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Soil Fertility Constraint Assessment Using Spatial NutrientMap at Three Selected Villages of Coastal Sundarbans
Tarik Mitran*1, Pabitra Kumar Mani1, Nirmalendu Basak1, Biswapati Mandal1
and Subrata Kumar Mukhopadhyay 2
1Directorate of Research, Bidhan Chandra Krishi Viswavidyalaya, Kalyani-741 252, Nadia, West Bengal, India2National Bureau of Soil Survey and Land Use Planning (NBSS&LUP), Kolkata-700 091, West Bengal, India
*Email: [email protected]
Abstract
Soil fertility constrains were studied in three representative villages i.e. Tushkhali, Duchnikhali and Korakatiwith collecting and interpreting a large number of geo-referenced soil samples of Sundarbans. The GlobalPositioning System (GPS) based soil reaction, salinity and nutrient maps were prepared by Inverse DistanceWeighted (IDW) spatial interpolation method using ArcGIS Ver. 10 software. Soils were mostly heavy in texture(clay > 50%), highly acidic to neutral in reaction and associated with marginal salinity. Although an extremeacidity (< 4.8) or higher salinity level > 4.0 dS m-1 was detected in some of the pockets. Among the threevillages, Tushkhali showed comparatively higher electrical conductivity (EC) with highest value of 5.23 dS m-1.There was a decreasing trend in EC recorded with higher level of organic C with irrespective of sampling sites.On an average, organic C of the studied area was medium to high in nature. Soils were poor in availablenitrogen and medium to low in available P and Zn. Higher availability status of K, Mn, Cu, Fe and S werefound in such soils. Nutrient Index values for all the studied plant nutrients were generated. These valuesrevealed that area is poor in available N; medium in available P and Zn but the native supply of available K, Sand micronutrients are in much pronounced. The available N, P and Zn contents significantly increased withincrease in organic C of soil.
Key words: Acid soil, ArcGIS, Salinity, Soil fertility, Spatial nutrient map, Sundarbans
Introduction
Coastal regions, home to a large and growingproportion of the world’s population, are undergoingenvironmental decline. The problem is particularly acutein developing countries. The reasons for environmentaldecline are complex, but population factors play asignificant role. Today, approximately 3 billion people-about half of the world’s population live within 200kilometres of a coastline. By 2025, figure is likely to bedouble. The Coastal agro-ecosystem occupies vast areaof land in India. About 20% of the population of Indialives in coastal areas. In coastal areas of India, salt affectedsoils stand to be one major challenge in preventingagricultural activities. On global basis salt affected soilsoccupy an estimated area of 952 million ha, nearly 7%of total land area or approximately 33% of the potentialarable land area of the world (Szabolcs, 1979). In India,out of an estimated area of 187.7 million hectares of totaldegraded land, 9.38 million hectares are salt affected soil
(Dagar, 2005) and out of which 3.1 million hectares arein the coastal region (Yadav et al., 1983). Among the statesin India, West Bengal has the largest area (0.82 millionhectares) of the salt affected soils in the coastal region,between 87º25' E and 89º.0' E latitude and longitude21º30' N and 23º15' N covering the district of North andSouth 24-Parganas, Howrah and East Midnapore. Thegreat Sundarbans, the delta region of the river Ganges,occur in the coastal tracts of North and South 24-Parganasconstitute a major portion of coastal region of India withwide variability in climatic, topographical and edaphicconditions. The climate is typical tropical with widevariation in the annual rainfall from 300-3000 mm. Therainfall is mainly concentrated during the South-Westmonsoon from June to September when more than 80%of the rainfall is experienced in the region. The rainfall inthe rest of the period of the year is meagre. Beside thatthe entire area of Sundarbans faces the problem of salinity,waterlogging and drainage congestion. In the absence ofupland water supply the area is exposed to tidal actionmaking the water highly brackish. The soil salinity showswide spatial and seasonal variability, being minimum inthe monsoon season and maximum in the summer season(Bandyopadhyay et al., 2001). The degraded soil and water
Journal of Soil Salinity and Water Quality 6(1), 1-8, 2014
*Corresponding author, Present address: Soil and LandResources Assessment Division, Land Resources, Land useMapping and Monitoring Group, National Remote SensingCentre, Hyderabad, India
