Predicting erodibility characteristics of soils formed over different parent
materials in southeastern Nigeria
E.U. Onweremadua, V.N. Onyiab, M.A.N.Anikwec*, F.O.R. Akamigbod and C.A. Igwed
Department of Soil Science and Technology, Federal University of Technology PMB 1526 Owerri
Imo State, Nigeria.
b Department of Crop Science and Technology, Federal University of Technology, P.M.B. 01526,
Owerri, Imo State, Nigeria
Department of Agronomy and Ecological Management, Enugu State University of Science and
Technology, Enugu, Nigeria
Department of Soil Science, University of Nigeria Nsukka,Nigeria
The heterogeneity of soil in time and in space tends to support the concept that soil
erodibility depends dynamically and spatially on the set of properties of a specific soil.
Seven soil erodibility indices and some soil properties were assessed to predict
vulnerability to soil erosion in southeastern Nigeria. Aggregated silt and clay (ASC,)
erodibility factor (K), dispersion ratio (DR,) clay ratio (CR) and erosion index (EI)
gave higher predictions as they correlated highly with selected soil properties
(P=0.05). Unlike earlier studies, the study revealed significant variations in the
erodibility characteristics of soils at different depths using Cr, K and ASC. A model
was generated which expressed good predictive relationship between soil erodibility
and clay content, organic matter, bulk density, ESP and total sand (R=0.85, R2 = 0.73,
1-R2 = 0.27, RMSE = 1.61, BIAS = 10.01). Soil erodibility varied with soil type
although, moderately (CV = 34.31%).
Key words: Erosion, erodibility, erodibility indices, parent materials, tropical soils, variability
* Corresponding author.
E-mail address: firstname.lastname@example.org (M.A.N. Anikwe).email@example.com(E.U.
Onweremadu) Onyianduvin@yahoo.com(V.N. Onyia)
Soil erosion under the agencies of water, wind, or ice is a major impediment to
agricultural and non-agricultural enterprises in the world. About 85 % of the causes
of land degradation worldwide is due to soil erosion by wind and water (Mbagwu and
Obi, 2003). In southeast Nigeria food and nutrition security status is highly threatened
by soil erosion which Igwe et al. (1995) related to nature of soils. Akamigbo (1983)
observed that gully erosion has reduced not only farmlands but threatened the
existence of towns and cities of southeastern Nigeria. Various agents of soil erosion
have been established, and in southeastern Nigeria, rainfall which exceeds 3000 mm
in a year, and which often has a long duration and with intensities of up to 200 mm/hr
could be expected to play a dominant role in soil loss (Obi and Asiegbu, 1980).
Attempts have been made by various scholars to assess some soil aggregate
indices which could be used to estimate erodibility potentials of soil. Soil erodibility is
thought of as the ease with which soil is detached by splash during rainfall or by
surface flow and soil erodibility is an important factor in determining the rate of soil
loss (Zhang et al., 2004). The concept of erodibility and how to assess it is
complicated since the susceptibility of the soil to erosion is influenced by a large
number of properties such as physical, mechanical, hydrologic, chemical, rheological,
mineralogical and biological, not to mention the soil profile characteristics such as the
depth of the soil and its influence on vegetative growth (Veihe, 2002). Finding a
suitable erodibility index for soils in the tropical ecosystem specifically, poses a
number of problems because the majority of the existing indices were developed for
soils from temperate regions (Zhang, et al., 2004). Many attempts have been made to
devise a simple erodibility index and several authors have pointed out that aggregate
stability is a critical factor in this sense (Barthès and Roose, 2002). The determination
of soil aggregation and structural stability affords an indirect measurement of
erodibility that can be used to help establish the mechanisms of particle detachment in
soils (Imeson and Vis, 1984 and Le Bissonnais and Arrouays, 1997).
