Evaluating the precision of eight spatial sampling schemes in estimating regional means of simulated yield for two crops
Gang Zhao
(1)
,
Holger Hoffmann
(1)
,
Jagadeesh Yeluripati
(2, 3)
,
Specka Xenia
(4)
,
Claas Nendel
(5)
,
Elsa Coucheney
(3)
,
Matthias Kuhnert
(6)
,
Fulu Tao
(7)
,
Julie J. Constantin
(8)
,
Helene H. Raynal
(9)
,
Edmar Teixeira
(10)
,
Balázs Grosz
(11)
,
Luca Doro
(12, 13)
,
Ralf Kiese
(14)
,
Henrik Eckersten
(15)
,
Edwin Haas
(14)
,
Davide Cammarano
(16)
,
Belay Kassie
(16)
,
Marco Moriondo
(17)
,
Giacomo Trombi
(18)
,
Marco Bindi
(18)
,
Christian Biernath
(19)
,
Florian Heinlein
(19)
,
Christian Klein
(19)
,
Eckart Priesack
(19)
,
Elisabet Lewan
(3)
,
Kurt-Christian Kersebaum
(5)
,
Reimund Rötter
(7)
,
Pier Paolo Roggero
(12)
,
Daniel D. Wallach
(8)
,
Senthold Asseng
(16)
,
Stefan Siebert
(1)
,
Thomas Gaiser
(1)
,
Frank Ewert
(1)
1
INRES -
Institute of Crop Science and Resource Conservation [Bonn]
2 The James Hutton Institute
3 Departement of Soil and Environment
4 ZALF - Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research
5 Institute of landscape systems analysis
6 INSTITUTE OF BIOLOGICAL AND ENVIRONMENTAL SCIENCES
7 Environmental Impacts Group
8 AGIR - AGroécologie, Innovations, teRritoires
9 MIAT INRA - Unité de Mathématiques et Informatique Appliquées de Toulouse
10 Systems Modelling Team (Sustainable Production Group)
11 Johann Heinrich von Thünen Institut
12 UNISS - Università degli Studi di Sassari = University of Sassari [Sassari]
13 Dipartimento di Agraria
14 KIT - Karlsruhe Institute of Technology
15 UAS - University of Agricultural Sciences
16 UF|ABE - Department of Agricultural and Biological Engineering [Gainesville]
17 CNR - National Research Council of Italy | Consiglio Nazionale delle Ricerche
18 Department of Agri-Food Production and Environmental Sciences
19 BIOP - Institute of Biochemical Plant Pathology
2 The James Hutton Institute
3 Departement of Soil and Environment
4 ZALF - Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research
5 Institute of landscape systems analysis
6 INSTITUTE OF BIOLOGICAL AND ENVIRONMENTAL SCIENCES
7 Environmental Impacts Group
8 AGIR - AGroécologie, Innovations, teRritoires
9 MIAT INRA - Unité de Mathématiques et Informatique Appliquées de Toulouse
10 Systems Modelling Team (Sustainable Production Group)
11 Johann Heinrich von Thünen Institut
12 UNISS - Università degli Studi di Sassari = University of Sassari [Sassari]
13 Dipartimento di Agraria
14 KIT - Karlsruhe Institute of Technology
15 UAS - University of Agricultural Sciences
16 UF|ABE - Department of Agricultural and Biological Engineering [Gainesville]
17 CNR - National Research Council of Italy | Consiglio Nazionale delle Ricerche
18 Department of Agri-Food Production and Environmental Sciences
19 BIOP - Institute of Biochemical Plant Pathology
Gang Zhao
- Fonction : Auteur correspondant
Claas Nendel
- Fonction : Auteur
- PersonId : 772081
- ORCID : 0000-0001-7608-9097
Julie J. Constantin
- Fonction : Auteur
- PersonId : 736250
- IdHAL : julie-constantin
- ORCID : 0000-0001-9647-5374
Helene H. Raynal
- Fonction : Auteur
- PersonId : 745427
- IdHAL : helene-raynal
- ORCID : 0000-0002-3492-0564
Luca Doro
- Fonction : Auteur
- PersonId : 773546
- ORCID : 0000-0003-1404-2255
Christian Klein
- Fonction : Auteur
- PersonId : 758553
- ORCID : 0000-0001-7594-7280
Frank Ewert
- Fonction : Auteur
- PersonId : 968227
Résumé
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.