Seasonal energy storage using bioenergy production from abandoned croplands

Abstract

Bioenergy has the unique potential to provide a dispatchable and carbon-negative component to renewable energy portfolios. However, the sustainability, spatial distribution, and capacity for bioenergy are critically dependent on highly uncertain land-use impacts of biomass agriculture. Biomass cultivation on abandoned agriculture lands is thought to reduce land-use impacts relative to biomass production on currently used croplands. While coarse global estimates of abandoned agriculture lands have been used for large-scale bioenergy assessments, more practical technological and policy applications will require regional, high-resolution information on land availability. Here, we present US county-level estimates of the magnitude and distribution of abandoned cropland and potential bioenergy production on this land using remote sensing data, agriculture inventories, and land-use modeling. These abandoned land estimates are 61% larger than previous estimates for the US, mainly due to the coarse resolution of data applied in previous studies. We apply the land availability results to consider the capacity of biomass electricity to meet the seasonal energy storage requirement in a national energy system that is dominated by wind and solar electricity production. Bioenergy from abandoned croplands can supply most of the seasonal storage needs for a range of energy production scenarios, regions, and biomass yield estimates. These data provide the basis for further down-scaling using models of spatially gridded land-use areas as well as a range of applications for the exploration of bioenergy sustainability.

A Review of the Application of Remote Sensing Data for Abandoned Agricultural Land Identification with Focus on Central and Eastern Europe

“…Other authors performed several methodological approaches for the classification of AAL. For this purpose, we refer to these studies as other combined methods for AAL identification using RS datasets: agricultural inventory with LU models [60], long-term inventory data with a cropland density map [30], multiple classification models [28], expert opinion with biophysical models [66], light detection and ranging (LiDAR) data and digital elevation model with ancillary data [63], hybrid classification: unsupervised iterative self-organizing data analysis technique (ISODATA) classification algorithm combined with supervised maximum likelihood algorithm [42], and ISODATA combined with the supervised classification approach and multitemporal images [96].…”

Cellulosic biofuel contributions to a sustainable energy future: Choices and outcomes

“…Marginal lands have considerable potential to produce cellulosic feedstocks from successional vegetation or perennial grasses, and their use can forestall food-fuel conflicts, including those resulting from ILUC effects (24,(97)(98)(99). Marginal lands denote nonforested lands that are not wetlands, are not used for row crops or livestock, but are sufficiently productive and accessible for bioenergy production.…”

Land‐based climate solutions for the United States

“…Perennial cellulosic bioenergy lands include 41 Mha of the 70–100 Mha of former cropland still unforested (Bandaru et al, 2015; Campbell et al, 2013), planted grasslands now enrolled in the USDA Conservation Reserve Program, and lands now used to grow corn for grain ethanol production. Cellulosic biofuels from perennial crops offer >5 times the climate benefit of grain‐based fuels, with CO 2 e emissions reductions relative to gasoline >100% as compared to corn grain ethanol's <20%, and as well numerous co‐benefits such as soil and water conservation and biodiversity enhancement (Mosier, Córdova, et al, 2021).…”