Spatial and temporal uncertainty of crop yield aggregations

Vera Porwollik (1) , Christoph Müller (1) , Joshua Elliott (2) , James Chryssanthacopoulos (2) , Toshichika Iizumi (3) , Deepak Ray (4) , Alex Ruane (5) , Almut Arneth (6) , Juraj Balkovič (7, 8) , Philippe Ciais (9) , Delphine Deryng (10) , Christian Folberth (7, 11) , Roberto Izaurralde (12) , Curtis Jones (13) , Nikolay Khabarov (7) , Peter Lawrence (14) , Wenfeng Liu (15) , Thomas A.M. Pugh (16, 17) , Ashwan Reddy (13) , Gen Sakurai (18) , Erwin Schmid (19) , Xuhui Wang (20) , Allard de Wit (21) , Xiuchen Wu (22)

1 PIK - Potsdam Institute for Climate Impact Research
2 CCSR - Center for Climate Systems Research [New York]
3 NIAES - National Institute of Agro-Environmental Sciences
4 Department of Civil, Environmental and Geo-Engineering [Minneapolis]
5 GISS - NASA Goddard Institute for Space Studies
6 IMK-IFU - Institut für Meteorologie und Klimaforschung - Atmosphärische Umweltforschung
7 IIASA - International Institute for Applied Systems Analysis [Laxenburg]
8 Comenius University in Bratislava
9 ICOS-ATC - ICOS-ATC
10 Tyndall Centre for Climate Change Research
11 LMU - Ludwig Maximilian University [Munich] = Ludwig Maximilians Universität München
12 University of Maryland [College Park]
13 Department of Geographical Sciences [College Park]
14 NCAR - National Center for Atmospheric Research [Boulder]
15 EAWAG - Swiss Federal Insitute of Aquatic Science and Technology [Dübendorf]
16 KIT - Karlsruher Institut für Technologie
17 School of Geography, Earth and Environmental Sciences [Birmingham]
18 NARO - National Agriculture and Food Research Organization
19 BOKU - Universität für Bodenkultur Wien = University of Natural Resources and Life [Vienne, Autriche]
20 Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences
21 WUR - Wageningen University and Research [Wageningen]
22 LSCE - Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette]

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.

Mots clés: Aggregation uncertainty; global crop model; crop yields; gridded data; harvested area

Domaines: Agronomie

Vera Porwollik, Christoph Müller, Joshua Elliott, James Chryssanthacopoulos, Toshichika Iizumi, et al.. Spatial and temporal uncertainty of crop yield aggregations. European Journal of Agronomy, 2017, 88, pp.10-21. ⟨10.1016/j.eja.2016.08.006⟩. ⟨hal-02903790⟩