Cited by 135Bioenergy crop production and managementClimate change impacts on agricultureAgricultural Productivity and Crop Improvement

The DOI matches its Crossref record; 33 of the 35 references it cites were found in a registry or an index.

Potential production and environmental effects of switchgrass and traditional crops under current and greenhouse-altered climate in the central United States: a simulation study

Brown, R.A, Rosenberg, N.J, Hays, C.J, Easterling, W.E, Mearns, L.O·2000·Agriculture, Ecosystems & Environment

Overview

Integrity signals

Registry

Crossref · 10.1016/s0167-8809(99)00115-2

Cites

33 of 33 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Perennial grass, especially switchgrass, could reduce sediment yield in streamflow and soil erosion and increase water use and infiltration regardless of climate conditions in the loess gully areas of the Chinese Loess Plateau.

    “the perennial grass, especially the switchgrass, could reduce the sediment yield in streamflow and soil erosion and increase the water use and infiltration regardless the climate conditions in the loess gully areas of the Chinese Loess Plateau”

    From Bioenergy production and environmental impacts (2018) · cites this paper as Brown et al. 2000

Citations

Cites

Flagged first, then by citations

2 of the 35 references couldn’t be matched to a known paper.

Research works printed but not linked: 8

  1. Brown, R.A., Rosenberg, N.J., 1996. The potential for biomass energy production in the Missouri–Iowa–Nebraska–Kansas (MINK) region. In: Proc. of the 1995 Society of American Foresters Convention, Society of American Foresters, Bethesda, MD, pp. 204–209.
  2. Kiniry, J.R., Spanel, D.A., Williams, J.R., Jones, C.A., 1990. Demonstration and validation of crop grain yield simulation by EPIC. In: Sharpley, A.N., Williams, J.R. (Eds.), EPIC – Erosion Productivity Impact Calculator, Model Documentation, USDA-ARS Tech. Bull. No. 1768, USDA-ARS Grassland, Soil and Water Research Laboratory, Temple, TX, pp. 220–234.
  3. Mearns, L.O., Giorgi F., Easterling, W.E., Weiss, A., 1996. Development of a nested regional model for the conterminous US and formation of high resolution climate change scenarios with an application to crop-climate models. In: Toward an Integrated Regional Research Program on Global Change and the Nation’s Major Grasslands, Report GPRC 96-01, Great Plains Reg. Center for Global Env. Change, Inst. of Agriculture and Nat. Resources, Univ. of Nebraska, Lincoln, NE, pp. 47–50.
  4. Richardson, C.W., Nicks, A.D., 1990. Weather generator description. In: Sharpley, A.N., Williams, J.R. (Eds.), EPIC – Erosion Productivity Impact Calculator, Model Documentation, USDA-ARS Tech. Bull. No. 1768, USDA-ARS Grassland, Soil and Water Research Laboratory, Temple, TX, pp. 93–104.
  5. Rosenberg, N.J. (Ed.), 1993. A methodology for assessing regional agricultural consequences of climate change: application to the Missouri–Iowa–Nebraska–Kansas (MINK) region. Agric. and For. Meteorology 59, 1–127.
  6. Rosenzweig, C., 1989. Potential effects of climate change on agricultural production in the Great Plains: a simulation study. In: Smith, J.B., Tripak, D. (Eds.), The Potential Effects of Global Climate Change on the United States, Appendix C, vol. 1. United States Environmental Protection Agency, Office of Policy, Planning, and Evaluation, Washington, DC
  7. Watterson, 1995, The CSIRO nine-level atmospheric general circulation model and its equilibrium present and doubled CO2 climates, Aust. Met. Mag., 44, 111
  8. Williams, J.R., Dyke, P.T., Fuchs, W.W., Benson, V.W., Rice, O.W., Taylor, E.D., 1990. EPIC – erosion productivity impact calculator: users manual. In: Sharpley, A.N., Williams, J.R. (Eds.), EPIC – Erosion Productivity Impact Calculator, Model Documentation, USDA-ARS Tech. Bull. No. 1768, USDA-ARS Grassland, Soil and Water Research Laboratory, Temple, TX, 127 pp.

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Metadata

Authors
Brown, R. A., Rosenberg, N. J., Hays, C. J., Easterling, W. E., & Mearns, L. O.
Affiliations
  • Pacific Northwest National Laboratory (US)
  • University of Nebraska–Lincoln (US)
  • Pennsylvania State University (US)
  • NSF National Center for Atmospheric Research (US)
Corresponding author
Brown, R.A
Venue
Agriculture, Ecosystems & Environment
Publisher
Elsevier BV
Published
2000-03
Volume
78
Issue
1
Pages
31-47
ISSN
0167-8809 (ISSN-L)
OpenAlex
W2164702246
Cited by
135
References
35
Citations by year
2023: 2 · 2021: 5 · 2020: 3 · 2019: 4 · 2018: 4 · 2017: 6 · 2016: 3 · 2015: 10 · 2014: 17 · 2013: 10 · 2012: 9
FWCI
11.64
Type
journal article
Language
en
Open access
closed
Locations
  • Agriculture Ecosystems & Environment (publishedVersion)
  • Lincoln (University of Nebraska) (submittedVersion)
Funders
  • U.S. Department of Energy
  • Pacific Northwest National Laboratory
Topics
  • Life Sciences › Agricultural and Biological Sciences › Agronomy and Crop Science › Bioenergy crop production and management
  • Life Sciences › Agricultural and Biological Sciences › Agronomy and Crop Science › Climate change impacts on agriculture
  • Life Sciences › Agricultural and Biological Sciences › Agronomy and Crop Science › Agricultural Productivity and Crop Improvement
Keywords
switchgrass, energy crops, climate effects on crop yield, elevated CO2, CO2 fertilization, heat stress, cold stress, crop water use, perennial grasses, maize, sorghum, soybean, winter wheat, EPIC model, land use conversion, biofuels

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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