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Zhao, G.* ; Hoffmann, H.* ; Yeluripati, J.* ; Xenia, S.* ; Nendel, C.* ; Coucheney, E.* ; Kuhnert, M.* ; Tao, F.* ; Constantin, J.* ; Raynal, H.* ; Teixeira, E.* ; Grosz, B.* ; Doro, L.* ; Kiese, R.* ; Eckersten, H.* ; Haas, E.* ; Cammarano, D.* ; Kassie, B.* ; Moriondo, M.* ; Trombi, G.* ; Bindi, M.* ; Biernath, C.J. ; Heinlein, F. ; Klein, C. ; Priesack, E. ; Lewan, E.* ; Kersebaum, K.C.* ; Rötter, R.P.* ; Roggero, P.P.* ; Wallach, D.* ; Asseng, S.* ; Siebert, S.* ; Gaiser, T.* ; Ewert, F.*

Evaluating the precision of eight spatial sampling schemes in estimating regional means of simulated yield for two crops.

Environ. Modell. Softw. 80, 100-112 (2016)
Postprint DOI
Open Access Green
We compared the precision of simple random sampling (SimRS) and seven types of stratified random sampling (StrRS) schemes in estimating regional mean of water-limited yields for two crops (winter wheat and silage maize) that were simulated by fourteen crop models. We found that the precision gains of StrRS varied considerably across stratification methods and crop models. Precision gains for compact geographical stratification were positive, stable and consistent across crop models. Stratification with soil water holding capacity had very high precision gains for twelve models, but resulted in negative gains for two models. Increasing the sample size monotonously decreased the sampling errors for all the sampling schemes. We conclude that compact geographical stratification can modestly but consistently improve the precision in estimating regional mean yields. Using the most influential environmental variable for stratification can notably improve the sampling precision, especially when the sensitivity behavior of a crop model is known.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Clustering ; Crop Model ; Model Comparison ; Precision Gain ; Simple Random Sampling ; Stratified Random Sampling ; Up-scaling; Species Distribution Models; Systems Simulation; Weather Data; Large-scale; Design; Soil; Optimization; Growth; Apsim; Autocorrelation
Sprache englisch
Veröffentlichungsjahr 2016
HGF-Berichtsjahr 2016
ISSN (print) / ISBN 1364-8152
e-ISSN 1873-6726
Zeitschrift Environmental Modelling and Software
Quellenangaben Band: 80, Heft: , Seiten: 100-112 Artikelnummer: , Supplement: ,
Verlag Elsevier
Verlagsort Oxford
Begutachtungsstatus Peer reviewed
Institut(e) Research Unit Environmental Simulation (EUS)
POF Topic(s) 30202 - Environmental Health
Forschungsfeld(er) Environmental Sciences
PSP-Element(e) G-504912-001
DOI 10.1016/j.envsoft.2016.02.022
WOS ID WOS:000376218300009
Scopus ID 84961774633
Erfassungsdatum 2016-04-11
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