Articles | Volume 11, issue 1
https://doi.org/10.5194/esd-11-113-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-11-113-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
11 Feb 2020
Research article | Highlight paper |
| 11 Feb 2020
| 11 Feb 2020
A multi-model analysis of teleconnected crop yield variability in a range of cropping systems
Water and Development Research Group, Aalto University, Finland,
Tietotie 1E, 02150 Espoo, Finland
Joseph H. A. Guillaume
Water and Development Research Group, Aalto University, Finland,
Tietotie 1E, 02150 Espoo, Finland
Christoph Müller
Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, 14473 Potsdam, Germany
Toshichika Iizumi
Institute for Agro-Environmental Sciences, National Agriculture and
Food Research Organization, 3-1-3 Kannondai, Tsukuba, 305-8604 Japan
Water and Development Research Group, Aalto University, Finland,
Tietotie 1E, 02150 Espoo, Finland
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- Forecasting global crop yields based on El Nino Southern Oscillation early signals J. Cao et al. 10.1016/j.agsy.2022.103564
- Weather Extremes Shock Maize Production: Current Approaches and Future Research Directions in Africa S. Du & W. Xiong 10.3390/plants13121585
- Assessment of machine learning model performance for seasonal precipitation simulation based on teleconnection indices in Iran J. Helali et al. 10.1007/s12517-022-10640-2
- County-scale crop yield prediction by integrating crop simulation with machine learning models S. Sajid et al. 10.3389/fpls.2022.1000224
- Effects of hydrothermal conditions on agrophysical properties of typical chernozem and crop rotation productivity in the system of organic farming S. Kudria 10.31073/mivg202002-250
- A review of global gridded cropping system data products K. Kim et al. 10.1088/1748-9326/ac20f4
- Identifying links between monsoon variability and rice production in India through machine learning C. Bowden et al. 10.1038/s41598-023-27752-8
- Connection between climatic change and international food prices: evidence from robust long-range cross-correlation and variable-lag transfer entropy with sliding windows approach Z. Dhifaoui 10.1140/epjds/s13688-024-00482-1
- Monthly Precipitation Forecasting in the Han River Basin, South Korea, Using Large-Scale Teleconnections and Multiple Regression Models C. Kim et al. 10.3390/w12061590
- ENSO influence on corn and soybean yields as a base of an early warning system for agriculture in Córdoba, Argentina A. de la Casa et al. 10.1016/j.eja.2021.126340
- Two Cases Detected with Pulmonary Embolism After COVID-19 Acute Period M. Yüksel Yavuz 10.4274/terh.galenos.2021.81084
- Climatic signatures in early modern European grain harvest yields F. Ljungqvist et al. 10.5194/cp-19-2463-2023
- Evidence for and projection of multi-breadbasket failure caused by climate change T. Hasegawa et al. 10.1016/j.cosust.2022.101217
- El Niño and positive Indian Ocean Dipole conditions simultaneously reduce the production of multiple cereals across India M. Gurazada et al. 10.1088/1748-9326/ad6a6f
- Growth phase-specific evaporative demand and nighttime temperatures determine Maize (Zea Mays L.) yield deviations as revealed from a long-term field experiment A. Singh et al. 10.1016/j.agrformet.2021.108543
- Global coordination of wheat sowing: A possible policy against climate variability G. Giulioni et al. 10.1002/wfp2.12052
- Global Within-Season Yield Anomaly Prediction for Major Crops Derived Using Seasonal Forecasts of Large-Scale Climate Indices and Regional Temperature and Precipitation T. Iizumi et al. 10.1175/WAF-D-20-0097.1
- Aligning the harvesting year in global gridded crop model simulations with that in census reports is pivotal to national-level model performance evaluations for rice T. Iizumi et al. 10.1016/j.eja.2021.126367
- Significant changes in global maize yield sensitivity to vapor pressure deficit during 1983–2010 L. Han & G. Leng 10.1016/j.agwat.2024.109107
- Enhancing references evapotranspiration forecasting with teleconnection indices and advanced machine learning techniques J. Helali et al. 10.1007/s13201-024-02289-x
- Assessing the Impact of ENSO on Agriculture Over Africa Using Earth Observation Data N. Sazib et al. 10.3389/fsufs.2020.509914
20 citations as recorded by crossref.
