Vol. 12(2), pp. 78-83, June 2024
https://doi.org/10.14662/arjasr2024210
Copyright 2024
Author(s) retain the copyright of this article
ISSN: 2360-7874
http://www.academicresearchjournals.org/ARJASR/Index.htm
 

Research paper

Design and Utilization of Multivariate Statistical Modeling as an Application to Cassava Crop Yield in Delta State: Implications for Boosting Food Security.

 

1Mrs Omokaro B; 2 Mr Charles Todo; 3 Mr Owens A

 

1,2.3Department of Statistics, Delta State Polytechnic, Otefe-Oghara

 

Accepted 29 May 2024

Abstract

This study investigates factors influencing cassava yield variations across Local Government Areas (LGAs) in Delta State, Nigeria, from 2020 to 2022. The goal is to understand these variations and their implications for food security in the region. Significant differences were observed in cassava yield across LGAs. The average yield ranged from 11.8 tons/ha in Sapele to 22.3 tons/ha in Warri south, highlighting the need for targeted interventions in lower-performing areas. K-means, Partitioning Around Medoids (PAM), and Fuzzy C-means clustering effectively identified distinct yield clusters. This information can be used to develop targeted agricultural strategies for each cluster, promoting food security. Analysis revealed a link between soil nutrient composition and yield variations. Specifically, nitrogen (N) levels were significantly associated with yield. Areas like Ndokwa East with sufficient nitrogen (1.3%) exhibited higher yields compared to Sapele (1.0%). Quadratic Discriminant Analysis (QDA) achieved a high classification accuracy (81%) in predicting whether a farmer's yield would be above or below the average. This suggests QDA's potential as a tool for strategic decision-making to optimize production and ensure food security. The study provides valuable insights for optimizing cassava production and enhancing food security in Delta State.

 

Key words: Cassava yield variations; Statistical Modeling Delta State, Nigeria; Food security; Soil nutrient composition; Clustering algorithms (K-means, PAM, Fuzzy C-means); Quadratic Discriminant Analysis (QDA)

 

How to cite this article (APA Style): Omokaro, B., Todo, C., Owens, A. (2024). Design and Utilization of Multivariate Statistical Modeling as an Application to Cassava Crop Yield in Delta State: Implications for Boosting Food Security . Acad. Res. J. Agri. Sci. Res. 12(2): 78-83