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Kollas, C.; Kersebaum, K.C.; Nendel, C.; Manevski, K.; Müller, C.; Palosuo, T.; Armas-Herrera, C.M.; Beaudoin, N.; Bindi, M.; Charfeddine, M.; Conradt, T.; Constantin, J.; Eitzinger, J.; Ewert, F.; Ferrise, R.; Gaiser, T.; Cortazar-Atauri, I.G. de; Giglio, L.; Hlavinka, P.; Hoffmann, H.; Hoffmann, M.P.; Launay, M.; Manderscheid, R.; Mary, B.; Mirschel, W.; Moriondo, M.; Olesen, J.E.; Öztürk, I.; Pacholski, A.; Ripoche-Wachter, D.; Roggero, P.P.; Roncossek, S.; Rötter, R.P.; Ruget, F.; Sharif, B.; Trnka, M.; Ventrella, D.; Waha, K.; Wegehenkel, M.; Weigel, H.-J.; Wu, L. |
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Title |
Crop rotation modelling—A European model intercomparison |
Type |
Journal Article |
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Year |
2015 |
Publication |
European Journal of Agronomy |
Abbreviated Journal |
European Journal of Agronomy |
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Volume |
70 |
Issue |
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Pages  |
98-111 |
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Keywords |
Model ensemble; Crop simulation models; Catch crop; Intermediate crop; Treatment; Multi-year; long-term experiment; climate-change; wheat production; n-fertilization; systems simulation; nitrogen dynamics; tillage intensity; winter-wheat; soil carbon; growth |
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Abstract |
• First model inter-comparison on crop rotations. • Continuous simulation of multi-year crop rotations yields outperformed single-year simulation. • Low accuracy of yield predictions in less commonly modelled crops such as potato, radish, grass vegetation. • Multi-model mean prediction was found to minimise the likely error arising from single-model predictions. • The representation of intermediate crops and carry-over effects in the models require further research efforts.
Diversification of crop rotations is considered an option to increase the resilience of European crop production under climate change. So far, however, many crop simulation studies have focused on predicting single crops in separate one-year simulations. Here, we compared the capability of fifteen crop growth simulation models to predict yields in crop rotations at five sites across Europe under minimal calibration. Crop rotations encompassed 301 seasons of ten crop types common to European agriculture and a diverse set of treatments (irrigation, fertilisation, CO2 concentration, soil types, tillage, residues, intermediate or catch crops). We found that the continuous simulation of multi-year crop rotations yielded results of slightly higher quality compared to the simulation of single years and single crops. Intermediate crops (oilseed radish and grass vegetation) were simulated less accurately than main crops (cereals). The majority of models performed better for the treatments of increased CO2 and nitrogen fertilisation than for irrigation and soil-related treatments. The yield simulation of the multi-model ensemble reduced the error compared to single-model simulations. The low degree of superiority of continuous simulations over single year simulation was caused by (a) insufficiently parameterised crops, which affect the performance of the following crop, and (b) the lack of growth-limiting water and/or nitrogen in the crop rotations under investigation. In order to achieve a sound representation of crop rotations, further research is required to synthesise existing knowledge of the physiology of intermediate crops and of carry-over effects from the preceding to the following crop, and to implement/improve the modelling of processes that condition these effects. |
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1161-0301 |
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CropM, ft_macsur |
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MA @ admin @ |
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4660 |
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Author |
Schils, R.; Olesen, J.E.; Kersebaum, K.-C.; Rijk, B.; Oberforster, M.; Kalyada, V.; Khitrykau, M.; Gobin, A.; Kirchev, H.; Manolova, V.; Manolov, I.; Trnka, M.; Hlavinka, P.; Palosuo, T.; Peltonen-Sainio, P.; Jauhiainen, L.; Lorgeou, J.; Marrou, H.; Danalatos, N.; Archontoulis, S.; Fodor, N.; Spink, J.; Roggero, P.P.; Bassu, S.; Pulina, A.; Seehusen, T.; Uhlen, A.K.; Zylowska, K.; Nierobca, A.; Kozyra, J.; Silva, J.V.; Macas, B.M.; Coutinho, J.; Ion, V.; Takac, J.; Ines Minguez, M.; Eckersten, H.; Levy, L.; Herrera, J.M.; Hiltbrunner, J.; Kryvobok, O.; Kryvoshein, O.; Sylvester-Bradley, R.; Kindred, D.; Topp, C.F.E.; Boogaard, H.; de Groot, H.; Lesschen, J.P.; van Bussel, L.; Wolf, J.; Zijlstra, M.; van Loon, M.P.; van Ittersum, M.K. |
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Title |
Cereal yield gaps across Europe |
Type |
Journal Article |
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Year |
2018 |
Publication |
European Journal of Agronomy |
Abbreviated Journal |
Europ. J. Agron. |
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Volume |
