Quantifying Soil Morphology In Tropical Environments .
DIVISION S-5—PEDOLOGYQuantifying Soil Morphology in Tropical Environments: Methods and Applicationin Soil ClassificationAnne Gobin,* Paul Campling, Jozef Deckers, and Jan FeyenABSTRACTcorded and mostly used to aid classification purposes(Soil Survey Staff, 1998; FAO, 1998). Standard soil morphological descriptions have been quantified and combined in a soil profile development index for evaluatingsoil development (Bilzi and Ciolkosz, 1977; Meixnerand Singer, 1981; Harden, 1982). In addition, McKenzieand Jacquier (1997) describe the use of soil morphological descriptions together with inexpensive field tests toderive soil hydraulic properties in low-surveyed regions.Last, soil color indices present a quantified approachfor assigning drainage class to a soil (Megonigal et al.,1993; Thompson and Bell, 1996) and assessing the Feoxide mineralogy (Torrent et al., 1980; Mokma, 1993).Farmers often describe soils in combinations of singlemorphological characteristics (e.g. red sand or stone)and often relate their decision-making on land use andmanagement to these soil descriptions (Gobin et al.,1998,1999). Quantifying these morphological characteristics opens new perspectives for incorporating farmers'knowledge into land resources information systems, andenables statistical modeling to be used. Farmers' knowledge could benefit scientific understanding of soils, contribute to international agricultural development, andfacilitate exchange between farmers and researchers(Sandor and Furbee, 1996; Alexander, 1996; Habarurema and Steiner, 1997; Norton et al., 1998).The objectives of this study were to quantify fieldobservations of soils made by scientists and local farmers, relate field observations to laboratory analysis, andidentify soil variables for distinguishing soils in a tropicalarea. Our method for quantifying field observations useslaboratory techniques and color indices measured on72 pedons. We developed two soil color indices thatincorporate a weighting factor for matrix and mottlingcolors, and we identified soil variables that distinguishthe soils using exploratory methods, analysis of variance,and multivariate analysis.We tested the hypothesis that readily observed and easily measuredmorphological variables can be used to characterize the soils sampledand described in southeastern Nigeria for purposes of land use andmanagement. Field tests were developed for estimating soil textureand amount of ironstone nodules. Two new soil color indices providedan immediate means of diagnosing the soil drainage regime in caseof the color index (CI) and soil forming processes in tropical soils incase of the redness index (RI). The indices correlated negatively withorganic C content (R - 0.39) and positively with dithionite-extractedFe2O3 (0.44) and A12O3 (0.51). Inexpensive field tests for color, texture,and ironstone can be quantified using color indices and laboratorymeasurements. The local soil classification was quantified by meansof color indices (RI, CI) and percentages of ironstone, sand, silt, andclay measured in the A horizon. A classification based on soil texture,ironstone, and color was used to define classes for the B horizon. Thetwo first principal components (PC) extracted from soil morphologicalvariables measured on the upper three horizons of 11 pedons explained 64.7% of the total variance. Nonhierarchical clustering performed on the two PCs produced seven clusters that compare wellwith the great groups of U.S. soil taxonomy. Principal componentanalysis on 20 soil chemical and morphological variables confirmedthat soil texture, ironstone, and soil color account for most of thevariation of the soils and provide an efficient means of characterizingtropical soils derived from sedimentary parent material.THE INCREASING USE of geographical information systems and earth observation techniques in land resources analysis has highlighted the need for quantitative data on the spatial distribution pattern of soilcharacteristics. However, many soil surveys have concentrated on the vertical sequence of horizons withinpedons, paying less attention to the spatial distribution(FAO, 1998). Parametric soil surveys concentrate onmeasuring single soil characteristics, and provide, inconcert with process modeling, a suitable paradigm forspatial prediction across low-surveyed regions (McKenzie and Austin, 1993; Moore et al., 1993). The use ofreadily observed and quantifiable morphological characteristics to distinguish between different soils as a firststep to determining their spatial extent is our majorconcern here.Soil morphological descriptions are commonly re-MATERIALS AND METHODSStudy areaThe 589-km2 study area comprises the River Ebonyi headwater catchment and a part of the Udi-Nsukka Plateau locatedinstitute for Land and Water Management, Katholieke UniversiteitLeuven, Vital Decosterstraat 102,3000 Leuven, Belgium. 1 Mar. 1999.* Corresponding author (anne.gobin@agr.kuleuven.ac.be).Abbreviations: BS, base saturation; CEC, cation-exchange capacity;CI, color index; CV, coefficient of variation; E A, exchangeable acidity;PC, principal component; RI, redness index; RR, redness rating; SD,standard deviation.Published in Soil Sci. Soc. Am. J. 64:1423-1433 (2000).1423
1424SOIL SCI. SOC. AM. J., VOL. 64, JULY-AUGUST 2000Table 1. Field test for soil textureShapeTextureHeap; soil remains loose and single grainedSandBall of around 2.5 cm diameterShort, thick cylinderLoamy sandCylinder of 15 cm that breaks when bentLoamCylinder of 15 cm can be bent in a U-formCircle that shows cracksCircle without cracksClay loamSilty clay; sandy clayClaySilt loamconcept was also employed in former soil surveys within thestudy area: 32 pedons were sited in the field and on the topographic map (Akamigbo and Opara, 1977; Federal Department of Agricultural Land Resources, 1985; Asadu, 1986;Nwadialor, 1989) (Fig. 1). A relational database (Prague andIrwin, 1997) was set up to process all information obtainedon the 72 pedons.Field MethodsFig. 1. Regional setting of the study area and location of pedons.in Enugu State, Nigeria (Fig. 1). The study area traverses fromwest to east a part of the Udi-Nsukka Plateau and Escarpmentand a section of the Cross River Plains, characterized by acomplex sedimentary history (Benkhelil, 1988). In general,argillaceous rocks underlie the Plains, whereas the Plateauand Escarpment are formed by sandstone. Flat-topped hillsor ridges with surface ironstone are common on the Plateauand east of the Escarpment and represent remnants of the CoalMeasure formations (Jungerius, 1964). The fluvial landscapefarther on the Plains is characterized by a meandering riverchannel bordered by narrow river banks, with seasonally submerged backswamps from combined river flooding and uplandrun-on (Gobin et al., 1998). The upper interfluve is undulatingto rolling, whereas the lower interfluve is flat to undulating.The region has a humid tropical climate with a distinct dryseason between November and March and an annual rainfallaveraging 1500 mm yr ' (Koppen climate classification isAw). The area is situated in the transition zone between lowland, Guinea-Congolian, wetter-type rainforest and Guineasavanna, resulting in a mosaic vegetation pattern (Hopkins,1979; White, 1992). Luxuriant evergreen forest fringes theriver and perennial streams on the regularly flooded soils ofthe valley bottom, whereas along seasonal streamlines corridors of semideciduous trees and bushes are found. Moist semideciduous forest occurs on the Plateau and shale dominantinterfluve, whereas drought-tolerant tree species and tallgrasses mainly occupy the Escarpment and denudated gravellyinterfluve areas.Location of PedonsGeneralized information on geology was derived from the1:250 000 geological maps of Nigeria (Shell-BP, 1957). Vegetation, drainage, and relief were obtained from stereoscopicanalysis of 1:40000 aerial photographs from 1962 and topographic maps at a scale of 1:50 000. The study area was stratified according to combined geology, drainage, relief, and vegetation characteristics. Based on this stratification, 40 pedonswere located along the steepest environmental gradients following the catena concept (Sommer and Schlichting, 1997) tosample the full range of soils in the study area. Soil pits weredug (Fig. 