Spatial sampling of weather data for regional crop yield simulations

Autor: Florian Heinlein, Guillermo A. Baigorria, Daniel Wallach, Davide Cammarano, Michael Glotter, Frank Ewert, Consuelo C. Romero, Eckart Priesack, Bruno Basso, Christian Klein, Senthold Asseng, Fulu Tao, Helene Raynal, Claas Nendel, James P. Chryssanthacopoulos, Christian Biernath, Holger Hoffmann, Andreas Enders, Julie Constantin, Reimund P. Rötter, Lenny G.J. van Bussel, Joshua Elliott, Xenia Specka, Kurt Christian Kersebaum, Gang Zhao
Přispěvatelé: Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Plant Production Systems, Wageningen University and Research [Wageningen] (WUR), AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de la Recherche Agronomique (INRA), Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE), Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS), University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF), University of Nebraska [Lincoln], University of Nebraska System, Department of geological sciences, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, German Research Center for Environmental Health - Helmholtz Center München (GmbH), Center for Climate Systems Research [New York] (CCSR), Columbia University [New York], University of Chicago, German Research Center for Environmental Health, Institute of Soil Ecology, Helmholtz-Zentrum München (HZM), Institute of landscape systems analysis, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Leibniz Centre for Agricultural Landscape Research (ZALF), Environmental Impacts Group, Natural resources institute Finland
Jazyk: angličtina
Rok vydání: 2016
Předmět:
Atmospheric Science
010504 meteorology & atmospheric sciences
Regional Crop Simulations
Stratified Sampling
Upscaling
Winter Wheat
Yield Estimates
Yield estimates
01 natural sciences
stratified sampling
Statistics
Range (statistics)
Regional crop simulations
[INFO.INFO-MS]Computer Science [cs]/Mathematical Software [cs.MS]
0105 earth and related environmental sciences
2. Zero hunger
Hydrology
Global and Planetary Change
Crop yield
Stratified sampling
Sampling (statistics)
Forestry
04 agricultural and veterinary sciences
15. Life on land
Missing data
PE&RC
Winter wheat
Stratification (seeds)
Plant Production Systems
Sample size determination
Plantaardige Productiesystemen
Weather data
040103 agronomy & agriculture
0401 agriculture
forestry
and fisheries
Environmental science
[INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM]
[SDE.BE]Environmental Sciences/Biodiversity and Ecology
Agronomy and Crop Science
Zdroj: Agricultural and Forest Meteorology, 220, 101-115
Agricultural and Forest Meteorology
Agricultural and Forest Meteorology, Elsevier Masson, 2016, 220, pp.101-115. ⟨10.1016/j.agrformet.2016.01.014⟩
Agricultural and Forest Meteorology 220 (2016)
Agric. For. Meteorol. 220, 101-115 (2016)
ISSN: 0168-1923
Popis: International audience; Field-scale crop models are increasingly applied at spatio-temporal scales that range from regions to the globe and from decades up to 100 years. Sufficiently detailed data to capture the prevailing spatio- temporal heterogeneity in weather, soil, and management conditions as needed by crop models are rarely available. Effective sampling may overcome the problem of missing data but has rarely been investigated. In this study the effect of sampling weather data has been evaluated for simulating yields of winter wheat in a region in Germany over a 30-year period (1982–2011) using 12 process-based crop models. A stratified sampling was applied to compare the effect of different sizes of spatially sampled weather data (10, 30, 50, 100, 500, 1000 and full coverage of 34,078 sampling points) on simulated wheat yields. Stratified sampling was further compared with random sampling. Possible interactions between sample size and crop model were evaluated. The results showed differences in simulated yields among crop models but all models reproduced well the pattern of the stratification. Importantly, the regional mean of simulated yields based on full coverage could already be reproduced by a small sample of 10 points. This was also true for reproducing the temporal variability in simulated yields but more sampling points (about 100) were required to accurately reproduce spatial yield variability. The number of sampling points can be smaller when a stratified sampling is applied as compared to a random sampling. However, differences between crop models were observed including some interaction between the effect of sampling on simulated yields and the model used. We concluded that stratified sampling can considerably reduce the number of required simulations. But, differences between crop models must be considered as the choice for a specific model can have larger effects on simulated yields than the sampling strategy. Assessing the impact of sampling soil and crop management data for regional simulations of crop yields is still needed.
Databáze: OpenAIRE
Externí odkaz: