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Data from: Predicting spatial-temporal patterns of diet quality and large herbivore performance using satellite time series

Analysis-ready tabular data from "Predicting spatial-temporal patterns of diet quality and large herbivore performance using satellite time series" in Ecological Applications, Kearney et al., 2021. Data is tabular data only, summarized to the pasture scale. Weight gain data for individual cattle and the STARFM-derived Landsat-MODIS fusion imagery can be made available upon request.

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FieldValue
Tags
cattle weight gain
Remote Sensing
diet quality
forage quality
data fusion
landsat
MODIS
rangeland
Grassland
NP215
Modified
2022-09-22
Release Date
2021-06-14
Identifier
abdc0d72-2073-4721-8d0c-0f0d8a497bf3
Spatial / Geographical Coverage Area
POLYGON ((-104.77746963501 40.799110428089, -104.77746963501 40.866389645718, -104.67962265015 40.866389645718, -104.67962265015 40.799110428089))
Publisher
Ag Data Commons
Spatial / Geographical Coverage Location
Central Plains Experimental Range, CO
Temporal Coverage
May 1, 2000 to October 31, 2019
License
Creative Commons CCZero
Data Dictionary

https://data.nal.usda.gov/dataset/data-predicting-spatial-temporal-patte...
Contact Name
Kearney, Sean P.
Contact Email
sean.kearney@usda.gov
Public Access Level
Public
Program Code
005:040 - Department of Agriculture - National Research
Bureau Code
005:00 - Department of Agriculture
Extended Metadata: 
FieldValue
Authors
Kearney, Sean P. (ORCID)
Porensky, Lauren M. (ORCID)
Augustine, David J. (ORCID)
Derner, Justin D. (ORCID)
Gao, Feng (ORCID)
Peer Reviewed
No
Intended Use
Data is made available to facilitate replication of statistical analysis of the associated publication.
Use Limitations
Data is tabular data only summarized to the pasture scale. Individual cattle data and satellite imagery can be made available upon request.
Funding Sources
Agricultural Research Service
3012-21610-003-00-D
Primary Article

Kearney, S. P., Porensky, L. M., Augustine, D. J., Derner, J. D., & Gao, F. (2022). Predicting spatial‐temporal patterns of diet quality and large herbivore performance using satellite time series. Ecological Applications, 32(2). https://doi.org/10.1002/eap.2503

AgID: 7690677
Method Citation

Gaffney, R., Porensky, L., Gao, F., Irisarri, J., Durante, M., Derner, J., & Augustine, D. (2018). Using APAR to Predict Aboveground Plant Productivity in Semi-Aid Rangelands: Spatial and Temporal Relationships Differ. Remote Sensing, 10(9), 1474. https://doi.org/10.3390/rs10091474
Dataset DOI (digital object identifier)
10.15482/USDA.ADC/1522609
Product Type
Dataset
Cites Other Datasets
STARFM
Related Content
A New Way to Predict Grazing Cattle Weight Gain on Rangelands From Satellite Imagery
FieldValue
OSTI Identifier
ISO Topic(s)
biota
farming
State or Territory
Colorado
National Agricultural Library Thesaurus Term
nutritional adequacy
herbivores
time series analysis
pastures
weight gain
cattle
remote sensing
forage quality
Landsat
rangelands
grasslands
crude protein
organic matter
average daily gain
stocking rate
models
grazing
yearlings
soil types
plant communities
topography
Ag Data Commons Keywords
  • Animals & Livestock
  • Animal production
  • Range management
  • Animals & Livestock
  • Animal production
  • Animals & Livestock
  • Agroecosystems & Environment
  • Geography
  • Remote sensing
  • Agroecosystems & Environment
  • Geography
  • Agroecosystems & Environment
  • Plant and animal
  • Agroecosystems & Environment
  • Management
  • Agroecosystems & Environment

Suggested Citations:

(original article) Kearney, S. P., Porensky, L. M., Augustine, D. J., Derner, J. D., & Gao, F. (2022). Predicting spatial‐temporal patterns of diet quality and large herbivore performance using satellite time series. Ecological Applications, 32(2). https://doi.org/10.1002/eap.2503

(dataset) Kearney, Sean P.; Porensky, Lauren M.; Augustine, David J.; Derner, Justin D.; Gao, Feng (2021). Data from: Predicting spatial-temporal patterns of diet quality and large herbivore performance using satellite time series. Ag Data Commons. https://doi.org/10.15482/USDA.ADC/1522609. Accessed 2023-11-29.