Morgan (1979) associated high silt/clay ratios with erodible areas where continual
removal of the soil does not allow sufficient time for a high degree of weathering to
occur. Obi et al. (1989) reported that the soil aggregate stability technique was least
satisfactory measure of erodibility of soils. In a toposequence study, Mbagwu (1986)
evaluated relative erodibility values of soils and observed that clay ratio (CR),
dispersion ratio (DR), Wischmeier’s erodibility index (K) and dispersion index (DI)
gave a better estimation of simulated soil loss than other indices. Igwe et al. (1995)
assessed soil erodibility using clay flocculation index (CFI), clay dispersion index
(CDI), dispersion ratio (DR), geometric mean diameter (GMD) and mean weight
diameter (MWD) and ranked their predictive abilities as follows: CFI > CDI > DR >
GMD > MWD. Because of the underling forces shaping the soils, the soil properties
vary with time and space and are affected by climate, organisms, topography and
parent materials interacting with time (Jenny, 1941). Climate factors, temperature and
rainfall, affect soils as well as plants growing on those soils. Plant community
succession due to the change of the soil physical environment is well observed and
change in plant composition in turn affects the soil properties. The soil properties vary
also in space because of the variation of soil formation factors. Thus, a soil erodibility
value for a specific soil may vary dynamically and spatially. Using the soil erodibility
values obtained previously from an extensive database to a specific area may lead to
uncertainty (Wang et al., 2001) it is necessary to include large databases from a
specific location, improve methods for mapping the soil loss and to revise published
values of soil erodibility over time.
The aim of this study was to predict soil erodibility in selected sites in
southeastern Nigeria using seven indices namely, clay dispersion index (CDI), clay
ratio (DR), erodibility factor (K), erosion index (EI), dispersion ratio (DR),
flocculation index (FI) and aggregated silt and clay (ASC).
2. Materials and Methods
The study site lies between latitude 40401 and 80 151 North and longitudes 60 401 and
80 151 East (Federal Department of Agricultural Land Resources, 1985). Due to its
latitudinal location the zone receives abundant and constant insolation. The actual
amount of insolation received is attenuated by the effects of cloud cover, rainfall and
harmattan haze. The mean annual temperature is between 27 – 320C. Humidity is
quite high being generally lowest during season as the rainy season begins. The
soils of the zone have isohyperthermic soil temperature regime (Anikwe et al., 2006)
and receives average annual rainfall of between 1600 mm – 4338 mm. Total rainfall
decreases progressively from the coast and the east towards to the interior (Unamma
et al., 1985). The parent materials comprise alluvium, coastal plain sands, shale,
lower coal measures, upper coal measures and false-bedded sandstone (Orajaka,
1975). The area is within the humid tropics (Ofomata, 1975) and of rainforest
vegetation (Igbozuruike, 1975). Farming is commonly practiced (Federal
Department of Agricultural Land Resources, 1985) and crops are planted in a
sequence over a complete cycle (Mutsaers et al., 1997). However, population
pressure has drastically reduced fallow length (Onweremadu, 1994).
A reconnaissance survey was carried out in the in the study area to identify
sampling points. Sampling points were chosen in each selected site using free survey
technique (observation points that are representative of the site are chosen by the
surveyors based on personal judgment and experience) (Mulla and McBratrey, 2000).
The 6 soil groups representing soils formed over alluvium, coastal plain sands, shale,
lower coal measures, upper coal measures and false bedded sandstone were selected.
Five profile pits were sunk in each soil group, giving a total of 30 pedons studied.
Three auger and three core samples were collected from 5 soil depths (0-20, 20-40,
40-60, 60- 80 and 80- 100 cm) in each of the 30 profile pits making a total of 150 soil
samples. The auger samples were composited and its sub-samples used for analysis
whereas the core samples were analyzed separately and mean results computed and
used for statistical analysis. Differences in management practices and edaphoclimatic
properties of the soils influenced the choice of the different sites. Sampled locations
are shown in Fig 2.
2.3. Laboratory analyses
Soil samples were air-dried and sieved using 2-mm mesh sieve before laboratory
determinations. Particle size distribution was determined in both distilled water and
calgon by hydrometer method (Gee and Bauder, 1986). Thereafter these indirect
measures of erodibility were computed as follows:
Clay dispersion index (CDI) = % clay (H20)
= x 100 … Dong et al. (1983)
% clay (calgon) 1
Clay ratio (CR) % sand
% silt + % clay
Erodibility factor (K) = % total sand + % silt
Erosion index (EI) = DR
% clay/ ½ WHC
Where DR = dispersion ratio
WHC = Water holding capacity
Dispersion ratio (DR) = % silt + % clay (H20)
% silt + % clay (calgon)
Flocculation index (FI) ASC 100
= SC (calgon)
Where ASC = aggregated silt and clay
Aggregated silt and clay (ASC) = silt + clay (calgon) – silt + clay (H20).
