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  • 2020-2024  (7)
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  • 1
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    AgMIP-Wheat multi-model simulations on climate change impact and adaptation for global wheat (2023)
    In:  Open Data Journal for Agricultural Research
    Details
    Publication Date: 2023-07-06
    Description: The climate change impact and adaptation simulations from the Agricultural Model Intercomparison and Improvement Project (AgMIP) for wheat provide a unique dataset of multi-model ensemble simulations for 60 representative global locations covering all global wheat mega environments. The multi-model ensemble reported here has been thoroughly benchmarked against a large number of experimental data, including different locations, growing season temperatures, atmospheric CO2 concentration, heat stress scenarios, and their interactions. In this paper, we describe the main characteristics of this global simulation dataset. Detailed cultivar, crop management, and soil datasets were compiled for all locations to drive 32 wheat growth models. The dataset consists of 30-year simulated data including 25 output variables for nine climate scenarios, including Baseline (1980-2010) with 360 or 550 ppm CO2, Baseline +2oC or +4oC with 360 or 550 ppm CO2, a mid-century climate change scenario (RCP8.5, 571 ppm CO2), and 1.5°C (423 ppm CO2) and 2.0oC (487 ppm CO2) warming above the pre-industrial period (HAPPI). This global simulation dataset can be used as a benchmark from a well-tested multi-model ensemble in future analyses of global wheat. Also, resource use efficiency (e.g., for radiation, water, and nitrogen use) and uncertainty analyses under different climate scenarios can be explored at different scales. The DOI for the dataset is 10.5281/zenodo.4027033 (AgMIP-Wheat, 2020), and all the data are available on the data repository of Zenodo (doi: 10.5281/zenodo.4027033). Two scientific publications have been published based on some of these data here.
    Language: English
    Type: info:eu-repo/semantics/article
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  • 2
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    Global crop yields can be lifted by timely adaptation of growing periods to climate change (2022)
    In:  Nature Communications
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    Publication Date: 2023-07-27
    Description: Adaptive management of crop growing periods by adjusting sowing dates and cultivars is one of the central aspects of crop production systems, tightly connected to local climate. However, it is so far underrepresented in crop-model based assessments of yields under climate change. In this study, we integrate models of farmers’ decision making with biophysical crop modeling at the global scale to simulate crop calendars adaptation and its effect on crop yields of maize, rice, sorghum, soybean and wheat. We simulate crop growing periods and yields (1986-2099) under counterfactual management scenarios assuming no adaptation, timely adaptation or delayed adaptation of sowing dates and cultivars. We then compare the counterfactual growing periods and corresponding yields at the end of the century (2080-2099). We find that (i) with adaptation, temperature-driven sowing dates (typical at latitudes 〉30°N-S) will have larger shifts than precipitation-driven sowing dates (at latitudes 〈30°N-S); (ii) later-maturing cultivars will be needed, particularly at higher latitudes; (iii) timely adaptation of growing periods would increase actual crop yields by ~12%, reducing climate change negative impacts and enhancing the positive CO2 fertilization effect. Despite remaining uncertainties, crop growing periods adaptation require consideration in climate change impact assessments.
    Language: English
    Type: info:eu-repo/semantics/article
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  • 3
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    Narrowing uncertainties in the effects of elevated CO2 on crops (2020)
    In:  Nature Food
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    Publication Date: 2023-07-27
    Description: Plant responses to rising atmospheric carbon dioxide (CO2) concentrations, together with projected variations in temperature and precipitation will determine future agricultural production. Estimates of the impacts of climate change on agriculture provide essential information to design effective adaptation strategies, and develop sustainable food systems. Here, we review the current experimental evidence and crop models on the effects of elevated CO2 concentrations. Recent concerted efforts have narrowed the uncertainties in CO2-induced crop responses so that climate change impact simulations omitting CO2 can now be eliminated. To address remaining knowledge gaps and uncertainties in estimating the effects of elevated CO2 and climate change on crops, future research should expand experiments on more crop species under a wider range of growing conditions, improve the representation of responses to climate extremes in crop models, and simulate additional crop physiological processes related to nutritional quality.
