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Author |
Persson, T.; Höglind, M.; Gustavsson, A.-M.; Halling, M.; Jauhiainen, L.; Niemeläinen, O.; Thorvaldsson, G.; Virkajärvi, P. |
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Title |
Evaluation of the LINGRA timothy model under Nordic conditions |
Type |
Journal Article |
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Year |
2014 |
Publication |
Field Crops Research |
Abbreviated Journal |
Field Crops Research |
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Volume  |
161 |
Issue |
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Pages |
87-97 |
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Keywords |
crop model; forage grass; perennial ley; simulation model; nutritive-value; climate-change; systems simulation; growth; dynamics; crop; performance; regrowth; calibration; pastures |
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Abstract |
Simulation models are frequently applied to determine the production potential of forage grasses under various scenarios, including climate change. Thorough calibrations and evaluations of forage grass models can help improve their applicability. This study evaluated the ability of the Light Interception and Utilization Simulator-GRAss (LINGRA) model to predict biomass yield of timothy (Phleum pratense L. cv. Grindstad) in the Nordic countries. Variety trial data for the first and second year after establishment were obtained for seven locations: Jokioinen, Finland (60 degrees 48 ‘ N; 23 degrees 29 ‘ E), Maaninka, Finland (63 degrees 09 ‘ N; 27 degrees 18 ‘ E), Korpa, Iceland (64 degrees 09 ‘ N; 21 degrees 45 ‘ W), Srheim, Norway (58 degrees 41 ‘ N; 5 degrees 39 ‘ E), Lillerud, Sweden (59 degrees 24’ N; 13 degrees 16 ‘ E), Ostersund, Sweden (63 degrees 15 ‘ N; 14 degrees 34 ‘ E) and Ulna Sweden (63 degrees 49 ‘ N; 20 degrees 13 ‘ E) from 1992 to 2012. Two calibrations of the LINGRA model were carried out using Bayesian techniques. In the first of these (SRrheim calibration), data on biomass yield and underlying variables obtained from independent field trials at Srheim were used. In the second (Nordic calibration), biomass data from the other locations were used as well. The model was validated against the remaining set of biomass yields from all locations not included in the Nordic calibration. The observed total seasonal yield the first and second year after establishment was 913 and 991 g DM m(-2) respectively on average across the locations. The corresponding average simulated yield after the Srheim calibration was 1044 (root mean square error (RMSE) 258) and 1112 g DM m(-2) (RMSE 312), respectively. After the Nordic calibration, the simulated average total seasonal yield was 863 (RMSE 242) the first year and 927 g DM m(-2) (RMSE 271) the second year after establishment. The differences between the observed and simulated first cut yield followed the same patterns, whereas the prediction accuracy for second cut yield did not differ substantially between the calibration approaches.Using the parameter set from the Nordic region decreased the model predictability at Srheim compared with only using model parameters derived from this location. These results show that using biomass data from several locations, instead of only one specific location, in the calibration of the LINGRA model improved the overall prediction accuracy of first cut dry matter yield and total seasonal dry matter yield across an environmentally heterogeneous region. To further analyse the usefulness of including multi-site data in forage grass model calibrations, other forage grass models could be evaluated against the same dataset. |
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0378-4290 |
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CropM, ft_macsur |
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MA @ admin @ |
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4634 |
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Author |
Vilvert, E.; Lana, M.; Zander, P.; Sieber, S. |
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Title |
Multi-model approach for assessing the sunflower food value chain in Tanzania |
Type |
Journal Article |
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Year |
2018 |
Publication |
Agricultural Systems |
Abbreviated Journal |
Agric. Syst. |
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Volume |
159 |
Issue |
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Pages |
103-110 |
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Keywords |
Sunflower; Food value chain; Modelling; Tanzania; Food security; Systems Simulation; Crop Model; Agricultural Systems; Farming Systems; Yield Response; Land-Use; Water; Aquacrop; Security; Stics |
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Abstract |
