Tropical Environments) Tropical Environments)
Volume 5 Number 2 Article 1
12-1-2021
EFFECTS OF HUMAN ACTIVITIES ON THE AFAKA EFFECTS OF HUMAN ACTIVITIES ON THE AFAKA
AFFORESTATION PROJECT, KADUNA NORTH, KADUNA STATE, AFFORESTATION PROJECT, KADUNA NORTH, KADUNA STATE, NIGERIA
NIGERIA
Mamman Shaba Jibril
Department of Geography, Nigerian Defense Academy, Kaduna, Nigeria Mary Oluyemisi Ariyo
Department of Geography, Nigerian Defense Academy, Kaduna, Nigeria Ali Williams Butu
Department of Geography, Nigerian Army University, Biu, Borno State Chukwudi Nnaemeka Emeribe
National Centre for Energy and Environment, Energy Commission of Nigeria, University of Benin, Benin City, [email protected]
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Recommended Citation Recommended Citation
Jibril, Mamman Shaba; Ariyo, Mary Oluyemisi; Butu, Ali Williams; and Emeribe, Chukwudi Nnaemeka (2021) "EFFECTS OF HUMAN ACTIVITIES ON THE AFAKA AFFORESTATION PROJECT, KADUNA NORTH, KADUNA STATE, NIGERIA," Jurnal Geografi Lingkungan Tropik (Journal of Geography of Tropical
Environments): Vol. 5: No. 2, Article 1.
Available at: https://scholarhub.ui.ac.id/jglitrop/vol5/iss2/1
This Research Article is brought to you for free and open access by UI Scholars Hub. It has been accepted for inclusion in Jurnal Geografi Lingkungan Tropik (Journal of Geography of Tropical Environments) by an authorized editor of UI Scholars Hub.
Journal homepage: www.jglitrop.ui.ac.id http://dx.doi.org/10.7454/jglitrop.v5i2.133
Effects of Human Activities on the Afaka
Afforestation Project, Kaduna North, Kaduna State, Nigeria
Mamman Shaba Jibril1,Mary Oluyemisi Ariyo1, Ali Williams Butu2: Chukwudi Nnaemeka Emeribe3
1Department of Geography, Nigerian Defense Academy, Kaduna, Nigeria;
2Department of Geography, Nigerian Army University, Biu, Borno State;
3National Centre for Energy and Environment, Energy Commission of Nigeria, University of Benin, Benin City
E-mail: [email protected]
Received: 24 October 2021; Accepted: 23 December 2021; Published: 30 December 2021
Abstract. The study aimed to investigate the effects of human activities on the Afaka afforestation project, Kaduna State, Nigeria. Structured interview was used to evaluate the level of community involvement in the modification of the forest project, their perceived environmental effects of land- cover loss. Landsat images of 1986, 1999 and Sentinel-2 image of 2017 were applied for detection of changes in land use/land cover over the years (1986-2017). The study found that the land cover structure of the forest reserve has changed significantly. In 1986, area under crop cultivation was 19.24%, built-up areas, 0.08%, disturbed forest, 7.57%, gully, 2.60%, riparian vegetation, 2.78%
and undisturbed forest, 61.49%. However, by 2017, there were significant changes as the area under crop cultivation 41.18%, built-up areas 0.17%, disturbed forest, 43.17%, gully 5.6%, riparian vegetation 4.66% and undisturbed forest, 1.66%, implying intensive human impacts on the Kaduna Afforestation project in recent time. This could be traced to the increased level of poverty in the community as 75% of the respondents who cannot afford alternative energy supplies such as kerosene and National grid-based electricity, rely on felling of trees for cooking. On the perceived effects of the afforestation project modification, reduced plantation size was 60.5%, decreased soil fertility 19.5%, reduced non-timber products, 11.1%, sheet erosion 6.1%, while flooding 2.8%. The result of the chi-square test reveals significant changes in the area coverage of the forest cover classification at P<0.05. Thus, it can be concluded that the afforestation project did not meet its objectives. There is need for sustainable programmes and policies towards alleviating poverty among the inhabitants of the study area most of which depend on the forest resources for livelihood.
This should be followed up with policies to encourage tree planting initiatives to promote forest restoration and ecological integrity of the study area.
