Explainable machine learning driven nutrient recommendation for maize production in Malawi
Addressing the persistent challenge of low maize productivity in Malawi requires spatially explicit and nutrient-specific fertilizer recommendations that align with soil heterogeneity and economic constraints. Existing blanket recommendations often overlook localized variability in soil properties,...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Abstract |
| Language: | Inglés |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/179331 |
| _version_ | 1855526821333827584 |
|---|---|
| author | Liben, Feyera Kihara, Job Abera, Wuletawu AbebeMesfin, Tewodrofins Homann-kee Tui, Sabine Munthali, Moses Munthali, Chandiona Nalivata, Patson Mzumara, Edward Tuimene, Lulseged |
| author_browse | AbebeMesfin, Tewodrofins Abera, Wuletawu Homann-kee Tui, Sabine Kihara, Job Liben, Feyera Munthali, Chandiona Munthali, Moses Mzumara, Edward Nalivata, Patson Tuimene, Lulseged |
| author_facet | Liben, Feyera Kihara, Job Abera, Wuletawu AbebeMesfin, Tewodrofins Homann-kee Tui, Sabine Munthali, Moses Munthali, Chandiona Nalivata, Patson Mzumara, Edward Tuimene, Lulseged |
| author_sort | Liben, Feyera |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Addressing the persistent challenge of low maize productivity in Malawi requires spatially explicit and nutrient-specific fertilizer recommendations that align with soil heterogeneity and economic constraints. Existing blanket recommendations often overlook localized variability in soil properties, climate, and nutrient responses, limiting their effectiveness. This study aimed to (1) evaluate and compare the predictive performance of multiple machine learning algorithms for maize yield estimation in Malawi; (2) identify the most important yield-determining features through recursive feature elimination (RFECV) and SHAP-based interpretation; (3) examine interaction effects between key nutrient inputs and environmental variables using two-dimensional partial dependence plots; and (4) translate model outputs into site-specific nutrient management insights for precision agronomy in smallholder maize systems. A Light Gradient Boosting Machine (LightGBM) model was selected based on performance and interpretability and trained on 20 selected features including nutrients management, soil, climate, topography, and hydrological covariates. Hyperparameter optimization and SHAP analysis enhanced both accuracy and model transparency. We generated fine-scale (250 m resolution) maize fertilizer recommendations for N, P, K, S, and Zn using the optimized LightGBM model. Results revealed substantial spatial variation in nutrient requirements, with nitrogen and phosphorus showing the widest ranges in both agronomic and economic rates. The economic optimum rates were slightly lower than agronomic ones, highlighting the importance of cost-benefit considerations in fertilizer planning. Spatial patterns also indicated that highly weathered soils in southern and central Malawi often required higher inputs, while nutrient-rich areas in the north exhibited lower optimal rates. These findings support a shift from national blanket recommendations to precision-guided input planning using high-resolution geospatial data and data-driven models.
