Geospatial Assessment of Agricultural Land Suitability in IFE South, Osun State, Nigeria

Global population growth is rapidly approaching 10 billion, intensifying food demand beyond the capacity of current agricultural systems, particularly in developing regions. This surge, coupled with urbanization and climate change, is shrinking arable land and straining food production capacity ^[1]. Despite progress in food security between 2000 and 2010, Africa has seen rising hunger level recently, with worsening malnutrition and food insecurity ^[2,3]. Nigeria exemplifies this crisis as the world’s seventh most populous nation, projected to become the third largest by 2050 ^[4]. Urban sprawl consumes prime agricultural land, while climate change manifesting in erratic rainfall, extreme weather, and rising temperatures further degrades soil quality and reduces cultivable areas ^[5]. These factors accelerate agricultural land degradation, and threatens sustainable agricultural practices. [^6,7,8]. To combat this, accurate land evaluation is critical for informed policymaking and predicting agricultural suitability ^[9]. Some countries rely on food imports as a short-term fix ^[10]. Nigeria is a notable example, heavily dependent on food imports, including sugar, which alone accounts for about 20% of its Gross Domestic Product (GDP) ^[11,12]. The country spent approximately $2.41 billion on rice imports between 2012 and 2015, highlighting its vulnerability to global market fluctuations ^[13,14]. This reliance is economically unsustainable given rising population ^[15]. Instead, self-sufficiency through improved land management and utilization is essential ^[16]. Land is a fixed, finite, and non-renewable resource ^[17], making it fundamental to food security and overall human well-being ^[18]. Mismanagement results in degradation, environmental harm, and deeper poverty. As such, sustainable land use is essential for maintaining food production while meeting the demands of growing populations, especially in Sub-Saharan Africa, where urban development is increasingly competing with agricultural land ^[19]. Agriculture remains the backbone of many economies, vital for food security and rural livelihoods. Selecting suitable land for farming maximizes yield, reduces environmental degradation, and ensures long-term sustainability ^[20]. Land suitability analysis helps identify optimal areas for specific crops by evaluating factors such as soil fertility, topography, climate, and water availability ^[21,22]. Reliable land evaluation is indispensable for shaping effective land use policies that promote sustainable rural development. In developing nations like Nigeria, achieving food self-sufficiency depends on using such evaluations to model land suitability for various agricultural activities ^[23]. Agricultural land suitability refers to a land area's capacity to sustainably support a specific crop or agricultural use ^[17,19]. Matching crops with suitable environmental conditions boosts productivity and preserves soil health. Studies emphasize that local-level land suitability assessments are crucial for enhancing food production in Nigeria ^[5,14]. Although about 80% of Nigeria’s landmass is cultivable ^[14,24], much of it is increasingly degraded due to erosion and conversion for urban development ^[25]. Agricultural Land Suitability (ALS) analysis is vital for increasing productivity per unit of land, as it not only improves yields but also predict area prone to soil erosion and land degradation which is a critical consideration given that remediation of degraded soil can take hundreds of years, making sustainable soil management essential for maintaining ecosystem services, which is why farmers endeavor to identify their soil characteristics to facilitate optimum yields and prevent loss of agricultural inputs through a good understanding of soil capabilities that form the basis of all crop production activities ^[18,26]. Traditional land evaluation methods, including field surveys and geophysical studies, are often time-consuming and imprecise. Advances in Geographic Information Systems (GIS) and remote sensing now offer more cost-effective, spatially accurate alternatives. These technologies have given rise to precision agriculture by accounting for the geographic variability of farming conditions ^[27]. When integrated with Multi-Criteria Decision Analysis (MCDA) tools like the Analytical Hierarchy Process (AHP), GIS allows for comprehensive spatial assessments that consider both biophysical and socioeconomic factors. Developed by ^[28], AHP structures complex decision-making by ranking criteria and alternatives. Applied to land evaluation, AHP combined with GIS can identify and rank suitable areas for mechanized agriculture using criteria such as soil texture, slope, rainfall, land cover, and accessibility ^[29]. This study employs an AHP-GIS framework to assess land suitability in Ife South Local Government Area, Osun State, Nigeria—a peri-urban zone facing urban encroachment and land use conflicts. The resulting suitability maps will guide sustainable agricultural planning and inform policy decisions to improve food security amid environmental and demographic pressures.

