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Land Suitability Analysis for Afforestation in Semi-arid Watershed of Western Ghat, India: A Groundwater Recharge Perspective

Land Suitability Analysis for Afforestation in Semi-arid Watershed of Western Ghat, India: A... GEOLOGY, ECOLOGY, AND LANDSCAPES 2021, VOL. 5, NO. 2, 136–148 INWASCON https://doi.org/10.1080/24749508.2020.1833643 RESEARCH ARTICLE Land Suitability Analysis for Afforestation in Semi-arid Watershed of Western Ghat, India: A Groundwater Recharge Perspective a b c d e f Ajaykumar Kadam , Rajasekhar M , Bhavana Umrikar , Vijay Bhagat , Vasant Wagh and R. N Sankua a b Department of Environmental Science, Savitribai Phule Pune University, Pune, India; Department of Geology, Yogi Vemana University, c d Kadapa, India; Department of Geology, Savitribai Phule Pune University, Pune, India; Department of Geography, Agasti Arts, Commerce and Dadasaheb Rupwate Science College, Ahmednagar, India; School of Earth Sciences, Swami Ramanand Teerth Marathwada University, Nanded, India; Basin Planning, Central Water Commission, New Delhi, India ABSTRACT ARTICLE HISTORY Received 30 March 2020 Land suitability analysis (LSA) for afforestation is an approach that stands amongst the most Accepted 4 October 2020 able frameworks revealing the concern for cultivable land and predicting the availability for sustainable development in semi-arid areas. In view of this, the objective of the present study is KEYWORDS to propose a conceptual procedure for LSA that would help enhancing the green cover to Land suitability; agriculture; combat environmental threats rendering groundwater recharge. LSA involves various the- MCDM; AHP; remote sensing; matic layers such as distribution of land use/landcover, slope, soil depth, soil type, pH, soil GIS calcium, soil magnesium, sodium, bulk density, organic matter, boron and run-off that have been derived using satellite images and collateral data. Food and Agriculture Organization framework and guidelines have been followed for LSA and it has been found that only 9.16% land is ‘highly suitable’, 14% of land is ‘moderately suitable’ whereas, marginally suitable lands for afforestation are estimated about 14% of total area and non-suitable areas for afforestation are about 61% of the total area. The multi-parametric based decision making AHP tool integrated with GIS proposes a novel methodology and outcomes of the research could be useful to identify suitable lands for agriculture in any various parts of the world. 1. Introduction (Bhagat, 2009). The green-covered area mainly the forest is reduced to 21.31% due to soil deprivation, Land under afforestation is a limited natural resource cut down of trees and minimum soil moisture during that facilitates sub-surface moisture, surface runoff, scarcity situations, etc. (Sonawane & Bhagat, 2017). vegetation, timber land, soil and mineral deposits Therefore, afforestation on degraded lands is primely which are getting degraded by heavy erosion, water suggested for environmental regulation of carbon, logging, increasing acidity and salinity, decreasing protection of natural resources such as soil and nutrients and soil productivity. Out of total land, water, biodiversity protection, etc. Many governmen- 1.68% productive land is reduced and 47% land has tal, non-governmental organisations, and forest con- degraded in the last twenty years (FAO). About 14.9% servation activists and movements are working on the of population of developing countries are suffering improvements in quality of soil and water resources from malnutrition (Fao, 2012). Demand of agricul- and land restoration. Land suitability analysis (LSA) tural products for food, fodder, industry, etc. is detects the inherent capacities, potentials and suitabil- increasing rapidly while productive land and produc- ity for plantation. Multi-criteria land suitability has tivity are decreasing remarkably. Therefore, the stu- been widely used for identification of potential parcels dies undertaken in this regard (Dumanski, 1997; for agriculture (R. B. Zolekar & Bhagat, 2015), planta- Nyeko, 2012; Schwilch et al., 2011) have been focussed tion (Bhagat, 2009; R.B. Zolekar & Bhagat, 2014), on sustainable land use development and watershed management (Gaikwad & Bhagat, 2018, management. A. K. Kadam et al., 2018; A. Kadam et al., 2019; Forestation, reforestation and protection of existing Rajasekhar et al., 2020), settlements, development pro- forests are suggested for conservation and enhance- jects, industries, etc. ment of CO storage in forest cover to reduce the The LSAs in existing methods in this paper is hav- atmospheric carbon (Patenaude et al., 2005). Forests ing the following parts such as the examination of soil increase subsurface recharge and help conservation of possessions is a significant stage. In view of this soil soil and groundwater resources. However, the area samples were analyzed in laboratory to understand the under forest and qualitative measures in terms of physicochemical properties. To know the present density, diversity, greenness and production are LULCs supervised categorization algorithm was used decreasing with alarming rate since centuries CONTACT Rajasekhar M sekhar.raja042@gmail.com Department of Geology, Yogi Vemana University, Kadapa, India © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the International Water, Air & Soil Conservation Society(INWASCON). This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. GEOLOGY, ECOLOGY, AND LANDSCAPES 137 for nine land utilization types. This map and informa- occupy mainly higher elevations, while local planta- tion were further used to derived the soil depth in tions are also scanty due the low soil cover in scrub connection with slope. Also, the other influencing land area. The government department planted the factors were also derived from base maps and field- trees but the scientific approach is missing in the based data. Finally, multi-criteria evaluation tool was action, resulting into wilting of plants, poor plant used for detection of land suitable for afforestation in growth with low recharge of water, etc., leading to semi-arid watershed of western Maharashtra, India. In failure of the project. The study area incorporates the study area, surface runoff utilized for artificial hilly undulating terrain in the peripheral proximity groundwater recharge has been considered as with high rainfall runoff ratio, depicting poor forest/ a probable source for “plantation”. The groundwater green cover therefore has been selected for the land recharge will be enhanced after the afforestation. suitability analysis for afforestation along with Hence, land with groundwater recharge potential is groundwater recharge perspective. taken to be a “preferable land for afforestation”. Thus, the land covered by grass/shrubs shows moderate run- off potential and hence could be considered as suitable 3. Data and software used for green growth. With this hypothesis, LSA for affor - Present study aims to evaluate the LS for afforestation estation with the perspective for groundwater conser- or plantation in Shivganga River basin, Maharashtra. vation has been carried out in Shivganga river basin. Study necessitates analysis of biophysical components such as soil properties, satellite image-based indices, weather conditions, etc. The soil samples were col- 2. Study area lected from well-distributed locations within the The area considered for study is drained by a tributary study area and analyzed for physico-chemical proper- of Nira River (Shivganga River) that flows in Bhor, ties such as soil depth (SD), soil texture (ST), soil Haveli and Purandar Tahsil of the Pune district, moisture (SM), soil organic matter (SOM), soil Maharashtra, India. It is covering an area of about pHand primary soil nutrients such nitrogen, phos- 17600 ha present in the eastern undulating part of phate and potassium to recognize the categories of the Western Ghats (Figure 1). The study area experi- LS. Further, remote sensing data i.e. LANDSAT-TM ences innumerable problems such as soil deprivation, imagery with 30 m spatial resolution was used to water scarcity, deforestation and soil pollution generate the land use/land cover classes. The bands (A. K. Kadam et al., 2018; A. Kadam et al., 2019), of satellite image were compiled, fused and stacked groundwater quality depletion (A. Kadam et al., using the open-source image processing software. All 2019) and change in hydrological response the thematic parameters were assigned a common (A. K. Kadam et al., 2018). Majority of the area coordinate system and projection and weighted over- under study possesses irrigated land covered by scanty lay analysis (WOA) was performed. to thick plantation. The forest cover is very poor and Figure 1. The study area: Shivgangabasin watershed. 