Particle size distribution (textures) was obtained by the hydrometer method
(Gee and Or, 2002). Organic carbon was measured using Walkley/Black procedure
(Nelson and Sommers, 1982). Soil water holding capacity was determined on
undisturbed samples as the difference of water contents at –0.03 MPa determined by
pressure plate and water content at –1.5 MPa determined by pressure membrane
(Dane and Hopmans, 2002).
Bulk density was estimated by core method (Grossman, and Reinsch. 2002). Cation
exchange capacity and exchangeable cations were determined with neutral 1N
NH4OAc (Thomas, 1982) whereas exchangeable sodium was got by flame
photometry. Exchangeable sodium percentage was obtained as ratio of exchangeable
sodium to cation exchange capacity.
2.4 Statistical analyses
Soil data were subjected to analysis of variance (ANOVA) using PROC Mix-model
of SAS (Little et al., 1996). Means were separated using a standard error of the
difference (SED) at 5% level of portability. Multiple regression was used to calculate
the variance associated with the best fitting linear combination of the variable
according to the model below:
Y = a+b1x1+b2x2+b3x3+…+bnxn
Where Y = predicted Y (soil erodibility)
a = intercept
bs = slopes corresponding to the xs
xs = independent soil variables
n = number variable used in the model
In order to evaluate average prediction uncertainty of the model root mean square
error (RMSE) was used as follows:
REMSE di … Moldrup et al. (2004)
n i 1
Where di = difference between predicted and measured values of predictors
n = number in the data set
Finally, model BIAS was applied to assess underestimation or overestimation thus:
n i 1
… Moldrup et al.(2004)
di = as defined in the formula for RMSE.
3. Results and Discussion
Correlation coefficients for linear relationships between soil erodibility indices
with selected soil properties are shown in Table 1. Significant negative correlations
were obtained between CDI and effective cation exchange capacity and organic matter
whereas positive correlations existed between CDI and total sand and clay at 5%
levels of probability. Using clay erosion prediction ranking follows this trend: ASC >
CR/K > EI > FI/DR > CDI. Similarly, negative correlation coefficients were obtained
when CR, K, EI and DR related with ECEC. Using ECEC, the erosion prediction is:
ASC > FI/DR > EI > CR > K > CDI. Aggregated silt and clay correlated positively
and significantly with all soil properties (P = 0.05) except with OM and ESP.
Aggregated silt and clay (ASC) gave the best significant correlation with total
sand (r = 0.92, P = 0.05), followed by CR (r = 0.83, P = 0.05), then erodibility factor
(r = 0.74, p = 0.05), dispersion ratio (r=0.69,P =0.05), erosion index (r =0.63, P
=0.05) and least in clay dispersion index (r=0.42, P =0.05), thus erosion prediction
is: ASC > CR > K > DR > FI > EI. Exchangeable sodium percentage (ESP) had no
significant relationship with erodibility indices (P =0.05). This agrees with the
findings of Mbagwu and Auerswald (1999) that soils of southeastern Nigeria contain
low values of ESP, indicating that dispersability of these soils may not be due to Na+
on the exchange sites.
There were significant correlations between organic matter and clay dispersion
index, erosion index, dispersion ratio and flocculation index (P>0.05) whereas no
significant relationship existed between organic matter and clay ratio, erodibility
factor and aggregated silt and clay (P>0.05) as shown in Table 2. Earlier, Igwe et al.
(1995) reported that significant correlation existed between organic carbon and DR,
CDI and CFI. These show that organic carbon could also be used for predictive
purposes as postulated by Mbagwu et al. (1983). Soil organic carbon (SOC) is
recognized as a main soil constituent and a key parameter to measure soil quality,
provided the fair correlation between the SOC content and many soil properties and
functions such as soil porosity, water holding capacity, nutrient availability, soil
biodiversity, soil structural stability, etc. (Karlen and Andrews, 2000 and Singer and
Ewing, 2000). The interrelationship between SOC and the stability of topsoil
aggregates has been well established. Several mechanisms are involved in soil
susceptibility to particle detachment, and they are mainly related with soil erodibility
(Rodriguez, 2006). It is difficult to measure soil erodibility, given the complex
interactions between soil properties and time-related conditions that make soil
erodibility a dynamic (rather than a constant) property (Roose, 2003). Based on results
in Table 2 and in terms of erosion prediction, these indices can be ranked as follows:
CDI/DR > EI/FI > ASC/K > CR. These showed slight deviations from the ranking of
Igwe et al. (1995).
