Chemical And Biological Indicators Of Soil Quality - Yadvinder Singh, Pau 6l6e31

Chemical and Biological Indicators of Soil Quality Yadvinder Singh Punjab Agricultural University PAU Ludhiana

INTRODUCTION • In our drive to meet the food and fiber needs of ever-increasing populations, we are taxing the resilience of the planet’s natural resources. This fevered quest to pursue ever-increasing crop yields has led to soil degradation due to widespread soil erosion, atmospheric pollution, over-cultivated fields, poor quality water supplies, decline in soil fertility and desertification, which is a closes associated with the loss of soil quality. • There is now growing concern over the ability of the 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 crop management.

Soil Quality (SQ)defined • The term "soil quality" has been coined to describe the combination of chemical, physical, and biological characteristics that enables soils to perform a wide range of functions. SQ requires an holistic approach, and it is not possible to consider one component of quality in isolation. • Doran et al. (1996) defined SQ as the “capacity of a soil to function, within ecosystem and land use boundaries, to sustain biological productivity, maintain environmental quality, and promote plant, animal and human health”. • This definition provides a focal point for assessing the intensity of soil degradation. • Soil quality is related to soil functions and soil health concepts & views soil as a finite and dynamic living resource (Doran and Zeiss, 2000). • Plant health is clearly a component of soil health but necessarily not of soil quality (Karlen et al., 1997).

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 critical component of the study of soil quality. • Soil quality researchers are asking how the whole production system (e.g., tillage, planting, harvest, and crop rotation) changes the pest, water, and nutrient cylces which change farm productivity and water quality over the long term • Soil quality research has expanded the understanding of the individual 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 of the soil (e.g. crop productivity).

Dynamic components of soil quality (Karlen et al., 1994)

Soil Quality Management Options • • • • •

Reducing or modifying Tillage Growing cover crops Crop Rotation Adding organic matter/crop residues Adding chemical amendments

Soil Quality Indicators SQ is rather dynamic and can affect the sustainability and productivity of land use. It is the end product of soil degradative or conserving processes and is controlled by chemical, physical, and biological components of a soil and their interactions . Indicators, however, will vary according to the location, and the level of sophistication at which measurements are likely to be made (Riley, 2001). Therefore, it is not possible to develop a single 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.

Criteria for selecting indicators of soil quality There are several criteria to consider when selecting soil health and 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 s • be sensitive to management & climate • be interpretable • easy to measure and rapid/less time consuming method • Some soil properties are relatively insensitive to degradation or pollution but are important for interpreting the results of indicator measurements. For example: soil texture; available water holding capacity; and CEC. • Other soil properties that are sensitive to degradation or pollution are suitable for routine and frequent measurement

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 soil contaminants and their availability for uptake by animals and plants. • Results of chemical tests are soil quality indicators which provide information on the capacity of soil to supply mineral nutrients.

Commonly Used chemical & biological Indicators of SQ Chemical Indicators % base saturation Cation-exchange capacity and exchangeable acidity Contaminants- Types (Zn, Cu, Pb), Availability, Conc., mobility Salinity (EC) Sodicity (ESP or SAR) pH Nutrients (content, availability, cycling rates) Total C and N contents

Biological Indicators Organic carbon content Biomass C Total, bacterial, fungi Total C and N contents Biomass 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 composition Nucleic acid composition Weed seed bank Glomalin content

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 the treatment 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.

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 Nutrient availability, chemical activity, pesticide absorption, and use in process models

pH

Electrical conductivity

Forms of N, PMN

Extractable N,P,K, and micronutrients Heavy metal pollutants and organic pollutants

Defines crop growth, soil structure, water infiltration; presently lacking in most process models Availability to crops, leaching potential, mineralization/immobilization rates, process modeling Capacity to plant growth, environmental quality indicators Plant quality, and human and animal health

Chemical Indicators of Soil Quality • Where pollution is suspected, analysis of the particular contaminants of concern should be conducted (eg. metals or organic chemicals). • Many countries already have guidelines in place for allowable contaminant concentrations in soils, however there are wide discrepancies between them, which result from the different philosophies used for setting the guidelines. • The limits of soil metals are set in of their total soil concentrations rather than a measure of metal bioavailability. • Obtaining general agreement on measures of bioavailable metals will no doubt be difficult, but it is clearly an issue that requires attention. • Establishing indicator thresholds for contaminants is extremely complex & requires integration of effects on human health, plants, animals, soil biota, & on other environments.

