Random forest machine learning for maize yield and agronomic efficiency prediction in Ghana.

ABSTRACT: Maize (Zea mays) is an important staple crop for food security in Sub-Saharan Africa. However, there is need to increase production to feed a growing population. In Ghana, this is mainly done by increasing acreage with adverse environmental consequences, rather than yield increment per unit area. Accurate prediction of maize yields and nutrient use efficiency in production is critical to making informed decisions toward economic and ecological sustainability. We trained the random forest machine learning algorithm to predict maize yield and agronomic efficiency in Ghana using soil, climate, environment, and management factors, including fertilizer application. We calibrated and evaluated the performance of the random forest machine learning algorithm using a 5 × 10-fold nested cross

SUBMITTER: Asamoah E 

PROVIDER: S-EPMC11403005 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature