Increasing the Global Climate Change Teams Technical Support Capacity to Global Climate Change-Program
DESCRIPTION: This grant provided support for the coordination and reporting of a workshop focused on advancing global agricultural assessments held at Columbia University on 9-11 April, 2014. Sub-Saharan Africa was the primary study region. This grant also provided support for research staff, which advanced multi model crop and climate impact assessment by further developing the Global Gridded Crop Model Initiative (GGCMI) Phase 1 output processing and validation pipeline. This has included the release of the first time-stamped validation archive for GGCMI to the participants and paper writing teams. Research staff directly contributed to publications, integrated a new global gridded soil dataset (the Chinese GSDE), implemented and tested a method for dynamic application of soil pedotransfer functions directly in the multi model psims platform, helped to perform several comparisons between gridded soil datasets in Africa, and participated in workshops on soil data and next generation IT for crop modeling.
OUTCOMES: Notable results from the project include the development of a distinct instance of S-world specific for Africa (S-world Africa) that uses the AfSIS soil profile database and greatly increased the number of output variables; simulations and preliminary comparisons of maize yields in Sub-Saharan Africa using 3 soil datasets of increasing complexity from Harvest Choice; translators for soil datasets (pSIMS), and pedotransfer functions to be used by modeling community and AgGRID; the incorporation of AfSIS dataset in AgGRID framework; achieving agreement on how best to harmonize soil initial conditions (H2O, residues, mineral nitrogen (NO3), and soil carbon pools (SOC) to allow for effective and prudent intercomparisons; and harmonizing point-based simulation comparisons between APSIM and DSSAT to evaluate importance of initial conditions and the importance of long-term continuous series (sequential analysis) for capturing soil dynamics, especially areas with poor soil management.