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African Journal of Environmental Economics and Management ISSN 2375-0707 Vol. 7 (5), pp. 001-007, May, 
2019. Available online at www.internationalscholarsjournals.org © International Scholars Journals 

 

Author(s) retain the copyright of this article. 
 
 
 

Full Length Research Paper 

 

Creating a soil data base in a reconnaissance 
soil fertility study of an encroached forest 

reserve in Northern Nigeria for a reforestation 
programme 

 
F. O. Adekayode1 and D. T. Akomolafe2

 
 

1
Department of Crop, Soil and Pest Management, Federal University of Technology, Akure, Nigeria. 

2
Department of Computer Science, Joseph Ayo Babalola University, Ikeji, Arakeji, Nigeria. 

 
Accepted 11 January, 2019 

 
The reconnaissance soil fertility study of 10,000 ha partly encroached forest reserve located between latitude 
11°47’N and 11°56’N and longitude 4°22’E and 4°32’E in Northern Nigeria was conducted in 2009 to generate a soil 
fertility data base of the reserve. The tracking of the forest reserve boundary was done using a Garmin 72 model 
global positioning system (GPS) receiver. The geographic coordinates were input into the computer to generate a 
digital map of the forest reserve. The entire forest reserve was divided into grids to guide in the location for soil 
sampling using the GPS/Geographic Information System (GIS) geospatial technique. Soil auger studies were made 
at 250 locations to site 60 sampling pits to collect soil samples for laboratory analysis of soil properties. Forest soils 
were classified into three groups using soil depth as a limiting parameter in the soil fertility assessment. The soils 
were sandy clay loam and the pH indicated a moderate to strong acid status with low content of organic matter, 
percentage nitrogen, available phosphorus, potassium, calcium and magnesium. A reforestation programme with 
the planting of acacia for soil rehabilitation was recommended for the reserve as the tree stands would serve to 
protect the land against erosive activity of wind and also serve in enriching the soil with nitrogen. 

 

Key words: Soil fertility assessment, reforestation program, forest reserve, geographic information system. 

 
INTRODUCTION 

 
Reforestation had aptly been described as the tree planting 
process undertaken to replenish already harvested trees in 
existing forest land in order to ameliorate the soil fertility that 
had been degraded as a result of uncontrolled felling of 
forest trees (Moffat and Boswell, 1990; Jaiyeoba, 2001; 
Saarsalmi et al., 2010). The need for tree replanting has 
become desirable as rampant deforestation destabilized the 
ecosystem and caused increasing green house effects, soil 
erosion, loss of biodiversity and drastic climatic changes 
(Kassas, 1995; Bashir, 2010; Putatunda, 2010). It had 
previously been explained that forests absorb carbon dioxide 
through their photosynthesis cycle and increasing forests 

with reforestation and discouraging deforestation would help 
mitigate global warming (Rosillo-Calle and Hall, 1992; 
Yokoyama, 1997;.  
 
 
 
*Corresponding author. E-mail: adekay98@yahoo.com. 

 
 
 

 
Several efforts have previously been made as discussed 

by Copperwiki (2010) for successful, sustainable and 
standardized afforestation and refores-tation management 
as exemplified by projects executed by the Britain’s Forestry 
Commission, the Jewish National Fund on the Yatri Forest 
and also the Green Wall of China which aimed to halt the 
expansion of the Gobi desert. The Yatri Forest project was a 
type whereby afforestation project had been successfully 
executed in harsh desert conditions to prevent further 
expansion of desert land.  

The need for maximizing the use of available land 
resources with high yield tree species had become desirable 
and achieving this goal greatly depends on climate and soil 
conditions (Ravindranath et al., 2006; Wu et al., 2007). The 
scope of afforestation would extend beyond just planting of 

trees to planting of the right types of trees at the right 
places which would require soil fertility investigation to 
confirm the soil fertility status that would support the 
particular tree types (Moshki and Lamersdorf, 2010). 



 
 
 

 

Soil fertility and management had been found essential to 
determine the type of forest trees to be planted as tree 
types influence soil properties (Ritter, 2009; Sartori et al., 
2007).  

