A novel protocol for assessment of aboveground biomass in rangeland environments

Abstract

Current methods to measure aboveground biomass (AGB) do not deliver adequate results in relation to the extent and spatial variability that characterise rangelands. An optimised protocol for the assessment of AGB is presented that enables calibration and validation of remote-sensing imagery or plant growth models at suitable scales. The protocol combines a limited number of destructive samples with non-destructive measurements including normalised difference vegetation index (NDVI), canopy height and visual scores of AGB. A total of 19 sites were sampled four times during two growing seasons. Fresh and dry matter weights of dead and green components of AGB were recorded. Similarity of responses allowed grouping into Open plains sites dominated by annual grasses, Bunch grass sites dominated by perennial grasses and Spinifex (Triodia spp.) sites. Relationships between non-destructive measurements and AGB were evaluated with a simple linear regression per vegetation type. Multiple regression models were first used to identify outliers and then cross-validated using a 'Leave-One-Out' and 'Leave-Site-Out' (LSO) approach on datasets including and excluding the identified outliers. Combining all non-destructive measurements into one single regression model per vegetation type provided strong relationships for all seasons for total and green AGB (adjusted R 2 values of 0.65-0.90) for datasets excluding outliers. The model provided accurate assessments of total AGB in heterogeneous environments for Bunch grass and Spinifex sites (LSO-Q 2 values of 0.70-0.88), whereas assessment of green AGB was accurate for all vegetation types (LSO-Q 2 values of 0.62-0.84). The protocol described can be applied at a range of scales while considerably reducing sampling time.

Relationships between dry matter yield and height of rotationally grazed dryland lucerne

“…A range of non-destructive methods have been assessed. These include visual observation, pasture height, pasture capacitance meters, rising plate meters, canopy/ground cover, board height and remote sensing technologies (Earle & McGowan 1979;Griggs & Stringer 1988;Litherland et al 2008;Michalk & Herbert 1977;Mundava et al 2015;Piggot 1988).…”

A new temporal prediction method of grazing pressure based on normalized difference vegetation index and precipitation using nonlinear autoregressive with exogenous input networks

“…In this study region, the values of U t , C o , H a , S f and D, respectively, estimated as 0.8, 0.5, 0.6, 2 (dry weight of 14% water content of herbage) and 365 were obtained by Su et al (). A represents the aboveground biomass of grassland, which can be calculated as (Hu, Li, & Zhou, ; Mundava et al, ),where I N represents the NDVI in the study area. Using Equations , the grazing pressure can be analytically calculated using the NDVI.…”

“…Su et al (2003). A represents the aboveground biomass of grassland, which can be calculated as (Hu, Li, & Zhou, 2014;Mundava et al, 2015), where I N represents the NDVI in the study area. Using Equations…”