The General Large-Area Model for annual crops (GLAM)
Andy Challinor and team (University of Leeds)
|Model category||CSM, gbCSM|
|Scale||Field, Regional, Global|
|Format of model inputs and outputs||Text or nectcdf files|
|Species studied||Groundnut, Wheat, Maize|
Design and optimisation of a large-area process-based model for annual cropsA.J. Challinor,T.R. Wheeler,P.Q. Craufurd,J.M. Slingo,D.I.F.GrimesAgricultural and Forest Meteorology, 2004 View paper
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. This model is characterized by multiple versions (2004 original; 2019 GLAM-Parti with new structure and improved partitioning; 2020 version incorporating ozone damage; and a nitrogen-focussed version in development). GLAM is designed primarily for use with climate models, but current work includes targeted use of the model for crop breeding under climate change. The original 2004 version, with subsequent modifications for heat stress and elevated CO2, is available under licence agreement.
Some case studies
Some recent studies using GLAM:
Climate Change Resilience: We lead the modelling component of the AFRICAP project (Agricultural and Food-system Resilience: Increasing Capacity and Advising Policy) in partnership with the pan-African Food, Agriculture and Natural Resources Policy Analysis Network. This project combines on-farm monitoring, laboratory research and policy analysis to enhance development of climate smart agri-food systems in sub-Saharan Africa.
Forecasting: We are working on the AfriCultuReS project - Enhancing Food Security in African Agricultural Systems with the Support of Remote Sensing. The goals of the AfriCultuReS project is to enrich decision making on food security through an open source agricultural geospatial decision support system. This project integrates crop growth models, weather and climate forecasts and satellite data to produce an integrated operational platform for crop monitoring and forecasting of food production.
Climate Smart Adaptation: In the BACO project, we are working with the International Center for Tropical Agriculture (CIAT) in Colombia to definite target population environments (TPEs) for heat stress. The aim of the project is to assist bean breeders to produce climate smart material.
Climate Variability and Human Health: We are in the process of developing new crop models to assess the impact of ozone on the production of major cereal crops.