Using the dry matter productivity model as an estimator of biomass production in native grassland communities

Abstract

Estimating biomass production in the grassland ecosystem is useful for applications such as determining how much forage is produced for wildlife and livestock, quantifying carbon stocks, monitoring the consequences of climate change, detecting land use change, and determining losses focusing on agricultural insurance. Historically, production estimates required field work in which the biomass went through the collection, drying, and weighing process. Despite being a common process that generates accurate results, this type of activity is time consuming, has high financial costs and has limited geographic coverage. To overcome these limitations, remote sensing emerges as a viable alternative, since the systematic imaging of the Earth allows the characterization of the canopy and the extraction of information that can be useful to estimate biomass production. The main objective of this study is to provide a more accurate method to estimate biomass production in native grasslands communities in three Canadian Prairies ecoregions, as well as to investigate the relationship between biomass production and the dry matter productivity (DMP) model and propose adjustments to increase the prediction power of the model. The highest R value (between biomass and DMP) was observed in the Moist-Mixed Ecoregion (R = 0.71) (R² = 0.50) and the lowest was observed in the Mixed Ecoregion (R = 0.66) (R² = 0.43). By correlating three vegetation indices derived from satellite images (the enhanced vegetation index, the green normalized difference vegetation index (GNDVI), and the normalized difference vegetation index (NDVI)), we identified that NDVI had the best performance to estimate biomass production in the study area. However, by including other variables such as the fraction of absorbed photosynthetically active radiation (FPAR), the leaf area index (LAI), precipitation, and temperature, the NDVI was surpassed by the LAI. It is worth noting that, the best results for estimating the biomass production were obtained via DMP (except for the Mixed Ecoregion where the LAI had a greater correlation with biomass production than the DMP). By including variables such as annual mean temperature and mean annual NDVI as biomass production penalty tools, the prediction power of the DMP increased by 19.10% in the Fescue Ecoregion, 5.47% in the Moist-Mixed Ecoregion, and 20.19% in the Mixed Ecoregion.