Field Diagnosis of Potato Nitrogen Nutrition Using a Bayesian Critical Nitrogen Dilution Curve and Canopy Spectral Sensing

Abstract

Accurate diagnosis of potato nitrogen status is critical for optimized fertilizer management and sustaining productivity. We used data from nine field experiments (2010-2018) across major potato-producing regions in northern China to develop a regional critical nitrogen dilution curve via a Bayesian hierarchical model. The curve, Nc = 4.179 × DW -0.417 (DW = whole-plant dry matter), provided the basis for calculating the nitrogen nutrition index (NNI), which was related to canopy spectral indices from a GreenSeeker sensor. Relationships between spectral indices and NNI were strongly growth-stage dependent. The tuber initiation-bulking period, approximately 29-70 days after emergence (DAE), represented the effective phenological window, with 29-55 DAE as the primary operational window for quantitative spectral diagnosis. Stage-specific ratio vegetation index (RVI) showed the most consistent association with NNI, whereas pooled whole-season models had low predictive power. The Bayesian framework quantified uncertainty, emphasizing that near-threshold NNI values require cautious interpretation. The resulting regional-average reference supports rapid field diagnosis of potato N status while accounting for cultivar, year, and site variability. These findings provide practical guidance for stage-specific N management and demonstrate the importance of growth-stage-aware spectral assessment in operational decision-making.

Plant phenotyping relevance

ジャガイモの窒素栄養状態を対象に、Bayesian窒素希釈曲線とキャノピー分光センシングを開発・評価し、成長段階別の診断性能と不確実性を検証しているため、植物表現型取得法が中心である。

Code and data availability

The paper's phenotype data (dry matter, PNC, NNI, GreenSeeker spectral indices from nine field experiments) are explicitly not public: the Data Availability Statement says they are available only on request from the corresponding author. No author analysis code, scripts, models, or supplementary datasets with a public,

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