Several methods of soil fertility investigation were 
employed in confirming the fertility status of soils (Assefa 
and Glatzel, 2010; Belachew and Abera, 2010). Some of 
the conventional methods of soil fertility investigation 
adopted in the past included locating study points 
randomly, zigzag direction and cutting of traverses at 
known regular intervals and these methods had not 
ensured the completion of soil fertility investigation within 
the specified time frame and the required degree of 
accuracy. The application of geospatial technology 
involving the use of Global Positioning System (GPS) and 
Geographic Information Systems (GIS) had greatly 
improved the old traverse techniques and forest soil 
fertility studies using remote sensing and GIS techniques 
had been reported (Solanke et al., 2005; Oza et al., 2006; 
Bhagat, 2009).  

The previous research in Medugu et al. (2010) had 
confirmed the role of afforestation programme in 
combating desertification in Nigeria and also in the 
research findings reported by Jaiyeoba (2001) in the 
evaluation of the performance of Eucalyptus and Pine 
plantations for soil rehabilitation in Nigeria Savanna 
environment. Ademoh and Abdullahi (2010) in Nigeria 
had discussed the use of Acacia species in soil 
rehabilitation and also in the economic values of 
production of binding resins products from it. The two 
species of Acacia trees Acacia senegal L. and Acacia 
seyal L. were reportedly planted in semi-arid conditions to 
prevent desert encroachment. Acacia is leguminous plant 
which in addition to enriching the soil with nitrogen also 
has the economic benefits of being used to produce 
acacia gum or gum arabic (Lupien, 2007). Furthermore, 
Acacia trees had been described as a natural shade tree 
and when planted properly could lower ground 
temperature and the nitrogen rich leaves when shed at 
the beginning of the rainy season could check soil 
erosion while the decomposition of the leaves would 
improve the fertility status of the soil.  

The soil fertility studies for reforestation programme in 
the studied forest reserve was found desirable in order to 
replant with trees in proportion to the reserve areas 
where the original forest trees/shrubs had been illegally 
felled principally for arable crop production and for 
domestic fire wood use. The objective of the research 
was to carry out a soil fertility investigation of the 
encroached forest reserve for Acacia tree reforestation 
programme. 
 
 
MATERIALS AND METHODS 
 
Site description 
 
The study site is situated between Latitude 11°47’N and 11° 56’N 
and Longitude 4°22’E and 4°32’E, it is a 10,000 ha forest reserve 

                     
 

 
located in Kebbi State in Northern Nigeria as shown in Figure 2. 
The climate is tropical with two distinct seasons of rainy and dry 
seasons. The annual rainfall varies between 650 and 1000 mm 
while the mean annual temperature is 26 and 33°C during the rainy 
and dry season respectively. The soils are Aridisols by the United 
States Soil Taxonomy Classification system. The soil investigation 
involved preliminary computer analysis, field work and the 
laboratory analysis. 

 

Preliminary computer analysis and field work 
 
The tracking of the boundary of the forest reserve was carried out 
with the use of the GPS Garmin 72 receiver and the data input into 
the computer to generate a digital map of the forest reserve. The 
entire forest reserve was divided into grids to guide in the location 
for soil sampling using the GPS/ GIS geospatial technique. The 
points for soil sampling indicated on the digital map were located on 
the ground with the use of the GPS receiver and soil samples taken 
from the sampling pits for laboratory analysis. 
 
 
Collections of soil samples, packing and transportation to 
laboratory 
 
Soil samples were collected from the three types of soils 
categorized according to depth. For soil depth category of 0 to 15 
cm (Group A soils), samples were taken from surface layer to 15 
cm while for soil depth category of 0 to 30 cm (Group B soils), 
samples were taken from surface to 15 cm and 15 to 30 cm and for 
soil depth category of 0 to 60 cm (Group C soils), samples were 
taken from surface to 15 cm, 15 to 30 cm and 30 to 60 cm. The soil 
samples were bagged in 350 cc sampling bag, labeled and 
transported to the laboratory for analysis. 

 

Laboratory analysis 
 
Soil samples were air-dried and sieved through 2 mm sieve and 
analysed following the laboratory procedures of Canadian Society 
of Soil Science (Carter, 1993). The particle size distribution was 
determined by the hydrometer method in which 50 g of sieved air 
dried soil was weighed into 250 ml beaker and 100 ml of calgon 
added and allowed to soak for 30 min. It was transferred to a 
dispersing cup and the suspension stirred for 3 min with mechanical 
stirrer. The suspension was transferred to a sedimentation cylinder 
and filled to the mark with distilled water. A plunger was inserted 
and used to mix the content thoroughly. The stirring was stopped 
and the time recorded. The hydrometer and thermometer were 
carefully lowered into the suspension and the readings taken after 
40 s. Two hours after, another hydrometer and thermometer 
readings were taken and the hydrometer reading corrected for 
temperature by adding 0.36 g/L for every 1°C above 20°C and 
subtracting 0.36 g/L for every 1°C below 20°C.  

