Development of a model for estimating the biomass of pastures using normalized difference vegetation index (NDVI): the case of the Niassa pastoral zone in Burkina Faso

In Burkina Faso, livestock feeding is based on pasture productivity, which is a key factor in managing livestock productivity. As such, this management requires a good knowledge of pastures. However, the high cost of producing pasture data and the inaccessibility of pastures due to insecurity mean that there is a low availability or lack of data on pasture. This makes it difficult for stakeholders to take decisions to improve livestock feed in the event of a local food crisis. This is why this study aims to facilitate data production by developing a model for estimating herbaceous biomass in pastures using land observation tools. To this end, the study correlated biomass data collected on the ground with satellite imagery. A total of 24 the wooded, shrubby, and grassy savannahs and gallery forests vegetation units were used as ground observation stations. Simple multilinear regression was chosen for the correlation test. The model parameters gave an R² of 0.70, an RMSE of 236.7 kg DM/ha with a p-value of less than 0.0001 and an average production of 1,423 kg DM/ha. Based on these parameters, there is a strong relationship between vegetation indices and pasture biomass. The equation Y=45*Vav+9*Vmx-19*Rrg-2,256 derived from the model can be used to estimate pasture herbaceous biomass. This will improve the availability of data on pasture forage resources for better action planning in a context of insecurity.

Authors

Publication Details

Journal
GSC Advanced Research and Reviews
Published
2024-08-23
DOI
https://doi.org/10.30574/gscarr.2024.20.2.0289
Primary Topic
Agriculture and Rural Development Research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Development of a model for estimating the biomass of pastures using normalized difference vegetation index (NDVI): the case of the Niassa pastoral zone in Burkina Faso

Wièmè Some, André Kiema, Abroulaye Sanfo, G. Bambara et al.
GSC Advanced Research and Reviews
Agriculture and Rural Development Research
article

Development of a model for estimating the biomass of pastures using normalized difference vegetation index (NDVI): the case of the Niassa pastoral zone in Burkina Faso

Wièmè Some, André Kiema, Abroulaye Sanfo, G. Bambara, Valérie M.C. Bougouma-Yameogo
article en

Abstract

In Burkina Faso, livestock feeding is based on pasture productivity, which is a key factor in managing livestock productivity. As such, this management requires a good knowledge of pastures. However, the high cost of producing pasture data and the inaccessibility of pastures due to insecurity mean that there is a low availability or lack of data on pasture. This makes it difficult for stakeholders to take decisions to improve livestock feed in the event of a local food crisis. This is why this study aims to facilitate data production by developing a model for estimating herbaceous biomass in pastures using land observation tools. To this end, the study correlated biomass data collected on the ground with satellite imagery. A total of 24 the wooded, shrubby, and grassy savannahs and gallery forests vegetation units were used as ground observation stations. Simple multilinear regression was chosen for the correlation test. The model parameters gave an R² of 0.70, an RMSE of 236.7 kg DM/ha with a p-value of less than 0.0001 and an average production of 1,423 kg DM/ha. Based on these parameters, there is a strong relationship between vegetation indices and pasture biomass. The equation Y=45*Vav+9*Vmx-19*Rrg-2,256 derived from the model can be used to estimate pasture herbaceous biomass. This will improve the availability of data on pasture forage resources for better action planning in a context of insecurity.

GSC Advanced Research and ReviewsVol. 20(2)
Zero hunger
Openalex Percentile: Top 90%
Agriculture and Rural Development Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.