Keywords: Afaka Afforestation Project, Land-use, Land cover, Human Activities, Remote Sensing
1. Introduction
Urbanization and unregulated land-use activities are among the most important drivers of biodiversity loss as well as reduction of global forest area. It is reported that the world has lost about 178 million ha of forest since 1990, which is an area about the size of Libya (FAO, 2020). Africa had the largest annual
rate of net forest loss from 2010–2020, at 3.9 million ha per year, followed by South America, at 2.6 million ha (FAO, 2020). In Nigeria, the trend is similar, as a FAO report has shown that from 2000- 2005, the country recorded the highest deforestation rate in the world at 12.2% equivalent of 11,089,000 hectares (FAO, 2005). Similarly, in 2010 another FAO report revealed that Nigeria has lost more than half of her forests within the last fifty years, making it one of the countries with the highest rate of deforestation in the world (FAO, 2010). More so, according to International Institute of Tropical Agriculture (IITA) (2011), Nigeria is ranked the worst country with the highest deforestation rate of 3.5% and 400,000 hectares every year. In another report, Babalola (2012) showed that 400 out of every 1,000 hectares of forestland are deforested every year and only 26 hectares of these are reforested thus leaving 374 hectares deforested. Adewuye and Olofin (2015) argues that one of the main reasons for the depletion of Nigerian forests resources is the uncontrolled wood harvesting for fuels and construction purposes. Other studies have also reported urbanization, wildfire, agriculture and logging as increasingly driving forest loss in Nigeria (Ehigiator and Anyata, 2011; Bamba, et al. 2011;
Ogundele, et al. 2016; Otum, et al. 2017; Oyetunji et al., 2020). Ogundele, et al. (2016), for example found urbanization, industrialization, infrastructural development, tourism, bush burning, mining, logging and fuelwood collection, corruption and poor policies implementation are some causative factors responsible for deforestation in Nigeria.
Deforestation is the process of clearing and conversion of forest to other types of activities for non- forest use, such as residential areas, industrial areas, road/rail construction, agricultural purposes and cutting down of forest trees for domestics and industrial use like fire-woods, timbers, paper production and charcoal production. Deforestation has resulted in habitat damage (Akachuku, 2006; Fakoya, 2010), biodiversity loss (Okojie, 1993; Akintoye et al, 2013), aridity of arable lands (Hunter et al, 2005), soil erosion, declining soil fertility, flooding and extinction of important flora and fauna species (IUCN, 1980; Sodimu et al., 2020). Deforestation has also been linked to global warming as studies have found that forest burning can lead to changes in air movements, increases in temperatures, and decreases in atmospheric moisture and cloud formation, whereby convection-related forest patterns are particularly sensitive (Berbet and Costa, 2003; Ghazoul and Sheil, 2010).
Deforestation is of great concern across the world and as a result, many countries and some international organizations including the United Nations and World Bank have begun to develop programmes to curb deforestation through afforestation programmes, and initiatives to reduce emissions from Deforestation and Forest Degradation (REDD) through direct monetary or other incentives to encourage developing countries to limit and/or roll back deforestation (Wajim, 2020). In Nigeria, one of such effort is the afforestation forest project in Afaka Kaduna State. The afforestation forest project was initiated in 1985 by the Nigerian Government, in collaboration with Japanese Incorporation Afforestation (JICA), and was commissioned on 3rd June, 1988. On economic ground, the afforestation project was targeted to provide opportunity for employment and sustainable supply of fuelwood for the local community as well as incentive to local timber industry (Oladele, 2006).
Over the years however, due to rapid urbanization and expansion of settlements around the study area, coupled with inadequate establishment of new plantation policies, improper planning of land use activities as reported by Adewuyi and Olafin, (2005) and Sodimu et al., (2020), the Afaka afforestation project has undergone intensive modification through bush burning, wood harvesting for fuels/construction works and farming activities etc. In Kaduna metropolis, which includes the study area, frequent seasonal flooding has reported with devastating effects on socio-economic life of affected communities as reported by Ijigah and Akinyemi, (2015); Sule et al., (2016). The 2012 seasonal flooding of Kaduna metropolis which was attributed to heavy rainfall was reported to have destroyed at least 178 homes, basic public infrastructures and surrounding farmlands (Ijigah and Akinyemi, (2015). Thus, if the problem of Afaka forest reserve encroachment as well as associated environmental impacts is to be addressed, there is need to understand the level and nature of land use/land cover changes since the establishment of the forest project and the driving factors of the observed changes.
Such information is key in policy making towards sustainable management of forest resources. Thus, the aim of the present study is to investigate the effects of expanding human activities on land cover changes and the Afaka afforestation project of the State Government and to ascertain whether the afforestation projects met its establishment objectives.
2. Materials and Methods 2.1. Study Area
The Afaka afforestation project is located in Kaduna north, Igabi Local Government Area of Kaduna State and is surrounded by Laima, Riggasa, Buruku and Mando communities. The area covers approximately 14 km2, and lies between latitude 10° 37' 48″, North of the Equator and 10° 35' 10″ N and Longitudes 7° 18' 49″ E and 7° 21' 58' of the Greenwich Meridian (Figure 1). The project is called a trial project because it was it was initially established Forestry Research Institute of Nigeria (FRIN) in collaboration with Japanese Incorporation Afforestation to provide data and material on forestry development project in this semi-arid region.
