Spatial Assessment of Land Suitability Potential for Agriculture in Nigeria
Abstract
:1. Introduction
2. Material and Methods
2.1. Overview of the Study Area
2.2. Data Sources
2.3. Calculation of Land Use Land Cover Change
2.4. Cropland Suitability Potential Assessment
- (i)
- First, the processing of the spatial datasets is explained below:
- (a)
- The mean surface soil moisture was downloaded from Google Earth Engine and analyzed using ArcGIS 10.6 to determine its values across the study area.
- (b)
- Land surface temperature (Day time LST), a product with a repeat-cycle of 8 days, was averaged and converted to degree Celsius using Google Earth Engine and analyzed using ArcGIS 10.6 to determine its values across the study area.
- (c)
- The spatial distribution pattern of annual precipitation was analyzed using the Inverse Distance Weighted (IDW) spatial analyst function in ArcGIS 10.6.
- (d)
- The digital elevation and (e) the percentage rise of slope was determined using the spatial analyst function in ArcGIS 10.6.
- (e)
- The spatial distribution of SOC in the country was extracted from the global soil organic carbon map, indicating SOC stock from 0 to 30 cm.
- (ii)
- Secondly, to evaluate the assessment of the cropland suitability potential of the country, weights were generated for the six 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) as explained by Saaty (2008) [49] 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 [50] and is effective in determining weights [44]. This implies that the higher the hierarchical value, the more suitable the potential for agriculture.
- (iii)
- From the analyzed pairwise comparison, the weighted values of each variable were fitted into a weighted overlay model in ArcGIS using the following formula:LS = n(SSM)wt(1–5) + n(LST)wt(1–5) + n(Precip)wt(1–5) + n(Elev)wt(1–5) + n(SLP)wt(1–5) + n(SOC)wt(1–5)LS = Land suitability;n = The weighted value from the pairwise comparison;wt(1–5) = The individually weighted variables on a scale of 1–5 based on their suitability using AHP;SSM = Surface soil moisture;LST = Land surface temperature;Precip = Precipitation;Elev = Elevation;SLP = Slope;SOC = Soil organic carbon.
- (iv)
- Lastly, to achieve the study objective, the weighted overlay model outcome was multiplied by the recent cropland land use land cover of 2020, using the spatial analyst function of ArcGIS to model the existing cropland suitability, which was reclassified into five (5) classes, namely, Very High Suitability (5), High Suitability (4), Moderate Suitability (3), Marginal Suitability (2), and Low Suitability (1). All dataset values are given in Table 3.
3. Results and Discussion
3.1. Analysis of Land Use Land Cover Changes
3.2. Environmental Suitability Potential Assessment for Agriculture
3.3. Cropland Suitability Assessment
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Data Source | Data | Resolution | Year |
---|---|---|---|
Land Use Cover Change Analysis | |||
National Geomatics Center of China (NGCC) | Land Use Cover | 30 m | 2000 and 2020 |
Cropland Suitability Analysis | |||
Food and Agricultural Organization (GSOC map) | Global Soil Organic Carbon | 30 arc seconds (eq to 111.2 km) | 2012 |
Climate Research Unit (University of East Anglia) | Precipitation | 0.5° (eq to 55.66 km) | 2020 |
Moderate Resolution Imaging Spectroradiometer (MODIS) | Land Surface Temperature | 1 km | 2020 |
