Abstract

We compared the precision of simple random sampling (SimRS) and seven types of stratified 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 modesty 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.

Keywords

crop model, stratified random sampling, simple random sampling, clustering, up-scaling, model comparison, precision gain

Published in

Environmental Modelling and Software
2016, Volume: 80, pages: 100-112

SLU Authors

• Coucheney, Elsa

  • Department of Soil and Environment, Swedish University of Agricultural Sciences
    

• Lewan, Elisabet

  • Department of Soil and Environment, Swedish University of Agricultural Sciences
    

• Eckersten, Henrik

  • Department of Crop Production Ecology, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Agricultural Science


Publication identifier

DOI: https://doi.org/10.1016/j.envsoft.2016.02.022

Permanent link to this page (URI)

https://res.slu.se/id/publ/78398