|   | 
Details
   web
Records
Author Peng, B.; Guan, K.; Tang, J.; Ainsworth EA; Asseng S; Bernacchi, C.J.; Bernacchi CJ; Cooper, M.; Delucia EH; Elliott, J.W.; Ewert F; Grant RF; Gustafson, D.I.; Hammer GL; Jin Z; Jones JW; Kimm, H.; Lawrence DM; Li Y; Lombardozzi, D.L.; Marshall-Colon, A.; Messina CD; Ort, D.R.; Schnable, J.C.; Vallejos, C.E.; Wu, A.; Yin X; Zhou, W.
Title Towards a multiscale crop modelling framework for climate change adaptation assessment. Type Journal Article
Year 2020 Publication Nature plants Abbreviated Journal Nat Plants
Volume 6 Issue 4 Pages 338-348
Keywords ELEVATED CARBON-DIOXIDE; HEAT-STRESS; EARTH SYSTEM; IN-SILICO; STOMATAL CONDUCTANCE; LEAF PHOTOSYNTHESIS; GENETIC-VARIABILITY; TROPOSPHERIC OZONE; SIMULATION-MODELS; DATA AGGREGATION
Abstract Predicting the consequences of manipulating genotype (G) and agronomic management (M) on agricultural ecosystem performances under future environmental (E) conditions remains a challenge. Crop modelling has the potential to enable society to assess the efficacy of G x M technologies to mitigate and adapt crop production systems to climate change. Despite recent achievements, dedicated research to develop and improve modelling capabilities from gene to global scales is needed to provide guidance on designing G x M adaptation strategies with full consideration of their impacts on both crop productivity and ecosystem sustainability under varying climatic conditions. Opportunities to advance the multiscale crop modelling framework include representing crop genetic traits, interfacing crop models with large-scale models, improving the representation of physiological responses to climate change and management practices, closing data gaps and harnessing multisource data to improve model predictability and enable identification of emergent relationships. A fundamental challenge in multiscale prediction is the balance between process details required to assess the intervention and predictability of the system at the scales feasible to measure the impact. An advanced multiscale crop modelling framework will enable a gene-to-farm design of resilient and sustainable crop production systems under a changing climate at regional-to-global scales.
Address Department of Natural Resources and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
Corporate Author Thesis
Publisher Place of Publication Editor
Language eng Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2055-0278 (Linking) ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number FCI @ refbase @ Serial 2415
Permanent link to this record
 

 
Author Zhao, G.; Hoffmann, H.; van Bussel, L.; Enders, A.; Specka, X.; Sosa, C.; Yeluripati, J.; Tao, F.; Constantin, J.; Raynal, H.; Teixeira, E.; Grosz, B.; Doro, L.; Zhao, Z.; Nendel, C.; Kiese, R.; Eckersten, H.; Haas, E.; Vanuytrecht, E.; Wang, E.; Kuhnert, M.; Trombi, G.; Moriondo, M.; Bindi, M.; Lewan, E.; Bach, M.; Kersebaum, K.; Rötter, R.; Roggero, P.; Wallach, D.; Cammarano, D.; Asseng, S.; Krauss, G.; Siebert, S.; Gaiser, T.; Ewert, F.
Title Effect of weather data aggregation on regional crop simulation for different crops, production conditions, and response variables Type Journal Article
Year 2015 Publication Climate Research Abbreviated Journal Clim. Res.
Volume 65 Issue Pages 141-157
Keywords Crop model; Model comparison; Spatial resolution; Data aggregation; Spatial heterogeneity; Scaling
Abstract We assessed the weather data aggregation effect (DAE) on the simulation of cropping systems for different crops, response variables, and production conditions. Using 13 process-based crop models and the ensemble mean, we simulated 30 yr continuous cropping systems for 2 crops (winter wheat and silage maize) under 3 production conditions for the state of North Rhine-Westphalia, Germany. The DAE was evaluated for 5 weather data resolutions (i.e. 1, 10, 25, 50, and 100 km) for 3 response variables including yield, growing season evapotranspiration, and water use efficiency. Five metrics, viz. the spatial bias (Δ), average absolute deviation (AAD), relative AAD, root mean squared error (RMSE), and relative RMSE, were used to evaluate the DAE on both the input weather data and simulated results. For weather data, we found that data aggregation narrowed the spatial variability but widened the Δ, especially across mountainous areas. The DAE on loss of spatial heterogeneity and hotspots was stronger than on the average changes over the region. The DAE increased when coarsening the spatial resolution of the input weather data. The DAE varied considerably across different models, but changed only slightly for different production conditions and crops. We conclude that if spatially detailed information is essential for local management decision, higher resolution is desirable to adequately capture the spatial variability for heterogeneous regions. The required resolution depends on the choice of the model as well as the environmental condition of the study area.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0936-577X ISBN Medium
Area Expedition Conference
Notes Approved no
Call Number FCI @ refbase @ Serial 901
Permanent link to this record