Prediction of spatial heterogeneity in nutrient-limited sub-tropical maize yield: Implications for precision management in the eastern Indo-Gangetic Plains
Year: 2024
Type: article
Abstract: Inadequate knowledge of factors affecting sub-tropical maize yield has constrained efforts to develop precision nutrient management recommendations to reduce input costs and increase yield and profits with reduced environmental externalities. We analyzed data from nutrient omission plot trials (NOPTs) conducted in 324 farmers' fields across ten agro-ecological zones (AEZs) in the Eastern Indo-Gangetic Plains (EIGP) of Bangladesh to explain maize yield variability and identify variables controlling nutrient-limited yields. An Additive Main Effect and Multiplicative Interaction (AMMI) model was used to explain maize yield variability to nutrient addition. Interpretable machine learning (ML) algorithms in automatic machine earning (AutoML) frameworks were subsequently used to rank variables that control yields limited by N, P, K, or Zn relative to NPKZn fertilization. The stack-ensemble model was identified as the best-performing model for predicting RYs of N, P, and Zn. In contrast, deep learning outperformed all base learners for predicting RYK. The best model's means square errors (RMSEs) were 0.122, 0.105, 0.123, and 0.104 for RYN, RYP, RYK, and RYZn, respectively. The permutation-based feature importance technique identified soil pH as the most critical variable controlling RYN and RYP. The RYK showed lower in the eastern longitudinal direction. Soil N and Zn were associated with RYZn. The predicted median RY of N, P, K, and Zn, representing average soil fertility, was 0.51, 0.84, 0.87, and 0.97, accounting for 44, 54, 54, and 48% upland dry season crop area of Bangladesh, respectively. Efforts are needed to update databases cataloging variability in land type inundation classes, soil characteristics, and INS and combine them with farmers' crop management information to develop more precise nutrient guidelines for maize in the EIGP.
Authors: Zia Uddin Ahmed, Timothy J. Krupnik, Jagadish Timsina, Saiful Islam, Khaled Hossain, A.S.M. Alanuzzaman Kurishi, Shah-Al Emran, M. Harun-Ar-Rashid, Andrew J. McDonald, Mahesh K. Gathala
Language: English
Subfield: Soil Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $1,100
OpenAlex ID: W4403319518