1) at representative locations of each landscape alongthe toposequences. These locations were identified using aclinometer, a compass, and a global positioning system (Trimble Pathfinder Basic and Software; Trimble, 1992). The catenaSoil profile observations were fully described according tothe FAO guidelines (1990). Soil texture of the fine earth wasapproximated using a manual field test (International LandDevelopment Consultants, 1981). The extent to which a tablespoon ( 15 mL) of moist fine earth can be shaped is indicativeof its texture (Table 1). On the Interfluve, the test was improved with the knowledge that loam is powdery when dryand leaves dirt on the skin when moist, and clay displaysshining faces when augering. The occurrence of ironstone nodules, usually between 2 and 20 mm in size, was expressed inpercentage surface covered: few (0-5%), common (5-15%),many (15-40%), abundant (40-80%), and dominant ( 80%).Soil colors were determined per horizon using the revisedstandard soil color charts (Takehara, 1992), which are basedon the Munsell soil color chart (Munsell, 1954). The surfacepercentage of mottling and of each soil matrix color was estimated in the field. The mottling size was recorded in millimeters as fine ( 5 mm), medium (5-15 mm), or coarse ( 15 mm),and the mottling abundance was described as few ( 2%),common (2-20%), and many (20-50%).The term local soil classification refers to the classificationthat local farmers use to name the soils in the Igbo language.Techniques borrowed from participatory rural appraisals(Chambers, 1992) were conducted along the toposequencewith the aid of two local village guides and trained interpretersand facilitators from outside the village to elicit the local soilclassification scheme. Open-ended and semistructured interviews (Mettrick, 1993) were administered to villagers cultivating or owning fields within 50 m from a profile pit. The soiladjacent to each field was augered and described to verifyits similarity with the reference soil pit and to facilitate theinterview. Villagers were asked to compare their descriptions,knowledge, and name of a particular soil with soils locatedfarther along the toposequence. Accounts of within-field variation were not incorporated into the classification scheme.Schematic diagrams were constructed for each of the toposequences, and the soil classification was verified during agroup discussion involving farmers and village elders.Laboratory MethodsSamples were taken per horizon, air-dried at an ambienttemperature of 25 C, crushed, and passed through a 2-mmsieve. The percentage of the fraction 2 mm was weighed,and the ironstones were weighed separately. Soil chemicalanalysis was carried out on the sieved fraction (Table 2). Soilclassifications according to U.S. soil taxonomy (Soil SurveyStaff, 1998) were inserted into the database. Local soil descrip-
GOBIN ET AL.: QUANTIFYING SOIL MORPHOLOGY IN TROPICAL ENVIRONMENTSTable 2. Soil variables and analysis methods for soil samples.Soil variable iMethodpH(H20) (pHw)pH(KCI) (pHK)Organic C (OC)Total N (N)Available P (P)Total Al and Fe (A12O3, Fe2O3)Exchangeable acidity (EA)Cation-exchangecapacity (CEC)Exchangeable cations(Ca, Mg, K, Na)Particle-size distribution(sand, silt, clay)Ironstone content (stone)Color indices (Cl, RI,MoCI, MoRI)1:5 soil solution ratio of distilledwater1:5 soil solution ratio of 1 M KCIWet oxidation method (Walkleyand Black, 1934)KjeldahlBray I (Bray and Kurtz, 1945)Dithionite extraction1 M KCI extractionPercolation with ammoniumacetate at pH 7First percolate (atomic absorptionspectrophotometer)Pipette methodmon-coarse, many-medium, and many-coarse. The sum of allmottling fractions was not allowed to exceed the field-estimated total fraction of mottle colors. The fraction of eachmatrix color (P,) is:[2]2; iWhere p{ is the percentage of each matrix color estimated inthe field description, m is the number of mottle colors, S; isthe fraction of each mottle color. A redness index (RI) anda color index (CI) were designed according to:MatrixRI( MaRI)AVI —"pMottleRI( MoRI) i'HT'Tl MatrixCI( MaCI)Soil Color IndicesIF 'YR', THEN HT 12.5 - hue, ELSE: HT 12.5[1]Where "hue" is the Munsell hue. Numerical values, based onthe mean of the possible abundance range, were assigned toeach mottling fraction depending on the combination of therecorded abundance and mottling size. A numerical value of0.01 was assigned to few-fine and few-medium mottling; 0.11corresponded with the combinations few-coarse, common-medium, common-fine, and many-fine; 0.35 was reserved for com-r \IwT.r.lL\ 3J\MottleCI( MoCI)mt Abbreviations used for each soil variable are presented in brackets.Soil color based on the soil Munsell color chart consists ofhue, value, and chroma. The color notations were convertedinto color indices to arrive at a single numerical value. Previously published work suggests that color indices are usefulfor quantifying differences in soil morphology. Hurst (1977)defined the Redness Rating (RR) as an assigned numericalvalue for hue, multiplied by chroma and divided by value.The RR proved a good discriminator between hematite andgoethite and correlated highly with the fine earth hematitecontent in red Mediterranean soils (Torrent et al., 1980).Chroma, augmented with or without an arbitrary value forhue, and in both cases corrected for all mottle and matrixfractions, corresponded well with the hydric conditions of seasonally saturated soils (Thompson and Bell, 1996). The decrease in Munsell value along a toposequence was found tobe highly correlated with increased organic C content (Fernandez et al., 1988). A decreasing color number, which was basedon value and chroma added to a numerical value for hue, washighly correlated with increasing organic C and to a lesserextent with Fe and Al content in Spodosols (Mokma, 1993).Harden (1982) and Harden et al. (1991) also developed Melanization, rubification, color lightening and color paling fordesert soils.Two new indices, a redness index (RI) and a color index(CI), combined and modified components from previous studies to suit soil colors typical for active tropical weathering andto incorporate a weighting for matrix and mottling colors.Hues of all soil horizons in the study area ranged from 10YR(Yellowish Red) to 10R (Red) and were transformed to anumerical value (HT) so that redder hues would correspondwith higher values:ml/'kWeighing ironstone in fraction 2 mmMunsell color notation and Eq.[3] and [4]tions were compared with soil variables measured by standardlaboratory techniques and expressed as numerical values.14252 P, [HT, 1 1V,]2 s, [HT;- q v,]/ ![4]Where n is the number of matrix colors, V is the Munsellvalue, C is the Munsell chroma. The second terms of Eq. [2]and [3] are referred to as the mottling redness index (MoRI)and mottling color index (MoCI), respectively.Statistical AnalysisThe statistical analysis aimed at identifying soil variablesthat can be used to distinguish the different soils of thestudy area.Exploratory and Correlation AnalysisExploratory data analysis included descriptive statistics anda check for normality (SAS Institute, 1990) on all soil characteristics per horizon. Most soil variables were transformed tonormal distribution using a natural logarithm or square root.Soil texture was transformed according to Eq. [5]; the amountof ironstone was transformed according to Eq. [6] (Websterand Olliver, 1990).as* arcsmliy In———'100/(y Q-i)[100 - (y 0.1)][5][6]Where x is sand, silt, or clay fraction expressed in percentageof fine earth, and y is percentage of ironstone. Correlationbetween the soil variables was examined.Analysis of VarianceMean, standard deviation (SD) and coefficient of variation(CV) were used to evaluate the within- and between-classeshomogeneity in the different soil characteristics. Classes ofthe A horizon from all 72 pedons were based on the localclassification, and classes of the B horizon were based on soiltexture, ironstone, and color. A Fisher test was used to assesswhether the class means are different from each other at the0.05 significance level (SAS Institute, 1990). Multiple comparison was performed on each normalized soil variable, and subsequent statistical grouping was accomplished through Duncan-Waller multiple range tests at the 0.05 significance level(SAS Institute, 1990).
