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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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Title |
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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Keywords |
Biomass; Grasslands; Modelling; Multi-model ensemble; Soil processes |
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Abstract |
• 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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LiveM |
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MA @ admin @ |
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4768 |
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Refsgaard, J.C.; Madsen, H.; Andréassian, V.; Arnbjerg-Nielsen, K.; Davidson, T.A.; Drews, M.; Hamilton, D.P.; Jeppesen, E.; Kjellström, E.; Olesen, J.E.; Sonnenborg, T.O.; Trolle, D.; Willems, P.; Christensen, J.H. |
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Title |
A framework for testing the ability of models to project climate change and its impacts |
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Journal Article |
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Year |
2014 |
Publication |
Climatic Change |
Abbreviated Journal |
Clim. Change |
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122 |
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1-2 |
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271-282 |
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Keywords |
simulation-models; shallow lakes; predictions; calibration; ensembles; terminology; uncertainty; temperature; adaptation; validation |
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Abstract |
Models used for climate change impact projections are typically not tested for simulation beyond current climate conditions. Since we have no data truly reflecting future conditions, a key challenge in this respect is to rigorously test models using proxies of future conditions. This paper presents a validation framework and guiding principles applicable across earth science disciplines for testing the capability of models to project future climate change and its impacts. Model test schemes comprising split-sample tests, differential split-sample tests and proxy site tests are discussed in relation to their application for projections by use of single models, ensemble modelling and space-time-substitution and in relation to use of different data from historical time series, paleo data and controlled experiments. We recommend that differential-split sample tests should be performed with best available proxy data in order to build further confidence in model projections. |
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0165-0009 1573-1480 |
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CropM |
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MA @ admin @ |
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4688 |
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Mansouri, M.; Destain, M.-F. |
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Title |
Predicting biomass and grain protein content using Bayesian methods |
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Journal Article |
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Year |
2015 |
Publication |
Stochastic Environmental Research and Risk Assessment |
Abbreviated Journal |
Stoch. Environ. Res. Risk Assess. |
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29 |
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4 |
Pages |
1167-1177 |
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Keywords |
crop model; particle filter; prediction; ensemble kalman filter; parameter-estimation; particle filters; decision-support; state estimation; model; nitrogen; navigation; tracking; systems |
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This paper deals with the problem of predicting biomass and grain protein content using improved particle filtering (IPF) based on minimizing the Kullback-Leibler divergence. The performances of IPF are compared with those of the conventional particle filtering (PF) in two comparative studies. In the first one, we apply IPF and PF at a simple dynamic crop model with the aim to predict a single state variable, namely the winter wheat biomass, and to estimate several model parameters. In the second study, the proposed IPF and the PF are applied to a complex crop model (AZODYN) to predict a winter-wheat quality criterion, namely the grain protein content. The results of both comparative studies reveal that the IPF method provides a better estimation accuracy than the PF method. The benefit of the IPF method lies in its ability to provide accuracy related advantages over the PF method since, unlike the PF which depends on the choice of the sampling distribution used to estimate the posterior distribution, the IPF yields an optimum choice of this sampling distribution, which also utilizes the observed data. The performance of the proposed method is evaluated in terms of estimation accuracy, root mean square error, mean absolute error and execution times. |
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1436-3240 1436-3259 |
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CropM |
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MA @ admin @ |
Serial |
4664 |
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Dumont, B.; Leemans, V.; Ferrandis, S.; Bodson, B.; Destain, J.-P.; Destain, M.-F. |
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Title |
Assessing the potential of an algorithm based on mean climatic data to predict wheat yield |
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Journal Article |
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Year |
2014 |
Publication |
Precision Agriculture |
Abbreviated Journal |
Precision Agric. |
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15 |
Issue |
3 |
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255-272 |
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Keywords |
stics model; yield prediction; real-time; proxy-sensing; stochastic weather generator; crop yield; mediterranean environment; simulation-model; variability; nitrogen; ensembles; forecasts; demeter; europe |
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The real-time non-invasive determination of crop biomass and yield prediction is one of the major challenges in agriculture. An interesting approach lies in using process-based crop yield models in combination with real-time monitoring of the input climatic data of these models, but unknown future weather remains the main obstacle to reliable yield prediction. Since accurate weather forecasts can be made only a short time in advance, much information can be derived from analyzing past weather data. This paper presents a methodology that addresses the problem of unknown future weather by using a daily mean climatic database, based exclusively on available past measurements. It involves building climate matrix ensembles, combining different time ranges of projected mean climate data and real measured weather data originating from the historical database or from real-time measurements performed in the field. Used as an input for the STICS crop model, the datasets thus computed were used to perform statistical within-season biomass and yield prediction. This work demonstrated that a reliable predictive delay of 3-4 weeks could be obtained. In combination with a local micrometeorological station that monitors climate data in real-time, the approach also enabled us to (i) predict potential yield at the local level, (ii) detect stress occurrence and (iii) quantify yield loss (or gain) drawing on real monitored climatic conditions of the previous few days. |
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1385-2256 1573-1618 |
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CropM |
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MA @ admin @ |
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4621 |
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Author |
Rötter, R.P.; Tao, F.; Höhn, J.G.; Palosuo, T. |
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Title |
Use of crop simulation modelling to aid ideotype design of future cereal cultivars |
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Journal Article |
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Year |
2015 |
Publication |
Journal of Experimental Botany |
Abbreviated Journal |
J. Experim. Bot. |
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66 |
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12 |
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3463-3476 |
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Keywords |
Breeding/*methods; Climate Change; *Computer Simulation; Ecotype; Edible Grain/*growth & development; *Models, Theoretical; cereals; climate extremes; crop growth simulation; ensemble modelling; future cultivars; genetic modelling; ideotype breeding; model improvement; model-aided design |
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A major challenge of the 21st century is to achieve food supply security under a changing climate and roughly a doubling in food demand by 2050 compared to present, the majority of which needs to be met by the cereals wheat, rice, maize, and barley. Future harvests are expected to be especially threatened through increased frequency and severity of extreme events, such as heat waves and drought, that pose particular challenges to plant breeders and crop scientists. Process-based crop models developed for simulating interactions between genotype, environment, and management are widely applied to assess impacts of environmental change on crop yield potentials, phenology, water use, etc. During the last decades, crop simulation has become important for supporting plant breeding, in particular in designing ideotypes, i.e. ‘model plants’, for different crops and cultivation environments. In this review we (i) examine the main limitations of crop simulation modelling for supporting ideotype breeding, (ii) describe developments in cultivar traits in response to climate variations, and (iii) present examples of how crop simulation has supported evaluation and design of cereal cultivars for future conditions. An early success story for rice demonstrates the potential of crop simulation modelling for ideotype breeding. Combining conventional crop simulation with new breeding methods and genetic modelling holds promise to accelerate delivery of future cereal cultivars for different environments. Robustness of model-aided ideotype design can further be enhanced through continued improvements of simulation models to better capture effects of extremes and the use of multi-model ensembles. |
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2016-10-31 |
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0022-0957 1460-2431 |
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Review |
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CropM, ft_macsur |
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MA @ admin @ |
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4804 |
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