This article proposes an integrated framework linking artificial intelligence, climate resilience, and nutrition, based on literature reviews, case studies in Cameroon, and conceptual modeling. The results show that artificial intelligence can become a catalyst for developing innovative strategies to strengthen food and nutrition security
Table of Contents
1. Introduction
2. Study Objectives
3. Literature Review
4. Methodology
5. Results
6. Discussion
7. Conclusion and Recommendations
Research Objectives and Focus
This study aims to investigate the role of artificial intelligence as a strategic lever to enhance climate resilience and nutritional security within the agricultural sector in Cameroon. By addressing the dual crisis of climate vulnerability and food insecurity, the research explores how predictive and analytical digital tools can be integrated into local food systems to improve decision-making and sustainable productivity.
- The potential of AI for climate-resilient agricultural practices.
- Contributions of digital innovation to nutritional security and dietary diversity.
- Identification of barriers to technology adoption in smallholder farming.
- Strategies for inclusive digital governance and capacity building.
- Comparative analysis of agricultural metrics and nutritional outcomes.
Excerpt from the Book
5. Results
The combined analysis of scientific literature, case studies conducted in Cameroon, and the conceptual model highlights three interdependent dimensions: climate resilience, nutritional security, and food system governance.
The integration of artificial intelligence in Cameroonian agriculture significantly improves the adaptive capacity of rural communities. Prediction models using meteorological, soil, and satellite data make it possible to anticipate climate hazards such as droughts, floods, and rainfall fluctuations, thus facilitating planting and harvest planning.
Furthermore, recommendations derived from artificial intelligence promote the adoption of drought-resistant varieties and the adaptation of irrigation practices to local conditions. The use of predictive measures optimizes the timing of interventions such as fertilization, irrigation, and pest control, reducing losses by 10 to 20% in certain pilot areas.
Concretely, maize yields increased by 15% in the West and Central regions, while in the North and Far North, drought anticipation reduced sorghum and millet losses by approximately 12%. Artificial intelligence also contributes to strengthening food and nutrition security in Cameroon by identifying crops with high nutritional value, thus helping to reduce problems caused by malnutrition.
Summary of Chapters
1. Introduction: Outlines the dual crisis of climate-induced agricultural vulnerability and malnutrition in Cameroon, framing AI as a potential tool for systemic improvement.
2. Study Objectives: Defines the three core goals of the research, focusing on climate resilience, nutritional security, and the identification of integration strategies.
3. Literature Review: Synthesizes existing research on AI in African agriculture, highlighting both the catalytic potential of these technologies and the necessity of addressing digital infrastructure gaps.
4. Methodology: Details the mixed-methods approach, combining semi-structured interviews, focus groups, and secondary data analysis to evaluate AI’s impact.
5. Results: Presents empirical findings showing improved crop yields and better drought management through AI-driven predictive modeling.
6. Discussion: Evaluates the study results in the context of broader regional challenges, confirming the decisive role of AI in guiding public policies and agricultural programs.
7. Conclusion and Recommendations: Summarizes the transformative potential of AI while emphasizing the need for infrastructure investment and inclusive governance.
Keywords
Artificial intelligence, Climate change, Nutrition, Food security, Cameroon, Agricultural productivity, Climate resilience, Smallholder farmers, Digital innovation, Nutritional security, Food systems, Data governance, Capacity building, Sustainable agriculture, Predictive modeling.
Frequently Asked Questions
What is the core focus of this research?
The research examines how artificial intelligence can be leveraged to address the combined challenges of climate change and food insecurity within Cameroon’s agricultural sector.
What are the primary themes discussed?
The key themes include climate resilience in farming, nutritional security, the role of predictive data in agriculture, and the importance of inclusive digital governance.
What is the central research goal?
The primary objective is to develop an integrated "Artificial Intelligence – Climate – Nutrition" framework that supports the sustainable development of the Cameroonian food system.
Which methodology is employed?
The study utilizes a mixed-methods approach, incorporating both quantitative data (yields, climate variations) and qualitative insights (interviews, focus groups) from farmers and experts.
What topics are covered in the main body?
The main body covers the current state of agricultural vulnerability in Cameroon, a review of existing literature on AI in Africa, detailed methodological processes, and a presentation of results regarding yield increases and improved food management.
Which keywords best describe this study?
The study is characterized by terms such as Artificial intelligence, Climate change, Nutrition, Food security, and Cameroon, reflecting its focus on tech-driven agricultural development.
How does AI specifically help with malnutrition in Cameroon?
AI assists by identifying crops with high nutritional value, facilitating dietary diversification, and helping to anticipate food shortages, which allows for better distribution planning.
What are the major barriers identified for AI adoption?
The study identifies insufficient digital infrastructure, a lack of reliable local data, high costs of equipment, and a lack of technical training for producers as the main obstacles.
What is the recommended approach for successful implementation?
Successful implementation requires a contextualized and participatory approach, including sustainable financing, local capacity building, and strong institutional frameworks for data protection.
- Quote paper
- Dr. Fosso Alain Ngouang (Author), 2025, Artificial intelligence, climate resilience and nutritional security in Cameroon, Munich, GRIN Verlag, https://www.grin.com/document/1619624