Sunflower is one of the major oilseeds produced in Tanzania, but due to insufficient domestic production more than half of the country’s demand is imported. The improvement of the sunflower food value chain (FVC) understanding is important to ensure an increase in the production, availability, and quality of edible oil. In order to analyse causes and propose solutions to increase the production of sunflower oil, a conceptual framework that proposes the combined use of different models to provide insights about the sunflower FVC was developed. This research focus on the identification of agricultural models that can provide a better understanding of the sunflower FVC in Tanzania, especially within the context of food security improvement. A FVC scheme was designed considering the main steps of sunflower production. Thereafter, relevant models were selected and placed along each step of the FVC. As result, the sunflower FVC model in Tanzania is organized in five steps, namely (1) natural resources; (2) crop production; (3) oil processing; (4) trade; and (5) consumption. Step 1 uses environmental indicators to analyse soil parameters on soil-water models (SWAT, LPJmL, APSIM or CroSyst), with outputs providing data for step 2 of the FVC. In the production step, data from step 1, together with other inputs, is used to run crop models (DSSAT, HERMES, MONICA, STICS, EPIC or AquaCrop) that analyse the impact on sunflower yields. Thereafter, outputs from crop models serve as input for bio-economic farm models (FSSIM or MODAM) to estimate production costs and farm income by optimizing resource allocation planning for step 2. In addition, outputs from crop models are used as inputs for macro-economic models (GTAP, MAGNET or MagPie) by adjusting supply functions and environmental impacts within steps 3, 4, and 5. These models simulate supply and demand, including the processing of products to determine prices and trade volumes at market equilibrium. In turn, these data is used by bio-economic farm models to assess sunflower returns for different farm types and agro-environmental conditions. Due to the large variety of models, it is possible to assess significant parts of the FVC, reducing the need to make assumptions, while improving the understanding of the FVC. |
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2018-01-25 |
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0308-521x |
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CropM, TradeM, ft_macsur |
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MA @ admin @ |
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5187 |
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Author |
Salo, T.J.; Palosuo, T.; Kersebaum, K.C.; Nendel, C.; Angulo, C.; Ewert, F.; Bindi, M.; Calanca, P.; Klein, T.; Moriondo, M.; Ferrise, R.; Olesen, J.E.; Patil, R.H.; Ruget, F.; Takáč, J.; Hlavinka, P.; Trnka, M.; Rötter, R.P. |
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Title |
Comparing the performance of 11 crop simulation models in predicting yield response to nitrogen fertilization |
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Journal Article |
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Year |
2016 |
Publication |
Journal of Agricultural Science |
Abbreviated Journal |
J. Agric. Sci. |
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154 |
Issue |
7 |
Pages |
1218-1240 |
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Keywords |
northern growing conditions; climate-change impacts; spring barley; systems simulation; farming systems; soil properties; winter-wheat; dynamics; growth; management |
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Abstract |
Eleven widely used crop simulation models (APSIM, CERES, CROPSYST, COUP, DAISY, EPIC, FASSET, HERMES, MONICA, STICS and WOFOST) were tested using spring barley (Hordeum vulgare L.) data set under varying nitrogen (N) fertilizer rates from three experimental years in the boreal climate of Jokioinen, Finland. This is the largest standardized crop model inter-comparison under different levels of N supply to date. The models were calibrated using data from 2002 and 2008, of which 2008 included six N rates ranging from 0 to 150 kg N/ha. Calibration data consisted of weather, soil, phenology, leaf area index (LAI) and yield observations. The models were then tested against new data for 2009 and their performance was assessed and compared with both the two calibration years and the test year. For the calibration period, root mean square error between measurements and simulated grain dry matter yields ranged from 170 to 870 kg/ha. During the test year 2009, most models failed to accurately reproduce the observed low yield without N fertilizer as well as the steep yield response to N applications. The multi-model predictions were closer to observations than most single-model predictions, but multi-model mean could not correct systematic errors in model simulations. Variation in soil N mineralization and LAI development due to differences in weather not captured by the models most likely was the main reason for their unsatisfactory performance. This suggests the need for model improvement in soil N mineralization as a function of soil temperature and moisture. Furthermore, specific weather event impacts such as low temperatures after emergence in 2009, tending to enhance tillering, and a high precipitation event just before harvest in 2008, causing possible yield penalties, were not captured by any of the models compared in the current study. |
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0021-8596 1469-5146 |
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CropM, ft_macsur |
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MA @ admin @ |
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4713 |