Table 3 shows significant variability existing in CR, K and ASC as depth changed
(P >0.01). This points to the fact that some layers in the pedons are more readily
eroded than others. The consequence of this is that exposure of sub-surface layers has
possible deleterious effects as soil layers differ in chemical and mineralogical
compositions caused by prevailing pedogenic processes. Using 5 independent
variables of clay, organic matter, bulk density, exchangeable sodium percentage and
total sand, the following erodibility model was obtained:
K = 5.48+0.05 clay + 0.30 OM + 4.31 BD +0.06 ESP
+ 0.08 Tsand
Where K = soil erodibility
OM = organic matter
BD = bulk density
ESP = exchangeable sodium percentage,
Tsand = total sand
Analysis of model attributes shows an R-value of 0.85 and R2 – value of 0.73,
indicating that the independent variables, namely clay, organic matter, bulk density,
exchangeable sodium percentage have highly significant relationship (P=0.01) with
soil erodibility. The erodibility of a certain soil is closely related to its particle-size
distribution, permeability, organic matter content and structure (Zhang, 2004).
Soils varied moderately (CV = 34.31%) This is in line with Aweto (1982) who
classified CV into 0-20% (little variation), 20-50% (moderate variation) and >50%
(high variation). Evaluating these attributes can therefore help soil conservationists to
draw reliable conclusions on the vulnerability of soils of the area to erosion given a
RMSE value of 1.61 and a coefficient of alienation of 0.27. The estimated BIAS of +
0.01 points to a high level of accuracy since it is a minor overestimation. These
results exist despite the fact that soils were derived from different parent materials,
drainage classes, slightly different climatic regimes and management practices.
Aggregated silt and clay (ASC), dispersion ratio (DR), clay ratio (CR) and erosion
index (EI) ranked higher in predicting vulnerability of soils to erosive forces. There
were significant variations in the erodibilities of surface and sub-surface layers of soils
when measured with clay ratio (CR), erodibility factor (K) and aggregated silt and
clay (ASC). Clay, organic matter, bulk density, exchangeable sodium percentage and
total sand were good predictors of soil erodibility in southeastern Nigeria (R2 = 0.73,
RMSE 1.61, BIAS = + 0.01). Finally soils of study site are generally erodible
irrespective of parent materials type.
We are grateful to the staff of Institute of Soil Erosion Studies, Federal University of
Technology, Owerri, Nigeria for their technical assistance.
Akamigbo, F.O.R. 1983. Influence of pedological processes on gully formation in
southeastern Nigeria. Niger J. Soil Sci., 4:12-127.
Anikwe, M.A.N., C.N. Mbah, P.I. Ezeaku , V.N. Onyia.2006. Tillage and plastic
mulch effects on soil properties and growth and yield of cocoyam (Colocasia
esculenta) on an ultisol in southeastern Nigeria. Soil & Tillage Research, In
press. Available on line June 2006.
Aweto, A.O. 1982. Variability of upper slope soils developed under sandstones in
southwestern Nigeria. Nigerian Geographical Journal 25:27-37.
Barthès, B and E. Roose.2002. Aggregate stability as an indicator of soil
susceptibility to runoff and erosion; validation at several levels, Catena 47,
Dane, J. H., and J. W. Hopmans. 2002.Water retention and storage: Laboratory
methods. In: J. H. Dane and G. C. Topp (eds.). Methods of soil analysis. Part
4. Physical Methods. Soil Sci. Soc. Am. Book Series No. 5 ASA and SSSA
Madison, WI. pp. 675–720.
Federal Department of Agricultural Land Resources (1985). The reconnaissance soil
survey of Imo State, Nigeria (1:250,000). Soils Report. 133pp.
Gee, G. W and D. Or. 2002. Particle size analysis. In: J. H. Dane and G. C. Topp
(eds.). Methods of soil analysis. Part 4. Physical Methods. Soil Sci. Soc. Am.
Book Series No. 5 ASA and SSSA Madison, WI. pp. 255–293.
Grossman, R. B., and T. G. Reinsch. 2002. Bulk density and linear extensibility. In:
J. H. Dane and G. C. Topp (eds.). Methods of soil analysis. Part 4. Physical
Methods. Soil Sci. Soc. Am. Book Series No. 5 ASA and SSSA Madison, WI.