ORGANIC MICROPOLLUTANTS • One difficulty in using organic contaminants as SQIs is the large number of possible contaminants. The European Commission (EC) working group on parameters and indicators suggested that halogenated compounds (HCH, DDT/E), PAHs, polychlorinated biphenyls (PCBs) and dibenzofurans/dioxins were likely to be of greatest concern. However, their monitoring would be restricted to specific sites (EC,2004).

Biological Indicators of Soil Quality Most processes in soil are driven by micro-organisms, the most abundant being bacteria and fungi. Microorganisms are the dominant component of soil biomass. Presence of specific organisms and their populations or community analysis (functional groups and biodiversity) (Linden et al., 1994). They are the main drivers for the turnover of soil organic matter, release of nutrients, promotion of plant growth, and degradation of organic pollutants and other potential pollutants. Identification of biological indicators of soil quality is critically important (Doran and Parkin, 1994; Abawi and Widmer, 2000) because SQ is strongly influenced by microbiological mediated processes (nutrient cycling, nutrient capacity, aggregate stability). • Of particular importance is to identify those components that rapidly respond to changes in soil quality.

Commonly Used chemical & biological Indicators of SQ Biological Indicators Organic carbon content Biomass C Total, bacterial, fungi Total C and N contents Biomass 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 composition Nucleic acid composition Weed seed bank Glomalin content (Glomalin is a glycoprotein produced abundantly on hyphae and spores of arbuscular mycorrhizal fungi in soil and in roots)

Biological Indicators of Soil Quality • Difficulties in classification of organisms at species level has a major constraint delimiting use of indicators based on soil organisms, more so the microfauna. Soil fauna (arthropods and invertebrates) populations influence soil biological processes, nutrient cycling and soil structure. Several properties or functions of soil fauna can be used as indicator of SQ. A faunal group, such as nematodes, is likely to be effective indicator of soil quality if it forms a dominant group and occurs in all soil types, has high abundance and high biodiversity and plays an important role in soil functioning, e.g., in food webs. • Parisi et al. (2000) proposed the index of biological quality of soil based on evaluation of microarthropods’ level of adaptation to the soil environment rather than the species richness/diversity.

Soil arthropods • Soil arthropods affect soil quality directly and indirectly depending on their size and specific activity. • Macroarthropods (millipedes, centipedes, insect larvae, termites, ants and others) have the ability to modify soil structure by decreasing bulk density, 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 of forest litter decomposition (Seastedt and Crossley, 1983).

Enzymes as indicators of SQ Soil enzyme assays generally provide a measure of the potential microbial activity. There are large number of 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 which enzymes are most relevant for soil quality characterization (Schloter et al., 2003). • Three enzymes viz., phosphomonoesterase (phosphatases), chitinase and phenol oxidase, as a group reflect relative importance of bacterial and fungi, 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.

Earthworms • Earthworms are the largest soil invertebrates. They are considered to be the main soil engineers and changes in their number and community structure can affect several soil characteristics, such as porosity, aeration, water holding capacity, density, recycling and distribution of organic matter and nutrients. • As they feed, earthworms participate in plant residue decomposition, nutrient cycling, and redistribution of nutrients in the soil profile. Their casts, as well as dead or decaying 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 and changing to no-till may not help their quick establishment unless they are introduced first. • There are about 20 earthworm species that can be divided into four groups: • epigeic species (litter dweller), endogeic species (shallow earth dweller) , anecic species (deep earth dweller) and compost worms

Microbial measurements • Biomass and respiration. Carbon dioxide evolution can be measured directly from soil that is held under controlled conditions. This is called basal respiration. It provides a measure of biological activity, but does not indicate how many or what kind of organisms are present. • Substrate-induced respiration is a measure of the CO2 evolved from 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 divided by the microbial biomass. • The ratio of microbial carbon to total organic carbon is another common measure of biomass. • Potentially mineralizable nitrogen. This test is an estimate of the amount of N that is immobilized in organic forms and potentially could be decomposed by microorganisms into a plant available form. The amount of potentially mineralizable N depends on the amount and form of N in the soil, the microbes available to degrade N-containing compounds, and a carbon source to feed the microbes.

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 plant roots. • Measurement of CO2 efflux yields an index of total carbon availability. • Respiratory quotient (qCO2) = CO2 released /O2 consumed), has been recommended by Anderson and Domsch (1990). ( • Brooks and McGrath (1984) observed higher respiratory quotients in soils containing heavy-metal contaminated sewage sludge, compared with control soils containing no heavy metals.

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 of these),Earthworms, microbial biomass carbon & N, Basal respiration, ATP, dehydrogenase, urease, protease, Acid and alkaline phosphatases and betaglucosidase acitivity estimated by methods as detailed in 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 the normalized and weighted values of the most important parameters.