Figure 1: Afaka Afforestation, Kaduna State.
The climate of this area is Aw (tropical dry-and-wet climate) according to Koppen classification.
The wet season lasts for about six to seven months (April to October) with an average annual rainfall of about 1323 mm. The month of August is the peak of the wet season. The mean annual rainfall in the study area is about 1000 mm (Adewuyi and Olofin, 2014). The rainfall intensity is very high within the months of July/August and can be as high as 99 mm/h (Oladipo, 1993). Average air temperature of the study area is 28.9oC and normally occurs in April while the lowest temperature value of 22.9oC to 23.1oC occurs in December through January (NiMet, 2016). The vegetation of the study area is Guinea savannah. The vegetation is characterized by grasslands, with trees and grasses occurring at varying degree of inter-mixture (Al-Amin and Aliyu 2014). The grasslands appear as product of mans prolonged exploitation on the wooded lands rather than a “naturally”, tall trees occur even into the margins of the deserts (Abaje and Oladipo, 2019). In the southern part, with higher rainfall amount, the vegetation
comprises of savannah wooded lands with trees of about 20-40ft for example shear butter. In the drier part in the northern part of the study area, the vegetation comprises of lower orchard shrub with scatter of shade trees like the baobab, silk cotton etc. Crop farming is predominantly the economic activities of inhabitants of the study area. They practice continuous cropping and mixed cropping. In addition, rearing of animals such as cattle, goats and sheep is a common in the study area.
2.2. Data collection
Changes in land sue and land cover have been linked to Population growth (Palamuleni, 2009, Bell et al., 2018 ) and associated urbanization (Zubair, 2006; Long, et. al.,2008; Mbaya et al., 2019, Nath et al., 2021), increase in cultivated land (Tobin et al., 1998, Ngwira et al., 2012), and residential area (Haque and Basak, 2017; kullo et al., 2021), heavy dependency on wood for energy (Hudak and Wessman, 2000, Mlotha, 2001, Taulo et al., 2015), expansion in macroeconomic activities such as increase in manufacturing industries and other businesses (Mzuza et al., 2019) and overdependence on land-based resources for income or food (Antwi et al., 2014) and unlawful felling of trees (Kamwi et al., 2017). Similarly, changes in land use and land cover changes will significantly affect water bodies, vegetation, manmade infrastructure and landscape-level biodiversity, soil erosion, and sediment loads (Lambin et al., 2006; Sultan, 2016). Hence, the primary data sources for this study direct observation and measurements (Structured interview, Landsat images of 1986, 1999 and Sentinel-2 image of 2017).
Data on specific demographics and socio-economic characteristics of respondents were generated through oral interview of the selected communities around the forest reserve project. The interview was structured to provide information on the activities of the communities which may affect the afforestation project, as well as perceived effects of these activities on the sampled communities. Multi-temporal land use/land cover Landsat images of the study area were also collected ascertain the pattern of temporal changes in land cover over the years. In carrying out the interview, only the male/female heads of household were selected. These groups were because first, they are saddled with the responsibility of providing for the home. Secondly, they are primarily farmers whose activities have implications for forest modification. According to the UN-department of Economic and Social Affairs (2005), household surveys can be used for collection of detailed and varied socio-demographic data pertaining to conditions under which people live, their well-being, activities in which they engage, demographic characteristics and cultural factors which influence behaviour, as well as social and economic change.
Four (4) villages within 2 km radius to the forest reserve project were purposively sampled. These include Mando, Laima, Buruku and Rigasa communities. These communities were selected due to their proximity to the afforestation project and the fact that they allowed to be interviewed. A total of 157 household heads were randomly sampled. To determine the sample size, the population figures of the sampled communities were first determined from the National Population Bulletin (NPC 2006), and from the Kaduna State Bureau of Statistics Office, Kaduna State. These figures were then subjected to Yaro Yamani’s method equation (Equation 1). Furthermore, from each computer sample size, 10% of the population was selected for interview. The choice of 10% was to minimize cost. Thus, one person per household was selected for the interview, while the interviews were proportionally distributed according to the population of the communities. Table 1 presents the population of the study area, sample size determined using Yaro Yamani equation and the 10% derived from each sample size.
n = 𝐍
𝟏+𝐍(𝐞)²
(1)
Where
n = sample size
N = population size 1 = constant
Allowable error margin (1% i.e 0.05 level of significance was used).
