Effect of weather data aggregation on regional crop simulation for different crops, production conditions, and response variables
Gang Zhao
(1)
,
Holger Hoffmann
(1)
,
Lenny G. J. van Bussel
(1, 2)
,
Andreas Enders
(1)
,
Xenia Specka
(3)
,
Carmen Sosa
(4)
,
Jagadeesh Yeluripati
(5, 6)
,
Fulu Tao
(7)
,
Julie J. Constantin
(8)
,
Helene H. Raynal
(8)
,
Edmar Teixeira
(9)
,
Balázs Grosz
(10)
,
Luca Doro
(11)
,
Zhigan Zhao
(12)
,
Claas Nendel
(3)
,
Ralf Kiese
(13)
,
Henrik Eckersten
(14)
,
Edwin Haas
(13)
,
Eline Vanuytrecht
(15)
,
Enli Wang
(12)
,
Matthias Kuhnert
(5)
,
Giacomo Trombi
(16)
,
Marco Moriondo
(17)
,
Marco Bindi
(16)
,
Elisabet Lewan
(4)
,
Michaela Bach
(10)
,
Kurt-Christian Kersebaum
(3)
,
Reimund Roetter
(7)
,
Pier Paolo Roggero
(11)
,
Daniel D. Wallach
(8)
,
Davide Cammarano
(18)
,
Senthold Asseng
(19)
,
Gunther Krauss
(1)
,
Stefan Siebert
(1)
,
Thomas Gaiser
(1)
,
Frank Ewert
(1)
1
INRES -
Institute of Crop Science and Resource Conservation [Bonn]
2 Plant Production Systems Group
3 Institute of landscape systems analysis
4 SLU - Swedish University of Agricultural Sciences = Sveriges lantbruksuniversitet
5 INSTITUTE OF BIOLOGICAL AND ENVIRONMENTAL SCIENCES
6 The James Hutton Institute
7 Agrifood Research Finland
8 AGIR - AGroécologie, Innovations, teRritoires
9 Systems Modelling Team (Sustainable Production Group)
10 Johann Heinrich von Thünen-Institut = Thünen Institute
11 UNISS - Università degli Studi di Sassari = University of Sassari [Sassari]
12 CSIRO - Commonwealth Scientific and Industrial Research Organisation [Canberra]
13 KIT - Karlsruhe Institute of Technology = Karlsruher Institut für Technologie
14 Department of Crop Production Ecology
15 Division Soil and Water Management
16 Department of Agri-Food Production and Environmental Sciences
17 IBIMET - Istituto di Biometeorologia [Firenze]
18 UF|ABE - Department of Agricultural and Biological Engineering [Gainesville]
19 UF - University of Florida [Gainesville]
2 Plant Production Systems Group
3 Institute of landscape systems analysis
4 SLU - Swedish University of Agricultural Sciences = Sveriges lantbruksuniversitet
5 INSTITUTE OF BIOLOGICAL AND ENVIRONMENTAL SCIENCES
6 The James Hutton Institute
7 Agrifood Research Finland
8 AGIR - AGroécologie, Innovations, teRritoires
9 Systems Modelling Team (Sustainable Production Group)
10 Johann Heinrich von Thünen-Institut = Thünen Institute
11 UNISS - Università degli Studi di Sassari = University of Sassari [Sassari]
12 CSIRO - Commonwealth Scientific and Industrial Research Organisation [Canberra]
13 KIT - Karlsruhe Institute of Technology = Karlsruher Institut für Technologie
14 Department of Crop Production Ecology
15 Division Soil and Water Management
16 Department of Agri-Food Production and Environmental Sciences
17 IBIMET - Istituto di Biometeorologia [Firenze]
18 UF|ABE - Department of Agricultural and Biological Engineering [Gainesville]
19 UF - University of Florida [Gainesville]
Gang Zhao
- Fonction : Auteur correspondant
Julie J. Constantin
- Fonction : Auteur
- PersonId : 736250
- IdHAL : julie-constantin
- ORCID : 0000-0001-9647-5374
Helene H. Raynal
- Fonction : Auteur
- PersonId : 745427
- IdHAL : helene-raynal
- ORCID : 0000-0002-3492-0564
Luca Doro
- Fonction : Auteur
- PersonId : 773546
- ORCID : 0000-0003-1404-2255
Claas Nendel
- Fonction : Auteur
- PersonId : 772081
- ORCID : 0000-0001-7608-9097
Frank Ewert
- Fonction : Auteur
- PersonId : 968227
Résumé
We assessed the weather data aggregation effect (DAE) on the simulation of cropping systems for different crops, response variables, and production conditions. Using 13 process-based crop models and the ensemble mean, we simulated 30 yr continuous cropping systems for 2 crops (winter wheat and silage maize) under 3 production conditions for the state of North Rhine-Westphalia, Germany. The DAE was evaluated for 5 weather data resolutions (i.e. 1, 10, 25, 50, and 100 km) for 3 response variables including yield, growing season evapotranspiration, and water use efficiency. Five metrics, viz. the spatial bias (Delta), average absolute deviation (AAD), relative AAD, root mean squared error (RMSE), and relative RMSE, were used to evaluate the DAE on both the input weather data and simulated results. For weather data, we found that data aggregation narrowed the spatial variability but widened the., especially across mountainous areas. The DAE on loss of spatial heterogeneity and hotspots was stronger than on the average changes over the region. The DAE increased when coarsening the spatial resolution of the input weather data. The DAE varied considerably across different models, but changed only slightly for different production conditions and crops. We conclude that if spatially detailed information is essential for local management decision, higher resolution is desirable to adequately capture the spatial variability for heterogeneous regions. The required resolution depends on the choice of the model as well as the environmental condition of the study area.