Abstract
Over the last 15 years, new important developments occurred in the reference method and use of optical sensors dedicated to in-season potato crop nitrogen (N) status (CNS) monitoring. The reference method is based on N nutrition index (NNI) defined as the ratio of critical N concentration (%Nc) and actual plant N concentration (%N) during the growing season, %Nc being deduced from the critical N dilution curve (CNDC) in total crop biomass. New statistical insights on the 11 different existing potato CNDCs worldwide, considered as site- and time-specific because they are built on specific combinations of potato cultivars (G), environment (E) and crop management practices (M), led to the assumption that it could be possible to derive more generic CNDCs providing large sets of data (pairs of biomass and plant N% assessments) from different G×E×M situations. Practical application of the reference method for CNS assessment is labour intensive and time consuming due to plant sampling and laboratory analysis for N content. However, a more generic NNI reference method is valuable to define threshold or critical values of plant N-related biophysical or biochemical variables (BVs) that can be retrieved from optical sensor readings and used as quick indicators for CNS assessment. Optical sensors are classically used to measure leaf or canopy transmittance, reflectance and chlorophyll fluorescence of visible (VIS) and near-infrared (NIR) wavelengths of the electromagnetic spectrum and related to leaf chlorophyll content (LCC) demonstrated as strongly correlated to leaf N content (LNC). Optical sensors applied at the leaf level are mainly handheld devices based on transmittance and chlorophyll fluorescence and have specific advantages and weaknesses that are extensively described to derive BVs and to assess potato CNS. Their main application is dedicated for CNS assessment at small field scale showing low within-field variability. Reflectance-based, remotely sensed optical devices are applied at canopy level from different ground-based, airborne or spaceborne platforms. Ground-based and airborne approaches, combined with different reflectance data analysis and processing mathematical and statistical methods (vegetation indices, chemometrics, machine learning) or physically-based approach such as inversion of radiative transfer models, allowed reliable retrieve of N-related BVs for CNS assessment such as LCC, canopy chlorophyll content (CCC), leaf area index (LAI) or others BVs. Remote sensing from spaceborne platforms, with the recent and forthcoming launches of satellite missions equipped with multispectral and hyperspectral sensors and with enhanced spatial, temporal, spectral and radiometric resolutions in the VIS and NIR regions but also shortwave infrared (SWIR) region allowing to consider plant N related to leaf proteins, have opened new avenues for real-time and reliable CNS monitoring at field and within-field scale over large territories and based on the setup of generic N-related BVs across different growing conditions and multiple G×E×M scenarios.
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Jean-Pierre Goffart: conceptualization, formal analysis and investigation, literature and reference investigation, resources, data collection and analysis, writing—original draft preparation, writing—review and editing, visualisation. Feriel Ben Abdallah: resources, data supply, writing—review and editing. Dimitri Goffart: resources, data supply, writing—review and editing. Yannick Curnel: writing—review and editing. Viviane Planchon: writing—review and editing.
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Goffart, J.P., Ben Abdallah, F., Goffart, D. et al. Potato Crop Nitrogen Status Monitoring for Sustainable N Fertilisation Management: Last 15 Years and Future-Expected Developments with Reference Method and Use of Optical Sensors. Potato Res. 66, 1257–1303 (2023). https://doi.org/10.1007/s11540-023-09644-6
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DOI: https://doi.org/10.1007/s11540-023-09644-6