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

Gang Zhao*, Holger Hoffmann, Jagadeesh Yeluripati, Specka Xenia, Claas Nendel, Elsa Coucheney, Matthias Kuhnert, Fulu Tao, Julie Constantin, Helene Raynal, Edmar Teixeira, Balazs Grosz, Luca Doro, Ralf Kiese, Henrik Eckersten, Edwin Haas, Davide Cammarano, Belay Kassie, Marco Moriondo, Giacomo Trombi & 14 others Marco Bindi, Christian Biernath, Florian Heinlein, Christian Klein, Eckart Priesack, Elisabet Lewan, Kurt-Christian Kersebaum, Reimund Rotter, Pier Paolo Roggero, Daniel Wallach, Senthold Asseng, Stefan Siebert, Thomas Gaiser, Frank Ewert

*Corresponding author for this work

Research output: Contribution to journalArticle

15 Citations (Scopus)

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. (C) 2016 Elsevier Ltd. All rights reserved.

Original languageEnglish
Pages (from-to)100-112
Number of pages13
JournalEnvironmental Modelling and Software
Volume80
Early online date27 Feb 2016
DOIs
Publication statusPublished - Jun 2016

Keywords

  • Crop model
  • Stratified random sampling
  • Simple random sampling
  • Clustering
  • Up-scaling
  • Model comparison
  • Precision gain
  • Species Distribution Models
  • Systems simulation
  • Weather Data
  • Large-Scale
  • Design
  • Soil
  • Optimization
  • Growth
  • Apism
  • Autocorrelation

Cite this

Evaluating the precision of eight spatial sampling schemes in estimating regional means of simulated yield for two crops. / Zhao, Gang; Hoffmann, Holger; Yeluripati, Jagadeesh; Xenia, Specka; Nendel, Claas; Coucheney, Elsa; Kuhnert, Matthias; Tao, Fulu; Constantin, Julie; Raynal, Helene; Teixeira, Edmar; Grosz, Balazs; Doro, Luca; Kiese, Ralf; Eckersten, Henrik; Haas, Edwin; Cammarano, Davide; Kassie, Belay; Moriondo, Marco; Trombi, Giacomo; Bindi, Marco; Biernath, Christian; Heinlein, Florian; Klein, Christian; Priesack, Eckart; Lewan, Elisabet; Kersebaum, Kurt-Christian; Rotter, Reimund; Roggero, Pier Paolo; Wallach, Daniel; Asseng, Senthold; Siebert, Stefan; Gaiser, Thomas; Ewert, Frank.

In: Environmental Modelling and Software, Vol. 80, 06.2016, p. 100-112.

Research output: Contribution to journalArticle

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, Heinlein, F, Klein, C, Priesack, E, Lewan, E, Kersebaum, K-C, Rotter, R, Roggero, PP, Wallach, D, Asseng, S, Siebert, S, Gaiser, T & Ewert, F 2016, 'Evaluating the precision of eight spatial sampling schemes in estimating regional means of simulated yield for two crops', Environmental Modelling and Software, vol. 80, pp. 100-112. https://doi.org/10.1016/j.envsoft.2016.02.022
Zhao, Gang ; Hoffmann, Holger ; Yeluripati, Jagadeesh ; Xenia, Specka ; Nendel, Claas ; Coucheney, Elsa ; Kuhnert, Matthias ; Tao, Fulu ; Constantin, Julie ; Raynal, Helene ; Teixeira, Edmar ; Grosz, Balazs ; Doro, Luca ; Kiese, Ralf ; Eckersten, Henrik ; Haas, Edwin ; Cammarano, Davide ; Kassie, Belay ; Moriondo, Marco ; Trombi, Giacomo ; Bindi, Marco ; Biernath, Christian ; Heinlein, Florian ; Klein, Christian ; Priesack, Eckart ; Lewan, Elisabet ; Kersebaum, Kurt-Christian ; Rotter, Reimund ; Roggero, Pier Paolo ; Wallach, Daniel ; Asseng, Senthold ; Siebert, Stefan ; Gaiser, Thomas ; Ewert, Frank. / Evaluating the precision of eight spatial sampling schemes in estimating regional means of simulated yield for two crops. In: Environmental Modelling and Software. 2016 ; Vol. 80. pp. 100-112.
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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. (C) 2016 Elsevier Ltd. All rights reserved.",
keywords = "Crop model, Stratified random sampling, Simple random sampling, Clustering, Up-scaling, Model comparison, Precision gain, Species Distribution Models, Systems simulation, Weather Data, Large-Scale, Design, Soil, Optimization, Growth, Apism, Autocorrelation",
author = "Gang Zhao and Holger Hoffmann and Jagadeesh Yeluripati and Specka Xenia and Claas Nendel and Elsa Coucheney and Matthias Kuhnert and Fulu Tao and Julie Constantin and Helene Raynal and Edmar Teixeira and Balazs Grosz and Luca Doro and Ralf Kiese and Henrik Eckersten and Edwin Haas and Davide Cammarano and Belay Kassie and Marco Moriondo and Giacomo Trombi and Marco Bindi and Christian Biernath and Florian Heinlein and Christian Klein and Eckart Priesack and Elisabet Lewan and Kurt-Christian Kersebaum and Reimund Rotter and Roggero, {Pier Paolo} and Daniel Wallach and Senthold Asseng and Stefan Siebert and Thomas Gaiser and Frank Ewert",
note = "Acknowledgments This work was financially supported by the German Federal Ministry of Food and Agriculture (BMEL) through the Federal Office for Agriculture and Food (BLE), grant number 2851ERA01J. The funding support of the Joint Programme Initiative FACCE, MACSUR Knowledge Hub, is greatly acknowledged. G.Z. was supported by the German Federal Ministry of Education and Research (BMBF) through the SPACES project “Living Landscapes Limpopo” and WASCAL (West African Science Service Center on Climate Change and Adapted Land Use) project. S.A. and D.C. were partly funded by a NOAA RISA grant. FT and RR was funded through the FACCE MACSUR project by the Finnish Ministry of Agriculture and Forestry (MMM). E.L. and E.C. was supported by The Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning (contract 220-2007-1218) and by the strategic funding “Soil-Water-Landscape” from the faculty of Natural Resources and Agricultural Sciences (Swedish University of Agricultural Sciences). We are grateful to Eric Casellas from MIA-T INRA Toulouse for performing STICS simulation under the RECORD platform and ACCAF INRA meta-program for funding JC, HR and DW. We thank Professor Per-Erik Jansson (Royal Institute of Technology in Stockholm) for valuable support and recent development of the CoupModel. We are grateful to Gunther Krauss for checking the mathematic equations and terminologies. Comments from three reviewers greatly improved the quality of the manuscript.",
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T1 - Evaluating the precision of eight spatial sampling schemes in estimating regional means of simulated yield for two crops

