Evaluating the precision of eight spatial sampling schemes in estimating regional means of simulated yield for two crops. (June 2016) - Zhao, Gang; Hoffmann, Holger; Yeluripati, Jagadeesh; Xenia, Specka; Nendel, Claas; Coucheney, Elsa; Kuhnert, Matthias; Tao, Fulu; Constantin, Julie; Raynal, Helene; Teixeira, Edmar; Grosz, Balázs; et al.
Abstract: 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. Highlights: We compare eight sampling schemes for estimating regional mean crop yield. Precision of eight schemes is compared across fourteen crop models. Compact geographical stratification can always improve the precision. Stratification with soil had very high gains of precision for twelve crop models. Our findings can improve the precision of site-based regional crop modeling.