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Ruane, A.C.; Hudson, N.I.; Asseng, S.; Camarrano, D.; Ewert, F.; Martre, P.; Boote, K.J.; Thorburn, P.J.; Aggarwal, P.K.; Angulo, C.; Basso, B.; Bertuzzi, P.; Biernath, C.; Brisson, N.; Challinor, &rew J.; Doltra, J.; Gayler, S.; Goldberg, R.; Grant, R.F.; Heng, L.; Hooker, J.; Hunt, L.A.; Ingwersen, J.; Izaurralde, R.C.; Kersebaum, K.C.; Kumar, S.N.; Müller, C.; Nendel, C.; O’Leary, G.; Olesen, J.E.; Osborne, T.M.; Palosuo, T.; Priesack, E.; Ripoche, D.; Rötter, R.P.; Semenov, M.A.; Shcherbak, I.; Steduto, P.; Stöckle, C.O.; Stratonovitch, P.; Streck, T.; Supit, I.; Tao, F.; Travasso, M.; Waha, K.; Wallach, D.; White, J.W.; Wolf, J. |
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Title |
Multi-wheat-model ensemble responses to interannual climate variability |
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Journal Article |
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Year |
2016 |
Publication |
Environmental Modelling & Software |
Abbreviated Journal |
Env. Model. Softw. |
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Volume |
81 |
Issue |
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86-101 |
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Keywords |
Crop modeling; Uncertainty; Multi-model ensemble; Wheat; AgMIP; Climate; impacts; Temperature; Precipitation; lnterannual variability; simulation-model; crop model; nitrogen dynamics; winter-wheat; large-area; systems simulation; farming systems; yield response; growth; water |
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We compare 27 wheat models’ yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981-2010 grain yield, and we evaluate results against the interannual variability of growing season temperature, precipitation, and solar radiation. The amount of information used for calibration has only a minor effect on most models’ climate response, and even small multi-model ensembles prove beneficial. Wheat model clusters reveal common characteristics of yield response to climate; however models rarely share the same cluster at all four sites indicating substantial independence. Only a weak relationship (R-2 <= 0.24) was found between the models’ sensitivities to interannual temperature variability and their response to long-term warming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs and motivating continuing analysis and model development efforts. Published by Elsevier Ltd. |
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1364-8152 |
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CropM, ft_macsur |
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MA @ admin @ |
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4769 |
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Zhao, G.; Hoffmann, H.; Yeluripati, J.; Xenia, S.; Nendel, C.; Coucheney, E.; Kuhnert, M.; Tao, F.; Constantin, J.; Raynal, H.; Teixeira, E.; Grosz, B.; Doro, L.; Kiese, R.; Eckersten, H.; Haas, E.; Cammarano, D.; Kassie, B.; Moriondo, M.; Trombi, G.; Bindi, M.; Biernath, C.; Heinlein, F.; Klein, C.; Priesack, E.; Lewan, E.; Kersebaum, K.-C.; Rötter, R.; Roggero, P.P.; Wallach, D.; Asseng, S.; Siebert, S.; Gaiser, T.; Ewert, F. |
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Title |
Evaluating the precision of eight spatial sampling schemes in estimating regional means of simulated yield for two crops |
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Journal Article |
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Year |
2016 |
Publication |
Environmental Modelling & Software |
Abbreviated Journal |
Env. Model. Softw. |
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Volume |
80 |
Issue |
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100-112 |
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Keywords |
Crop model; Stratified random sampling; Simple random sampling; Clustering; Up-scaling; Model comparison; Precision gain; species distribution models; systems simulation; weather data; large-scale; design; soil; optimization; growth; apsim; autocorrelation |
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We compared the precision of simple random sampling (SimRS) and seven types of stratified random sampling (StrRS) schemes in estimating regional mean of water-limited yields for two crops (winter wheat and silage maize) that were simulated by fourteen crop models. We found that the precision gains of StrRS varied considerably across stratification methods and crop models. Precision gains for compact geographical stratification were positive, stable and consistent across crop models. Stratification with soil water holding capacity had very high precision gains for twelve models, but resulted in negative gains for two models. Increasing the sample size monotonously decreased the sampling errors for all the sampling schemes. We conclude that compact geographical stratification can modestly but consistently improve the precision in estimating regional mean yields. Using the most influential environmental variable for stratification can notably improve the sampling precision, especially when the sensitivity behavior of a crop model is known. |
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1364-8152 |
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CropM, ft_macsur |
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MA @ admin @ |
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4724 |
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