Table 1: Study area population and sampled size
Community Year
2006 2012 2019 Total Sample size 10 % of
sample size
Rigasa 149,600 17,381 207057 530,463 400 40
Buruku 206,215 239,588 285408 731,201 400 40
Mando 7,500 8714 10,383 26,594 394 39
Laima 2,650 3079 3.668 8,397 383 38
Total 157
Source: Field work (2019)
Archival topographic maps of the study area (1:50,000) were collected from the Kaduna State Ministry of Environment. These maps served as the baseline for the production of land-use/ land-cover maps of the area. To complement the archival maps, multi-temporal satellite images, Landsat (30 m resolution) of 1986, 1999 and Sentinel-2 image (10 m resolution) of 2017 were downloaded from Glovis-USGS free data archive (Table 2). The reliability of the satellite images was verified by ground measurements using a Montana Garmin 650 handheld Global Positioning System (GPS).
Points/features such as rivers road network etc were identified and measured around each community (Mando, Laima, Buruku and Rigasa) that make up the study area. The measurements obtained were compared with corresponding measurements on the satellite image and it was found that the ground measurements and the measurements on both Landsat and Sentinel images were close. Hence, the measurement of the parameters from satellite images could be considered reliable.
2.3. Data Analysis
Data from topographic maps and satellite imagery were further subjected to processing operations such as scanning, geo-referencing, image processing, image re-projection and transformation to aid raster data viewing, querying and other spatial analysis, corrections, ensure proper scaling and comparison of data sets (i.e 1986, 1999 and 2017).
Image processing: The satellite imagery was radiometrically and geometrically corrected when they did not have such corrections already applied. To start the extraction of the geographical features, the satellite data was first radiometrically calibrated following Landsat 7 Handbook (2001) guidelines.
Dark Object Subtraction-1 (DOS-1) method for atmospheric correction was applied to all the images.
This correction was implemented using the semi-automatic classification plugin in QGIS. With the DOS-1 method, elements such as water bodies and shadows are considered dark objects since their reflectance values are close to zero and these objects are identified automatically by selecting the 1%
pixel with the lowest reflectance value (Landsat 7 Data Users Handbook 2001 and 2016). After these initial steps, the data was geometrically and radiometrically corrected and transformed to a geometry that can be combined with the other data sets. Further processing was required for the images to be analysed and this post-processing included subset, mosaic and re-projection processes.
Scanning and geo-referencing: The topographic and satellite images to be used in the GIS environment, the topographic maps and satellite images were first processed to extract spatial data.
First, the relevant analogue maps were scanned and georeferenced. Georeferencing the scanned maps define their location using map coordinates by assigning the coordinate system of the data frame to selected control points.
Image Re-projection: The Sentinel-2 images and rest of the spatial data for the project were in Universal Transverse Mercator (UTM) system. In order to ensure proper scaling and comparison of data sets, the subset Sentinel-2 images were re-projected from geographic coordinates (latitudes and longitudes) in degrees, to Universal Transverse Mercator (UTM) in metres.
Image Mosaic. Since the study area is covered by multiple Sentinel-2 tiles a mosaic process was carried out to obtain a contiguous image covering the AOI for the year 2017. This was done using the Mosaic Utility tool in (ERDAS) software.
Projection and Transformation: The results of the processing performed on the scanned map could be adversely affected if the projection and transformation are not carefully managed. To avoid this, the scanned maps were projected and transformed to the same datum and spheroid, the World Geodetic System of 1984 (WGS 84) Datum and Spheroid which are commonly used for most mapping in the country were adopted for this study.
Table 2: Data types and sources
S/N Satellite Date Resolution Band Acquisition Source 1
Landsat5 15/5/1986 30M USGS Global Visualization
Viewer (GloVis)
2
Landsat7 12/06/1999 30M 8 USGS Global Visualization
Viewer (GloVis)
3
Sentinel-2 28/01/2017 10M 14 USGS Global Visualization
Viewer (GloVis) Source: Field work (2019)
For the land cover maps, a semi-supervised classification method and the Maximum Likelihood Classification Algorithm were used. A semi-supervised classification method involves, first of all, using a non-supervised method to classify the pixels into a given number of clusters, followed by the use of training data to assign the pixels into different land cover classes. A suitable classification scheme was thus developed based on a prior knowledge of the study area and a reconnaissance survey (Table 3).
The scheme was modified from Anderson system of classification (Anderson, et al., 1976). This scheme was adopted since it is designed to mainly rely on remote-sensing; therefore only land-use and land- cover types identifiable by remote-sensing are used as the basis for organizing this classification (Zomeni et al., 2008). Although this classification scheme is coarse, it eliminates misclassification errors and makes delineation of categories more substantial (Mallinis et al., 2011). The classification scheme utilizes eight land cover classes (Built-up, undisturbed-forest, area under crop cultivation, Riparian, gully, disturbed forest vegetation, Barren land and water (Anderson, et al., 1976).
