| Records |
| Author |
Sharif, B.; Olesen, J.E.; Schelde, K. |
| Title |
Statistical learning approach for modelling the effects of climate change on oilseed rape yield |
Type |
Conference Article |
| Year |
2014 |
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Statistical learning is a fairly new term referring to a set of supervised and unsupervised modelling and prediction techniques. It is based on traditional statistics but has been highly enhanced inspired by developments in machine learning and data mining. The main focus of statistical learning is to estimate the functions that quantify relations between several parameters and observed responses. These functions are further used for prediction, inference or a combination of both. For a particular case of quantitative responses, regularization techniques in regression are developed to overcome the weaknesses of ordinary least square (OLS) regression in prediction. These new shrinkage methods outperform OLS for prediction, especially in large datasets. In this study, a large dataset of field experiments on winter oilseed rape in Denmark for 22 years (1992 to 2013) was collected. Biweekly climatic data along with sowing date, harvest date, soil type and previous crop are considered as the explanatory variables. Yield of winter oilseed rape is considered as response variable. LASSO and Elastic Nets are the regularization techniques used to estimate the functions. Hold-one-out cross validation method for testing the prediction power reveals that these techniques are much useful in both prediction and inference. Since these techniques are included in recent versions of some software packages (e.g. R), they can be easily implemented by users at any level. The estimated function (model) is further used to predict the oilseed rape yield responses to climate change for several scenarios using representative weather data produced by a weather generator. |
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FACCE MACSUR Mid-term Scientific Conference |
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3(S) Sassari, Italy |
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FACCE MACSUR Mid-term Scientific Conference, 2014-04-01 to 2014-04-04, Sassari, Italy |
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no |
| Call Number |
MA @ admin @ |
Serial |
5129 |
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| Author |
Cammarano, D.; Rötter, P.; Ewert, F.; Palosuo, T.; Bindi, M.; Kersebaum, K.C.; Olesen, J.E.; Trnka, M.; van Ittersum, M.K.; Janssen, S.; Rivington, M.; Semenov, M.; Wallach, D.; Porter, J.R.; Stewart, D.; Verhagen, J.; Angulo, C.; Gaiser, T.; Nendel, C.; Martre, P.; de Wit, A. |
| Title |
Challenges for Agro-Ecosystem Modelling in Climate Change Risk Assessment for major European Crops and Farming systems |
Type |
Conference Article |
| Year |
2013 |
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Issue |
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Pages |
555-564 |
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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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no |
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MA @ admin @ |
Serial |
2765 |
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| Author |
Olesen, J.E. |
| Title |
Socio-economic impacts – agricultural systems |
Type |
Book Chapter |
| Year |
2016 |
Publication |
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Pages |
397-407 |
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Quante, M.; Colijn, F. |
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North Sea Region climate change assessment |
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Regional Climate Studies |
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1862-0248 (series) |
ISBN |
978-3-319-39745-0 (eBook), 978-3-319-39745-0 (hardcover) |
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no |
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Serial |
4828 |
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Yin, X.; Kersebaum, K.C.; Kollas, C.; Armas-Herrera, C.M.; Baby, S.; Beaudoin, N.; Bindi, M.; Charfeddine, M.; Conradt, T.; Cortazar-Atauri, I.G.D.; Ewert, F.; Ferrise, R.; Hoffmann, H.; Lana, M.; Launay, M.; Manderscheid, R.; Manevski, K.; Mary, B.; Mirschel, W.; Moriondo, M.; Müller, C.; Nendel, C.; Öztürk, I.; Palosuo, T.; Ripoche-Wachte, D.; Rötter, R.P.; Ruget, F.; Sharif, B.; Ventrella, D.; Weigel, H.-J.; Olesen, J.E. |
| Title |
Uncertainty in simulating N uptakes, N leaching and N use efficiency in crop rotation systems across Europe |
Type |
Conference Article |
| Year |
2016 |
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Berlin (Germany) |
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Conference |
International Crop Modelling Symposium iCROPM 2016, 2016-05-15 to 2016-05-17, Berlin, Germany |
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Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4899 |
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Nendel, C.; Thorburn, P.; Melzer, D.; Cerri, C.E.P.; Claessens, L.; Aggarwal, P.K.; Adam, M.; Angulo, C.; Asseng, S.; Baron, C.; Basso, B.; Bassu, S.; Bertuzzi, P.; Biernath, C.; Boogaard, H.; Boote, K.J.; Brisson, N.; Cammarano, D.; Conijn, S.; Corbeels, M.; Deryng, D.; Sanctis, G.D.; Doltra, J.; Durand, J.L.; Ewert, F.; Gayler, S.; Goldberg, R.; Grant, R.; Grassini, P.; Heng, L.; Hoek, S.B.; Hooker, J.A.U.-, L.A.H.; Ingwersen, J.; Izaurralde, C.; Jongschaap, R.; Kemanian, A.; Kersebaum, K.C.; Lizaso, J.; Makowski, D.; Martre, P.; Müller, C.; Kim, S.H.; Kumar, S.N.; O’Leary, G.; Olesen, J.E.; Osborne, T.; Palosuo, T.; Pravia, M.V.; Priesack, E.; Ripoche, D.A.U.-, R.P.R.; Sau, F.; Semenov, M.A.; Shcherbak, I.; Steduto, P.; Stöckle, C.; Stratonovitch, P.; Streck, T.; Supit, I.; Tao, F.L.; Teixeira, E.; Timlin, D.; Travasso, M.; Waha, K.; Wallach, D.; White, J.W.; Wolf, J. |
| Title |
Soil nitrogen mineralisation simulated by crop models across different environments and the consequences for model improvement |
Type |
Conference Article |
| Year |
2016 |
Publication |
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Berlin (Germany) |
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Conference |
International Crop Modelling Symposium iCROPM 2016, 2016-05-15 to 2016-05-17, Berlin, Germany |
| Notes |
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Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4903 |
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