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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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| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | phase 2 | Original Title | ||
| Series Editor | Series Title | Abbreviated Series Title | |||
| 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 | Siebert, S.; Webber, H.; Zhao, G.; Ewert, F.; Siebert, S.; Webber, H.; Zhao, G.; Ewert, F. | ||||
| Title | Heat stress is overestimated in climate impact studies for irrigated agriculture | Type | Journal Article | ||
| Year | 2017 | Publication | Environmental Research Letters | Abbreviated Journal | Environ. Res. Lett. |
| Volume | 12 | Issue | 5 | Pages | 054023 |
| Keywords | heat stress; climate change impact assessment; irrigation; canopy temperature; CANOPY TEMPERATURE; WINTER-WHEAT; WATER-STRESS; CROP YIELDS; GROWTH; MAIZE; DROUGHT; UNCERTAINTY; ENVIRONMENT; PHENOLOGY | ||||
| Abstract | Climate change will increase the number and severity of heat waves, and is expected to negatively affect crop yields. Here we show for wheat and maize across Europe that heat stress is considerably reduced by irrigation due to surface cooling for both current and projected future climate. We demonstrate that crop heat stress impact assessments should be based on canopy temperature because simulations with air temperatures measured at standard weather stations cannot reproduce differences in crop heat stress between irrigated and rainfed conditions. Crop heat stress was overestimated on irrigated land when air temperature was used with errors becoming larger with projected climate change. Corresponding errors in mean crop yield calculated across Europe for baseline climate 1984-2013 of 0.2 Mg yr(-1) (2%) and 0.6 Mg yr(-1) (5%) for irrigated winter wheat and irrigated grain maize, respectively, would increase to up to 1.5 Mg yr (1) (16%) for irrigated winter wheat and 4.1 Mg yr (1) (39%) for irrigated grain maize, depending on the climate change projection/GCM combination considered. We conclude that climate change impact assessments for crop heat stress need to account explicitly for the impact of irrigation. | ||||
| Address | 2017-06-22 | ||||
| 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 | 1748-9326 | ISBN | Medium | ||
| Area | Expedition | Conference | |||
| Notes | CropM, ft_macsur | Approved | no | ||
| Call Number | MA @ admin @ | Serial |
5035 | ||
| Permanent link to this record | |||||
| 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 | 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. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Editor | |||
| Language | 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 |
4966 | ||
| Permanent link to this record | |||||