To determine whether there is statistical difference in land-cover land-use changes between the years under study, a Chi-square test was carried out with 0.05 level of significance Equation 2. A chi- square test is a statistical test used to compare observed results with expected results. The purpose is to determine if a difference between observed data and expected data is due to chance, or if it is due to a relationship between the variables under investigation (in this case land-cover changes between 1986 and 2017).
𝛘𝟐 =
∑(𝐎𝒊– 𝐄𝒊)𝟐𝑬𝒊
(2)
Where, O is the observed value, E is the expected value and “i” is the “ith” position in the contingency Table (Table 3).
Table 3: The landuse and landcover of the study area
SN Land-use/land-cover Component features
1 Area under crop cultivation Area under crop cultivation: This area show a vast area of land cleared which consist of bare land surface with scanty or no vegetation, farm lands
2 Built up area Settlements (rural and urban) which are places of human habitation with varying network of roads
3 Disturbed forest This is area covered by scattered trees, few climbers, parches, it occurs all over the forest
4 Gully The area has been destroyed by erosion; it is widely shown all over the forest
5 Riparian vegetation this is the vegetation around a narrow stream which was dam, the vegetation is around the dam and stream
6 Tree plantation An area or region covered with tree cover mosaics of with the same species distinguished by their height present in a roll pattern, with little or no human activities which appeared in row with definite pattern
7 Undisturbed forest An area or region covered with tree cover mosaics of different species distinguished by their height, with little or no human activities
8 Water body Areas covered with water body such as dam and rivers.
Source: Adapted from (Anderson, et al., 1976)
3. Results
The gender distribution of the respondents is shown in Figure 2. Female accounted for 53% and the male, 47%. This indicates that females are more than male in number of the respondents that depends on the resources from the forest project. Women were engaged in farming of vegetables and the collection of some forest product like herbs, fruits, ropes, leaves and firewood for the household.
In Figure 3, the marital status shows that 89% are married and the single 1%, widow 8% and divorced are 2%. This implies that a higher percentage people depending on the forest products, crop cultivation and livestock are married, an indication that more of the forest products will be exploited to sustain the wellbeing of their households. The single mostly constitute the labour force in the government establishments like Nigeria Defence Academy, airport, and train station that are close to the study area.
Figure 2: Gender Distribution of the respondents Figure 3: Marital Status of the respondents Female
Male 53%
47% Married
89%
Single 1%
Widow 8%
Divorced 2%
Figure 4: Products Obtain from the Afforestation
Figure 4, shows that 75% of the respondents agreed to harvesting of fuelwoods from the forest reserve project for cooking, heating and for sales. Other products collected from the forest reserve project by respondents were vegetables, herbs, leaves, ropes, poles, and fish from tributaries of the Kaduna River.
Figure 5: Occupational Distribution of the Respondents
[
The response on the occupation of the inhabitants of the study area shows that majority of the people in the study area are farmers, followed by herdsmen (Figure 5). This shows that agriculture is important and essential to the development of the economy of Nigeria and the study area.
Leaves 2.2% Rope 2.2% Pole 7.8%
Fish 2.8%
Herds 2.8%
Vegetables 7.2%
Firewood 75%
Students 2% Civil Srveants 6%
Traders 11%
Herdsmen 12%
Famers 69%
Figure 6: Energy Used by the Respondents
In Figure 6, of the available sources, 85.8% of the respondents use fuelwood for cooking and heating, kerosene7.7%, straws4.4%, national grid-based electricity 0.5%, while 1.6% make use of charcoal. In figure 7 the views of respondents on the adverse effect of human activities on the forest reserve project is presented.
Figure 7: Respondents view on the Effect of human activities on Afforestation Area
The results indicates that 60.5% of the respondents have observed drastic reduction on the forest trees size through continuous clearing of forest land for crop cultivation. Burning of bush to capture animals/preparation for farming season also have reduce the forest resources and this revealed to be the reason for the reduction of non-timber forest produce which accounted for 11%. Also 19.5% of the respondents indicated that there is decrease in the soil fertility. Landuse/landcover maps of the study area for the period under review are presented in Figures 8, 9 and 10. The year 1986 was adopted as the base year since the forest was commissioned in 1988, while the year 2017 was considered as assumed change year. Figure 8 and Table 4, shows that in 1986 undisturbed forest, accounting for about 61.49%, followed by area under crop cultivation, 19.24%. Built up and water body had a very low percent of 0.08% each, while the disturb forest and tree plantation were moderate with 7.57% and 6.16%
Gas;
0.50%
Electricity; 1.10% Charcoal; …Straws;
4.40%
Kerosene;
7.70%
Fuelwood;
85.80%
Reduced plantation size 60.5%
Flooding 2.8%
Reduced non-timber
products 11.1%
Decreased soil fertility
19.5%
Sheet erosion 6.1%
respectively. The other part of the area is crop land and disturbed forest and riparian vegetation 2.8%.
