Mangroves are vital components of the environment providing diverse functions from biodiversity conservation to climate regulation. Understanding the soil quality in these systems is essential for appropriate decision making and developing strategies for sustainable mangrove soil management. The objective of the study was to develop a Geochemical Quality Index (GQI) and Relative Geochemical Index (RGQI) for soil quality assessment of the mangrove systems. A total of 63 soil samples were collected from the surface layer 0 to 30 cm, for assessing the physico – chemical parameters from the mangrove systems along the South Western Coast of Penisnsular India. The datasets were subjected to principal component analysis (PCA) to derive the minimum data set for the GQI and RGQI. The selected parameters were ranked based on their performance of soil functions and combined to form the indices. The PCA results showed that, percent sand, percent silt, percent clay, Cu, EC, Fe and K were the most representative variables of mangrove ecosystem health. The RGQI values indicated the relative quality of mangrove soils in the region with respect to maximum attainable geochemical health and classified the mangrove systems into 3 types vis - a - vis; good, medium and poor. Evaluation of the existing mangrove systems using RGQI revealed that majority of the mangrove systems in the South Western coast of Peninsular India had a high probability to progressively alter from ‘good’ to the ‘poor’ category, hence mandate crucial management strategies.
Research Article
Developing a geochemical quality index for mangrove systems in the South Western Coast of Indian Peninsula
https://doi.org/10.21203/rs.3.rs-1972696/v1
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Mangroves
geochemical quality index
relative geochemical quality index
principal component analysis
minimum data set
soil quality assessment
Mangroves are very vibrant ecosystems of global significance which provide multitude of benefits to society. As such, these wetland systems are highly productive and diverse systems representing ecotonal zones between aquatic environments (i.e., rivers) and terrestrial uplands. They offer a critical nursery ground for many faunal species (Secor and Rooker, 2005). By temporarily storing large amounts of water, wetlands play a significant role in decreasing floods and enriching groundwater. They support to maintain water quality by filtering out pollutants and sediments, and aid to regulate erosion by trapping soil leached from adjacent uplands (Fennessy and Mitsch, 2001). The productivity of mangrove wetlands indicate that they deliver sufficient food supplies to sustain the complex food chains in these systems (Alongi, 2016). Continuous interactions among the mangrove vegetation and upland terrestrial areas occur through exchanges of energy, nutrients, and species and thus these systems function as highly integrated aerobic-anaerobic interfacial systems.The juxtaposition of aerobic and anaerobic zones is a crucial part in the sustenance of mangrove systems which in turn enable them to support a wide range of specific processes, activities and vegetation. Hence, soil-monitoring mechanisms play a key role in planning and developing strategies for their conservation.
The development of sound concepts and methodologies for monitoring these pristine system requires an understanding of their structure and function. Given the high cost of environmental monitoring in terms of time, human resources and funding, methods need to be developed that are simple, efficient, scientifically rigorous and ecologically meaningful. One of the most attractive approaches is to develop scientifically rigorous indicators integrating physical, chemical and biological properties or processes. As such, a fundamental understanding of the biogeochemical processes regulating the functions of the mangrove ecosystem is critical in evaluating their health and designing successful restoration efforts. The certainty associated with such assessments decreases if the factors that affect biogeochemical processes regulating the fate and transport of nutrients in these systems are not well understood (Andrews et al, 2002). Decision tools that help arrange soil test information into a single comprehensible value or index would help easy understanding of the system with a few selected parameters (Beinat and Nijkamp, 1998). Soil Quality Index (SQI) is one such decision tool that combine a variety of parameters or information for informed decision making (Jernigan et al. 2020, Williams et al. 2020). However, indices should be selected based on the soil functions of interest in a specified system and the intended management goals. Further, the index should be based on the fewest possible parameters that would help reduce the myriad of complex soil tests and recommendations that present management dilemmas (Beinat and Nijkamp, 1998). The SQI technique is preferred over dynamic soil quality models (Larson and Pierce 1994), soil quality cards, test kits (Ditzler and Tugel, 2002) and fuzzy association rules (Xue et al. 2010) because of its quantitative flexibility and easiness (Chen et al, 2013).
The concept of a minimum data set for soil quality indicators for specific ecosystems is widely accepted but has relied primarily on expert opinions or field experiences to select the components (Karlen et al., 1996; Andrew et al., 2002). A more prudent approach of variable selection would be by way of statistical methods and thereby reduce the possibility of biases (Doran and Parkin, 1996; Walter et al., 1997; Bachmann and Kinzel, 1992). Recent advances in combining parameters into reliable indices and affixing value ranges to grade the system vary from simple linear scoring techniques to complex curvilinear scoring functions (Liebig et al., 2001; Karlen and Stott, 1994; Andrews and Carroll, 2001). Soil quality indices that vary widely in their complexity have been developed and used extensively for agroecosystems, but such indices and their use are scarce in forest systems.
The quality of mangrove soils is a subject of "unusual scientific and ecological interest”. The chemical changes in these mangrove soils will influence (a) the character of the sediment or soil that forms, (b) the suitability of wet soils for plants, (c) the distribution of plant species around lakes and streams and in estuaries, deltas, and marine flood plains, (d) the quality and quantity of aquatic life, and (e) the capacity of lakes and seas to serve as sinks for terrestrial wastes.
Wetlands are areas of marsh, fen, peat land or water, whether natural or artificial, permanent or temporary, with water that is static or flowing, fresh, brackish or salt, including areas of marine water the depth of which at low tide does not exceed six meters' (Ramsar convention, 1971).
Wetlands are highly productive and diverse systems representing ecotonal zones between aquatic environments (i.e., rivers) and terrestrial uplands. Continuous interactions among the vegetation and upland terrestrial areas occur through exchanges of energy, nutrients, and species and thus these systems function as highly integrated units. Such ecosystems are particularly important in the hydrologic relations of the watershed (Mitsch and Gosselink, 2000). Water is stored and its quality improved by filtering or removing nutrients and pollutants from the water (Fennessy and Mitsch, 2001). Finally, wetlands sequester carbon dioxide and act as a sink for carbon. Recently these systems have experienced the greatest decline of all wetland types.