2 Mitran et al.
quality together with climatic adversities like cyclone,tsunami, heavy rains, flood etc. contributed to the poorlivelihood security and low agricultural productivity ofthe area. The coastal area is generally mono-cropped withonly rice during the monsoon season. In this region, soilsalinity, imbalance of nutrients, unfavourable pH, lackof good quality irrigation water etc. account for pooryields of crops (Sen and Bandyopadhyay, 2001). Due topresence of brackish water table at a shallow depth thereis always an increase in soil and water salinity in drymonths. The land remains almost fallow throughout theyear after Kharif season (wet season, July-October).During the remaining part of the year (Rabi and Summer),90% of the land remains fallow due to soil and watersalinity and lack of good quality irrigation water. Thus,continuous cropping of the land by almost a single cropalong with non-scientific fertility management has resultedin low soil productivity. A suitable soil managementstrategy is, therefore, required to mitigate the ill effects ofdegraded coastal land for sustained productivity byidentifying the constrains. The present study hasundertaken to make an inventory of nutrients status ofsuch coastal soils to identify the fertility constraints bypreparing spatial nutrient map as Global PositioningSystem (GPS) and Geo-graphical Information System(GIS) help in collecting a systematic set of georeferencedsamples and generating spatial data regarding distributionof nutrients.
Materials and Methods
A total of 150 GPS based soil samples (0-20 cmdepth) containing 50 samples each from the representativevillages such as Tushkhali, Duchnikhali and Korakati of(22°22′ N latitude and 88°54′ E, Longitude) Sandeshkhali-II, North-24-Parganas, West Bengal, were collected duringthe peak summer month during May 2011.
Particle size distributions of the soils were determinedfollowing the Boyoucous hydrometer method (Gee andBouder, 1986). Bulk density was determined by coresampler method following the method of Blake andHartge (1986). The pH of the soil was determined in 1:2.5:: soil: water and 0.02 M CaCl2 solution, respectivelysuspension, by using digital pH meter described byJackson (1973). Electrical conductivity of the soilsaturation extract was determined at room temperaturein a soil water suspension ratio of 1:2.5 with the help ofWheatstone Conductivity Bridge as described by Jackson(1973). Organic C in soil was determined by methodsdescribe by Nelson and Sommers (1982). Estimation ofAvailable N was done by alkaline permanganate method(Subbiah and Asija, 1956). Available phosphorus in soilwas determined calorimetrically following ascorbic acidreluctant method as outlined by Bray and Kurtz (1945).Available potassium of the soil samples was extracted withneutral 1N ammonium acetate solution followed bymeasured by a Flame Photometer as described by Jackson
Fig. 1. Location of the study area
Fig. 2. Location of the observation sites at different villages ofSandeshkhali-II
Soil Fertility Constraint Assessment Using Spatial Nutrient Map 3
(1973). The available DTPA extractable Fe, Mn, Zn andCu of the soil samples were estimated using atomicabsorption spectrophotometer (AAS) following themethod of Lindsay and Norvell (1978). Available sulphur(S) was estimated by turbidmetrically barium chlorideprocedure as described by Chesnin and Yien (1951).Nutrient index values (NIV) calculated from theproportion of soils under low, medium and high availablenutrient categories, as represented by the followingexpression:
Where, NIV is nutrient index value., Nl, Nm and Nh arethe number of soil samples falling in the category of low,medium and high nutrient status and are given weightageof 1, 2 and 3, respectively. Accordingly, areas with nutrientindex value > 2.33 could be considered high, those withNIV between 1.34 and 2.33 or 1.51-2.50 could beconsidered medium, and those with values < 1.5 or <1.33 could be regarded low in native supply of thatnutrient.
Spatial soil nutrient maps were prepared by inversedistance weighted (IDW) interpolation method availablein the sub-mode of interpolation in the spatial analysttools of ArcGIS Ver. 10 by using a linearly weightedcombination of a set of sample points. The IDW methodfor spatial interpolation estimates values of cells byweighting of values (point) of geometric data in theneighbourhood of each processed cell. The points locatedcloser to the cell center have more influence or weight inthe process of weighting. Soil fertility levels were classifiedinto low, medium, or high categories according to existingnorms (Ali, 2005) used for nutrient indexing andpreparation of nutrients map. Ranges of soil salinity (EC),pH and organic C are depicted in Fundamentals of SoilScience (ISSS, 2009) used for map preparation. Simplecorrelation coefficients and regression analysis wereperformed by the windows based SPSS program (ver. 16.0,SPSS Inc.).