In 1999, when the afforestation programme was completed, undisturbed forest was 1.60%, area under crop cultivation 21.55%, build-up area, 0.12, water bodies 0.19%, disturbed forest 9.72%, while tree plantation was 22.27% (figure. 9 and Table 4). By the year 2017, there were significant modifications in the land cover structure. Undisturbed forest was 1.65%; area under crop cultivation to 41.2%; built up area, 0.17%; water bodies (0.16%); disturbed forest, (43.2%); tree crop (36.77%), gully (5.69%) and riparian vegetation (4.7%) (Figure 10 and Table 4). This pattern reveals that over the years, the rate of forest degradation has rapidly increased in line with settlement expansion and pressure on forest resources. The result of Chi-square test to determine the level of significant in coverage of the forest cover classifications during the years under study, shows a Chi-square value of 27600.9, which is significant at P<0.05 is greater than the critical value of 23.68. Hence, there is significant difference in the forest cover of afforestation reserve in the study period (Tables 5&6).
Table 4: Area coverage of the land-cover/ land-use classification over the examined period
Table 5: Chi-square Calculations
Land Cover Classification 1986 1999 2017 TOTAL
Area under crop cultivation 2870.7 3215.8 6144.6 12231.1
Built up area 11.7 17.6 25.2 54.5
Disturbed forest 1129.4 6441.6 1449.9 9020.9
Gully 388.3 747 849 1984.3
Riparian vegetation 414.1 910.8 696.4 2021.3
Tree plantation 919.6 3322.5 5486.1 9728.2
Undisturbed forest 9175.7 238.2 246.9 9660.8
Water body 11.9 28.1 23.5 63.5
TOTAL 14922 14922 14922 44765
Table 6: Chi-square for differences in land cover classifications
Oj Ej (Oj - Ej) (Oj- Ej)2 χ2 =∑(Oj– Ej)2 𝐸𝑗
2870.7 4077 -1206.3 1455159.7 359.9
3215.8 4077 -861.2 741665.4 181.9
6144.6 4077 2067.6 4274969.8 1048.6
11.7 18.2 -6.5 42.3 2.3
Classification 1986 1999 2017
Area (km2) % Area (km2) % Area (km2) % Area under crop
cultivation
28.70 19.24 32.16 21.55 61.44 41.18
Built up area 0.12 0.08 0.18 0.12 0.25 0.17
Disturbed forest 11.29 7.57 64.41 9.72 14.50 43.17
Gully 3.88 2.60 7.47 5.00 8.49 5.69
Riparian vegetation 4.14 2.78 9.11 6.10 6.96 4.66
Tree plantation 9.20 6.16 33.23 22.27 54.86 36.77
Undisturbed forest 91.76 61.49 2.38 1.60 2.47 1.66
Water body 0.12 0.08 0.28 0.19 0.23 0.15
Total 149.22 100.00 149.22 100.00 149.22 100.00
Oj Ej (Oj - Ej) (Oj- Ej)2 χ2 =∑(Oj– Ej)2 𝐸𝑗
17.6 18.2 -0.6 0.4 0.02
25.2 18.2 7 49 2.7
1129.4 3007 -1877.6 3525381.8 1172.3
6441.6 3007 3434.6 11796477 3923
1449.9 3007 -1557.1 2424560.4 806.3
388.3 661.4 -273.1 75483.7 112.8
747 661.4 85.6 7327.4 11.1
849 661.4 18S7.6 35193.8 53.2
414.1 673.8 -259.7 67444.1 100.1
910.8 673.8 237 56169 83.4
696.4 673.8 22.6 510.8 0.8
919.6 3242.7 -2323.1 5396793.6 1664.3
3322.5 3242.7 79.8 6368 2
5486.1 3242.7 2243.4 5032843.6 1552.1
9175.7 3220.3 5955.4 35466789 11013.5
238.2 3220.3 -2982.1 8892920.4 2761.5
246.9 3220.3 -2973.4 8841107.6 2745.4
11.9 21.2 -9.3 86.5 4.1
28.1 21.2 6.9 47.6 2.3
23.5 21.2 2.3 5.3 0.25
TOT. 44766 44766 0 ∑= 27600.9
Figure 8: land cover and land use of Afaka afforestation project site, in 1986 Sources: Field work (2019)
Figure 9: land cover and land use of Afaka afforestation project site in 1999 Sources: Field work (2019)
Figure 10: land cover and land use of Afaka afforestation project site in 2017 Sources: Field work (2019)
The disappearing forest resources as seen in Figures 8, 9, 10 and Table 4 could be linked to population expansion, coupled with rising demand for forest products and increasing rate of poverty.
