The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Crop Yield Gaps: Their Importance, Magnitudes, and Causes
Abstract
Future trajectories of food prices, food security, and cropland expansion are closely linked to future average crop yields in the major agricultural regions of the world. Because the maximum possible yields achieved in farmers' fields might level off or even decline in many regions over the next few decades, reducing the gap between average and potential yields is critical. In most major irrigated wheat, rice, and maize systems, yields appear to be at or near 80% of yield potential, with no evidence for yields having exceeded this threshold to date. A fundamental constraint in these systems appears to be uncertainty in growing season weather; thus tools to address this uncertainty would likely reduce gaps. Otherwise, short-term prospects for yield gains in irrigated agriculture appear grim without increased yield potential. Average yields in rainfed systems are commonly 50% or less of yield potential, suggesting ample room for improvement, though estimation of yield gaps for rainfed regions is subject to more errors than for irrigated regions. Several priorities for future research are identified.
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
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
Agricultural Intensification and Ecosystem Properties
Matson, P. A., Parton, W. J., Power, A. G., & Swift, M. J. (1997) · Science · doi:10.1126/science.277.5325.504 · Cited by 3,160
Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000
Ramankutty, N., Evan, A. T., Monfreda, C., & Foley, J. A. (2008) · Global Biogeochemical Cycles · doi:10.1029/2007gb002952 · Cited by 1,942
The McCree-de Wit-Penning de Vries-Thornley respiration paradigms: 30 years later.
Amthor, J. (2000) · Annals of Botany · doi:10.1006/anbo.2000.1175 · Cited by 822 · cited 1×
“These include the facts that about half of the light energy is in wavelengths too long to be used in photosynthesis and that all plants need to consume some of the photosynthate they fix in the mitochondrial respiration that powers construction and maintenance of plant tissues (16).”
BackgroundThe global potential of bioenergy on abandoned agriculture lands
Campbell, J. E., Lobell, D. B., Genova, R. C., & Field, C. B. (2008) · Environmental Science & Technology · doi:10.1021/es800052w · Cited by 709
Self-citation
Cited by
Global food demand and the sustainable intensification of agriculture
Tilman, D., Balzer, C., Hill, J., & Befort, B. L. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1116437108 · Cited by 7,692
Yield trends are insufficient to double global crop production by 2050
Ray, D. K., Mueller, N. D., West, P. C., & Foley, J. A. (2013) · PLoS ONE · doi:10.1371/journal.pone.0066428 · Cited by 3,415
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
Ecological intensification: harnessing ecosystem services for food security
Bommarco, R., Kleijn, D., & Potts, S. G. (2013) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2012.10.012 · Cited by 1,862
Yield 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
Self-citation
Metadata
- Authors
- Lobell, D. B., Cassman, K. G., & Field, C. B.
- Venue
- Annual Review of Environment and Resources
- Publisher
- Annual Reviews
- Published
- 2009-11-01
- Volume
- 34
- Issue
- 1
- Pages
- 179-204
- ISSN
- 1543-5938 (ISSN-L)
- 1545-2050
- OpenAlex
- W2159292710
- Cited by
- 1,523
- References
- 5
- FWCI
- 5.08
- Type
- journal article
- Language
- en
- Open access
- closed
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: Crop Yield Gaps: Their Importance, Magnitudes, and Causes. https://app.cochat.ai/papers/10.1146/annurev.environ.041008.093740
- The paper
- Lobell, D. B., Cassman, K. G., & Field, C. B. (2009). Crop Yield Gaps: Their Importance, Magnitudes, and Causes. Annual Review of Environment and Resources. https://doi.org/10.1146/annurev.environ.041008.093740
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.