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Author |
Toscano, P.; Genesio, L.; Crisci, A.; Vaccari, F.P.; Ferrari, E.; La Cava, P.; Porter, J.R.; Gioli, B. |
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Title |
Empirical modelling of regional and national durum wheat quality |
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Journal Article |
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Year |
2015 |
Publication |
Agricultural and Forest Meteorology |
Abbreviated Journal |
Agricultural and Forest Meteorology |
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Volume |
204 |
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Pages |
67-78 |
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Keywords |
durum wheat; grain protein content; forecasting tool; modelling; gridded data; red winter-wheat; grain quality; climate-change; mediterranean conditions; interannual variability; protein-composition; co2 concentration; vapor-pressure; carbon-dioxide; crop yield |
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Abstract |
The production of durum wheat in the Mediterranean basin is expected to experience increased variability in yield and quality as a consequence of climate change. To assess how environmental variables and agronomic practices affect grain protein content (GPC), a novel approach based on monthly gridded input data has been implemented to develop empirical model, and validated on historical time series to assess its capability to reproduce observed spatial and inter-annual GPC variability. The model was applied in four Italian regions and at the whole national scale and proved reliable and usable for operational purposes also in a forecast ‘real-time’ mode before harvesting. Precipitable water during autumn to winter and air temperature from anthesis to harvest were extremely important influences on GPC; these and additional variables, included in a linear model, were able to account for 95% of the variability in GPC that has occurred in the last 15 years in Italy. Our results are a unique example of the use of modelling as a predictive real-time platform and are a useful tool to understand better and forecast the impacts of future climate change projections on durum wheat production and quality. |
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2016-10-31 |
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ISSN |
0168-1923 |
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CropM, ft_macsur |
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Call Number |
MA @ admin @ |
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4818 |
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Author |
Trnka, M.; Hlavinka, P.; Semenov, M.A. |
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Title |
Adaptation options for wheat in Europe will be limited by increased adverse weather events under climate change |
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Journal Article |
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Year |
2015 |
Publication |
Journal of the Royal Society Interface |
Abbreviated Journal |
J. R. Soc. Interface |
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Volume |
12 |
Issue |
112 |
Pages |
20150721 |
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Keywords |
climate change; extreme events; food security; winter wheat |
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Abstract |
Ways of increasing the production of wheat, the most widely grown cereal crop, will need to be found to meet the increasing demand caused by human population growth in the coming decades. This increase must occur despite the decrease in yield gains now being reported in some regions, increased price volatility and the expected increase in the frequency of adverse weather events that can reduce yields. However, if and how the frequency of adverse weather events will change over Europe, the most important wheat-growing area, has not yet been analysed. Here, we show that the accumulated probability of 11 adverse weather events with the potential to significantly reduce yield will increase markedly across all of Europe. We found that by the end of the century, the exposure of the key European wheat-growing areas, where most wheat production is currently concentrated, may increase more than twofold. However, if we consider the entire arable land area of Europe, a greater than threefold increase in risk was predicted. Therefore, shifting wheat production to new producing regions to reduce the risk might not be possible as the risk of adverse events beyond the key wheat-growing areas increases even more. Furthermore, we found a marked increase in wheat exposure to high temperatures, severe droughts and field inaccessibility compared with other types of adverse events. Our results also showed the limitations of some of the presently debated adaptation options and demonstrated the need for development of region-specific strategies. Other regions of the world could be affected by adverse weather events in the future in a way different from that considered here for Europe. This observation emphasizes the importance of conducting similar analyses for other major wheat regions. |
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2016-10-31 |
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1742-5689 1742-5662 |
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CropM, ft_macsur |
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MA @ admin @ |
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4819 |
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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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2016 |
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Environmental Modelling & Software |
Abbreviated Journal |
Env. Model. Softw. |
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81 |
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Pages |
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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Abstract |
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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Author |
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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Year |
2017 |
Publication |
Ecological Modelling |
Abbreviated Journal |
Ecol. Model. |
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Volume |
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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Author |
Zhao, G.; Hoffmann, H.; van Bussel, L.G.J.; Enders, A.; Specka, X.; Sosa, C.; Yeluripati, J.; Tao, F.L.; Constantin, J.; Raynal, H.; Teixeira, E.; Grosz, B.; Doro, L.; Zhao, Z.G.; Nendel, C.; Kiese, R.; Eckersten, H.; Haas, E.; Vanuytrecht, E.; Wang, E.; Kuhnert, M.; Trombi, G.; Moriondo, M.; Bindi, M.; Lewan, E.; Bach, M.; Kersebaum, K.C.; Rotter, R.; Roggero, P.P.; Wallach, D.; Cammarano, D.; Asseng, S.; Krauss, G.; Siebert, S.; Gaiser, T.; Ewert, F. |
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Title |
Effect of weather data aggregation on regional crop simulation for different crops, production conditions, and response variables |
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Journal Article |
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Year |
2015 |
Publication |
Climate Research |
Abbreviated Journal |
Clim. Res. |
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Volume |
65 |
Issue |
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Pages |
141-157 |
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Keywords |
crop model; model comparison; spatial resolution; data aggregation; spatial heterogeneity; scaling; climate-change scenarios; sub-saharan africa; winter-wheat; spatial-resolution; yield response; input data; systems simulation; large-scale; soil data; part i |
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Abstract |
We assessed the weather data aggregation effect (DAE) on the simulation of cropping systems for different crops, response variables, and production conditions. Using 13 process-based crop models and the ensemble mean, we simulated 30 yr continuous cropping systems for 2 crops (winter wheat and silage maize) under 3 production conditions for the state of North Rhine-Westphalia, Germany. The DAE was evaluated for 5 weather data resolutions (i.e. 1, 10, 25, 50, and 100 km) for 3 response variables including yield, growing season evapotranspiration, and water use efficiency. Five metrics, viz. the spatial bias (Delta), average absolute deviation (AAD), relative AAD, root mean squared error (RMSE), and relative RMSE, were used to evaluate the DAE on both the input weather data and simulated results. For weather data, we found that data aggregation narrowed the spatial variability but widened the., especially across mountainous areas. The DAE on loss of spatial heterogeneity and hotspots was stronger than on the average changes over the region. The DAE increased when coarsening the spatial resolution of the input weather data. The DAE varied considerably across different models, but changed only slightly for different production conditions and crops. We conclude that if spatially detailed information is essential for local management decision, higher resolution is desirable to adequately capture the spatial variability for heterogeneous regions. The required resolution depends on the choice of the model as well as the environmental condition of the study area. |
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0936-577x |
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CropM, ft_macsur |
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MA @ admin @ |
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4754 |
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