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Spatial Variation of Soil Moisture Content and Total Porosity As Influenced by Land UseTypes in Lafia, North Central Nigeria

Jayeoba O J*1, Amana S M1, and Ogbe V B1

1 Agronomy Department, Faculty of Agriculture, Nasarawa State University, Keffi, LafiaCampus

*Corresponding Author: [email protected]

AbstractsThe study was carried out to evaluate the spatial variaility of soil moisture content and totalporosity as influenced by land use, in Faculty of Agriculture, teaching and Research farm,Shabu, Lafia. A total of 81observations and samples were collected at every 10m along the X-axis and 10m apart along the Y-axis and soil moisture content of the samples was determinedgravimetrically, while total porosity was calculated using bulk density from gravimetricmeasurement. Spatial analyses of the physical properties were done in a GIS environment.Geo-statistical procedure (Mvariogram) of Genstat Package was used to determine and selectappropriate spatial models for all the data set. Win- Surfer Version 7.0 (Golden Software Inc,Golden, Colorado) was used for interpolation technique called “Ordinary Kriging“ toproduce the spatial distribution of soil moisture content and total porosity. The results showedthat soil moisture content was negatively skewed but positive kurtosis, while kurtosis andskewness values in soil total porosity were both positive . This implies that both, testedparameters were highly variable and were not normally distributed. Anisotropy was notevident in the directional semivariograms for any of the properties. Therefore, isotropicmodels were fitted. Relative Nugget Effect (RNE) (The nugget-to-sill ratio (Co/Co + C)) oftotal porosity was 50% and 2.3% for moisture content. Contour Isoline mapping showed thatthe surface soil physical properties varied between the experimental plots as determined byland use type and management practices, plots with soil amendments having higher but highlyvaried surface soil moisture and better porosity. The results of this study demonstrated theneed for Site-specific management as required in Precision Agriculture.Keyword: Physical soil properties, Soil compaction, Kriging, Contour Plotting and Precision Agriculture

IntroductionSoil moisture plays a critical role in plant growth and vegetation restoration. However, soilmoisture exhibits highly variable in space and time. Consequently, high resolution ground-basedmonitoring is required to characterize these variations. At present, rapid and reliablemeasurements of soil moisture are possible with Time Domain Reflectometry (TDR), enablingdetailed measurement campaigns for spatial and temporal pattern of soil moisture in small areas(Dasberg and Dalton, 1985). This variability of soil moisture results from the differences intopography (Burt and Butcher, 1985), soils (Hawley et al., 1983), vegetation (Le Roux et al.,1995) and land use. Land use, also plays an important role in controlling spatial patterns of soilmoisture by influencing the infiltration, runoff and evapotransipiration, particularly during thegrowth season (Fu et al., 2000). Also Tominaga et al. (2005) found that the variability of soilwater content and bulk density was greatly affected by soil management. A betterunderstanding of the characteristics of soil moisture variability is important for improving

PAT June, 2014; 10 (1): 53-66 ISSN: 0794-5213

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hydrological models (Grayson et al., 1992) and land management in runoff and erosion control(Fitzjohn et al., 1998). Due to the importance of soil moisture, there have been a number ofpapers indicating the soil moisture variability ( Bárdossy and Lehmann, 1998; Zhao et al.,1999). Soil moisture plays a key role in the decline of soil functions including productivity andstability of mediating the nutrient and water flow for plant growth (Chen and Wang, 2008).Furthermore, soil moisture is one of the major controls on the structure, function, and diversityof ecosystems (Wang et al., 2004). These studies evaluated the factors controlling soil moisture,determine the significance in ecosystem processes and predict soil moisture in catchment orlarge scale. However, a little attention just is paid to influence of land use structure (pattern) onsoil moisture. Evaluating the effects of land use and its pattern on soil moisture is difficult,because the differences in land uses which produce a change in the soil properties andevapotranspiration are likely to increase soil moisture variability across the landscape (Andrewet al., 1998). The objective of this study is to investigate the application of Geo-statisticstechniques in evaluating the relationships between land use and some soil physical properties(Soil moisture content and total porosity) by means of intensive monitoring in space.

Materials and MethodsStudy site and sampling designThe study was conducted at the Faculty of Agriculture Research Farm, Nasarawa StateUniversity Keffi, Lafia Campus. The location is situated by latitude 8oN and 9oN and longitude8oE and 9oE Green witch meridian. It covers a total land area of 72 hectares with an altitude of200m above sea level. The area is surrounded by streams flowing down to the major river ofLafia, with an undulating land form, of slope ranging from 3-4.5 percent. The vegetation isderived savanna mostly dominated by tall grasses and shrubby trees of indigenous species. Thesoil is predominantly sandy loam. Eighty-one core soil samples were collected on theexperimental sites using a grid method at a distance of 10m apart along the X axis and 10malong the Y axis. The soils were collected at depths of 0-15cm. The sampling tube or soil augerwas driven into the soil at the desired depth and carefully pulled out and transferred into alabeled can, instantaneously covered with a lid and placed in a wooden box for ease oftransportation. The procedure was repeated for varying points of the site.