    Type: info:eu-repo/semantics/article
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  • 4
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    Emergent constraint on crop yield response to warmer temperature from field experiments (2020)
    In:  Nature Sustainability
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    Publication Date: 2023-07-27
    Description: Responses of global crop yields to warmer temperatures are fundamental to sustainable development under climate change but remain uncertain. Here, we combined a global dataset of field warming experiments (48 sites) for wheat, maize, rice and soybean with gridded global crop models to produce field-data-constrained estimates on responses of crop yield to changes in temperature (ST) with the emergent-constraint approach. Our constrained estimates show with 〉95% probability that warmer temperatures would reduce yields for maize (−7.1 ± 2.8% K−1), rice (−5.6 ± 2.0% K−1) and soybean (−10.6 ± 5.8% K−1). For wheat, ST was 89% likely to be negative (−2.9 ± 2.3% K−1). Uncertainties associated with modelled ST were reduced by 12–54% for the four crops but data constraints do not allow for further disentangling ST of different crop types. A key implication for impact assessments after the Paris Agreement is that direct warming impacts alone will reduce major crop yields by 3–13% under 2 K global warming without considering CO2 fertilization effects and adaptations. Even if warming was limited to 1.5 K, all major producing countries would still face notable warming-induced yield reduction. This yield loss could be partially offset by projected benefits from elevated CO2, whose magnitude remains uncertain, and highlights the challenge to compensate it by autonomous adaptation.
    Type: info:eu-repo/semantics/article
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  • 5
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    Workflow for Global crop yields can be lifted by timely adaptation of growing periods to climate change (2022)
    CERN / Zenodo
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    Publication Date: 2023-10-11
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  • 6
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    The optimization of model ensemble composition and size can enhance the robustness of crop yield projections (2023)
    In:  Communications Earth and Environment
    Details
    Publication Date: 2024-01-17
    Description: Linked climate and crop simulation models are widely used to assess the impact of climate change on agriculture. However, it is unclear how ensemble configurations (model composition and size) influence crop yield projections and uncertainty. Here, we investigate the influences of ensemble configurations on crop yield projections and modeling uncertainty from Global Gridded Crop Models and Global Climate Models under future climate change. We performed a cluster analysis to identify distinct groups of ensemble members based on their projected outcomes, revealing unique patterns in crop yield projections and corresponding uncertainty levels, particularly for wheat and soybean. Furthermore, our findings suggest that approximately six Global Gridded Crop Models and 10 Global Climate Models are sufficient to capture modeling uncertainty, while a cluster-based selection of 3-4 Global Gridded Crop Models effectively represents the full ensemble. The contribution of individual Global Gridded Crop Models to overall uncertainty varies depending on region and crop type, emphasizing the importance of considering the impact of specific models when selecting models for local-scale applications. Our results emphasize the importance of model composition and ensemble size in identifying the primary sources of uncertainty in crop yield projections, offering valuable guidance for optimizing ensemble configurations in climate-crop modeling studies tailored to specific applications.
    Language: English
    Type: info:eu-repo/semantics/article
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  • 7
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    Assessing the impact on crop modelling of multi- and uni-variate climate model bias adjustments (2024)
    In:  Agricultural Systems
    Details
    Publication Date: 2024-03-14
    Description: Crop models are essential tools for assessing the impact of climate change on national or regional agricultural production. Starting from meteorology, soil and crop management, fertilization and irrigation practices, they predict the yield of specific crop varieties. For long term assessments, climate models are the source of primary information. To make climate model results usable in a specific time frame context, bias adjustment (BA) is required. In fact, climate models tend to deviate from day-to-day values of the physical parameters while conserving the climate variability signal. BA brings the climatic signal to the actual values observed in a specific location and period, and to be representative of a specific period in absolute terms. BA techniques come in different flavours. The broadest categorization is univariate and multivariate methods. Multivariate methods adjust the variables considering possible cross-correlations while univariate methods treat the variables one by one without accounting for possible dependence on one another.
    Language: English
    Type: info:eu-repo/semantics/article
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