The soil pH was determined in water using a glass electrode pH 
meter. Organic carbon was determined by oxidising soil sample 
with dichromate solution and later titrated with ferrous sulphate 
solution (Walkley and Black, 1934). The total nitrogen was 
determined using micro-kjeldahl method and the available 
phosphorus colorimetrically by the molybdenum blue method (Bray 
and Kurtz, 1945). The exchangeable cations were extracted by 
leaching 5 g of soil with 50 ml of ammonium acetate at pH 7. The 
potassium and sodium in the leachate were determined with a 
column model 21 flame spectrophotometer while the calcium and  
magnesium were determined with atomic absorption 
spectrophotometer. The exchangeable acidity was determined by 
adding barium chloride buffer solution to soil sample and titrated 
against 0.1 N HCl. 



 
 
 

 
 N   

W E   

 4°25' 4°30' 4°35' 
 S   

11°55'   11°55'  
 
 
 
 
 
 
 
 

 
11°50' 11°50' 

 
 
 
 
 
 
 
 

 
   4°25' 4°30' 4°35' 

7  0 7  Kilometers Scale: 1:150000 
          

 
Figure 1. Digital boundary map of the forest reserve. 

 
 

 
Creation of attribute table and link to digital map 
 
In creating the attribute table, the soil parameters which included 
the coordinates specifying the latitudes and longitudes of all the 
sampling point locations and the soil properties were entered as a 
table in Excel software and saved in a dBASE IV (DBF 4) format. 
The table was automatically linked to the digital map generated 
from the data and ArcView, statistical analysis run to calculate the 
minimum and maximum values to obtain the average values of soil 
properties for each of the three soil depth categories of 0 to 15 cm 
depth (Group A soils), 0 to 30 cm depth (Group B Soils) and 0 to 60 
cm depth (Group C Soils). 
 

 

RESULTS 

 

Table 1 showed the coordinates specifying the latitude 
and longitude of the forest boundary while Figure 1 
showed the digital map boundary generated from the 
coordinates. Table 2 showed the coordinates of the 
sampling pits from which soil samples were taken for 
laboratory analysis of the physical and chemical 
properties while Figure 2 showed the representations of 
the 60 sampling pit locations on the digital map. Nine 
sampling pits were 0 to 15 cm depth while 27 and 24 

 
 
 
 
 

 

sampling pits were 0 to 30 cm and 0 to 60 cm depth 
respectively. Table 3 showed the mean values of soil 
properties as generated with ArcView statistical analysis. 
The soil was sandy clay loam with the clay content having 
an average of 25%. 
 

 

DISCUSSION 

 

The latitude and longitude readings of the forest reserve 
boundary in Table 1 were taken with the global 
positioning system receiver which was a satellite linked 
device that gave the precise locations on the ground. This 
was used to generate the boundary map of the reserve in 
Figure 1. The principle was used in a previous research 
of a farm layout which was part of the digital farm map of 
a crop type museum (Adekayode et al., 2007). 
 

The coordinates of the sampling pits as shown in Table 
2 and the representation of the points in Figure 2 showed 
the application of GIS to obtain accurate location of soil 
sample points in the forest reserve for the determination 
of soil properties at different locations. This principle was 



  
 
 

 

N             
 

 
4°25' 

      
4°30' 

   Location of 
 

          Forest Reserve  

            
 

11°55'           11°55'  
 

          %  # 
 

       
$ 

$ 
$ % 

  
 

     $     
 

       

$     
 

           

MAP OF NIGERIA 
 

        $    
 

   $ %  %       
 

 #            
 

## #            
 

$
# #     $ $     

 

#  

% %  %     

%         
 

   % $ % $  $   %  
 

  %  $    %     
 

      $       
 

   

$ 
 % $  %     

 

     $  $     
 

11°50'           11°50'  
 

  % % $  $ % $    

LEGEND 
 

            
 

    $ %   $    
SAMPLING PITS 

 

           $ 
 

            OF 0 TO 60 CM DEPTH 
 

   %  %  % %     
 

     
$  #     SAMPLING PITS  

    

$ 
 #    

% 
 

      $    
OF 0 TO 30 CM DEPTH  

            
 

           
# SAMPLING PITS 

 

           

OF 0 TO 15 CM DEPTH 
 

            
 

 4°25'       4°30'     
 

6     0   6 Kilometers   
  

 

Scale: 1:100000 
 
Figure 2. Map of the forest reserve showing locations of sampling pits with map of Nigeria and location of forest reserve in the inset. 
 