101 |
Issue |
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Pages |
109-120 |
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Keywords |
Wheat, Barley, Grain maize, Crop modelling, Yield potential, Nitrogen; Nitrogen Use Efficiency; Sustainable Intensification; Climate-Change; Land-Use; Wheat; Soil; Agriculture; Impacts; Fertility; Emissions |
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Abstract |
Europe accounts for around 20% of the global cereal production and is a net exporter of ca. 15% of that production. Increasing global demand for cereals justifies questions as to where and by how much Europe’s production can be increased to meet future global market demands, and how much additional nitrogen (N) crops would require. The latter is important as environmental concern and legislation are equally important as production aims in Europe. Here, we used a country-by-country, bottom-up approach to establish statistical estimates of actual grain yield, and compare these to modelled estimates of potential yields for either irrigated or rainfed conditions. In this way, we identified the yield gaps and the opportunities for increased cereal production for wheat, barley and maize, which represent 90% of the cereals grown in Europe. The combined mean annual yield gap of wheat, barley, maize was 239 Mt, or 42% of the yield potential. The national yield gaps ranged between 10 and 70%, with small gaps in many north-western European countries, and large gaps in eastern and south-western Europe. Yield gaps for rainfed and irrigated maize were consistently lower than those of wheat and barley. If the yield gaps of maize, wheat and barley would be reduced from 42% to 20% of potential yields, this would increase annual cereal production by 128 Mt (39%). Potential for higher cereal production exists predominantly in Eastern Europe, and half of Europe’s potential increase is located in Ukraine, Romania and Poland. Unlocking the identified potential for production growth requires a substantial increase of the crop N uptake of 4.8 Mt. Across Europe, the average N uptake gaps, to achieve 80% of the yield potential, were 87, 77 and 43 kg N ha(-1) for wheat, barley and maize, respectively. Emphasis on increasing the N use efficiency is necessary to minimize the need for additional N inputs. Whether yield gap reduction is desirable and feasible is a matter of balancing Europe’s role in global food security, farm economic objectives and environmental targets. |
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2019-01-07 |
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1161-0301 |
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CropM, TradeM, ft_macsur |
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Call Number |
MA @ admin @ |
Serial |
5213 |
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Author |
Fan, F.; Henriksen, C.B.; Porter, J. |
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Title |
Valuation of ecosystem services in organic cereal crop production systems with different management practices in relation to organic matter input |
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Journal Article |
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Year |
2016 |
Publication |
Ecosystem Services |
Abbreviated Journal |
Ecosystem Services |
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Volume |
22 |
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117-127 |
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Keywords |
soil physical-properties; carbon sequestration; microbial biomass; farming systems; nitrogen mineralization; earthworm populations; straw; incorporation; economic valuation; agricultural soils; different tillage; Organic farming; Ecosystem services; Economic valuation; Management; Informed decision making |
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As the degradation of global ecosystem services (ES) continues in the last five decades, maintaining or even enhancing the ES of agro-ecosystem is one of the approaches to mitigate the global ES loss. This study provides the first estimate of an economic valuation of ES provided by organic cereal crop production systems with different management practices in relation to organic matter input (low, medium and high). Our results show that organic matter inputs significantly affect the total ES value on organic cereal crop production systems. The system with high organic matter input has the highest gross total ES value (US$ 1969 ha(-1) yr(-1)), followed by the low organic matter input system (US$ 1688 ha(-1) yr(-1)), and the lowest ES value are found in the medium organic matter input system (US$ 1492 ha(-1) yr(-1)). Organic matter inputs have strong positive relationship with non-marketable ES values, while this relationship was not found in marketable ES values. Monetizing the ES can be used by land managers and policy makers to adjust management practices in terms of organic matter input in cereal production system with a long term goal for sustainable agriculture. |
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2017-01-12 |
