The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.
Simulating the effects of climate and agricultural management practices on global crop yield: SIMULATING GLOBAL CROP YIELD
Open accessAbstract
[1] Climate change is expected to significantly impact global food production, and it is important to understand the potential geographic distribution of yield losses and the means to alleviate them. This study presents a new global crop model, PEGASUS 1.0 (Predicting Ecosystem Goods And Services Using Scenarios) that integrates, in addition to climate, the effect of planting dates and cultivar choices, irrigation, and fertilizer application on crop yield for maize, soybean, and spring wheat. PEGASUS combines carbon dynamics for crops with a surface energy and soil water balance model. It also benefits from the recent development of a suite of global data sets and analyses that serve as model inputs or as calibration data. These include data on crop planting and harvesting dates, crop-specific irrigated areas, a global analysis of yield gaps, and harvested area and yield of major crops. Model results for present-day climate and farm management compare reasonably well with global data. Simulated planting and harvesting dates are within the range of crop calendar observations in more than 75% of the total crop-harvested areas. Correlation of simulated and observed crop yields indicates a weighted coefficient of determination, with the weighting based on crop-harvested area, of 0.81 for maize, 0.66 for soybean, and 0.45 for spring wheat. We found that changes in temperature and precipitation as predicted by global climate models for the 2050s lead to a global yield reduction if planting and harvesting dates remain unchanged. However, adapting planting dates and cultivar choices increases yield in temperate regions and avoids 7–18% of global losses.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Agricultural sustainability and intensive production practices
Tilman, D., Cassman, K. G., Matson, P. A., Naylor, R., & Polasky, S. (2002) · Nature · doi:10.1038/nature01014 · Cited by 7,957
Forecasting Agriculturally Driven Global Environmental Change
Tilman, D., Fargione, J., Wolff, B., D'Antonio, C., Dobson, A., Howarth, R., Schindler, D., Schlesinger, W. H., Simberloff, D., & Swackhamer, D. (2001) · Science · doi:10.1126/science.1057544 · Cited by 3,992
Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change
Schlenker, W., & Roberts, M. J. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0906865106 · Cited by 3,420
An Introduction to Environmental Biophysics
Campbell, G. S. (1977) · Heidelberg Science Library · doi:10.1007/978-1-4684-9917-9 · Cited by 3,185
Prioritizing Climate Change Adaptation Needs for Food Security in 2030
Lobell, D. B., Burke, M. B., Tebaldi, C., Mastrandrea, M. D., Falcon, W. P., & Naylor, R. L. (2008) · Science · doi:10.1126/science.1152339 · Cited by 3,105
A high-resolution data set of surface climate over global land areas
New, M., Lister, D., Hulme, M., & Makin, I. (2002) · Climate Research · doi:10.3354/cr021001 · Cited by 2,246
Adapting agriculture to climate change
Howden, S. M., Soussana, J.-F., Tubiello, F. N., Chhetri, N., Dunlop, M., & Meinke, H. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0701890104 · Cited by 2,124
Statistics for the evaluation and comparison of models
Willmott, C. J., Ackleson, S. G., Davis, R. E., Feddema, J. J., Klink, K. M., Legates, D. R., O'Donnell, J., & Rowe, C. M. (1985) · Journal of Geophysical Research: Oceans · doi:10.1029/jc090ic05p08995 · Cited by 2,026
Monfreda, C., Ramankutty, N., & Foley, J. A. (2008) · Global Biogeochemical Cycles · doi:10.1029/2007gb002947 · Cited by 1,895
Self-citationPotential impact of climate change on world food supply
Rosenzweig, C., & Parry, M. L. (1994) · Nature · doi:10.1038/367133a0 · Cited by 1,891
Cited by
Rosenzweig, C., Elliott, J., Deryng, D., Ruane, A. C., Müller, C., Arneth, A., Boote, K. J., Folberth, C., Glotter, M., Khabarov, N., et al. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1222463110 · Cited by 2,319
Self-citationYield gap analysis with local to global relevance—A review
van Ittersum, M. K., Cassman, K. G., Grassini, P., Wolf, J., Tittonell, P., & Hochman, Z. (2013) · Field Crops Research · doi:10.1016/j.fcr.2012.09.009 · Cited by 1,652
Increase in crop losses to insect pests in a warming climate
Deutsch, C. A., Tewksbury, J. J., Tigchelaar, M., Battisti, D. S., Merrill, S. C., Huey, R. B., & Naylor, R. L. (2018) · Science · doi:10.1126/science.aat3466 · Cited by 1,549
The Inter-Sectoral Impact Model Intercomparison Project (ISI–MIP): Project framework
Warszawski, L., Frieler, K., Huber, V., Piontek, F., Serdeczny, O., & Schewe, J. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1312330110 · Cited by 1,253
The Agricultural Model Intercomparison and Improvement Project (AgMIP): Protocols and pilot studies
Rosenzweig, C., Jones, J. W., Hatfield, J. L., Ruane, A. C., Boote, K. J., Thorburn, P., Antle, J. M., Nelson, G. C., Porter, C., Janssen, S., et al. (2013) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2012.09.011 · Cited by 1,048
Metadata
- Authors
- Deryng, D., Sacks, W. J., Barford, C. C., & Ramankutty, N.
- Venue
- Global Biogeochemical Cycles
- Publisher
- American Geophysical Union (AGU)
- Published
- 2011-05-20
- Volume
- 25
- Issue
- 2
- Pages
- n/a-n/a
- ISSN
- 0886-6236 (ISSN-L)
- OpenAlex
- W1634020409
- Cited by
- 366
- References
- 54
- FWCI
- 21.47
- Type
- journal article
- Language
- en
- Open access
- bronze
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Simulating the effects of climate and agricultural management practices on global crop yield: SIMULATING GLOBAL CROP YIELD. https://app.cochat.ai/papers/10.1029/2009gb003765
- The paper
- Deryng, D., Sacks, W. J., Barford, C. C., & Ramankutty, N. (2011). Simulating the effects of climate and agricultural management practices on global crop yield: SIMULATING GLOBAL CROP YIELD. Global Biogeochemical Cycles. https://doi.org/10.1029/2009gb003765
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.