HOW MACHINE LEARNING CAN EVALUATE THE INFLUENCE OF SOCIOECONOMIC AND CLIMATIC FACTORS IN AGRICULTURAL YIELD

HOW MACHINE LEARNING CAN EVALUATE THE INFLUENCE OF SOCIOECONOMIC AND CLIMATIC FACTORS IN AGRICULTURAL YIELD: A CASE OF NIGERIA

Abstract:
Agricultural productivity is heavily influenced by various socio-economic and climatic factors. Understanding the complex relationships between these factors and agricultural yield is essential for effective decision-making and policy formulation in the agricultural sector. This abstract presents a study that utilizes machine learning techniques to evaluate the influence of socio-economic and climatic factors on agricultural yield in Nigeria.

The. study focuses on Nigeria due to its significant agricultural sector and vulnerability to climate change. The dataset used in this research comprises historical records of agricultural yield, socio-economic indicators, and climatic variables from various regions of Nigeria. Machine learning algorithms, including regression models, random forest, and neural networks, are employed to develop predictive models that can capture the intricate relationships between the predictor variables and agricultural yield.

The socio-economic factors considered in the analysis include population density, literacy rate, access to credit, infrastructure development, and agricultural investments. Climatic factors encompass rainfall patterns, temperature variations, and humidity levels. The models are trained and validated using historical data, and their performance is assessed based on metrics such as mean square error, R-squared, and accuracy.

The results of this study will provide valuable insights into the relative importance and interactions of socio-economic and climatic factors on agricultural yield in Nigeria. The machine learning models can help identify the critical variables that significantly impact crop production and guide policymakers in developing targeted interventions to improve agricultural productivity. Moreover, the findings can contribute to the development of climate-smart agricultural practices and strategies that are resilient to changing climatic conditions.

By leveraging machine learning techniques, this research aims to enhance our understanding of the complex dynamics between socio-economic and climatic factors and their influence on agricultural yield. The findings will offer valuable guidance for policymakers, researchers, and stakeholders involved in the agricultural sector in Nigeria to promote sustainable agricultural practices and ensure food security in the face of changing socio-economic and climatic conditions.

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