138 A. KADAM ET AL. reserve plantation were classified into deep, moder- 4. Methodology ately deep and marginally deep soils, respectively. 4.1 Soil analysis The analysis of soil properties is an important step in 4.3 Baseline data creation LSA. Total 34 sites in the proximity of dug wells were The study area map was derived using the drainage selected using random sampling technique for collec- divide from contour of survey of India and updated tion of soil samples. These soil samples were analyzed with watershed atlas map. Physical elements has direct in laboratory to understand the physicochemical relation with land efficiency and agriculture activities properties (Table 1). Along with these physicochem- (Adgo et al., 2013; Adimassu et al., 2016; Moeletsi ical soil parameters, land slopes were also considered et al., 2011). The decisive factor such slope, LULC, in finding and assessing suitable area for plantation SD, ST, SM, soil EC, N, P and K have been frequently and afforestation. Moisture content in soil has vital impact on vegetation growth and distribution. Bhagat used for land suitability of plantation area (R. B. Zolekar & Bhagat, 2015; Jamali, 2014; Mustafa (2009) considered SM as an important parameter for et al., 2011; Romano et al., 2015; Yalew et al., 2016) but finding potential areas for afforestation. The metho- dology involved in this study is shown in Figure 2. the surface runoff firstly used for such analysis. The effect and wealth of these parameters are diverse according to the land physiognomies. The thematic 4.2 Remote sensing image analysis layers such as spatial variation of soil parameters (ST, SM, SOM, pH. N, P and K) were generated by the Supervised categorization algorithm was used for IDW interpolation algorithm in GIS. The runoff is LULC analysis considering nine land utilization generated using intersect of LULC and rainfall data types. The map of soil depth was derived from the in ArcGIS environment. slope and LULC classes. Land with and without scrub is comprised into the shallow and thin soil class respectively. Irrigated crop lands, fallow lands and 4.4 MCDM analysis The LSA have been done using MCDM-based AHP method. The procedure for LSA in the current study Table 1. Soil properties with the experimental methods of (Figure 2) is performed through six steps: (1) genera- analysis. tion of base maps with other thematic layer, (2) assort- Soil Property Analysis Method Unit ment of criterion, (3) defining of sub-criteria, (4) Texture International pipette method (mechanical formation of pairwise comparison matrix, (5) creation analysis) Moisture Oven drying method % of normalized pairwise judgement matrix and finally, Organic Titrimetric method (Walkley and Black, 1934). % (6) weights calculation. matter Bulk density Core sampling method g/ The researcher’s expertise inputs were taken into cm consideration while deciding the ranks of influential CaCO acid neutralization method % pH pH meter - factors. Analytical hierarchy process establishes judg- Ca EDTA titrimetric method ppm ments between criterions that help to arrange com- Mg EDTA titrimetric method ppm Na Flame photometer method ppm plete list of factors (Saaty, 1997). The comparison Boron Spectrophotometric method mg/ matrix supports the assessment to allocate various kg Figure 2. Methodology flow chart. GEOLOGY, ECOLOGY, AND LANDSCAPES 139 stages of significance of the features that present in land suitability analysis (R. B. Zolekar & Bhagat, 2015). The weights to the LSA studies have been given for interpretation of comparative rank and land physiognomies (Bandyopadhyay et al., 2009; Mendas & Delali, 2012; Reshmidevi et al., 2009; Romano et al., 2015; Yalew et al., 2016). FAO (1976) guidelines have been used prerequisite of land-use classification. The high land suitability shows maximum effect of sub-condition while lowest values display least LS for afforestation (Tables 3). Weights for the parameters namely % slope, LULC, SD, ST, soil erosion, OM Ca, Mg and Na were allotted, as per their significance. Finally, land suitability map was prepared using overlay technique in GIS to detect suitable sites for afforestation to improve infiltration rate for groundwater conservation in the region. The resultant map was categorized into highly suitable, moderately suitable, marginally suitable and not- suitable classes. Figure 3. Land use and land cover. penetration of water in sub-surface. This area is 5. Results and discussion suitable for afforestation (Figure 4). The area with 5.1 Land use and land cover (LULC) 15–35% slope is main concern for afforestation to reduce soil loss and increase groundwater recharge. LULC plays major role in identification of area poten- tial for LSA, for these two seasons’ satellite images were used for detail understanding. The LULC map 5.3 Soil depth was generated using level-II classification system that includes agriculture land as major class that has been Soil depth is a function of slope, runoff and rainfall further classified into horticulture land, crop land, pattern of the area (Bandyopadhyay et al., 2009; Rejani fruit orchard, etc., thus encompassing the whole et al., 2017; Worqlul et al., 2017). Slope, LULC map, soil gamut of green growth. Similarly, the waste land sample and field-based measurement were defining the major class has been further classified into barren depth of soil layer. The soil depth was also crosschecked land, land with scrub, land without scrub and stony with the surrounding well section as well as the geophy- waste helpful in identification of land suitable for sical investigation done in the study area validation of afforestation (Figure 3). 5.2 Slope The elevation-based slope is responsible for varia- tion in soil humidity. For understanding LSA suit- ability for plantation, it is essential to consider percent slope and its direction for amas- sing of water (Olshevsky, 2008). The elevation of the study area ranges from 560 to1257m above MSL. The % slopes are classified into seven cate- gories according to IMSD specification (IMSD, 1995). The nearly level 0–1% area is measured as large storage land owing a little rolling landscape with less surface flow. The part with comparatively moderate runoff and excellent penetration of water is very gently slope 1–3% favourable for plantation. The moderate surface flow causes relatively moder- ate penetration of water area with slope of 3–5%. The area with 10–15% slope having moderate soil depth and high surface flow causes relatively low Figure 4. Slope. 140 A. KADAM ET AL. the field experimental methods. Based on field observa- tion the agriculture lands with nearly levelled land hav- ing deep soil depth were found at lower part of watershed. While the peripheral part of basin having forest as land use with moderate to steep slope having moderate to marginal depth of soil (Figure 5). The cliff as well as near pediment part having steep slope show shallow soil. The soil depth represents the cropping pattern in the area, deep soil having rice as well as sugarcane, while parsley, etc., are found on thin soils. 5.4 Soil type Soil has an important role in supporting the plant life in that area. The soil type is also validating with the experi- mental methods in laboratory analysis. As per the NBSS and LUP categorization and the experimental methods, the area is characterizing into sandy loamy soil, clayey loamy soil and clayey soil types (Figure 6). The study Figure 6. Soil type. region is majorly covered with well-drained sandy loamy soils having 48% of area with low nutrient pre- sent on moderate strong to steep slope goo for affores - difference in the pH range in the study area (Figure tation. The moderately drained loamy clay soil covers 7). All superficial soil samples excluding S6, S8 and S16 43% of area, sandy soil has high infiltration rate due to having pH higher than 8, showing alkaline type of greater porosity and permeability therefore it will be common soil samples (Table 2), is the results of high assigned highest priority. Clayey soil is compact and CaCO concentration in host rock. Cumulative quan- impervious hence given low priority rank. tity of CaCO in soil, may affect in increasing the heavy metal absorption. This could result into elevated 5.5 pH of soil buffering volume of soils and non-appearance of CO The utmost informative features of soils are its pH. It in the saturation extract (Bagyaraj et al., 2013). The defines the comparative contents of hydrogen ions in presence of solvable and transferrable Na with bicar- liquid. The pH of a regular soil is measured as an bonate ions results in such high values. Further, there indicator of its transferable cation’s saturation is a precipitation of CaCO and MgCO carbonates 3 3 (USDA, 1954). The pH of soil ranged from 7.39 to during evaporation contributing to such high pH 8.63 reflecting alkaline nature (Table 2) and slight values. High pH contents result into the growth of salinity/sodicity in soil of study area. 5.6 Soil calcium 2+ Ca is main cation over other found cations in soil. This 2+ is because of the more adsorption potency of Ca ion in soil related to other positively charged ion (Miller, 1995). 