Effect of retaining or burning stubble on soil properties after 17 yrs (Hoyle and Murphy, 2006a,b)

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.

Soil OM as SQ indicator • Total organic matter is strongly affected by soil texture and climate, and requires decades to change significantly 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 physical structure and biological activity of the soil. • Highly labile compounds are sources of nutrients for microorganisms and plants.

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 same as 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 researchers want to study. • The best proxy measures for the biologically active portion of soil OM seem to be particulate organic matter and lightfraction 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 lightfraction OM. Light-fraction has a specific density less than 2g/cm3, and macro-OM is .05 to 2 mm in size.

carbon management index Blair et al. (1995) proposed carbon management index (CMI), a multiplicative function of carbon pool index (I) and lability index (LI) as an indicator of the rate of change of SOM in response to land management changes, relative to a more stable reference soil. • Carbon pool index (I) = Total C of a given land use/Total C of the reference land use • Lability index (LI) = [Labile C content of a given land use/Nonlabile C content of a given land use] * [Labile C content of the reference land use/Non-labile carbon content of the reference land use] • Carbon management index (CMI) = I * 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 the system. • Labile C is measured by using 333 mM KMn04 oxidation method and 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.

Carbon management index • Blair et al. (1995) method using strong KMnO4 involves several important limitations . The highly concentrated solution of KMnO4 (333mM) is both difficult to prepare and maintain and somewhat hazardous to use. . • Weil et al. (2003) attempted to overcome in developing a simplified, improved method for determination of active soil C. They modified the Blair et al. (1995) method to develop 20 mM KMnO4 oxidation method which is more sensitive to the effects of soil management, more rapid, reliable and -friendly to carry out, and suitable for routine use.

Sensitivity of total and active carbon under CT and NT in wheat-based system

Methods for analysis of SQ Indicators • Several well-established soil tests are included in most soil quality minimum data sets. Further descriptions of these tests can be found in Methods of Soil Analysis(SSSA Book 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)

Chemical Indicators of Soil Quality •

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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. (Doran and Parkin, 1996). SOC plays an important role in soil quality due to improvement in infiltration and crop 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 used to guide management decisions over the period of a rotation. It is important to monitor these indicators as they can act as constraints to yield, restricting crop growth and preventing the yield potential from being achieved. Electrical conductivity (EC) of soil is a measure of the concentration of ions in solution. It is generally used as an indicator of salinity, but where nitrate levels are high and depending on the time of year and the climatic zone, EC can be an indicator of soil nitrate status. Exchangeable sodium percentage measures exchangeable sodium ions as a percentage of other exchangeable cations. Sodium adsorption ratio is the ratio of sodium concentration to calcium and magnesium concentrations. These three measurements are most useful in arid soils.

Minimum data set (MDS) • Various studies have attempted MDS of properties which can characterize a soil process or processes in regard to a specific soil function. Such data sets are composed of a minimum number of soil properties that will provide a practical assessment of one or several soil processes of importance for a specific soil function. • Once a property is identified for a specific soil type or situation, information is needed in regard to SQ standards for a given set of conditions. This involves information on the critical level and range of the attribute that is associated with optimum crop production.

Conceptual model for converting MDS indicators to SQ index values

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 soil function and (iv) give a weight to each parameter , with values ranging from 0.1 to 1.0 and integrate the indicator scores into a comparative index of SQ. • To select a representative minimum data set (MDS) only those soil properties that show significant treatment differences 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, factor analysis, cluster analysis and star plots, which may be used to select the variables for inclusion in index, avoiding the possibilities of disciplinary biases in expert opinion based approaches.

Identifying critical limits The success and usefulness of a SQI mainly depends on setting the appropriate critical limits for individual soil properties. • Critical limit is the desirable range of values for a selected soil indicator that must be maintained for normal functioning of the soil ecosystem health. • Thresholds for each soil quality indicator are set based on the range of values measured in natural ecosystems or in best-managed systems and on critical values in the literature in the last 5–10 years relating to the MDS. • For example, to grow most crops the pH may be 6.5– 7.0. • Selection of critical limits for SQ indicators poses several difficult problems. For example, a pH below about 6.5 reduces the yield of alfalfa, but pH must drop below about 4.0 before critical yield reduction occurs in blueberries (Doll, 1964).

SQI (Scoring)-1 • The first step in soil quality assessment is the identification of critical parameters or arranging the minimum data set (MDS). • The contribution of the individual soil properties are assigned weights. • Indicator weights can be derived by using statistical tools like regression equations, principal component analysis, etc., expert opinion 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.).

