Effects of input data aggregation on simulated crop yields in temperate and Mediterranean climates
Résumé
Soil-crop models are used to simulate ecological processes from the field to the regional scale. Main inputs are soil and
climate data in order to simulate model response variables such as crop yield. We investigate the effect of changing the
resolution of input data on simulated crop yields at a regional scale using up to ten dynamic crop models. For these
models we compared the effects of spatial input data aggregation for wheat and maize yield of two regions with
contrasting climate conditions (1) Tuscany (Italy, Mediterranean climate) and (2) North Rhine Westphalia (NRW,
Germany, temperate climate). Soil and climate data of 1 km resolution were aggregated to resolutions of 10, 25, 50, and
100 km by selecting the dominant soil class (and corresponding soil properties) and by arithmetic averaging, respectively.
Differences in yield simulated at coarser resolutions from the yields simulated at 1 km resolution were calculated
to quantify the effect of the aggregation of the input data (soil and climate data) on simulation results.
The mean yield difference (bias) at the regional level was positive due to the upscaling of productive
dominant soil(s) to coarser resolution. In both regions and for both crops, aggregation effects (i.e. errors in
simulation of crop yields at coarser spatial resolution) due to the combined aggregation of soil and climate input
data increased with decreasing resolution, whereby the aggregation error for Tuscany was larger than for North
Rhine Westphalia (NRW). The average absolute percentage yield differences between grid cell yields at the
coarsest resolution (100 km) compared to the finest resolution (1 km) were by about 20–30% for Tuscany and
less than 15 and 20% for NRW for winter wheat and silage maize, respectively.
In the Mediterranean area, the prediction errors of the simulated yields could reach up to 60% when looking
at individual crop model simulations. Additionally, aggregating soil data caused larger aggregation errors in
both regions than aggregating climate data.
Those results suggest that a higher spatial resolution of climate and especially of soil input data are necessary
in Mediterranean areas than in temperate humid regions of central Europe in order to predict reliable regional
yield estimations with crop models. For generalization of these outcomes, further investigations in other subhumid
or semi-arid regions will be necessary.
Auteurs, date et publication :
Auteurs Ganga Maharjan , Holger Hoffmann , Heidi Webber , Amit Srivastava , Lutz Weihermüller , A. Villa Solis , Elsa Coucheney , Elisabet Lewan , Giacomo Trombi , Marco Moriondo , Marco Bindi , Balazs Grosz , Rene Dechow , Matthias Kuhnert , Luca Doro , Kurt Kersebaum , Tommaso Stella , Xenia Specka , Claas Nendel , Thomas Gaiser
Publication : European Journal of Agronomy
Date : 2019
Volume : 103
Pages : 32-46