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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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2017-08-07 |
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1161-0301 |
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CropM, ft_macsur |
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MA @ admin @ |
Serial |
5170 |
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Author |
Makowski, D. |
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Title |
A simple Bayesian method for adjusting ensemble of crop model outputs to yield observations |
Type |
Journal Article |
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Year |
2017 |
Publication |
European Journal of Agronomy |
Abbreviated Journal |
Europ. J. Agron. |
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Volume |
88 |
Issue |
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Pages |
76-83 |
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Keywords |
Bayesian method; Climate change; Ensemble modelling; Uncertainty; Yield; Linear-Approach; Climate-Change; CO2 |
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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. |
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2017-08-07 |
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1161-0301 |
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CropM, ft_macsur |
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MA @ admin @ |
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5171 |
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Author |
Yin, X.G.; Kersebaum, K.C.; Kollas, C.; Manevski, K.; Baby, S.; Beaudoin, N.; Ozturk, I.; Gaiser, T.; Wu, L.H.; Hoffmann, M.; Charfeddine, M.; Conradt, T.; Constantin, J.; Ewert, F.; de Cortazar-Atauri, I.G.; Giglio, L.; Hlavinka, P.; Hoffmann, H.; Launay, M.; Louarn, G.; Manderscheid, R.; Mary, B.; Mirschel, W.; Nende, C.; Pacholskin, A.; Palosuo, T.; Ripoche-Wachter, D.; Rotter, R.P.; Ruget, F.; Sharif, B.; Trnka, M.; Ventrella, D.; Weigel, H.J.; Olesen, J.E.; Yin, X.; Kersebaum, K.C.; Kollas, C.; Manevski, K.; Baby, S.; Beaudoin, N.; Ozturk, I.; Gaiser, T.; Wu, L.; Hoffmann, M.; Charfeddine, M.; Conradt, T.; Constantin, J.; Ewert, F.; de Cortazar-Atauri, I.G.; Giglio, L.; Hlavinka, P.; Hoffmann, H.; Launay, M.; Louarn, G.; Manderscheid, R.; Mary, B.; Mirschel, W.; Nende, C.; Pacholskin, A.; Palosuo, T.; Ripoche-Wachter, D.; Roetter, R.P.; Ruget, F.; Sharif, B.; Trnka, M.; Ventrella, D.; Weigel, H.-J.; Olesen, J.E. |
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Title |
Performance of process-based models for simulation of grain N in crop rotations across Europe |
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Journal Article |
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Year |
2017 |
Publication |
Agricultural Systems |
Abbreviated Journal |
Agric. Syst. |
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Volume |
154 |
Issue |
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Pages |
63-77 |
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Keywords |
Calibration, Crop model, Crop rotation, Grain N content, Model evaluation, Model initialization; Climate-Change; Winter-Wheat; Nitrogen-Fertilization; Agroecosystem; Models; Multimodel Ensembles; Yield Response; Use Efficiency; Soil-Moisture; Oilseed Rape; Elevated Co2 |
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Abstract |
The accurate estimation of crop grain nitrogen (N; N in grain yield) is crucial for optimizing agricultural N management, especially in crop rotations. In the present study, 12 process-based models were applied to simulate the grain N of i) seven crops in rotations, ii) across various pedo-climatic and agro-management conditions in Europe, under both continuous simulation and single year simulation, and for iv) two calibration levels, namely minimal and detailed calibration. Generally, the results showed that the accuracy of the simulations in predicting grain N increased under detailed calibration. The models performed better in predicting the grain N of winter wheat (Triticum aestivum L.), winter barley (Hordewn vulgare L.) and spring barley (Hordeum vulgare L.) compared to spring oat (Avena saliva L.), winter rye (Secale cereale L.), pea (Piswn sativum L.) and winter oilseed rape (Brassica napus L.). These differences are linked to the intensity of parameterization with better parameterized crops showing lower prediction errors. The model performance was influenced by N fertilization and irrigation treatments, and a majority of the predictions were more accurate under low N and rainfed treatments. Moreover, the multi-model mean provided better predictions of grain N compared to any individual model. In regard to the Individual models, DAISY, FASSET, HERMES, MONICA and STICS are suitable for predicting grain N of the main crops in typical European crop rotations, which all performed well in both continuous simulation and single year simulation. Our results show that both the model initialization and the cover crop effects in crop rotations should be considered in order to achieve good performance of continuous simulation. Furthermore, the choice of either continuous simulation or single year simulation should be guided by the simulation objectives (e.g. grain yield, grain N content or N dynamics), the crop sequence (inclusion of legumes) and treatments (rate and type of N fertilizer) included in crop rotations and the model formalism. |
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2017-06-12 |
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0308-521x |
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Notes |
CropM, ft_macsur |
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no |
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Call Number |
MA @ admin @ |
Serial |
4963 |
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Author |
Malone, R.W.; Kersebaum, K.C.; Kaspar, T.C.; Ma, L.; Jaynes, D.B.; Gillette, K. |
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Title |
Winter rye as a cover crop reduces nitrate loss to subsurface drainage as simulated by HERMES |
Type |
Journal Article |
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Year |
2017 |
Publication |
Agricultural Water Management |
Abbreviated Journal |
Agric. Water Manage. |
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Volume |
184 |
Issue |
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Pages |
156-169 |
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Keywords |
Subsurface drainage, Cover crop, Nitrate loss, Modeling, Denitrification; NITROGEN DYNAMICS; TILE DRAINAGE; AGROECOSYSTEM MODELS; MISSISSIPPI; RIVER; GROWTH-MODEL; RZWQM-DSSAT; DRAINMOD-N; CATCH CROP; SOIL; WATER |
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Abstract |
HERMES is a widely used agricultural system model; however, it has never been tested for simulating N loss to subsurface drainage. Here, we integrated a simple drain flbw component into HERMES. We then compared the predictions to four years of data (2002-2005) from central Iowa fields in corn-oybean with winter rye as a cover crop (CC) and without winter rye (NCC). We also compared the HERMES predictions to the more complex Root Zone Water Quality Model (RZWQM) predictions for the same dataset. The average annual observed and simulated N loss to drain flow were 43.8 and 44.4 kg N/ha (NCC) and 17.6 and 18.9 kg N/ha (CC). The slightly over predicted N loss for CC was because of over predicted nitrate concentration, which may be partly caused by slightly under predicted average annual rye shoot N (observed and simulated values were 47.8 and 46.0 kg N/ha). Also, recent research from the site suggests that the soil field capacity may be greater in CC while we used the same soil parameters for both treatments. A local sensitivity analysis suggests that increased field capacity affects HERMES simulations, which includes reduced drain flow nitrate concentrations, increased denitrification, and reduced drain flow volume. HERMES-simulated cumulative monthly drain flow and annual drain flow were reasonable compared to field data and HERMES performance was comparable to other published drainage model tests. Unlike the RZWQM simulations, however, the modified HERMES did riot accurately simulate the year to year variability in nitrate concentration difference between NCC and CC, possibly due in part to the lack of partial mixing and displacement of the soil solution. The results suggest that 1) the relatively simple model HERMES is a promising tool to estimate annual N loss to drain flow under corn-soybean rotations with winter rye as a cover crop and 2) soil field capacity is a critical parameter to investigate to more thoroughly understand and appropriately model denitrification and N losses to subsurface drainage. Published by Elsevier B.V. |
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2017-04-28 |
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0378-3774 |
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CropM, ft_macsur |
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Call Number |
MA @ admin @ |
Serial |
4946 |
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Author |
Schauberger, B.; Rolinski, S.; Müller, C. |
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Title |
A network-based approach for semi-quantitative knowledge mining and its application to yield variability |
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Journal Article |
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Year |
2016 |
Publication |
Environmental Research Letters |
Abbreviated Journal |
Environ. Res. Lett. |
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Volume |
11 |
Issue |
12 |
Pages |
123001 |
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Keywords |
yield variability; crop models; interaction network; plant process; wheat; maize; rice; Global Food Security; Climate-Change; Crop Production; Stress Tolerance; Wheat Yields; Heat-Stress; Temperature Variability; Environmental-Factors; United-States; Elevated CO2 |
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Abstract |
Variability of crop yields is detrimental for food security. Under climate change its amplitude is likely to increase, thus it is essential to understand the underlying causes and mechanisms. Crop models are the primary tool to project future changes in crop yields under climate change. Asystematic overview of drivers and mechanisms of crop yield variability (YV) can thus inform crop model development and facilitate improved understanding of climate change impacts on crop yields. Yet there is a vast body of literature on crop physiology and YV, which makes a prioritization of mechanisms for implementation in models challenging. Therefore this paper takes on a novel approach to systematically mine and organize existing knowledge from the literature. The aim is to identify important mechanisms lacking in models, which can help to set priorities in model improvement. We structure knowledge from the literature in a semi-quantitative network. This network consists of complex interactions between growing conditions, plant physiology and crop yield. We utilize the resulting network structure to assign relative importance to causes of YV and related plant physiological processes. As expected, our findings confirm existing knowledge, in particular on the dominant role of temperature and precipitation, but also highlight other important drivers of YV. More importantly, our method allows for identifying the relevant physiological processes that transmit variability in growing conditions to variability in yield. We can identify explicit targets for the improvement of crop models. The network can additionally guide model development by outlining complex interactions between processes and by easily retrieving quantitative information for each of the 350 interactions. We show the validity of our network method as a structured, consistent and scalable dictionary of literature. The method can easily be applied to many other research fields. |
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2017-04-07 |
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1748-9326 |
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Review |
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CropM, ft_macsur |
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MA @ admin @ |
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4942 |
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