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Kuhnert, M., Yeluripati, J., Smith, P., Hoffmann, H., Constantin, J., Coucheney, E., et al. (2016). Impacts of soil and weather data aggregation in spatial modelling of net primary production of croplands.. Belrin (Germany).
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Kuhnert, M., Yeluripati, J., Smith, P., Hoffmann, H., van Oijen, M., Zhao, G., et al. (2016). Impact of climate aggregation over different scales on regional NPP modelling.. Vienna (Austria).
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Ewert, F., van Bussel, L. G. J., Zhao, G., Hoffmann, H., Gaiser, T., Specka, X., et al. (2015). Uncertainties in Scaling up Crop Models for Large Area Climate-change Impact Assessments. In C. Rosenzweig, & D. Hillel (Eds.), (pp. 261–277). Handbook of Climate Change and Agroecosystems: The Agricultural Model Intercomparison and Improvement Project (AgMIP) Integrated Crop and Economic Assessments — Joint Publication with American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America (In 2 Parts), ICP Series on Climate Change Impacts, Adaptation, . London: Imperial College Press.
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Zhao, G., Hoffmann, H., Yeluripati, J., Xenia, S., Nendel, C., Coucheney, E., et al. (2016). Evaluating the precision of eight spatial sampling schemes in estimating regional means of simulated yield for two crops. Env. Model. Softw., 80, 100–112.
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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