Igbozuruike, M.U. 1975. Vegetation In: Ofomata, G.E.K. (ed). Nigeria in maps:
Eastern States. Ethiope Publ. House, Benin City. Pp 30-31
Igwe, C.A., Akamigbo, F.O.R., Mbagwu, J.S.C. 1995. The use of some soil
aggregate indices to assess potential soil loss in soils of southeastern Nigeria.
Int. Agro-physics, 9:95-100.
Imeson, A and M. Vis. 1984. Assessing soil aggregate stability by water-drop
impact and ultrasonic dispersion, Geoderma 34, 185–200.
Jenny, H.1941. In: Factors of Soil Formation, McGraw-Hill Book, New York. p.
Karlen, D.L and S.S. Andrews .2000. The soil quality concept: a tool for evaluating
sustainability. In: S. Elmholt, B. Stenberg, A. Gronlund and V. Nuutinen,
Editors, Soil Stresses, Quality and Care, DIAS Report vol. 38, Danish Institute
of Agricultural Sciences, Tjele, Denmark.
Le Bissonnais, Y and D. Arrouays. 1997. Aggregate stability and assessment of soil
crustability and erodibility: II. Application to humic loamy soils with various
organic carbon contents, European Journal of Soil Science 48, 39–48.
Little, R.C, Milliken, G.A., Stroup, W.W., Wolfiager, R.C. 1996. SAS system for
mixed models. Statistical system Inc. Cary, North Carolina, USA. 633pp.
Mbagwu, J.S.C 1986. Erodibility of soils formed on a catenary toposequence in
southeastern Nigeria as evaluated by different indexes. East Africa Agric For.
J. 52 (2), 74-80.
Mbagwu, J.S.C.,R, Lal., T.W. Scott .1983. Physical properties of three soils in
southern Nigeria. Soil Sci., 136: 48-55.
Mbagwu, JSC., K. Auerswald.1999. Relationship of percolation stability of soil
aggregate to land use, selected properties, structural indicted and simulated
rainfall erosion. Soil and Tillage Research, 50: 197-206.
Mbagwu. JSC., M.E.Obi . 2003. Land degradation, agricultural productivity and
rural poverty: Environmental implications. Proc of the 28 th Ann. Conf of the
Soil Science Soc of Nigeria, National Root, Crops Research Institute, Pp 1-11.
Middleton, H.E. 1930. Properties of soil which influence soil erosion U.S. Dept
Agronomy Tech. Bull. 178pp.
Moldrup, R., R, Olesen., S. Yoshikawa, T. Komatsu., D.E. Polston. .2004. Three
porosity model for predicting the gas diffusion coefficient in undisturbed soil.
Soil Sci. Soc Am J 68: 750 -759.
Morgan, R.P.C. (1979). Topics in applied geography: Soil erosion. Longman Group
Mulla, D.J and A.B. McBratrey.2000. Soil spatial variability. In: Sumner, M.E. (Ed),
Handbook of Soil Science. CRC Books, New York.
Mutsaers, H.J.W., G.K. Weber., P. Walker., N.M. Fisher. 1997. A field guide for on-
farm experimentation. CTA Wageningen,The Netherlands. 231 pp.
Nelson, D.W., L.E.Sommers.1982. Total carbon, organic carbon and organic matter.
In: Page, A.L. Miller, R.H., and Keeney, D.R. (eds). Methods of soil analysis,
part 2. American Soc Agronomy Madison, WI, Pp. 539-579.
Obi, M.E., F.K. Salako., R. Lal, 1989. Relative Susceptibility of some southeastern
Nigeria soils to erosion. Catena, 16: 215-225.
Obi, ME, Asiegbu 1980. The physical properties of some soils of southeastern
Nigeria. Soil Sci. 130:39-48.
Ofomata, G.E.K .1975. Landform regions. In: Ofomata GEK. (ed). Nigeria in maps:
Eastern States. Ethiope Publ House Benin City, pp 33-37.
Onweremadu, E.U. 1994. Investigation of soil and other related constraints to
sustained agricultural productivity of soils of Owerri Agricultural zone in Imo
State, Nigeria. M.Sc. Thesis University of Nigeria Nsukka. 164pp.
Orajaka, S.O. 1995. Geology In: Ofomata, G.E. K. (ed). Nigeria in maps: Eastern
States. Ethiope Publ House Benin City pp 5-7.
Rodríguez A., C.D. Arbelo, J.A. Guerra, J.L. Mora, J.S. Notario and C.M.
Armas.2006. Organic carbon stocks and soil erodibility in Canary Islands
Andosols Catena 66, 228-235.
