Your search found 3 records
1 Asseng, S.; Ewert, F.; Martre, P.; Rotter, R. P.; Lobell, D. B.; Cammarano, D.; Kimball, B. A.; Ottman, M. J.; Wall, G. W.; White, J. W.; Reynolds, M. P.; Alderman, P. D.; Prasad, P. V. V.; Aggarwal, Pramod Kumar; Anothai, J.; Basso, B.; Biernath, C.; Challinor, A. J.; De Sanctis, G.; Doltra, J.; Fereres, E.; Garcia-Vila, M.; Gayler, S.; Hoogenboom, G.; Hunt, L. A.; Izaurralde, R. C.; Jabloun, M.; Jones, C. D.; Kersebaum, K. C.; Koehler, A-K.; Muller, C.; Kumar, S. N.; Nendel, C.; O’Leary, G.; Olesen, J. E.; Palosuo, T.; Priesack, E.; Rezaei, E. E.; Ruane, A. C.; Semenov, M. A.; Shcherbak, I.; Stockle, C.; Stratonovitch, P.; Streck, T.; Supit, I; Tao, F.; Thorburn, P. J.; Waha, K.; Wang, E.; Wallach, D.; Wolf, J.; Zhao, Z.; Zhu, Y. 2015. Rising temperatures reduce global wheat production. Nature Climate Change, 5:143-147. [doi: https://doi.org/10.1038/nclimate2470]
(Location: IWMI HQ Call no: e-copy only Record No: H046906)
Crop models are essential tools for assessing the threat of climate change to local and global food production1. Present models used to predict wheat grain yield are highly uncertain when simulating how crops respond to temperature2. Here we systematically tested 30 different wheat crop models of the Agricultural Model Intercomparison and Improvement Project against field experiments in which growing season mean temperatures ranged from 15 °C to 32 °C, including experiments with artificial heating. Many models simulated yields well, but were less accurate at higher temperatures. The model ensemble median was consistently more accurate in simulating the crop temperature response than any single model, regardless of the input information used. Extrapolating the model ensemble temperature response indicates that warming is already slowing yield gains at a majority of wheat-growing locations. Global wheat production is estimated to fall by 6% for each °C of further temperature increase and become more variable over space and time.
2 Constantin, J.; Raynal, H.; Casellas, E.; Hoffmann, H.; Bindi, M.; Doro, L.; Eckersten, H.; Gaiser, T.; Grosz, B.; Haas, E.; Kersebaum, K.-C.; Klatt, S.; Kuhnert, M.; Lewan, E.; Maharjan, G. R.; Moriondo, M.; Nendel, C.; Roggero, P. P.; Specka, X.; Trombi, G.; Villa, A.; Wang, E.; Weihermuller, L.; Yeluripati, J.; Zhao, Z.; Ewert, F.; Bergez, J.-E. 2019. Management and spatial resolution effects on yield and water balance at regional scale in crop models. Agricultural and Forest Meteorology, 275:184-195. [doi: https://doi.org/10.1016/j.agrformet.2019.05.013]
Crop management ; Crop yield ; Water balance ; Crop modelling ; Crop forecasting ; Strategies ; Evapotranspiration ; Drainage ; Wheat ; Maize / Germany / North Rhine-Westphalia
(Location: IWMI HQ Call no: e-copy only Record No: H049327)
(2.99 MB)
Due to the more frequent use of crop models at regional and national scale, the effects of spatial data input resolution have gained increased attention. However, little is known about the influence of variability in crop management on model outputs. A constant and uniform crop management is often considered over the simulated area and period. This study determines the influence of crop management adapted to climatic conditions and input data resolution on regional-scale outputs of crop models. For this purpose, winter wheat and maize were simulated over 30 years with spatially and temporally uniform management or adaptive management for North Rhine-Westphalia (˜34 083 km²), Germany. Adaptive management to local climatic conditions was used for 1) sowing date, 2) N fertilization dates, 3) N amounts, and 4) crop cycle length. Therefore, the models were applied with four different management sets for each crop. Input data for climate, soil and management were selected at five resolutions, from 1 × 1 km to 100 × 100 km grid size. Overall, 11 crop models were used to predict regional mean crop yield, actual evapotranspiration, and drainage. Adaptive management had little effect (<10% difference) on the 30-year mean of the three output variables for most models and did not depend on soil, climate, and management resolution. Nevertheless, the effect was substantial for certain models, up to 31% on yield, 27% on evapotranspiration, and 12% on drainage compared to the uniform management reference. In general, effects were stronger on yield than on evapotranspiration and drainage, which had little sensitivity to changes in management. Scaling effects were generally lower than management effects on yield and evapotranspiration as opposed to drainage. Despite this trend, sensitivity to management and scaling varied greatly among the models. At the annual scale, effects were stronger in certain years, particularly the management effect on yield. These results imply that depending on the model, the representation of management should be carefully chosen, particularly when simulating yields and for predictions on annual scale.
3 Deng, C.; Wang, H.; Gong, S.; Zhang. J.; Yang, B.; Zhao, Z.. 2020. Effects of urbanization on food-energy-water systems in mega-urban regions: a case study of the Bohai MUR, China. Environmental Research Letters, 15(4):044014. [doi: https://doi.org/10.1088/1748-9326/ab6fbb]
Urbanization ; Food systems ; Energy consumption ; Water systems ; Nexus ; Urban areas ; Water resources ; Land use ; Economic development ; Indicators ; Population growth ; Case studies / China / Bohai Mega-Urban Region / Beijing / Hebei / Tianjin / Liaoning / Shandong
(Location: IWMI HQ Call no: e-copy only Record No: H049630)
https://iopscience.iop.org/article/10.1088/1748-9326/ab6fbb/pdf
https://vlibrary.iwmi.org/pdf/H049630.pdf
https://vlibrary.iwmi.org/pdf/H049630.pdf
(3.37 MB) (3.37 MB)
The security of food-energy-water (FEW) systems is an issue of global concern, especially in mega-urban regions (MURs) with high-density populations, industries and carbon emissions. To better understand the hidden links between urbanization and FEW systems, the pressure on FEW systems was quantified in a typical rapidly urbanizing region—the Bohai MUR. The correlations between urbanization indicators and the pressure on FEW systems were analyzed and the mechanism of the impact of urbanization on FEW systems was further investigated. The results showed that approximately 23% of cropland was lost, 61% of which was lost via conversion to construction land and urban areas expanded by 132.2% in the Bohai MUR during 1980–2015. The pressure on FEW systems showed an upward trend, with the stress index of the pressure on FEW systems (FEW_SI) ranging from 80.49% to 134.82%. The dominant pressure consisting of that has converted from water system pressure to energy system pressure since 2004. The FEW_SI in the Bohai MUR was enhanced with cropland loss and increases in urbanization indicators. Additionally, land use, populations, incomes, policies and innovation are the main ways that urbanization affects FEW systems in MURs. This study enhances our understanding of the variation in pressure on FEW systems in MURs and the effects of urbanization on FEW systems, which will help stakeholders to enhance the resilience of FEW systems and promote sustainable regional development.
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