Pasture Productivity Assessment under Mob Grazing and Fertility Management Using Satellite and UAS Imagery

  1. Sangjan, Worasit
  2. Carpenter-Boggs, Lynne A.
  3. Hudson, Tipton D.
  4. Sankaran, Sindhuja
  5. González Aguilera, Diego ed. lit. 1
  6. Rodríguez-Gonzálvez, Pablo ed. lit.
  1. 1 Universidad de Salamanca
    info

    Universidad de Salamanca

    Salamanca, España

    ROR https://ror.org/02f40zc51

Revista:
Drones

ISSN: 2504-446X

Año de publicación: 2022

Volumen: 6

Número: 9

Páginas: 232

Tipo: Artículo

DOI: 10.3390/DRONES6090232 GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Drones

Resumen

Pasture management approaches can determine the productivity, sustainability, and ecological balance of livestock production. Sensing techniques potentially provide methods to assess the performance of different grazing practices that are more labor and time efficient than traditional methods (e.g., soil and crop sampling). This study utilized high-resolution satellite and unmanned aerial system (UAS) imagery to evaluate vegetation characteristics of a pasture field location with two grazing densities (low and high, applied in the years 2015–2019) and four fertility treatments (control, manure, mineral, and compost tea, applied annually in the years 2015–2019). The pasture productivity was assessed through satellite imagery annually from the years 2017 to 2019. The relation and variation within and between the years were evaluated using vegetation indices extracted from satellite and UAS imagery. The data from the two sensing systems (satellite and UAS) demonstrated that grazing density showed a significant effect (p < 0.05) on pasture crop status in 2019. Furthermore, the mean vegetation index data extracted from satellite and UAS imagery (2019) had a high correlation (r ≥ 0.78, p < 0.001). These results show the potential of utilizing satellite and UAS imagery for crop productivity assessment applications in small to medium pasture research and management.

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