Authors

Abstract

Bioenergy and bio-based materials are critical for sustainable economic growth under changing climate conditions by reducing our dependence on fossil fuels and fossil-derived materials. The key to the successful development of a sustainable biomass-based economy is an integrated production of food, feed, and fiber and bioenergy crops over finite land resource while protecting wildlife and natural resources. A viable solution would be growing advanced or high-yielding bioenergy crop cultivars that require lower production inputs on marginal areas while keeping commodity and food crops in inherently productive on marginal lands while growing commodity and food crops on inherently productive parts agricultural landscape. Because marginal areas are often small and spread across the agricultural landscape, this proposed cropping system requires rapid, accurate, and cost-effective estimation of biomass yield at harvest time at a plot or sub-field scale. Remotely sensed imagery collected during the growing season has been successfully applied for estimating yields across scales for various crop types. This paper demonstrates (1) the initial investigation of multispectral optical remote sensing for predicting warm-season perennial grass yields at harvest using a linear regression model with a spectral vegetation index, more specifically the Green Normalized Difference Vegetation Index (GDNVI), and (2) the models predictive power for at-harvest dry biomass yields evaluated using five study locations in the U.S. Midwest. The results show that the linear regression model using mid-summer GNDVI predicted at-harvest switchgrass yields with R2 as high as 0.879 and a mean absolute error and root mean squared error as low as 0.592 Mg/ha and 0.539 Mg/ha, respectively, except for the establishment year. The model also indicated at-harvest switchgrass yields may be predicted as early as 152 days before the date of harvest on average, except for the establishment year. Additionally, the study showed that the green band appeared to have a greater contribution for predicting at-harvest switchgrass dry biomass yields than the red band, which is consistent with an increase in chlorophyll content during the early growing season. Although additional testing is warranted, this initial investigation showed a great promise for a remote sensing approach utilizing a spectral vegetation index for forecasting perennial bioenergy grass yields in advance to support the important economic and logistical decisions of farmers, breeders, policy makers, and future biomass refineries.