General Large-Area Model for annual crops (GLAM)
The General Large-Area Model for annual crops (GLAM) is a tool used for research purposes 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 is a regional-scale crop model that was developed to operate on the grid of global and regional climate models. Hence GLAM is process-based, but is less complex than field scale models. It parameterises the impact of weather and climate on crops; it does not explicitly simulate biotic stresses but implicitly includes their impact using a yield gap parameter.
GLAM simulates the impact of climate variability and change on crops by using daily weather information to determine the growth and development of the crop, from sowing to harvest. By simulating different varietal properties, the model can be used in developing and assessing genotypic adaptation strategies.
The model can be used as part of studies that need to turn gridded weather data into crop productivity outcomes. Our setup is particularly well-suited to producing tens of thousands of simulations in order to quantify uncertainty and obtain robust results.
The model requires daily time series of rainfall and solar radiation, and either: i. maximum and minimum temperatures or ii. humidity and mean temperature. If daily data are not available then, with the exception of rainfall, data may be interpolated. Soil hydrological properties can also be used, though these are not required. The planting window is set as an external input. The model also needs crop yield data for calibration.
Crop yield, biomass, leaf area index, water balance (transpiration, runoff, evaporation, drainage) and many other outputs can be analysed at seasonal and daily timesteps.
Crops and regions
GLAM has been used across the globe; principal regional foci at Leeds include India, Africa and China. The model was originally designed for groundnut (peanut) in India and has since been extended for spring and winter wheat, sorghum, soybean, millet, potato and maize. It can be run for any region for which there is crop yield data. Re-running existing crop/region combinations is quicker than applying the model to new regions.
- Challinor, A. J., Wheeler, T. R., Slingo, J. M., Craufurd, P., & Grimes, D. (2004), Design and optimisation of a large-area process-based model for annual crops, Agricultural and Forest Meteorology, 124(1-2), 99-120
- Droutsas, I., Challinor, A. J., Arnold, S. R., Mikkelsen, T. N., & Hansen, E. M. Ø (2020), A new model of ozone stress in wheat including grain yield loss and plant acclimation to the pollutant, European Journal of Agronomy, 120
- Droutsas, I., Challinor, A. J., Swiderski, M., & Semenov, M. A. (2019), New modelling technique for improving crop model performance - Application to the GLAM model, Environmental Modelling and Software, 118, 187-200
- Challinor, A. J., Wheeler, T. R., Craufurd, P., & Slingo, J. M. (2005), Simulation of the impact of high temperature stress on annual crop yields, Agricultural and Forest Meteorology, 135(1-4), 180-189
- Challinor, A. J., Wheeler, T. R., Slingo, J. M., & Hemming, D. (2005), Quantification of physical and biological uncertainty in the simulation of the yield of a tropical crop using present-day and doubled CO2 climates, Philosophical transactions of the Royal Society of London. B: Biological Sciences, 360, 2085-2094
- Challinor, A. J., Slingo, J. M., Wheeler, T. R., & Doblas-Reyes, F. J. (2005), Probabilistic simulations of crop yield over western India using the DEMETER seasonal hindcast ensembles, Tellus, 57(3), 498-512
- Challinor, A. J., Wheeler, T. R., Slingo, J. M., Craufurd, P., & Grimes, D. (2005), Simulation of crop yields using ERA-40: limits to skill and nonstationarity in weather–yield relationships, Journal of Applied Meteorology, 44(4), 516-531
- Watson, J., Challinor, A. J., Fricker, T. E., & Ferro, C. A. T. (2015), Comparing the effects of calibration and climate errors on a statistical crop model and a process-based crop model, Climatic Change, 132(1), 93-109
