9.

4 15

.7

32.7

Ran

ge

8.6-

9.1

4.8-

5.3

6.6-

8.8

79-9

7 46

-55.

6 23

.5-2

7.4

60.1

-63

1.48

-1.5

9 33

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7.6

0.26

-0.3

7

Mea

n 8.

80

5.00

8.

0 85

.0

49.6

0 25

.37

62.5

1.

53

35.9

4 0.

31

18

Cdp

-3 m

A3

SS1

CV

18

.8

23.3

23

.9

28.2

16

.1

29.2

8.

9 9.

0 15

.9

35.4

Ran

ge

8.7-

9.2

5.6-

6.4

6.0-

7.7

82-9

9 47

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5 16

.4-1

9.4

61-6

4.1

1.59

-1.6

7 40

.3-4

5.2

0.18

-0.2

8

Mea

n 8.

85

5.95

7.

2 92

.5

51.0

0 18

.30

63.0

1.

62

42.9

3 0.

22

19

Cdp

-3 m

C1

SS1

CV

18

.4

22.8

23

.8

26.9

16

.2

28.4

9.

2 9.

2 16

.0

33.2

Ran

ge

8.6-

9.0

5.7-

6.3

5.5-

6.9

87-1

15

51.2

-59.

8 15

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9.6

60-6

3 1.

55-1

.64

44.4

-49.

5 0.

26-0

.34

Mea

n 8.

9 5.

90

6.2

105.

0 56

.4

17.6

6 62

.0

1.60

47

.06

0.30

20

Cdp

-3 m

B2

SS1

CV

21

.5

22.9

23

.6

29.4

16

.5

22.2

9.

4 7.

9 17

.0

34.4

K.D. Kale et al.

69 R

ange

8.

4-8.

9 6.

4-7.

20

6.5-

8.2

71-9

0 48

.5-5

5.0

15.1

-16.

5 62

.5-6

5 1.

46-1

.58

37.3

-42.

2 0.

16-0

.24

Mea

n 8.

70

6.85

7.

0 80

.0

51.0

15

.51

64.0

1.

52

39.0

0 0.

20

21

Cdp

-3 m

C2

SS1

CV

18

.1

20.7

23

.1

25.3

16

.7

23.4

9.

3 8.

0 16

.8

34.7

Ran

ge

8.4-

8.9

7.2-

8.0

6.4-

7.8

82-1

10

50.1

-59.

2 30

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1.4

61-6

3 1.

53-1

.62

36.3

-41.

2 0.

17-0

.23

Mea

n 8.

70

7.70

6.

8 95

.0

54.4

30

.89

62.0

1.

56

38.0

0 0.

20

22

Cdp

-3 m

B S

S2

CV

17

.5

21.5

21

.4

23.2

16

.9

25.7

9.

7 8.

2 16

.9

33.8

Ran

ge

8.6-

9.0

2.1-

2.5

5.9-

8.1

84-1

15

45-5

2.0

15.9

-17.

2 63

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6 1.

54-1

.65

22.2

-26.

4 0.

10-0

.16

Mea

n 8.

70

2.33

7.

40

105.

0 48

.60

16.9

5 65

.0

1.60

24

.50

0.12

23

Kbk

-1 m

A1

SD1

CV

17

.9

19.4

22

.4

22.9

17

.3

23.9

10

.2

8.5

16.8

29

.7

Ran

ge

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8.8

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3.1

4.0-

7.2

122-

167

47.2

-56.

0 30

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2.8

60.1

-63

1.66

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36

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0.1

0.07

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2

Mea

n 8.

65

2.97

5.

7 15

0.0

50.4

0 32

.19

61.5

1.

74

38.5

0 0.

08

24

Kbk

-1 m

B S

D2

CV

18

.8

19.8

21

.5

20.4

17

.4

22.8

10

.5

8.7

17.0

30

.7

Ran

ge

8.5-

8.6

2.2-

3.0

5.0-

7.0

125-

165

49.1

-55.

7 30

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2.0

60.2

-63

1.68

-1.8

2 34

.9-3

9.2

0.06

-0.1

Mea

n 8.

52

2.40

5.

7 15

0.0

52.8

0 31

.85

62.0

1.

74

37.3

4 0.

07

25

Kbk

-1 m

A1

SD2

CV

20

.4

22.6

25

.3

19.5

17

.8

22.9

10

.4

8.4

17.4

34

.6

Ran

ge

8.5-

8.8

2.81

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3.