1426SOIL SCI. SOC. AM. J., VOL. 64, JULY-AUGUST 2000I JBIcteillst tlzelStoneSandStickySand(Hea*y)ClayFig. 2. Local soil classification scheme. Local farmers refer to the part of the Cross River Plains within the study area as Ebonyi or Isi Uzo.Ebonyi is the main river of the study area; Isi Uzo is the district named after the old main road crossing the study area. Dark soil is a synonymfor black soil; clay is often referred to as sticky.correlation matrix (R x ) was defined:Multivariate AnalysisMultivariate analysis was used to identify interrelationshipsamong soil chemical and morphological variables and to studydifferentiation patterns among the different soils using twomatrices. A 72 by 27 matrix, X, consisted of nine normalizedmorphological variables per horizon for the upper three horizons (p) of 72 pedons (n), namely horizon thickness, sand,silt, clay, percentage of ironstone, and four soil color indices(RI, CI, MoRI, and MoCI), A second 72 by 60 matrix, Y, wascreated from 72 pedon observations on 20 normalized soilchemical, textural, and morphological variables for the upperthree horizons. The calculations explained hereafter for Xwere also performed on Y. All normalized soil variables werestandardized to zero mean and unit variance:[7]Where x are the normalized observation values of the n by pmatrix, xj is the mean vector of each normalized variable, andSj the corresponding variance. The corresponding standardized0 5 10 15200 1 2 3 0 5 10 15 0 5 10 15 20R, X'X[8]Rx was examined according to three criteria (Jobson, 1992;SPSS, 1994):Test for Zero Correlation. The correlation matrix equalsthe identity matrix when « has a x2 distribution with 0.5p(p —1) degrees of freedom, whereby u equals:u -[n- 6 \2p 11)] 2 In X,[9]Where Xj are the eigenvalues of the correlation matrix. Thetest statistic is highly significant for matrices that are not orthogonal and thus appropriate for factoring.Kaiser-Mayer-Olkin (KMO) Measure of Sampling Adequacy.2KMO [10]22 4 22Where rtj is the correlation coefficient and a is the partialcorrelation coefficient between variables i and j. Values forKMO below 0.5 are unacceptable.Individual Measure of Sampling Adequacy (MSA,-) forEach Variable i. A value close to one is excellent, whereasvalues smaller than 0.5 require elimination of the particularvariable.MSA; 24/Vi24[11]Principal components (SAS Institute, 1990) were extractedfrom the correlation matrix, using the equation Z XV, whereFig. 3. Scatterplots and correlation coefficients between CI, RI, andsoil chemical properties (** indicates significance at the 0.01 level).Table 3. Values for redness index (RI) and color index (CI) fromsoils in the study area.fMunsell colorFeatureRICIOxic horizon10R4/8252.45Argillic horizon7.57.5YR4/615Iron mottling152.5YR4/620Yellow mottling10YR6/83.316.5Redoximorphic10YR6/20.810.5t RI is calculated by Eq. [2], and CI by Eq. [3].
