Project
Machine learning for fertilizer recommendation in Ghana
PhD project Eric Asamoah
As conventional methods to generate fertilizer recommendations are time consuming, we will develop a machine-learning approach for a site-specific fertilizer recommendation strategy that will be relevant for all actors involved in maize production in Ghana, while accounting for uncertainties in model predictions.
Information on site-specific fertilizer recommendations for efficient nutrient use and yield prediction prior to harvesting is critical for adequate planning along the agricultural value chain in Ghana. Conventional methods to generate fertilizer recommendations are time consuming, expensive and practically not possible to cover large areas, hence unable to arrive at accurate site- and crop-specificity. Among such existing methods are data collected from surveys, the soil-crop simulation models such as the Decision Support System for Agrotechnology Transfer (DSSAT) and descriptive empirical models such as Quantitative Evaluation of the Fertility of Tropical Soils (QUEFTS).
In this study, a novel approach that addresses prevailing challenges to existing methods is proposed. A machine learning model will be developed and tested for maize to derive fertilizer recommendations to improve the yields and ensure efficient nutrient use in Ghana. We develop and apply a Random Forest model that predicts nutrient use efficiencies and its associated yields from maize yield datasets. We will derive fertiliser recommendations from the calibrated Random Forest model and compare with recommendations derived from existing mechanistic and empirical methods such as DSSAT and QUEFTS. Furthermore, we will develop other machine learning models such as the support vector machine, extreme gradient boosting and the K nearest neighbors algorithms and evaluate their predicted nutrient use efficiencies and associated fertiliser recommendations. We will then develop a fertilizer recommendation strategy that accounts for uncertainties in the model predictions that will be relevant for actors in the fertiliser value chain.
The results from this research will be highly relevant to actors in the fertilizer value chain for decision making to boost maize production in Ghana.
Partners:
ISRIC -- World soil information
Université Mohammed VI Polytechnique (Morocco)
Kwame Nkrumah University of Science and Technology (Ghana)
International Fertilizer Development Center