7-6.

0 13

4-17

4 50

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7.2

30.0

-31.

7 62

-64.

9 1.

7-1.

81

32.1

-36.

4 0.

07-0

.13

Mea

n 8.

65

2.90

4.

9 16

0.0

53.6

0 30

.40

64.0

1.

76

34.8

8 0.

09

26

Kbk

-1 m

B1

SD2

CV

19

.3

25.2

25

.2

23.4

17

.4

23.3

10

.3

8.8

17.6

33

.7

Ran

ge

8.5-

8.8

1.6-

3.2

6.3-

8.1

84-1

10

45.4

-51.

5 23

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8.5

58-6

1.2

1.6-

1.69

22

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7.4

0.12

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9

Mea

n 8.

60

2.18

7.

60

97.5

0 48

.00

26.2

7 59

.5

1.64

25

.03

0.15

27

Kkd

-3 m

A1

SD2

CV

21

.2

23.1

25

.4

25.4

16

.3

27.8

11

.4

8.9

17.7

30

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Ran

ge

8.5-

8.8

2.85

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5.

1-7.

2 14

1-18

2 51

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7.6

25.1

-29.

5 61

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4 1.

63-1

.73

31.1

-37.

2 0.

08-0

.14

Mea

n 8.

55

2.97

5.

5 16

7.5

54.4

0 28

.7

63.0

1.

68

33.0

0 0.

11

28

Kkd

-3 m

B2

SD1

CV

18

.6

21.5

24

.8

26.5

16

.2

29.4

11

.8

9.0

17.3

30

.1

Ran

ge

8.5-

8.8

2.7-

3.00

4.

7-5.

9 13

5-16

8 52

-59.

5 30

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2.9

64-6

6.1

1.62

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40

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5.2

0.07

-0.1

1

Mea

n 8.

55

2.89

5.

1 15

0.5

55.6

0 32

.5

65.0

1.

66

42.2

5 0.

09

29

Kkd

-3 m

A1

SD2

CV

20

.8

23.3

24

.9

28.2

16

.5

30.2

11

.3

9.2

17.5

30

.8

Ran

ge

8.5-

8.6

2.55

-2.9

4.

5-5.

5 11

0-13

2 50

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5 31

.4-3

2.9

62-6

4.5

1.59

-1.7

5 41

.8-4

6.2

0.07

-0.1

2

Mea

n 8.

50

2.75

5.

1 12

2.5

52.4

0 32

.0

63.0

1.

68

44.0

0 0.

08

30

Kkd

-3 m

B S

D2

CV

22

.3

23.4

21

.5

29.4

16

.9

30.5

11

.9

9.5

17.6

29

.8


70 R

ange

8.

5-8.

6 2.

4-3.

0 4.

8-6.

2 11

6-13

6 46

.1-5

2.3

30.1

-31.

4 55

-58.

1 1.

64-1

.72

36.4

-40.

2 0.

17-0

.23

mea

n 8.

55

2.60

5.

7 12

2.5

48.8

0 30

.6

56.0

1.

68

38.0

0 0.

20

31

Kkd

-3 m

B2

SD

2

cv

18.8

25

.2

22.4

25

.4

17.0

30

.8

11.1

9.

7 17

.1

29.6

Ran

ge

8.7-

9.2

1.95

-2.6

5 6.

3-8.

1 85

-118

45

.6-5

3.5

24-2

8.5

56-5

9.3

1.61

-1.7

1 16

.9-2

0.4

0.19

-0.2

5

Mea

n 8.

90

2.25

7.

20

105.

0 48

.4

27.0

2 58

.0

1.65

18

.03

0.22

32

Kkd

-4 m

A1

SD1

CV

19

.4

22.9

22

.8

21.9

15

.4

25.2

9.

5 8.

8 18

.2

31.5

Ran

ge

8.7-

9.15

1.

8-2.

10

5.0-

6.9

110-

135

48.2

-53.

5 31

.2-3

3.0

60.8

-63

1.65

-1.7

9 21

.5-2

4.9

0.16

-0.2

3

Mea

n 8.

95

1.97

5.

9 12

7.5

51.6

0 32

.71

62.0

1.

72

23.0

5 0.

19

33

Kkd

-4 m

B S

D2

CV

23

.2

23.4

23

.2

23.4

15

.3

25.9

9.

7 8.

4 18

.8

31.8

Ran

ge

8.8-

9.20

2.

2-2.

8 4.

8-6.

