Assessment of land capability and suitability classification for crop production in Katsina, Nigeria

• Main Author: Sani, Abdullahi
• Format: Thesis
• Language: English
• Published: 2022
• Subjects: Land capability for agriculture - Nigeria; Soils - Nigeria; Soil surveys - Nigeria
• Online Access: http://psasir.upm.edu.my/id/eprint/104561/1/FP%202022%2026%20IR.pdf; http://psasir.upm.edu.my/id/eprint/104561/
• Institution: Universiti Putra Malaysia

ArcGIS and remote sensing play a vital role in generation of spatial information, mapping of natural resources and inventory such as mapping for optimal land use for sustainable agriculture. Lack of sufficient and adequate information on climate and soils characteristics are among the major limiting factors affecting agricultural development in Nigeria. Thus, the study was conducted to determine the physiochemical properties of soil, land characteristics, land capability and suitability for selected crops in Katsina State, Nigeria. The agriculture area was divided into land units and a total of 5 soil profile pits were excavated corresponding to each unit. The different soil horizon in soil profile were described using United State Department of Agriculture soil taxonomy, with 500g of sample were collected from each soil horizon. A total of fifteen (15) samples were collected from the profile pits (three in each pit from three different horizons) for land capability analysis. For suitability analysis fifty-five (55) sub surface samples were collected. Soil survey was conducted on each land unit to record the physical and chemical properties of the soil. Soil samples coordinates were marked with GPS Garmin 60csx and subjected to geospatial distribution analysis. Data collected for climatic (rainfall and temperature) and soil physio-chemical characteristics were analyzed using descriptive statistics (SAS v9.4). The soil properties analyses results indicate that the area is highly susceptible to erosion and low in soil fertility that limit the land capability for agricultural production. Soil properties distribution map were generated with ArcGIS v10.3 using Inverse Distance Weighted (IDW) techniques. The spatial distribution of soil properties of the land units was showed in variation map of each soil properties. The land capability assessment was undertaken based on United State Department of Agriculture (USDA) criteria. The results showed that three land unit maps were rated capable for rain fed farming of major crops under different management practices which included in the category of classes II, III, and IV, whereas the V and VI land unit was not capable due to permanent limitations associated with slope, stoniness and soil depth. In order to have more detail and direct information on land suitability for use by specific crops, land evaluation for selected crops was carried out using Food and Agricultural Organization (FAO) framework of land suitability. The generation of crops suitability map was prepared using two modelling techniques of GIS. Analytical hierarchical processes (AHP). and Food Agricultural Organization (FAO) Frame work of land evaluation. The weightage and score of each parameter and their classes are based on administered questionnaire to Nigeria millet expert opinion. The suitability for millet in Katsina from the climate and physical-chemical parameters indicates that annual rainfall (604- 702mm), elevation (434.75-558.5◦), temperature (26.50-26.99◦C), drainage, erosion, soil depth (0-30cm), soil pH (6.4-6.7), organic carbon (OC, 1.67-2.22) and organic matter and (OM, 02.96-3.0) are noted within the acceptable suitability index values (for Class S1 to Class S3), that represent sustainable crop production. While, cation exchange capacity (CEC, 5-15 cmol(+)/kg), total nitrogen (TN, 0.5-5.0%), exchangeable acidity (EC,0.03-0.65dS/m), phosphorus (P, 4.40-10.23%) and effective sodium percentage (ESP, 1.06-1.53%) were noted below average value for crop production. Land Suitability Class S1 (highly suitable) covers 1328.40ha which is about 21.19% of the study area. While land suitability Class S2 (moderately suitable) covers 1098ha (17.53% area). The land suitability Class S3 cover 1767ha (28.19% area). Besides that, Class N1 (potentially not suitable) covers about 851.33ha (13.58% area) and, finally Class N2 (potentially and actually not suitable) covers about 1223.08ha (19.51% area) with scores below average selected crops. Further, the Class N2 areas marked with rock outcrop and inherent low fertility. Studied area (Katsina) suitability class for crop production as follow: S3>S1>N2>S2>N1. This indicates that, land area under Class S3 (28.19%) requires moderate level of soil amendment to improve millet, sorghum, beans and groundnut production. Whereas, Class S2 (17.53%), requires minimal level of soil amendment, whereas Class N1 and N2 with total land area of percentage of 30.09%, requires high input of soil amendment. The result indicates that there are general limitation factors in each land unit such as slope, soil depth, CEC, erosion, and rainfall for groundnut production. Meanwhile, OC, OM, CEC, soil depth, for millet, CEC, EC, ESP and stoniness for sorghum cultivation, and for beans, soil depth, pH, texture, rainfall, temperature. From the study data, climatic condition (rainfall and temperature) and soil properties are the first step (primary factor) in site specific crop production. Therefore, different land unit requires different level of input and land management to facilitate (improve) crops production in Katsina state for sustainable agriculture. Government and other non-governmental organization should encourage mix-cropping and mixed farming in the area to enhance soil fertility, there is also emphasize of avoiding using non-agricultural land for agricultural use, long term soil monitoring sites should be established using a localize soil map by the government using regular soil samples and management aspect should be taken and stored in database. The study also recommends for further studies in combining Fuzzy-AHP for fertility variability in the area and advance statistical analysis such as non-descriptive analysis and nuclear magnetic resources (NMR) study should be use on physiochemical properties of soil.