Many species depend upon wetlands for successful completion of their life cycle and most require, or benefit from, nearby aquatic habitat. Changes in the structure and function of a wetland will eventually have far-reaching effects on the biota of the wetland and the surrounding uplands. Monitoring wetland systems can provide information on environmental change, including changes in community structure and function, in both the wetland and adjacent upland watershed.
Aerobic-anaerobic interfaces in the water column (e.g. surfaces of detrital plant tissue and benthic periphyton mats), at the soil water interface, and in the root zone of aquatic macrophytes are unique features of wetlands, as compared to upland landscapes. The juxtaposition of aerobic and anaerobic zones in wetlands supports a wide range of microbial populations and associated metabolic activities, with oxygen-reduction occurring in the aerobic interface of the substrate, and reduction of alternate electron acceptors occurring in the anaerobic zone (Reddy et al, 2008). Under continuously saturated soil conditions, vertical layering of different metabolic activities can be present, with oxygen-reduction occurring at and just below the soil–floodwater interface. Much of the aerobic decomposition of plant detritus occurs in the water column; however, the supply of oxygen may be insufficient to meet demands and drive certain microbial groups to utilize alternate electron acceptors, e.g., nitrate, oxidized forms of Fe and Mn, sulfate and HCO3- (Ponnamperuma, 1972; Aldous et al, 2005; Chen et al. 1999).
Development of sound concepts and methodologies for ecosystem monitoring require an understanding of ecosystem structure and function. Given the high cost of environmental monitoring in terms of time, human resources and funding, methods need to be developed that are simple, efficient, scientifically rigorous and ecologically meaningful. One of the most attractive approaches to developing scientifically rigorous methods is based on the concept of using physical, chemical or biological properties or processes as indicators of wetland condition, change or response to anthropogenic impacts. A fundamental understanding of the biogeochemical processes regulating the functions of the ecosystem is critical to evaluating nutrient impacts and successes of restoration efforts. The certainty associated with an assessment decreases if the factors that affect biogeochemical processes regulating the fate and transport of nutrients in wetlands are not well understood; i.e., the risk assessment is only as good as the information/knowledge available at the time (Andrews et al, 2002).
Mangrove ecosystems are one of the major wetland systems in Kerala. These are unique pristine systems and are known to provide a number of ecological services and economic benefits, but with no information on its soil geochemistry. In Kerala, mangrove forests that once occupied about 700 km2, have now dwindled to 25 km2 (Vidyasagaran and Madhusoodanan 2014). Understanding the underlying soil processes in tandem with the vegetation dynamics will help us evolving better management, restoration and conservation strategies for these ecosystems. This study intends to outline the underlying soil physico - chemical processes in relation to the vegetation in mangrove ecosystems of Kerala. The objective of this study was to develop a soil quality indexing method for the mangrove systems and use it to assess the health of the existing mangrove patches along the South Western Coastal belt of Peninsular India.
Study area
The study area is located in the south-western coast of India between 100 51ꞌ 1ꞌꞌ N and 760 16ꞌ 15ꞌꞌ E spanning a coastal belt of ~ 590 km with the mangrove systems growing in narrow strips along the coast. The region is flanked on the eastern side by the Western Ghats with a maximum altitude of 2695 m and a drop to the sea level within 30–120 kms The Western Ghats is a continuous stretch of mountains with two natural gaps at Palakkad and Shenkottah. The study area was classified into northern (north of Palakkad gap), central (between Palakkad and Shenkottah gaps) and southern zones (south of Palakkad gap) based on their geographical peculiarities (Fig. 1). Northern zone lies between 750 28ꞌ 14.3ꞌ Nꞌ and 120 0ꞌ 98.14ꞌꞌ E with a mean annual temperature of 27.20C and mean average rainfall of 3351 mm. Avicinnia officinalis, A.marina, Acanthus ilicifolius, Aegiceras corniculatum, Bruguiria cylindrica, Kandelia candal, Rhizophora mucronata, R.apiculata, Sonneratia alba and Sonneratia caseolaris were the major mangrove species identified in this zone. Central zone (760 06ꞌ 67.1ꞌꞌN and 100 54ꞌ 12.1ꞌꞌ E) had thick patches of natural mangrove growth with Avicinnia officinalis, Acanthus ilicifolius, Aegiceras corniculatum, Bruguiria cylindrica and Rhizophora mucronata as the major species. The region has a tropical humid climate with 3001 mm average annual rainfall and mean annual temperature of 27.60 C. Southern zone (760 55ꞌ 62.4ꞌꞌN and 80 93ꞌ 59.6ꞌꞌ E) had Avicinnia marina, Acanthus ilicifolius, Aegiceras corniculatum, Bruguiria cylindrica, Rhizophora mucronata, R.apiculata and Sonneratia alba as the major species with a mean annual temperature and rainfall are 27 0C and 2468 mm, respectively.
Sample collection of soil and analysis
In each sampling location, the mangrove patches were sub divided into three substrata based on canopy coverage (FSI, 2019) as disturbed (> 70 % canopy cover), medium disturbed (40 – 70 % canopy cover) and undisturbed (< 40 % canopy cover) and soil samples were collected from each substratum in triplicates. Surface soil samples (0-30 cm) were collected from selected locations in each zone by core method. A total of 63 samples were collected from thethree zones (7 locations x 3 substrata x 3 replications). Separate cores were collected for bulk density (BD) determination. The collected samples were air dried, powdered and sieved (2mm sieve) for further analysis.
The processed soil samples were used for assessing the physico- chemical properties and heavy metal contents by standard protocols. The sand, silt and clay contents in the samples were assessed by Bouyoucos hydrometer method (Bouyoucos 1962). A soil: suspension ratio of 1:2.5 was used for pH, electrical conductivity and redox potential measurements (Jackson 1957). Organic carbon (OC) was determined by wet oxidation method of Walkley and Black (1934). The cation exchange capacity (CEC) was determined by neutral normal ammonium acetate method (Chapman 1965). Available phosphorus (P) was extracted with Bray No. 1 solution and estimated by colorimetry in a UV - vis spectrophotometer (Xplorer XP 2001) at 420nm. Available potassium (K), calcium (Ca) and magnesium (Mg) were extracted with neutral normal ammonium acetate and estimated in a flame photometer (Elico CL 378; available K) and atomic absorption spectrophotometer (Varian AA 240 ; available Ca and Mg), respectively. Micronutrents such as iron (Fe), copper (Cu), zinc (Zn) and manganese (Mn) and heavy metals such as cadmium (Cd), lead (Pb), chromium (Cr), nickel (Ni) and cobalt (Co) were extracted with 0.1 N HCL and determined using atomic absorption spectrophotometer.