5 11

2-14

2 51

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8.4

30.8

-32.

0 62

.5-6

5 1.

64-1

.75

23.1

-27.

4 0.

12-0

.18

Mea

n 9.

05

2.60

5.

70

130.

0 54

.40

31.3

6 64

.0

1.68

25

.21

0.15

34

Kkd

-4 m

B1

SD

1

CV

20

.2

21.8

23

.4

25.4

15

.0

26.4

9.

4 8.

3 18

.4

30.2

Ran

ge

8.7-

9.10

1.

9-2.

5 5.

9-7.

7 12

5-14

0 54

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0.1

24.5

-29

58-6

1 1.

6-1.

68

24.6

-27.

8 0.

17-0

.22

Mea

n 9.

0 2.

11

7.2

137.

5 57

.00

27.8

2 60

.0

1.64

26

.00

0.19

35

Kkd

-4 m

A2

SD1

CV

21

.3

20.8

24

.5

26.3

15

.7

26.6

9.

3 8.

1 19

.4

29.8

Ran

ge

8.7-

9.1

2.15

-2.7

5.

0-6.

8 85

-115

43

.4-4

8.3

18.9

-22.

2 56

.4-5

9 1.

62-1

.73

19.2

-23.

4 0.

31-0

.39

Mea

n 9.

0 2.

33

5.7

105.

0 45

.60

20.8

0 57

.0

1.68

21

.00

0.35

36

Kkd

-4 m

B2

SD

1

CV

20

.4

19.6

25

.2

27.9

15

.9

25.9

9.

8 8.

5 19

.1

28.9

Ran

ge

8.6-

8.9

1.8-

2.2

4.5-

7.2

72-9

4 42

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7.8

30.9

-32.

2 57

.5-6

1 1.

69-1

.8

23.1

-28.

1 0.

12-0

.18

Mea

n 8.

75

1.90

6.

0 85

.0

45.6

0 31

.18

59.0

1.

74

25.9

9 0.

15

37

Kbk

-2 m

A S

D2

CV

20

.2

20.3

20

.5

28.4

18

.5

30.3

9.

4 7.

9 15

.2

29.5

Ran

ge

8.55

-9.0

1.

7-2.

2 5.

6-7.

1 14

4-16

9 50

.1-5

5.3

19.5

-24.

7 55

.4-5

8 1.

72-1

.82

36.2

-40.

2 0.

06-0

.01

Mea

n 8.

70

1.83

6.

60

157.

5 52

.40

22.8

2 56

.2

1.78

38

.86

0.08

38

Kbk

-2 m

A S

D1

CV

18

.7

19.8

21

.3

28.1

18

.4

31.3

9.

7 7.

3 15

.4

30.5

Ran

ge

8.55

-8.9

1.

55-2

.1

4.7-

7.1

110-

152

49.7

-55.

4 18

.4-2

5.3

53.9

-57

1.6-

1.7

41.4

-45.

4 0.

07-0

.12

Mea

n 8.

71

1.76

5.

5 13

7.5

52.0

0 21

.32

55.0

1.

66

43.0

0 0.

09

39

Kbk

-2 m

B S

D1

CV

17

.5

18.7

22

.4

28.9

18

.8

32.2

8.

8 7.

2 15

.8

30.9

Ran

ge

8.55

-9.0

1.

6-1.

9 5.

0-6.

6 10

5-13

9 48

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3.2

18.7

-22.

3 55

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9 1.

68-1

.79

37.2

-42.

2 0.

12-0

.17

Mea

n 8.

70

1.69

5.

70

127.

5 51

.00

21.7

5 57

.5

1.72

39

.88

0.14

40

Kbk

-2 m

B1

SD

1

CV

19

.9

18.8

22

.5

27.4

18

.3

31.4

7.

9 7.

5 15

.8

29.4

K.D. Kale et al.

71

The higher values of pH > 9.0 in some soils are

because of calcification during pedogenesis where pres-ence of sodium carbonate and bicarbonate accumulationthereby increasing the soil pH. The Electrical Conduc-

tivity of saturation extract (ECe) varied from 1.69 to 8.50dSm-1. Excessive irrigation raises the water table caus-ing secondary salinization or sodiumization. The vari-

ability in pH and salinity indicates different stages of al-kalization and salinization associated with rising watertable. The CEC was in general high ranging from 45.6 to

62.4 cmol (p+) kg-1. The higher CEC is attributed to thedominant smectitic mineralogy of these shrink- swell soils.The ESP of the soils varied from 5.50 to 33.00. The soils

with high sodium on the exchange complex (ESP >10) inassociation with high clay content of smectitic natureshowed the problems and caused severe restriction in the

drainage.

