chemical and biological indicators of soil quality - yadvinder singh, pau

7/31/2019 Chemical and Biological Indicators of Soil Quality - Yadvinder Singh, PAU

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Chemical and Biological

Indicators of Soil Quality

Yadvinder Singh

Punjab Agricultural University

PAU Ludhiana


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INTRODUCTION In our drive to meet the food and fiber needs of

ever-increasing populations, we are taxing the

resilience of the planets natural resources. Thisfevered quest to pursue ever-increasing cropyields has led to soil degradation due towidespread soil erosion, atmospheric pollution,over-cultivated fields, poor quality water supplies,

decline in soil fertility and desertification, which isa closes associated with the loss of soil quality.

There is now growing concern over the ability ofthe soil to sustain the increasing demands that we

place upon it Soil quality assessment has been suggested as a

tool for evaluating sustainability of soil and cropmanagement.


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Soil Quality (SQ)defined

The term "soil quality" has been coined to describe thecombination of chemical, physical, and biologicalcharacteristics that enables soils to perform a widerange of functions. SQ requires an holistic approach,

and it is not possible to consider one component ofquality in isolation.

Doran et al. (1996) defined SQ as the capacity of a soilto function, within ecosystem and land use boundaries,to sustain biological productivity, maintainenvironmental quality, and promote plant, animal andhuman health.

This definition provides a focal point for assessing theintensity of soil degradation.

Soil quality is related to soil functions and soil healthconcepts & views soil as a finite and dynamic livingresource (Doran and Zeiss, 2000).

Plant health is clearly a component of soil health butnecessarily not of soil quality (Karlen et al., 1997).


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Goals of Soil Quality Research Current soil quality research has several motivations. The most

important is the desire to improve environmental quality and

productivity through better site-specific (and soil-specific)management decisions. Because site-specific assessment is important to this work, the

relationship between researchers and farmers is a criticalcomponent of the study of soil quality.

Soil quality researchers are asking how the whole productionsystem (e.g., tillage, planting, harvest, and crop rotation)changes the pest, water, and nutrient cylces which changefarm productivity and water quality over the long term

Soil quality research has expanded the understanding of theindividual components. For example, it has promoted

development of new measures of biological characteristics. A major goal in SQ studies is to ascertain, where possible, links

between properties (or indicators) and a specific function ofthe soil (e.g. crop productivity).


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Dynamic components of soilquality (Karlen et al., 1994)


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Soil Quality Management Options

Reducing or modifying Tillage

Growing cover crops

Crop Rotation

Adding organic matter/crop residues Adding chemical amendments


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Soil Quality IndicatorsSQ is rather dynamic and can affect thesustainability and productivity of land use. It is

the end product of soil degradative or conservingprocesses and is controlled by chemical,physical, and biological components of a soil andtheir interactions .

Indicators, however, will vary according to thelocation, and the level of sophistication at whichmeasurements are likely to be made (Riley,2001). Therefore, it is not possible to develop asingle short list which is suitable for all purposes.

Typical soil tests only look at chemical indicators. SQ attempts to integrate all three types of

indicators.


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There are several criteria to consider when selecting soil healthand soil

quality indicators. In general, ideal indicators should: able to measure changes in soil function both at plot and

landscape scales. assessed by both qualitative and/or quantitative approaches. correlate well with ecosystem processes integrate soil physical, chemical, and biological properties &

processes be accessible to many users be sensitive to management & climate be interpretable easy to measure and rapid/less time consuming method

Some soil properties are relatively insensitive to degradation orpollution but are important for interpreting the results ofindicator measurements. For example: soil texture; availablewater holding capacity; and CEC.

Other soil properties that are sensitive to degradation orpollution are suitable for routine and frequent measurement

Criteria for selecting indicators of soil quality


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Chemical Indicators of Soil Quality

Chemical indicators can give you information

about the equilibrium between soil solution(soil water and nutrients) and exchange sites(clay particles, organic matter); plant health;the nutritional requirements of plant and soil

animal communities; and levels of soilcontaminants and their availability for uptakeby animals and plants.