2+ The Ca ion present in abundant amount to form the major transferable complex in soil media specially in the study area to occupy a major position on the exchange complex (Sharma & Raghubanshi, 2009). A brief look at the information from Table 2 shows that the transfer- 2+ rable Ca ion varies from 40.08 to 180.63 ppm (Figure 2+ 8). The high proportion of Ca in the majority of the samples is suggestive of the occurrence of Ca-clay. Such 2+ soils with high Ca possess a good physical condition, develop a good crumb structure and allow the free 2+ movement of water and air. The soil Ca ion helps in maintaining the soil pH with playing important role in cell wall development for plant growth. Figure 5. Soil depth. GEOLOGY, ECOLOGY, AND LANDSCAPES 141 Table 2. Soil chemical analysis. BD Boron Ca Mg Na % ID pH (ppm) (ppm) (ppm) OM (g/cm ) % CaCO3 % Moisture mg/kg S1 8.54 180.36 73.09 23.8 1.6 1.2 8.75 4.16 1.16 S2 8.04 60.12 12.18 10.85 0.48 1.41 9.75 3.05 0.35 S3 8.6 140.28 36.55 18.9 1.03 1.23 3.5 6.61 1.38 S4 8.24 80.16 12.18 12.95 2.01 1.15 8.75 4.05 0.36 S5 8.12 60.12 12.18 7.7 1.7 1.06 14 5.7 0.2 S6 8.4 60.12 12.18 10.5 1.36 1.13 6.8 0.02 2.43 S7 8.4 40.08 12.18 11.2 0.84 1.24 14 3.06 2.3 S8 7.39 60.12 12.18 10.15 1.86 1.06 12.5 3.87 0.12 S9 8.41 60.12 12.18 10.5 1.74 1.09 11.75 5.88 0.25 S10 8.42 128.26 53.6 25.2 1.94 1.17 13.45 7.46 0.36 S11 7.62 180.36 36.55 23.8 1.82 1.13 14.25 5.46 0.23 S12 8.48 96.19 26.8 20.65 1.74 1.2 10.5 4.32 0.13 S13 8.46 44.09 9.75 12.6 1.91 1.06 12.75 4.35 0.6 S14 8.63 56.11 14.62 8.4 2.1 1.04 6.8 3.99 0.8 S15 8.39 40.08 12.18 14.7 2.41 1.13 3 4.47 0.29 S16 8.58 64.13 19.49 16.8 1.96 1.16 7.05 6.15 0.3 S17 8.44 40.08 14.62 14 2.1 1.07 10 5.6 0.87 S18 8.47 52.1 29.24 12.6 1.2 1.06 8.1 6.43 1.56 S19 8.55 68.14 19.49 14.7 1.46 1.09 7.75 5.08 0.25 S20 8.4 96.19 38.98 15.75 1.74 1.09 10.3 13.04 1.41 S21 7.9 56.11 14.62 9.8 1.2 1.07 13.75 3.7 1.2 S22 8.43 48.1 14.62 11.9 1.05 1.2 11.25 7.51 4.45 S23 8.52 64.13 21.93 19.6 0.96 1.46 12.25 2.99 0.91 S24 8.24 52.1 17.06 11.9 1.67 1.03 8.25 7.53 2.1 S25 8.25 56.11 24.36 9.8 1.36 1.17 7.5 11.89 2.67 S27 8.23 64.13 21.93 7.35 1.79 1.2 6.25 5.39 0.49 S28 8.08 72.14 17.06 9.45 1.41 1.04 9.75 5.77 0.21 S29 8.53 80.16 24.36 12.95 1.2 1.03 12.5 10.61 3.12 S30 8.34 64.13 21.93 8.4 1.46 1.04 9.45 5.82 0.23 S31 8.41 72.14 29.24 16.45 1.72 1.1 6.75 7.47 1.4 S32 8.35 64.13 21.93 14.7 1.94 1.03 14.25 4.22 1.25 S33 8.46 80.16 24.36 11.9 1.6 1.13 4.75 2.35 1.2 S34 8.24 80.16 36.55 14 1.2 1.34 6.5 1.3 6.7 Min 7.39 40.08 9.75 7.35 0.48 1.03 3 0.02 0.12 Max 8.63 180.36 73.09 25.2 2.41 1.46 14.25 13.04 6.7 Avg 8.32 74.57 23.04 13.76 1.56 1.14 9.60 5.43 1.25 Std. dev 0.27 35.14 13.51 4.72 0.42 0.11 3.19 2.72 1.41 Ca, Mg. Na is having unit, ppm; while OM, CaCO , Moisture is having unit % and pH is unitless Table 3. Normalised matrix. LULC SD Slope ST SBD OM B pH Ca Mg Na Runoff % weight LULC 0.14 0.12 0.09 0.24 0.17 0.14 0.12 0.13 0.09 0.08 0.12 0.13 13.14 SD 0.05 0.04 0.09 0.24 0.17 0.14 0.12 0.13 0.09 0.08 0.12 0.13 11.73 Slope 0.42 0.12 0.26 0.24 0.29 0.23 0.12 0.22 0.15 0.14 0.2 0.08 20.6 ST 0.05 0.01 0.09 0.08 0.17 0.14 0.2 0.13 0.09 0.08 0.12 0.13 10.84 SBD 0.05 0.01 0.05 0.03 0.06 0.23 0.12 0.13 0.09 0.08 0.12 0.08 8.78 OM 0.05 0.01 0.05 0.03 0.01 0.05 0.12 0.13 0.09 0.08 0.12 0.08 6.88 B 0.05 0.01 0.09 0.02 0.02 0.02 0.04 0.01 0.15 0.14 0.2 0.08 6.89 pH 0.05 0.01 0.05 0.03 0.02 0.02 0.12 0.04 0.09 0.08 0.12 0.13 6.37 Ca 0.05 0.01 0.05 0.03 0.02 0.02 0.01 0.01 0.03 0.08 0.12 0.01 3.67 Mg 0.05 0.01 0.05 0.03 0.02 0.02 0.01 0.01 0.01 0.03 0.12 0.01 3.03 Na 0.05 0.01 0.05 0.03 0.02 0.02 0.01 0.01 0.01 0.01 0.04 0.01 2.19 Runoff 0.03 0.01 0.09 0.02 0.02 0.02 0.01 0.01 0.09 0.08 0.08 0.03 3.97 5.7. Soil magnesium ppm. The less residence time of water in upstream parts of study region shows that low leaching of Magnesium is plentiful in the earth’s shell. It is 2+ 2+ Mg ion (Figure 9). The prevalence of Mg in the present in a varied variability of minerals. majority of the soil samples due to photosynthesis, Weathering or break down of these minerals leads as it is a building block of the chlorophyll, which to the availability of magnesium for plant use. makes leaves appear green. This is particularly 2+ Though magnesium (Mg ) is a vital element for observed in the irrigated areas (S4, S7, S8, S13, vegetal growth, its use in a manure activity obtains S21 and S24). 2+ only slight importance. Mg is held on the super- ficial part of clay soil particle and organic matter particles. A brief look at the data from Table 2 2+ shows that the Mg ion varies from 9.75 to 73.09 142 A. KADAM ET AL. Table 4. Criteria, sub-criteria, MCDM for land suitability analysis. Zolekar and Bhagat, 2015. Control features Parameter Weight Types Topographical parameters Landuse/landcover 13.14 Agriculture Land without scrub Plantations Land with scrub Barren land Dense forest Settlement Sparse forest Water body Slope 20.6 Nearly level Very gentle Gentle Moderately strong Strong Moderately steep Very steep Soil parameters Soil depth 11.73 Thin soil Shallow soil Marginal depth Moderate depth Deep soil Water bodies Soil type 10.84 Loam soil on strong to very steep slope Clay loam soil on gentle to Moderately strong slope Clay soil on gentle slopes Soil Boron 6.89 Very low Low Moderate High Soil organic matter 6.88 Not suitable (<0.20) Marginally suitable (0.20–0.40) Moderately suitable (0.40–0.60) Highly suitable (0.61–1.00) Soil bulk density 8.78 Low (<100) Moderate (200–100) High (200–400) Very High (>400) Soil pH 6.37 Marginally suitable Moderately suitable Highly suitable Soil Ca 3.67 Not suitable Marginally suitable Moderately suitable Highly suitable Soil Mg 3.03 Not suitable Marginally suitable Moderately suitable Soil Na 2.19 Marginally suitable Moderately suitable Highly suitable Weather parameters Run-off 3.97 Low runoff Moderate runoff High runoff Very high runoff 5.8. Soil sodium 5.9. Soil bulk density Plants are provided with sodium (Na ) from the soil The ideal bulk density (BD) of sandy clay loam or naturally, this is obtained from manures, insecticides, loam for plant growth should be less than 1.60 gm/ runoff from phreatic salt-loaded waters and the weath- cm and for clay loam it should be less than 1.10 gm/ + 3 ering of minerals which dissolve salt into the soil. Na cm . Soil sample numbers from 1 to 21 have bulk content in soil that is not at poisonous levels can simply densities in the ideal range for plant growth; whereas, be seeped out by reddening the soil with fresh water. The soil samples 22–25 have clay content >30% with max- less percolation of water in upstream parts of study area imum value 65.52% and sand <35% indicating that the shows comparatively high concentration of Na ion due soil samples with higher percentage of clay than sand less leaching (Figure 10). The concentration of sodium show possibilities of affecting the root growth of in soil is generally less in the watershed, the sodium plants. Because of the pore spaces, clay has the char- concentration shows the standard deviation of 4.72 acteristics to hold water molecules; hence, the density ppm in soil, while overall concentration ranges from of soil mass declines with increasing clay content, 7.25 to 25.20 ppm in the watershed (Table 2). while with more compact soil with less pores, the GEOLOGY, ECOLOGY, AND LANDSCAPES 143 Figure 7. Soil pH. Figure 9. Soil magnesium. Figure 8. Soil calcium. Figure 10. Soil sodium. bulk density of soil is higher. The bulk density is higher in the upstream part of basin but there is not incomplete decay products, and soil biomass. The much variation in it, spatial distribution map portraits biological and physical characteristics of the soil are similar pictures (Figure 11). This moderate value of influenced by the organic matter. It improves WHC, BD shows the modest water holding capacity. aggregation, ventilation and soil structure. It is con- sidered as a significant source of nutrients (N, P, S, B and Mo). The detailed investigation of data showed 5.10. Soil organic matter in Table 2 illustrations that OM concentration varies The organic matter (OM) is a vital constituent of soil from 0.48 to 2.41% in the soils from the area. It was that is main component to soil productiveness. observed that 45% of samples have low OM (Figure According to Piccolo and Stevenson (1982), Soil OM 12). The moderate soil OM shows that modest will be includes all organic compounds in soils, excluding the water-holding capacity of soil. Therefore, these undecayed plant and mammals’ tissues, their soils need adequate fertilizers through organic 144 A. KADAM ET AL. Figure 11. Soil bulk density. Figure 13. Soil boron. compost, agricultural compost, plant manure, etc. The of soil (Welch & Eswarakrishnan, 1991). The boron upper part of river basin sample S2 having lowest OM concentration in soil varies from 0.0874 to 0.967 mg/ (0.48%) is moderately suitable for agriculture. kg, while high concentration is observed in lower part of basin due accumulation in soil particle. 