Most of the soils were calcareous with freeCaCO

3 content varied from 60 to 182 g kg-1. Formation

of pedogenic calcium carbonate is the prime chemicalreaction responsible for the increase in pH with depthand in the development of sub-soil sodicity. The forma-

tion of pedogenic CaCO3 has been active in semi-arid cli-

mate which is responsible for the development of sodicity(Pal et al. 2000). The organic carbon content of soils

ranged from 4.0 to 9.0 g kg-1 and was decreased drasti-cally in sodic soils as compared to other soils. The low

organic carbon in sodic soils is attributed to the high so-

dium, high pH and low biological activity in these soils,which is not conducive for both the accumulation of or-ganic matter and its mineralization (Naidu and

Rangasamy 1993).

Selection of sensitive indicators and development of Soil

Quality Index (SQI)

Multivariate data sets due to their multi dimen-sionality are difficult to interpret. In such circumstance,use of principle component analysis is very useful. The

analysis requires computation of eigen values and vec-tors of correlation matrix with many variables. The di-rection of maximum variability was estimated by eigen

vectors while the eigen values specifies the variance ofthe vector. The entire data set was subjected to PCA toidentify the critical soil parameters under different land

uses that can be considered as soil indicators. Amongthe 40 variables analyzed, 21 variables are highlyweighted and retained in the different PCs. In the PCA of

21 variables, 10 PCs had eigen value > 1 and explained72 % of the variance in the data (Table 4). Highlyweighted variables under PC

1 were Ca/Mg, O.C., ECe,

H.C. and Aggregate stability (A.S.). A correlation ma-trix for the highly weighted variables under different PCswas run separately. It was assumed that the variables hav-

ing the highest correlation sum best represented the group.

Table 4. Results of principle component analysis of soil parameters for development of soil quality indicators


PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10

Eigen V. 12.568 3.203 2.666 2.091 1.937 1.635 1.3 1.266 1.109 1.074

% Var. 31.422 8.009 9.664 5.228 4.844 4.088 3.25 3.165 2.775 2.684

Cumul. % 31.422 39.43 46.09 51.32 56.167 60.51 63.50 66.67 69.44 72.131

Factor loading /eigen vector variable

C.sand 0.148 -0.144 0.328 -0.063 -0.632 0.148 -0.019 -0.015 0.051 0.103

F.sand -0.002 -0.055 0.545 -0.018 -0.685 0.028 0.086 -0.144 0.085 -0.043

Silt -0.733 0.041 -0.199 -0.008 0.241 0.154 -0.108 0.019 0.091 0.112

Clay -0.657 0.063 -0.348 0.060 0.401 -0.190 0.058 0.077 -0.122 -0.089

B.D. -0.804 -0.119 0.031 -0.012 0.521 0.099 0.059 0.084 0.001 -0.146

72

Itallic bold face factor loadings are considered highly weighted with 15 % of the absolute values of the highest factor loading ineach PC

K.D. Kale et al.

