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Calibration of an Unmanned Aerial Vehicle for Prediction of Herbage Mass in Temperate Pasture

Author

Listed:
  • Celina M. Laplacette

    (Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires C1425FQB, Argentina
    Estación Experimental Agropecuaria Balcarce, Instituto Nacional de Tecnología Agropecuaria (INTA), Balcarce, Buenos Aires 7620, Argentina)

  • Germán D. Berone

    (Estación Experimental Agropecuaria Balcarce, Instituto Nacional de Tecnología Agropecuaria (INTA), Balcarce, Buenos Aires 7620, Argentina
    Facultad de Ciencias Agrarias, Universidad Nacional de Mar del Plata, Balcarce 7620, Argentina)

  • Santiago A. Utsumi

    (Department of Animal and Range Sciences, New Mexico State University, Las Cruces, NM 88003, USA)

  • Juan R. Insua

    (Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires C1425FQB, Argentina
    Facultad de Ciencias Agrarias, Universidad Nacional de Mar del Plata, Balcarce 7620, Argentina)

Abstract

Accurate estimation of herbage mass is crucial for managing pastoral livestock systems. The Normalized Difference Vegetation Index (NDVI) from Unmanned Aerial Vehicle (UAV) sensors shows promise for high-resolution estimations of pasture herbage mass, but it is still unknown how this method differs among forage species, seasons, and pasture management practices. A commercial sensor was calibrated to predict herbage mass using NDVI. Additionally, the effect of different forage species, days of regrowth, and nitrogen (N) status on the relationship between NDVI and herbage mass was evaluated. Two pastures of tall wheatgrass ( Thinopyrum ponticum ) and tall fescue ( Festuca arundinacea ), divided into 30 and 72 plots, respectively, were assessed during spring and autumn regrowth over two years in Balcarce, Argentina. Doses of 0, 50, and 100 kg N ha −1 were applied to tall wheatgrass, and 0, 50, 100, 200, 400, and 600 kg N ha −1 were applied to tall fescue to create variability in herbage mass and N status. Exponential regression models of herbage mass (y) fitted against NDVI (x) showed an average R 2 of 0.83 ± 0.04 and a mean absolute error of 170 ± 60 kg DM ha −1 . The relationship between NDVI and herbage mass differed ( p ≤ 0.05) between species, seasons, and regrowth stage, but was not influenced by N status ( p > 0.05). Results suggest that accurate predictions of herbage mass using NDVI measurements by an UAV require frequent model recalibrations to account for observed differences among forage species, days of regrowth, and years.

Suggested Citation

  • Celina M. Laplacette & Germán D. Berone & Santiago A. Utsumi & Juan R. Insua, 2025. "Calibration of an Unmanned Aerial Vehicle for Prediction of Herbage Mass in Temperate Pasture," Agriculture, MDPI, vol. 15(5), pages 1-17, February.
  • Handle: RePEc:gam:jagris:v:15:y:2025:i:5:p:492-:d:1599254
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    References listed on IDEAS

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    1. Niko Viljanen & Eija Honkavaara & Roope Näsi & Teemu Hakala & Oiva Niemeläinen & Jere Kaivosoja, 2018. "A Novel Machine Learning Method for Estimating Biomass of Grass Swards Using a Photogrammetric Canopy Height Model, Images and Vegetation Indices Captured by a Drone," Agriculture, MDPI, vol. 8(5), pages 1-28, May.
    2. Juan R Insua & Santiago A Utsumi & Bruno Basso, 2019. "Estimation of spatial and temporal variability of pasture growth and digestibility in grazing rotations coupling unmanned aerial vehicle (UAV) with crop simulation models," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-21, March.
    3. François Gastal & Gilles Lemaire, 2015. "Defoliation, Shoot Plasticity, Sward Structure and Herbage Utilization in Pasture: Review of the Underlying Ecophysiological Processes," Agriculture, MDPI, vol. 5(4), pages 1-26, November.
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