5.11. Soil boron 5.12. Runoff Boron (B) is a distinctive micronutrient necessary for regular development and progress of vegetation. The Run-off is a function of soil type and LULC through total content of B varies from 20 to 200 mg/kg (Mengel undulating topography. Meadows with gradient nor- & Kirkby, 1987), and its content also differ signifi - mally have small shallow soil storing space to retain cantly from one type of soil to other (Figure 13). run. Normally, agriculture yield and water inferiority Various soil parameters that affect the uptake of problems are directly related to superficial overflow. Boron are soil pH, organic matter content and type Surface overland flow can remove and transfer top soil units, manures and insecticides from the ground loca- tion. Additional possible problem related with over- land flow comprises of an absence of soil moistness in local zones of the field, crop nutrient deficit and eroded out seeds or plant (Rajasekhar et al., 2019). Study area is categorised into three zones as high run- off potential, moderately run-off potential and low run-off potential (Figure 14). 5.13. Land suitability analysis The ground signatures were used to allocate the scores to sub-criteria units. Lands having low to moderate gradients with thick soil cover are most suitable for agricultural practices. Uncultivated lands are dis- persed in pediment regions with moderately deep th soils; hence 4 rank has been assigned to it which shows potentials for extension of agricultural activity. Third rank to moderate suitability land which is observed to be occupying sparse forest characterized Figure 12. Soil organic matter. by modest gradients and shallow soil depth. GEOLOGY, ECOLOGY, AND LANDSCAPES 145 includes environmentally protected biodiversity hot spots. Hence, the thick-forested parts are categorized into the “null class” with rank one. In the present attempts, FAO specification has been used for LS analysis and found that only 9.16% land is “highly suitable” for plantation. These lands have 3–5% slopes, HSG-C and B soil type, more water- holding capability, soil moisture and low EC value. Nutrients like N-P-K are abstemiously available and exterior contributions are essential for plantation. About 14% of the lands are of “moderately suitable” class (Figure 15). The physiognomies of these lands are high slopes with small plain land, having thick soils in pediment region (50–90 cm), moderate water- holding capacity, soil moisture and runoff (Table 5). The area under vegetation and very scant scrub have also been recommended for afforestation. However, it needs extra efforts for planning, natural resources conservation, etc. Marginally suitable area for afforestation is assessed Figure 14. Soil Run-off. as around 14% of total river basin. It particularly shows thin soil cover, high slope, little water-holding volumes, poor soil moistness and potencies and high runoff. Undergrowth lands representing sharp gradients and Stepped lands having the problem of high soil erosion, thin topsoils are marginally fit whereas waste and so the use of this land for afforestation can also be done stony outcrop lands are unsuitable for green growth. after proper perquisite conservation work. The watershed is a representative of the semi-arid region from Western part, Maharashtra (India) that Figure 15. Land suitability map: Afforestation. 146 A. KADAM ET AL. Table 5. Land suitability classes: Afforestation. Area for Level plantation Land characteristics/qualities Remarks Sq km % Area Highly 16.70 9.16 3–5% slopes Highly suitable land for plantation. If afforestation conveniences are available suitable HSG C and B Good WHC High SM moderate runoff Scrub land Moderately 24.36 14 High slopes with small plain land Good area for plantation under suitable farm management practices. suitable thick soils at pediment area (50–90 cm) Clay texture Modest WHC Medium SM Moderate runoff fallow land with Sparse forest Marginally 26.10 15 >15% slope Medium suitability for afforestation under cautious management. It is likely to be suitable Thin soils at a place done on flat land but there is need to conservation practices to be done on the land Loam soil texture to reduced intensive erosion and drainage Less MWHC Less SM Accessibility of nutrients are low due to steep slope Terrace farming and fallow land Fallow and scrub land on shallow soil and moderate slope Unsuitable areas for afforestation were assessed marginally used. The land suitability types namely about 61% of the studied area. These areas have very “moderate suitable” and “not suitable” in suitability steep gradients with stony area, waste area, shallow, map are accurately assessed than the marginally and dry soils, etc. (Table 4 and Figure 15). Farming area highly suitable in producers and users’ point of and medium to thick forests are not recommended for view. The methodology, techniques and results of afforestation. the study may be useful to assess the suitability of the land for plantation in the study area. This tech- nique provides an effective method for creating land 6. Conclusions suitability maps based on complex evaluation cri- teria. This can considerably facilitate negotiations Land suitability maps for irrigated and dry farm- between land users and other stakeholders. Using ing in study area from Western India were gener- the resulting set of suitability maps, one for each ated using MCDM tool supported by GIS. Various possible land use at a certain location, planners can derived thematic layers such as LULC, slope, soil quickly compare the scenarios. Therefore, the deci- depth, soil type, pH, soil calcium, soil magnesium, sions could be taken with a good knowledge of the soil sodium, BD, OM, soil boron and run-off have consequences, as well as the limitations in future been evaluated through RS and field data. The land use development. Finally, the LSA is conducive expert’s opinion and analyses were used to iden- to negotiate the objectives and constraints of data, tify the impact of specific criterion and calculated making it an excellent tool to promote for demo- based on MCDM using the AHP that was opted to cratic decision-making in the areas under spatial assign the weights. The present research has been planning. carried out using FAO framework for LS analysis and it is found that only 9.16% land is “highly suitable”, 14% of lands are of “moderately suita- Disclosure statement ble”, marginally suitable lands for plantation are assessed as about 14% of total study region and No potential conflict of interest was reported by the authors. unsuitable areas for plantation were estimated about 61% of the studied area. Further, these zones were compared with an updated LULC ORCID map of the study region. Ajaykumar Kadam http://orcid.org/0000-0001-5043- The results show that the areas that are cate- gorised as “highly suitable” for plantation are Rajasekhar M http://orcid.org/0000-0002-5042-2506 already cultivated. However, areas categorised as Bhavana Umrikar http://orcid.org/0000-0002-6248-4069 Vasant Wagh http://orcid.org/0000-0002-1815-3625 “moderately suitable” are currently only being GEOLOGY, ECOLOGY, AND LANDSCAPES 147 Moeletsi, M. E., Walker, S., & Landman, W. A. (2011). References ENSO and implications on rainfall characteristics with Adgo, E., Teshome, A., & Mati, B. (2013). Impacts of reference to maize production in the Free State Province long-term soil and water conservation on agricultural of South Africa. Physics and Chemistry of the Earth, Parts productivity: The case of Anjenie watershed, Ethiopia. A/B/C, 36(14–15), 715–726. https://doi.org/10.1016/j.pce. Agricultural Water Management, 117, 55–61. https://doi. 2011.07.043 org/10.1016/j.agwat.2012.10.026 Mustafa, A. 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Land Suitability Analysis for Afforestation in Semi-arid Watershed of Western Ghat, India: A Groundwater Recharge Perspective

