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Liu, B.; Martre, P.; Ewert, F.; Porter, J.R.; Challinor, A.J.; Mueller, C.; Ruane, A.C.; Waha, K.; Thorburn, P.J.; Aggarwal, P.K.; Ahmed, M.; Balkovic, J.; Basso, B.; Biernath, C.; Bindi, M.; Cammarano, D.; De Sanctis, G.; Dumont, B.; Espadafor, M.; Rezaei, E.E.; Ferrise, R.; Garcia-Vila, M.; Gayler, S.; Gao, Y.; Horan, H.; Hoogenboom, G.; Izaurralde, R.C.; Jones, C.D.; Kassie, B.T.; Kersebaum, K.C.; Klein, C.; Koehler, A.-K.; Maiorano, A.; Minoli, S.; San Martin, M.M.; Kumar, S.N.; Nendel, C.; O’Leary, G.J.; Palosuo, T.; Priesack, E.; Ripoche, D.; Roetter, R.P.; Semenov, M.A.; Stockle, C.; Streck, T.; Supit, I.; Tao, F.; Van der Velde, M.; Wallach, D.; Wang, E.; Webber, H.; Wolf, J.; Xiao, L.; Zhang, Z.; Zhao, Z.; Zhu, Y.; Asseng, S. |
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Title |
Global wheat production with 1.5 and 2.0 degrees C above pre-industrial warming |
Type |
Journal Article |
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Year |
2019 |
Publication |
Global Change Biology |
Abbreviated Journal |
Glob. Chang. Biol. |
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Volume |
25 |
Issue |
4 |
Pages |
1428-1444 |
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Keywords |
1.5 degrees C warming; climate change; extreme low yields; food security; model ensemble; wheat production; Climate-Change; Crop Yield; Impacts; Co2; Adaptation; Responses; Models; Agriculture; Simulation; Growth |
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Abstract |
Efforts to limit global warming to below 2 degrees C in relation to the pre-industrial level are under way, in accordance with the 2015 Paris Agreement. However, most impact research on agriculture to date has focused on impacts of warming >2 degrees C on mean crop yields, and many previous studies did not focus sufficiently on extreme events and yield interannual variability. Here, with the latest climate scenarios from the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project, we evaluated the impacts of the 2015 Paris Agreement range of global warming (1.5 and 2.0 degrees C warming above the pre-industrial period) on global wheat production and local yield variability. A multi-crop and multi-climate model ensemble over a global network of sites developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP) for Wheat was used to represent major rainfed and irrigated wheat cropping systems. Results show that projected global wheat production will change by -2.3% to 7.0% under the 1.5 degrees C scenario and -2.4% to 10.5% under the 2.0 degrees C scenario, compared to a baseline of 1980-2010, when considering changes in local temperature, rainfall, and global atmospheric CO2 concentration, but no changes in management or wheat cultivars. The projected impact on wheat production varies spatially; a larger increase is projected for temperate high rainfall regions than for moderate hot low rainfall and irrigated regions. Grain yields in warmer regions are more likely to be reduced than in cooler regions. Despite mostly positive impacts on global average grain yields, the frequency of extremely low yields (bottom 5 percentile of baseline distribution) and yield inter-annual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producer-India, which supplies more than 14% of global wheat. The projected global impact of warming <2 degrees C on wheat production is therefore not evenly distributed and will affect regional food security across the globe as well as food prices and trade. |
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2019-04-27 |
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1354-1013 |
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CropM, ft_macsur |
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MA @ admin @ |
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5219 |
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Author |
Webber, H.; Ewert, F.; Kimball, B.A.; Siebert, S.; White, J.W.; Wall, G.W.; Ottman, M.J.; Trawally, D.N.A.; Gaiser, T. |
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Title |
Simulating canopy temperature for modelling heat stress in cereals |
Type |
Journal Article |
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Year |
2016 |
Publication |
Environmental Modelling & Software |
Abbreviated Journal |
Env. Model. Softw. |
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77 |
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Pages |
143-155 |
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Keywords |
canopy temperature; heat stress; cereals; crop models; profile relationships; crop production; climate-change; spring wheat; field plots; growth; maize; water; yields; variability |
