ASSESSMENT OF INDIGENOUS AND MODERN AGRICULTURAL PRACTICES TO ENHANCE FOOD SECURITY IN MUBI NORTH L.G.A., ADAMAWA STATE, NIGERIA

This study assessed indigenous and modern agricultural practices to enhance food security in Mubi North LGA, Adamawa State Nigeria. The objectives included identifying potential barriers and opportunities for integrating indigenous farming knowledge with modern agricultural practices to enhance regional food security in study area and identifying the contributions of indigenous farming knowledge to income generation among farmers in the area. Using a mixed-methods approach, data were collected through structured questionnaires and interviews of randomly selected 135 farmers and 15 agricultural officials in Mubi North LGA of Adamawa State. Data were analysed using descriptive statistics for research questions while hypothesis was tested using t-test analysis. Findings reveal that economic pressures, market forces, perceived inefficiency, government policies, and social perceptions are primary factors affecting the adoption of indigenous practices. Farmers perceive that adopting these methods supports and enhanced food security underscoring their potential for financial stability. However, analysis of yield outcomes shows no significant difference between adopters and non-adopters, suggesting that with access to larger farmlands, adopters could achieve similar or better yields (p<0.05). The study concludes that integrating traditional knowledge with modern techniques can strengthen agricultural resilience, with recommendations for government support in training, improved market access, and collaboration among stakeholders to foster sustainable practices.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-04-19
DOI
https://doi.org/10.5281/zenodo.15465195
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
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article

ASSESSMENT OF INDIGENOUS AND MODERN AGRICULTURAL PRACTICES TO ENHANCE FOOD SECURITY IN MUBI NORTH L.G.A., ADAMAWA STATE, NIGERIA

Zenodo (CERN European Organization for Nuclear Research)
Model Reduction and Neural Networks
article

ASSESSMENT OF INDIGENOUS AND MODERN AGRICULTURAL PRACTICES TO ENHANCE FOOD SECURITY IN MUBI NORTH L.G.A., ADAMAWA STATE, NIGERIA

article en

Abstract

This study assessed indigenous and modern agricultural practices to enhance food security in Mubi North LGA, Adamawa State Nigeria. The objectives included identifying potential barriers and opportunities for integrating indigenous farming knowledge with modern agricultural practices to enhance regional food security in study area and identifying the contributions of indigenous farming knowledge to income generation among farmers in the area. Using a mixed-methods approach, data were collected through structured questionnaires and interviews of randomly selected 135 farmers and 15 agricultural officials in Mubi North LGA of Adamawa State. Data were analysed using descriptive statistics for research questions while hypothesis was tested using t-test analysis. Findings reveal that economic pressures, market forces, perceived inefficiency, government policies, and social perceptions are primary factors affecting the adoption of indigenous practices. Farmers perceive that adopting these methods supports and enhanced food security underscoring their potential for financial stability. However, analysis of yield outcomes shows no significant difference between adopters and non-adopters, suggesting that with access to larger farmlands, adopters could achieve similar or better yields (p<0.05). The study concludes that integrating traditional knowledge with modern techniques can strengthen agricultural resilience, with recommendations for government support in training, improved market access, and collaboration among stakeholders to foster sustainable practices.

Zenodo (CERN European Organization for Nuclear Research)
Adamawa State University (NG), Modibbo Adama University of Technology (NG)
Zero hunger
Openalex Percentile: Top 59%
Model Reduction and Neural Networks
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