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GEOLOGY, ECOLOGY, AND LANDSCAPES 2021, VOL. 5, NO. 2, 136–148 INWASCON https://doi.org/10.1080/24749508.2020.1833643 RESEARCH ARTICLE Land Suitability Analysis for Afforestation in Semi-arid Watershed of Western Ghat, India: A Groundwater Recharge Perspective a b c d e f Ajaykumar Kadam , Rajasekhar M , Bhavana Umrikar , Vijay Bhagat , Vasant Wagh and R. N Sankua a b Department of Environmental Science, Savitribai Phule Pune University, Pune, India; Department of Geology, Yogi Vemana University, c d Kadapa, India; Department of Geology, Savitribai Phule Pune University, Pune, India; Department of Geography, Agasti Arts, Commerce and Dadasaheb Rupwate Science College, Ahmednagar, India; School of Earth Sciences, Swami Ramanand Teerth Marathwada University, Nanded, India; Basin Planning, Central Water Commission, New Delhi, India ABSTRACT ARTICLE HISTORY Received 30 March 2020 Land suitability analysis (LSA) for afforestation is an approach that stands amongst the most Accepted 4 October 2020 able frameworks revealing the concern for cultivable land and predicting the availability for sustainable development in semi-arid areas. In view of this, the objective of the present study is KEYWORDS to propose a conceptual procedure for LSA that would help enhancing the green cover to Land suitability; agriculture; combat environmental threats rendering groundwater recharge. LSA involves various the- MCDM; AHP; remote sensing; matic layers such as distribution of land use/landcover, slope, soil depth, soil type, pH, soil GIS calcium, soil magnesium, sodium, bulk density, organic matter, boron and run-off that have been derived using satellite images and collateral data. Food and Agriculture Organization framework and guidelines have been followed for LSA and it has been found that only 9.16% land is ‘highly suitable’, 14% of land is ‘moderately suitable’ whereas, marginally suitable lands for afforestation are estimated about 14% of total area and non-suitable areas for afforestation are about 61% of the total area. The multi-parametric based decision making AHP tool integrated with GIS proposes a novel methodology and outcomes of the research could be useful to identify suitable lands for agriculture in any various parts of the world. 1. Introduction (Bhagat, 2009). The green-covered area mainly the forest is reduced to 21.31% due to soil deprivation, Land under afforestation is a limited natural resource cut down of trees and minimum soil moisture during that facilitates sub-surface moisture, surface runoff, scarcity situations, etc. (Sonawane & Bhagat, 2017). vegetation, timber land, soil and mineral deposits Therefore, afforestation on degraded lands is primely which are getting degraded by heavy erosion, water suggested for environmental regulation of carbon, logging, increasing acidity and salinity, decreasing protection of natural resources such as soil and nutrients and soil productivity. Out of total land, water, biodiversity protection, etc. Many governmen- 1.68% productive land is reduced and 47% land has tal, non-governmental organisations, and forest con- degraded in the last twenty years (FAO). About 14.9% servation activists and movements are working on the of population of developing countries are suffering improvements in quality of soil and water resources from malnutrition (Fao, 2012). Demand of agricul- and land restoration. Land suitability analysis (LSA) tural products for food, fodder, industry, etc. is detects the inherent capacities, potentials and suitabil- increasing rapidly while productive land and produc- ity for plantation. Multi-criteria land suitability has tivity are decreasing remarkably. Therefore, the stu- been widely used for identification of potential parcels dies undertaken in this regard (Dumanski, 1997; for agriculture (R. B. Zolekar & Bhagat, 2015), planta- Nyeko, 2012; Schwilch et al., 2011) have been focussed tion (Bhagat, 2009; R.B. Zolekar & Bhagat, 2014), on sustainable land use development and watershed management (Gaikwad & Bhagat, 2018, management. A. K. Kadam et al., 2018; A. Kadam et al., 2019; Forestation, reforestation and protection of existing Rajasekhar et al., 2020), settlements, development pro- forests are suggested for conservation and enhance- jects, industries, etc. ment of CO storage in forest cover to reduce the The LSAs in existing methods in this paper is hav- atmospheric carbon (Patenaude et al., 2005). Forests ing the following parts such as the examination of soil increase subsurface recharge and help conservation of possessions is a significant stage. In view of this soil soil and groundwater resources. However, the area samples were analyzed in laboratory to understand the under forest and qualitative measures in terms of physicochemical properties. To know the present density, diversity, greenness and production are LULCs supervised categorization algorithm was used decreasing with alarming rate since centuries CONTACT Rajasekhar M sekhar.raja042@gmail.com Department of Geology, Yogi Vemana University, Kadapa, India © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group on behalf of the International Water, Air & Soil Conservation Society(INWASCON). This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. GEOLOGY, ECOLOGY, AND LANDSCAPES 137 for nine land utilization types. This map and informa- occupy mainly higher elevations, while local planta- tion were further used to derived the soil depth in tions are also scanty due the low soil cover in scrub connection with slope. Also, the other influencing land area. The government department planted the factors were also derived from base maps and field- trees but the scientific approach is missing in the based data. Finally, multi-criteria evaluation tool was action, resulting into wilting of plants, poor plant used for detection of land suitable for afforestation in growth with low recharge of water, etc., leading to semi-arid watershed of western Maharashtra, India. In failure of the project. The study area incorporates the study area, surface runoff utilized for artificial hilly undulating terrain in the peripheral proximity groundwater recharge has been considered as with high rainfall runoff ratio, depicting poor forest/ a probable source for “plantation”. The groundwater green cover therefore has been selected for the land recharge will be enhanced after the afforestation. suitability analysis for afforestation along with Hence, land with groundwater recharge potential is groundwater recharge perspective. taken to be a “preferable land for afforestation”. Thus, the land covered by grass/shrubs shows moderate run- off potential and hence could be considered as suitable 3. Data and software used for green growth. With this hypothesis, LSA for affor - Present study aims to evaluate the LS for afforestation estation with the perspective for groundwater conser- or plantation in Shivganga River basin, Maharashtra. vation has been carried out in Shivganga river basin. Study necessitates analysis of biophysical components such as soil properties, satellite image-based indices, weather conditions, etc. The soil samples were col- 2. Study area lected from well-distributed locations within the The area considered for study is drained by a tributary study area and analyzed for physico-chemical proper- of Nira River (Shivganga River) that flows in Bhor, ties such as soil depth (SD), soil texture (ST), soil Haveli and Purandar Tahsil of the Pune district, moisture (SM), soil organic matter (SOM), soil Maharashtra, India. It is covering an area of about pHand primary soil nutrients such nitrogen, phos- 17600 ha present in the eastern undulating part of phate and potassium to recognize the categories of the Western Ghats (Figure 1). The study area experi- LS. Further, remote sensing data i.e. LANDSAT-TM ences innumerable problems such as soil deprivation, imagery with 30 m spatial resolution was used to water scarcity, deforestation and soil pollution generate the land use/land cover classes. The bands (A. K. Kadam et al., 2018; A. Kadam et al., 2019), of satellite image were compiled, fused and stacked groundwater quality depletion (A. Kadam et al., using the open-source image processing software. All 2019) and change in hydrological response the thematic parameters were assigned a common (A. K. Kadam et al., 2018). Majority of the area coordinate system and projection and weighted over- under study possesses irrigated land covered by scanty lay analysis (WOA) was performed. to thick plantation. The forest cover is very poor and Figure 1. The study area: Shivgangabasin watershed. 