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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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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords 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
ISSN (print) / ISBN 1364-8152
e-ISSN 1873-6726
Journal Environmental Modelling and Software
Quellenangaben Volume: 80, Issue: , Pages: 100-112 Article Number: , Supplement: ,
Publisher Elsevier
Publishing Place Oxford
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Biochemical Plant Pathology (BIOP)