Environmental Influences on Soil Chemistry in Central Semiarid Tanzania

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    Environmental Influences on Soil Chemistry in Central Semiarid TanzaniaJulia Alien Jones*

    ABSTRACTThis study assessed the effectiveness of landscape features for

    predicting the variation in a set of chemical properties from 81 soilprofiles sampled in a 236-km2 site in central Tanzania. Soil sampleswere treated as points and coded by horizon and by groups, includinglandform, parent material, vegetation type, vegetation percent cover,and presence or absence of hypogeal termites. The variation cap-tured by these groups in eight soil chemical properties (organic C,total N, acid-extractable P, exchangeable cations, and extractableAl) was assessed using analysis of variance, Duncan's multiplerange test, and discriminant analysis. Soil development fits the ca-tena model along hillslope transects, but lateral subsurface transportof dissolved Fe and cations has produced plinthite and highly base-saturated horizons just above the lithic or paralithic contact down-slope of transitions between ferruginous sediments and granitic sed-iments. Nevertheless, biotic factors (vegetation type and density oftermites) captured more variation than other groups at the regionallevel. Vegetation species composition and the density and type oftermites in central Tanzania reflect soil depth, texture, clay miner-alogy, and drainage characteristics influenced by landform and par-ent material. Root symbioses of native miombo and Acacia spp.woodlands may also modify local soil chemistry via selective uptakeof C arid exchangeable cations. Termites contribute to the depletionof organic C, total N, and associated nutrients, throughout the soilsthey occupy in this region.

    SOIL SURVEYS provide basic information on soil fer-tility for agricultural planning at the regional levelin the semiarid tropics. Although a wide range of land-scape features could be used to design soil map unitsfor sampling of representative peddns, the compara-tive effectiveness with which different landscape fea-tures capture variation in soil chemistry has not beentested at the regional scale in the dry tropics. In Tan-zania, Milne's catena concept (Milne, 1935), originallyintended as a description of soil sequences on a hill-slope, has been extrapolated as the basis for map unitson a regional or national scale (Hathout, 1972; Scott,1972). Other soil maps of Tanzania (Baker, 1970)have been based on the land systems concept, treatingsoils and slopes as integral units (Moss, 1968). How-ever, sources of landscape information such as geo-logic maps or remotely sensed imagery are availableas a basis for soil map unit generalization in centralTanzania. These provide information on vegetationtype and percent cover, and even on the distributionand density of termite mounds. On the basis of fieldand laboratory observations from a detailed soil sur-Dept. of Geography, Univ. of California, Santa Barbara, CA 93106.Contribution from the Dep. of Geography and the EnvironmentalStudies Program, Univ. of California at Santa Barbara/Received 5Feb. 1988. 'Corresponding author.Published in Soil Sci. Soc. Am. J. 53:1748-1758 (1989).

    vey of a 236 km2 portion of central Tanzania (Jones,1989, unpublished data), it was hypothesized that veg-etation type and termite-mound occurrence incorpo-rate the most information about pedogenesis in cen-tral Tanzania, and would therefore be most closelyrelated to soil variability. This hypothesis was testedby comparing the variation in soil chemical data fromthe soil survey, captured by a series of groups basedon hillslope position, landform, parent material, veg-etation, and termite activity.


    The study site has a topography, geologic history, climate,vegetation, and biota similar to much of East and CentralAfrica. The landscape consists of steep mountains rising outof a gently sloping, soil-mantled pediment (Fig. 1). Complexpedogenetic factors have influenced soils in the 236 km2study site, which is on the "immense pedimented land-scape" of the central plateau of Tanzania (King, 1962). Theplateau has been subject to repeated cycles of erosion sinceplanation in the early Tertiary period, and the study sitenow lies at the extreme southeast corner of the dischargelessEast African Rift basin (King, 1962). The underlying rockconsists of the Dodoman system: a belt of schist, ferruginousquartzites, amphibolites, and hornblende gneisses much in-truded by granite and pegmatite, and dated at older than 2.5billion yr (Cahen and Snelling, 1984). A wide variety of par-ent materials (Fig. 2) (Government of Tanzania, 1963, 1964)have been warped and tilted to form an irregular basin some20 km southeast of the site (Wade and Gates, 1938). Local-ized faulting follows the preexisting northeast trend of thecountry rock (King, 1962). Although underlain by Precam-brian rocks, the soils of the study site Have formed on veryyoung sediments (Wade and Gates, 1938), which are beingcontinually reworked by faulting and uplift associated withthe East African Rift.

