Towards Food Security: the Prediction of Climatic Factors in Nigeria using Random Forest Approach

Abstract

With the explosive growth in the world’s population which has little or no corresponding rise in the food production, food insecurity has become eminent, and hence, the need to seek for opportunities to increase food production in order to cater for this population is paramount. The second goal of the Sustainable Development Goals (SDGs) (i.e., ending hunger, achieving food security and improved nutrition, and promoting sustainable agriculture) set by the United Nations (UN) for the year 2030 clearly acknowledged this fact. Improving food production cannot be achieved using the obsolete conventional methods of agriculture by our farmers; hence, this study focuses on developing a model for predicting climatic conditions with a view to reducing their negative impact, and boosting the yield of crop. Temperature, wind, humidity and rainfall were considered as the effect of these factors is more devastating in Nigeria as compared to sun light which is always in abundance. We implemented random forest algorithm using Python programming language to predict the aforementioned climate parameters. The data used was gotten from the Nigerian Meteorological (NiMet) Agency, Lokoja, Kogi State between 1988 and 2018. The result shows that random forest algorithm is effective in climate prediction as the accuracy from the model based on the climatic factors considered was 94.64%. With this, farmers would be able to plan ahead to prevent the impact of the fluctuations in these climatic factors; thus, the yield of crops would be increased. This would dwarf the negative impact of food insecurity to the populace.

Rice Yield Forecasting: A Comparative Analysis of Multiple Machine Learning Algorithms

“…Moreover, it houses a significant population of rice consumers. Although some research has been conducted in other states such as Kogi [11] and Ebonyi [7], limited studies have focused on predicting rice yield in Katsina using climate variables. Therefore, to facilitate effective policy formulation, planning, and intervention for food security in Katsina State, the development of rice yield prediction models incorporating climate variables becomes crucial [12].…”

“…The findings suggested that the model's accuracy was sufficiently reliable for future rice yield forecasts in Sri Lanka. Similarly, [11] employed the Random Forest algorithm to model yield in Kogi State, Nigeria, with rainfall, wind, and temperature as input features.…”

Measuring Food Quality and Safety for Food Security Related Assessments: A Microscopic Review

“…Babu & Gajanan (2022) have classified household groups based on certain socioeconomic characteristics to assess food security using K-mean cluster analysis. Egbunu et al (2021) used Random Forest (RF) to predict climatic changes, helping farmers prepare in advance to avoid the influence of the climate variations; therefore, the yield of crops would certainly be boosted. A binary Logistic Regression (LR) model built by Omotesho et al (2016) was established to identify factors affecting Nigeria's household food security.…”