Pasture Quality Assessment through NDVI Obtained by Remote Sensing: A Validation Study in the Mediterranean Silvo-Pastoral Ecosystem

João Serrano (), Shakib Shahidian, Luís Paixão, José Marques da Silva and Luís Lorenzo Paniágua
Additional contact information
João Serrano: MED—Mediterranean Institute for Agriculture, Environment and Development and CHANGE—Global Change and Sustainability Institute, University of Évora, Mitra, Ap. 94, 7006-554 Évora, Portugal
Shakib Shahidian: MED—Mediterranean Institute for Agriculture, Environment and Development and CHANGE—Global Change and Sustainability Institute, University of Évora, Mitra, Ap. 94, 7006-554 Évora, Portugal
Luís Paixão: AgroInsider Lda., 7005-841 Évora, Portugal
José Marques da Silva: MED—Mediterranean Institute for Agriculture, Environment and Development and CHANGE—Global Change and Sustainability Institute, University of Évora, Mitra, Ap. 94, 7006-554 Évora, Portugal
Luís Lorenzo Paniágua: Escuela de Ingenierías Agrarias, Universidad de Extremadura, Avenida Adolfo Suárez, S/N, 06007 Badajoz, Spain

Agriculture, 2024, vol. 14, issue 8, 1-21

Abstract: Monitoring the evolution of pasture availability and quality throughout the growing season is the basis of grazing management in extensive Mediterranean livestock systems. Remote sensing (RS) is an innovative tool that, among many other applications, is being developed for detailed spatial and temporal pasture quality assessment. The aim of the present study is to evaluate the potential of satellite images (Sentinel-2) to assess indicators of pasture quality (pasture moisture content, PMC, crude protein, CP and neutral detergent fiber, NDF) using the normalized difference vegetation index (NDVI). Field measurements were conducted over three years at eight representative fields of the biodiversity and variability of dryland pastures in Portugal. A total of 656 georeferenced pasture samples were collected and processed in the laboratory. The results show a significant correlation between pasture quality parameters (PMC, CP and NDF) obtained in standard laboratory methods and NDVI satellite-derived data (R 2 of 0.72, 0.75, and 0.50, respectively). The promising findings obtained in this large-scale validation study (three years and eight fields) encourage further research (i) to test and develop other vegetation indexes for monitoring pasture nutritive value; (ii) to extend this research to pastures of the other Mediterranean countries, building large and representative datasets and developing more robust and accurate monitoring models based on freely available Sentinel-2 images; (iii) to implement an extension program for agricultural managers to popularize the use of these technological tools as the basis of grazing and pasture management.

Keywords: Sentinel-2; vegetation index; pasture nutritive value; degradation; Montado (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/14/8/1350/pdf (application/pdf)
https://www.mdpi.com/2077-0472/14/8/1350/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:14:y:2024:i:8:p:1350-:d:1455196

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().