Geochemical Quality Index (GQI)
The development of the index involved three main steps (i) selection of minimum data set (MDS) (ii) scoring of MDS based on their performance of soil functions and (iii) integration of the indicator scores into a comparative index of soil quality.
i. Selection of minimum data set (MDS) A primary data set reduction was affected by subjecting the soil parameters to Pearson correlation (one-tailed) test and selecting only one of the parameters when they had a significant correlation with another parameter at p < 0.05. Estimated soil parameters were subjected to principal component analysis (PCA) using SPSS ver. 21 to select the representative MDS for mangrove systems.. Principal Components (PC) that received eigen values ≥ 1 were considered to best represent variations in the system and were selected for index development. For the MDS, only the variables with maximum weights were retained in each PC. Highly weighed factors were defined as those parameters having their absolute values within 10% of the highest factor loading or ≥ 0.40 (Wander and Bollero 1999). When under a single PC. it was necessary to retain more than one factor based on the above criteria, the redundancy of the variables was determined by multivariate correlation coefficients (Andrews et al. 2001).
(ii) Scoring of MDS based on their performance of soil functions
After determining the variables for the MDS, each observation of the selected MDS parameter was transformed into scores (Fig. 2). Each parameter was ranked depending on whether a higher value was considered good or bad in terms of soil function. Thus there were three types of scoring functions namely (1) more is better (2) less is better and (3) optimum. This selection was based on relationship between the assessed parameter and soil quality as determined by secondary literature and field experiences ie., ‘more is better’ function was selected for parameters which shows increase in soil quality with increasing levels. ‘less is better’ function was selected for parameters, which affected the soil quality negatively. Indicator, which shows positive association with soil quality up to an optimal level beyond which soil quality decreases, was taken as ‘optimum’ function. For indicators with optimum ranges, observations were scored as ‘lower is better’ up to a threshold value and then scored as ‘higher is better’ above the threshold (Liebig et al. 2001).
(iii) Development of index
The scores were combined into an overall weighed additive geochemical quality index (GQI) using the formula
where Wi and Si are respectively the weighing factor and score of the indicator. Weights were determined by the percent of variation in the dataset obtained by the PC that contributed the variable divided by the total percentage of variation explained by all PCs with Eigen values ≥ 1.
The relative soil quality in a mangrove system was measured by assessing the relative geochemical quality index (RGQI) using the equation proposed by Karlen and Scott (1994).
RGQI = GQI/GQIm * 100 -------(Eq. 2)
Where GQI is the computed Geochemical Quality Index and GQIm is the theoretical maximum obtained in a zone. The mangrove patches along the in the south western coast of India was rated based on their RGQI value as poor (RGQI < 50%), medium (50–70%) and good (RGQI > 70%).
Biomass estimation and Statistical analysis
Non-destructive estimation of above ground biomass (Wtop) and below ground biomass (WR) was done using allometric equations developed by Komiyama et al. (2005).The data obtained for GQI and biomass were subjected to a correlation analysis by Pearson correlation and the level of significance was set at p<0.05 (one-tailed).
Characterization of soil
The mangrove soils in central zone had a finer sandy clay loam texture flanked on both sides (both south and north) by a coarser sandy loam. Accordingly, the bulk density in the central zone (1.70 ± 0.04 g/cm3) was found to be higher than that of southern (1.10 ± 0.01 g/cm3) and northern mangrove zones (1.00 ± 0.01 g/cm3). The pH in all the zones were in the neutral range (6.50 - 7.50) and the redox potential (Eh) varied from -167 ± 2.31 mV to -215 ± 0.17mV. The electrical conductivity and cation exchange capacity were 2.22 ± 0.04 dS/m and 10.43 ± 0.14 cmol (P+)/ kg soil in northern zone, 2.19 ± 0.03 dS/m and 12.00 ± 0.18 cmol (P+)/ kg soil in central zone and 2.24 ± 0.03 dS/m and 7.90 ± 0.34 cmol (P+)/ kg soil in southern zone (Table 1).
Development of GQI and RGQI of mangrove ecosystem
A total of twenty-soil parameters vis – a – vis sand, silit, clay, Mn, Pb, Na, Cu, EH, K, Ni, pH, EC, Fe, Cd, Zn, Co, B P, OC an Ca. From the correlation matrix (Table 2), the correlation coefficient <0.60 was considered for the PCA analysis. Thus eleven soil parameters namely percent clay (-0.864**), percent silt (0.765**), percent sand (-0.985**), EC (0.699**), Eh (-0.676**), organic carbon (0.744**), Cu (-0.843**), Fe (0.801**), K (0.751**), Ca (0.66**) and bulk density (-0.614**) were selected for PCA.
Three principal components (PCs) (eigen values> 1) were found to contribute 30.29%, 16.26% and 13.99% of variances, respectively (Table 3). Cumulatively, these three PCs explained 60.54 percent variance in the system. From each of the selected PCs, the variables having their absolute values within 10% of the highest factor loading or with values ≥ 0.40 were retainedin the MDS
Accordingly, from the first PC, percent sand (-0.962), percent silt (0.914) and percent clay (0.933) that showed higher values of coefficients of correlation were retained in the MDS. From the second PC, EC (0.574) and Cu (0.767) and from the third PC, Fe (0.613) and K (0.605) were selected for the MDS. The final database of MDS thus contained a set of seven attributes, reduced from the original twenty parameters (Table 4).
The selected MDS variables were scored by using ‘more is better’ function based on their roles in soil function. The indicators were transformed into unit less data using the scoring functions (Table 5) and the GQI was calculated. The weights for each MDS variablewas determined by dividing the per cent of variation of the respective PC for the variable by the total percentage of variation explained by all PCs with eigen values >1 (Table 4).
The calculated GQI (Table 6) of different mangrove zones showed that the southern zone had a relatively lower soil quality than the other two zones with the values ranging from 1.11 to 1.17. The GQI in central and northern zones were found to vary from 1.14 to 1.63 and 1.17 to 2.45, respectively. Biomass was found to have a significant correlation with the developed index (Figure 3).
