Land productivity and land availability for growing bioenergy crop in the Contiguous US. Center for Advanced Bioenergy and Bioproducts Innovation (CABBI)
Cite this dataset:
Yang, Pan; Zhao, Qiankun; Cai, Ximing (2019): Land productivity and land availability for growing bioenergy crop in the Contiguous US. Center for Advanced Bioenergy and Bioproducts Innovation (CABBI). University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-4584681_V1
Use this persistent URL to link to this dataset:
Dataset Description

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

Subject Life Sciences
Keywords Land productivity;marginal land;land use
License CC BY
Funder U.S. Department of Energy (DOE)-Grant:DE-SC0018420
Corresponding Creator Ximing Cai
Downloaded 1687 times
Version DOI Comment Publication Date
1 10.13012/B2IDB-4584681_V1 2019-12-10
Open in Globus File Manager what's this?
5.55 KB File
15.7 MB File
13.2 MB File
18.1 MB File
16.6 MB File
14 MB File
1.24 GB File
1.24 GB File
1.24 GB File
1.24 GB File
1.24 GB File
1.24 GB File
6.45 KB File

Contact the Research Data Service for help interpreting this log.

RelatedMaterial create: {"material_type"=>"Article", "availability"=>nil, "link"=>"https://doi.org/10.1088/1748-9326/ab865f", "uri"=>"10.1088/1748-9326/ab865f", "uri_type"=>"DOI", "citation"=>"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", "dataset_id"=>1154, "selected_type"=>"Article", "datacite_list"=>"IsSupplementTo"} 2020-04-23T21:37:55Z
Dataset update: {"keywords"=>["Land productivity; marginal land; land use", "Land productivity;marginal land;land use"]} 2019-12-12T20:05:55Z