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Heinschink, K.; Sinabell, F.; Url, T. |
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Elements of an Index-based Margin Insurance. An Application to Wheat Production in Austria |
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2017 |
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WIFO Working Papers |
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536 |
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16 |
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TradeM |
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MA @ admin @ |
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5016 |
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Semenov, M.A.; Stratonovitch P |
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ELPIS: delivering local-scale climate scenarios for impact assessments. Impacts World 2013 |
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2013 |
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Impacts World 2013, International Conference on Climate Change Effects, Potsdam, Germany, 2013-05-27 to 2013-05-30 |
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MA @ admin @ |
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2817 |
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Sharif, B.; Mankowski, D.; Kersebaum, K.C.; Trnka, M.; Schelde, K.; Olsesen, J.E. |
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Title |
Empirical analysis on crop-weather relationships |
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2015 |
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FACCE MACSUR Reports |
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6 |
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D-C2.5 |
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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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MA @ admin @ |
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2092 |
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Toscano, P.; Genesio, L.; Crisci, A.; Vaccari, F.P.; Ferrari, E.; La Cava, P.; Porter, J.R.; Gioli, B. |
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Empirical modelling of regional and national durum wheat quality |
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2015 |
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Agricultural and Forest Meteorology |
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Agricultural and Forest Meteorology |
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204 |
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67-78 |
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durum wheat; grain protein content; forecasting tool; modelling; gridded data; red winter-wheat; grain quality; climate-change; mediterranean conditions; interannual variability; protein-composition; co2 concentration; vapor-pressure; carbon-dioxide; crop yield |
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The production of durum wheat in the Mediterranean basin is expected to experience increased variability in yield and quality as a consequence of climate change. To assess how environmental variables and agronomic practices affect grain protein content (GPC), a novel approach based on monthly gridded input data has been implemented to develop empirical model, and validated on historical time series to assess its capability to reproduce observed spatial and inter-annual GPC variability. The model was applied in four Italian regions and at the whole national scale and proved reliable and usable for operational purposes also in a forecast ‘real-time’ mode before harvesting. Precipitable water during autumn to winter and air temperature from anthesis to harvest were extremely important influences on GPC; these and additional variables, included in a linear model, were able to account for 95% of the variability in GPC that has occurred in the last 15 years in Italy. Our results are a unique example of the use of modelling as a predictive real-time platform and are a useful tool to understand better and forecast the impacts of future climate change projections on durum wheat production and quality. |
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2016-10-31 |
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English |
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0168-1923 |
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CropM, ft_macsur |
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MA @ admin @ |
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4818 |
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Bartley, D.J.; Skuce, P.J.; Zadoks, R.N.; MacLeod, M. |
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Endemic sheep and cattle diseases and greenhouse gas emissions |
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2016 |
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Advances in Animal Biosciences |
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Advances in Animal Biosciences |
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7 |
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03 |
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253-255 |
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2040-4700 |
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LiveM, ft_macsur |
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MA @ admin @ |
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4865 |
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