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| Author | Özkan Gülzari, Ş.; Åby, B.A.; Persson, T.; Höglind, M.; Mittenzwei, K. | ||||
| Title | Combining models to estimate the impacts of future climate scenarios on feed supply, greenhouse gas emissions and economic performance on dairy farms in Norway | Type | Journal Article | ||
| Year | 2017 | Publication | Agricultural Systems | Abbreviated Journal | Agric. Syst. |
| Volume | 157 | Issue | Pages | 157-169 | |
| Keywords | Climate change; Dairy farming; Dry matter yield; Economics; Greenhouse gas emission; Modelling | ||||
| Abstract | • This study combines crop, livestock and economic models. • Models interaction is through use of relevant input and output variables. • Future climate change will result in increased grass and wheat dry matter yields. • Changes in grass, wheat and milk yields in future reduce farm emissions intensity. • Changes in future dry matter yields and emissions lead to increased profitability. There is a scientific consensus that the future climate change will affect grass and crop dry matter (DM) yields. Such yield changes may entail alterations to farm management practices to fulfill the feed requirements and reduce the farm greenhouse gas (GHG) emissions from dairy farms. While a large number of studies have focused on the impacts of projected climate change on a single farm output (e.g. GHG emissions or economic performance), several attempts have been made to combine bio-economic systems models with GHG accounting frameworks. In this study, we aimed to determine the physical impacts of future climate scenarios on grass and wheat DM yields, and demonstrate the effects such changes in future feed supply may have on farm GHG emissions and decision-making processes. For this purpose, we combined four models: BASGRA and CSM-CERES-Wheat models for simulating forage grass DM and wheat DM grain yields respectively; HolosNor for estimating the farm GHG emissions; and JORDMOD for calculating the impacts of changes in the climate and management on land use and farm economics. Four locations, with varying climate and soil conditions were included in the study: south-east Norway, south-west Norway, central Norway and northern Norway. Simulations were carried out for baseline (1961–1990) and future (2046–2065) climate conditions (projections based on two global climate models and the Special Report on Emissions Scenarios (SRES) A1B GHG emission scenario), and for production conditions with and without a milk quota. The GHG emissions intensities (kilogram carbon dioxide equivalent: kgCO2e emissions per kg fat and protein corrected milk: FPCM) varied between 0.8 kg and 1.23 kg CO2e (kg FPCM)− 1, with the lowest and highest emissions found in central Norway and south-east Norway, respectively. Emission intensities were generally lower under future compared to baseline conditions due mainly to higher future milk yields and to some extent to higher crop yields. The median seasonal above-ground timothy grass yield varied between 11,000 kg and 16,000 kg DM ha− 1 and was higher in all projected future climate conditions than in the baseline. The spring wheat grain DM yields simulated for the same weather conditions within each climate projection varied between 2200 kg and 6800 kg DM ha− 1. Similarly, the farm profitability as expressed by total national land rents varied between 1900 million Norwegian krone (NOK) for median yields under baseline climate conditions up to 3900 million NOK for median yield under future projected climate conditions. |
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| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | phase 2 | Original Title | ||
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| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | CropM, LiveM, TradeM, ft_macsur | Approved | no | ||
| Call Number | MA @ admin @ | Serial  |
5172 | ||
| Permanent link to this record | |||||
| Author | Makowski, D. | ||||
| Title | A simple Bayesian method for adjusting ensemble of crop model outputs to yield observations | Type | Journal Article | ||
| Year | 2017 | Publication | European Journal of Agronomy | Abbreviated Journal | Europ. J. Agron. |
| Volume | 88 | Issue | Pages | 76-83 | |
| Keywords | Bayesian method; Climate change; Ensemble modelling; Uncertainty; Yield; Linear-Approach; Climate-Change; CO2 | ||||
| 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. | ||||
| Address | 2017-08-07 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | English | Summary Language | Original Title | ||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 1161-0301 | ISBN | Medium | Article | |
| Area | Expedition | Conference | |||
| Notes | CropM, ft_macsur | Approved | no | ||
| Call Number | MA @ admin @ | Serial |
5171 | ||
| Permanent link to this record | |||||
| Author | Zhang, S.; Tao, F.; Zhang, Z. | ||||
| Title | Uncertainty from model structure is larger than that from model parameters in simulating rice phenology in China | Type | Journal Article | ||
| Year | 2017 | Publication | European Journal of Agronomy | Abbreviated Journal | Europ. J. Agron. |
| Volume | 87 | Issue | Pages | 30-39 | |
| Keywords | Crop model, Extreme weather, Impacts, Rice development rate, Uncertainty; Climate-Change; Growth Duration; Crop Model; Ceres-Rice; Wheat; Temperature; Impact; Yield; Optimization; Performance | ||||
| 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. | ||||
| Address | 2017-08-07 | ||||
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| Publisher | Place of Publication | Editor | |||
| Language | English | Summary Language | Original Title | ||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 1161-0301 | ISBN | Medium | Article | |
| Area | Expedition | Conference | |||
| Notes | CropM, ft_macsur | Approved | no | ||
| Call Number | MA @ admin @ | Serial |
5170 | ||
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| Author | Janssen, S. | ||||
| Title | Open data journal as a publishing and data sharing mechanism | Type | Report | ||
| Year | 2017 | Publication | FACCE MACSUR Reports | Abbreviated Journal | |
| Volume | 10 | Issue | 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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| Area | Expedition | Conference | |||
| Notes | CropM | Approved | no | ||
| Call Number | MA @ admin @ | Serial |
5169 | ||
| Permanent link to this record | |||||
| Author | Francioni, M. | ||||
| Title | Soil CO2 emissions and C stock as ecosystem services: a comparison between transhumant and conventional farming systems | Type | Book Whole | ||
| Year | 2017 | Publication | Abbreviated Journal | ||
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| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Università Politecnica delle Marche | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | PhD | ||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | LiveM | Approved | no | ||
| Call Number | MA @ admin @ | Serial |
5167 | ||
| Permanent link to this record | |||||