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In-Season Potato Crop Nitrogen Status Assessment from Satellite and Meteorological Data

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A Correction to this article was published on 21 September 2023

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Abstract

For a conventional potato crop, splitting nitrogen (N) application is recognised as an efficient strategy to improve tuber yield and quality and to mitigate N losses to the environment. This approach requires the assessment of in-season crop N status for decisions on supplemental mineral N fertiliser application. This study focuses on the assessment of potato crop biophysical variables useful to establish crop N status. Field, satellite and meteorological data were collected in farmer’s fields during 3 years (2017–2019) with contrasted meteorological conditions. Degree days (DD) and water balance from planting date were computed from meteo data, and a selection of relevant vegetation indices (VIs) was derived from Sentinel-2 reflectance. Multiple linear regression (MLR) and random forest regression (RFR) models predicting shoots biomass, shoots N content and shoots N uptake from a combination of meteo and/or satellite-based variables were defined and evaluated. The best combinations integrate DD and two to four VIs and perform with cross-validation RMSE of about 0.38 DM t ha−1, 0.41%, 21 kg ha−1 for MLR and 0.32 DM t ha−1, 0.31%, 19 kg ha−1 for RFR. Despite these performances, MLR was shown to be more robust. From these estimated variables, two methods are proposed to derive total N uptake and nitrogen nutrition index. The most relevant method uses shoots N uptake and biomass. It allows future estimation of in-season supplemental N fertiliser to be applied to reach a targeted tuber yield.

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Data Availability

Data is stored on Walloon Agricultural Research Center (CRA-W) servers and can be made available on demand through specific agreement.

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Acknowledgements

Many thanks to all the farmers that allowed us to sample their fields, to the CRA-W crew who contributed to the field work (Amaury Leclef, Gregory Cloux, Daniel Deloze, William Philippe), to Alban Jago (CRA-W) for the processing of the L2A and L3B level Sentinel-2 images, to Yves Brostaux for its expert advices on random forest algorithm and to Belgian Scientific Policy (BELSPO) for the funding of this work. R (R Core Team, 2018) was used for the data processing. Thanks to the whole team and community for developing such a powerful and versatile tool.

Author Contribution (CRediT)

Dimitri Goffart: conceptualisation, methodology, software, formal analysis, investigation, data curation, writing—original draft, writing—review and editing, visualisation. Feriel Ben Abdallah: conceptualisation, methodology, resources, writing—review and editing. Yannick Curnel: conceptualisation, methodology, writing—review and editing, supervision, funding acquisition. Viviane Planchon: conceptualisation, methodology, resources, project administration, funding acquisition. Jean-Pierre Goffart: conceptualisation, methodology, resources, writing—review and editing, supervision, funding acquisition. Pierre Defourny: conceptualisation, writing—review and editing, supervision, funding acquisition

Code Availability

R code used for the data processing, analysis and figures production is a custom code written for the only purpose of this paper.

Funding

This work was supported by the SPP Politique scientifique–BELSPO under Grants SR/00/300–BELCAM and SR/42/203–STARGATE.

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Authors and Affiliations

  1. Agriculture, Territory and Technology Integration Unit, Walloon Agricultural Research Centre, 5030, Gembloux, Belgium

    D. Goffart, Y. Curnel & V. Planchon

  2. Crop Production Unit, Walloon Agricultural Research Centre, 5030, Gembloux, Belgium

    F. Ben Abdallah

  3. Earth and Life Institute, Université Catholique de Louvain, 1348, Louvain-la-Neuve, Belgium

    P. Defourny

  4. General Direction, Walloon Agricultural Research Centre, 5030, Gembloux, Belgium

    J.-P. Goffart

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  1. D. Goffart
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Appendix

Appendix

Table 3 For each explained shoots variables and each regression type, classification of the ten best combinations of explanatory variables used to build empirical models according to the predictive RMSE from a tenfold cross-validation procedure (RMSECV)
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Goffart, D., Abdallah, F.B., Curnel, Y. et al. In-Season Potato Crop Nitrogen Status Assessment from Satellite and Meteorological Data. Potato Res. 65, 729–755 (2022). https://doi.org/10.1007/s11540-022-09545-0

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