Leveraging Big Data Analytics for Predictive Modeling and Forecasting in Agriculture in Zambia

Abstract

Big Data is defined using characteristics and concepts beyond size, pinpointing to the volume, velocity, variety, and veracity of the data. The integration of big data analytics in agriculture is revolutionizing farming practices, crop management, and decision-making processes. Much of the existing research has utilized limited datasets and simplistic analytical methods, such as basic statistical approaches and opaque machine learning models, which hinder clear interpretation by farmers and stakeholders. The study aimed to develop a predictive model and forecasting accuracy using data analytics that will improve crop yield in Agriculture, applied advanced data analytics approaches with tree-based machine learning techniques to pinpoint key factors that influence agricultural productivity and used key factors to build a model that predicts crop yield. The study implemented experimental methodology. Utilizing the LightGBM framework - a gradient boosting model known for its interpretability, analyzed an amalgamation of data from surveys, farm records, and climatic information to assess feature importance. It also integrated diverse datasets from governmental reports and agricultural archives. This analysis included various socio-economic factors such as access to water, soil quality, type of seeds, weather pattern, educational levels of farmers, and market access, which were identified as critical variables affecting agricultural success. The LightGBM model not only achieved high accuracy and reliability but also provide transparent insights, outperforming other methods like XGBoost, decision trees, and random forests in our evaluations.