MATERIALS AND METHOD

STUDY AREA

This study was conducted in Ife South Local Government Area of Osun State, Nigeria. The study area is located between latitudes 7 ? 1' 00"N and 7 ? 29' 30"N and longitudes 4 ? 25' 22.5"E and 4 ? 45' 40.61"E and an altitude of 176m above sea level (Fig. 1). The area experiences rainy season starting from mid-March to late October with mean annual rainfall of about 1400mm, relative humidity is about 75.8% and 86% while the dry season runs from November to March with temperature ranging between 28⁰C to 34⁰C. It has an area of 730 km² and population was about 135,338 persons ^[30]. The study area is characterized by two types of soil: deep clay soil formed on the lower smooth hill crests and upper slopes; and sandy (hill wash) soil on the lower slopes. The mixture of clay and sandy soil forms loamy soil and this helps water retention from seepage. The people are mostly farmers producing such food crops as yam, maize, cassava, cocoyam and cash crops which include cocoa and oil palm produce ^[31].

Fig. 1: Study Area Map

DATA

This research utilized seven environmental factor criteria to determine land suitability for agricultural cultivation purpose: elevation, slope, precipitation, land surface temperature (LST), soil moisture, soil organic carbon (SOC) and land use land cover. The selection of these variables is similar to research done by ^[14]. The data used in this study were extracted from various sources as listed (Table 1). All data were processed using Google Earth Engine and ArcGIS 10.8, they were further resampled to 30 m spatial resolution.

Table 1: Data Source

2.3 Agricultural Land Suitability Analysis

In order to determine land suitability for agriculture, three major different cartographic materials were used: climatic data (precipitation and land surface temperature), soil data (soil moisture, Soil organic carbon and LULC) and topographic data (slope and elevation). These environmental variables were fitted into a weighted overlay model using ArcGIS 10.8 based on a Multi-Criteria Decision Analysis (MCDA). While the parameters selected for this study are logical ^[32].  Processing of the variables to calculate the suitability assessment are as follows:

1. The mean soil moisture was downloaded from Google Earth Engine and exported to ArcGIS 10.8 for further analysis
2. Land surface temperature (LST), extracted from Landsat 8 and converted to degree Celsius using Google Earth Engine.
3. The annual precipitation data for the study area was extracted from CHIRPS dataset on Google Earth Engine.
4. The digital elevation extracted from SRTM data and the percentage rise of slope was determined using the spatial analyst tool on ArcGIS 10.8.
5. The spatial distribution of SOC in the study area was extracted from OpenLandMap Soil Organic Carbon Content on Google Earth Engine, mean SOC stock for top soil of 0 to 30 cm extracted for analysis.
6. The LULC data used in this study was clipped to ROI and exported from Global land cover - GLC_FCS30D data on Google Earth Engine. LULC was then classified into built-up, waterbody, dense forest, vegetation, cultivation and bare surface.

2.4 Analytical Hierarchy Process (AHP) Method

AHP is a widely recognized and utilized method for multi-criteria analysis, allowing individuals to establish the relative importance of various parameters in addressing a multi-criteria problem. This method employs a hierarchical structure comprising objectives, criteria, sub-parameters, and alternatives specific to each problem being addressed ^[33]. To evaluate the assessment for agricultural practice suitability, the parameters of the problem are organized in a hierarchical structure once they have been identified. A scoring system developed by ^[27] was used to determine the relative importance of criteria within the hierarchy.  Weights were generated for the seven variables using Pairwise Comparison Analysis (Table 2) which is a part of multicriteria decision method, after quantifying the influence of the variables based on an individual analytical hierarchical process (AHP). After performing pairwise comparisons, the consistency ratio was checked. The consistency ratio of the study was calculated as 0.009, which indicates that the decision matrix was consistent ^[27]. Weights generated on a numerical scale (1–5) to indicate the suitability values over other values within the same variable (Table 3). This Analytical Hierarchy Process (AHP) is based on a hierarchical structure and is effective in determining weights. This implies that the higher the hierarchical value, the more suitable the potential for agriculture ^[14].