The RGQI values (Table 6) were used to have a relative assessment of the mangrove systems in the region with respect to the system having maximum GQI values. Accordingly, it was found that the RGQI values varied from 45.26 to 47.79% in the southern zone , from 47.75 to 66.44% in the central zone and from 46.53 to 100%. in the northern zone. Accodingly, the mangrove soils in the southern and central zones varied from poor to medium with respect to the maximum assessed quality. Northern zone had the maximum soil quality variation in the mangrove systems with the RGQI values varying from 46.53 to 100%.
Soil characterization
The higher bulk density in the central zone may be attributed to the relatively higher clay contents in these soils, which effectively clog the pore spaces and thereby reduce porosity. aaaa et al. (1998) had observed that mangrove soils are bound with low-energy waters, which is favourable for the sedimentation of clay particles +thereby effectively reducing the pore volume. As bulk density is inversely related to the pore volume, reduction in porosity will enhance the bulk density (Chaudhari et al. 2013; Kakaire et al.. 2015). The mangrove soils were found to have near neutral pH values ranging from 6.64 (northern zone) to 7.31 (central zone). Generally, soils in the humid tropics tend to be acidic owing to the leaching of bases under high precipitation (Sandeep and Sujatha, 2014; Nanganoa et al., 2019). On the other hand, mangrove systems are inundated with water rich in bases (Na, K, Ca and Mg), which effectively gets deposited in these soils and raise the pH to a near neutral range.
Soil organic carbon contents of the mangrove systems in the region revealed a greater concentration in central zone, which may be attributed to litter fall and high decomposition rate in this zone than other zones. The wide range of variations was attributed to nature of vegetation, topography, soil texture, rate of accumulation of dead and decayed foliage and soil texture (Hussain and Badola 2008, Sahoo et al. 2017). Species composition changes the soil properties in southern zone with respect to northern and central zones. This results are in confirmation with the studies by Naidoo (1980) and Saha and Choudhury (1995) wherein they showed that climatic variations such as tidal inundation, temperature and precipitation influence soil characteristics which in turn control the mangrove species that grows in that particular soil ((Banerjee, 1987) . Hence, a soil – vegetation loop can be assumed wherein the soil physico-chemical characteristics would have influenced the species composition and structure of mangroves in each zone and this vegetation would have, in turn, manipulated the soil variables where they grow in accordance with their inherent capacities .
Soil quality assessment using GQI and RGQI
The PCA and the subsequent data reduction yielded seven soil attributes as the most important variables for soil quality evaluation in the mangrove systems of South Western India: sand, silt, clay, EC, Cu, Fe and K . The derived factors have already been reported to have significant correlations with mangrove growth as for example, mangrove density is positively correlated with EC (Dasgupta et al 2018). Sand, silt and clay determine the texture of the soil and a finer texture capable of relatively higher water retention would be the most ideal base matrix for mangrove growth. The mangrove soils are continuously replenished with fresh sediments during the high tide – low tide cycle. Studies have shown that soils with an almost equal proportion of sand silt and clay with approximate quantities of 35, 40 and 45%, respectively would be ideal for mangrove growth (Sah et al., 1989; Ukpong, 1997; Sukardjo, 1994) wherein it promotes good root growth (Dasgupta et al 2018). Besides regulating soil porosity and bulk density, the type and quantity of clay sized fractions are crucial in regulating an array of soil processes and functions ranging from heavy metal retention to nutrient dynamics. However, there would be a critical limit for the soil fineness where after there would be an impedance to mangrove root growth and development. Hence, the derived textural parameters of sand, silt and clay could be considered as critical indicators of mangrove geochemical quality at a particular site. .
Electrical conductivity, another independent variable in the index represents soil salinity by tidal inundation in mangrove systems. Though mangrove species are halophytes, the relative salinity tolerance varies among the species. Studies in the mangrove systems of Sundarbans indicates that certain species such as Avicennia officinalis and A. marina are tolerant to a wider soil salinity range than species such as Ceriops decandra, Aegiceras corniculatum, Excoecaria agallocha and Derris trifoliate. In general, mangrove growth is relatively less luxuriant in higher salinities due to the diversion of energy expenditure from growth to maintenance of ion and water balances in the plants (Lin and Sternberg, 1993; Kathiresan et al., 1996; Suwa et al., 2009). Accordingly, a list of common mangrove species with their salinity tolerance ranges would be critical in developing mangrove restoration strategies once its determined that the electrical conductivity is the detrimental factor in the GQI of a particular site.
Among the soil nutrients K, Fe and Cu were found to be determining factors influencing geochemical quality of mangrove sites. Potassium, a major essential nutrient is involved in ion balances ,growth and reproduction regulations (Das et al. 1997). There exists a data gap on K influences on mangrove productivity. Few studies have indicated that a high K- Na ratio in the mangrove cells is essential in maintaining the semi permeability of plasma lemma and thereby ion exchanges (Yates et al., 2002). Mangroves, being subjected to intense saline environments would have to rely on the balances of these ions in soils to maintain this ratio in cells and survive in the harsh conditions (Ukpong 1997). On the other hand, Fe and Cu are essential micronutrients actively involved in a series of regulatory functions in plants (Alongi 2021). Humid tropical soils are inherently high in Fe and their geochemistry have profound influences on the soil chemical processes. Besides being a micronutrient, Fe is also associated with some important mangrove ecosystem services such as toxic element retention and carbon sequestration. Queiroz et al. (2022) revealed that the biogeochemistry of Fe in mangrove systems is highly sensitive to climate change and could initiate a mangrove mortality – iron content reduction loop in the ecosystem. Similar to Fe, Cu is essential in different plant metabolic processes ranging from lignin synthesis to photosynthesis, but can be toxic at higher concentrations, making it a critical variable in quality indices for mangroves (Pilon et al., 2006; Almahasheer et al. , 2018).
