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Ahmadi, B.V.; Shrestha, S.; Thomson, S.G.; Barnes, A.P.; Stott, A.W. |
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Health, welfare and profitability in Scottish sheep farms: assessing the impacts of CAP 2015 reforms |
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2014 |
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TradeM |
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88th Annual Conference, Paris, France, 2014-04-09 to 2014-04-11 |
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MA @ admin @ |
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2275 |
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Author |
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.; Rötter, R.; Roggero, P.P.; Wallach, D.; Asseng, S.; Siebert, S.; Gaiser, T.; Ewert, F. |
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Title |
Evaluating the precision of eight spatial sampling schemes in estimating regional means of simulated yield for two crops |
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Journal Article |
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2016 |
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Environmental Modelling & Software |
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Env. Model. Softw. |
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80 |
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100-112 |
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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; apsim; autocorrelation |
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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. |
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English |
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1364-8152 |
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CropM, ft_macsur |
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MA @ admin @ |
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4724 |
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Author |
Banse, M. |
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Title |
MACSUR European experience in addressing the complexity of climate impact research in agriculture: Lessons for replication elsewhere |
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2014 |
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Global Forum on Climate Change, Food Security and Trade, FAO, Rome, 2014-06-03 to 2014-06-04 |
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MA @ admin @ |
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2291 |
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Author |
Banse, M.; Köchy, M. |
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Title |
Opportunities for collaboration: MACSUR |
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2013 |
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4th Annual AgMIP Workshop, New York, USA, 2013-10-28 to 2013-10-30 |
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MA @ admin @ |
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2292 |
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Banse, M.; Köchy, M.; Tiffin, R. |
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MACSUR – A European network of crop, livestock and trade modelling activities for assessing impacts of climate change on food security |
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2013 |
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United Nations Convention to Combat Desertification 2nd Scientific Conference, Bonn, Germany, 2013-04-09 to 2013-04-12 |
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MA @ admin @ |
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2293 |
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