Soil Moisture MeasurementsAfterwards the collected samples were transported to the laboratory for weighing and the

moisture content of the samples were determined gravimetrically.( involves weighing the wetsoil sample, removing the water content of the soil by oven drying at 105oC for 48 hours. Eachsample was reweighed to determine the amount of water removed).

g = (Ww – Wd)/ (Wd – Wc) ………… (eq. 1)

Where g = gravimetric water content

Ww = Mass of wet soil and container (g)

Wd = Mass of dry soil and container (g)

Wc = Mass of container (g)

PAT 2014; 10 (1):53-66: ISSN: 0794-5213; Jayeoba et al;; Spatial Variation of Soil Moisture Content .…54

Total porosity was calculated from bulk density and particle density (using a value of 2.65Mgm3).

φ = 1 – ρb / ρpwhere, φ is the total porosity, ρb is bulk density and ρp is particle density

Statistical analysis

Exploratory data analysis was performed by SPSS (version 16) software. The data distributionswere analyzed by classical statistics (mean maximum, minimum, standard deviation, skewness,kurtosis and coefficient of variation). Histograms and Box-plots for soil moisture content andtotal porosity data were inspected for the possible outliers which affect the descriptive statisticsand the characterization of spatial variation. Geostatistical methods require using data withnormal distribution values, the data were also checked for normality and transformed asappropriate. Spatial analysis of the classified soil moisture was performed in a GISenvironment. Experimental semi-variograms were calculated for the 0-15cm depth usingequation (1)

…………………….. (1)

Where γ(h) is the semivariance for the lag distance h. N (h) is the number of sample pairsseparated by the lag distance h, z(xα) is the measured value at αth sample location and z(xα+h) isthe measured value at point α+hth sample location. Theoretical models (Spherical, RationalQuadratic, Hole effect, Exponential, K-Bessel or Gaussian) were fitted to experimentalsemivariograms. Geo-statistics analysis and semi-variogram model selection were done withGENSTAT. Soil surface plotting was done using Win- Surfer Version 7.0 (Golden SoftwareInc, Golden, Colorado).

ResultsStatistical parameters of soil physical propertiesThe summary statistical parameters of the soil physical properties data set were listed in Table1. To evaluate the data set, the mean, minimum values, maximum values, median values,standard deviation and variance were calculated. The soil Porosity of the study area (0-15cm)depth had a mean value of 56.27% and ranged from 51.79 – 68.07%, The soil moisture contentat field capacity (gravimetric) ranged from 0.01 – 0.08% with a mean value of 0.45. Krigingmethods work best if the data is approximately normally distributed. In SPSS, the histogram andnormal QQPlots were used to see what transformations, if any, are needed to make the datamore normally distributed. Normal QQPlots provides an indication of univariate normality.Histogram and normal QQPlots analysis were applied for each soil parameter and the results arepresented in Figures1 and 2. It was found that all the parameters required transformation for it toconform to the normality requirement of Kriging. For these parameters a log transformationswere applied to make the distribution close to normal


Semivariogram models

The prediction of the spatial process at nonsampling sites using geostatistics requires atheoretical semivariogram. It is necessary to decide on a theoretical variogram based on theexperimental variogram. It is vital to choose an appropriate model to estimate spatial statisticsas each model yields different values for nugget variance and range which are essential forgeostatistical analyses (Trangmar, 1985). In this study, the semivariogram models (Circular,Spherical, Tetraspherical, Penaspherical, Exponential, Gaussian, Rational Quadratic, Holeeffect, K-Bessel, J-Bessel, Stable) were tested for each soil parameter data set. Predictionperformances were assessed by cross validation.

Table 1: Statistical parameters of soil physical variablesMoisture

Content (%)Porosity (%)

Sampling size 81 81

Mean 0.045 56.276Std. Error of Mean 0.001 0.336Std. Deviation 0.013 3.022Variance 0.000 9.133Skewness -0.213 1.065Std. Error of Skewness 0.267 0.267Kurtosis 0.705 1.800Std. Error of Kurtosis 0.529 0.529Range 0.07 16.28Minimum 0.01 51.79Maximum 0.08 68.07

Figure 1. Histogram of Soil moisture content showing a normal probability distribution and Normal

Q-Q Plot at 0-15cm depth.