- Watson, J., & Challinor, A. (2013), The relative importance of rainfall, temperature and yield data for a regional-scale crop model, Agricultural and Forest Meteorology, 170, 47-57
- Challinor, A. J., Osborne, T., Morse, A., Shaffrey, L., Wheeler, T., Weller, H., & Vidale, P. L. (2009), Methods and resources for climate impacts research achieving synergy, Bulletin of the American Meteorological Society, 90(6), 836-848
- Challinor, A. J., Wheeler, T., Hemming, D., & Upadhyaya, H. D. (2009), Ensemble yield simulations: crop and climate uncertainties, sensitivity to temperature and genotypic adaptation to climate change, Climate research, 38(2), 117-127
- Challinor, A. J., & Wheeler, T. R. (2008), Crop yield reduction in the tropics under climate change: processes and uncertainties, Agricultural and Forest Meteorology, 148(3), 343-356
- Challinor, A. J., & Wheeler, T. R. (2008), Use of a crop model ensemble to quantify CO2 stimulation of water-stressed and well-watered crops, Agricultural and Forest Meteorology, 148(6-7), 1062-1077
- Challinor, A. J., Wheeler, T. R., Craufurd, P., Ferro, C. A. T., & Stephenson, D. B. (2007), Adaptation of crops to climate change through genotypic responses to mean and extreme temperatures, Agriculture, Ecosystems and Environment, 119(1-2), 190-204
- Falconnier, G. N., Corbeels, M., Boote, K. J., Affholder, F., Adam, M., MacCarthy, D. S., Webber, H. (2020), Modelling climate change impacts on maize yields under low nitrogen input conditions in sub‐Saharan Africa, Global Change Biology
- Yang, H., Dobbie, S., Ramirez-Villegas, J., Chen, B., Qiu, S., Ghosh, S., & Challinor, A. (2020), South India projected to be susceptible to high future groundnut failure rates for future climate change and geo-engineered scenarios, Science of The Total Environment
- Ramirez-Villegas, J., Koehler, A. K., & Challinor, A. J. (2017), Assessing uncertainty and complexity in regional-scale crop model simulations, European Journal of Agronomy, 88, 84-95
- Yang, H., Dobbie, S., Ramirez-Villegas, J., Feng, K., Challinor, A. J., Chen, B., Ghosh, S. (2016), Potential negative consequences of geoengineering on crop production: a study of Indian groundnut, Geophysical Research Letters, 43(22), 11786-11795
- Ramirez-Villegas, J., & Challinor, A. J. (2016), Towards a genotypic adaptation strategy for Indian groundnut cultivation using an ensemble of crop simulations, Climatic Change, 138(1), 223-238
- Ruane, A. C., Hudson, N. I., Asseng, S., Camarrano, D., Ewert, F., Martre, P., Wolf, J. (2016), Multi-wheat-model ensemble responses to interannual climate variability, Environmental Modelling and Software, 81, 86-101
- Parkes, B., Challinor, A. J., & Nicklin, K. (2015), Crop failure rates in a geoengineered climate: impact of climate change and marine cloud brightening, Environmental Research Letters, 10(8)
- Bergamaschi, H., Costa, S. M. S. D., Wheeler, T. R., & Challinor, A. J. (2013), Simulating maize yield in sub‑tropical conditions of southern Brazil using Glam model, Pesquisa Agropecuária Brasileira, 48(2), 132-140
- Koehler, A. -K., Challinor, A. J., Hawkins, E., & Asseng, S. (2013), Influences of increasing temperature on Indian wheat: quantifying limits to predictability, Environmental Research Letters, 8(3), 034016
- Challinor, A. J., Simelton, E. S., Fraser, E. D. G., Hemming, D., & Collins, M. (2010), Increased crop failure due to climate change: assessing adaptation options using models and socio-economic data for wheat in China., Environmental Research Letters, 5(3)
- Sanai, L. I., Wheeler, T., Challinor, A. J., Erda, L. I. N., & Yinlong, W. U. (2010), Simulating the impacts of global warming on wheat in China using a large area crop model, Acta Meteorologica Sinica, 24(1), 123-135
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