1427GOBIN ET AL.: QUANTIFYING SOIL MORPHOLOGY IN TROPICAL ENVIRONMENTSTable 4. Variation of properties within the A horizon, as categorized by the local soil.tSandSiltClayStoneCl Si 2 jun 50 ionRIpHOC 2mm 50 x50-2 1a1. Plateau, Sticky Sand, Red20.215.9 11.94.812.11.53.80.359.99.432.35.6cdaaa2. Steep land, Sand, Red5.18.013.810.90.72.40.95.96.413.230.553.9abbd3. Hill, Stone, Red54.315.38.65.014.52.73.00.626.818.0 35.3 12.8aababb4. Small hill, Stony Clay, 37.6ab17.59.051.35.06.0119.8be23.75. Upland, 82.95.310.41.31.10.320.712.138.26.3bcca6. Waterlogged Lowland, Heavy Clay63A1L64ifl5 015.02.01.70.323.717.0 42.46.0acca7. Riverside, Sand, and Sticky Sand2811L848sT cd2.489.5bbb16.469.2abt Where mean is the arithmetic mean; SD is the standard deviation; CV is the coefficient of variation in percentage ( 100 x SD/mean); Group DuncanWaller grouping at the 0.05 significance level shown as letters in alphabetical order according to the magnitude of the class mean; stone is expressed inweight percentage of soil; sand, silt and clay are expressed in percentage of the fine earth fraction; Cl Si is the silt plus clay fraction; CI and RI arethe color indices defined by Eq. [2] and Eq. [3]; pH is pH in H,O; OC is organic C; N is total N; P is available P; EA is exchangeable acidity; CEC ismeasured soil cation-exchange capacity; BS is base saturation.Z is the matrix of unstandardized PCs, X the data matrix, Vthe matrix of eigenvectors with V'V W I, and I is theidentity matrix (Jobson, 1992). The solution is an eigenvalueproblem according to:of the total variance. All PCs were given a rank (eigenvaluenumber) according to their eigenvalue. The mean eigenvaluewas used as a criterion to retain a reduced set of q components:x, X'X- A V 0[12]Where A is a diagonal matrix of eigenvalues of Rx (Eq. [8]),and V is orthogonal to Rx. As the PCs are standardized, thevalues of the standardized components for n observations weredetermined using:X Z*V*[13]1/2Where Z* ZA V2 and V* A V.The p PCs are standardized linear combinations of the poriginal soil variables, with coefficients equal to the eigenvectors of the correlation matrix. The total variance (sx) is givenby:[14]Where X, is the eigenvalue or variance of the tth PC (PC,).Larger eigenvalues correspond with a larger explained portion[15]The correlation between the original variables and PCs (loadings) are used for interpreting the PCs. Scatterplots of theobservations in the plane of the PCs with the largest eigenvalues (scores) (Eq. [14]) provide a good estimate of the patternof variation. The same procedure (Eq. [7]-[15]) was employedto extract PCs from matrix Y.Nonhierarchical cluster analysis following the k means algorithm was performed on the first two unstandardized PCs ofmatrix Z derived from matrix X (Eq. [12] and [13]). TheEuclidean distance between group centroids was used to measure the proximity between groups:& 2 (Zn- - Z sj ) 2[16]/ !Where d is the Euclidean distance between class r and s, andZj contain the coordinates of the centroids. The dispersionwithin classes, expressed as the sums of squares of deviationsfrom the group means, was minimized through subsequentiterations in order to arrive at an optimal number of classes.
1428SOIL SCI. SOC. AM. J., VOL. 64, JULY-AUGUST 2000Records are reassigned until they are located in the groupwith the nearest centroid. The resulting clusters were compared with classification based on soil texture, ironstone, andsoil color and with classification according to U.S. soiltaxonomy.RESULTSField ObservationsIn general, all pedons examined are closely relatedto parent material, which in turn is strongly associatedwith landform and landscape. On the Plateau, soilsformed from sandstones and colluvial material derivedfrom Upper Coal Measures occur on nearly flat slopesand dry valleys. They consist of reddish brown sandyloam over crumbly red sandy clay loam (2.5YR 4/8-10R4/8). At Escarpment, soils are formed on sandstone andcontain high amounts of sand throughout the profile;the subsoil contains weak crumb-structured sand witha very friable consistence. Soils rich in ironstone nodulesdominate the Residual Hills. The Interfluve is characterized by soils derived from shale and