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 is weighted depending on the importance of the attribute to the particular soil function, and all of the relevant characteristics can be multiplied into a single index. Using the scoring curve equation, three types of standardized scoring functions typically used for SQ assessment can be generated: (1) ‘More is better,’ (2) ‘Less is better,’ and (3) ‘Optimum.’ The equation defines a ‘More is better’ scoring curve for positive slopes, a ‘Less is better’ curve for negative slopes, and an ‘Optimum’ curve when a positive curve is reflected at 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 in computer spreadsheet software programs.

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 the highest observed value received a score of 1. For ‘less is better’ indicators, the lowest observed value (in the numerator) is divided by each observation (in the denominator) such that the lowest observed value receives a score of 1. • For some indicators, observations are scored as ‘higher is better’ up to a threshold value and as ‘lower is better’ above the threshold (Lebig et al., 2001). • The values of different variables can be transformed to a common 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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Principal Component Analysis (PCA) is a data compression technique designed for data that are in the form of continuous measurements, though it has been also been applied to other kind of data such as presence/absence of an element or measurements in the form of discrete variables. PCs for a data set are defined as linear combinations of the variables that for maximum variance within the set by describing vectors of closest fit to the n observations in p-dimensional feature space, subject to being orthogonal to one another. The PCA output gives as many PCs as the input variables but it is assumed that PCs receiving high eigenvalues (setting a threshold, e.g., eigenvalues > 1) or those explaining variation in the data exceeding a limit (e.g., > 5% of the variability) are ‘important’ and not the others. Contribution of a variable to a particular PC is represented by a weight or factor loading. Only the highly weighted variables are retained from each PC and highly weighted factor loadings identified based on thresholds such as those variables with absolute values 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 if variables could be considered redundant and if the variables are correlated, that with the highest value is chosen for MDS.

Rotated loadings on the principal components of the chemical 0–5 cm data

groups

SQI (scoring-100) • The scoring used here assigns a higher score to the value of a given soil condition (parameter) most suitable for plant growth and lower scores as values depart from the most suitable scenario or acceptable value. • Criteria can be weighted according to the relative importance of a given indicator within a component and its relationship with other indicators. The maximum score for an overall soil quality is 100. • This is partitioned into physical, chemical, biological, and organic matter (OM) components. Each component is assigned a score of 25. • OM is treated as a separate component because of its importance in controlling overall soil health. • Next, assign weighted scores to indicators based on importance. For example, within the chemical component, pH is assigned the maximum possible score. Other factors, such as EC or NPK may be altered by changing pH; thus they are assigned lower scores. The total scores of selected indicators should add up to the component score. • Indicators shouldn’t be assigned a score of ‘0’ since a soil cannot be totally non-functional.

Maximum possible scores for different soil components

Asg values and scores to indicators

Transformations of data

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 how each measure relates to the soil function of interest and to allow indicators to be integrated by eliminating unit differences. • A common scoring method is the use of non-linear scoring functions.

SQI • Numerical weights for each SQ indicator are multiplied by indicator scores calculated through the use of the standardized scoring functions that normalize indicator measurements to a value between 0 and 1.0 as proposed by Wymore (1993). • Then the weighted MDS variables scores for each observation can be summed up using the following equation: • Where, S is the score for the subscripted variable and Wi is the weighing factor derived from the PCA. • Here the assumption is that higher index scores meant better SQ or greater performance of soil function. Further, the percent contribution of each final key indicator is also calculated. The SQI values so obtained are tested for their level of significance at P = 0.05.

Research Needs • Much more development work is required for minimum data set of indicators required for different ecological situations to assess soil quality. • There is an obvious need that these indices to be validated under various land and crop management systems before their successful use. • There is little research that tracks the changes in soil characteristics over the year, or compares annual cycles among management systems.

Research Needs • There has been a generous amount of research into the effects of management on specific soil characteristics. More work is needed that links management practices and soil characteristics to soil function. • The study of temporal patterns—over seasonal cycles and through management transition periods—has been neglected. • Long-term experiments (10–30 years) should be conducted to establish the positive and negative effects of different land uses on soil indicators for developing models so that appropriate action could be taken accordingly.

Biplot of principal components of 0-5 cm chemical data

Relative changes of soil C in the surface 10 cm of soil after adoption of different CA managements. A: SB/CT–SR/ZT, conversion from stubble burning (SB) and CT to SR and ZT; B: SB/CT–SR/CT, conversion from SB and CT to SR and CT; C: SB/CT– SB/ZT, conversion from SB and CT to SB and ZT; D: SB–SR, conversion from SB to SR; E: CT–ZT, conversion from CT to ZT.