The world is faced with a difficult multiple challenge of meeting nutritional, energy, and other basic needs, under a limited land and water budget, of between 9 and 10 billion people in the next three decades, mitigating impacts of climate change, and making agricultural production resilient. More productivity is expected from agricultural lands, but intensification of production could further impact the integrity of our finite surface water and groundwater resources. Integrating perennial bioenergy crops in agricultural lands could provide biomass for biofuel and potential improvements on the sustainability of commodity crop production. This article provides an overview of ways in which research has shown that perennial bioenergy grasses and short rotation woody crops can be incorporated into agricultural production systems with reduced indirect land use change, while increasing water quality benefits. Current challenges and opportunities as well as future directions are also highlighted. WIREs Energy Environ 2018, 7:e275. doi: 10.1002/wene.275 This article is categorized under: Bioenergy > Climate and Environment Bioenergy > Systems and Infrastructure

As the global population increases and becomes more affluent, biomass demands for food and biomaterials will increase. Demand growth is further accelerated by the implementation of climate policies and strategies to replace fossil resources with biomass. There are, however, concerns about the size of the prospective biomass demand and the environmental and social consequences of the corresponding resource mobilization, especially concerning impacts from the associated land-use change. Strategically integrating perennials into landscapes dominated by intensive agriculture can, for example, improve biodiversity, reduce soil erosion and nutrient emissions to water, increase soil carbon, enhance pollination, and avoid or mitigate flooding

A sustainable bioeconomy would require growing high-yielding bioenergy crops on marginal agricultural areas with minimal inputs. To determine the cost competitiveness and environmental sustainability of such production systems, reliably estimating biomass yield is critical. However, because marginal areas are often small and spread across the landscape, yield estimation using traditional approaches is costly and time-consuming. This paper demonstrates the (1) initial investigation of optical remote sensing for predicting perennial bioenergy grass yields at harvest using a linear regression model with the green normalized difference vegetation index (GNDVI) derived from Sentinel-2 imagery and (2) evaluation of the model’s performance using data from five U.S. Midwest field sites. The linear regression model using midsummer GNDVI predicted yields at harvest with R2 as high as 0.879 and a mean absolute error and root mean squared error as low as 0.539 Mg/ha and 0.616 Mg/ha, respectively, except for the establishment year. Perennial bioenergy grass yields may be predicted 152 days before the harvest date on average, except for the establishment year. The green spectral band showed a greater contribution for predicting yields than the red band, which is indicative of increased chlorophyll content during the early growing season. Although additional testing is warranted, this study showed a great promise for a remote sensing approach for forecasting perennial bioenergy grass yields to support critical economic and logistical decisions of bioeconomy stakeholders.

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