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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 |
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Book Whole |
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Year |
2017 |
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Abbreviated Journal |
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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 |
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Book Whole |
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Year |
2017 |
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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 |
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Abbreviated Journal |
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Ph.D. thesis |
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Publisher |
Università Politecnica delle Marche |
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PhD |
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LiveM |
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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 |
Issue |
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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 |
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no |
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Call Number |
MA @ admin @ |
Serial |
5169 |
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Author |
Zhang, S.; Tao, F.; Zhang, Z. |
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Title |
Uncertainty from model structure is larger than that from model parameters in simulating rice phenology in China |
Type |
Journal Article |
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Year |
2017 |
Publication |
European Journal of Agronomy |
Abbreviated Journal |
Europ. J. Agron. |
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Volume |
87 |
Issue |
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Pages |
30-39 |
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Keywords |
Crop model, Extreme weather, Impacts, Rice development rate, Uncertainty; Climate-Change; Growth Duration; Crop Model; Ceres-Rice; Wheat; Temperature; Impact; Yield; Optimization; Performance |
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Abstract |
Rice models have been widely used in simulating and predicting rice phenology in contrasting climate zones, however the uncertainties from model structure (different equations or models) and/or model parameters were rarely investigated. Here, five rice phenological models/modules (Le., CERES-Rice, ORYZA2000, RCM, Beta Model and SIMRIW) were applied to simulate rice phenology at 23 experimental stations from 1992 to 2009 in two major rice cultivation regions of China: the northeastern China and the southwestern China. To investigate the uncertainties from model biophysical parameters, each model was run with randomly perturbed 50 sets of parameters. The results showed that the median of ensemble simulations were better than the simulation by most models. Models couldn’t simulate well in some specific years despite of parameters optimization, suggesting model structure limit model performance in some cases. The models adopting accumulative thermal time function (e.g., CERES-Rice and ORYZA2000) had better performance in the southwestern China, in contrast, those adopting exponential function (e.g., Beta model and RCM model) had better performance in the northeastern China. In northeastern China, the contribution of model structure and model parameters to model total variance was, respectively, about 55.90% and 44.10% in simulating heading date, and about 75.43% and 24.57% in simulating maturity date. In the southwestern China, the contribution of model structure and model parameters to model total variance was, respectively, about 79.97% and 27.03% in simulating heading date, about 92.15% and 7.85% in simulating maturity date. Uncertainty from model structure was the most relevant source. The results highlight that the temperature response functions of rice development rate under extreme climate conditions should be improved based on environment-controlled experimental data. |
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Address |
2017-08-07 |
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Corporate Author |
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Publisher |
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Place of Publication |
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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 |
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Article |
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Notes |
CropM, ft_macsur |
Approved |
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Call Number |
MA @ admin @ |
Serial |
5170 |
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| Permanent link to this record |