Keywords: Artificial intelligence; machine learning; nutrient; maize; LightGBM; SHAP |
| format | Abstract |
| id | CGSpace179331 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| record_format | dspace |
| spelling | CGSpace1793312025-12-31T02:08:31Z Explainable machine learning driven nutrient recommendation for maize production in Malawi Liben, Feyera Kihara, Job Abera, Wuletawu AbebeMesfin, Tewodrofins Homann-kee Tui, Sabine Munthali, Moses Munthali, Chandiona Nalivata, Patson Mzumara, Edward Tuimene, Lulseged maize machine learning artificial intelligence fertilizer application Addressing the persistent challenge of low maize productivity in Malawi requires spatially explicit and nutrient-specific fertilizer recommendations that align with soil heterogeneity and economic constraints. Existing blanket recommendations often overlook localized variability in soil properties, climate, and nutrient responses, limiting their effectiveness. This study aimed to (1) evaluate and compare the predictive performance of multiple machine learning algorithms for maize yield estimation in Malawi; (2) identify the most important yield-determining features through recursive feature elimination (RFECV) and SHAP-based interpretation; (3) examine interaction effects between key nutrient inputs and environmental variables using two-dimensional partial dependence plots; and (4) translate model outputs into site-specific nutrient management insights for precision agronomy in smallholder maize systems. A Light Gradient Boosting Machine (LightGBM) model was selected based on performance and interpretability and trained on 20 selected features including nutrients management, soil, climate, topography, and hydrological covariates. Hyperparameter optimization and SHAP analysis enhanced both accuracy and model transparency. We generated fine-scale (250 m resolution) maize fertilizer recommendations for N, P, K, S, and Zn using the optimized LightGBM model. Results revealed substantial spatial variation in nutrient requirements, with nitrogen and phosphorus showing the widest ranges in both agronomic and economic rates. The economic optimum rates were slightly lower than agronomic ones, highlighting the importance of cost-benefit considerations in fertilizer planning. Spatial patterns also indicated that highly weathered soils in southern and central Malawi often required higher inputs, while nutrient-rich areas in the north exhibited lower optimal rates. These findings support a shift from national blanket recommendations to precision-guided input planning using high-resolution geospatial data and data-driven models. Keywords: Artificial intelligence; machine learning; nutrient; maize; LightGBM; SHAP 2025-12-22 2025-12-30T15:06:53Z 2025-12-30T15:06:53Z Abstract https://hdl.handle.net/10568/179331 en Open Access application/vnd.openxmlformats-officedocument.wordprocessingml.document Liben, F.; Kihara, J.; Abera, W.; AbebeMesfin, T.; Homann-kee Tui, S.; Munthali, M..; Munthali, C.; Nalivata, P..; Mzumara, E.; Tuimene, L. (2025) Explainable machine learning driven nutrient recommendation for maize production in Malawi. 1 p. |
| spellingShingle | maize machine learning artificial intelligence fertilizer application Liben, Feyera Kihara, Job Abera, Wuletawu AbebeMesfin, Tewodrofins Homann-kee Tui, Sabine Munthali, Moses Munthali, Chandiona Nalivata, Patson Mzumara, Edward Tuimene, Lulseged Explainable machine learning driven nutrient recommendation for maize production in Malawi |
| title | Explainable machine learning driven nutrient recommendation for maize production in Malawi |
| title_full | Explainable machine learning driven nutrient recommendation for maize production in Malawi |
| title_fullStr | Explainable machine learning driven nutrient recommendation for maize production in Malawi |
| title_full_unstemmed | Explainable machine learning driven nutrient recommendation for maize production in Malawi |
| title_short | Explainable machine learning driven nutrient recommendation for maize production in Malawi |
| title_sort | explainable machine learning driven nutrient recommendation for maize production in malawi |
| topic | maize machine learning artificial intelligence fertilizer application |
| url | https://hdl.handle.net/10568/179331 |
| work_keys_str_mv | AT libenfeyera explainablemachinelearningdrivennutrientrecommendationformaizeproductioninmalawi AT kiharajob explainablemachinelearningdrivennutrientrecommendationformaizeproductioninmalawi AT aberawuletawu explainablemachinelearningdrivennutrientrecommendationformaizeproductioninmalawi AT abebemesfintewodrofins explainablemachinelearningdrivennutrientrecommendationformaizeproductioninmalawi AT homannkeetuisabine explainablemachinelearningdrivennutrientrecommendationformaizeproductioninmalawi AT munthalimoses explainablemachinelearningdrivennutrientrecommendationformaizeproductioninmalawi AT munthalichandiona explainablemachinelearningdrivennutrientrecommendationformaizeproductioninmalawi AT nalivatapatson explainablemachinelearningdrivennutrientrecommendationformaizeproductioninmalawi AT mzumaraedward explainablemachinelearningdrivennutrientrecommendationformaizeproductioninmalawi AT tuimenelulseged explainablemachinelearningdrivennutrientrecommendationformaizeproductioninmalawi |