Sustainable livestock farming: Estimating forage biomass with RPAS and 3D modeling

Eduardo Tacuri Espinoza; Mateo López Espinoza; Alberto  Macancela Herrera; Lucía Lupercio Novillo

doi:10.17268/sci.agropecu.2026.024

Autores

Eduardo Tacuri Espinoza Laboratory of Geomatic, Agricultural Sciences Faculty, University of Cuenca. Av. 12 de Octubre y Diego de Tapia, Cuenca, Ecuador https://orcid.org/0000-0002-4094-209X
Mateo López Espinoza Laboratorio de Geomática, Facultad de Ciencias Agropecuarias, Universidad de Cuenca https://orcid.org/0000-0002-6996-8701
Alberto Macancela Herrera Laboratorio de Geomática, Facultad de Ciencias Agropecuarias, Universidad de Cuenca https://orcid.org/0000-0003-1461-4364
Lucía Lupercio Novillo G.R.A.S.S. Research Group, Agricultural Sciences Faculty, University of Cuenca. Av. 12 de Octubre y Diego de Tapia, Cuenca, Ecuador https://orcid.org/0000-0002-4798-6108

DOI:

https://doi.org/10.17268/sci.agropecu.2026.024

Palavras-chave:

grass height, grass volume, pasture mixture, structure from motion, drone

Resumo

An alternative to support sustainable and technological livestock farming is using aerial images through Remotely Piloted Aircraft Systems (RPAS). This method has demonstrated effective outcomes in assessing agricultural variables including height, volume, and biomass across vegetation and crops like pastures. The study was carried out at Nero farm in southern Ecuador. The objectives of this research were: i) demonstrate the validity of the aerial imagery method with traditional field methods for characterizing grassland agronomic parameters (height, volume, and biomass) and ii) evaluate which of the variables studied (height and volume) is the best predictor of grass fresh mass and dry mass. The first methodology consists of collecting in filed (paddock) height and volume of grass using a frame of 1 m², then biomass was measured in laboratory. For the second method, aerial images were obtained through RPAS and processed to generate digital surface model (DSM) and digital terrain model (DTM). Finally, linear models were performed with respective R² and error. The height and volume of grass of both methods represent up to 98% of data variability (p < 0.0001), also, the measures of central tendency and dispersion were so similar. Regarding the models of fresh and dry mass with height and volume digital of grass representing over 40% (p < 0.05), the digital height being the best predictor for dry (R²: 48%) and fresh mass (R²: 42%). This research revalidates the effectiveness use of aerial images in important crops from Ecuador.

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