Graef, F., Sieber, S., Mutabazi, K., Asch, F., Biesalski, H. K., Bitegeko, J., et al. (2014). Framework for participatory food security research in rural food value chains. Global Food Security, 3(1), 8–15.
Abstract: Enhancing food security for poor and vulnerable people requires adapting rural food systems to various driving factors. Food security-related research should apply participatory action research that considers the entire food value chain to ensure sustained success. This article presents a research framework that focusses on determining, prioritising, testing, adapting and disseminating food securing upgrading strategies across the multiple components of rural food value chains. These include natural resources, Food production, processing, markets, consumption and waste management. Scientists and policy makers jointly use tools developed for assessing potentials for enhancing regional food security at multiple spatial and temporal scales. The research is being conducted in Tanzania as a case study for Sub-Saharan countries and is done in close collaboration with local, regional and national stakeholders, encompassing all activities across all different food sectors. (C) 2014 Elsevier B.V. All rights reserved.
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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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Sharif, B., Mankowski, D., Kersebaum, K. C., Trnka, M., Schelde, K., & Olsesen, J. E. (2015). Empirical analysis on crop-weather relationships (Vol. 6).
Abstract: There have been several studies, where process-based crop models are developed, used and compared in order to project crop production and corresponding model uncertainties under climate change. Despite many advances in this field, there are some correlations between climate variables and crop growth, such as pest and diseases, that is often absent in process-based models. Such relationships can be simulated using empirical models. In this study, several statistical techniques were applied on winter oilseed rape data collected in some European countries. The empirical models were then used to predict yield of winter oilseed rape in the field experiments during more than 20 years, up to 2013. Results suggest that newly developed regression techniques such as shrinkage methods work well both in yield projections and finding the influential climatic variables. Many of regression techniques agree in terms of yield prediction; however, choice of significant climate variables is rather sensitive to the choice of regression technique. No Label
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Hoffmann, H., Zhao, G., Constantin, J., Raynal, H., Wallach, D., Coucheney, E., et al. (2015). Effects of soil and climate input data aggregation on modelling regional crop yields. MACSUR Science Conference.
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Hoffmann, H., Zhao, G., Van Bussel, L. G. J., Enders, A., Specka, X., Sosa, C., et al. (2014). Effects of climate input data aggregation on modelling regional crop yields. CropM International Symposium and Workshop.
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