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Crop models must be improved to account for the effects of heat stress events on crop yields. To date, most approaches in crop models use air temperature to define heat stress intensity as the cumulative sum of thermal times (TT) above a high temperature threshold during a sensitive period for yield formation. However, observational evidence indicates that crop canopy temperature better explains yield reductions associated with high temperature events than air temperature does. This study presents a canopy level energy balance using Monin ObukhovSimilarity Theory (MOST) with simplifications about the canopy resistance that render it suitable for application in crop models and other models of the plant environment. The model is evaluated for a uniform irrigated wheat canopy in Arizona and rainfed maize in Burkina Faso. No single variable regression relationships for key explanatory variables were found that were consistent across sowing dates to explain the deviation of canopy temperature from air temperature. Finally, thermal times determined with simulated canopy temperatures were able to reproduce thermal times calculated with observed canopy temperature, whereas those determined with air temperatures were not. (C) 2015 Elsevier Ltd. All rights reserved. |
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1364-8152 |
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CropM, ft_macsur |
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MA @ admin @ |
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4730 |
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Author |
Ma, S.; Lardy, R.; Graux, A.-I.; Ben Touhami, H.; Klumpp, K.; Martin, R.; Bellocchi, G. |
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Title |
Regional-scale analysis of carbon and water cycles on managed grassland systems |
Type |
Journal Article |
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Year |
2015 |
Publication |
Environmental Modelling & Software |
Abbreviated Journal |
Env. Model. Softw. |
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Volume |
72 |
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356-371 |
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Keywords |
carbon flux; eddy flux measurements; model evaluation; pasture simulation model (pasim); water balance; pasture simulation-model; nitrous-oxide emissions; primary productivity npp; comparing global-models; net ecosystem exchange; greenhouse-gas balance; climate-change; agricultural systems; co2 exchange; european grasslands |
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Predicting regional and global carbon (C) and water dynamics on grasslands has become of major interest, as grasslands are one of the most widespread vegetation types worldwide, providing a number of ecosystem services (such as forage production and C storage). The present study is a contribution to a regional-scale analysis of the C and water cycles on managed grasslands. The mechanistic biogeochemical model PaSim (Pasture Simulation model) was evaluated at 12 grassland sites in Europe. A new parameterization was obtained on a common set of eco-physiological parameters, which represented an improvement of previous parameterization schemes (essentially obtained via calibration at specific sites). We found that C and water fluxes estimated with the parameter set are in good agreement with observations. The model with the new parameters estimated that European grassland are a sink of C with 213 g C m(-2) yr(-1), which is close to the observed net ecosystem exchange (NEE) flux of the studied sites (185 g C m(-2) yr(-1) on average). The estimated yearly average gross primary productivity (GPP) and ecosystem respiration (RECO) for all of the study sites are 1220 and 1006 g C m(-2) yr(-1), respectively, in agreement with observed average GPP (1230 g C m(-2) yr(-1)) and RECO (1046 g C m(-2) yr(-1)). For both variables aggregated on a weekly basis, the root mean square error (RMSE) was similar to 5-16 g C week(-1) across the study sites, while the goodness of fit (R-2) was similar to 0.4-0.9. For evapotranspiration (ET), the average value of simulated ET (415 mmyr(-1)) for all sites and years is close to the average value of the observed ET (451 mm yr(-1)) by flux towers (on a weekly basis, RMSE similar to 2-8 mm week(-1); R-2 = 0.3-0.9). However, further model development is needed to better represent soil water dynamics under dry conditions and soil temperature in winter. A quantification of the uncertainties introduced by spatially generalized parameter values in C and water exchange estimates is also necessary. In addition, some uncertainties in the input management data call for the need to improve the quality of the observational system. |
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2015-10-09 |
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1364-8152 |
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CropM, LiveM, ft_macsur |
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MA @ admin @ |
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4695 |
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Coucheney, E.; Buis, S.; Launay, M.; Constantin, J.; Mary, B.; García de Cortázar-Atauri, I.; Ripoche, D.; Beaudoin, N.; Ruget, F.; &rianarisoa, K.S.; Le Bas, C.; Justes, E.; Léonard, J. |
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Title |