 

 
Table 1. The coordinates of the forest boundary.  

 
 Boundary points Latitude Longitude Boundary points Latitude Longitude 

 1 11.872391 4.403176 12 11.806707 4.485918 

 2 11.864977 4.406828 13 11.855968 4.529515 

 3 11.842722 4.418315 14 11.915204 4.528312 

 4 11.841300 4.418831 15 11.909216 4.508716 

 5 11.832878 4.421458 16 11.901658 4.488075 

 6 11.824518 4.424462 17 11.895715 4.471336 

 7 11.816641 4.428395 18 11.887013 4.445890 

 8 11.807432 4.432798 19 11.881212 4.429336 

 9 11.803468 4.433649 20 11.877390 4.418026 

 10 11.805174 4.443000 21 11.874895 4.410660 

 11 11.804927 4.466656 22 11.872391 4.403176 



 
 
 

 
Table 2. The coordinates of the sampling pit locations.  
 

Sampling pit   Sampling pit   Sampling pit   

(0-15 cm Latitude Longitude (0-30 cm Latitude Longitude (0-60 cm Latitude Longitude 
depth)   depth   depth)   

1 11.872000 4.404000 1 11.870000 4.410000 1 11.870000 4.510000 

2 11.871000 4.405000 2 11.880000 4.430000 2 11.910000 4.520000 

3 11.872700 4.408700 3 11.860000 4.440000 3 11.880000 4.460000 

4 11.870000 4.405000 4 11.840000 4.480000 4 11.870000 4.440000 

5 11.873730 4.409190 5 11.870000 4.490000 5 11.850000 4.420000 

6 11.868800 4.416480 6 11.890000 4.510000 6 11.830000 4.430000 

7 11.869490 4.411540 7 11.870000 4.470000 7 11.820000 4.450000 

8 11.806000 4.468000 8 11.860000 4.460000 8 11.860000 4.450000 

9 11.809000 4.469700 9 11.890000 4.470000 9 11.850000 4.490000 

   10 11.850000 4.440000 10 11.840000 4.480000 

   11 11.840000 4.430000 11 11.840000 4.450000 

   12 11.830000 4.440000 12 11.870000 4.410000 

   13 11.820000 4.440000 13 11.860000 4.530000 

   14 11.830000 4.500000 14 11.890000 4.520000 

   15 11.860000 4.480000 15 11.830000 4.470000 

   16 11.830000 4.460000 16 11.830000 4.420000 

   17 11.820000 4.480000 17 11.880000 4.440000 

   18 11.840000 4.454500 18 11.860000 4.430000 

   19 11.841700 4.454880 19 11.870000 4.440000 

   20 11.842670 4.454890 20 11.810000 4.480000 

   21 11.892410 4.488310 21 11.810000 4.470000 

   22 11.888870 4.448857 22 11.810000 4.450000 

   23 11.886470 4.488570 23 11.810000 4.430000 

   24 11.884910 4.489300 24 11.870000 4.450000 

   25 11.805140 4.468730    

   26 11.805250 4.434620    

   27 11.806690 4.449270    
 
 

 

applied in previous research to map fertility levels across 
a farm to serve as basis for the application of farm inputs 
and also for establishing accurate location of yield data 
for the production of yield maps for yield monitoring 
(Ziadat, 2005; Sudhanshu et al., 2009).  

Liaghat and Balasundram (2010) had discussed the 
use of GIS in precision farming to produce a production 
based farming system that was designed to increase long 
term, site-specific and whole-farm production efficiency, 
productivity and profitability. Song et al. (2009) in a 
research on the delineation of agricultural management 
zones using remotely sensed data concluded that remote 
sensing was a valuable tool for assessing the variation in 
soil properties and yield in arable fields. Hossain (2009) 
used the geographic information system to develop a 
site-specific geospatial database in assessing spatial land 
use/cover changes in a study site in Thailand while 
Adekayode (2006 ) used the soil data base on soil 
physical and chemical properties to generate the soil map 
of part of Owena Forest Reserve in Ondo State Nigeria. 

