ORCID Profile
0000-0002-8551-6617
Current Organisation
University of Leeds
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Publisher: Springer Science and Business Media LLC
Date: 15-05-2014
DOI: 10.1038/NCOMMS4712
Abstract: The monitoring and prediction of climate-induced variations in crop yields, production and export prices in major food-producing regions have become important to enable national governments in import-dependent countries to ensure supplies of affordable food for consumers. Although the El Niño/Southern Oscillation (ENSO) often affects seasonal temperature and precipitation, and thus crop yields in many regions, the overall impacts of ENSO on global yields are uncertain. Here we present a global map of the impacts of ENSO on the yields of major crops and quantify its impacts on their global-mean yield anomalies. Results show that El Niño likely improves the global-mean soybean yield by 2.1-5.4% but appears to change the yields of maize, rice and wheat by -4.3 to +0.8%. The global-mean yields of all four crops during La Niña years tend to be below normal (-4.5 to 0.0%). Our findings highlight the importance of ENSO to global crop production.
Publisher: Elsevier BV
Date: 03-2013
Publisher: Wiley
Date: 18-08-2020
DOI: 10.1111/GCB.15261
Publisher: Springer Science and Business Media LLC
Date: 21-07-2013
DOI: 10.1038/NCLIMATE1945
Publisher: Springer Science and Business Media LLC
Date: 12-09-2016
DOI: 10.1038/NCLIMATE3115
Publisher: Springer Science and Business Media LLC
Date: 12-08-2014
Publisher: Springer Science and Business Media LLC
Date: 17-07-2017
Abstract: Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections.
Publisher: Springer Science and Business Media LLC
Date: 16-03-2014
DOI: 10.1038/NCLIMATE2153
Publisher: Springer Science and Business Media LLC
Date: 22-12-2014
DOI: 10.1038/NCLIMATE2470
Publisher: American Geophysical Union (AGU)
Date: 19-11-2016
DOI: 10.1002/2016GL071209
Publisher: Cambridge University Press (CUP)
Date: 25-03-2011
DOI: 10.1017/S0014479711000123
Abstract: Global food security is under threat by climate change, and the impacts fall disproportionately on resource-poor small producers. With the goal of making agricultural and food systems more climate-resilient, this paper presents an adaptation and mitigation framework. A road map for further agricultural research is proposed, based on the CGIAR Research Program on Climate Change, Agriculture and Food Security. We propose a holistic, integrated approach that takes into account trade-offs and feedbacks between interventions. We ide the agenda into four research areas, three tackling risk management, accelerated adaptation and emissions mitigation, and the fourth facilitating adoption of research outputs. After reviewing specific technical, agronomic and policy options for reducing climate change vulnerability, we acknowledge that science and good-faith recommendations do not necessarily translate into effective and timely actions. We therefore outline impediments to behavioural change and propose that future research overcomes these obstacles by linking the right institutions, instruments and scientific outputs. Food security research must go beyond its focus on production to also examine food access and utilization issues. Finally, we conclude that urgent action is needed despite the uncertainties, trade-offs and challenges.
Publisher: Springer Science and Business Media LLC
Date: 15-10-2014
Publisher: Oxford University Press (OUP)
Date: 07-03-2015
DOI: 10.1093/JXB/ERV014
Abstract: Genotypic adaptation involves the incorporation of novel traits in crop varieties so as to enhance food productivity and stability and is expected to be one of the most important adaptation strategies to future climate change. Simulation modelling can provide the basis for evaluating the biophysical potential of crop traits for genotypic adaptation. This review focuses on the use of models for assessing the potential benefits of genotypic adaptation as a response strategy to projected climate change impacts. Some key crop responses to the environment, as well as the role of models and model ensembles for assessing impacts and adaptation, are first reviewed. Next, the review describes crop-climate models can help focus the development of future-adapted crop germplasm in breeding programmes. While recently published modelling studies have demonstrated the potential of genotypic adaptation strategies and ideotype design, it is argued that, for model-based studies of genotypic adaptation to be used in crop breeding, it is critical that modelled traits are better grounded in genetic and physiological knowledge. To this aim, two main goals need to be pursued in future studies: (i) a better understanding of plant processes that limit productivity under future climate change and (ii) a coupling between genetic and crop growth models-perhaps at the expense of the number of traits analysed. Importantly, the latter may imply additional complexity (and likely uncertainty) in crop modelling studies. Hence, appropriately constraining processes and parameters in models and a shift from simply quantifying uncertainty to actually quantifying robustness towards modelling choices are two key aspects that need to be included into future crop model-based analyses of genotypic adaptation.
Publisher: Wiley
Date: 06-2013
DOI: 10.1890/120126
Publisher: Springer Science and Business Media LLC
Date: 09-06-2013
DOI: 10.1038/NCLIMATE1916
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Andrew Challinor.