Results and Discussion
Physico-chemical properties of soils
The soils were mostly heavy in texture with high claycontent generally more than 50% (Table 1). The claycontent varied marginally from 50.7 to 66.1% that mightbe due to the origin of soil, parent materials and soilvariability. The textural compositions of soils underdifferent villages were more or less similar and fall underclayey class. These finding was corroborated withBandyopadhyay et al. (1998) and Maji et al. (1998) whoreported that coastal soils are mostly heavy textured andvary widely from place to place depending on theirphysiographic locations, climatic conditions and soilparent materials. Among representative villages, bulk Tab
le 1
. P
hysi
co-c
hem
ical
cha
ract
eris
tics
of
soils
Site
sSa
ndSi
ltC
lay
Bul
kpH
WpH
Ca
EC
2.5
Org
anic
Av.
NA
v. P
Av.
KA
v. S
Av.
Fe
Av.
Mn
Av.
Zn
Av.
Cu
%de
nsity
1:2
.5dS
m-1
Ckg
ha-1
mg
kg-1
M
g m
-3g
kg-1
Tus
hkha
li (n
= 5
0)R
ange
3.3-
13.2
28.0
-38.
054
.7-6
2.7
1.26
-1.3
64.
36-7
.53
4.06
-7.1
91.
12-5
.23
4.5-
9.8
104.
5-28
6.4
10.1
-28.
530
3.4-
682.
010
.2-4
6.2
14.1
-87.
88.
36-3
8.2
0.44
-3.6
13.
35-9
.81
Mea
n+SE
m (±
)8.
0±3.
234
.0±
3.7
58.0
±2.
51.
3±0.
035.
54±
0.93
5.25
±0.
913.
16±
1.02
7.3±
1.6
193.
0±52
.717
.3±
4.64
496.
0±94
.221
.3±
8.1
56.5
±21
.320
.9±
8.32
1.4±
0.47
7.1±
1.62
Duc
hnik
hali
(n=
50)
Ran
ge1.
3-13
.326
.4-3
8.0
50.7
-61.
31.
22-1
.36
5.16
-7.1
54.
82-6
.88
1.04
-4.8
92.
1-10
.590
.5-3
23.4
9.6-
27.4
337.
3-66
5.3
10.7
-38.
17.
04-1
24.5
10.9
-42.
20.
56-2
.27
3.5-
9.39
Mea
n+SE
m (±
)9.
0±3.
833
.0±
3.9
58.4
±2.
51.
29±
0.04
6.06
±0.
465.
67±
0.42
2.84
±0.
958.
0±2.
020
8.6±
65.9
19.2
±4.
8654
9.5±
87.8
25.4
±7.
1759
.3±
7.9.
023
.7±
7.38
1.31
±0.
446.
75±
1.37
Kor
akat
i (n=
50)
Ran
ge5.
3-13
.322
.6-3
7.0
52.7
-66.
11.
23-1
.31
4.85
-7.5
84.
65-7
.26
1.1-
4.3
2.8-
9.7
98.3
-330
.98.
8-28
.835
7.1-
709.
110
.1-4
4.2
8.5-
163.
45.
20-3
8.3
0.18
-3.1
82.
9-9.
35M
ean+
SEm
(±)
9.0±
3.3
32.0
±4.
059
.0±
3.6
1.27
±0.
035.
85±
0.75
5.47
±0.
742.
92±
0.90
7.4±
1.9
192.
5±74
.318
.1±
4.65
562.
9±89
.425
.5±
9.05
79.0
0±41
.61
22.8
±8.
421.
76±
0.88
7.06
±1.