138 A. KADAM ET AL. reserve plantation were classified into deep, moder- 4. Methodology ately deep and marginally deep soils, respectively. 4.1 Soil analysis The analysis of soil properties is an important step in 4.3 Baseline data creation LSA. Total 34 sites in the proximity of dug wells were The study area map was derived using the drainage selected using random sampling technique for collec- divide from contour of survey of India and updated tion of soil samples. These soil samples were analyzed with watershed atlas map. Physical elements has direct in laboratory to understand the physicochemical relation with land efficiency and agriculture activities properties (Table 1). Along with these physicochem- (Adgo et al., 2013; Adimassu et al., 2016; Moeletsi ical soil parameters, land slopes were also considered et al., 2011). The decisive factor such slope, LULC, in finding and assessing suitable area for plantation SD, ST, SM, soil EC, N, P and K have been frequently and afforestation. Moisture content in soil has vital impact on vegetation growth and distribution. Bhagat used for land suitability of plantation area (R. B. Zolekar & Bhagat, 2015; Jamali, 2014; Mustafa (2009) considered SM as an important parameter for et al., 2011; Romano et al., 2015; Yalew et al., 2016) but finding potential areas for afforestation. The metho- dology involved in this study is shown in Figure 2. the surface runoff firstly used for such analysis. The effect and wealth of these parameters are diverse according to the land physiognomies. The thematic 4.2 Remote sensing image analysis layers such as spatial variation of soil parameters (ST, SM, SOM, pH. N, P and K) were generated by the Supervised categorization algorithm was used for IDW interpolation algorithm in GIS. The runoff is LULC analysis considering nine land utilization generated using intersect of LULC and rainfall data types. The map of soil depth was derived from the in ArcGIS environment. slope and LULC classes. Land with and without scrub is comprised into the shallow and thin soil class respectively. Irrigated crop lands, fallow lands and 4.4 MCDM analysis The LSA have been done using MCDM-based AHP method. The procedure for LSA in the current study Table 1. Soil properties with the experimental methods of (Figure 2) is performed through six steps: (1) genera- analysis. tion of base maps with other thematic layer, (2) assort- Soil Property Analysis Method Unit ment of criterion, (3) defining of sub-criteria, (4) Texture International pipette method (mechanical formation of pairwise comparison matrix, (5) creation analysis) Moisture Oven drying method % of normalized pairwise judgement matrix and finally, Organic Titrimetric method (Walkley and Black, 1934). % (6) weights calculation. matter Bulk density Core sampling method g/ The researcher’s expertise inputs were taken into cm consideration while deciding the ranks of influential CaCO acid neutralization method % pH pH meter - factors. Analytical hierarchy process establishes judg- Ca EDTA titrimetric method ppm ments between criterions that help to arrange com- Mg EDTA titrimetric method ppm Na Flame photometer method ppm plete list of factors (Saaty, 1997). The comparison Boron Spectrophotometric method mg/ matrix supports the assessment to allocate various kg Figure 2. Methodology flow chart. GEOLOGY, ECOLOGY, AND LANDSCAPES 139 stages of significance of the features that present in land suitability analysis (R. B. Zolekar & Bhagat, 2015). The weights to the LSA studies have been given for interpretation of comparative rank and land physiognomies (Bandyopadhyay et al., 2009; Mendas & Delali, 2012; Reshmidevi et al., 2009; Romano et al., 2015; Yalew et al., 2016). FAO (1976) guidelines have been used prerequisite of land-use classification. The high land suitability shows maximum effect of sub-condition while lowest values display least LS for afforestation (Tables 3). Weights for the parameters namely % slope, LULC, SD, ST, soil erosion, OM Ca, Mg and Na were allotted, as per their significance. Finally, land suitability map was prepared using overlay technique in GIS to detect suitable sites for afforestation to improve infiltration rate for groundwater conservation in the region. The resultant map was categorized into highly suitable, moderately suitable, marginally suitable and not- suitable classes. Figure 3. Land use and land cover. penetration of water in sub-surface. This area is 5. Results and discussion suitable for afforestation (Figure 4). The area with 5.1 Land use and land cover (LULC) 15–35% slope is main concern for afforestation to reduce soil loss and increase groundwater recharge. LULC plays major role in identification of area poten- tial for LSA, for these two seasons’ satellite images were used for detail understanding. The LULC map 5.3 Soil depth was generated using level-II classification system that includes agriculture land as major class that has been Soil depth is a function of slope, runoff and rainfall further classified into horticulture land, crop land, pattern of the area (Bandyopadhyay et al., 2009; Rejani fruit orchard, etc., thus encompassing the whole et al., 2017; Worqlul et al., 2017). Slope, LULC map, soil gamut of green growth. Similarly, the waste land sample and field-based measurement were defining the major class has been further classified into barren depth of soil layer. The soil depth was also crosschecked land, land with scrub, land without scrub and stony with the surrounding well section as well as the geophy- waste helpful in identification of land suitable for sical investigation done in the study area validation of afforestation (Figure 3). 5.2 Slope The elevation-based slope is responsible for varia- tion in soil humidity. For understanding LSA suit- ability for plantation, it is essential to consider percent slope and its direction for amas- sing of water (Olshevsky, 2008). The elevation of the study area ranges from 560 to1257m above MSL. The % slopes are classified into seven cate- gories according to IMSD specification (IMSD, 1995). The nearly level 0–1% area is measured as large storage land owing a little rolling landscape with less surface flow. The part with comparatively moderate runoff and excellent penetration of water is very gently slope 1–3% favourable for plantation. The moderate surface flow causes relatively moder- ate penetration of water area with slope of 3–5%. The area with 10–15% slope having moderate soil depth and high surface flow causes relatively low Figure 4. Slope. 140 A. KADAM ET AL. the field experimental methods. Based on field observa- tion the agriculture lands with nearly levelled land hav- ing deep soil depth were found at lower part of watershed. While the peripheral part of basin having forest as land use with moderate to steep slope having moderate to marginal depth of soil (Figure 5). The cliff as well as near pediment part having steep slope show shallow soil. The soil depth represents the cropping pattern in the area, deep soil having rice as well as sugarcane, while parsley, etc., are found on thin soils. 5.4 Soil type Soil has an important role in supporting the plant life in that area. The soil type is also validating with the experi- mental methods in laboratory analysis. As per the NBSS and LUP categorization and the experimental methods, the area is characterizing into sandy loamy soil, clayey loamy soil and clayey soil types (Figure 6). The study Figure 6. Soil type. region is majorly covered with well-drained sandy loamy soils having 48% of area with low nutrient pre- sent on moderate strong to steep slope goo for affores - difference in the pH range in the study area (Figure tation. The moderately drained loamy clay soil covers 7). All superficial soil samples excluding S6, S8 and S16 43% of area, sandy soil has high infiltration rate due to having pH higher than 8, showing alkaline type of greater porosity and permeability therefore it will be common soil samples (Table 2), is the results of high assigned highest priority. Clayey soil is compact and CaCO concentration in host rock. Cumulative quan- impervious hence given low priority rank. tity of CaCO in soil, may affect in increasing the heavy metal absorption. This could result into elevated 5.5 pH of soil buffering volume of soils and non-appearance of CO The utmost informative features of soils are its pH. It in the saturation extract (Bagyaraj et al., 2013). The defines the comparative contents of hydrogen ions in presence of solvable and transferrable Na with bicar- liquid. The pH of a regular soil is measured as an bonate ions results in such high values. Further, there indicator of its transferable cation’s saturation is a precipitation of CaCO and MgCO carbonates 3 3 (USDA, 1954). The pH of soil ranged from 7.39 to during evaporation contributing to such high pH 8.63 reflecting alkaline nature (Table 2) and slight values. High pH contents result into the growth of salinity/sodicity in soil of study area. 5.6 Soil calcium 2+ Ca is main cation over other found cations in soil. This 2+ is because of the more adsorption potency of Ca ion in soil related to other positively charged ion (Miller, 1995). 