Results of chemical tests are soil quality

indicators which provide information on thecapacity of soil to supply mineral nutrients.


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Commonly Used chemical & biologicalIndicators of SQ

Chemical Indicators Biological Indicators

% base saturation Organic carbon content

Cation-exchange capacity andexchangeable acidity

Biomass C

Total, bacterial, fungi

Total C and N contentsContaminants- Types (Zn, Cu, Pb), Biomass C/total organic C

Availability, Conc., mobility Potentially mineralizable N

Salinity (EC) Earthworm population

Sodicity (ESP or SAR) Enzymes (Dehydrogenase,Phosphatase, Arlysulfatase)

pH Nematode population (Beneficial andParasitic)

Nutrients (content, availability,

cycling rates)

Substrate utilization

Microbial community (Composition ,Size, Distribution, Respiration)

Total C and N contents Fatty acid composition

Nucleic acid composition

Weed seed bank

Glomalin content


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Chemical indicators of Soil Quality Soil responses to different management

practices may include a large number of

variables. When averaged over the other factors, tillage

and residue management can influence pH,EC, OC, available N, P, K, S, Fe, Cu, Mn, B and

total N, PMN, P, K, Mg, Cu, Mn. For initial screening of indicators, parameters

showing significance in two or more of thetreatment effects are considered important

and retained for principal component analysis( PCA) . Using these criteria, less important soil

properties are dropped and the remaining

properties are selected for PCA.


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Key chemical indicators of SQ (Adapted from

Arshad and Martin, 2002)Selected indicator Rationale for selection

Organic matter Defines soil fertility and soil structure,

pesticide and water retention, and use in

process models

pH Nutrient availability, chemical activity,

pesticide absorption, and use in process models

Electrical conductivity Defines crop growth, soil structure, waterinfiltration; presently lacking in most process

models

Forms of N, PMN Availability to crops, leaching potential,

mineralization/immobilization rates, process

modeling

Extractable N,P,K, and

micronutrients

Capacity to support plant growth,

environmental quality indicators

Heavy metal pollutants

and organic pollutants

Plant quality, and human and animal health


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Chemical Indicators of Soil Quality

Where pollution is suspected, analysis of the particularcontaminants of concern should be conducted (eg. metalsor organic chemicals).

Many countries already have guidelines in place for

allowable contaminant concentrations in soils, howeverthere are wide discrepancies between them, which resultfrom the different philosophies used for setting theguidelines.

The limits of soil metals are set in terms of their total soilconcentrations rather than a measure of metalbioavailability.

Obtaining general agreement on measures of bioavailable

metals will no doubt be difficult, but it is clearly an issuethat requires attention.

Establishing indicator thresholds for contaminants isextremely complex & requires integration of effects on

human health, plants, animals, soil biota, & on otherenvironments.


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ORGANIC MICROPOLLUTANTS One difficulty in using organic

contaminants as SQIs is the largenumber of possible contaminants. TheEuropean Commission (EC) workinggroup on parameters and indicators

suggested that halogenated compounds(HCH, DDT/E), PAHs, polychlorinatedbiphenyls (PCBs) and di-benzofurans/dioxins were likely to be

of greatest concern. However, theirmonitoring would be restricted tospecific sites (EC,2004).


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Biological Indicators of Soil QualityMost processes in soil are driven by micro-organisms,the most abundant being bacteria and fungi. Micro-organisms are the dominant component of soil biomass.

Presence of specific organisms and their populations orcommunity analysis (functional groups and biodiversity)(Linden et al., 1994).

They are the main drivers for the turnover of soil organicmatter, release of nutrients, promotion of plant growth,and degradation of organic pollutants and otherpotential pollutants.Identification of biological indicators of soil quality iscritically important (Doran and Parkin, 1994; Abawi andWidmer, 2000) because SQ is strongly influenced bymicrobiological mediated processes (nutrient cycling,nutrient capacity, aggregate stability).

