Abstract
Remote sensing based grassland carrying capacity assessments are not commonly applied in rangeland management. Possible reasons for this include non-equilibrium thinking in rangeland management, and the costliness of existing remotely sensed biomass estimation that carrying capacity assessments require. Here, we present a less demanding approach for grassland biomass estimation using the MODIS Net Primary Production (NPP) product and demonstrate its use in carrying capacity assessment over the mountain grasslands of Azerbaijan. Based on publicly available estimates of the fraction of total NPP partitioned to aboveground NPP (fANPP) we calculate the aboveground biomass produced from 2005 to 2014. Validation of the predicted aboveground biomass with independent field biomass data collected in 2007 and 2008 confirmed the accuracy of the aboveground biomass product and hence we considered it appropriate for further use in the carrying capacity assessment. A first assessment approach, which allowed for consumption of 65% of aboveground biomass, resulted in an average carrying capacity of 12.6 sheep per ha. A second more realistic approach, which further restricted grazing on slopes steeper than 10%, resulted in a stocking density of 6.20 sheep per ha and a carrying capacity of 3.93 million sheep. Our analysis reveals overgrazing of the mountain grasslands because the current livestock population which consists of at least 8 million sheep, 0.5 million goats and an unknown number of cattle exceeds the predicted carrying capacity of 3.93 million sheep. We consider that the geographically explicit advice on sustainable stocking densities is particularly attractive to regulate grazing intensity in geographically varied terrain such as the mountain grasslands of Azerbaijan. We further conclude that the approach, given its generic nature and the free availability of most input data, could be replicated elsewhere. Hence, we advise considering its use where traditional carrying capacity assessments are difficult to implement.
NSTL
Elsevier