Records |
Author |
Olesen, J.E.; Niemeyer, S.; Ceglar, A.; Roggero, P.-P.; Lehtonen, H.; Schönhart, M.; Kipling, R. |
Title |
Section 5.3. Agriculture |
Type |
Book Chapter |
Year |
2017 |
Publication |
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Abbreviated Journal |
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Volume |
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Issue ![sorted by Issue field, descending order (down)](img/sort_desc.gif) |
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Pages |
223-243 |
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Publisher |
European Environmental Agency |
Place of Publication |
Copenhagen, Denmark |
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Original Title |
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Series Title |
Climate change, impacts and vulnerability in Europe 2016. An indicator-based report |
Abbreviated Series Title |
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Series Volume |
EEA Report (1/2017) |
Series Issue |
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Edition |
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ISBN |
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Medium |
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Area |
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Expedition |
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Conference |
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Notes |
CropM, LiveM, TradeM |
Approved |
no |
Call Number |
MA @ admin @ |
Serial |
4964 |
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Author |
Sharif, B.; Makowski, D.; Plauborg, F.; Olesen, J.E. |
Title |
Comparison of regression techniques to predict response of oilseed rape yield to variation in climatic conditions in Denmark |
Type |
Journal Article |
Year |
2017 |
Publication |
European Journal of Agronomy |
Abbreviated Journal |
Europ. J. Agron. |
Volume |
82 |
Issue ![sorted by Issue field, descending order (down)](img/sort_desc.gif) |
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Pages |
11-20 |
Keywords |
Winter oilseed rape; Statistical models; Yield; Climate; Regression |
Abstract |
Highlights • Regularization techniques for regression outperformed the classical regression techniques in predicting crop yields. • Different regression techniques with similar prediction accuracy showed different responses of major climatic variables to crop yield. • The regression models showed some responses of crop yield to climatic conditions that is mostly absent in process based crop models. Abstract Statistical regression models represent alternatives to process-based dynamic models for predicting the response of crop yields to variation in climatic conditions. Regression models can be used to quantify the effect of change in temperature and precipitation on yields. However, it is difficult to identify the most relevant input variables that should be included in regression models due to the high number of candidate variables and to their correlations. This paper compares several regression techniques for modeling response of winter oilseed rape yield to a high number of correlated input variables. Several statistical regression methods were fitted to a dataset including 689 observations of winter oilseed rape yield from replicated field experiments conducted in 239 sites in Denmark, covering nearly all regions of the country from 1992 to 2013. Regression methods were compared by cross-validation. The regression methods leading to the most accurate yield predictions were Lasso and Elastic Net, and the least accurate methods were ordinary least squares and stepwise regression. Partial least squares and ridge regression methods gave intermediate results. The estimated relative yield change for a +1°C temperature increase during flowering was estimated to range between 0 and +6 %, depending on choice of regression method. Precipitation was found to have an adverse effect on yield during autumn and winter. It was estimated that an increase in precipitation of +1 mm/day would result in a relative yield change ranging from 0 to −4 %. Soil type was also important for crop yields with lower yields on sandy soils compared to loamy soils. Later sowing was found to result in increased crop yield. The estimated effect of climate on yield was highly sensitive to the chosen regression method. Regression models showing similar performance led in some cases to different conclusions with respect to effect of temperature and precipitation. Hence, it is recommended to apply an ensemble of regression models, in order to account for the sensitivity of the data driven models for projecting crop yield under climate change. |
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Edition |
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ISSN |
1161-0301 |
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article |
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Notes |
CropM, ft_macsur |
Approved |
no |
Call Number |
MA @ admin @ |
Serial |
4966 |
Permanent link to this record |
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Author |
Raymundo, R.; Asseng, S.; Prassad, R.; Kleinwechter, U.; Concha, J.; Condori, B.; Bowen, W.; Wolf, J.; Olesen, J.E.; Dong, Q.; Zotarelli, L.; Gastelo, M.; Alva, A.; Travasso, M.; Quiroz, R.; Arora, V.; Graham, W.; Porter, C. |
Title |
Performance of the SUBSTOR-potato model across contrasting growing conditions |
Type |
Journal Article |
Year |
2017 |
Publication |
Field Crops Research |
Abbreviated Journal |
Field Crops Research |
Volume |
202 |
Issue ![sorted by Issue field, descending order (down)](img/sort_desc.gif) |
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Pages |
57-76 |
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ISSN |
0378-4290 |
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article |
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Notes |
CropM, ftnotmacsur |
Approved |
no |
Call Number |
MA @ admin @ |
Serial |
4967 |
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Author |
Tomozeiu, R.; Pasqui, M.; Quaresima, S. |
Title |
Future changes of air temperature over Italian agricultural areas: a statistical downscaling technique applied to 2021–2050 and 2071–2100 periods |
Type |
Journal Article |
Year |
2017 |
Publication |
Meteorology and Atmospheric Physics |
