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Bodin, P. |
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Simulating the sensitivity of carbon and water fluxes as well as yield within the ClimAfrica project |
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2014 |
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Sub Saharan Africa (SSA) is a region expected to be particularly sensitive to climate change effects on crop yield (Barrios et al. 2008). Annual precipitation, calculated as averages for each African country, is expected to change by −39 to +64 mm by 2030 (Jarvis et al. 2012). The effect of climate also becomes larger as ~97 % of all agricultural land in SSA is rain fed (Rockström et al. 2004). The aim of the ClimAfrica project (FP7) is to better understand and predict climate change in SSA and to analyse the impacts on ecosystems and populations. Within the modeling Work Package (WP3) the main goal is to quantify the sensitivity of vegetation productivity and water resources to seasonal interannual decadal variability in weather and climate using a set of crop models. Here we present some results on the sensitivity of simulated carbon fluxes and FAPAR for different representations of cropland in a vegetation model (LPJ-GUESS: Lindeskog et al. 2013) as well as the sensitivity on simulated fluxes of carbon water and crop yield using a range of vegetation and crop models (LPJ-GUESS, LPJmL, ORCHIDEE and DSSAT), climate datasets, GCM output and bias correction/downscaling techniques. |
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FACCE MACSUR Mid-term Scientific Conference |
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3(S) Sassari, Italy |
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FACCE MACSUR Mid-term Scientific Conference, 2014-04-01 to 2014-04-04, Sassari, Italy |
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no |
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MA @ admin @ |
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5125 |
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Jing, Q.; Bélanger, G.; Baron, V.; Bonesmo, H.; Virkajärvi, P. |
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Title |
Simulating the Nutritive Value of Timothy Summer Regrowth |
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Journal Article |
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2013 |
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Agronomy Journal |
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Agronomy Journal |
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105 |
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3 |
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563 |
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varying n nutrition; cation-anion difference; spring growth; swine manure; leaf-area; nitrogen; yield; model; digestibility; dynamics |
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The process-based grass model, CATIMO, simulates the spring growth and nutritive value of timothy (Phleum pratense L.), a forage species widely grown in Scandinavia and Canada, but the nutritive value of the summer regrowth has never been simulated. Our objective was to improve CATIMO for simulating the N concentration, neutral detergent fiber (NDF), in vitro digestibility of NDF (dNDF), and in vitro true digestibility of dry matter (IVTD) of summer regrowth. Daily changes in summer regrowth nutritive value were simulated by modifying key crop parameters that differed from spring growth. More specifically, the partitioning fraction to leaf blades was increased to increase the leaf-to-weight ratio, and daily changes in NDF and dNDF of leaf blades and stems were reduced. The modified CATIMO model was evaluated with data from four independent experiments in eastern and western Canada and Finland. The model performed better for eastern Canada than for the other locations, but the nutritive value attributes of the summer regrowth across locations (range of normalized RMSE = 8-25%, slope < 0.17, R-2 < 0.10) were not simulated as well as those of the spring growth (range of normalized RMSE = 4-16%, 0.85 < slope < 1.07, R-2 > 0.61). These modeling results highlight knowledge gaps in timothy summer regrowth and prospective research directions: improved knowledge of factors controlling the nutritive value of the timothy summer regrowth and experimental measurements of leaf-to-weight ratio and of the nutritive value of leaves and stems. |
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0002-1962 |
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CropM, LiveM |
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MA @ admin @ |
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4493 |
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de Wit, A.; Rötter, R.P.; Palosuo, T.; Bergjord, A.K.; Virchenko, O.; Kleshenko, A. |
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Simulating the impact of winter conditions on the survival and yield potential of winter wheat |
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2016 |
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Berlin (Germany) |
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International Crop Modelling Symposium iCROPM 2016, 2016-05-15 to 2016-05-17, Berlin, Germany |
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MA @ admin @ |
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4905 |
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Author |
Doltra, J.; Olesen, E.; Báez, D.; Chirinda, N. |
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Simulating seasonal nitrous oxide emissions from maize and wheat crops grown in two different cropping systems in Atlantic Europe |
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2014 |
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CropM |
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MACSUR CropM International Symposium and Workshop: Modelling climate change impacts on crop production for food security, Oslo, Norway, 2014-02-10 to 2014-02-12 |
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MA @ admin @ |
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2392 |
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Nendel, C.; Wieland, R.; Mirschel, W.; Specka, X.; Guddat, C.; Kersebaum, K.C. |
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Simulating regional winter wheat yields using input data of different spatial resolution |
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Journal Article |
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2013 |
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Field Crops Research |
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Field Crops Research |
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145 |
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67-77 |
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monica; agro-ecosystem model; dynamic modelling; scaling; input data; climate-change; crop yield; nitrogen dynamics; food security; mineral nitrogen; soil-moisture; scaling-up; model; maize; water |
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The success of using agro-ecosystem models for the high-resolution simulation of agricultural yields for larger areas is often hampered by a lack of input data. We investigated the effect of different spatially resolved soil and weather data used as input for the MONICA model on its ability to reproduce winter wheat yields in the Federal State of Thuringia, Germany (16,172 km(2)). The combination of one representative soil and one weather station was insufficient to reproduce the observed mean yield of 6.66 +/- 0.87 t ha(-1) for the federal state. Use of a 100 m x 100 m grid of soil and relief information combined with just one representative weather station yielded a good estimator (7.01 +/- 1.47 t ha(-1)). The soil and relief data grid used in combination with weather information from 14 weather stations in a nearest neighbour approach produced even better results (6.60 +/- 1.37 t ha(-1)); the same grid used with 39 additional rain gauges and an interpolation algorithm that included an altitude correction of temperature data slightly overpredicted the observed mean (7.36 +/- 1.17 t ha(-1)). It was concluded that the apparent success of the first two high-resolution approaches over the latter was based on two effects that cancelled each other out: the calibration of MONICA to match high-yield experimental data and the growth-defining and -limiting effect of weather data that is not representative for large parts of the region. At the county and farm level the MONICA model failed to reproduce the 1992-2010 time series of yields, which is partly explained by the fact that many growth-reducing factors were not considered in the model. (C) 2013 Elsevier B.V. All rights reserved. |
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0378-4290 |
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CropM, ftnotmacsur |
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MA @ admin @ |
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4498 |
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