 
 

 

Table 3 shows the mean values of soil physical and 
chemical properties was the result of ArcView statistical 
analysis of the soil properties entered as field in the 
attribute table. Soil samples from the sixty sampling pits 
were analyzed for the physical and chemical properties. 
Each soil property was entered as a field to calculate the 
maximum, minimum and the average values in relation to 
the various depths of 0 to 15 cm, 0 to 30 cm and 0 to 60 
cm. The physical and chemical properties of soils in the 
forest reserve were similar and the limitation of Group 1 
soils was the shallow depth being within 15 cm of the 
surface and this was caused by hard pan just below the 
surface. Such shallow sampling pits were mostly found in 
the north western part of the study area and this would 
make mechanical cultivation impracticable. The 
remaining part of soils in the forest reserve was 30 cm 
deep or even deeper. The generally low level of cations 
reflected the low level of organic matter which reflected in 
the low nutrient level as previous observation had 
revealed a direct relationship between organic matter and 



  
 
 

 
Table 3. The mean values of soil physical and chemical properties.  

 
 

Soil properties 
Group A soils Group B soils  Group C soils  

 

 

(0 – 15 cm) (0 – 15 cm) (15 – 30 cm) (0 – 15 cm) (15 – 30 cm) (30 – 60 cm) 
 

  
 

 Percentage gravel (%) 5.1 6.0 9.8 5.2 5.7 6.2 
 

 Percentage sand (%) 61 67 59 68 61 61 
 

 Percentage silt (%) 14 10 12 11 13 12 
 

 Percentage clay 25 23 29 21 26 27 
 

 Texture       
 

 pH 5.1 4.9 4.9 4.8 4.7 4.6 
 

 Organic matter (%) 0.56 0.90 0.64 1.11 0.93 0.72 
 

 Available phosphorus (ppm) 3 2.7 2.3 2.6 2.5 2.4 
 

 Total nitrogen (%) 0.23 0.35 0.15 0.36 0.15 0.12 
 

 Sodium (cmol/kg) 0.17 0.16 0.20 0.15 0.16 0.11 
 

 Potassium (cmol/kg) 0.16 0.21 0.15 0.18 0.16 0.08 
 

 Calcium (cmol/kg) 1.23 1.42 1.40 1.38 1.36 1.25 
 

 Magnesium (cmol/kg) 0.58 0.68 0.62 0.71 0.66 1.02 
 

 Vegetation/land-use Shrub Shrub Shrub Shrub Shrub Shrub 
 

 

 

nutrient availability to plants (Takata et al., 2007). The low 
level of nutrients showed the high degree of 
encroachment of the forest reserve by people for arable 
cropping whereas they did not make serious attempt to 
replenish the soil fertility with the addition of organic and 
inorganic fertilizers. The response of forest trees to soil 
nutrient content had been discussed in previous research 
(Liao, 1977; Alfaia et al., 2004; John et al., 2007). Tang 
and Robson (1993) in investigating the effects of pH on 
nodulation of Lupinus species observed that pH above 6 
had a specific effect in the impairment of nodulation in 
Lupins and the lateral roots to be greatly reduced 
resulting in decreased uptake of iron and phosphorus 
while Ojeniyi and Agbede (1980) in investigating the 
effect of organic matter on the yield of tree crops in 
different ecological zones of Nigeria observed a positive 
correlation between soil organic matter and girth of 
Gmelina arborea. The nutrient requirement of some forest 
trees as reported previously in Nwoboshi (2000) stated 
the requirements for G. arborea to be 960 kg/ha Nitrogen, 
371 kg/ha Phosphorus, 2425 kg/ha Potassium and 615 
kg/ha Calcium while the requirements for Tectona grandis 
to be 640 kg/ha Nitrogen, 170 kg/ha Phosphorus, 719 
kg/ha Potassium and 199 kg/ha Calcium. This 
corroborated the earlier research findings of Akinsanmi 
and Akindele (1995) while Adekayode (2005) had 
previously reported the potentiality of soil to support the 
growth of species of forest trees to be highly influenced 
by topography and parent materials. 
 

 

Conclusion 

 

The assessment of the fertility status of the forest reserve 
had been based on the soil data base of soil properties 

 

 

created. The soils generally had low fertility but which 
could be improved with the addition of both organic and 
inorganic manure and the productivity sustained with the 
planting of Acacia trees. The digital map of the forest 
reserve generated with GIS technique would allow a 
periodic review of the soil fertility status. 
 

 
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