Chen et al. (2013) showed that soil quality indicators selection after scoring of soil attributes and the incorporation of such indicators into a single index could carry information about alteration of soil quality and offer a good indication of soil evolution than individual properties. A correlation between Geochemical Quality Index and biomass showed that there exists a strong relationship of the developed index with biomass (R2=0.89, Figure 3). Earlier studies have also showed that above and below ground biomass were influenced by soil attributes (Johnen and Sauerbeck 1977, Batjes 1996, Bolinder et al. 2002, Dodd and Mackay 2011, McNally et al. 2015, Ibrahim 2019). The vegetation in turn plays a significant role in regulating soil quality through supplementing soil organic matter bulk density and nutrient availability (Ngo-Mbogba et al. 2015). The results of the present study revealed that the extracted MDS and developed GQI and RGQI would be useful tools for evaluating the mangrove geochemical quality and aid in developing management and afforestation strategies.
The GQI was developed to integrate seven measured physical and chemical properties in to a single index, which can be used to assess the health of mangrove soil and establish baseline soil health levels of mangroves across the South Western coast of India. A perusal of the RGQI values revealed that apart from a few mangroves in the Northern zone, no good category mangroves were found in the entire region, Good correlations between the developed index and biomass contents indicates the practical utility of these index in evaluating the mangrove systems of south west coast of India.
Acknowledgements We are indebted to KSCSTE – Kerala Forest Research Institute for providing the facilities and support to execute the work.
Authors’ Contributions All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Rajan Renuka, Sasidharan Sandeep, Kalapuzha Ashtamoorthy Sreejith, Vadakethil Balakrishnan Sreekumar and Rakkodan Anil Kumar. The first draft of the manuscript was written by Rajan Renuka and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.”
Data Availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript
Conflicts of Interest/Competing Interests Authors declare that they have no conflict of interest
Ethics Approval We responsible for integrity of our research
Consent to Participate Not applicable.
Consent for Publication Not applicable
- Alongi DM (2016) Mangroves. In: Kennish, M.J. (eds) Encyclopedia of Estuaries. Encyclopedia of Earth Sciences Series. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-8801-4_3
- Andrews SS, Karlen DL, Mitchell JP (2002) A comparison of Geochemical Quality Indexing methods for vegetable production systems in Northern California. Agriculture, Ecosystem and Environment 90: 25–45
- Andrews SS, Mitchell JP, Mancinelli R, Karlen DL, Hartz, TK, Horwath WR, Pettygrove GS, Scow KM, Munk DS (2001) On-farm assessment of soil quality in California’s Central Valley. Journal of Agronomy 94:12–23
- Anup KC, Govinda B, Subigya PW, Yubraj B (2013) Status of soil fertility in a community forest of Nepal. International Journal of Environment 1: 56-67
- Armenise E, Redmile-Gordon MA, Stellacci AM, Ciccarese A, Rubino P (2013) Developing a soil quality index to compare soil fitness for agricultural use under different managements in the Mediterranean environment. Soil Tillage Research 130: 91–98
- Bai JH, Xiao R, Zhang KJ, Gao HF (2012) Arsenic and heavy metal pollution in wetland soils from tidal freshwater and salt marshes before and after the flowsediment regulation regime in the Yellow River Delta, China. Journal of Hydrology 244–253.
- Bai Z, Caspari T, Gonzalez MR, Batjes NH, Mader P et al. (2018) Effects of agricultural management practices on soil quality: A review of long-term experiments for Europe and China. Agriculture, Ecosystem and Environment 265: 1–7. https://doi.org/10.1016/j.agee.2018.05.028
- Bastida F, Moreno JL, Hernández T, García C (2006) Microbiological degradation index of soils in a semiarid climate. Soil Biology and Biochemisrey 38: 3463–3473
- Batjes NH (1996) Total carbon and nitrogen in the soils of the world. European Journal of Soil science 47: 151–163
- Bayraktarov E, Roy E P, Timothy GF, Kai F (2013) The pH and pCO2 dependence of sulfate reduction in shallow-sea hydrothermal CO2 – venting sediments (Milos Island, Greece). Front Microbiology 4: 111-115
- Bhowmik A, Kukal SS, Saha D, Sharma H, Kalia A, Sharma, S (2019) Potential indicators of soil health degradation in different land use-based ecosystems in the Shiwaliks of North western India, Sustainability. 11:3908. https://doi.org/10.3390/su11143908
- Bolinder MA, Angers DA, Bélanger G, Michaud R, Laverdière MR (2002) Root biomass and shoot to root ratios of perennial forage crops in eastern Canada. Canadian Journal of Plant Science 82:731–737
- Bouyoucos GJ (1962) Hydrometer method improved for making particle size analysis of soils. Agronomy Journal 54: 464-465
- Bucher E (2002) Soil quality characterization and remediation in relation to soil management; PhD thesis, Department of Crop and Soil Sciences, Pennsylvania State University, USA
- Chapman HD (1965) Cation-exchange capacity 1.Methods of soil analysis. Part 2. Chemical and microbiological properties (methods of soil analysis). Pp 891-901
- Chaudhari R, Ahire V, Vidya D, Chkravarty M, Maity S (2013) Soil bulk density as related to soil texture, organic matter content and available total nutrients of Coimbatore Soil. International Journal of Scientific and Research Publications 3: 123-131
- Chen YD, Wang HY, Zhou JM, Xing ., Zhu BS, Zhao YC, Chen XQ (2013) Minimum data set for assessing soil quality in farmland of northeast China. Pedosphere 23 (5): 564–576
- Dasgupta MA, Ghosh SM, Sen SNP (2018) Influence of soil texture on nature of mangrove vegetation in Sundarbans Tiger Reserve forest of India. International Journal of Environment Agriculture and Biotechnology 3(2): 626-656
- Ditzler CA, Tugel AJ (2002) Soil quality field tools. Journal of Agronomy 94 (1): 33–38
- Dodd MB, Mackay AD (2011) Effects of contrasting soil fertility on root mass, root growth, root decomposition and soil carbon under a New Zealand perennial ryegrass/white clover pasture. Plant and Soil 349: 291–302
- Dolcater DL, Jackson ML, Syers JK (1972) Cation exchange selectivity in mica and vermiculite. The American Mineralogy 57: 1823-1831.
