Development of a weather-based predictive system for optimizing cassava and cocoa crops yields for farmers in Ondo state
DOI:
https://doi.org/10.51459/jostir.2025.1.Special-Issue.0228Keywords:
agriculture, crop production, predictive model, XGBoost, gradient boostingAbstract
Agriculture is a very crucial sector in the Nigerian economy, it provides food and income to millions of residents. However, the increasing unpredictability of climate regimes which is a direct effect of anthropogenic climate change has a highly negative impact on the total agricultural output in terms of crop yield. This paper aims at creating a predictive model based on weather, which maximizes the productivity of cassava and cocoa in Ondo State, Nigeria. Kaggle repository data on temporal crop yield were collected from 1991 to 2020 using meteorological measures provided by NASA POWER, this weather data was used to gain a calibration of forecasting models. XGBoost (Gradient Boosting) machine learning model was used to develop salient climatic factors like precipitation, temperature, and soil moisture. The findings show that the cassava model had moderate predictability (R² = 0.61) and the cocoa model had less predictability (R² = 0.55). The system seeks to provide the agricultural sector stakeholders with actionable knowledge, which will empower the stakeholders to make evidence-based decisions on how to advance agricultural production and strengthen agric-food systems during climatic disturbances. It follows that the developed predictive system have a significant contribution to sustainable agricultural development and food security enhancement across the region.
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