The aggregation of simulated gridded crop yields to national or regional scale requires information on temporal and spatial patterns of crop-specific harvested areas. This analysis estimates the uncertainty of simulated gridded yield time series related to the aggregation with four different harvested area data sets. We compare aggregated yield time series from the Global Gridded Crop Model Intercomparison project for four crop types from 14 models at global, national, and regional scale to determine aggregation-driven differences in mean yields and temporal patterns as measures of uncertainty. The quantity and spatial patterns of harvested areas differ for individual crops among the four data sets applied for the aggregation. Also simulated spatial yield patterns differ among the 14 models. These differences in harvested areas and simulated yield patterns lead to differences in aggregated productivity estimates, both in mean yield and in the temporal dynamics. Among the four investigated crops, wheat yield (17% relative difference) is most affected by the uncertainty introduced by the aggregation at the global scale. The correlation of temporal patterns of global aggregated yield time series can be as low as for soybean (r = 0.28). For the majority of countries, mean relative differences of nationally aggregated yields account for 10% or less. The spatial and temporal difference can be substantial higher for individual countries. Of the top-10 crop producers, aggregated national multi-annual mean relative difference of yields can be up to 67% (maize, South Africa), 43% (wheat, Pakistan), 51% (rice, Japan), and 427% (soybean, Bolivia). Correlations of differently aggregated yield time series can be as low as r = 0.56 (maize, India), r = 0.05 (wheat, Russia), r = 0.13 (rice, Vietnam), and r = −0.01 (soybean, Uruguay). The aggregation to sub-national scale in comparison to country scale shows that spatial uncertainties can cancel out in countries with large harvested areas per crop type. We conclude that the aggregation uncertainty can be substantial for crop productivity and production estimations in the context of food security, impact assessment, and model evaluation exercises.
Bibliographical noteFunding Information:
We acknowledge the support and data provision by the Agricultural Intercomparison and Improvement Project (AgMIP), the Intersectoral Impact Model Intercomparison Project (ISIMIP), and the contributing modelers. V.P. and C.M. acknowledge financial support from the MACMIT project (01LN1317A) funded through the German Federal Ministry of Education and Research (BMBF). A.A. and T.A.M.P. were supported by the European Commission's 7th Framework Programme under Grant Agreement number 603542 (LUC4C) and by the Helmholtz Association through its research program ATMO. This represents paper number 21 of the Birmingham Institute of Forest Research.
- Aggregation uncertainty
- Crop yields
- Global crop model
- Gridded data
- Harvested area