Predictive models for assessing 'Spartina argentinensis' biomass
Vast areas of Argentina are covered by rangelands, which provide important ecosystem services. Traditionally, livestock farming has been the main productive activity in these environments, with the use of fire as a common management practice. Although burning stimulates the regrowth of grasses with...
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Artículo revista |
| Lenguaje: | Español |
| Publicado: |
Facultad de Ciencias Agronómicas - UNR
2025
|
| Materias: | |
| Acceso en línea: | https://cienciasagronomicas.unr.edu.ar/index.php/agro/article/view/98 |
| Aporte de: |
| Sumario: | Vast areas of Argentina are covered by rangelands, which provide important ecosystem services. Traditionally, livestock farming has been the main productive activity in these environments, with the use of fire as a common management practice. Although burning stimulates the regrowth of grasses with better forage quality, it also poses environmental challenges due to the emission of CO2eq into the atmosphere. In response to the growing demand for renewable energy sources, rangelands under livestock production systems with frequent burning present a sustainable alternative for bioenergy production. In the province of Santa Fe, rangelands dominated by Spartina argentinensis in the Bajos Submeridionales region, which covers more than two million hectares, have high potential for bioenergy production without compromising the existing biodiversity. This work focuses on the development and evaluation of predictive models for S. argentinensis biomass using spectral images obtained by drones. Multiple linear regression and classification models were developed, considering spectral variables, site, and seasons, to predict total biomass and its green and senescent fractions. The models obtained to estimate the total biomass of Spartina explained up to 62% of its variability. The green and senescent biomass fractions were predicted with greater accuracy, showing an R² of 66% for both. These findings highlight the potential of remote sensing technology to optimize the planning and sustainable management of biomass resources in Argentine grasslands. |
|---|