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2212-0416 |
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CropM, ft_MACSUR |
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MA @ admin @ |
Serial |
4934 |
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Author |
Dumont, B.; Leemans, V.; Mansouri, M.; Bodson, B.; Destain, J.-P.; Destain, M.-F. |
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Title |
Parameter identification of the STICS crop model, using an accelerated formal MCMC approach |
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Journal Article |
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Year |
2014 |
Publication |
Environmental Modelling & Software |
Abbreviated Journal |
Env. Model. Softw. |
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Volume |
52 |
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121-135 |
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Keywords |
crop model; parameter estimation; bayes; stics; dream; global sensitivity-analysis; simulation-model; nitrogen balances; bayesian-approach; generic model; wheat; prediction; water; optimization; algorithm |
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This study presents a Bayesian approach for the parameters’ identification of the STICS crop model based on the recently developed Differential Evolution Adaptive Metropolis (DREAM) algorithm. The posterior distributions of nine specific crop parameters of the STICS model were sampled with the aim to improve the growth simulations of a winter wheat (Triticum aestivum L) culture. The results obtained with the DREAM algorithm were initially compared to those obtained with a Nelder-Mead Simplex algorithm embedded within the OptimiSTICS package. Then, three types of likelihood functions implemented within the DREAM algorithm were compared, namely the standard least square, the weighted least square, and a transformed likelihood function that makes explicit use of the coefficient of variation (CV). The results showed that the proposed CV likelihood function allowed taking into account both noise on measurements and heteroscedasticity which are regularly encountered in crop modelling. (C) 2013 Elsevier Ltd. All rights reserved. |
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1364-8152 |
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CropM |
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MA @ admin @ |
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4520 |
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Author |
Webber, H.; Zhao, G.; Wolf, J.; Britz, W.; Vries, W. de; Gaiser, T.; Hoffmann, H.; Ewert, F. |
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Title |
Climate change impacts on European crop yields: Do we need to consider nitrogen limitation |
Type |
Journal Article |
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Year |
2015 |
Publication |
European Journal of Agronomy |
Abbreviated Journal |
European Journal of Agronomy |
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Volume |
71 |
Issue |
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123-134 |
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Keywords |
Climate impact assessment; Nitrogen limitation; European crop yields; SIMPLACE Crop modelling framework; model calibration; winter-wheat; scale; co2; productivity; agriculture; strategies; scenarios; systems; growth |
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Global climate impact studies with crop models suggest that including nitrogen and water limitation causes greater negative climate change impacts on actual yields compared to water-limitation only. We simulated water limited and nitrogen water limited yields across the EU-27 to 2050 for six key crops with the SIMPLACE<LINTUL5, DRUNIR, HEAT> model to assess how important consideration of nitrogen limitation is in climate impact studies for European cropping systems. We further investigated how crop nitrogen use may change under future climate change scenarios. Our results suggest that inclusion of nitrogen limitation hardly changed crop yield response to climate for the spring-sown crops considered (grain maize, potato, and sugar beet). However, for winter-sown crops (winter barley, winter rapeseed and winter wheat), simulated impacts to 2050 were more negative when nitrogen limitation was considered, especially with high levels of water stress. Future nitrogen use rates are likely to decrease due to climate change for spring-sown crops, largely in parallel with their yields. These results imply that climate change impact studies for winter-sown crops should consider N-fertilization. Specification of future N fertilization rates is a methodological challenge that is likely to need integrated assessment models to address. |
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1161-0301 |
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CropM, ft_macsur |
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MA @ admin @ |
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4726 |
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