Of particular importance is to identify those componentsthat rapidly respond to changes in soil quality.


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Commonly Used chemical & biologicalIndicators of SQ

Biological Indicators

Organic carbon content

Biomass C

Total, bacterial, fungi

Total C and N contentsBiomass C/total organic C

Potentially mineralizable N

Earthworm population

Enzymes (Dehydrogenase, Phosphatase, Arlysulfatase)Nematode population (Beneficial and Parasitic)

Substrate utilization

Microbial community (Composition , Size, Distribution,Respiration)

Fatty acid compositionNucleic acid composition

Weed seed bank

Glomalin content (Glomalin is a glycoprotein producedabundantly on hyphae and spores of arbuscular mycorrhizal

fungi in soil and in roots)


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Difficulties in classification of organisms at specieslevel has a major constraint delimiting use ofindicators based on soil organisms, more so the

microfauna.Soil fauna (arthropods and invertebrates) populationsinfluence soil biological processes, nutrient cycling andsoil structure. Several properties or functions of soilfauna can be used as indicator of SQ.

A faunal group, such as nematodes, is likely to beeffective indicator of soil quality if it forms a dominantgroup and occurs in all soil types, has high abundanceand high biodiversity and plays an important role insoil functioning, e.g., in food webs.

Parisi et al. (2000) proposed the index of biologicalquality of soil based on evaluation of microarthropodslevel of adaptation to the soil environment rather thanthe species richness/diversity.

Biological Indicators of Soil Quality


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Soil arthropods

Soil arthropods affect soil quality directly andindirectly depending on their size and specificactivity.

Macroarthropods (millipedes, centipedes, insectlarvae, termites, ants and others) have the abilityto modify soil structure by decreasing bulkdensity, increasing soil pore space, mixing soil

horizons and improving aggregate structure(Abbott, 1989). Microarthropods, primarily mites and

collembolans, affect soil structure indirectly and

nutrient cycling directly (Powers et al., 1998). Field experiment using insecticides showed that

excluding microarthropods reduces rates offorest litter decomposition (Seastedt andCrossley, 1983).


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Enzymes as indicators of SQ

Soil enzyme assays generally provide a measure of thepotential microbial activity. There are large numberof enzymes and one has decide as to which enzymes

would be the best indicators for soil quality. There are at least 500 enzymes participating in the C

and N cycles and it is difficult to know as to whichenzymes are most relevant for soil quality

characterization (Schloter et al., 2003). Three enzymes viz., phosphomonoesterase

(phosphatases), chitinase and phenol oxidase, as agroup reflect relative importance of bacterial andfungi, as well as the nature of organic matter

complex (Giai and Boerner, 2007). Urease activity is used as a SQ indicator because it is

influenced by soil factors such as cropping history,OM content, soil depth, management practices, heavy

metals and environmental factors.


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Earthworms Earthworms are the largest soil invertebrates. They are

considered to be the main soil engineers and changes in theirnumber and community structure can affect several soilcharacteristics, such as porosity, aeration, water holding

capacity, density, recycling and distribution of organic matterand nutrients. As they feed, earthworms participate in plant residue

decomposition, nutrient cycling, and redistribution ofnutrients in the soil profile. Their casts, as well as dead ordecaying earthworms, are a source of nutrients.

No-till increases plant residues and improves soil structure,providing improved habitat for earthworms.

Deep soil-burrowers are lacking in ploughed fields andchanging to no-till may not help their quick establishmentunless they are introduced first.

There are about 20 earthworm species that can be dividedinto four groups:

epigeic species (litter dweller), endogeic species (shallowearth dweller) , anecic species (deep earth dweller) andcompost worms


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Microbial measurements Biomass and respiration. Carbon dioxide evolution can be

measured directly from soil that is held under controlledconditions. This is called basal respiration. It provides ameasure of biological activity, but does not indicate how manyor what kind of organisms are present.