Figure 2. Histogram of Soil Total Porosity showing a normal probability distribution andNormal Q-Q Plot at 0-15cm depth

After applying different models for each soil parameter examined in this study, the error wascalculated using cross validation and models giving best results were determined. Figures 3 and4 shows plots of different models tested for each of the soil physical properties data set. Severalmodels were fitted to the semivariograms and the semivariance statistics of measured soilproperties are shown in Table 2.Exponential model was obtained as the best fit for moisture content and total porosity.Anisotropy was not evident in the directional semivariograms for any of the properties.Therefore, isotropic models were fitted. The entire semivariogram models displayed positivenugget effect, which may be as a result of sampling error, random, inherent variability or short-range variability. In order to identify the spatial distribution patterns of soil properties in thestudy area, it is necessary to present the data in the form of a map. For this purpose, moisturecontent and total porosity distribution maps were obtained by the ordinary kriging based on theExponential model. Spatial distribution maps of moisture content and total porosity for Facultyof Agriculture Teaching and Research farm are presented in Figure 3-4. The spatialinterpolation of soil physical properties at the surface soil in the study area revealed that all theparameters showed similar spatial trends. Total porosity (Figure 6) had the highest value in partof the study area amended with annual application of over 30ton/ha cow dung, while the lowesttotal porosity was observed in un- amended patch of the study area. Similar spatial patternswere also observed for moisture content.


Lag distance

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Figure 3. Plot of lag against semi-variance for different models for soil moisture conten.


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Figure 4. Plot of lag against semi-variance for different models for soil Porosity


Table 2 Parameter values of different model fittings of semivariogram of soil properties

DiscussionSoil management systems play an important role in sustainable agriculture andenvironmental quality. Management practices have greater effect on the direction anddegree of changes in soil properties. Soil properties vary spatially from a field to alarger regional scale affected by both intrinsic (soil forming factors) and extrinsicfactors (soil management practices, fertilization, and crop rotation) (Cambardella andKarlen, 1999). The soil Porosity had a mean value of 56.27% and ranged from 51.79 –68.07%. Total porosity was characterized by very high means, moderate variance(9.133), low standard deviation (3.022), and with low but positive skewness andkurtosis. The standard error of both skewness and kurtosis in the topsoil were extremelylow. The positively skewed variables in the top soils indicated that there were fewextreme high values of the tested variables. This result agreed with Ngailo andViera(2012). The negative skewness and positive kurtosis observed on the surface soilmoisture probably implied that the top soil had been affected by some external activitiessuch as management practices. the Q–Q plots of surface soil moisture and total porosity

exhibited normal distributions, this agreed with Zhang et al; (2011) For this study,ordinary Kriging method was used for making optimal, unbiased estimates ofregionalized variables at unsampled locations using the structural properties of thesemivariogram and the initial set of measured data. A useful feature of kriging is that anerror term expressing the estimation variance or uncertainty in estimation is calculatedfor each interpolated value. Kriging always produce an estimate equal to the measuredvalue if it is interpolating at a location where a measurement is obtained (Burrough,

Parameters Model Nugget (Co) Partial sill(C )

Range(h)/m

Sill (Co+C) RatioCo/(Co

+ C)

RNE(%)

R2 SE +

Porosity Exponential 9.164 3.60 1.90 12.764 0.717 71 56.3 10.4

Gaussian 3.592 3.59 1.91 7.182 0.500 50 - 19.0

Bessel 9.164 3.60 1.90 12.764 0.717 71 41.7 12.0

MoistureContent

Linear 0.00060036 0.00000082 - 0.00000118 0.305 30.5 5.3 0.000514

Exponential 0.000064 0.00017 9.7 0.000174 0.0245 2.35 29.4 0.00045

Gaussian 0.000098 0.000081 19.2 0.000179 0.547 54 31.9 0.00044

Bassel 0.000056 0.00012 8.09 0.000176 0.318 31 29.9 0.00045

Circular 0.000086 0.000093 36.2 0.000179 0.480 48 32.4 0.00044

Pentaspherical 0.00076 0.000103 - 0.000863 0.88 88 32.4 0.000044


1997; Stein and Bouma 1997; Jayeoba et al., 2012). Distinct classes of spatialdependence (Relative Nugget Effect) for the soil properties were obtained by the ratioof the nugget to sill. If the ratio was <25%, between 25 and 75% or >75%, the variablewas considered strongly, moderately or weakly spatially dependent respectively(Cambardella, et al., 1994). In accordance to the nugget effect, the semivariogramincreased until the variance of the data called Sill C was reached. Under thissemivariogram value, the regionalized variables at the sampling locations are spatiallycorrelated. The sill value represents total experimental errors (Ersoy, 2004). If thedistance of two pairs increases, the variogram of those two pairs will also increase.Eventually, the increase of the distance cannot cause an increase in the variogram. Thedistance, which causes the variogram to reach the plateau, is called the range. In otherwords, the range is considered as the distance beyond which observations are notspatially dependent (Gallardo, 2003). The range of spatial dependence varied from 9.7mfor moisture content to 1.9m for total porosity. Total porosity had a lower range (1.9m)