- Duval ME, Martinez JM, Galantini JA (2020) Assessing soil quality indices based on soil organic carbon fractions in different long-term wheat systems under semiarid conditions. Soil Use and Management 36:71–82. https://doi.org/10.1111/sum.12532
- Giannitsopoulos ML, Burgess PJ, Rickson RJ (2019) Effects of conservation tillage systems on soil physical changes and crop yields in a wheat–oilseed rape rotation. Journal of Soil, Water and Conservation 74: 247–258. https://doi.org/10.2489/jswc.74.3.247
- Glover JD, Reganold JP, Andrews PK (2000) Systematic method for rating soil quality of conventional, organic, and integrated apple orchards in Washington State. Agricuture, Ecosystem and Environment 80: 29–45
- Gong L, Ran QY, He GX, Tiyip T (2015) A soil quality assessment under different land use types in Keriya river basin, Southern Xinjiang, China. Soil Tillage Research 146: 223–229
- Griffiths BS, Daniell TJ, Hallet PD, Neilson R, Wheatley RE, Osler G, Bohanec M (2010) Integrating soil quality changes to arable agricultural systems following organic matter addition or adoption of aley-arable rotation. Applied Soil Ecology 46: 43-53
- Hussain S A, Badola R (2008) Valuing mangrove ecosystem services: Linking nutrient retention function of mangrove forests to enhanced agroecosystem production. Wetlands Ecology and Management 16: 441-450
- Ibrahim JA (2019). Evaluation of soil quality under Date Palm plantation for climate change and food security in Gombe State University, Gombe Nigeria. International Journal of Environmental Quality 35: 51-64
- Imaz MJ,Virto I, Bescansa P, Enrique A, Fernandex-Ugalde O, Karlen DL (2010) Soil quality indicator response to tillage and residue management on semi-arid Mediterranean cropland. Soil and Tillage Research 107: 17-25
- Jackson ML (1957) Soil chemical analysis by Prentice Hall of India Pvt. Ltd. New Delhi.
- Jernigan AB, Wickings K, Mohler CL, Caldwell BA, Pelzer CJ, Wayman S, Ryan MR (2020) Legacy effects of contrasting organic grain cropping systems on soil health indicators, soil invertebrates, weeds, and crop yield. Agricultural Systems 177: 102719. https://doi.org/10.1016/j.agsy.2019.102719
- Jian J, Lester BJ, Du X, Reiter MS, Stewart RD (2020) A calculator to quantify cover crop effects on soil health and productivity. Soil Tillage and Research 199: 104575. https://doi.org/10.1016/j.still.2020.104575
- Johnen BG, Sauerbeck DR (1977) A tracer technique for measuring growth, mass and microbial breakdown of plant roots during vegetation. Ecology Bulletin 366– 373
- Karlen DL, Ditzler CA, Andrews SS (2003) Soil quality: why and how? Geoderma 114: 145–156
- Karlen DL, Stott DE (1994) A framework for evaluating physical and chemical indicators of soil quality. p. 53–72. In J.W. Doran quality: A minimum data set. p. 25–37. In J.W. Doran and A.J. et al. (ed.) Defining soil quality for a sustainable environment. SSSA Spec. Publ. No. 35. SSSA, Madison, WI
- Khan MI, Gwon HS, Alam MA, Song HJ, Das S Kim PJ (2020) Short term effects of different green manure amendments on the composition of main microbial groups and microbial activity of a submerged rice cropping system. Applied Soil Ecology 147: 103400. https://doi.org/10.1016/j.apsoil.2019.103400
- Komiyama A, Poungparn S, Kato S (2005) Common allometric equations for estimating the tree weight of mangroves. Journal of Tropical Ecology 21. 10.1017/S0266467405002476
- Larson WE, Pierce FJ (1994) The dynamics of soil quality as a measure of sustainable management. In: Doran, J.W., Coleman, D.C., Bezdicek, D.F., Stewart, B.A. (Eds.), Defining Soil Quality for A Sustainable Environment. Soil Science Society of America Inc., Madison, USA, pp 37–51
- Li T, Sun Z, He C, Ge X, Ouyang Z,Wu L (2020) Changes in soil bacterial community structure and microbial function caused by straw retention in the North China Plain. Archieves of AgronomyandSoilScience 66: 46–57, https://doi.org/10.1080/03650340.2019.1593382
- Liebig MA, Varvel G, Doran JW (2001) A simple performance-based index for assessing multiple agroecosystem functions. Agronomy and Horticulture- Faculty Publications. 336: https://digitalcommons.unl.edu/agronomyfacpub/336
- Liebig MA, Varvel G, Doran JW (2001) A simple performance-based index for assessing multiple agroecosystem functions. Agronomy and Horticulture- Faculty Publications. 336: https://digitalcommons.unl.edu/agronomyfacpub/336
- Liu ZJ, Zhou W, Shen JB, Li ST, He, LGQ (2014a) Soil quality assessment of Albic soils with different productivities for eastern China. Soil Tillage Research 140: 74–81
- Liu, ZJ, Zhou W, Shen JB, Li ST, Ai C (2014b). Soil quality assessment of yellow clayey paddy soils with different productivity. Biology and Fertility of Soils 50(3): 537-548
- Liu M, Wang C, Wang F, Xie Y (2019) Vermi compost and humic fertilizer improve coastal saline soil by regulating soil aggregates and the bacterial community. Archieves of Agronomy and Soil Science 65:281–293.https://doi.org/10.1080/03650340.2018.1498083
- Nanganoa Lawrence, Justin Okolle , Missi Valentine, Tueche Jacques Roberto, Levai Lewis, Nkengafac, Njukeng (2019) Impact of Different Land-Use Systems on Soil Physicochemical Properties and Macrofauna Abundance in the Humid Tropics of Cameroon. Applied and Environmental Soil Science. 1-9. 10.1155/2019/5701278.