Substrate-induced respirationis a measure of the CO2 evolvedfrom a soil sample after adding sugar.

The ratio of these two numbers is called the metabolic quotient,and is often more informative than either measure alone. The

metabolic quotient is the amount of biological activity dividedby the microbial biomass.

The ratio of microbial carbon to total organic carbonis anothercommon measure of biomass.

Potentially mineralizable nitrogen. This test is an estimate of

the amount of N that is immobilized in organic forms andpotentially could be decomposed by microorganisms into aplant available form. The amount of potentially mineralizable Ndepends on the amount and form of N in the soil, the microbesavailable to degrade N-containing compounds, and a carbon

source to feed the microbes.


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Carbon availability as indicator of SBQ The availability of carbon (C) is important in controlling

nutrient cycling and soil biological activity. CO2 efflux, microbial biomass C (Cmic), respiratory

quotient (qCO2); & microbial efficiency quotient(qCmic) can be used to evaluate soil quality. Soil CO2 efflux is an index of total soil biological activity

including soil microorganisms, macro-fauna and plantroots.

Measurement of CO2 efflux yields an index of totalcarbon availability.

Respiratory quotient (qCO2) = CO2 released /O2 consumed),has been recommended by Anderson and Domsch(1990). (

Brooks and McGrath (1984) observed higherrespiratory quotients in soils containing heavy-metalcontaminated sewage sludge, compared with controlsoils containing no heavy metals.


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Soil microbiological Quality index

Computation of this index involves : (i) selection of appropriate parameters, e.g., total

organic carbon, water soluble carbon, Potentially

Mineralizable Nitrogen,water soluble carbohydrates,microbial biomass(total, bacterial, fungal, or all ofthese),Earthworms, microbial biomass carbon & N,Basal respiration, ATP, dehydrogenase, urease,protease, Acid and alkaline phosphatases and beta-

glucosidase acitivity estimated by methods as detailedin Bastida et al. (2006), (ii) transformation and weighting of values and (iii) combining the scores into an index.

The soil microbiological quality index is the sum of thenormalized and weighted values of the most importantparameters.


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Effect of retaining or burning stubble

on soil properties after 17 yrs (Hoyleand Murphy, 2006a,b)


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Effect of tillage and crop on earthworm number/m2

CT=conventional till, NT= no-till; W=wheat, C=corn,S=soybean Adapted from Hubbard, et al. 1999.


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Soil OM as SQ indicator Total organic matter is strongly affected by soil

texture and climate, and requires decades to changesignificantly in response to most management

changes. The active fractions of organic matter respond much

more quickly to management changes.

Analyzing organic matter requires chemical tests,but the results are strongly linked to the physicalstructure and biological activity of the soil.

Highly labile compounds are sources of nutrients

for microorganisms and plants.


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Soil OM as SQ indicator The challenge of this research is that the pools of OM that

can be isolated using laboratory methods are not the sameas the pools that researchers want to study (Parton et al.,

1994). Unfortunately, chemists can only divide soil OM into

physical categories of light and heavy fractions, or chemical categories such as fulvic or humic acids, or

polyphenols, but none of these categories match neatly

with the active vs. highly resistant pools that researcherswant to study. The best proxy measures for the biologically active portion

of soil OM seem to be particulate organic matter and light-fraction organic matter.

Particulate OM has been isolated based on size by sieving(Elliot et al. 1994), and based on weight by centrifugation. Organic matter isolated by weight is also called light-

fraction OM. Light-fraction has a specific density less than2g/cm3, and macro-OM is .05 to 2 mm in size.


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carbon management indexBlair et al. (1995) proposed carbon management index (CMI), a

multiplicative function of carbon pool index (CPI) and labilityindex (LI) as an indicator of the rate of change of SOM inresponse to land management changes, relative to a morestable reference soil.