Table 2: Pairwise Comparison Matrix Weight Criteria

Table 3: Analytical Hierarchical Process

Weighted Overlay Analysis

This study employed GIS technology to identify suitable areas for agricultural activities through weighted overlay analysis ^[34]. Each environmental factor was assigned an importance weight derived from pairwise comparison matrices following methods by ^[35,36]. The analysis calculated suitability scores using equation (1):

S = Σ (Wi × Xi) ………………………………. (1)

where Wi represents factor weights and Xi represents criterion scores. The process was implemented in ArcGIS 10.8, combining reclassified spatial layers to produce a comprehensive agricultural land suitability map based on objective evaluation criteria.

RESULTS AND DISCUSSION

Combined influence of the seven selected variables as shown in (Fig. 2) was used to establish agricultural land suitability within the study area based on a classification system. These areas were classified as very high suitability, high suitability, moderate suitability, marginal suitability and low suitability. Following the overlay analysis, a map illustrating the agricultural land suitability of Ife-South was generated.

Fig. 2: Seven Environmental Variable Maps (A) Precipitation, (B) Land Surface Temperature, (C) Soil Moisture, (D) Soil Organic Carbon, (E) Elevation, (F) Slope and (G) Land Use Land Cover

Table 4: Environmental variables spatial distribution and corresponding suitability classes

3.1  Environmental Factor Suitability Potential Assessment

The agricultural practices in the study area primarily rely on rainfall, with precipitation analysis (Table 4) revealing that only 7% of the land area has very high suitability and 18.8% has high suitability. Nearly 50% of the study area falls within marginal and low suitability classes for precipitation. Areas with limited rainfall may not support water-intensive crops when depending solely on natural precipitation. It should be noted that these assessments did not account for climate change impacts on rainfall patterns or the potential use of irrigation as an alternative water source for food production. Surface soil moisture (SM), which indicates the degree of soil wetness or dryness and contributes to various ecological functions, shows a more favorable distribution. Analysis reveals that 29% of the land area has very high suitability and 43% has high suitability for soil moisture. Only about 7% shows moderate suitability, while 9% and 6% fall under marginal and low suitability classifications, respectively. Land surface temperature (LST), serving as a proxy for areas prone to water stress and crop growth constraints, indicates that 17.6% of the study area has very high suitability, with 44.2% classified as having high suitability. Approximately 30% falls under moderate suitability, while areas with marginal and low suitability LST comprise only 6% and 1.7%, respectively. Regarding elevation, 31.8% of the area is categorized as having very high suitability (41-51m) and 39% as having high suitability (52-64m) for food production, suggesting that the topography is sufficiently flat for mechanized farming operations. Additionally, 21% of land areas demonstrate moderate suitability (65-82m), while only 6% (83-107m) and 1% (108-176m) are classified as having marginal and low suitability, respectively, due to excessive elevation. Slope analysis indicates that areas with favorable gradient characteristics occupy 48.5% (very high suitability) and 34% (high suitability) of the land. Approximately 12% falls under moderate suitability, while 4% and 1.4% are classified as having marginal and low suitability, respectively, due to steeper slopes. It is important to note that greater percentage rises in slope correlate with increased susceptibility to soil erosion. The terrain is predominantly characterized as lowlands, with most areas being moderately suitable for agriculture. This terrain classification represents a favorable spatial determinant for arable land, suggesting significant potential for intensive and mechanized food production across vast areas. Soil Organic Carbon (SOC), a critical indicator of soil health, reveals concerning conditions throughout the study area. Only approximately 1% and 7% of the land demonstrates very high and high suitability, respectively, while about 16% shows moderate suitability. The importance of high soil organic matter content cannot be overstated, as it provides essential nutrients and improves water availability for crops ^[37]. Research has shown that both organic and inorganic fertilizers can effectively replenish SOC content ^[38]. However, common agricultural practices in Africa, such as mono-cropping and extensive tillage, negatively impact SOC stocks and overall soil health.

3.2  Agricultural Land Suitability Potential Assessment

Based on the result of this analysis, it was determined that 8.4% of the study area have very high suitability, 21.1% have a high suitability, and 31.1% have moderate suitability and 26.4% have marginal suitability. However, 13% of the area have low suitability for agricultural production; these areas, which are primarily composed urban area and low soil moisture, are inaccessible and are not conducive for agricultural cultivation.

Table 5: Agricultural Land Suitability Area

This information is highlighted in Table 5, which presents the land suitability and percentage area. The map (Fig. 3) illustrates the suitability of the study area, with different colors indicating varying levels of suitability.

Fig. 3: Agricultural Land Suitability Map