Modeling of Climate Effect on Arable Crops Production of Oryza Sativa and Zea Mays in Nigeria Using Fuzzy Logic

Abstract

In Nigeria, farmers account for more than a third of the labor force. Despite the government's and other relevant stakeholders' strategies and measures to address climate change's impact on agricultural activities in Nigeria, the country has continued to be threatened by climate change, which has had negative consequences for food production, particularly rice and maize, which are the country's main staple foods. High precipitation owing to moisture levels negates agricultural yields, according to the impacts of temperature and moisture levels on agriculture productivity. Weather disasters, deforestation, disease transmission, and species extinction are all climate variables that limit agricultural production rates. The goal of this research is to use machine learning algorithms to determine the best weather conditions for arable crops. To model and predict agricultural yields, fuzzy logic was used. Secondary data, pre-processing and processing, modeling, and the user interface are the five sections that make up the system design. The harvesting outcomes for each of the months specified From January through December, the majority of the data obtained revealed Harvest values of low (54.8%), fair (23.6%), and good (21.5%) quality. Agricultural activities should be carried out between MAY and SEP, whereas crops that require a lot of rain should be sown between JUL and AUG when rainfall is at its highest. The study concluded that agricultural operations should be carried out between MAY and SEP in order to achieve good harvest quality, based on the data collected, extracted, and assessed. The findings of the study will guide policymakers and farmers toward a bumper harvest, increased income, and a rise in the country's GDP. Finally, crops that require a lot of rain, like rice, should be planted between July and August, according to the research.