- McNally SR, Daniel CL, Susanna R Dodd M B, Johan S, Louis A S (2015) Root carbon inputs under moderately diverse sward and conventional ryegrass-clover pasture: implications for soil carbon sequestration and Plant Soil :289–299
- Mundepi A, Cabrera M, Norton J, Habteselassie M (2019) Ammonia oxidizers as biological health indicators of elevated Zn and Cu in poultry litter amended soil, Water Air and Soil Pollution 230-239, https://doi.org/10.1007/s11270-019-4283-x
- Navas M, Benito M, Rodríguez I, Masaguer A (2011) Effect of five forage legume covers on soil quality at the Eastern plains of Venezuela. Application of Soil Ecology 49: 242–249
- Ngo-Mbogba M, Yemefack M, Nyeck B (2015) Assessing soil quality under different land cover types within shifting agriculture in South Cameroon Soil and Tillage Research. https://doi.org/10.1016/j.still.2015.01.007
- Pazi MAM, Khan WR, Nuruddin AA, Adam MB, Gandaseca S (2021)Development of Mangrove Sediment Quality Index in Matang Mangrove Forest Reserve, Malaysia: A Synergetic Approach. Forests 12:1279. https:// doi.org/10.3390/f12091279
- Perez-Jaramillo, JE, de Hollander M, Ramírez CA, Mendes R, Raaijmakers JM, Carrión VJ ( 2019) Deciphering rhizosphere microbiome assembly of wild and modern common bean (Phaseolus vulgaris) in native and agricultural soils from Colombia. Microbiome 7:114. https://doi.org/10.1186/s40168- 019-0727-1
- Qi Y, Wijeratne AJ, Tmsho LP, Hu Y, Schuster SC, Ma H (2009) Evaluating soil quality indices in an agricultural region of Jiangsu Province, China. Geoderma 149: 325-334.
- Rahmanipour F, Marzaioli R, Bahrami HA, Fereidouni Z, Bandarabadi SR (2014)Assessment of soil quality indices in agricultural lands of Qazvin province, Iran. Ecological Indicator 40 :19–26. https://doi.org/10.1016/j.ecolind.2013.12.003
- Rezaei SA, Gilkes RJ, Andrews SS (2006) A minimum data set for assessing soil quality in rangelands. Geoderma 136: 229–234
- Rinot O, Levy GJ, Steinberger Y, Svoray T, Eshel G (2019) Soil health assessment: A critical review of current methodologies and a proposed new approach. Science of Total Environment 648: 1484– 149. https://doi.org/10.1016/j.scitotenv.2018.08.259
- Sahoo AK, Singh SK, Nayak DC, Mukhopadhyay S, Banerjee T, Sarkar D, Sarkar A K, Agarwal BK, Shahi DK, Arvind Kumar (2017) Soil acidity and poor nutrient status: Emerging issues for agricultural land use planning in Jamtara district of Jharkhand. Sustainable Management of Land Resources 7: 485-510
- Sandeep S, M P Sujatha (2014) Mineralogy of kaolin clays in different forest ecosystems of southern Western Ghats, India. Current Science 107: 5
- Secor H, Rooker JR (2005) Connectivity in the life histories of fishes that use estuaries. Estuary, Coast an Shelf Science 64: 1–3
- Silvestri S, Defina A, Marani M (2005) Tidal regime, salinity and salt marsh plant zonation. Estuarine, Coastal and Shelf Science 62:119–130
- Queiroz, H.M., Ferreira, T.O., Fandiño, V.A. et al. (2022) Changes in soil iron biogeochemistry in response to mangrove dieback. Biogeochemistry 158 357–372.
- Takkar (1969) Effect of organic matter on soil Iron and Manganese; Soil science. 108: 108-112
- VeVerka JS, Udawatta RP, Kremer R J (2019) Soil health indicator responses on Missouri claypan soils affected by landscape position, depth, and management practices. Journal of Soil and Water Conservation 74: 126–137. https://doi.org/10.2489/jswc.74.2.126
- Walkley A, Black IA (1934) An examination of the Degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method. Soil science. 37: 29-38
- Wander M M, Bollero G A (1999) Soil quality assessment of tillage impacts in Illinois; Soil Science Society America Journal. 63: 961–971
- Weissengruber L, Moller K, Puschenreiter M, Jurgen KF (2018) Long-term soil accumulation of potentially toxic elements and selected organic pollutants through application of recycled phosphorus fertilizers for organic farming conditions; Nutrient Cycle and Agroecosystem 110: 427–449
- Williams H, Colombi T, Keller T (2020) The influence of soil management on soil health: An on-farm study in southern Sweden. Geoderma 360: 114010, https://doi.org/10.1016/j.geoderma.2019.114010
- Wolanski E, Gibbs RJ, Spagnol S, King B, Brunskill G (1998) Inorganic sediment budget in the mangrove-fringed Fly River delta, Papua New Guinea. Mangroves Salt Marshes 2: 85-98
- Woldendorp J W (1965) Nitrogen and grassland. Annales de I’Institut Pasteur (Paris). 3: 316-329
- Woldendorp J W (1968) Loss of soil nitrogen; Stikstof.12: 32-46
- Xue YJ, Liu SG, Hu YM, Yang JF (2010) Soil quality assessment using weighted fuzzy association rules. Pedosphere 20, 334–341
- Yang C, Liu N, Zhang, Y (2019) Soil aggregates regulate the impact of soil bacterial and fungal communities on soil respiration. Geoderma 337: 444 - 452. https://doi.org/10.1016/j.geoderma.2018.10.002