2+ The Ca ion present in abundant amount to form the major transferable complex in soil media specially in the study area to occupy a major position on the exchange complex (Sharma & Raghubanshi, 2009). A brief look at the information from Table 2 shows that the transfer- 2+ rable Ca ion varies from 40.08 to 180.63 ppm (Figure 2+ 8). The high proportion of Ca in the majority of the samples is suggestive of the occurrence of Ca-clay. Such 2+ soils with high Ca possess a good physical condition, develop a good crumb structure and allow the free 2+ movement of water and air. The soil Ca ion helps in maintaining the soil pH with playing important role in cell wall development for plant growth. Figure 5. Soil depth. GEOLOGY, ECOLOGY, AND LANDSCAPES 141 Table 2. Soil chemical analysis. BD Boron Ca Mg Na % ID pH (ppm) (ppm) (ppm) OM (g/cm ) % CaCO3 % Moisture mg/kg S1 8.54 180.36 73.09 23.8 1.6 1.2 8.75 4.16 1.16 S2 8.04 60.12 12.18 10.85 0.48 1.41 9.75 3.05 0.35 S3 8.6 140.28 36.55 18.9 1.03 1.23 3.5 6.61 1.38 S4 8.24 80.16 12.18 12.95 2.01 1.15 8.75 4.05 0.36 S5 8.12 60.12 12.18 7.7 1.7 1.06 14 5.7 0.2 S6 8.4 60.12 12.18 10.5 1.36 1.13 6.8 0.02 2.43 S7 8.4 40.08 12.18 11.2 0.84 1.24 14 3.06 2.3 S8 7.39 60.12 12.18 10.15 1.86 1.06 12.5 3.87 0.12 S9 8.41 60.12 12.18 10.5 1.74 1.09 11.75 5.88 0.25 S10 8.42 128.26 53.6 25.2 1.94 1.17 13.45 7.46 0.36 S11 7.62 180.36 36.55 23.8 1.82 1.13 14.25 5.46 0.23 S12 8.48 96.19 26.8 20.65 1.74 1.2 10.5 4.32 0.13 S13 8.46 44.09 9.75 12.6 1.91 1.06 12.75 4.35 0.6 S14 8.63 56.11 14.62 8.4 2.1 1.04 6.8 3.99 0.8 S15 8.39 40.08 12.18 14.7 2.41 1.13 3 4.47 0.29 S16 8.58 64.13 19.49 16.8 1.96 1.16 7.05 6.15 0.3 S17 8.44 40.08 14.62 14 2.1 1.07 10 5.6 0.87 S18 8.47 52.1 29.24 12.6 1.2 1.06 8.1 6.43 1.56 S19 8.55 68.14 19.49 14.7 1.46 1.09 7.75 5.08 0.25 S20 8.4 96.19 38.98 15.75 1.74 1.09 10.3 13.04 1.41 S21 7.9 56.11 14.62 9.8 1.2 1.07 13.75 3.7 1.2 S22 8.43 48.1 14.62 11.9 1.05 1.2 11.25 7.51 4.45 S23 8.52 64.13 21.93 19.6 0.96 1.46 12.25 2.99 0.91 S24 8.24 52.1 17.06 11.9 1.67 1.03 8.25 7.53 2.1 S25 8.25 56.11 24.36 9.8 1.36 1.17 7.5 11.89 2.67 S27 8.23 64.13 21.93 7.35 1.79 1.2 6.25 5.39 0.49 S28 8.08 72.14 17.06 9.45 1.41 1.04 9.75 5.77 0.21 S29 8.53 80.16 24.36 12.95 1.2 1.03 12.5 10.61 3.12 S30 8.34 64.13 21.93 8.4 1.46 1.04 9.45 5.82 0.23 S31 8.41 72.14 29.24 16.45 1.72 1.1 6.75 7.47 1.4 S32 8.35 64.13 21.93 14.7 1.94 1.03 14.25 4.22 1.25 S33 8.46 80.16 24.36 11.9 1.6 1.13 4.75 2.35 1.2 S34 8.24 80.16 36.55 14 1.2 1.34 6.5 1.3 6.7 Min 7.39 40.08 9.75 7.35 0.48 1.03 3 0.02 0.12 Max 8.63 180.36 73.09 25.2 2.41 1.46 14.25 13.04 6.7 Avg 8.32 74.57 23.04 13.76 1.56 1.14 9.60 5.43 1.25 Std. dev 0.27 35.14 13.51 4.72 0.42 0.11 3.19 2.72 1.41 Ca, Mg. Na is having unit, ppm; while OM, CaCO , Moisture is having unit % and pH is unitless Table 3. Normalised matrix. LULC SD Slope ST SBD OM B pH Ca Mg Na Runoff % weight LULC 0.14 0.12 0.09 0.24 0.17 0.14 0.12 0.13 0.09 0.08 0.12 0.13 13.14 SD 0.05 0.04 0.09 0.24 0.17 0.14 0.12 0.13 0.09 0.08 0.12 0.13 11.73 Slope 0.42 0.12 0.26 0.24 0.29 0.23 0.12 0.22 0.15 0.14 0.2 0.08 20.6 ST 0.05 0.01 0.09 0.08 0.17 0.14 0.2 0.13 0.09 0.08 0.12 0.13 10.84 SBD 0.05 0.01 0.05 0.03 0.06 0.23 0.12 0.13 0.09 0.08 0.12 0.08 8.78 OM 0.05 0.01 0.05 0.03 0.01 0.05 0.12 0.13 0.09 0.08 0.12 0.08 6.88 B 0.05 0.01 0.09 0.02 0.02 0.02 0.04 0.01 0.15 0.14 0.2 0.08 6.89 pH 0.05 0.01 0.05 0.03 0.02 0.02 0.12 0.04 0.09 0.08 0.12 0.13 6.37 Ca 0.05 0.01 0.05 0.03 0.02 0.02 0.01 0.01 0.03 0.08 0.12 0.01 3.67 Mg 0.05 0.01 0.05 0.03 0.02 0.02 0.01 0.01 0.01 0.03 0.12 0.01 3.03 Na 0.05 0.01 0.05 0.03 0.02 0.02 0.01 0.01 0.01 0.01 0.04 0.01 2.19 Runoff 0.03 0.01 0.09 0.02 0.02 0.02 0.01 0.01 0.09 0.08 0.08 0.03 3.97 5.7. Soil magnesium ppm. The less residence time of water in upstream parts of study region shows that low leaching of Magnesium is plentiful in the earth’s shell. It is 2+ 2+ Mg ion (Figure 9). The prevalence of Mg in the present in a varied variability of minerals. majority of the soil samples due to photosynthesis, Weathering or break down of these minerals leads as it is a building block of the chlorophyll, which to the availability of magnesium for plant use. makes leaves appear green. This is particularly 2+ Though magnesium (Mg ) is a vital element for observed in the irrigated areas (S4, S7, S8, S13, vegetal growth, its use in a manure activity obtains S21 and S24). 2+ only slight importance. Mg is held on the super- ficial part of clay soil particle and organic matter particles. A brief look at the data from Table 2 2+ shows that the Mg ion varies from 9.75 to 73.09 142 A. KADAM ET AL. Table 4. Criteria, sub-criteria, MCDM for land suitability analysis. Zolekar and Bhagat, 2015. Control features Parameter Weight Types Topographical parameters Landuse/landcover 13.14 Agriculture Land without scrub Plantations Land with scrub Barren land Dense forest Settlement Sparse forest Water body Slope 20.6 Nearly level Very gentle Gentle Moderately strong Strong Moderately steep Very steep Soil parameters Soil depth 11.73 Thin soil Shallow soil Marginal depth Moderate depth Deep soil Water bodies Soil type 10.84 Loam soil on strong to very steep slope Clay loam soil on gentle to Moderately strong slope Clay soil on gentle slopes Soil Boron 6.89 Very low Low Moderate High Soil organic matter 6.88 Not suitable (<0.20) Marginally suitable (0.20–0.40) Moderately suitable (0.40–0.60) Highly suitable (0.61–1.00) Soil bulk density 8.78 Low (<100) Moderate (200–100) High (200–400) Very High (>400) Soil pH 6.37 Marginally suitable Moderately suitable Highly suitable Soil Ca 3.67 Not suitable Marginally suitable Moderately suitable Highly suitable Soil Mg 3.03 Not suitable Marginally suitable Moderately suitable Soil Na 2.19 Marginally suitable Moderately suitable Highly suitable Weather parameters Run-off 3.97 Low runoff Moderate runoff High runoff Very high runoff 5.8. Soil sodium 5.9. Soil bulk density Plants are provided with sodium (Na ) from the soil The ideal bulk density (BD) of sandy clay loam or naturally, this is obtained from manures, insecticides, loam for plant growth should be less than 1.60 gm/ runoff from phreatic salt-loaded waters and the weath- cm and for clay loam it should be less than 1.10 gm/ + 3 ering of minerals which dissolve salt into the soil. Na cm . Soil sample numbers from 1 to 21 have bulk content in soil that is not at poisonous levels can simply densities in the ideal range for plant growth; whereas, be seeped out by reddening the soil with fresh water. The soil samples 22–25 have clay content >30% with max- less percolation of water in upstream parts of study area imum value 65.52% and sand <35% indicating that the shows comparatively high concentration of Na ion due soil samples with higher percentage of clay than sand less leaching (Figure 10). The concentration of sodium show possibilities of affecting the root growth of in soil is generally less in the watershed, the sodium plants. Because of the pore spaces, clay has the char- concentration shows the standard deviation of 4.72 acteristics to hold water molecules; hence, the density ppm in soil, while overall concentration ranges from of soil mass declines with increasing clay content, 7.25 to 25.20 ppm in the watershed (Table 2). while with more compact soil with less pores, the GEOLOGY, ECOLOGY, AND LANDSCAPES 143 Figure 7. Soil pH. Figure 9. Soil magnesium. Figure 8. Soil calcium. Figure 10. Soil sodium. bulk density of soil is higher. The bulk density is higher in the upstream part of basin but there is not incomplete decay products, and soil biomass. The much variation in it, spatial distribution map portraits biological and physical characteristics of the soil are similar pictures (Figure 11). This moderate value of influenced by the organic matter. It improves WHC, BD shows the modest water holding capacity. aggregation, ventilation and soil structure. It is con- sidered as a significant source of nutrients (N, P, S, B and Mo). The detailed investigation of data showed 5.10. Soil organic matter in Table 2 illustrations that OM concentration varies The organic matter (OM) is a vital constituent of soil from 0.48 to 2.41% in the soils from the area. It was that is main component to soil productiveness. observed that 45% of samples have low OM (Figure According to Piccolo and Stevenson (1982), Soil OM 12). The moderate soil OM shows that modest will be includes all organic compounds in soils, excluding the water-holding capacity of soil. Therefore, these undecayed plant and mammals’ tissues, their soils need adequate fertilizers through organic 144 A. KADAM ET AL. Figure 11. Soil bulk density. Figure 13. Soil boron. compost, agricultural compost, plant manure, etc. The of soil (Welch & Eswarakrishnan, 1991). The boron upper part of river basin sample S2 having lowest OM concentration in soil varies from 0.0874 to 0.967 mg/ (0.48%) is moderately suitable for agriculture. kg, while high concentration is observed in lower part of basin due accumulation in soil particle. 