Consultative Group for International Agricultural Research (CGIAR) | Digital Elevation | 90 m | 2000 |
Consultative Group for International Agricultural Research (CGIAR) | Slope | 90 m | 2000 |
National Aeronautics and Space Administration/United States Department of Agriculture (NASA-USDA) | Surface Soil Moisture | 10 km | 2020 |
Criteria | SSM | LST | Precipitation | Elevation | Slope | SOC | TOTAL | Weights | % |
---|---|---|---|---|---|---|---|---|---|
SSM | 1 | 5 | 2 | 5 | 5 | 3 | 21 | 0.32 | 32 |
LST | 0.2 | 1 | 3 | 1 | 0.5 | 0.2 | 6 | 0.09 | 9 |
Precipitation | 0.5 | 0.3 | 1 | 5 | 5 | 1 | 13 | 0.19 | 20 |
Elevation | 0.2 | 0.2 | 0.2 | 1 | 5 | 1 | 8 | 0.12 | 12 |
Slope | 0.2 | 5 | 0.2 | 1 | 1 | 1 | 8 | 0.13 | 13 |
SOC | 0.3 | 5 | 1 | 1 | 1 | 1 | 9 | 0.14 | 14 |
Total | 65 | 1 |
Variables | Value | AHP | Suitability Class |
---|---|---|---|
Surface Soil Moisture | 21–25 | 5 | Very High Suitability |
17–20 | 4 | High Suitability | |
13–16 | 3 | Moderate Suitability | |
8–12 | 2 | Marginal Suitability | |
3–8 | 1 | Low Suitability | |
Land Surface Temperature (°C) | 21–28 | 5 | Very High Suitability |
28–31 | 4 | High Suitability | |
31–33 | 3 | Moderate Suitability | |
33–35 | 2 | Marginal Suitability | |
35–41 | 1 | Low Suitability | |
Precipitation (mm) | 2200–2900 | 5 | Very High Suitability |
1.600–2100 | 4 | High Suitability | |
1200–1500 | 3 | Moderate Suitability | |
850–1100 | 2 | Marginal Suitability | |
380–840 | 1 | Low Suitability | |
Elevation (m) | −40–190 | 5 | Very High Suitability |
200–360 | 4 | High Suitability | |
370–570 | 3 | Moderate Suitability | |
580–970 | 2 | Marginal Suitability | |
980–2400 | 1 | Low Suitability | |
Slope (%) | 0–4 | 5 | Very High Suitability |
4–24 | 4 | High Suitability | |
25–52 | 3 | Moderate Suitability | |
53–100 | 2 | Marginal Suitability | |
Above 100 | 1 | Low Suitability | |
Soil Organic Carbon (Top Soil) (cm) | 3.14 | 5 | Very High Suitability |
2.02 | 4 | High Suitability | |
1.60 | 3 | Moderate Suitability | |
1.15 | 2 | Marginal Suitability | |
0.5 | 1 | Low Suitability |
NORTH CENTRAL | NORTH EAST | NORTH WEST | SOUTH EAST | SOUTH SOUTH | SOUTH WEST | |
---|---|---|---|---|---|---|
Rice | 3017 | 1376 | 1993 | 398 | 464 | 577 |
Sorghum | 1538 | 1914 | 3207 | nil | nil | nil |
Maize | 3108 | 3145 | 2827 | 625 | 725 | 1678 |
Cassava | 14,651 | 3516 | 4559 | 10,132 | 11,076 | 11,135 |
Yam | 18,536 | 5445 | 3381 | 8744 | 10,023 | 7955 |
Cowpea | 841 | 1146 | 861 | 307 | 182 | 539 |
Onion | 98 | 551 | 776 | nil | nil | 8 |
Tomato | 540 | 906 | 961 | 102 | 50 | 249 |
TOTAL | 45,126 | 17,999 | 18,565 | 20,309 | 22,520 | 22,141 |
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Chiaka, J.C.; Zhen, L.; Xiao, Y.; Hu, Y.; Wen, X.; Muhirwa, F. Spatial Assessment of Land Suitability Potential for Agriculture in Nigeria. Foods 2024, 13, 568. https://doi.org/10.3390/foods13040568
Chiaka JC, Zhen L, Xiao Y, Hu Y, Wen X, Muhirwa F. Spatial Assessment of Land Suitability Potential for Agriculture in Nigeria. Foods. 2024; 13(4):568. https://doi.org/10.3390/foods13040568
Chicago/Turabian StyleChiaka, Jeffrey Chiwuikem, Lin Zhen, Yu Xiao, Yunfeng Hu, Xin Wen, and Fabien Muhirwa. 2024. "Spatial Assessment of Land Suitability Potential for Agriculture in Nigeria" Foods 13, no. 4: 568. https://doi.org/10.3390/foods13040568