Roose, E., 2003. Soil erosion research in Africa: a review. In: Gabriels, D., Cornelis,
W. (Eds), 25 Years of Assessment of Erosion, Proceedings of International
Symposium, Ghent, Belgium, ICE and Universiteit Gent, pp. 29–43.
Singer, M.J and Ewing, S., 2000. Soil Quality. In: Sumner, M.E. (Ed.), Handbook of
Soil Science, CRC Press, Boca Raton, FL, USA pp. G-271/G-298.
Thomas, G.W., 1982. Exchangeable cations. In: Page, A.L., Miller, R.H., Keeny,
D.R. (Eds.), Methods of Soil Analysis. Part 2, 2nd ed. Agronomy Monograph
No. 9. ASA and SSSA, Madison, WI, pp. 159–165
Unamma, R.P.A, S.O.Odurukwe, H.E. Okereke, L.S.O.Ene and O.O. Okoli
(Ed.).1985. Farming Systems in Nigeria: Report of the benchmark survey of
the eastern agricultural zone of Nigeria. NRCRI Umudike Umuahia Nigeria.
Veihe, A. 2002. The spatial variability of erodibility and its relation to soil types: a
study from northern Ghana. Geoderma 106, 101-120.
Wang, G, G. Gertner, X, Liu and A. Anderson. 2001. Uncertainty assessment of soil
erodibility factor for revised universal soil loss equation Catena 46, 1-14.
Zhang, K S. Li, W. Peng and B. Yu . 2004. Erodibility of agricultural soils on the
Loess Plateau of China. Soil and Tillage Research 76, 157-165.
Correlation coefficients for linear relationships between soil erodibility indices and soil
Soil CDI CR K EI DR FI ASC
ECEC -0.22* -0.42* -0.34* -0.51* -0.60* 0.60* 0.71*
OM -0.26* -0.01ns 0.13ns -0.23* -0.26* 0.23* 0.13ns
ESP 0.12ns 0.04ns 0.07ns 0.15ns 0.12ns -0.14ns 0.14ns
Total Sand 0.42* 0.83* 0.74* 0.63* 0.69* -0.68* 0.92*
Clay 0.38* 0.74* -0.74* -0.63* -0.62* 0.62* 0.88*
* = significant at p = 0.05
ns = not significant at p = 0.05.
Correlation between organic matter and soil erodibility characteristics
Factor correlated R Significance (P = 0.05)
OM Vs CDI -0.26 0.0012
OM Vs CR -0.01 ns
OM Vs K -0.13 ns
OM Vs EL -0.23 0.0043*
OM Vs DR -0.26 0.0014*
OM Vs FI 0.23 0.0038*
OM Vs ASC 0.13 ns
* = significant at p < 0.05
ns = not significant at P < 0.05
Erodibility characteristics of the study site with depth (MeanSEM)
Soil Depth CDI CR K EI DR FI ASC
0 – 20 cm 61.63 4.18 4.29 0.60 7.23 0.97 47.80 3.42 62.87 4.34 34.83 4.37 13.23 2.77
20 – 40 cm 60.23 4.32 3.58 0.44 5.53 0.61 48.40 4.20 64.00 4.99 36.63 4.85 13.20 2.70
40 – 60 cm 58.23 4.08 2.48 0.29 3.47 0.37 47.83 4.01 63.50 4.90 35.57 4.82 16.53 3.41
60 – 80 cm 59.40 3.71 2.20 0.26 2.97 0.30 45.93 4.27 62.70 4.80 35.37 4.85 17.40 3.48
80 – 100 cm 62.16 3.63 3.13 0.46 4.20 0.60 50.63 4.87 65.07 4.84 35.03 4.71 16.67 3.48
S.E.D (P=0.05) 1.63 0.27 0.47 2.51 1.77 1.76 0.89
Pr > F ns <.0001 <.0001 Ns Ns ns <.0001
Erodibility model attributes (P < 0.01)
Dependent mean 4.68
CV (%) 34.31
Isuochi Scale 1: 500,000
TE M 9000 0 9000 18000 27000 m
AM ndiz ne
Ihube ru AT
irib Isiama Ohafia E
lo e ere Ab
Oforola uahia Itumbuzo
Owerri Mbaise Um Arochukwu
520430mE 540430mE 560430m 580430m 600430m 620430mE
Fig.2: Location map of the study site showing sampled points