5.11. Soil boron 5.12. Runoff Boron (B) is a distinctive micronutrient necessary for regular development and progress of vegetation. The Run-off is a function of soil type and LULC through total content of B varies from 20 to 200 mg/kg (Mengel undulating topography. Meadows with gradient nor- & Kirkby, 1987), and its content also differ signifi - mally have small shallow soil storing space to retain cantly from one type of soil to other (Figure 13). run. Normally, agriculture yield and water inferiority Various soil parameters that affect the uptake of problems are directly related to superficial overflow. Boron are soil pH, organic matter content and type Surface overland flow can remove and transfer top soil units, manures and insecticides from the ground loca- tion. Additional possible problem related with over- land flow comprises of an absence of soil moistness in local zones of the field, crop nutrient deficit and eroded out seeds or plant (Rajasekhar et al., 2019). Study area is categorised into three zones as high run- off potential, moderately run-off potential and low run-off potential (Figure 14). 5.13. Land suitability analysis The ground signatures were used to allocate the scores to sub-criteria units. Lands having low to moderate gradients with thick soil cover are most suitable for agricultural practices. Uncultivated lands are dis- persed in pediment regions with moderately deep th soils; hence 4 rank has been assigned to it which shows potentials for extension of agricultural activity. Third rank to moderate suitability land which is observed to be occupying sparse forest characterized Figure 12. Soil organic matter. by modest gradients and shallow soil depth. GEOLOGY, ECOLOGY, AND LANDSCAPES 145 includes environmentally protected biodiversity hot spots. Hence, the thick-forested parts are categorized into the “null class” with rank one. In the present attempts, FAO specification has been used for LS analysis and found that only 9.16% land is “highly suitable” for plantation. These lands have 3–5% slopes, HSG-C and B soil type, more water- holding capability, soil moisture and low EC value. Nutrients like N-P-K are abstemiously available and exterior contributions are essential for plantation. About 14% of the lands are of “moderately suitable” class (Figure 15). The physiognomies of these lands are high slopes with small plain land, having thick soils in pediment region (50–90 cm), moderate water- holding capacity, soil moisture and runoff (Table 5). The area under vegetation and very scant scrub have also been recommended for afforestation. However, it needs extra efforts for planning, natural resources conservation, etc. Marginally suitable area for afforestation is assessed Figure 14. Soil Run-off. as around 14% of total river basin. It particularly shows thin soil cover, high slope, little water-holding volumes, poor soil moistness and potencies and high runoff. Undergrowth lands representing sharp gradients and Stepped lands having the problem of high soil erosion, thin topsoils are marginally fit whereas waste and so the use of this land for afforestation can also be done stony outcrop lands are unsuitable for green growth. after proper perquisite conservation work. The watershed is a representative of the semi-arid region from Western part, Maharashtra (India) that Figure 15. Land suitability map: Afforestation. 146 A. KADAM ET AL. Table 5. Land suitability classes: Afforestation. Area for Level plantation Land characteristics/qualities Remarks Sq km % Area Highly 16.70 9.16 3–5% slopes Highly suitable land for plantation. If afforestation conveniences are available suitable HSG C and B Good WHC High SM moderate runoff Scrub land Moderately 24.36 14 High slopes with small plain land Good area for plantation under suitable farm management practices. suitable thick soils at pediment area (50–90 cm) Clay texture Modest WHC Medium SM Moderate runoff fallow land with Sparse forest Marginally 26.10 15 >15% slope Medium suitability for afforestation under cautious management. It is likely to be suitable Thin soils at a place done on flat land but there is need to conservation practices to be done on the land Loam soil texture to reduced intensive erosion and drainage Less MWHC Less SM Accessibility of nutrients are low due to steep slope Terrace farming and fallow land Fallow and scrub land on shallow soil and moderate slope Unsuitable areas for afforestation were assessed marginally used. The land suitability types namely about 61% of the studied area. These areas have very “moderate suitable” and “not suitable” in suitability steep gradients with stony area, waste area, shallow, map are accurately assessed than the marginally and dry soils, etc. (Table 4 and Figure 15). Farming area highly suitable in producers and users’ point of and medium to thick forests are not recommended for view. The methodology, techniques and results of afforestation. the study may be useful to assess the suitability of the land for plantation in the study area. This tech- nique provides an effective method for creating land 6. Conclusions suitability maps based on complex evaluation cri- teria. This can considerably facilitate negotiations Land suitability maps for irrigated and dry farm- between land users and other stakeholders. Using ing in study area from Western India were gener- the resulting set of suitability maps, one for each ated using MCDM tool supported by GIS. Various possible land use at a certain location, planners can derived thematic layers such as LULC, slope, soil quickly compare the scenarios. Therefore, the deci- depth, soil type, pH, soil calcium, soil magnesium, sions could be taken with a good knowledge of the soil sodium, BD, OM, soil boron and run-off have consequences, as well as the limitations in future been evaluated through RS and field data. The land use development. Finally, the LSA is conducive expert’s opinion and analyses were used to iden- to negotiate the objectives and constraints of data, tify the impact of specific criterion and calculated making it an excellent tool to promote for demo- based on MCDM using the AHP that was opted to cratic decision-making in the areas under spatial assign the weights. The present research has been planning. carried out using FAO framework for LS analysis and it is found that only 9.16% land is “highly suitable”, 14% of lands are of “moderately suita- Disclosure statement ble”, marginally suitable lands for plantation are assessed as about 14% of total study region and No potential conflict of interest was reported by the authors. unsuitable areas for plantation were estimated about 61% of the studied area. Further, these zones were compared with an updated LULC ORCID map of the study region. Ajaykumar Kadam http://orcid.org/0000-0001-5043- The results show that the areas that are cate- gorised as “highly suitable” for plantation are Rajasekhar M http://orcid.org/0000-0002-5042-2506 already cultivated. However, areas categorised as Bhavana Umrikar http://orcid.org/0000-0002-6248-4069 Vasant Wagh http://orcid.org/0000-0002-1815-3625 “moderately suitable” are currently only being GEOLOGY, ECOLOGY, AND LANDSCAPES 147 Moeletsi, M. E., Walker, S., & Landman, W. A. (2011). References ENSO and implications on rainfall characteristics with Adgo, E., Teshome, A., & Mati, B. (2013). Impacts of reference to maize production in the Free State Province long-term soil and water conservation on agricultural of South Africa. Physics and Chemistry of the Earth, Parts productivity: The case of Anjenie watershed, Ethiopia. A/B/C, 36(14–15), 715–726. https://doi.org/10.1016/j.pce. Agricultural Water Management, 117, 55–61. https://doi. 2011.07.043 org/10.1016/j.agwat.2012.10.026 Mustafa, A. 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Journal

Geology Ecology and Landscapes – Taylor & Francis

Published: Apr 3, 2021

Keywords: Land suitability; agriculture; MCDM; AHP; remote sensing; GIS

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