H.C. 0.687 0.385 0.022 0.046 -0.029 -0.413 -0.191 -0.071 0.053 0.175

A.S. 0.621 0.058 0.148 0.239 0.097 -0.267 0.396 0.104 0.256 -0.077

MWD 0.305 -0.110 0.228 0.509 0.184 -0.272 0.424 0.150 0.163 -0.094

COLE -0.438 -0.340 0.027 0.014 -0.062 0.235 0.420 -0.213 0.039 -0.053

O.C. 0.808 0.084 -0.117 0.098 0.067 -0.022 -0.074 -0.066 0.001 0.055

CaCO3 -0.687 -0.464 0.583 0.193 0.167 -0.017 -0.002 0.039 0.502 0.059

Ex.Ca 0573 -0.177 -0.061 -0.145 0.345 0.329 -0.082 0.232 -0.201 -0.012

Ex.Mg 0.057 0320 -0.231 0.531 -0.306 0.014 -0.115 0.031 -0.504 -0.244

Ex.Na -0.839 -0.043 0.391 0.379 0.006 -0.001 -0.046 0.005 0.049 -0.038

Ex. K -0.476 0.520 0.319 0.121 0.069 0.047 -0.088 -0.077 0.100 -0.230

ESP -0.846 0.771 -0.002 -0.058 0.007 0.226 -0.014 0.002 0.052 -0.067

EMP -0.327 0.218 -0.340 0.582 -0.281 0.012 -0.117 0.034 -0.034 0.195

CEC 0.585 0.465 0.419 -0.285 0.063 -0.061 -0.169 0.178 -0.228 -0.155

CEC/clay 0.680 -0.124 0.666 0.256 -0.011 0.044 -0.026 -0.033 -0.253 -0.269

%BS -0.431 0.162 -0.360 0.147 -0.044 0.437 0.153 -0.013 0.022 0.029

Ca/Mg 0.876 -0.211 0.112 -0.423 0.243 0.324 -0.052 0.112 0.058 0.125

pHs -0.768 0.001 0.084 0.100 0.091 0.065 0.135 0.051 -0.038 -0.112

ECe 0.737 0.191 -0.024 -0.064 0.002 -0.015 -0.035 0.003 -0.040 -0.072

SAR -0.652 -0.069 0.354 0.102 0.136 -0.027 -0.036 0.029 -0.137 0.420

Cas 0.514 0.402 0.084 0.315 0.107 0.086 -0.090 -0.152 0.030 -0.083

Mgs 0.474 -0.044 0.125 0.331 0.155 0.033 -0.119 -0.184 0.033 -0.060

Nas -0.181 -0.105 0.533 0.324 0.258 0.096 -0.243 0.053 -0.104 0.388

Ks 0.239 0.326 -0.186 -0.059 -0.092 0.109 0.097 -0.015 -0.050 -0.095

CO3 -0.366 0.304 0.060 -0.217 0.034 -0.128 0.059 -0.119 0.127 0.174

HCO3 -0.236 0.242 -0.524 0.079 0.200 0.336 -0.051 0.298 -0.019 -0.092

Cls 0.273 0.391 0.158 -0.019 0.115 0.195 0.126 -0.405 -0.184 0.367

SO4s -0.063 0.677 -0.457 0.077 0.250 -0.030 -0.146 -0.024 0.080 -0.177

HCO3/Ca -0.695 0.300 0.139 -0.355 -0.005 0.535 0.134 0.225 0.015 0.004

Av. N 0.541 -0.095 -0.300 0.114 0.246 0.071 0.183 0.011 -0.039 0.019

Av. P 0.497 0.143 -0.012 0.270 -0.185 0.222 0.510 0.331 0.144 -0.015

Av. K -0.583 -0.411 -0.051 0.356 -0.004 0.079 -0.011 0.524 0.005 0.063

Fe 0.389 0.128 -0.081 -0.018 -0.076 -0.143 0.174 0.494 -0.002 0.336

Mn 0.303 0.027 -0.040 0.022 -0.263 -0.155 -0.345 0.170 0.470 0.062

Cu 0.390 0.226 -0.164 0.099 -0.124 0.339 0.241 0.155 -0.06 0.069

Zn 0.026 -0.220 -0.170 0.143 0.105 0.329 -0.327 -0.13 0.126 -0.076

73

Among the eight variables in PC1, Ca/Mg and

OC were chosen for the MDS because of the highest cor-

relation sum and greater contribution to the soil quality

and hence retained in MDS. Other variables with lower

correlation sum were observed highly correlated with Ca/

Mg and hence they were also dropped. In PC2,

ESP, Sulphate (SO4s), CEC and Chloride (Cls) were

highly weighted variables. These variables are highly

correlated (negatively and positively) to each other and

hence these were eliminated from MDS except ESP.

In PC3 CEC/clay, CaCO

3, Sand, CEC and ex-

changeable sodium were the highly weighted variables.

The CEC/clay had the highest correlation sum and hence

it was retained on MDs. In PC4 EMP, exchangeable Mg,

MWD and exchangeable Na, were highly weighted vari-

ables. The EMP had the highest correlation sum and hence

retained in MDS and other variables were dropped. In

PC5 BD, clay and exchangeable Ca were highly weighted

variables. The highest correlation sum of BD was retained

in MDS. In PC6 and PC

7, HCO

3/Ca, available P, BS%

and MWD were highly weighted variables contributing

the soil quality and hence retained in MDS. Under PC8

and PC9, available K, free CaCO

3, Fe and Mn were highly

weighted variables and hence they were retained on MDS.

Under PC10

SAR, soluble Na+ and Chlorides were highly

weighted variables and hence they were retained in MDS

(Andrews et al. 2000a).