Carbon pool index (CPI) = Total C of a given land use/Total C ofthe reference land use Lability index (LI) = [Labile C content of a given land use/Non-

labile C content of a given land use] * [Labile C content of thereference land use/Non-labile carbon content of the referenceland use]

Carbon management index (CMI) = CPI * LI * 100 CL declines faster and is restored faster than CNL or CT, and

hence is a more sensitive indicator of the C dynamics of thesystem.

Labile C is measured by using 333 mM KMn04 oxidation methodand total C is measured by Walkley and Black method The results are expressed as mg C g-1 soil. Labile C (CL) = the C oxidized by 333 mM KMn04 and non-labile

C (CNL) = the C not oxidized by KMn04.


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Blair et al. (1995) method using strong KMnO4involves several important limitations . The

highly concentrated solution of KMnO4(333mM) is both difficult to prepare andmaintain and somewhat hazardous to use. .

Weil et al. (2003) attempted to overcome in

developing a simplified, improved method fordetermination of active soil C. They modifiedthe Blair et al. (1995) method to develop 20mM KMnO4 oxidation method which is moresensitive to the effects of soil management,more rapid, reliable and user-friendly to carryout, and suitable for routine use.

Carbon management index


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Sensitivity of total and active carbon under

CT and NT in wheat-based system


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Methods for analysis of SQ

Indicators Several well-established soil tests are

included in most soil quality minimum datasets. Further descriptions of these tests canbe found in Methods of Soil Analysis(SSSABook Series No 5) (2002)

Methods of Soil Analysis. Part 2.Microbiological and Biochemical R. W.Weaver et al.

Methods of Soil Analysis. Part 3. Chemical

Methods Donald L. Sparks (Author, Editor) Methods of Soil Analysis. Part 4. Physical

Methods Jacob H. Dane (Author, Editor),Clarke Topp (Editor)


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Chemical Indicators of Soil Quality Common indicators of soil properties (minimum data set) are recommended to

evaluate soil quality. Soil organic C, total organic N, pH, EC, and extractable N,P,K,micronutrients have been recommended as useful soil quality indicators. (Doranand Parkin, 1996).

SOC plays an important role in soil quality due to improvement in infiltration andcrop available soil water.

Most indicators of soil chemical quality measure dynamic soil properties i.e.properties that change over time and with management. These indicators are usedto guide management decisions over the period of a rotation. It is important tomonitor these indicators as they can act as constraints to yield, restricting cropgrowth and preventing the yield potential from being achieved.

Electrical conductivity (EC) of soil is a measure of the concentration of ions insolution. It is generally used as an indicator of salinity, but where nitrate levels arehigh and depending on the time of year and the climatic zone, EC can be anindicator of soil nitrate status.

Exchangeable sodium percentage measures exchangeable sodium ions as apercentage of other exchangeable cations.

Sodium adsorption ratio is the ratio of sodium concentration to calcium andmagnesium concentrations. These three measurements are most useful in aridsoils.


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Minimum data set (MDS)

Various studies have attempted MDS of propertieswhich can characterize a soil process or processesin regard to a specific soil function. Such data setsare composed of a minimum number of soil

properties that will provide a practical assessmentof one or several soil processes of importance for aspecific soil function.

Once a property is identified for a specific soil typeor situation, information is needed in regard to SQstandards for a given set of conditions. This involvesinformation on the critical level and range of the

attribute that is associated with optimum cropproduction.


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Conceptual model for converting MDS

indicators to SQ index values


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Development of soil quality index (SQI)

To determine a SQI, four main steps are involved:

(i) define the goal, (ii) select a minimum data set (MDS)of indicators that best represent soil function, (iii) score

the MDS indicators based on their performance of soilfunction and (iv) give a weight to each parameter , withvalues ranging from 0.1 to 1.0 and integrate the indicatorscores into a comparative index of SQ.

