Land Productivity and Land Availability for Growing Bioenergy Crop in the Contiguous U.S.
CABBI Theme: Sustainability
Keywords: Economics, Modeling
Yang, P., Zhao, Q., Cai, X. Dec. 10, 2019. “Land productivity and land availability for growing bioenergy crop in the Contiguous U.S. Center for Advanced Bioenergy and Bioproducts Innovation (CABBI).” University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-4584681_V1
The dataset consists of two types of data: the estimate of land productivity (the maximum productivity, MP) and the estimate of land that has low productivity for any major crops planted in the Contiguous United States and then may be available for growing bioenergy crops (the marginal land, ML). All data items are in GeoTiff format, under the World Geodetic System (WGS) 84 project, and with a resolution of 0.0020810045 degree (~250 m).
The MP values are calculated based on machine learning model estimated yields of major crops in the CONUS, and its expected value (MP_mean.tif), and associated uncertainty (MP_IDP.tif). The ML availability data have two versions: a deterministic version and a version with uncertainty. The deterministic MLs are determined as the land pixels with expected MP values falling in the range defined in the following criteria, and the MLs with uncertainty are determined as the probability that the MP value of a land pixel falls in the range defined in the following criteria:
Criteria ____ Description
S1 ________ Current crop and pasture land with MP <= P50
S2 ________ Current crop and pasture land with MP <= P25
S3 ________ S1 + current grass and shrub land with P25 < MP < P50
S4 ________ S2 + current grass and shrub land with P10 < MP < P25
Economic ___ Current crop and pasture land with potential profitability < 0
Here P10, P25 and P50 are the 10th, 25th and 50th percentile of crop MP values
Productivity and Availability Data
Yang, P., Zhao, Q., Cai, X. April 3, 2020. “Machine Learning-Based Estimation of Land Productivity in the Contiguous U.S. Using Biophysical Predictors.” Environmental Research Letters. DOI: 10.1088/1748-9326/ab865f.