Towards improving farmers livelihood in Nigeria using food price forecasting
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Date
2025-12
Publication Type
Journal Article
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yes
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Abstract
Nigeria's agricultural sector represents approximately 25 % of the country's overall GDP and is a major source of employment for its population. This sector is largely driven by smallholder farmers who grow fruits and vegetables on farms under 4 ha. Despite their significant contribution to food production in Nigeria, most smallholder farmers, approximately 70 %, live in poverty, earning less than $1.9 per day. One of the key factors contributing to this situation is a lack of access to market price information. Farmers currently rely only on historical prices observed in local markets to decide on when, what, where and the price to sell their produce. This can lead to suboptimal decisions, resulting in food loss and loss of potential income. To address this challenge, we developed a machine learning online pipeline. It utilizes a Random Forest model trained on historical monthly fresh produce prices and other macroeconomic factors like currency exchange rates for Nigeria, that are regularly scraped from the internet. We deployed our trained model through an open-source mobile application, Coldtivate. Our model accurately predicted market prices for crops such as tomatoes, onions, potatoes, and plantains in various Nigerian states. The prediction success rate of our model varied across the various states in Nigeria. It ranged from 1 % to 20 % in Mean Absolute Percentage Error (MAPE) for predictions up to 8 months ahead. When evaluated on a hold-out test set, it yielded an RMSE of ₦45.16. The average MAPE of our model, when considering state-time-commodity averages, is up to 5 % lower than other baseline models, including the benchmark rolling-average, CatBoost, XGBoost, and SARIMA. By detecting patterns and trends in food prices, farmers can use our tool to make more informed decisions about when and what to sell to optimize profit, thereby improving revenue. Furthermore, our model provides a foundation for future machine learning model development in food price forecasting in agrarian countries.
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published
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Journal / series
Volume
24
Pages / Article No.
102365
Publisher
Elsevier
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Software
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Subject
Food price forecasting; Market intelligence; Online machine learning; Smallholders; Farmer’s income; Food waste; Food loss