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Please use this identifier to cite or link to this item:
http://hdl.handle.net/10174/39349
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Title: | MontadoDB: A Comprehensive Dataset of Pasture Parameters in the Southern Region of Portugal |
Authors: | Rodrigues, Samuel Defalque, Guilherme Serrano, João Santos, Ricardo |
Keywords: | Satellite remote sensing pasture moisture content crude protein neutral detergent fiber biomass |
Issue Date: | 2025 |
Publisher: | Elsevier |
Citation: | Rodrigues, Samuel; Defalque, Guilherme; Serrano, João; Santos, Ricardo (2025). MontadoDB: A Comprehensive Dataset of Pasture Parameters in the Southern Region of Portugal. Data in Brief, Elsevier, 62, 112029. https://doi.org/10.1016/j.dib.2025.112029
Aceite para publicação em 28/08/2025 |
Abstract: | This article presents MontadoDB, a dataset comprising samples of pasture quality parameters,
weather data, and satellite images from a set of paddocks in the Alentejo region (southern Portugal),
the Beira Interior region (Portugal), and Extremadura (Spain). These regions are part of the Montado
system, an agro-silvo-pastoral system featuring low-density trees, pastures, and livestock grazing.
The pasture parameters were collected along three vegetative cycles from 2018 to 2021 in 8 different
paddocks. After the collecting procedure, pasture samples were submitted to laboratory analysis to
determine the reference values of pasture moisture content (PMC, in %), crude protein (CP, in g/100g),
and neutral detergent fiber (NDF, in g/100g), using standard analytical methods. For each pasture
sample collected, GPS coordinates were recorded. Using these coordinates, the Google Earth Engine
(GEE) platform was utilized to collect satellite multispectral data and weather information. The
MontaDB dataset is publicly available, enabling research focused on the analysis and design of
machine learning models for predicting pasture parameters. It can also work as a reference dataset
for further experiments based on pasture samples, thereby enhancing research on advanced
algorithms that rely on large-scale datasets. |
URI: | http://hdl.handle.net/10174/39349 |
Type: | article |
Appears in Collections: | MED - Publicações - Artigos em Revistas Internacionais Com Arbitragem Científica
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