Crop Suitability Mapping for Rice, Cassava, and Yam in North Central Nigeria

Abstract

Agricultural production has contributed over time to food security and rural economic development in developing countries particularly supporting the countryside. Evidence of crop yield decline exist in the Lower River Benue Basin. This was a crop suitability mapping for rice, cassava, and yam to guide policy makers in strategic planning for sustainable agricultural development. Data was collected on various themes including climate, drainage, soil, satellite imagery, and maps. Remote Sensing was used to analyse satellite imagery to produce a digital elevation model, land use and land cover map, and normalised difference vegetation index map. GIS was used to produce thematic maps, weighted percentages of attribute data, and to produce crop suitability maps through weighted overlay. Soils in the study area require fertility enhancement with inorganic fertilisers for better crop yield. Soils in the Lower River Benue Basin are suitable for yam, cassava, and rice cultivation on maps of suitable areas. Some areas were found to be highly suitable for the cultivation of rice (34.22%), cassava (17.08%) and yam (16.08%). Some other areas were found to be moderately suitable for the cultivation of cassava (48.18%), rice (45.46%), and yam (48.85%). Areas with low suitability were 14.99% (rice), 33.68% (cassava), and 29.57% (yam). This study has demonstrated the importance of crop suitability mapping and recommends that farmers' cooperative societies and policy makers utilise the information presented to improve decision making methods and policies for agricultural development.

Land Suitability Assessment for Cassava var. Jarak Towo, Using Determinant Factors as the Strategy Fundament in Hilly Area Jatiyoso-Indonesia

“…The process to determine land suitability requires land evaluation which produces rational data for land use planning [12]. and suitability evaluation includes several aspects such as secondary data collection, primary data acquisition by survey, laboratory analysis, and data analysis [13]. FAO [14] divides land suitability classes into four classes: (1) very suitable class/S1 i.e.…”

Development of geo-referenced agricultural map and management information system for Samar Island

“…The geo-referenced agricultural map and management information system can be an important tool both at operational as well as policy level overviews of land suitability assessment [16]- [20]. At the operational level of this demonstration application, for instance, local farming extension workers in Samar can find it an important spatial decision support tool as their task involves advisory service and recommendation to farmers of agricultural land suitability for various purposes.…”

Land Suitability and Crop Pattern Model using Integrated Pollination Intelligence Algorithm and Remote Sensing

“…To implement the model, qualitative evaluation of farmers' region is carried out by matching the climate condition, vegetation and soil properties of the proposed land for cultivation with respect to crop requirement. In this work, suitability data of three crops grown in Benue State, Nigeria, were used as well as remote sensing adopted from Abah and Petja [20]. Descriptive statistics of annual crop area in the region between the year 1989 and 2011 obtained from Benue State Agricultural Development Authority (BNARDA) Markurdi, was harvested from Terdoo et al [21].…”

“…In this research work, a crop model with land suitability analysis obtained from Geographic Information System (GIS) with remote sensing is developed and used as a tool to aid farm managers decision making process. The soil data from Abah and Petja [20], was analyzed based on Likert scale to handle land suitability constraints in the model. A new modified class of metaheuristic algorithm, Pollination Intelligence Algorithm (PIA), Ejieji and Akinsunmade [14], was adopted with parameter modification to solve the new developed model.…”