having an argillichorizon. On the ridges and residual hills of the Interfluve, ironstone material occurs at the surface as laggravel or at shallow depth, mostly in concretionary form.Fairly well-drained soils of the Upper Interfluve displayTable 5. Variation of soil properties within the subsoil (B horizon), as classified by soil morphology.!Stone 2 mmSand 50 (AmSilt50-2 funClay 2 unCIRIpHOCNP. „ -iI*S d76.814.518.9b9.514.4ac63.1c37.1de44.47.41. Plateau, Sandy Clay Loam, .91.568.420.67.713.39.846.533.3127.6dcdaacdbeba2. Escarpment, Loamy Sand, .73.059.245.29.021.515.754.532.1114.4deabddba3. Residual Hill, Ironstone, 12.20.129.011.510.717.35.642.730.886.7bababdaaa4. Upper Interfluve, Ridge, Ironstone, 213.70.632.843.38.28.76.136.527.3131.0abbeaba b cbaa5A. Interfluve , Lowland, Clay Loam and Ironstone, Red edbeaaSB. Upper interfluve, Upland, Clay Loam, acda6A. Lower Interfluve, Waterlogged Lowland, Clay, Yellow Mottling in Gray be6B. Floodplain, Clay Loam, Redoximorphic Mottling in Gray cbed7. Riverbank, Loamy 4f30.130.4100.9ab1.60.532.55.733.3cc75.6at Classes 5A and SB correspond with Class 5 in Table 4, and classes 6A and 6B with Class 6. Where mean is the arithmetic mean; SD is the standarddeviation; CV is the coefficient of variation in percentage ( 100 x SD/mean); Group Duncan-Waller grouping at the 0.05 significance level shownas letters in alphabetical order according to the magnitude of the class mean; stone is expressed in weight percentage of soil; sand, silt and clay areexpressed in percentage of the fine earth fraction; Cl Si is the silt plus clay fraction; CI and RI are the color indices defined by Eq. [2] and Eq. [3];pH is pH in H2O; OC is organic C; N is total N; P is available P; EA is exchangeable acidity, CEC is measured soil cation-exchange capacity; BS isbase saturation.
GOBIN ET AL.: QUANTIFYING SOIL MORPHOLOGY IN TROPICAL ENVIRONMENTSargillic subsurface horizons without pronounced mottling. Soils displaying red Fe mottles (2.5YR 4/6) withinthe 2-m profile depth occur on the slopes and RiverTerraces of the Upper Interfluve and on the somewhatbetter-drained areas of the Lower Interfluve. Seasonallywaterlogged soils of the Lower Interfluve contain plinthite and a yellow mottling (10YR 6/8) in a gray soilmatrix. Sandy to very sandy soils have developed onthe riverbanks, whereas the soils of the backswamp havea finer texture and display redoximorphic features.Farmers of the Nsukka Agricultural Zone can identifymajor soil types according to morphological characteristics of the soil to the depth they till or position in thelandscape (Gobin et al., 1998,1999). Although the localsoil descriptions are not the same for every village, theunderlying concepts or characteristics are similar. Thecharacteristics used are easy to recognize and includethe occurrence of ironstone, texture, and color, to whichsecondary soil properties such as workability, drainage,and water-holding capacity are attributed. The local soilnames are based on the soil morphological characteristics, and a translation of the local names was used toreconstruct the local classification (Fig. 2). The soilnames are directly linked to decision-making in landuse and management. For example, soils of the residualhills and ridges that are named stone are consideredmarginal for agricultural uses.Quantifying Field ObservationsThe huge differences in soils enabled the field estimation method to be tested for a wide range of soil textures.Compared with the laboratory textural analysis, the accuracy was 100% in detecting USDA soil texturalclasses. Field estimates of ironstone content corresponded with the following classes by weight percentage: few ( 10%), common (10-30%), many (30-50%),abundant (50-70%), and dominant ( 70%). RI andCI correlated positively with dithionite-extracted Fe2O3(R 0.
Munsell color notation and Eq. [3] and [4] t Abbreviations used for each soil variable are presented in brackets. tions were compared with soil variables measured by standard laboratory techniques and expressed as numerical values. Soil Color Indices Soil color based on the soil Munsell color chart consists of hue, value, and chroma.
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