AU - Zhao, Gang

AU - Hoffmann, Holger

AU - Yeluripati, Jagadeesh

AU - Xenia, Specka

AU - Nendel, Claas

AU - Coucheney, Elsa

AU - Kuhnert, Matthias

AU - Tao, Fulu

AU - Constantin, Julie

AU - Raynal, Helene

AU - Teixeira, Edmar

AU - Grosz, Balazs

AU - Doro, Luca

AU - Kiese, Ralf

AU - Eckersten, Henrik

AU - Haas, Edwin

AU - Cammarano, Davide

AU - Kassie, Belay

AU - Moriondo, Marco

AU - Trombi, Giacomo

AU - Bindi, Marco

AU - Biernath, Christian

AU - Heinlein, Florian

AU - Klein, Christian

AU - Priesack, Eckart

AU - Lewan, Elisabet

AU - Kersebaum, Kurt-Christian

AU - Rotter, Reimund

AU - Roggero, Pier Paolo

AU - Wallach, Daniel

AU - Asseng, Senthold

AU - Siebert, Stefan

AU - Gaiser, Thomas

AU - Ewert, Frank

N1 - Acknowledgments This work was financially supported by the German Federal Ministry of Food and Agriculture (BMEL) through the Federal Office for Agriculture and Food (BLE), grant number 2851ERA01J. The funding support of the Joint Programme Initiative FACCE, MACSUR Knowledge Hub, is greatly acknowledged. G.Z. was supported by the German Federal Ministry of Education and Research (BMBF) through the SPACES project “Living Landscapes Limpopo” and WASCAL (West African Science Service Center on Climate Change and Adapted Land Use) project. S.A. and D.C. were partly funded by a NOAA RISA grant. FT and RR was funded through the FACCE MACSUR project by the Finnish Ministry of Agriculture and Forestry (MMM). E.L. and E.C. was supported by The Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning (contract 220-2007-1218) and by the strategic funding “Soil-Water-Landscape” from the faculty of Natural Resources and Agricultural Sciences (Swedish University of Agricultural Sciences). We are grateful to Eric Casellas from MIA-T INRA Toulouse for performing STICS simulation under the RECORD platform and ACCAF INRA meta-program for funding JC, HR and DW. We thank Professor Per-Erik Jansson (Royal Institute of Technology in Stockholm) for valuable support and recent development of the CoupModel. We are grateful to Gunther Krauss for checking the mathematic equations and terminologies. Comments from three reviewers greatly improved the quality of the manuscript.

PY - 2016/6

Y1 - 2016/6

N2 - 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. (C) 2016 Elsevier Ltd. All rights reserved.

AB - 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. (C) 2016 Elsevier Ltd. All rights reserved.

KW - Crop model

KW - Stratified random sampling

KW - Simple random sampling

KW - Clustering

KW - Up-scaling

KW - Model comparison

KW - Precision gain

KW - Species Distribution Models

KW - Systems simulation

KW - Weather Data

KW - Large-Scale

KW - Design

KW - Soil

KW - Optimization

KW - Growth

KW - Apism

KW - Autocorrelation

U2 - 10.1016/j.envsoft.2016.02.022

DO - 10.1016/j.envsoft.2016.02.022

M3 - Article

VL - 80

SP - 100

EP - 112

JO - Environmental Modelling and Software

JF - Environmental Modelling and Software

SN - 1364-8152

ER -