1. Evaluating the precision of eight spatial sampling schemes in estimating regional means of simulated yield for two crops
- Author
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Elisabet Lewan, Eckart Priesack, Edmar Teixeira, Davide Cammarano, Balázs Grosz, Julie Constantin, Elsa Coucheney, Senthold Asseng, Jagadeesh Yeluripati, Pier Paolo Roggero, Giacomo Trombi, Henrik Eckersten, Stefan Siebert, Specka Xenia, Reimund P. Rötter, Frank Ewert, Thomas Gaiser, Marco Bindi, Marco Moriondo, Matthias Kuhnert, Luca Doro, Christian Klein, Fulu Tao, Gang Zhao, Helene Raynal, Ralf Kiese, Kurt Christian Kersebaum, Claas Nendel, Belay T. Kassie, Christian Biernath, Holger Hoffmann, Edwin Haas, Florian Heinlein, Daniel Wallach, Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, The James Hutton Institute, Departement of Soil and Environment, Swedish University of Agricultural Sciences (SLU), Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Institute of landscape systems analysis, INSTITUTE OF BIOLOGICAL AND ENVIRONMENTAL SCIENCES, University of Aberdeen, Environmental Impacts Group, Natural resources institute Finland, AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de la Recherche Agronomique (INRA), Systems Modelling Team (Sustainable Production Group), Plant & Food Research, Johann Heinrich von Thünen Institut, Università degli Studi di Sassari, Dipartimento di Agraria, University of Sassari, Karlsruhe Institute of Technology (KIT), University of Agricultural Sciences (UAS), Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE), Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS), University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF), Consiglio Nazionale delle Ricerche (CNR), Department of Agri-Food Production and Environmental Sciences, University delgi Studi di Firenze, Institute of Biochemical Plant Pathology (BIOP), German Research Center for Environmental Health - Helmholtz Center München (GmbH), German Federal Ministry of Food and Agriculture (BMEL) through the Federal Office for Agriculture and Food (BLE) [2851ERA01J], Joint Programme Initiative FACCE, MACSUR Knowledge Hub, German Federal Ministry of Education and Research (BMBF) through the SPACES project 'Living Landscapes Limpopo', WASCAL (West African Science Service Center on Climate Change and Adapted Land Use) project, NOAA RISA grant, FACCE MACSUR project by the Finnish Ministry of Agriculture and Forestry (MMM), Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning [220-2007-1218], strategic funding 'Soil-Water-Landscape' from the faculty of Natural Resources and Agricultural Sciences (Swedish University of Agricultural Sciences), ACCAF INRA meta-program, and Università degli Studi di Sassari = University of Sassari [Sassari] (UNISS)
- Subjects
Environmental Engineering ,010504 meteorology & atmospheric sciences ,Simple random sampling ,[SDV]Life Sciences [q-bio] ,Stratified random sampling ,01 natural sciences ,[SHS]Humanities and Social Sciences ,Environment variable ,Ecosystem model ,Statistics ,[INFO]Computer Science [cs] ,Crop model ,[MATH]Mathematics [math] ,0105 earth and related environmental sciences ,Mathematics ,2. Zero hunger ,Ecological Modeling ,Crop yield ,fungi ,food and beverages ,Sampling (statistics) ,Model comparison ,04 agricultural and veterinary sciences ,15. Life on land ,Simple random sample ,Stratified sampling ,Sample size determination ,[SDE]Environmental Sciences ,Soil water ,Up-scaling ,040103 agronomy & agriculture ,0401 agriculture, forestry, and fisheries ,Clustering ,Crop Model ,Model Comparison ,Precision Gain ,Simple Random Sampling ,Stratified Random Sampling ,Precision gain ,Software - 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. 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.
- Published
- 2016
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