Abstract
Timely monitoring of plant biomass is critical for the management of forage resources in Sahelian rangelands. The estimation of annual biomass production in the Sahel is based on a simple relationship between satellite annual Normalized Difference Vegetation Index (NDVI) and in situ biomass data. This study proposes a new methodology using multi-linear models between phenological metrics from the SPOT-VEGETATION time series of Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) and in situ biomass. A model with three variables—large seasonal integral (LINTG), length of growing season, and end of season decreasing rate—performed best (MAE = 605 kg·DM/ha; R2 = 0.68) across Sahelian ecosystems in Senegal (data for the period 1999–2013). A model with annual maximum (PEAK) and start date of season showed similar performances (MAE = 625 kg·DM/ha; R2 = 0.64), allowing a timely estimation of forage availability. The subdivision of the study area in ecoregions increased overall accuracy (MAE = 489.21 kg·DM/ha; R2 = 0.77), indicating that a relation between metrics and ecosystem properties exists. LINTG was the main explanatory variable for woody rangelands with high leaf biomass, whereas for areas dominated by herbaceous vegetation, it was the PEAK metric. The proposed approach outperformed the established biomass NDVI-based product (MAE = 818 kg·DM/ha and R2 = 0.51) and should improve the operational monitoring of forage resources in Sahelian rangelands.
Highlights
Livestock farming is the most widespread human activity and most dominant form of land use in rangeland ecosystems [1]
At the scale of the whole study area, the results confirmed that large seasonal integral (LINTG) was the most important phenological metric, whereas at the ecoregion scale, the maximum Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) (PEAK) was the most important (Figure 4)
The results showed that the model with the three input variables LINTG, LOS, and RDERIV was the most suitable for estimating total biomass production across the study area, with a high adjusted R2, while minimizing the Mean Absolute Error (MAE), indicating good fit and accuracy of the model
Summary
Livestock farming is the most widespread human activity and most dominant form of land use in rangeland ecosystems [1]. Worldwide, it contributes 40% of the agricultural gross domestic product, and provides income for more than 1.3 billion people and nourishment for at least 800 million food-insecure people [2]. Depending on the needs of the agricultural monitoring systems, estimates should be provided as early as possible in the growing season so that stakeholders can take early decisions and identify areas with large variation in (and potential for) vegetation productivity [9]
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