Optimising nitrogen nutrition index (NNI) for maize cultivation with controlled release fertilizer treatments guided by UAV remote sensing technology

Ma, H, Li, X, Sun, Y, Cui, J, Li, Y, Feng, B, Falsone, G and Di Bonito, M ORCID: https://orcid.org/0000-0001-8590-0267, 2026. Optimising nitrogen nutrition index (NNI) for maize cultivation with controlled release fertilizer treatments guided by UAV remote sensing technology. Precision Agriculture, 27 (2): 30. ISSN 1385-2256

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Abstract

Purpose: Efficient nitrogen (N) management is essential for improving maize yield and resource use efficiency. The nitrogen nutrition index (NNI) is a common tool to assess crop N status. However, its response under controlled-release fertilizer (CRF) management is still not clear. This study evaluated how different N fertilizer types affect maize NNI. It also tested the use of UAV multispectral data for accurate N monitoring.

Methods: A two-year field experiment with five N treatments was conducted to evaluate the effects of fertilizer type on the maize’s CNDC and the NNI at the V6, VT and R2 growth stages. CNDCs and NNI models were developed from leaf area index (LAI) and aboveground biomass (AGB) and compared using Akaike (AIC) and Bayesian (BIC) information criteria. A gradient boosting decision tree (GBDT) model was built from UAV multispectral data.

Results: CRFs significantly improved N distribution uniformity and model accuracy. CRF had stronger effects on LAI than on AGB, but NNI based on AGB better reflected actual N balance. Based on accumulated N deficit conditions characterised using AIC and BIC information criteria, a fertilisation range of 150–225 kg ha− 1 was determined. Furthermore, a CNDC model established using measured NNI_AGB was implemented to utilise unmanned aerial vehicles for predicting NNI. The GBDT model based on UAV multispectral data and by screening sensitive variables through variance inflation factors (VIF) achieved the highest accuracy and stability (R2 = 0.88, RMSE = 0.04, nRMSE = 4.98%).

Conclusion: CRF management combined with UAV-based monitoring offers an effective way to diagnose maize N status. This approach supports precise N application and sustainable agriculture.

Item Type:	Journal article
Publication Title:	Precision Agriculture
Creators:	Ma, H., Li, X., Sun, Y., Cui, J., Li, Y., Feng, B., Falsone, G. and Di Bonito, M.
Publisher:	Springer
Date:	April 2026
Volume:	27
Number:	2
ISSN:	1385-2256
Identifiers:	Number Type 10.1007/s11119-026-10329-6 DOI 2581973 Other
Rights:	This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s11119-026-10329-6
Divisions:	Schools > School of Animal, Rural and Environmental Sciences
Record created by:	Jonathan Gallacher
Date Added:	09 Mar 2026 12:18
Last Modified:	09 Mar 2026 12:25
URI:	https://irep.ntu.ac.uk/id/eprint/55377

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