49
4 Mitran et al.
densities of the soils under Korakati village werecomparatively lower than the others; this might be due tothe marginally higher clay and organic matter content ofsuch soils. It was found from the correlation study thatthe bulk density was negatively correlated with bothorganic carbon (r= -0.071; p <0.05) and clay content (r=-0.024; p <0.05) under the studied soil. Gulser (2006) andHillel (1998) reported that bulk density had significantnegative correlation with soil organic matter as organicmatter is generally lowers in density. The soil reactionsof the studied areas were highly acidic to neutral in nature.These results are in agreement with Sarkar et al. (2001)who reported the pH of the soils of the Sunderbans deltaranged from 5.3 to 8.1. However, an extreme acidity (pH< 4.8) was observed in some pockets of studied villages,might be due to the occurrence of acid sulphate soils.Kalyan and Sarkar (2009) also reported on nature ofacidity of some coastal acid soils of Sunderbans of WestBengal and found that the soils under study wereextremely acidic in reactions having pH value marginallyabove 4.0. Highly acidic soils with abundance ofappreciable amount of sulphate at surface/ sub-surfacesoil horizons were reported in Coastal areas of Kerala,West Bengal, Orissa and Andaman and Nicobar groupof Islands (Bandyopadhyay and Bandyopadhyay, 1984;Bandyopadhyay and Maji, 1995). Among the threevillages, Tushkhali showed comparatively higher EC withhighest value of 5.23 dS m-1 whereas a lowest value of
EC ~1.04 dS m-1 was detected in soils under Duchnikhalivillage. Some locations showed higher salinity level > 4.0dS m-1 indicated critical condition for crop cultivation.On an average the studied area was marginally saline tosaline in nature. However, a decreasing trend in EC hadrecorded with increment in level of organic C irrespectiveto the sampling sites (Table 2). The correlation studyshowed that EC is negatively correlated with the organicC (r= -0.594; p <0.01). This might be due to the fact thatcarbon content improves soil physical properties i.e. soilstructure, permeability, infiltration rate etc; whichfacilitates the leaching and drainage of soluble salts.Tripathi et al. (2006) reported that the organic C contentof soil decreased by increasing salinity (r= -0.38; p <0.01).Kaur et al. (1998) also reported significant negativerelationship between organic C and EC. It is documentedthat EC of coastal soils of West Bengal mostly variedfrom 0.5 dS m-1 during monsoon to 50 dS m-1; duringsummer months. Bandyopadhyay et al. (1988, 2003) alsoreported similar salinity pattern in coastal areas.. Kalyanand Sarkar (2009) reported that EC of some coastal acidsoils of Sunderbans of West Bengal was high and variedfrom 1.9 to 3.9 d Sm-1. On an average the organic C ofthe studied area was medium to high in nature. However,among the sites soil samples from Duchnikhali showedcomparatively higher organic C compared to others. Theorganic C in the coastal soils are relatively high compared
Fig. 3. Spatial soil reaction (pH) Map of the studied area Fig. 4. Spatial soil salinity (EC dS m-1) Map of the studied area
Soil Fertility Constraint Assessment Using Spatial Nutrient Map 5
with that in similar types of soils located in identicalclimatic zones in non-coastal regions of India(Bandyopadhyay and Bandyopadhyay, 1983). Joshi andKadrekar (1987) reported that organic C in the coastalsoils varied from < 0.5% to > 0.75%. However, we foundlow organic C in some pockets. Available nitrogen contentof the studied sites did not varied significantly. On an averagethe studied area was low in available nitrogen (Fig. 6).
The observed result detected a higher availability ofN in soil is usually associated with the higher level oforganic C (r= 0.776; p <0.01). Mineralization of organicmatter may release plant available N in soil. The availablephosphorus content of the studied soils was low tomedium in range. Soils were high in available potassiumcontent. The available sulphur content under different siteswas more or less similar. The highest value (46.2 mg kg-1)was associated with the soil samples collected fromTushkhali, where the lowest (10.1 kg ha-1) was recordedin soils collected from Korakati. On an average theavailable S content of the studied area was medium tohigh. The available Zn content of soil under different siteswas more or less similar. However, higher Zn content wasrecorded with the samples collected from Korakati.Analytical values described that studied areas weremedium to low in available Zn (Fig. 7). The available Mn
content of the soils under Duchnikhali and Korakati didnot significantly differed and more or less similar; whereas,available Mn content of the soil samples collected fromTushkhali were medium in range (Fig. 8). This might bedue to the lower pH of soils in Tushkhali compared toDuchnikhali and Korakati. Available Cu content of thesoils in different sites did not differed significantly andwas high in status. From the correlation matrix it wasobserved that pH was negatively correlated with availableZn (r = -0.097*; p <0.05), Fe (r = -0.170; p <0.01) and Cu(r = -0.277; p <0.01) among the soils collected fromSandeshkhali-II (Table 2). The available Fe showed anegative correlation (r= -0.194; p <0.01) with EC andpositive correlation (r= 0.156; p <0.05) with organic C.Organic C showed a significant and negative correlationwith EC (r = -0.594; p <0.05) and bulk density (r= -0.071;p <0.05) and positive correlation (r = 0.198; p <0.01) withZn content of soils. The available nitrogen content of thesoil showed a significant and positive correlation withorganic carbon (r= 0.776; p <0.01) and negativecorrelation with EC (r= -0.383; p <0.01). From the resultsit can be inferred that the available P was negativelycorrelated with Zn (r= -0.443; p <0.01) content of soiland positively correlated with the organic carbon (r=0.056; p<0.05) and Fe (r= 0.538; p <0.01).
Fig. 5. Spatial map of organic C (g kg-1) content of the studiedsoil
Fig. 6. Spatial map of available nitrogen (kg ha-1) content ofthe studied soil
6 Mitran et al.
Fig. 7. Spatial map of available Zn (mg kg-1) content of thestudied soil
Fig. 8. Spatial map of available Mn (mg kg-1) content of thestudied soil T
able
2.
Cor
rela
tion
am
ong
the
phys
ico-
chem
ical
cha
ract
eris
tics
of
soils
Par
amet
ers
pHC
aE
C2.
5O
rgan
ic C
Av.
NA
v. P
Av.
Fe
Av.
Mn
Av.
Zn
Av.
Cu
Av.
KA
v. S
BD
Cla
y
pHC
a1.
000
EC
2.5
ns1.
000
Org
anic
C-0
.594
**1.
000
Av.
Nns
-0.3
83**
0.77
6**
1.00
0A
v. P
nsns
0.05
6*ns
1.00
0A
v. F
e-0
.170
**-0
.194
**0.
156*
*0.
114*
*-0
.538
**1.
000
Av.
Mn
0.01
0*-0
.134
*0.
147*
0.06
2-0
.288
**0.
710*
*1.
000
Av.
Zn
-0.0
97*
-0.1
67**
0.19
8**
0.14
6-0
.443
**0.
902*
*0.
785*
*1.
000
Av.
Cu
-0.2
77**
0.02
0*0.
149*
*0.
170*
0.83
9**
-0.4
71**
-0.3
63**
-0.3
88*
1.00
0A
v. K
nsns
nsns
-0.4
85**
-0.3
96**
-0.4
20**
-0.4
49**
-0.4
42**
1.00
0A
v. S
nsns
0.16
0**
0.22
1**
nsns
nsns
0.22
7**
ns1.
000
BD
nsns
-0.0
71*
nsns
nsns
nsns
nsns
1.00
0C
lay
nsns
nsns
nsns
nsns
0.02
1*ns
ns-0
.024
*1.
000
** in
dica
tes
sign
ific
ant a
t 1%
leve
l and
* in
dica
tes
sign
ific
ant a
t 5%
leve
l. (n
=15
0)
Soil Fertility Constraint Assessment Using Spatial Nutrient Map 7
Nutrient index
The Nutrient index value (NIV) of available nitrogenranged from 1.04 to 1.20 for the studied area whichindicated that the area was low in available nitrogen. Onthe other hand the NIV for available P ranged from 1.92to 1.99 which showed a medium status in terms of fertility.The NIV for available K was much (Table 3) higher i.e.3.00 irrespective to the sampling sites which indicatedthat the native supply of K was high in the studied area.NVI for available sulphur ranged from 2.72 to 2.88 whichindicated a higher availability of sulphur in such soil. Thearea was medium in available Zn content with nutrientindex values ranged from 1.64 to 2.04. Among therepresentative sites, soil samples collected from Korakatishowed a higher NIV for available Zn compared to others.The soils of the studied area were high in available Fe,Mn and Cu content with nutrient index value of 2.78 to2.82, 2.60-2.84 and 3.00, respectively. The results indicatedthat the area is poor in available nitrogen content andmedium in available phosphorus and Zn but the nativesupply of K, S and micronutrients except Zn are muchpronounced. However, among the three representativevillages soil samples collected from Tushkhali showed acomparatively lower nutrient index values for all nutrientsfollowed by Korakati and Duchnikhali, respectively.
Conclusions
The unique set of characteristics of a region arisesfrom the location specific variations of the naturalresource base. The sites selected for the present study wasin the Sundarbans region on the Bay of Bengal, WestBengal, India. The soils of this region are naturally salineand agriculture is severely constrained. Rice is theprincipal crop of this area, which is mainly grown duringmonsoon season. Improving productivity of crop byidentifying fertility constrains using spatial nutrients mapfollowed by suitable soil management strategies was theprimary focus of the study. This study produced high-quality spatial soil nutrient maps to apply the site-specificmanagement for crop cultivation in villages of coastalSunderbans. The spatial soil nutrient maps indicated thatmost of the studied soils were marginally saline to salinein nature. In some of the locations soil shows highersalinity level > 4.0 dS m-1 which was critical for cropcultivation. The available N, P and Zn content showedlow content which significantly increased with increasein organic C. On the other hand organic C was negativelycorrelated with the soil salinity level. Nitrogen basedfertilization supplemented with organic inputs should berecommended for the studied area for optimum growthand yield of crops.
Acknowledgements
The Authors would like to thank University GrantCommission (UGC), India for the financial supports forcarried out the research work.
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Received: November, 2013; Accepted: March, 2014