With high demand for charcoal/fuelwood for cooking and heating as well as the desire to expand farming activities, the expected results would be high rate of deforestation.
4. Discussion
The increasing demand for forest products and forest encroachment as can be seen in the study may be linked to the expanding population and urbanization of the study area. In 2006, Kaduna State had a population of 6.1 million people, next only to Kano and Lagos States. It is projected that at 3.18 per cent growth rate, this population would reach 12.96 million by 2050 (Kaduna State Infrastructure
Master Plan, 2018). Similarly, the fact that women were predominantly engaged in farming of vegetation and collecting of forest products in the study area confirms findings by Sunderland et al., (2014) who found that in many places, particularly in Africa, it is women and girls who are the main collectors of fuelwood. They may have to walk many hours, sometimes under highly perilous conditions, especially where accessibility of resources near the home is affected by deforestation, natural disasters or conflict (WFP, 2012). Resources important to most forest women include fuelwood, fodder and foods as they are saddled with the responsibility of preparing meals at home. Data from East African highlands reveals that it is mostly women who plant and manage agroforestry fodder shrubs (Franzel and Wambugu, 2007). Other studies have also shown that women’s customary use of fuelwood, forest medicines, fibers, fruits, vegetables and bush meat makes them repositories of considerable ecological knowledge (Boer and Lamxay 2009, Eyzaguirre 2006, Johnson and Grivetti 2002). These activities support domestic livestock production, enhance milk and meat supplies and contribute to higher household incomes in the study area.
Harvesting of fuelwoods from the forest reserve project for cooking, heating and for sales accounted for 75%. The finding conforms to Zaku et al., (2013) report that wood energy has remained the major fuel for over half of the world’s population. Fuel wood gathered from the forest is considered an important source of domestic energy in the rural areas of developing countries (Cecelski, et al 1979;
Umar et al., 2016). More so, it has been estimated that more than 2.4 billion people rely directly on traditional plant biomass for cooking and heating and in poor countries, plant biomass use represent half of the residential energy consumption (International Energy Agency, 2007). Fuelwood is a source of the energy derived by burning wood biomass like logs and twigs and is common among the rural population of developing countries. In Nigeria, Forest Resources Assessment (FRA) of the country shows that in the year 2005, the total wood removal from the county’s forests for wood fuel was 72,710,935 m3 (FAO, 2005). Our findings on fuelwood usage also shows that the majority of the inhabitants of the sampled communities use fuelwood as source of energy. Across the world, wood fuels contribute an estimated 7 percent of the world’s total energy supply, even though they are often viewed as a primitive energy form due environmental concerns (FAO, 2000). In most developing countries, low income households without access to modern energy source rely on traditional energy such as fuel wood and charcoal. The heavy reliance on fuelwood for fuel country is expected mainly due to instability in the price of petroleum products (Kerosene and Gas) in the study area and the increasing demand for wood as feedstock for biogas. Even when these products are available the prices are beyond the reach to ordinary people. This has forced many households to rely on wood for heating and cooking. Available data shows that fuelwood constitutes the main source of fuel for cooking by over 76% of the Nigerian population (Babanyara and Saleh 2010). Olusegun (2009) showed Nigeria consuming 262,783 metric tonnes of fuelwood compared with 7,210 tonnes for South Africa and 35,313 tonnes for Thailand.
The fact that most of the respondents are farmers clearly portrays the potentials impacts of their activities on the forest cover through clearing, bush fire and cultivation, while the herdsmen feed their cattle on other plant materials. This observation is also in agreement with finding from Adewuyi and Olofin (2015), that uncontrolled wood harvesting and forest fire are major drivers of forest depletion in Nigeria. The agricultural sector always has been the highest contributor to Nigeria GDP, followed by the petroleum industry. In 2013 the agriculture sector contributed about 22% of Nigeria GDP while Crude Oil 14%, telecommunication 9% and manufacturing 7% (US Department of State, 2014). In 2016 the agriculture sector contributed 24.18% of the GDP more than oil and manufacturing combined (CBN, 2016).
