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Estimation of Micronutrients in Maize yield in Malawi using Machine Learning and spatially explicit environmental data
Bian, Xinran (2025) Estimation of Micronutrients in Maize yield in Malawi using Machine Learning and spatially explicit environmental data.
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| Abstract: | Micronutrient deficiencies in staple crops contribute to “hidden hunger” across sub-Saharan Africa, yet field measurements of grain nutrient content remain spatially sparse and costly. This study develops a framework for predicting four key micronutrients, calcium (Ca), iron (Fe), zinc (Zn) and selenium (Se), in Malawian maize grain by combining one-time ground observations with 61 terrain, climate, soil, remote-sensing variables, and Global Agro-Ecological Zones. An XGBoost algorithm was trained for each micronutrient using spatial cross-validation approach to address the potential spatial autocorrelation bias. Model skill varied: test R² reached 0.15 for Ca, 0.14 for Zn, 0.08 for Fe and was negligible for Se; RMSEs were 18.9 mg kg⁻¹ (Ca), 25.1 mg kg⁻¹ (Fe), 3.7 mg kg⁻¹ (Zn) and 0.03 mg kg⁻¹ (Se). Elevation, precipitation, top-soil pH and organic carbon strongly influenced Ca, Fe and Zn predictions, while radar-derived canopy metrics dominated Se. The spatial distribution of the maize nutrient revealed a south-to-north decrease in micronutrient levels, aligning with the known gradients in soil pH, organic matter and climate. By exploiting low-cost geodata, the framework can guide site-specific fertiliser use, deployment of biofortified varieties and nutrition programmes, helping Malawian authorities combat hidden hunger and progress toward Sustainable Development Goal. |
| Item Type: | Essay (Master) |
| Faculty: | ITC: Faculty of Geo-information Science and Earth Observation |
| Subject: | 43 environmental science |
| Programme: | Geo-informatics MSc (60031) |
| Link to this item: | https://purl.utwente.nl/essays/107286 |
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