Machine Learning Prediction of the Influence of Socioeconomic and Climatic Factors on Agricultural Yield in Nigeria

In today’s world, global food insecurity is a growing concern, especially in regions like Africa where population growth and climate change are driving up the demand for food. To tackle this, machine learning (ML) can assist farmers and policymakers in making data-driven decisions for improving agricultural productivity.

The majority of climate change research explains agricultural output in terms of Climate related and biophysical elements like temperature, rainfall, and soil. But is that all that affects crop yield? Research also proves that socioeconomic factors also play a part. This necessitated the need to investigate the way socioeconomic and climatic factors interact to affect yield given both the agriculture industry's ongoing extensive economic reforms and investment.

In my research paper at the African University of Science and Technology, I explored how machine learning models can predict agricultural yields in Nigeria by analysing 30 years of data (1990-2020) on three critical crops: cocoa, sesame, and cashew.

The Key Findings were:

• KNN achieved an impressive 99.81% accuracy for predicting cashew yields using climatic factors.
• Random Forests outperformed other models across most crops and factor combinations, proving especially effective when integrating both socioeconomic and climatic data.

With Nigeria’s agricultural sector being a major part of its economy, machine learning provides the ability to:

• Forecast Crop Yield: This allows for better planning and resource allocation, ensuring that farmers can prepare for favorable or adverse growing seasons. It also help mitigate food insecurity by anticipating shortages or surpluses.
• Climate-Smart Agriculture: This empowers farmers to adopt climate-smart farming techniques, such as choosing the best planting times, selecting drought-resistant crops, or optimizing irrigation schedules, which are crucial for enhancing resilience to climate change.
• Data-Driven Policy Making: Policymakers can use AI-driven insights to formulate better agricultural policies, such as subsidies, resource distribution, and food security programs.

As an AI researcher, I am uniquely positioned to guide businesses in leveraging AI solutions to solve real-world challenges. Whether it's forecasting agricultural yields, improving operational efficiency, or developing custom machine learning models.

Let’s connect and explore how AI can elevate your business in agriculture or any other industry.

PS. For the full research paper, click on the university link: https://repository.aust.edu.ng/xmlui/bitstream/handle/123456789/5135/Dappa%20Tamuno%20opubo%20%2841051%29.pdf?sequence=1&isAllowed=y

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