Abbreviated Journal |
Meteorology and Atmospheric Physics |
Volume |
in press |
Issue ![sorted by Issue field, descending order (down)](img/sort_desc.gif) |
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Pages |
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Keywords |
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Abstract |
Climate change scenarios of seasonal minimum and maximum temperature over different Italian agricultural areas, during the periods 2021–2050 and 2071–2100 against 1961–1990, are assessed. The areas are those selected in the framework of the Agroscenari project and are represented by: Padano–Veneta plain, Marche, Beneventano, Destra Sele, Oristano, Puglia and Sicilia, all areas of prominent agricultural vocation with excellence productions. A statistical downscaling technique applied to ENSEMBLES global climate simulations, emission scenario A1B, is used to achieve this objective. The statistical scheme consists of a multivariate regression based on Canonical Correlation Analysis. The scheme is constructed using large-scale fields derived from ECMWF reanalysis and seasonal mean minimum, maximum temperature derived from national observed daily gridded data that cover 1959–2008 period. Once the most skillful model has been selected for each season and variable, this is then applied to GCMs of ENSEMBLES runs. The statistical downscaling method developed reveals good skill over the case studies of the present work, underlying the possibility to apply the scheme over whole Italian peninsula. In addition, the results emphasize that the temperature at 850 hPa is the best predictor for surface air temperature. The future projections show that an increase could be expected to occur under A1B scenario conditions in all seasons, both in minimum and maximum temperatures. The projected increases are about 2 °C during 2021–2050 and between 2.5 and 4.5 °C during 2071–2100, respect to 1961–1990. The spatial distribution of warming is projected to be quite uniform over the territory to the end of the century, while some spatial differences are noted over 2021–2050 period. For example, the increase in minimum temperature is projected to be slightly higher in areas from northern and central part than those situated in the southern part of Italian peninsula, during 2021–2050 period. The peak of changes is projected to appear during summer season, for both minimum and maximum temperature. The probability density function tends to shift to warmer values during both periods, with increases more intense during summer and to the end of the century, when the lower tail is projected to shift up to 3 °C and the upper tail up to 6 °C. All these projected changes have important impacts on viticulture, intensive fruit and tomatoes, some of the main agricultural systems analyzed in the Agroscenari project. |
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ISSN |
0177-7971 |
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CropM |
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Conference |
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Notes |
CropM, ft_macsur |
Approved |
no |
Call Number |
MA @ admin @ |
Serial |
4970 |
Permanent link to this record |
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Author |
Ginaldi, F.; Bindi, M.; Marta, A.D.; Ferrise, R.; Orlandini, S.; Danuso, F. |
Title |
Interoperability of agronomic long term experiment databases and crop model intercomparison: the Italian experience |
Type |
Journal Article |
Year |
2016 |
Publication |
European Journal of Agronomy |
Abbreviated Journal |
Europ. J. Agron. |
Volume |
77 |
Issue ![sorted by Issue field, descending order (down)](img/sort_desc.gif) |
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Pages |
209-222 |
Keywords |
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Abstract |
• ICFAR-DB organises and stores data from 16 Italian long term agronomic experiments. • ICFAR-DB fulfils interoperability using system dynamics ontology and AgMIP nomenclature. • ICFAR information management system moves closer data to model and vice versa. The IC-FAR national project (Linking long term observatories with crop system modelling for better understanding of climate change impact, and adaptation strategies for Italian cropping systems) initiated in 2013 with the primary aim of implementing data from 16 long term Italian agronomic experiments in a common, interoperable structure. The building of a common database (DB) structure demands a harmonization process aimed at standardising concepts, language and data in order to make them clear, and has to produce a well-documented and easily available tool for the whole scientific community. The Agricultural Model Intercomparison and Improvement Project (AgMIP) has made a great effort in this sense, improving the vocabulary developed by the International Consortium for Agricultural Systems Applications (ICASA) and defining harmonization procedures. Nowadays, these ones have also to be addressed to facilitate the extraction of input files for crop model simulations. Substantially, two alternative directions can be pursued: adapting data to models, building a standard storage structure and using translators that convert DB information to model input files; or adapting models to data, using the same storage structure for feeding modelling solutions constituted by combining model components, re-implemented in the same model platform. The ICFAR information management system simplifies data entry, improves model input extraction (implementing System Dynamics ontology), and satisfies both the paradigms. This has required the development of different software tools: ICFAR-DB for data entry and storage; a model input extractor for feeding the crop models (MoLInEx); SEMoLa platform for building modelling solutions and performing via scripts the model intercomparison. The use of the standard AgMIP/ICASA nomenclature in the ICFAR-DB and the opportunity to create files with MoLInex for feeding AgMIP model translators allow full system interoperability. |
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ISSN |
1161-0301 |
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article |
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Notes |
CropM, ftnotmacsur |
Approved |
no |
Call Number |
MA @ admin @ |
Serial |
4972 |
Permanent link to this record |