To select a representative minimum data set (MDS) only

those soil properties that show significant treatmentdifferences may be selected. Significant variables are then chosen for the next step in

MDS formation through principle component analysis(PCA).

Other statistical tools include multiple correlation, factoranalysis, cluster analysis and star plots, which may beused to select the variables for inclusion in index,avoiding the possibilities of disciplinary biases in expertopinion based approaches.


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Identifying critical limitsThe success and usefulness of a SQI mainly depends on

setting the appropriate critical limits for individual soilproperties.

Critical limit is the desirable range of values for a

selected soil indicator that must be maintained fornormal functioning of the soil ecosystem health.

Thresholds for each soil quality indicator are set basedon the range of values measured in natural ecosystems

or in best-managed systems and on critical values inthe literature in the last 510 years relating to theMDS.

For example, to grow most crops the pH may be 6.57.0.

Selection of critical limits for SQ indicators posesseveral difficult problems. For example, a pH belowabout 6.5 reduces the yield of alfalfa, but pH mustdrop below about 4.0 before critical yield reductionoccurs in blueberries (Doll, 1964).


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SQI (Scoring)-1 The first step in soil quality assessment is theidentification of critical parameters or arrangingthe minimum data set (MDS).

The contribution of the individual soil propertiesare assigned weights.

Indicator weights can be derived by usingstatistical tools like regression equations,

principal component analysis, etc., expertopinion and relevant literatures. The next stage is the conversion of indicators

value into unit less scores (0 to 1) based on

critical values. Fixing critical values depend on the nature of the

soil, climate, goals, and values in reference soil(forest, virgin soil, undisturbed ecosystem etc.).


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SQI (scoring a 0-1 scale) Soil data, to be used in scaling functions, need to be

converted to a 0 to 1 scale. This unitless value isweighted depending on the importance of theattribute to the particular soil function, and all of the

relevant characteristics can be multiplied into a singleindex. Using the scoring curve equation, three types ofstandardized scoring functions typically used for SQassessment can be generated: (1) More is better, (2)

Less is better, and (3) Optimum. The equation

defines a More is better scoring curve for positiveslopes, a Less is better curve for negative slopes, andan Optimum curve when a positive curve is reflectedat the upper threshold value.

The shape of the curves generated by the scoring

curve equation is determined by critical values. Slopes of scoring curves at the baseline point may be

determined using the optimization functions incomputer spreadsheet software programs.


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D f i (0 1 l )


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Data transformation (0-1 scale) The selected indicators can be transformed following a

linear or a non-linearing scoring rule. For more is better indicators, each observation is

divided by the highest observed value such that thehighest observed value received a score of 1. For less isbetter indicators, the lowest observed value (in thenumerator) is divided by each observation (in thedenominator) such that the lowest observed valuereceives a score of 1.

For some indicators, observations are scored as higher isbetter up to a threshold value and as lower is betterabove the threshold (Lebig et al., 2001).

The values of different variables can be transformed to acommon range, between 0.1 to 1.0 (Velasquez et al.,

2007): y = 0.1 + (x-b)/(a-b) * 0.9 Where, y = value of the variable after transformation x = the variable to transform

a = maximum value and b = minimum value of variable

Data compression


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Data compression

Principal Component Analysis (PCA) is a data compressiontechnique designed for data that are in the form of continuousmeasurements, though it has been also been applied to other kindof data such as presence/absence of an element or measurementsin the form of discrete variables.

PCs for a data set are defined as linear combinations of the variablesthat account for maximum variance within the set by describingvectors of closest fit to the n observations in p-dimensional featurespace, subject to being orthogonal to one another.

The PCA output gives as many PCs as the input variables but it isassumed that PCs receiving high eigenvalues (setting a threshold,

e.g., eigenvalues > 1) or those explaining variation in the dataexceeding a limit (e.g., > 5% of the variability) are important andnot the others.