The continuous clearing of forest land for crop cultivation and burning of bush to capture animals/preparation for farming season as revealed in the study have reduced the forest resources and this is believed to be the reason for the reduction of non-timber forest produce decrease in the soil fertility. Thus, most of the farmers now resort to the use of fertilizer and organic manure to aid soil fertility. Flooding accounted for 2.8% and this may be due to the fact that the study area is drained by two of the tributaries of Kaduna basin such as the Kaduna and Gurara rivers. Sheet erosion which accounted for 6% maybe attributed to the soil texture of the study area, coupled with intensive farming.
According to Ogbozige et al., (2018) the soils in the upland areas are rich in red clay and sand but poor
in organic matter. Jeb and Aggarwal (2008), also found that soils within the “fadama” areas are richer in kaolinitic clay and organic matter, very heavy and poorly drained characteristics of vertisols. These attributes make the study area susceptible to both flooding and erosion. Studies have also established some negative effects of complex deforestation on the environment such as loss of biodiversity and soil erosion etc (Heltberg et al, 2000; Yahaya 2000; de Shebinin et al, 2007; Ogbozige et al., 2018). Forest loss can also results in economic losses with several social consequences, more especially as one of the objectives of the Afaka Forest project was livelihood enhancement. The economic losses caused by deforestation could in the form of loss of potential revenue for the government, increase in unemployment as many people who depend on forest resources for raw materials to be used in production as reported by Oyetunji et al., (2020) for southern Nigeria. These negative consequences will be exacerbated by population increase, as dependence on forest resources for food, fuel and farming is expected to also rise. Evidence from field surveys already suggest that majority of the respondents are low income earners, and thus depend heavily on the natural resources such as trees, vegetables, and poles of the area for livelihood. Given these few choices, they are forced to adopt short line survival strategies and unsustainable natural resources exploitation. To buttress this finding, several authors have argued that forest loss and degradation are driven by a combination of economic, political and institutional factors. Aliyu, et al (2014) found that the most subtle and often neglected cause of forest degradation in the country is incidence of poverty, energy shortage and unregulated land-use practices.
Ogunwale (2015), on the other hand reported that the unwise use of the natural environment due to ignorance, poverty, greed and overpopulation amongst others have led to deforestation and degradation of the environment in Nigeria. Ebe (2014) also affirmed that in spite of the fact that income, prices of fuelwood and its substitutes are major determinants of the consumption of fuelwood, there was uniformity in reporting that the larger population are still mainly relying on this source of energy for most of their domestic uses. Mustapha et al., (2012) found that of determinants of adaptation to deforestation among farmers in Madagali Local Government Area of Adamawa State, Nigeria, energy shortage is the greatest challenge, particularly in rural areas of Nigeria. Another study has also confirmed that the increase rate of poverty in the country contributes to deforestation because 95% of the population depends on kerosene which is expensive and therefore many depend on fuelwood (JA’AFAR-FURO, 2014).
5. Conclusion and Recommendations
The study aimed to investigate the success level of the Afaka afforestation project in Kaduna State, which was commissioned in 1988 between the Nigerian Government and Japanese Incorporation Afforestation (JICA). The study revealed that land-cover and land-use classes of the forest reserve have significantly changed as a result of intensive anthropogenic activities within and around the afforestation project location. Of these activities, crop farming, fuelwood harvesting and bush clearing were the major human activities that have affected the forest reserve project. The study showed that from 1986 to 2017, the natural forest (afforestation project) has been almost replaced with farmlands and bare land. Low income level of inhabitants of the study area, coupled with illiteracy are important drivers of the observed activities responsible for forest depletion. Continuous deforestation will have major effects on the environment, such as soil erosion, flooding, loss of habitation, siltation, and desertification, some of which are already evident in the study area. In view of these findings, it can therefore be concluded that the objectives of the afforestation project have not been met. This is because the objectives of the project were to conserve forest resources, develop forest conservation programmes to promote silvicultural practices and to generate management related data and material on forestry development projects in the semi-arid region. Thus, there is need immediate intervention by the government to address these unregulated activities by the inhabitants of the study area that pose threat to the forest reserve project. Such intervention should include policies and programmes toward alleviating poverty in the study area, creating public awareness programmes at community levels on sustainable use of the forest resources including farming activities. A similar intervention is the Peatland restoration project in Indonesia (Ibnu et al., 2020).The peatland ecosystem is an important ecosystem in sustainable development, particularly in the land use sector. It provides multiple ecosystem services for rural livelihoods, and plays a vital role in stabilizing water flows, preventing devastating peat fires, enriching soil nutrients, providing clean water, and offering carbon storage for climate change
mitigation (Bonn et al., 2014). Finally, government should enact laws to encourage tree planting initiatives to promote forest restoration and ecological integrity of the study area.
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