Dalal and Melony (2000) reported that choice

among well correlated variables could also be based on

the practicability of the variables. Hence one could use

the option to retain or drop the variables from the final

MDS considering the ease of sampling, cost of estima-

tion and logic and sampling, cost of estimation and logic

and interpretability. Considering these options were uti-

lized to retain or eliminate the variables utilized to retain

or eliminate the variables from the MDS. Hence, the fi-

nal MDS consisted of Ca/Mg, organic carbon, ESP, CEC/

clay, EMP, bulk density, available phosphorus, HCO3/

Ca, available potassium and CaCO3. The indicators that

were retained at the minimum data set like Ca/Mg, or-

ganic carbon, CEC/clay, available phosphorus and avail-

able potassium were considered “good” when in increas-

ing order, they were scored, as ‘more is better’ whereas

the remaining indicators like ESP, EMP, bulk density,

HCO3/Ca and CaCO

3 were considered ‘good’ in decreas-

ing order. They were scored, as less is better.

Furthermore to develop the soil quality index

for salt affected soils in Mula command area and to as-

sess the relative degradation status, the selected indica-

tors were normalized to 0-1 scale by using function i.e.

more is better and less is better concept. After that the

soil quality was computed by using the additive model

(By averaging the 0-1 scale value of sensitive indicators).

It is important to note that the SQI is based on the results

obtained from the salt affected soils of Mula command

area. The soil quality index ranged from 0.62 to 0.91.

The highest soil quality index was obtained for Kkd-2-3

(0.91) soil units on the midland in command area. The

soils on midland (Kkd-2-1) and upland Kkd-2-4 (0.89)

also showed higher soil quality index. The lowest soil

quality index value was obtained for the soils under well

water irrigation (Kkd-4-2, 0.62). The soils in the tail re-

gion also recorded lowest value of soil quality index (Kkd-

1-3, 0.67).

Contribution of soil indicators in soil quality assessment

The contribution of each indicator in governing

the soil quality is depicted by Fig. 1. The Ca/Mg ratio

was found to be most prominent soil quality indicator

having highest contribution (19 %) in determining the

soil quality. This was reflected earlier in the correlation

of Ca/Mg with soil properties. The organic carbon in soil

was found to be the next important soil quality indicator

in deciding the soil quality having contribution of 17 %

which was closely followed by contribution of Ca/Mg.

Similar results were reported by Dongre (2010) in

Godavari command area (Ahmednagar) of Maharashtra.


74

Fig.1. Percent contribution of soil indicators in soil quality assessment

Yield of major crops

The highest per cent yield reduction was re-corded in severely sodic soils (P

8 and P

7) of lowland (tail)

region of Dy. No. 2 of Mula command area. 30 per centyield reduction in sugarcane, 31.00 per cent in cotton,34.38 per cent in wheat and 35.94 per cent in soybean

were recorded in sodic soils of tail region as comparedto normal soils. The low yields in these soils may be dueto poor physical and chemical properties of soil. The low-

est per cent yield reduction was observed in saline soilsof head and mid region of Dy. No. 2 (Pedon 1 and 2).The per cent yield reduction was 13.75 per cent in sugar-

cane, 12 per cent in cotton, 11.88 per cent in wheat and

10.63 per cent soybean. The highest yield of major cropswere obtained from normal soil (sugarcane-160 t/ha, cot-ton-40 q/ha, wheat-40 q/ha and soybean-32 q/ha) fol-

lowed by saline soils in head and mid region of Dy. No.2 whereas lowest yields were recorded from sodic andsaline-sodic soils (sugarcane-118.92, cotton-29.3 q/ha,

wheat-28.96 q/ha and soybean- 22.47 q/ha ) in tail re-gion of Dy. No. 2 of Mula command area. The highestper cent yield reduction was recorded in strongly sodic

soils (Pedon 7 and 8) of low land (tail) region of Dy. No.2 of Mula command area. 30 per cent yield reduction insugarcane, 31 per cent in cotton, 34.4 per cent in wheat

and 35.9 per cent in soybean were recorded in sodic soilsof lowland region as compared to normal soils.

K.D. Kale et al.

75

Table 3. Soil properties retained in MDS for soil quality index

Sr. No.