Contribution of a variable to a particular PC is represented by aweight or factor loading. Only the highly weighted variables areretained from each PC and highly weighted factor loadings

identified based on thresholds such as those variables with absolutevalues within 10% of the highest factor loading or > 0.40. When more than one factor is retained under a single PC,

multivariate correlation coefficients are employed to determine ifvariables could be considered redundant and if the variables arecorrelated, that with the highest value is chosen for MDS.

Rotated loadings on the principal


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Rotated loadings on the principalcomponents of the chemical 05 cm data

groups

SQI ( i 100)


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The scoring used here assigns a higher score to the value of agiven soil condition (parameter) most suitable for plant growthand lower scores as values depart from the most suitablescenario or acceptable value.

Criteria can be weighted according to the relative importance

of a given indicator within a component and its relationshipwith other indicators. The maximum score for an overall soilquality is 100.

This is partitioned into physical, chemical, biological, andorganic matter (OM) components. Each component is assigneda score of 25.

OM is treated as a separate component because of itsimportance in controlling overall soil health.

Next, assign weighted scores to indicators based onimportance. For example, within the chemical component, pHis assigned the maximum possible score. Other factors, such asEC or NPK may be altered by changing pH; thus they areassigned lower scores. The total scores of selected indicatorsshould add up to the component score.

Indicators shouldnt be assigned a score of 0 since a soilcannot be totally non-functional.

SQI (scoring-100)

Maximum possible scores for


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Maximum possible scores for

different soil components

A i i l d t


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Assigning values and scores to

indicators


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Transformations of data


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Integrating SQIs There are basically two ways of integrating indicators

to derive one soil quality index

by summing the scores from MDS indicators and - by summing MDS variables after weighting them by

considering the % variation explained by a PC,standardized to unity, as the weight for variable(s)chosen under a given PC.

Scoring of indicators is necessary to interpret howeach measure relates to the soil function of interestand to allow indicators to be integrated by eliminatingunit differences.

A common scoring method is the use of non-linearscoring functions.

SQ


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SQI Numerical weights for each SQ indicator are multiplied

by indicator scores calculated through the use of thestandardized scoring functions that normalize indicatormeasurements to a value between 0 and 1.0 asproposed by Wymore (1993).

Then the weighted MDS variables scores for eachobservation can be summed up using the followingequation:

Where, S is the score for the subscripted variable andWi is the weighing factor derived from the PCA.

Here the assumption is that higher index scores meantbetter SQ or greater performance of soil function.

Further, the percent contribution of each final keyindicator is also calculated. The SQI values so obtainedare tested for their level of significance at P= 0.05.


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Research Needs


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Research Needs

Much more development work isrequired for minimum data set ofindicators required for different

ecological situations to assess soilquality.

There is an obvious need that these

indices to be validated under variousland and crop management systemsbefore their successful use.

There is little research that tracks thechanges in soil characteristics overthe year, or compares annual cycles

among management systems.

Research Needs


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Research Needs There has been a generous amount of

research into the effects of management onspecific soil characteristics. More work is

needed that links management practices andsoil characteristics to soil function.

The study of temporal patternsover seasonalcycles and through management transition

periodshas been neglected. Long-term experiments (1030 years) should

be conducted to establish the positive and

negative effects of different land uses on soilindicators for developing models so thatappropriate action could be taken accordingly.


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Biplot of principal components of 0-5


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Biplot of principal components of 0 5

cm chemical data

Relative changes of soil C in the surface 10 cm of soil after


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adoption of different CA managements. A: SB/CTSR/ZT,

conversion from stubble burning (SB) and CT to SR and ZT; B:SB/CTSR/CT, conversion from SB and CT to SR and CT; C: SB/CTSB/ZT, conversion from SB and CT to SB and ZT; D: SBSR,conversion from SB to SR; E: CTZT, conversion from CT to ZT.

chemical and biological indicators of soil quality - yadvinder singh, pau

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