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Remote Sensing Dynamics for Analyzing Nitrogen Impact on Rice Yield in Limited Environments

Author

Listed:
  • David Fita

    (Department de Producción Vegetal, Universitat Politècnica de València, 46022 Valencia, Spain
    These authors contributed equally to this work.)

  • Alberto San Bautista

    (Department de Producción Vegetal, Universitat Politècnica de València, 46022 Valencia, Spain
    These authors contributed equally to this work.)

  • Sergio Castiñeira-Ibáñez

    (Centro de Tecnologías Físicas, Universitat Politècnica de València, 46022 Valencia, Spain)

  • Belén Franch

    (Global Change Unit, Image Processing Laboratory, Universitat de València, 46980 Valencia, Spain
    Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA)

  • Concha Domingo

    (Instituto Valenciano de Investigaciones Agrarias, 46113 Valencia, Spain)

  • Constanza Rubio

    (Centro de Tecnologías Físicas, Universitat Politècnica de València, 46022 Valencia, Spain)

Abstract

Rice production remains highly dependent on nitrogen (N). There is no positive linear correlation between N concentration and yield in rice cultivation because an excess of N can unbalance the distribution of photo-assimilates in the plant and consequently produce a lower yield. We intended to study these imbalances. Remote sensing is a useful tool for monitoring rice crops. The purpose of this study was to evaluate the effectiveness of using remote sensing to assess the impact of N applications on rice crop behavior. An experiment with three different doses (120, 170 and 220 kg N·ha −1 ) was carried out over two years (2021 and 2022) in Valencia, Spain. Biomass, Leaf Area Index (LAI), plants per m 2 , yield, N concentration and N uptake were determined. Moreover, reflectance values in the green, red, and NIR bands of the Sentinel-2 satellite were acquired. The two data matrices were merged in a correlation study and the resulting interpretation ended in a protocol for the evaluation of the N effect during the main phenological stages. The positive effect of N on the measured parameters was observed in both years; however, in the second year, the correlations with the yield were low, being attributed to a complex interaction with climatic conditions. Yield dependence on N was optimally evaluated and monitored with Sentinel-2 data. Two separate relationships between NIR–red and NDVI–NIR were identified, suggesting that using remote sensing data can help enhance rice crop management by adjusting nitrogen input based on plant nitrogen concentration and yield estimates. This method has the potential to decrease nitrogen use and environmental pollution, promoting more sustainable rice cultivation practices.

Suggested Citation

  • David Fita & Alberto San Bautista & Sergio Castiñeira-Ibáñez & Belén Franch & Concha Domingo & Constanza Rubio, 2024. "Remote Sensing Dynamics for Analyzing Nitrogen Impact on Rice Yield in Limited Environments," Agriculture, MDPI, vol. 14(10), pages 1-24, October.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:10:p:1753-:d:1492376
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    References listed on IDEAS

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    2. Bouman, B.A.M. & van Laar, H.H., 2006. "Description and evaluation of the rice growth model ORYZA2000 under nitrogen-limited conditions," Agricultural Systems, Elsevier, vol. 87(3), pages 249-273, March.
    3. Gao, Ya & Sun, Chen & Ramos, Tiago B. & Huo, Zailin & Huang, Guanhua & Xu, Xu, 2023. "Modeling nitrogen dynamics and biomass production in rice paddy fields of cold regions with the ORYZA-N model," Ecological Modelling, Elsevier, vol. 475(C).
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