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Author |
Van Oijen, M.; Höglind, M. |
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Title |
Toward a Bayesian procedure for using process-based models in plant breeding, with application to ideotype design |
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Journal Article |
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Year |
2016 |
Publication |
Euphytica |
Abbreviated Journal |
Euphytica |
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Volume |
207 |
Issue |
3 |
Pages |
627-643 |
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Keywords |
BASGRA; cold tolerance; genotype-environment interaction; plant breeding; process-based modelling; yield stability; grassland productivity; timothy regrowth; climate-change; water-deficit; forest models; late blight; leaf-area; calibration; growth; tolerance |
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Abstract |
Process-based grassland models (PBMs) simulate growth and development of vegetation over time. The models tend to have a large number of parameters that represent properties of the plants. To simulate different cultivars of the same species, different parameter values are required. Parameter differences may be interpreted as genetic variation for plant traits. Despite this natural connection between PBMs and plant genetics, there are only few examples of successful use of PBMs in plant breeding. Here we present a new procedure by which PBMs can help design ideotypes, i.e. virtual cultivars that optimally combine properties of existing cultivars. Ideotypes constitute selection targets for breeding. The procedure consists of four steps: (1) Bayesian calibration of model parameters using data from cultivar trials, (2) Estimating genetic variation for parameters from the combination of cultivar-specific calibrated parameter distributions, (3) Identifying parameter combinations that meet breeding objectives, (4) Translating model results to practice, i.e. interpreting parameters in terms of practical selection criteria. We show an application of the procedure to timothy (Phleum pratense L.) as grown in different regions of Norway. |
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2016-10-31 |
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0014-2336 |
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CropM, ft_macsur |
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MA @ admin @ |
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4820 |
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Author |
Sándor, R.; Barcza, Z.; Hidy, D.; Lellei-Kovács, E.; Ma, S.; Bellocchi, G. |
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Title |
Modelling of grassland fluxes in Europe: evaluation of two biogeochemical models |
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Journal Article |
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Year |
2016 |
Publication |
Agriculture, Ecosystems and Environment |
Abbreviated Journal |
Agric. Ecosyst. Environ. |
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215 |
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Pages |
1-19 |
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Keywords |
carbon-water fluxes; climate change; grasslands; model comparison; net ecosystem exchange; terrestrial carbon balance; pasture simulation-model; climate-change; nitrous-oxide; land-use; co2; photosynthesis; responses; water |
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Abstract |
Two independently developed simulation models – the grassland-specific PaSim and the biome-generic Biome-BGC MuSo (BBGC MuSo) – linking climate, soil, vegetation and management to ecosystem biogeochemical cycles were compared in a simulation of carbon (C) and water fluxes. The results were assessed against eddy-covariance flux data from five observational grassland sites representing a range of conditions in Europe: Grillenburg in Germany, Laqueuille in France with both extensive and intensive management, Monte Bondone in Italy and Oensingen in Switzerland. Model comparison (after calibration) gave substantial agreement, the performances being marginal to acceptable for weekly-aggregated gross primary production and ecosystem respiration (R-2 similar to 0.66 – 0.91), weekly evapotranspiration (R-2 similar to 0.78 – 0.94), soil water content in the topsoil (R-2 similar to 0.1 -0.7) and soil temperature (R-2 similar to 0.88 – 0.96). The bias was limited to the range -13 to 9 g C m(-2) week(-1) for C fluxes (-11 to 8 g C m(-2) week(-1) in case of BBGC MuSo, and -13 to 9 g C m(-2) week(-1) in case of PaSim) and -4 to 6 mm week for water fluxes (with BBGC MuSo providing somewhat higher estimates than PaSim), but some higher relative root mean square errors indicate low accuracy for prediction, especially for net ecosystem exchange The sensitivity of simulated outputs to changes in atmospheric carbon dioxide concentration ([CO2]), temperature and precipitation indicate, with certain agreement between the two models, that C outcomes are dominated by [CO2] and temperature gradients, and are less due to precipitation. ET rates decrease with increasing [CO2] in PaSim (consistent with experimental knowledge), while lack of appropriate stomatal response could be a limit in BBGC MuSo responsiveness. Results of the study indicate that some of the errors might be related to the improper representation of soil water content and soil temperature. Improvement is needed in the model representations of soil processes (especially soil water balance) that strongly influence the biogeochemical cycles of managed and unmanaged grasslands. (C) 2015 Elsevier B.V. All rights reserved. |