    This complex landscape history is compounded by evi-dence of change since 12 000 yr BP toward a warmer, drierclimate (Livingstone and Van der Hammen, 1978). Presentclimate is characterized by mean annual precipitation ofabout 550 mm and mean monthly temperatures rangingfrom 20 to 25 C. The soil moisture regime is ustic and thesoil temperature regime is isohyperthermic (Van Wambeke,1982).

    The natural vegetation of the study site includes three dry-deciduous formations: miombo woodland, dominated byBrachystegia spiciformis (Benth.) and Julbernardia globi-flora (Benth.) Troupin; Commiphora woodland dominatedby Commiphora spp., and Acacia wooded bushland domi-nated by Acacia spp., especially Acacia tortilis (Forssk.)Hayne subsp. spirocarpa (Hochst. ex A. Rich.) Brenan andCombretum apiculatum Sond. (Jones, 1989, unpublisheddata). Miombo woodland extends from Kenya to Zambia,and wooded bushland dominated by Acacia and Combre-tum spp. ranges from northern Ethiopia to Zimbabwe(White, 1983).



    Fig. 1. Study site location, topography, and location of pedons. Dotted lines indicate elevation in m, solid lines indicate boundaries of soilunits defined by landform, parent material, vegetation, and presence or absence of termites. A = Lithic Ustropepts on summits, backslopes,and toeslopes of granite or gneiss mountains with miombo woodland and no termites. B = Lithic Ustropepts on summits, backslopes, andtoeslopes of amphibole schist or ferruginous quartzite schist mountains with Commiphora woodland and no termites. C = Oxic Ustropeptsand Ustoxic Dystropepts on red sediments on the upper pediment, with Acacia wooded bushland and cultivated fields, no Macrotermitinae,but other termites. D = Ustic Dystropepts, Ustoxic Dystropepts, Typic Ustipsamments, Ultic Paleustalfs, Plinthustalfs, and Typic Ha-plustalfs on reworked granite sediments on the upper and lower pediment, with Acacia wooded bushland and cultivated fields, and Ma-crotermitinae and other termites. E = Fluventic Ustropepts and Fluventic Dystropepts on granitic sediments in narrow floodplains andchannels of nonperennial streams, with riparian woodland and cultivated fields, and no termites. Unit E occupies only the upper segmentsof drainages shown in Fig. 2. Pedons marked with a "p" have one or more layers in which plinthite occupies >50% of the matrix.


    Reworked Granitic SedimentsRed SedimentsAmphibole SchistFerruginous Ouartzite, SchistMicaceous Quartzite, SchistBiotitlc Quartzo-Feldspathic Gneiss, w/ AmphiboliteSynorogenic GraniteBasic, Ultrabasic Intrusive (w/ Soapstone & Talc Schist)AmphiboliteQuartz Tourmaline-Serlcite Schist

    Inclined Foliation

    Vertical FoliationObserved Faults

    _ Inferred Faults

    Concealed FaultsSeasonal Streams

    Fig. 2. Study site geology. Source: Government of Tanzania, 1963, 1964.

    Much of the site is occupied by Macrotermitinae or othertermites, with up to 200 large termite mounds km-2 (Jones,1989). Soils at the site are Lithic Ustropepts, Lithic Dystrop-epts, Oxic Ustropepts, Ustoxic Dystropepts, Ustic Dystrop-epts, Typic Ustipsamments, Ultic Paleustalfs, Plinthustalfs,Fluventic Ustropepts, Ruventic Dystropepts, and Typic Ha-plustalfs (Jones, 1989, unpublished data).Experimental Design

    Nine groups of landscape and soil features observablefrom maps, aerial photographs, and satellite imagery wereconstructed and compared to determine which group cap-tured the most variation in soil chemical properties. Eightof the groups were based on landform (LF), hillslope posi-tion (HP), parent material (PM), vegetation type (VT), veg-

    etation cover (VC), termite occurrence and density (T), land-form and parent material (LPM), and termite occurrenceand vegetation type (TVT). A ninth group, based on soilparticle size and mineralogy differentiae (PSM), capturedmost of the variation among the 11 family-level taxonomicclasses in the data set (Jones, 1989, unpublished data). Fig-ure 3 specifies the hypothesized causal links between thesegroups and soil chemistry. Because of pedogenic processes,all of these groups are interdependent, rather than indepen-dent. This means that causal relationships implied by thestatistical comparisons among groups must be substantiatedby examination of all interrelated factors shown in Fig. 3.