indicating spatial correlation within a smaller distance among the studied soilproperties. This property is variable within a short distance. Exponential model wasobtained as the best fit for moisture content and total porosity. Anisotropy was notevident in the directional semivariograms for any of the properties. Therefore, isotropicmodels were fitted. The entire semivariogram models displayed positive nugget effect,which may be as a result of sampling error, random, inherent variability or short-rangevariability. In order to identify the spatial distribution patterns of soil moisture contentand total porosity distribution maps were obtained by the ordinary kriging based on theExponential model. The spatial interpolation of soil physical properties at the surfacesoil in the study area revealed that the two parameter showed similar spatial trends. Thenugget effect (C0) for total porosity had low values in the models used with a uniformrange and sill except for Gausian model. Relative Nugget Effect (ratio of the nugget tosill) of Total porosity was 50% (Gaussian model in Table 2) showing a moderatelyspatial dependence due to soil mineralogy and soil formation processes (Cambardella etal. 1994; Shukla. et al. 2004; Jayeoba et al., 2012). The model parameters for soilmoisture are shown in Table. 2. The positive nugget effect (C0) for all the modelparameter showed low values, which could be explained by the sampling error, short-range variability, random and inherent variability (Wang et al. 2009. The sill (C0 + C)also showed very low values which meant that the spatial variance of soil moisture waslow. Relative Nugget Effect of surface moisture content was 2.4% (Table 2). Accordingto Cambardella (1994), this is a strongly structured spatial dependence (<25%). whichmeans that the soil moisture variability might be attributed to the extrinsic factorsincluding tillage and management practices. The range, as a measure of the spatialcontinuity of the soil moisture, ranges from small to high among the models. The rangecould be used as a guide to indicate the size of spatial classes, and to indicate the size of


areas of different moisture contents, thus the spatial frequency of soil moisture changes.Therefore, the ranges of soil moisture demonstrated a variable soil moisture patterncharacterized by distinct areas, which were spatially isolated and uncorrelated to thesurrounding areas. The difference could be explained by different processes controllingthe soil moisture pattern. The extrapolation mapping shown in Fig.5-6 indicates thatsurface physical properties of the field varied between the experimental plots asdetermined by land use type and management practices, plots with soil amendments

having higher but highly varied surface soil moisture and better porosity. Cultivatedplots also had more favorable physical properties compared with uncultivated plots.This was probably due to the effect of minimal tillage operation, which mostly employsthe use of local implements such as hoe etc. This system maintains at least 30 percent ofthe soil surface covered with residue of the previous season crops. The burying effectsof grown weeds during hoeing add humus to the soil after decomposition, whichincreases the fertility level and water holding capacity of the soil. There is highervariation and non-uniformity in this experimental plot. The low surface moisturecontent observed in un tilled plots was due to the zero tillage. Tillage operationimproves the porosity of the soil and aids rapid infiltration of soil water (Chang andLindwall, 1990). In the light of this, the uniformity of the soil moisture content at fieldcapacity could be attributed to the uniform application of tillage operation employedwhich reduced variation of surface moisture content through proper soil mixing. It is ofgreat importance for researchers to adapt one system of tillage operation throughout theplanting season in attempt to avoid mechanically induced spatial variability.

Total porosity (Figure 5) had the highest value in part of the study area amended withannual application of over 30ton/ha poultry manure, while the lowest total porosity wasobserved in un- amended patch of the study area. Similar spatial patterns were alsoobserved for moisture content. This agreed with the finding of ( Zhang et al; 2011) thatsoil moisture and total porosity variability might be attributed to the intrinsic (soil-forming processes) and extrinsic factors(land use) including tillage and managementpractices.


Fig 5 Contour plot of Soil Total Porosity (%) as influenced by land use pattern inLafia, Nigeria


Fig 6 Contour plot of Soil moisture content (cm3/cm3) as influenced by land use pattern inLafia, Nigeria

ConclusionGeostatistical characterization of the spatial variability through semivariograms orautocorrelation generally brings new insight into the way soil attributes are influenced by theenvironment such as geographical distribution of soil types or topography and land use. Thepredicted maps obtained could be helpful to the farmers and soil management experts to designland management and fertilizer recommendation taking into account the spatial heterogeneity of


soil properties for particular part of the field. The results of this study as demonstrated the needfor Site-specific nutrient management as required in Precision Agriculture.

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PAT June 2014 vol 10 no 1bpatnsukjournal.net/Vol10No1/p5.pdf · Title: Microsoft Word - PAT June 2014 vol 10 no 1b Author: User Created Date: 10/11/2014 3:39:24 PM