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2016-10-31 |
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0167-8809 |
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CropM, LiveM, ft_macsur |
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MA @ admin @ |
Serial |
4808 |
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Klosterhalfen, A.; Herbst, M.; Weihermueller, L.; Graf, A.; Schmidt, M.; Stadler, A.; Schneider, K.; Subke, J.-A.; Huisman, J.A.; Vereecken, H. |
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Title |
Multi-site calibration and validation of a net ecosystem carbon exchange model for croplands |
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Journal Article |
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2017 |
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Ecological Modelling |
Abbreviated Journal |
Ecol. Model. |
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363 |
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137-156 |
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Keywords |
AgroC; Soil respiration; Carbon balance; Winter wheat; Grassland; NEE; LOLIUM-PERENNE L; SOIL HETEROTROPHIC RESPIRATION; LAND-SURFACE MODELS; EDDY-COVARIANCE; WINTER-WHEAT; CARBOHYDRATE CONTENT; TURNOVER MODEL; ROTHC MODEL; ROOT RATIOS; CO2 EFFLUX |
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Croplands play an important role in the carbon budget of many regions. However, the estimation of their carbon balance remains difficult due to diversity and complexity of the processes involved. We report the coupling of a one-dimensional soil water, heat, and CO2 flux model (SOILCO2), a pool concept of soil carbon turnover (RothC), and a crop growth module (SUCROS) to predict the net ecosystem exchange (NEE) of carbon. The coupled model, further referred to as AgroC, was extended with routines for managed grassland as well as for root exudation and root decay. In a first step, the coupled model was applied to two winter wheat sites and one upland grassland site in Germany. The model was calibrated based on soil water content, soil temperature, biometric, and soil respiration measurements for each site, and validated in terms of hourly NEE measured with the eddy covariance technique. The overall model performance of AgroC was sufficient with a model efficiency above 0.78 and a correlation coefficient above 0.91 for NEE. In a second step, AgroC was optimized with eddy covariance NEE measurements to examine the effect of different objective functions, constraints, and data-transformations on estimated NEE. It was found that NEE showed a distinct sensitivity to the choice of objective function and the inclusion of soil respiration data in the optimization process. In particular, both positive and negative day- and nighttime fluxes were found to be sensitive to the selected optimization strategy. Additional consideration of soil respiration measurements improved the simulation of small positive fluxes remarkably. Even though the model performance of the selected optimization strategies did not diverge substantially, the resulting cumulative NEE over simulation time period differed substantially. Therefore, it is concluded that data transformations, definitions of objective functions, and data sources have to be considered cautiously when a terrestrial ecosystem model is used to determine NEE by means of eddy covariance measurements. (C) 2017 Elsevier B.V. All rights reserved. |
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2017-11-09 |
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0304-3800 |
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CropM, ft_MACSUR |
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MA @ admin @ |
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5216 |
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Kipling, R.P.; Virkajärvi, P.; Breitsameter, L.; Curnel, Y.; De Swaef, T.; Gustavsson, A.-M.; Hennart, S.; Höglind, M.; Järvenranta, K.; Minet, J.; Nendel, C.; Persson, T.; Picon-Cochard, C.; Rolinski, S.; Sandars, D.L.; Scollan, N.D.; Sebek, L.; Seddaiu, G.; Topp, C.F.E.; Twardy, S.; Van Middelkoop, J.; Wu, L.; Bellocchi, G. |
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Title |
Key challenges and priorities for modelling European grasslands under climate change |
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Journal Article |
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Year |
2016 |
Publication |
Science of the Total Environment |
Abbreviated Journal |
Science of the Total Environment |
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566-567 |
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851-864 |
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Keywords |
Climate change; Grasslands; Horizon scanning; Livestock production; Models; Research agenda |
