The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
Wheat yield loss attributable to heat waves, drought and water excess at the global, national and subnational scales
Open accessAbstract
Heat waves and drought are often considered the most damaging climatic stressors for wheat. In this study, we characterize and attribute the effects of these climate extremes on wheat yield anomalies (at global and national scales) from 1980 to 2010. Using a combination of up-to-date heat wave and drought indexes (the latter capturing both excessively dry and wet conditions), we have developed a composite indicator that is able to capture the spatio-temporal characteristics of the underlying physical processes in the different agro-climatic regions of the world. At the global level, our diagnostic explains a significant portion (more than 40%) of the inter-annual production variability. By quantifying the contribution of national yield anomalies to global fluctuations, we have found that just two concurrent yield anomalies affecting the larger producers of the world could be responsible for more than half of the global annual fluctuations. The relative importance of heat stress and drought in determining the yield anomalies depends on the region. Moreover, in contrast to common perception, water excess affects wheat production more than drought in several countries. We have also performed the same analysis at the subnational level for France, which is the largest wheat producer of the European Union, and home to a range of climatic zones. Large subnational variability of inter-annual wheat yield is mostly captured by the heat and water stress indicators, consistently with the country-level result.
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Cites
Vicente-Serrano, S. M., Beguería, S., & López-Moreno, J. I. (2010) · Journal of Climate · doi:10.1175/2009jcli2909.1 · Cited by 9,493
Solutions for a cultivated planet
Foley, J. A., Ramankutty, N., Brauman, K. A., Cassidy, E. S., Gerber, J. S., Johnston, M., Mueller, N. D., O’Connell, C., Ray, D. K., West, P. C., et al. (2011) · Nature · doi:10.1038/nature10452 · Cited by 8,476
Estimating and Testing Linear Models with Multiple Structural Changes
Bai, J., & Perron, P. (1998) · Econometrica · doi:10.2307/2998540 · Cited by 6,112
Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting
Cleveland, W. S., & Devlin, S. J. (1988) · Journal of the American Statistical Association · doi:10.1080/01621459.1988.10478639 · Cited by 5,742
Investigating soil moisture–climate interactions in a changing climate: A review
Seneviratne, S. I., Corti, T., Davin, E. L., Hirschi, M., Jaeger, E. B., Lehner, I., Orlowsky, B., & Teuling, A. J. (2010) · Earth-Science Reviews · doi:10.1016/j.earscirev.2010.02.004 · Cited by 5,638
MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications
Rienecker, M. M., Suarez, M. J., Gelaro, R., Todling, R., Bacmeister, J., Liu, E., Bosilovich, M. G., Schubert, S. D., Takacs, L., Kim, G.-K., et al. (2011) · Journal of Climate · doi:10.1175/jcli-d-11-00015.1 · Cited by 4,852
Influence of extreme weather disasters on global crop production
Lesk, C., Rowhani, P., & Ramankutty, N. (2016) · Nature · doi:10.1038/nature16467 · Cited by 3,883
Rising temperatures reduce global wheat production
Asseng, S., Ewert, F., Martre, P., Rötter, R. P., Lobell, D. B., Cammarano, D., Kimball, B. A., Ottman, M. J., Wall, G. W., White, J. W., et al. (2015) · Nature Climate Change · doi:10.1038/nclimate2470 · Cited by 2,491
Haylock, M. R., Hofstra, N., Klein Tank, A. M. G., Klok, E. J., Jones, P. D., & New, M. (2008) · Journal of Geophysical Research: Atmospheres · doi:10.1029/2008jd010201 · Cited by 2,437
Climate variation explains a third of global crop yield variability
Ray, D. K., Gerber, J. S., MacDonald, G. K., & West, P. C. (2015) · Nature Communications · doi:10.1038/ncomms6989 · Cited by 2,046
Cited by
Leng, G., & Hall, J. (2019) · Science of The Total Environment · doi:10.1016/j.scitotenv.2018.10.434 · Cited by 729
Kulkarni, M., Soolanayakanahally, R., Ogawa, S., Uga, Y., Selvaraj, M. G., & Kagale, S. (2017) · Frontiers in Chemistry · doi:10.3389/fchem.2017.00106 · Cited by 230
Estimating resilience of crop production systems: From theory to practice
Zampieri, M., Weissteiner, C. J., Grizzetti, B., Toreti, A., van den Berg, M., & Dentener, F. (2020) · Science of The Total Environment · doi:10.1016/j.scitotenv.2020.139378 · Cited by 113
Self-citationBrown, S. J. (2020) · Weather and Climate Extremes · doi:10.1016/j.wace.2020.100278 · Cited by 108
The impact of climate change in wheat and barley yields in the Iberian Peninsula
Bento, V. A., Ribeiro, A. F. S., Russo, A., Gouveia, C. M., Cardoso, R. M., & Soares, P. M. M. (2021) · Scientific Reports · doi:10.1038/s41598-021-95014-6 · Cited by 72
Metadata
- Authors
- Zampieri, M., Ceglar, A., Dentener, F., & Toreti, A.
- Venue
- Environmental Research Letters
- Publisher
- IOP Publishing
- Published
- 2017-06-05
- Volume
- 12
- Issue
- 6
- Pages
- 064008
- ISSN
- 1748-9326 (ISSN-L)
- OpenAlex
- W2624472554
- Cited by
- 689
- References
- 46
- FWCI
- 37.14
- Type
- journal article
- Language
- en
- Open access
- gold
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- Zampieri, M., Ceglar, A., Dentener, F., & Toreti, A. (2017). Wheat yield loss attributable to heat waves, drought and water excess at the global, national and subnational scales. Environmental Research Letters. https://doi.org/10.1088/1748-9326/aa723b
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