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Author |
Sharif, B. |
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Title |
Data mining techniques for quantifying and projecting crop yield responses to climate change |
Type |
Book Whole |
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Year |
2017 |
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Abbreviated Journal |
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Thesis |
Ph.D. thesis |
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Publisher |
Aarhus University |
Place of Publication |
Aarhus |
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PhD |
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CropM |
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no |
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Call Number |
MA @ admin @ |
Serial |
5165 |
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Author |
Iocola, I. |
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Title |
Past experience supports future choices for cropping systems management: the Italian long-term agro-ecosystem experiments (LTAEs) through the IC-FAR network |
Type |
Book Whole |
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Year |
2017 |
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Thesis |
Ph.D. thesis |
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Publisher |
University of Sassari |
Place of Publication |
Sassari |
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PhD |
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CropM |
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no |
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Call Number |
MA @ admin @ |
Serial |
5166 |
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Author |
Francioni, M. |
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Title |
Soil CO2 emissions and C stock as ecosystem services: a comparison between transhumant and conventional farming systems |
Type |
Book Whole |
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Year |
2017 |
Publication |
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Abbreviated Journal |
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Thesis |
Ph.D. thesis |
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Publisher |
Università Politecnica delle Marche |
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PhD |
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Notes |
LiveM |
Approved |
no |
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Call Number |
MA @ admin @ |
Serial |
5167 |
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Author |
Janssen, S. |
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Title |
Open data journal as a publishing and data sharing mechanism |
Type |
Report |
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Year |
2017 |
Publication |
FACCE MACSUR Reports |
Abbreviated Journal |
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Volume |
10 |
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Pages |
C2.3-D |
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Abstract |
This deliverable lays out the work as done as part of MACSUR CropM on data publishing, with the focus on improving data sharing and discovery and have shared data curation for future use. As part of the first phase MACSUR, The Open Data Journal for Agricultural Research (www.odjar.org) was started and documented in Deliverable C2.2 as part of Crop M. Odjar.org mainly focuses on long term data archival and citation of data sets, as input and outputs to the modelling work, as part of MACSUR, lead by Wageningen UR This deliverable is a short update on the process of creating such a data journal by demonstrating a set of articles published through the journal, some of which are based on MACSUR results, as well as related networks. The deliverable does not further explain what the journal is, as this is part of the previous deliverable. |
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Notes |
CropM |
Approved |
no |
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Call Number |
MA @ admin @ |
Serial |
5169 |
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Author |
Makowski, D. |
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Title |
A simple Bayesian method for adjusting ensemble of crop model outputs to yield observations |
Type |
Journal Article |
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Year |
2017 |
Publication |
European Journal of Agronomy |
Abbreviated Journal |
Europ. J. Agron. |
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Volume |
88 |
Issue |
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Pages |
76-83 |
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Keywords |
Bayesian method; Climate change; Ensemble modelling; Uncertainty; Yield; Linear-Approach; Climate-Change; CO2 |
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Abstract |
Multi-model forecasting has drawn some attention in crop science for evaluating effect of climate change on crop yields. The principle is to run several individual process-based crop models under several climate scenarios in order to generate ensembles of output values. This paper describes a simple Bayesian method – called Bayes linear method- for updating ensemble of crop model outputs using yield observations. The principle is to summarize the ensemble of crop model outputs by its mean and variance, and then to adjust these two quantities to yield observations in order to reduce uncertainty. The adjusted mean and variance combine two sources of information, i.e., the ensemble of crop model outputs and the observations. Interestingly, with this method, observations collected under a given climate scenario can be used to adjust mean and variance of the model ensemble under a different scenario. Another advantage of the proposed method is that it does not rely on a separate calibration of each individual crop model. The uncertainty reduction resulting from the adjustment of an ensemble of crop models to observations was assessed in a numerical application. The implementation of the Bayes linear method systematically reduced uncertainty, but the results showed the effectiveness of this method varied in function of several factors, especially the accuracy of the yield observation, and the covariance between the crop model output and the observation. (C) 2015 Elsevier B.V. All rights reserved. |
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Address |
2017-08-07 |
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Corporate Author |
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Editor |
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Language |
English |
Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
1161-0301 |
ISBN |
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Medium |
Article |
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Notes |
CropM, ft_macsur |
Approved |
no |
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Call Number |
MA @ admin @ |
Serial |
5171 |
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| Permanent link to this record |
