The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Impact of generated solar radiation on simulated crop growth and yield
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
Simulations of total ET, above-ground biomass, and seed cotton yield were similar for generated and measured solar radiation.
“Simulations of total ET, above-ground biomass, and seed cotton yield were similar for generated and measured solar radiation.”
From Development and Application of Process-based Simulation Models for Cotton Production: A Review of Past, Present, and Future Directions (2014) · cites this paper as Garcia y Garcia et al. (2008)
Generated solar radiation data could be reliably used as input to cotton simulation models in locations where measured data were not available.
“They concluded that generated solar radiation data could be reliably used as input to cotton simulation models in locations where measured data were not available.”
From Development and Application of Process-based Simulation Models for Cotton Production: A Review of Past, Present, and Future Directions (2014) · cites this paper as Garcia y Garcia et al. (2008)
Citations
Cites
The DSSAT cropping system model
Jones, J. W., Hoogenboom, G., Porter, C. H., Boote, K. J., Batchelor, W. D., Hunt, L. A., Wilkens, P. W., Singh, U., Gijsman, A. J., & Ritchie, J. T. (2003) · European Journal of Agronomy · doi:10.1016/s1161-0301(02)00107-7 · Cited by 4,465
Self-citationModeling Photosynthesis of Row Crop Canopies
Boote, K. J., & Pickering, N. B. (1994) · HortScience · doi:10.21273/hortsci.29.12.1423 · Cited by 144
Flagged first, then by citations
Research works printed but not linked: 35
- Angstrom, 1924, Solar and terrestrial radiation, Q. J. R. Meteorol. Soc., 50, 121, 10.1002/qj.49705021008 · DOI
- Ball, 2004, Evaluation of solar radiation prediction models in North America, Agron. J., 96, 391, 10.2134/agronj2004.0391 · DOI
- Bert, 2007, Sensitivity of CERES-Maize simulated yields to uncertainty in soil properties and daily solar radiation, Agric. Syst., 94, 150
- Bristow, 1984, On the relationship between incoming solar radiation and daily maximum and minimum temperature, Agric. For. Meteorol., 31, 159, 10.1016/0168-1923(84)90017-0 · DOI
- Cooter, 1995, A solar radiation model for use in biological applications in the south and southeastern USA, Agric. For. Meteorol., 78, 31, 10.1016/0168-1923(95)02241-4 · DOI
- Cooter, 1990, The impact of climate change on continuous corn production in the southern USA, Climatic Change, 16, 53, 10.1007/BF00137346 · DOI
- Donatelli, 2003, RadEst 3.0: software to estimate daily radiation data from commonly available meteorological variables, Eur. J. Agron., 18, 363, 10.1016/S1161-0301(02)00130-2 · DOI
- Dubrovský, 2000, Sensitivity of CERES-Maize yields to statistical structure of daily weather series, Climatic Change, 46, 447, 10.1023/A:1005681809065 · DOI
- Earl, 2003, Effect of drought stress on leaf and whole canopy radiation use efficiency and yield of maize, Agron. J., 95, 688, 10.2134/agronj2003.0688 · DOI
- Elizondo, 1994, Development of a neural network model to predict daily solar radiation, Agric. For. Meteorol., 71, 115, 10.1016/0168-1923(94)90103-1 · DOI
- Fodor, 2003, 4M-software package for modelling cropping systems, Eur. J. Agron., 18, 389, 10.1016/S1161-0301(02)00126-0 · DOI
- Fodor, 2005, Sensitivity of crop models to the inaccuracy of meteorological observations, Phys. Chem. Earth, 30, 53, 10.1016/j.pce.2004.08.020 · DOI
- Garcia y Garcia, 2005, Evaluation of an improved daily solar radiation generator for the southeastern USA, Climate Res., 29, 91, 10.3354/cr029091 · DOI
- Goodin, 1999, Estimating solar irradiance fro crop modeling using daily air temperature data, Agron. J., 91, 845, 10.2134/agronj1999.915845x · DOI
- Grant, 2004, Ability to predict solar radiation values from interpolated climate records for use in crop simulation models, Agric. For. Meteorol., 127, 65, 10.1016/j.agrformet.2004.07.016 · DOI
- Hansen, 1999, Stochastic daily solar irradiance for biological modeling applications, Agric. For. Meteorol., 94, 63
- Hartkamp, 2003, Comparison of three weather generators for crop modeling: a case study for subtropical environments, Agric. Syst., 76, 539, 10.1016/S0308-521X(01)00108-1 · DOI
