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
- Statements and Declarations
- References
Objective & Thematic Focus
This article aims to propose an integrated framework linking artificial intelligence, climate resilience, and nutrition in Cameroon. It assesses current and potential applications of AI within the Cameroonian food system, identifies conditions for its successful implementation, and addresses obstacles to overcome, thereby contributing to the development of a sustainable and innovative food system.
- Analyzing the potential of Artificial Intelligence in strengthening agricultural climate resilience.
- Assessing AI's contributions to nutritional security, including food availability, accessibility, and diversity.
- Identifying constraints and developing strategies for inclusive and sustainable integration of AI into Cameroon's food system.
- Examining the role of AI in improving food systems governance through data analysis and policy monitoring.
- Highlighting the importance of data availability, local capacity building, and inclusive digital governance for AI effectiveness.
Excerpt from the Book
1. Introduction
The dual structural crisis that Cameroon faces which are the high vulnerability of its agriculture to climate change and ongoing food insecurity is resulting in acute and persistent forms of malnutrition. According to the FAO (2023), approximately 16% of Cameroon's population is undernourished, with marked regional disparities. The North, Far North, and East regions are particularly affected by drought, rainfall variability, and soil degradation. FAO and WFP (2025) also report an intensification of food crises in these areas.
The advent of artificial intelligence is an opportunity for agriculture because it opens new perspectives to improve the performance of agricultural and nutritional systems. It makes it possible to predict yields, optimize the use of inputs, detect diseases early, and recommend crops adapted to local realities (Musa, 2023; Leal Filho, 2024).
The AgroXAI and IoT projects for resilient maize demonstrate the value of intelligent use of data. Artificial intelligence can also promote dietary diversification, reduce nutritional deficiencies, and support national food security (Gikunda, 2024).
However, the integration of artificial intelligence in Cameroon remains limited by insufficient local data, a lack of technical skills, and governance constraints (World Bank, 2025; UNU, 2024). The objective of this article is therefore to propose an integrated “Artificial Intelligence – Climate – Nutrition" framework for Cameroon, by assessing current and potential applications, conditions for success, and obstacles to overcome.
2. Study Objectives
This study has three main objectives:
1. To analyze the potential of Artificial Intelligence in strengthening the climate resilience of agricultural systems in Cameroon.
2. To assess its potential contributions to nutritional security, particularly in relation to food availability, accessibility, and diversity.
3. To identify constraints and strategies to consider for the inclusive and sustainable integration of Artificial Intelligence into the Cameroonian food system.
Chapter Summaries
1. Introduction: This chapter highlights Cameroon's agricultural vulnerability to climate change and widespread malnutrition, introducing artificial intelligence as a promising solution to enhance agricultural and nutritional systems, and outlining the study's aim to propose an integrated AI-Climate-Nutrition framework.
2. Study Objectives: This section details the three core goals of the research, which are to analyze AI's potential in climate resilience and nutritional security, and to identify strategies for its sustainable integration into Cameroon's food system.
3. Literature Review: This chapter examines how AI addresses climate change and food security challenges in agriculture, acknowledging its role as a catalyst in Africa while also discussing limitations such as data scarcity and the risk of excluding smallholder farmers.
4. Methodology: This section describes the mixed-methods approach used, combining qualitative and quantitative data from interviews, focus groups, and institutional sources across various agro-ecological zones in Cameroon, complemented by statistical analysis and thematic content analysis.
5. Results: This chapter presents the findings on AI's significant positive impact on climate resilience, nutritional security, and food system governance in Cameroon, illustrating increased yields, reduced losses, and improved dietary diversification.
6. Discussion: This chapter confirms the decisive role of AI in adapting Cameroon's food systems to climate change, validating the observed improvements in agricultural resilience and nutritional quality against existing literature, while also recognizing persistent limitations.
7. Conclusion and Recommendations: This chapter summarizes AI's transformative potential for climate resilience and nutritional security, reiterating its positive effects on productivity and food diversification, alongside key constraints and practical recommendations for successful and inclusive implementation.
Keywords
Artificial intelligence, Climate resilience, Nutritional security, Food systems, Cameroon, Agriculture, Sustainable development, Digital innovation, Malnutrition, Data governance, Capacity building, Smallholder farmers, Policy, Predictive modeling, Dietary diversification.
Frequently Asked Questions
What is this work fundamentally about?
This work fundamentally explores the transformative potential of artificial intelligence to strengthen climate resilience and improve nutritional security in Cameroon, proposing an integrated framework for its sustainable integration into the national food system.
What are the central thematic areas?
The central thematic areas include the application of artificial intelligence in agriculture, climate change adaptation, enhancing food security, improving nutrition, and developing robust food system governance in Cameroon.
What is the primary objective or research question?
The primary objective is to propose an integrated "Artificial Intelligence – Climate – Nutrition" framework for Cameroon by assessing current and potential applications, identifying conditions for success, and outlining obstacles to overcome.
What scientific method is used?
The research employs a mixed-methods approach, combining qualitative data from semi-structured interviews and focus groups with quantitative data derived from statistical processing of yields, technology access, and climate variations.
What is covered in the main body?
The main body covers a literature review on AI's role in agriculture and food security, a detailed methodology of the study, the results demonstrating AI's impact on climate resilience and nutrition, and a discussion contextualizing these findings with existing research and limitations, before concluding with recommendations.
What keywords characterize the work?
Key terms that characterize this work include Artificial intelligence, Climate resilience, Nutritional security, Food systems, Cameroon, Agriculture, Sustainable development, Digital innovation, Malnutrition, Data governance, Capacity building, Smallholder farmers, Policy, Predictive modeling, and Dietary diversification.
How does AI contribute to dietary diversification in Cameroon?
Artificial intelligence contributes to dietary diversification by identifying crops with high nutritional value, promoting the combination of complementary crops, and anticipating food deficits to facilitate distribution planning, leading to more varied and balanced diets.
What are the main limitations hindering AI integration in Cameroon?
The main limitations include insufficient digital infrastructure in rural areas, a lack of reliable local data, high equipment costs, a shortage of technical training for agricultural producers, and the absence of a comprehensive institutional framework.
What specific results were observed regarding maize yields and losses?
The study observed a 15% increase in maize yields in the West and Central regions, and a reduction of approximately 12% in sorghum and millet losses in the North and Far North due to improved drought anticipation through AI.
Why is inclusive digital governance crucial for AI adoption in Cameroonian agriculture?
Inclusive digital governance is crucial to ensure that both smallholder farmers and the more affluent have equitable access to advanced technologies, protect farmers' data, ensure transparent algorithms, and address social inequalities in the adoption of AI.
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- Dr. Fosso Alain Ngouang (Autor:in), 2025, Artificial intelligence, climate resilience and nutritional security in Cameroon, München, GRIN Verlag, https://www.grin.com/document/1619624