Soil unit Ca/Mg O.C. (%)

ESP (%)

CEC/ Clay

EMP BD Av. P HCO3/ Ca

Av. K CaCO3 BS (%)

SQI

1. Cdp-1 mA1 S2 2.08 8.9 6.58 0.94 31.3 1.48 14.33 0.48 482 9.50 101.9 0.90

2. Cdp-1 m B1 S2 1.86 8.0 8.40 0.80 34.6 1.44 10.19 0.45 459 7.50 114.1 0.87

3. Cdp-1 mB S1 2.10 7.0 5.88 0.86 32.3 1.38 13.78 0.46 538 8.75 115.7 0.90

4. CdP-1 mA SS1 1.84 7.2 22.46 0.85 29.1 1.60 13.50 1.58 571 8.00 108.2 0.88

5. Cdp-2 mA S1 1.93 8.1 8.35 0.97 30.0 1.42 11.85 0.31 482 8.25 100.7 0.89

6. Cdp-2 mB1 S2 1.98 6.6 12.86 0.84 29.2 1.57 14.05 0.41 538 8.00 101.7 0.86

7. Cdp-2 mA S2 2.00 6.7 11.09 0.97 25.8 1.51 11.85 0.34 448 6.75 90.2 0.85

8. Cdp-2 mB S3 2.08 5.8 10.59 0.95 27.9 1.48 9.37 0.51 493 7.75 98.0 0.81

9. Kkd-1 mA SS1 1.51 7.6 15.81 0.63 41.2 1.50 9.64 1.06 538 7.5 131.0 0.84

10. Kkd-1 mB1 SS2 1.74 7.4 18.47 0.75 36.6 1.56 11.02 1.06 470 9.50 131.0 0.76

11. Kkd-1 mB SS2 1.57 7.8 31.66 0.89 27.4 1.62 11.30 0.68 538 7.50 91.10 0.67

12. Kkd-1 mA SS1 1.88 7.2 19.29 0.87 31.1 1.54 11.30 0.32 706 9.75 110.7 0.82

13. Kkd-2 mA1 SS1 2.87 8.0 17.18 0.88 19.4 1.55 12.40 3.19 582 9.50 100.8 0.83

14. Kkd-2 mA3 SS1 1.84 5.3 17.50 0.96 24.3 1.54 10.74 2.60 650 11.00 85.7 0.89

15. Kkd-2 mC1 SS1 1.29 6.8 16.62 0.85 33.3 1.58 12.95 1.16 582 9.50 109.7 0.91

16. Kkd-2 mA2 SS1 1.40 7.9 18.09 0.73 35.4 1.62 14.33 1.36 650 9.25 105.0 0.89

17. Kkd-2 mB1 SS1 1.06 6.0 16.64 0.69 45.0 1.56 10.19 0.72 650 9.00 117.5 0.88

18. Cdp-3 mA3 SS1 2.13 8.0 25.37 0.77 34.0 1.53 11.57 0.47 459 8.50 126.7 0.77

19. Cdp-3 mC1 SS1 1.80 7.2 18.30 0.89 33.7 1.62 12.12 0.56 571 9.25 115.3 0.87

20. Cdp-3 mB2 SS1 1.44 6.2 17.66 0.84 40.5 1.60 10.19 0.93 538 10.50 121.7 0.89

21. Cdp-3 mC2 SS1 1.78 7.0 15.51 0.77 37.9 1.52 13.22 0.76 582 8.00 120.4 0.90

22. Cdp-3 mB SS2 2.26 6.8 30.19 0.89 24.4 1.56 9.37 0.92 638 9.50 100.4 0.70

23. Kbk-1 mA1 SD1 1.40 7.4 22.19 0.79 35.3 1.60 9.64 1.24 683 10.50 109.6 0.75

24. Kbk-1 mB SD2 1.57 5.7 32.10 0.91 27.3 1.74 7.71 1.44 683 15.00 99.6 0.66

25. Kbk-1 mA1 SD2 1.76 5.7 31.85 0.91 27.3 1.74 7.71 1.44 683 15.00 99.6 0.66

26. Kbk-1 mB1 SD2 1.75 4.9 30.40 0.71 35.1 1.76 8.82 1.76 694 16.00 131.0 0.71

27. Kkd-3 mA1 SD2 1.40 7.6 30.7 0.73 37.5 1.64 11.57 1.75 717 9.75 120.9 0.73

28. Kkd-3 mB2 SD1 1.45 5.5 28.7 0.74 33.1 1.68 10.19 1.50 650 16.75 112.7 0.76

29. Kkd-3 mA1 SD2 1.57 5.1 32.5 0.69 32.5 1.66 8.82 1.68 616 15.50 112.0 0.67

30. Kkd-3 mB SD2 1.56 5.1 32.0 0.70 377 1.68 11.57 1.04 728 12.25 105.8 0.69

31. Kkd-3 mB2 SD2 1.45 5.7 30.1 0.81 32.3 1.68 11.57 1.04 728 12.25 105.8 0.69

32. Kkd-4 mA1 SD1 0.96 7.2 27.02 0.82 39.3 1.65 14.05 1.15 694 10.50 105.3 0.70

33. Kkd-4 mB SD2 1.51 5.9 32.71 0.84 34.9 1.72 8.00 0.69 762 12.75 112.7 0.62

34. Kkd-4 mB1 SD1 1.72 5.7 25.36 0.72 29.0 1.68 6.61 0.85 773 13.00 106.8 0.72

35. Kkd-4 mA2 SD1 1.45 7.2 27.82 0.78 40.2 1.64 6.89 1.00 784 13.75 128.8 0.73