Accuracy, robustness and behavior of the STICS soil–crop model for plant, water and nitrogen outputs: Evaluation over a wide range of agro-environmental conditions in France |
Type |
Journal Article |
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Year |
2015 |
Publication |
Environmental Modelling & Software |
Abbreviated Journal |
Env. Model. Softw. |
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Volume |
64 |
Issue |
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Pages |
177-190 |
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Keywords |
soil-crop model; stics; model performances; plant biomass; soil nitrogen; soil water; remote-sensing data; goodness-of-fit; hydrological model; simulation-models; solar-radiation; regional-scale; climate-change; generic model; data set; validation |
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Abstract |
Soil-crop models are increasingly used as predictive tools to assess yield and environmental impacts of agriculture in a growing diversity of contexts. They are however seldom evaluated at a given time over a wide domain of use. We tested here the performances of the STICS model (v8.2.2) with its standard set of parameters over a dataset covering 15 crops and a wide range of agropedoclimatic conditions in France. Model results showed a good overall accuracy, with little bias. Relative RMSE was larger for soil nitrate (49%) than for plant biomass (35%) and nitrogen (33%) and smallest for soil water (10%). Trends induced by contrasted environmental conditions and management practices were well reproduced. Finally, limited dependency of model errors on crops or environments indicated a satisfactory robustness. Such performances make STICS a valuable tool for studying the effects of changes in agro-ecosystems over the domain explored. (C) 2014 Elsevier Ltd. All rights reserved. |
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CropM |
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MA @ admin @ |
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4554 |
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Mandryk, M.; Reidsma, P.; Kanellopoulos, A.; Groot, J.C.J.; van Ittersum, M.K. |
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The role of farmers’ objectives in current farm practices and adaptation preferences: a case study in Flevoland, the Netherlands |
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Journal Article |
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2014 |
Publication |
Regional Environmental Change |
Abbreviated Journal |
Reg Environ Change |
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14 |
Issue |
4 |
Pages |
1463-1478 |
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multi-criteria decision-making; multi-objective optimization; agriculture; arable farm; vegetable farms; climate-change; south uruguay; land-use; design; agriculture; model; management; options; systems |
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The diversity in farmers’ objectives and responses to external drivers is usually not considered in integrated assessment studies that investigate impacts and adaptation to climate and socio-economic change. Here, we present an approach to assess how farmers’ stated objectives relate to their currently implemented practices and to preferred adaptation options, and we discuss what this implies for assessments of future changes. We based our approach on a combination of multi-criteria decision-making methods. We consistently assessed the importance of farmers’ objectives and adaptation preferences from what farmers say (based on interviews), from what farmers actually do (by analysing current farm performance) and from what farmers want (through a selected alternative farm plan). Our study was performed for six arable farms in Flevoland, a province in the Netherlands. Based on interviews with farmers, we reduced the long list of possible objectives to the most important ones. The objectives we assessed included maximization of economic result and soil organic matter, and minimization of gross margin variance, working hours and nitrogen balance. In our sample, farmers’ stated preferences in objectives were often not fully reflected in realized farming practices. Adaptation preferences of farmers largely resembled their current performance, but generally involved a trend towards stated preferences. Our results suggest that in Flevoland, although farmers do have more objectives, in practical decision-making they focus on economic result maximization, while for strategic decision-making they account for objectives influencing long-term performance and indicators associated with sustainability, in this case soil organic matter. |
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2016-10-31 |
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1436-3798 1436-378x |
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CropM |
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MA @ admin @ |
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4794 |
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