    Each group is composed of classes. The three LF classesare: mountains, pediment, and channels. The seven HPclasses are: summit, shoulder, backslope, toeslope (on the


    soil depth

    soil chemistryFig. 3. Schematic diagram of the relationships tested between groups

    and soil chemistry, and the interrelation among groups.

    mountains) and upper pediment, lower pediment, and nar-row floodplains and channels (on the warped and tilted pe-diment), following Ruhe (1975) and Gerrard (1981). Thepediment is an extremely long, flat planar surface, lackingsummits or backslopes, so the HP and LF groups are some-what redundant. The six PM classes are: synorogenic gran-ite, amphibole schist, ferruginous quartzite schist and mi-caceous quartzite, gneiss, red sediments, and reworkedgranitic sediments, following terminology in Government ofTanzania (1963, 1964). The five VT classes are: miombowoodland, Commiphora woodland, Acacia wooded bush-land, riparian woodland, and cultivated fields, determinedfrom field sampling (Goldschmidt and Jones, 1989). Thefour VC classes are: 0 to 10, 11 to 40, 41 to 80, and 81 to100%, photointerpreted from 1978 aerial photographs andverified during field sampling (Goldschmidt and Jones,

    1989). The three T classes are: without termites, withoutMacrotermitinae (i.e., fungus-growing, mound-building ter-mites of Macrotermes and Odontotermes spp.) but withother termites (i.e., Hodotermes, Amitermes, and Microcer-otermes spp.), and with Macrotermitinae and other termites.Areas with termite mounds were photointerpreted followingHoward (1959), and termite species collected during fieldsampling were identified by J.P.E.C. Darlington. The sixLPM classes are: granite and gneiss mountains, amphiboleschist and ferruginous quartzite schist mountains, red sed-iments on the upper pediment (>1090 m), reworked gran-itic sediments on the upper pediment, reworked granitic sed-iments on the lower pediment (


    tured by a set of landscape features using all eight chemicalproperties at once. Discriminant analysis is frequently usedfor vegetation classification (Barbour et al., 1987), and hasrecently been applied to soil classification (Edmonds andLentner, 1987).

    The following brief description of discriminant analysis(or discriminant ordination) is modified from Pielou (1984)and Becker and Chambers (1984). Data for a discriminantordination is ordered in a matrix X with s + k ~ I rowsand n columns, where 5 = number of observed variables(eight soil properties in this analysis); k = number of classes(ranging from three to seven); and n = number of obser-vations (soil samples). The entries in the first k - 1 rows areinteger (dummy) variables indicating an observation's mem-bership in a class. The matrix X is post-multiplied by itstranspose to produce the sum of squares and cross-products(SSCP) matrix S. The matrix S is then subdivided into foursubmatrices: S,, (the k 1 by k - 1 matrix of the SSCPof dummy variables [classes]); S22 (the sby s matrix of SSCPof observed variables [soil properties]); S12 (the k I by smatrix of sums of cross-products of dummy variables[classes] and observed variables [soil properties]); and S2i(S',2). The inverses of Su and S22 (87! and S22) are used tocalculate the matrix product D

    the eigenvalues X corresponding to the rows of W(fc_t)equivalent to



    D = 822From an eigenanalysis of D, the eigenvectors correspondingto the eigenvalues X become the k 1 by s matrix W^_1}.This matrix is pre-multiplied by the matrix X/s) (the originalobservations [soil properties] stripped of the k 1 dummyvariables) to determine the matrix Y

    The first two columns of Y are the first two discriminantvariables, each of which is a linear combination of the dataX grouped by k. The discriminant ordination routine usedhere is more general than described above, in the sense that

    1 - PYCwhere pYC is the correlation between the discriminant vari-able (column of Y) and the variable C that describes thecorresponding contrast between the classes k (Becker andChambers, 1984). The values of pYC, C, and Y can be inter-preted to indicate how well the discriminant variable dis-tinguishes among the classes k, which of the classes k aremost strongly contrasting, and which of the eight soil chem-ical properties are responsible for the contrast.

    Analyses were carried out using the S statistical package(Becker and Chambers, 1984) and UNIX software (Ker-nighan and Pike, 1984) on an IBM VAX 750. Data from alleight soil properties were log-normally distributed, and weretransformed to fit a normal distribution following Alien(1985). Statistical analyses were performed on subsets ofsamples by horizon, with n = 108 for surface horizon, n =51 for B horizon, and n = 52 for C horizon subsets.

    RESULTSComparison of Groupings

    Means of the soil chemical data set and numbers ofsamples from A, B, and C horizons are grouped ac-cording to TVT in Table 1. In ANOVA analyses, A-horizon chemical properties were found to differ sig-nificantly (P < 0.01) for all nine groups (Table 2).Only two groups (PSM and TVT) capture significantvariation in all eight chemical properties of A hori-zons. The variation in chemical properties of B andC horizons is less well explained in a statistical senseby any of the groups. Two groups (HP and T) capturesome variation in acid-extractable P in B and C ho-

    Table 2. Significance levels (excedence probabilities) of analysis of variance results from nine groups used to capt...


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