36. Kkd-4 mB2 SD1 1.93 5.7 20.80 0.80 31.4 1.68 10.19 1.73 650 10.50 115.2 0.81

37. Kbk-2 mA SD2 1.15 6.0 31.18 0.72 43.3 1.74 7.16 0.92 706 8.50 116.3 0.71

38. Kbk-2 mA SD1 1.57 6.6 22.82 0.79 31.8 1.78 8.82 1.13 571 15.75 100.4 0.86

39. Kbk-2 mB SD1 1.50 5.5 21.32 0.86 32.3 1.66 10.19 1.24 582 13.75 106.9 0.89

40. Kbk-2 mB1 SD1 1.74 5.7 21.75 0.83 34.8 1.72 6.06 1.53 650 12.75 124.6 0.87


76

Multiple regression correlation analysis

The MDS soil parameters (pHs, ESP, ECe)were correlated with yield of major crops. It is observedthat the crop yield was negatively correlated with ESP (r

= -0.64**), ECe (r = -0.51**) and pHs (r = -0.45**)indicating that the soil quality is declined with the in-crease in pHs, ECe and ESP in the command area and

the MDS soil parameters were positively correlated withthe yield of sugarcane (r = 0.79**), cotton (r = 0.83**),

Conclusions

The soil quality index in mula command arearanged from 0.62 to 0.91 and it was high (0.91) for soilsof midland region, while it was low (0.62) in the tail re-

gion. The MDS soil parameters were negatively corre-lated with ESP, ECe and pHs indicating soil quality deg-radation due to salinity and sodicity hazards. The Ca :

Mg ratio was found to be the most predominant soil qual-ity indicator followed by organic carbon, ESP, CEC/clayand EMP in determination of soil quality. Thus, the soil

quality in Mula command area was severely degraded intail region as compared to head and mid region due tosalinity and sodicity. The SQI developed for assessment

of degradation due to salinity and sodicity could be used

Sugarcane :

Y = 116.12- 8.50 pHs + 1.77 ECe + 0.78 ESP + 3.88 Ca/Mg -

0.68 OC + 0.25 CaCO3 + 7.79 HC R2= 0.76

Cotton :

Y= 31.25 - 2.76 pH + 0.66 ECe + 0.31 ESP + 0.34 Ca/Mg -

0.66 OC. + 0.06 CaCO3 + 3.09 HC. R2= 0.84

Wheat :

Y= 47.06- 4.64 pHs + 0.87 ECe + 0.36 ESP + 0.23 Ca/Mg -

0.41 OC + 0.02 CaCO3 + 1.10 HC R2= 0.78

Soybean :

Y= 29.84- 3.27 pHs + 1.03 ECe + 0.30 ESP + 0.56 Ca/Mg -

0.92 OC + 0.035 CaCO3 + 3.10 HC R2= 0.88

wheat (r = 0.75**) and soybean (r = 0.78**) indicatingthat the soil quality causes significant variation in the

yield of different crops grown in the command area.

The multiple regression equations between yieldof major crops and MDS soil parameters like pHs, ECe,

ESP, Ca/Mg, O.C., CaCO3 and H.C. were derived to pre-dict the variation in the yield of major crops like sugar-cane, cotton, wheat and soybean. Four multiple regres-

sion equations were derived

as a decision making tool for formulation of polices lead-

ing to land restoration and reclamation of sodic soils inthe command areas.

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soil quality evaluation in salt-affected soils with yield of major...

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