Development of a platform for pasture management in the dehesa

The GRASSEN operational group aims to develop a platform to support pasture management in the dehesa; integrating information from various types of remote and proximal sensors; combined with field data. Key recommendations include analyzing experiences in sensorisation and remote detection; compiling georeferenced data on Mediterranean pastures; and developing mathematical models to estimate pasture production; structure; and quality. Results obtained so far include a list of the most common pasture varieties in the region; reports on cultivated pasture types; protocols for image analysis using different methods (SENTINEL-2; unmanned flights; multispectral cameras); and mathematical models to estimate pasture production; structure; and quality. These findings are crucial for improving pasture management in the dehesa; enabling managers to make informed decisions to maximize productivity and sustainability in this important agroforestry system.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-05-09
DOI
https://doi.org/10.5281/zenodo.15370453
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
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article

Development of a platform for pasture management in the dehesa

Zenodo (CERN European Organization for Nuclear Research)
Remote Sensing in Agriculture
article

Development of a platform for pasture management in the dehesa

article en

Abstract

The GRASSEN operational group aims to develop a platform to support pasture management in the dehesa; integrating information from various types of remote and proximal sensors; combined with field data. Key recommendations include analyzing experiences in sensorisation and remote detection; compiling georeferenced data on Mediterranean pastures; and developing mathematical models to estimate pasture production; structure; and quality. Results obtained so far include a list of the most common pasture varieties in the region; reports on cultivated pasture types; protocols for image analysis using different methods (SENTINEL-2; unmanned flights; multispectral cameras); and mathematical models to estimate pasture production; structure; and quality. These findings are crucial for improving pasture management in the dehesa; enabling managers to make informed decisions to maximize productivity and sustainability in this important agroforestry system.

Zenodo (CERN European Organization for Nuclear Research)
Universidade de Santiago de Compostela (ES)
Openalex Percentile: Top 54%
Remote Sensing in Agriculture
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