- Forecasting global crop yields based on El Nino Southern Oscillation early signals J. Cao et al. 10.1016/j.agsy.2022.103564
- Weather Extremes Shock Maize Production: Current Approaches and Future Research Directions in Africa S. Du & W. Xiong 10.3390/plants13121585
- Assessment of machine learning model performance for seasonal precipitation simulation based on teleconnection indices in Iran J. Helali et al. 10.1007/s12517-022-10640-2
- County-scale crop yield prediction by integrating crop simulation with machine learning models S. Sajid et al. 10.3389/fpls.2022.1000224
- Effects of hydrothermal conditions on agrophysical properties of typical chernozem and crop rotation productivity in the system of organic farming S. Kudria 10.31073/mivg202002-250
- A review of global gridded cropping system data products K. Kim et al. 10.1088/1748-9326/ac20f4
- Identifying links between monsoon variability and rice production in India through machine learning C. Bowden et al. 10.1038/s41598-023-27752-8
- Connection between climatic change and international food prices: evidence from robust long-range cross-correlation and variable-lag transfer entropy with sliding windows approach Z. Dhifaoui 10.1140/epjds/s13688-024-00482-1
- Monthly Precipitation Forecasting in the Han River Basin, South Korea, Using Large-Scale Teleconnections and Multiple Regression Models C. Kim et al. 10.3390/w12061590
- ENSO influence on corn and soybean yields as a base of an early warning system for agriculture in Córdoba, Argentina A. de la Casa et al. 10.1016/j.eja.2021.126340
- Two Cases Detected with Pulmonary Embolism After COVID-19 Acute Period M. Yüksel Yavuz 10.4274/terh.galenos.2021.81084
- Climatic signatures in early modern European grain harvest yields F. Ljungqvist et al. 10.5194/cp-19-2463-2023
- Evidence for and projection of multi-breadbasket failure caused by climate change T. Hasegawa et al. 10.1016/j.cosust.2022.101217
- El Niño and positive Indian Ocean Dipole conditions simultaneously reduce the production of multiple cereals across India M. Gurazada et al. 10.1088/1748-9326/ad6a6f
- Growth phase-specific evaporative demand and nighttime temperatures determine Maize (Zea Mays L.) yield deviations as revealed from a long-term field experiment A. Singh et al. 10.1016/j.agrformet.2021.108543
- Global coordination of wheat sowing: A possible policy against climate variability G. Giulioni et al. 10.1002/wfp2.12052
- Global Within-Season Yield Anomaly Prediction for Major Crops Derived Using Seasonal Forecasts of Large-Scale Climate Indices and Regional Temperature and Precipitation T. Iizumi et al. 10.1175/WAF-D-20-0097.1
- Aligning the harvesting year in global gridded crop model simulations with that in census reports is pivotal to national-level model performance evaluations for rice T. Iizumi et al. 10.1016/j.eja.2021.126367
- Significant changes in global maize yield sensitivity to vapor pressure deficit during 1983–2010 L. Han & G. Leng 10.1016/j.agwat.2024.109107
- Enhancing references evapotranspiration forecasting with teleconnection indices and advanced machine learning techniques J. Helali et al. 10.1007/s13201-024-02289-x
1 citations as recorded by crossref.
Latest update: 17 Nov 2024
Short summary
In this study, we analyse the impacts of three major climate oscillations on global crop production. Our results show that maize, rice, soybean, and wheat yields are influenced by climate oscillations to a wide extent and in several important crop-producing regions. We observe larger impacts if crops are rainfed or fully fertilized, while irrigation tends to mitigate the impacts. These results can potentially help to increase the resilience of the global food system to climate-related shocks.
In this study, we analyse the impacts of three major climate oscillations on global crop...
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