- Yao R, Yang J, Gao P, Zhang J, Jin W (2013) Determining minimum data set for soil quality assessment of typical salt-affected farmland in the coastal reclamation area. Soil and Tillage Research 128: 137-148
Table 1. Physico – chemical characters of mangrove soils (Mean ± SE) in the South Western Coast of Peninsular India
|
Soil attributes |
Central zone |
Northern zone |
Southern zone |
|
Texture |
Sandy clay loam |
Sandy loam |
Sandy loam |
|
Sand (%) |
61.42 ± 0.24 |
81.60 ± 0.14 |
81.51 ± 0.34 |
|
Clay (%) |
30.20 ± 0.15 |
16 ± 0.05 |
15.90 ± 0.10 |
|
Silt (%) |
8.00 ± 0.05 |
1.90± 0.05 |
1.90 ± 0.05 |
|
BD (g/cm3) |
1.7 ± 0.04 |
1 ± 0.01 |
1.1 ± 0.01 |
|
pH |
6.64 ± 0.20 |
6.81 ± 0.29 |
7.31 ± 0.30 |
|
Eh (mV) |
-167.00 ± 2.31 |
-215.00 ± 0.17 |
-177.85 ± 0.11 |
|
EC (dS/m) |
2.19 ± 0.03 |
2.22 ± 0.04 |
2.24 ± 0.03 |
|
OC (%) |
2.00 ± 0.03 |
1.83 ± 0.01 |
0.81 ± 0.03 |
|
CEC (cmol(p+)/kg soil) |
12.00 ± 0.18 |
10.43 ± 0.14 |
7.90 ± 0.34 |
Table 2 Correlation between soil parameters of the mangrove systems in the South Western Coast of Peninsular India
|
Soil attributes |
Sand |
Silt |
Clay |
Mn |
Pb |
Na |
Cu |
Eh |
K |
Ni |
pH |
EC |
Fe |
Cd |
Zn |
Co |
BD |
P |
OC |
Ca |
|
Sand |
1 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Silt |
-.985** |
1 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Clay |
-.864** |
.765** |
1 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Mn |
.068 |
-.021 |
-.193 |
1 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Pb |
.006 |
.042 |
-.148 |
.868** |
1 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Na |
.017 |
.005 |
-.078 |
.562** |
.553** |
1 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Cu |
.213 |
-.212 |
-.180 |
.108 |
.843 |
.129 |
1 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Eh |
-.147 |
.186 |
.007 |
.101 |
.002 |
.027 |
-.676** |
1 |
|
|
|
|
|
|
|
|
|
|
|
|
|
K |
.079 |
-.061 |
-.118 |
.127 |
.038 |
.751 |
-.393** |
.170 |
1 |
|
|
|
|
|
|
|
|
|
|
|
|
Ni |
.163 |
-.226 |
.048 |
.051 |
.010 |
.118 |
.129 |
-.260 |
.214 |
1 |
|
|
|
|
|
|
|
|
|
|
|
pH |
.328* |
-.366** |
-.158 |
-.225 |
-.151 |
-.115 |
.058 |
-.193 |
.410** |
.616** |
1 |
|
|
|
|
|
|
|
|
|
|
EC |
.257 |
-.236 |
-.273* |
.101 |
-.043 |
-.033 |
.699** |
-.191 |
-.342* |
.137 |
-.070 |
1 |
|
|
|
|
|
|
|
|
|
Fe |
-.175 |
.161 |
.186 |
-.120 |
-.151 |
-.230 |
.019 |
.035 |
-.025 |
.377** |
.801 |
.267 |
1 |
|
|
|
|
|
|
|
|
Cd |
-.130 |
.115 |
.150 |
-.043 |
.051 |
.356** |
.241 |
-.296* |
-.389** |
.022 |
-.083 |
-.163 |
-.128 |
1 |
|
|
|
|
|
|
|
Zn |
-.201 |
.168 |
.260 |
-.032 |
-.040 |
-.059 |
.071 |
-.083 |
-.119 |
.059 |
.077 |
.147 |
.126 |
.115 |
1 |
|
|
|
|
|
|
Co |
-.252 |
.253 |
.204 |
.138 |
.230 |
.088 |
.287* |
-.280* |
-.185 |
.111 |
-.250 |
.071 |
.105 |
-.014 |
.116 |
1 |
|
|
|
|
|
BD |
-.614** |
.448** |
.205 |
.052 |
.057 |
.126 |
-.110 |
.142 |
.044 |
-.179 |
-.238 |
-.022 |
.097 |
.075 |
.115 |
.105 |
1 |
|
|
|
|
P |
.002 |
-.015 |
.041 |
-.097 |
-.043 |
-.052 |
-.210 |
.799** |
-.891** |
.040 |
.151 |
-.181 |
-.056 |
-.066 |
-.062 |
-.151 |
-.134 |
1 |
|
|
|
OC |
-.204** |
.248** |
.215 |
.092 |
.061 |
.126 |
-.110 |
-.742** |
-.744** |
-.179 |
-.238 |
-.022 |
.097 |
.075 |
.115 |
.105 |
.125 |
.115 |
1 |
|
|
Ca |
-.301 |
.168 |
.660 |
-.032 |
-.040 |
-.059 |
.061 |
-.073 |
-.129 |
.049 |
.057 |
.127 |
.116 |
.125 |
-.280 |
.051 |
.205 |
-.024 |
-0.231 |
1 |
|
*.Significant at the 0.05 level, **. Significant at the 0.01 level |
|
|
||||||||||||||||||
Table 3 Principal Component Analysis of soil parameters
|
Particulars |
PC1 |
PC2 |
PC3 |
|
Eigen value |
3.331 |
1.789 |
1.539 |
|
% of variance |
30.29 |
16.26 |
13.99 |
|
Cumulative % |
30.29 |
46.55 |
60.54 |
|
Sand |
-0.962 |
-0.301 |
0.136 |
|
Clay |
0.933 |
0.421 |
0.389 |
|
Silt |
0.914 |
0.369 |
-0.412 |
|
OC |
0.547 |
0.333 |
0.444 |
|
Cu |
0.337 |
0.767 |
0.396 |
|
EC |
0.531 |
0.574 |
-0.372 |
|
Eh |
0.442 |
-0.553 |
-0.391 |
|
Fe |
0.521 |
0.412 |
0.613 |
|
K |
0.348 |
-0.344 |
0.605 |
|
Ca |
0.321 |
0.459 |
0.585 |
|
BD |
0.449 |
0.423 |
-0.532 |
Table 4 Minimum data set and weight of each principal components
|
|
PC1 |
PC2 |
PC3 |
|
MDS |
Sand |
Cu |
Fe |
|
Silt |
EC |
K |
|
|
Clay |
|
|
|
|
|
|
|
|
|
Weights (Wi) |
0.50 |
0.27 |
0.23 |
Table 5 Mean of scores obtained for each MDS variable
|
|
Southern zone |
Central zone |
Northern zone |
|
Sand |
0.341 |
0.378 |
0.488 |
|
Silt |
0.567 |
0.512 |
0.251 |
|
Clay |
0.512 |
0.567 |
0.268 |
|
Cu |
0.052 |
0.231 |
0.106 |
|
EC |
0.051 |
0.252 |
0.069 |
|
Fe |
0.129 |
0.231 |
0.183 |
|
K |
0.025 |
0.154 |
0.069 |
Table 6 Computed GQI and RGQI of mangrove soils in the South Western Coast of Peninsular India
|
|
Southern zone |
Central zone |
Northern zone |
|
Computed GQI of mangrove soils |
1.11 – 1.17 |
1.14- 1.63 |
1.17 – 2.45 |
|
Relative Geochemical Index of mangrove soil (RGQI) |
45.26 - 47.79 |
47.75-66.44 |
46.53–100 |