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General Large-Area Model for annual crops - GLAMPrincipal Investigator: Dr Andy Challinor AimThe General Large-Area Model for annual crops (GLAM) is a tool for assessing the impacts of climate variability and change on annual crops. It has been designed for use with regional and global climate model output and remotely sensed data.GLAM has a growing user community and the model is under continuing development. Below, the underlying concepts behind GLAM are explained, some results are presented, and some key references are provided. The model is available for non-profit use under a free license agreement. TechniquesThe model aims to combine the benefits of process-based plot-scale crop models such as the DSSAT suite, with the benefits of empirical models, in order to simulate yields over large areas (ie the regional scale). It does this by being process-based whilst having a small input data requirement. GLAM employs crop and climate science, as well as numerical modelling methods of carefully-chosen complexity. Challinor et al. (2009a,b) explain the philosophy behind the large-area approach, whilst Challinor et al. (2004) presents the original model. DescriptionTo gain access to the software, users must abide by the license agreement. Once the license has been completed you will be given access to the software. To proceed to the registration form, click here. ResultsGLAM is able to simulate interannual variability in crop yield (Figure 1), as well as picking out areas where climate extremes are likely to affect crops and assessing how a change in crop variety can be used to adapt to these changes (Figure 2). GLAM is often used with ensemble techniques, to produce seasonal hindcasts of crop yield (e.g. Challinor et al., 2005a) and to quantify the uncertainty associated with climate change (e.g. Challinor et at., 2009c). Figure 1. Observed and simulated crop yield (lines) for grid cell GJ, redrawn from the study of Challinor et al. (2004).
Figure 2. The impacts of extremes of temperature during anthesis in GLAM, for the A2 scenario for 2071-2100, using parameters and data from Challinor et al. (2005b,2007). Colours indicate the number of years when the total number of pods setting is below 50%, due to a high temperature threshold event during anthesis. The simulation tolerant crop simulation has not only a smaller area affected, but also a smaller magnitude of impact ReferencesChallinor, A. J., T. Osborne, A. Morse, L. Shaffrey, T. Wheeler, H. Weller (2009a). Methods and resources for climate impacts research: achieving synergy. Bulletin of the American Meteorological Society, 90 (6), 825–835. Challinor, A. J., F. Ewert, S. Arnold, E. Simelton and E. Fraser (2009b). Crops and climate change: progress, trends, and challenges in simulating impacts and informing adaptation. Journal of Experimental Botany 60 (10), 2775–2789. doi: 10.1093/jxb/erp062 Challinor, A. J., T. R. Wheeler, D. Hemming and H. D. Upadhyaya (2009c). Crop yield simulations using a perturbed crop and climate parameter ensemble: sensitivity to temperature and potential for genotypic adaptation to climate change. Climate Research, 38 117-127. Challinor, A. J., T. R. Wheeler, P. Q. Craufurd, C. A. T. Ferro and D. B. Stephenson (2007). Adaptation of crops to climate change through genotypic responses to mean and extreme temperatures. Agriculture, Ecosystems and Environment, 119 (1-2) 190-204. Challinor, A. J., J. M. Slingo, T. R. Wheeler and F. J. Doblas-Reyes (2005a). Probabilistic hindcasts of crop yield over western India. Tellus 57A 498-512. Challinor, A. J., T. R. Wheeler, P. Q. Craufurd, and J. M. Slingo (2005b). Simulation of the impact of high temperature stress on annual crop yields. Agric. For. Meteorol, 135 (1-4) 180-189. Challinor, A. J., T. R. Wheeler, J. M. Slingo, P. Q. Craufurd and D. I. F. Grimes (2004). Design and optimisation of a large-area process-based model for annual crops. Agricultural and Forest Meteorology, 124, (1-2) 99-120. Related Links
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