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<p>Accurate nitrogen management in rice (<i>Oryza sativa</i> L.) is essential for optimizing both crop productivity and environmental sustainability. This study evaluated the potential of Near-Infrared Spectroscopy (NIRS) combined with chemometric modeling to classify leaf nitrogen content (LNC) in five rice genotypes (Nerica, Rufipogon, IR64, Ciherang, and Curinga) subjected to five nitrogen fertilization levels (0%, 25%, 50%, 75%, 100%). Spectral data (350–2500&#xa0;nm) were processed using Principal Component Analysis followed by Linear Discriminant Analysis (PCA-LDA) to distinguish nitrogen treatments and explore genotype-specific spectral responses. The 1700–2200&#xa0;nm spectral region yielded the highest classification accuracy, consistently exceeding 94%, indicating strong sensitivity to nitrogen-related biochemical variation. Compared to conventional destructive methods, NIRS provides a non-invasive, rapid, and scalable alternative for nitrogen assessment in field conditions. This is the first study to demonstrate NIRS-based discrimination of nitrogen levels across multiple rice genotypes, offering new avenues for genotype-informed fertilization strategies and improved nitrogen use efficiency (NUE). The results support the objectives of the Green Campus Initiative at the Alliance Bioversity International &amp; CIAT and contribute to broader Sustainable Development Goals (SDGs 2, 12, 13, and 15), promoting data-driven, environmentally responsible nutrient management in rice production.</p>

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Improving nitrogen use efficiency in rice by estimating leaf nitrogen content with near-infrared spectroscopy and chemometric modeling

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月15日

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  • Audberto Quiroga-Mosquera,
  • Mariana Santos-Rivera,
  • Diego Guzmán-Prada,
  • Jorge Casas,
  • Manabu Ishitani,
  • Michael Gomez Selvaraj

摘要

Accurate nitrogen management in rice (Oryza sativa L.) is essential for optimizing both crop productivity and environmental sustainability. This study evaluated the potential of Near-Infrared Spectroscopy (NIRS) combined with chemometric modeling to classify leaf nitrogen content (LNC) in five rice genotypes (Nerica, Rufipogon, IR64, Ciherang, and Curinga) subjected to five nitrogen fertilization levels (0%, 25%, 50%, 75%, 100%). Spectral data (350–2500 nm) were processed using Principal Component Analysis followed by Linear Discriminant Analysis (PCA-LDA) to distinguish nitrogen treatments and explore genotype-specific spectral responses. The 1700–2200 nm spectral region yielded the highest classification accuracy, consistently exceeding 94%, indicating strong sensitivity to nitrogen-related biochemical variation. Compared to conventional destructive methods, NIRS provides a non-invasive, rapid, and scalable alternative for nitrogen assessment in field conditions. This is the first study to demonstrate NIRS-based discrimination of nitrogen levels across multiple rice genotypes, offering new avenues for genotype-informed fertilization strategies and improved nitrogen use efficiency (NUE). The results support the objectives of the Green Campus Initiative at the Alliance Bioversity International & CIAT and contribute to broader Sustainable Development Goals (SDGs 2, 12, 13, and 15), promoting data-driven, environmentally responsible nutrient management in rice production.