ArticleHeliyon2024
Evaluating crop nitrogen status in maize leaves: A predictive modelling approach using chlorophyll fluorescence parameters.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Machine learning techniques for accessing the chlorophyll content and fluorescence in maize based on UAV multispectral sensor.BMC plant biology · 2026Article
- Nitrogen threshold under straw returning: optimizing nitrogen application to synergize the yield and nutritional stoichiometry of maize.Frontiers in plant science · 2026Article
- Efficient but Elusive Rhizobia Fix Nitrogen in the Wild Legumes Bituminaria bituminosa and Coronilla Viminalis.Journal of basic microbiology · 2025Article
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Authors and funding
8 authors.
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Abstract
The consumption of chemical fertilizers has increased eight-fold since the 19th century, outstripping crop yields increases and, emphasizing the need for precise nitrogen (N) assessment in crops to optimize fertilization and mitigate environmental impacts. This study developed a model using chlorophyll fluorescence technology to accurately evaluate the N status in maize leaves while addressing the limitations of current labor-intensive and environmentally sensitive methods. Based on a long-term experiment initiated in 2011, maize hybrid Fumin 985 was sampled in 2021 and 2022 under two crop-straw management strategies (SM: no tillage with surface straw mulch, SP: plough tillage with straw incorporation) and six N application rates. Partial least squares regression (PLSR) models were formulated using chlorophyll fluorescence parameters (ChlF) to assess leaf N content (N leaf). The results indicated that a N application rate of 270 kg ha
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