Remote sensing for real time estimate of aboveground biomass productivity in mountain pasture - Ronchi B.; Primi R. (Innovation for sustainability in sheep and goats, 2019)
Precision grazing may increase the farm business productivity, through improved pasture and animal management. One of the main issue is to estimate the real productivity of the pasture, in order to program the correct grazing load at each moment of the year. The aim of the study was to verify if the data acquired by the new European satellite system named “Sentinel 2” could be used for estimation of aboveground biomass in a mountain pasture. During 2016 growing season, in a Central Italy mountain pasture (M. Tilia, Rieti), we built nine grazing exclusion fences (12 m x 12 m), including the Sentinel 2 acquisition grid (10 m x 10 m pixel). Inside each plot, we cut grass (5 m2) once a month for three months. The grass was weighed and analyzed for chemical and physical parameters. At the dates matching with cuts, free-of-charge red and infrared bands were acquired from Sentinel 2 data hub, in order to calculate NDVI (Normalized Difference Vegetation Index). In a GIS environment, we extract NDVI values for each plot. To explore how NDVI relate to biomass, linear regression analyses were performed. NDVI, varying from 0.41 to 0.83, showed strong linear relationships with green biomass (R2 = 0.6356). A lower relationship was observed between NDVI and dried biomass (R2 = 0.4678). The model, after being further refined, can be used to spatialise data over vast grazing areas, with high temporal frequency (5-10 days), helping in a more precise planning of livestock grazing.
2. Joint Seminar of the Subnetworks on Nutrition and on Production Systems of the FAO-CIHEAM Network for Research and Development in Sheep and Goats, 2017/10/03-05, Vitoria-Gasteiz (Spain)