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Grassland-based ruminant production systems are integral to sustainable food production in Europe, converting plant materials indigestible to humans into nutritious food, while providing a range of environmental and cultural benefits. Climate change poses significant challenges for such systems, their productivity and the wider benefits they supply. In this context, grassland models have an important role in predicting and understanding the impacts of climate change on grassland systems, and assessing the efficacy of potential adaptation and mitigation strategies. In order to identify the key challenges for European grassland modelling under climate change, modellers and researchers from across Europe were consulted via workshop and questionnaire. Participants identified fifteen challenges and considered the current state of modelling and priorities for future research in relation to each. A review of literature was undertaken to corroborate and enrich the information provided during the horizon scanning activities. Challenges were in four categories relating to: 1) the direct and indirect effects of climate change on the sward 2) climate change effects on grassland systems outputs 3) mediation of climate change impacts by site, system and management and 4) cross-cutting methodological issues. While research priorities differed between challenges, an underlying theme was the need for accessible, shared inventories of models, approaches and data, as a resource for stakeholders and to stimulate new research. Developing grassland models to effectively support efforts to tackle climate change impacts, while increasing productivity and enhancing ecosystem services, will require engagement with stakeholders and policy-makers, as well as modellers and experimental researchers across many disciplines. The challenges and priorities identified are intended to be a resource 1) for grassland modellers and experimental researchers, to stimulate the development of new research directions and collaborative opportunities, and 2) for policy-makers involved in shaping the research agenda for European grassland modelling under climate change. |
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0048-9697 |
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LiveM, ft_macsur |
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MA @ admin @ |
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4761 |
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Sándor, R.; Barcza, Z.; Acutis, M.; Doro, L.; Hidy, D.; Köchy, M.; Minet, J.; Lellei-Kovács, E.; Ma, S.; Perego, A.; Rolinski, S.; Ruget, F.; Sanna, M.; Seddaiu, G.; Wu, L.; Bellocchi, G. |
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Multi-model simulation of soil temperature, soil water content and biomass in Euro-Mediterranean grasslands: Uncertainties and ensemble performance |
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Journal Article |
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Year |
2016 |
Publication |
European Journal of Agronomy |
Abbreviated Journal |
European Journal of Agronomy |
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Biomass; Grasslands; Modelling; Multi-model ensemble; Soil processes |
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• We simulate biomass, soil water content (SWC) and temperature (ST) in grasslands. • We compare nine models to the multi-model median (MMM) at nine sites. • With model calibration, we obtain satisfactory estimates of ST, less of SWC and biomass. • We observe discrepancies across models in the simulation of grassland processes. • We improve performance with multi-model approach. This study presents results from a major grassland model intercomparison exercise, and highlights the main challenges faced in the implementation of a multi-model ensemble prediction system in grasslands. Nine, independently developed simulation models linking climate, soil, vegetation and management to grassland biogeochemical cycles and production were compared in a simulation of soil water content (SWC) and soil temperature (ST) in the topsoil, and of biomass production. The results were assessed against SWC and ST data from five observational grassland sites representing a range of conditions – Grillenburg in Germany, Laqueuille in France with both extensive and intensive management, Monte Bondone in Italy and Oensingen in Switzerland – and against yield measurements from the same sites and other experimental grassland sites in Europe and Israel. We present a comparison of model estimates from individual models to the multi-model ensemble (represented by multi-model median: MMM). With calibration (seven out of nine models), the performances were acceptable for weekly-aggregated ST (R² > 0.7 with individual models and >0.8–0.9 with MMM), but less satisfactory with SWC (R² < 0.6 with individual models and < ∼ 0.5 with MMM) and biomass (R² < ∼0.3 with both individual models and MMM). With individual models, maximum biases of about −5 °C for ST, −0.3 m3 m−3 for SWC and 360 g DM m−2 for yield, as well as negative modelling efficiencies and some high relative root mean square errors indicate low model performance, especially for biomass. We also found substantial discrepancies across different models, indicating considerable uncertainties regarding the simulation of grassland processes. The multi-model approach allowed for improved performance, but further progress is strongly needed in the way models represent processes in managed grassland systems. |
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1161-0301 |
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MA @ admin @ |
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4768 |
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