- Heinemann, 2002, The impact of potential errors in rainfall observation on the simulation of crop growth, development and yield, Ecol. Model., 157, 1, 10.1016/S0304-3800(02)00209-0 · DOI
- Hodges, 1987, Using the CERES-Maize model to estimate production for the U.S. corn belt, Agric. For. Meteorol., 40, 293, 10.1016/0168-1923(87)90043-8 · DOI
- Hoogenboom, 1995, Decision support system to study climate change impacts on crop production, Agron. J., 59, 51
- Hoogenboom, 2000, Contribution of agrometeorology to the simulation of crop production and its applications, Agric. For. Meteorol., 103, 157
- Hubbard, 1994, Spatial variability of daily weather variables in the High Plains of the USA, Agric. For. Meteorol., 68, 41
- Hunt, 2001, Agronomic data: advances in documentation and protocols for exchange and use, Agric. Syst., 70, 477, 10.1016/S0308-521X(01)00056-7 · DOI · Paper page
- Hunt, 1998, Estimation of solar radiation for use in crop modelling, Agric. For. Meteorol., 91, 300
- Kiniry, 1992, A general, process-oriented model for two competing plant species, Trans. ASAE, 35, 801, 10.13031/2013.28665 · DOI
- Kobayashi, 2000, Comparing simulated and measured values using mean squared deviation and its components, Agron. J., 92, 345, 10.2134/agronj2000.922345x · DOI
- Lizaso, 2005, Development of a leaf-level canopy assimilation model for CERES-Maize, Agron. J., 97, 722, 10.2134/agronj2004.0171 · DOI
- Meinke, 1995, Evaluation of radiation and temperature data generators in the Australian tropics and sub-tropics using crop simulation models, Agric. For. Meteorol., 72, 295, 10.1016/0168-1923(94)02159-H · DOI
- Priestley, 1972, On the assessment of surface heat and evaporation using large scale parameters, Mon. Weather Rev., 100, 81, 10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2 · DOI
- Richardson, 1981, Stochastic simulation of daily precipitation, temperature, and solar radiation, Water Resources Res., 17, 182, 10.1029/WR017i001p00182 · DOI
- Rivington, 2005, Evaluation of three model estimations of solar radiation at 24 UK stations, Agric. For. Meteorol., 132, 228, 10.1016/j.agrformet.2005.07.013 · DOI
- Rivington, 2006, Evaluating uncertainty introduced to process-based simulation model estimates by alternative sources of meteorological data, Agric. Syst., 88, 451, 10.1016/j.agsy.2005.07.004 · DOI
- Soltani, 2000, Evaluation of WGEN for generating long term weather data for crop simulations, Agric. For. Meteorol., 102, 1, 10.1016/S0168-1923(00)00100-3 · DOI
- Weiss, 2004, Simulation of daily solar irradiance, Agric. For. Meteorol., 123, 187, 10.1016/j.agrformet.2003.12.002 · DOI
- Xie, 2003, The ALMANAC's model sensitivity to input variables, Agric. Syst., 78, 1, 10.1016/S0308-521X(03)00002-7 · DOI
Cited by
Sorted by citations
Thorp, K. R., Ale, S., Bange, M. P., Barnes, E. M., Hoogenboom, G., Lascano, R. J., McCarthy, A. C., Nair, S., Paz, J. O., Rajan, N., et al. (2014) · Journal of Cotton Science · doi:10.56454/oovc6073 · Cited by 96
Self-citationLi, M., Du, Y., Zhang, F., Bai, Y., Fan, J., Zhang, J., & Chen, S. (2019) · Agricultural Water Management · doi:10.1016/j.agwat.2019.03.041 · Cited by 71
Metadata
- Authors
- Garcia y Garcia, A., Guerra, L. C., & Hoogenboom, G.
- Affiliations
- University of Georgia (US)
- ORCID
- Hoogenboom, Gerrit
- Venue
- Ecological Modelling
- Publisher
- Elsevier BV
- Published
- 2008-01
- Volume
- 210
- Issue
- 3
- Pages
- 312-326
- ISSN
- 0304-3800 (ISSN-L)
- OpenAlex
- W2021319150
- Cited by
- 49
- References
- 2
- Citations by year
- 2024: 1 · 2023: 1 · 2022: 2 · 2021: 1 · 2020: 2 · 2019: 1 · 2018: 1 · 2017: 3 · 2016: 2 · 2015: 3 · 2014: 8 · 2013: 2 · 2012: 5
- FWCI
- 3.42
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Ecological Modelling (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Life Sciences › Agricultural and Biological Sciences › Agronomy and Crop Science › Climate change impacts on agriculture
- Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
- Life Sciences › Agricultural and Biological Sciences › Soil Science › Irrigation Practices and Water Management
- Keywords
- solar radiation estimation, daily solar radiation, crop yield simulation, cotton, maize, groundnut, CERES-Maize, DSSAT, rainfed and irrigated conditions, evapotranspiration, aboveground biomass, model bias
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review