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Author  |
Makowski, D. |
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Title |
A simple Bayesian method for adjusting ensemble of crop model outputs to yield observations |
Type |
Journal Article |
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Year |
2017 |
Publication |
European Journal of Agronomy |
Abbreviated Journal |
Europ. J. Agron. |
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Volume |
88 |
Issue |
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Pages |
76-83 |
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Keywords |
Bayesian method; Climate change; Ensemble modelling; Uncertainty; Yield; Linear-Approach; Climate-Change; CO2 |
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Abstract |
Multi-model forecasting has drawn some attention in crop science for evaluating effect of climate change on crop yields. The principle is to run several individual process-based crop models under several climate scenarios in order to generate ensembles of output values. This paper describes a simple Bayesian method – called Bayes linear method- for updating ensemble of crop model outputs using yield observations. The principle is to summarize the ensemble of crop model outputs by its mean and variance, and then to adjust these two quantities to yield observations in order to reduce uncertainty. The adjusted mean and variance combine two sources of information, i.e., the ensemble of crop model outputs and the observations. Interestingly, with this method, observations collected under a given climate scenario can be used to adjust mean and variance of the model ensemble under a different scenario. Another advantage of the proposed method is that it does not rely on a separate calibration of each individual crop model. The uncertainty reduction resulting from the adjustment of an ensemble of crop models to observations was assessed in a numerical application. The implementation of the Bayes linear method systematically reduced uncertainty, but the results showed the effectiveness of this method varied in function of several factors, especially the accuracy of the yield observation, and the covariance between the crop model output and the observation. (C) 2015 Elsevier B.V. All rights reserved. |
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2017-08-07 |
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ISSN |
1161-0301 |
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Notes |
CropM, ft_macsur |
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Call Number |
MA @ admin @ |
Serial |
5171 |
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Author |
Maiorano, A.; Martre, P.; Asseng, S.; Ewert, F.; Müller, C.; Rötter, R.P.; Ruane, A.C.; Semenov, M.A.; Wallach, D.; Wang, E.; Alderman, P.D.; Kassie, B.T.; Biernath, C.; Basso, B.; Cammarano, D.; Challinor, A.J.; Doltra, J.; Dumont, B.; Rezaei, E.E.; Gayler, S.; Kersebaum, K.C.; Kimball, B.A.; Koehler, A.-K.; Liu, B.; O’Leary, G.J.; Olesen, J.E.; Ottman, M.J.; Priesack, E.; Reynolds, M.; Stratonovitch, P.; Streck, T.; Thorburn, P.J.; Waha, K.; Wall, G.W.; White, J.W.; Zhao, Z.; Zhu, Y. |
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Title |
Crop model improvement reduces the uncertainty of the response to temperature of multi-model ensembles |
Type |
Journal Article |
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Year |
2016 |
Publication |
Field Crops Research |
Abbreviated Journal |
Field Crops Research |
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Volume |
202 |
Issue |
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Pages |
5-20 |
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Keywords |
Impact uncertainty; High temperature; Model improvement; Multi-model ensemble; Wheat crop model |
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Abstract |
To improve climate change impact estimates and to quantify their uncertainty, multi-model ensembles (MMEs) have been suggested. Model improvements can improve the accuracy of simulations and reduce the uncertainty of climate change impact assessments. Furthermore, they can reduce the number of models needed in a MME. Herein, 15 wheat growth models of a larger MME were improved through re-parameterization and/or incorporating or modifying heat stress effects on phenology, leaf growth and senescence, biomass growth, and grain number and size using detailed field experimental data from the USDA Hot Serial Cereal experiment (calibration data set). Simulation results from before and after model improvement were then evaluated with independent field experiments from a CIMMYT world-wide field trial network (evaluation data set). Model improvements decreased the variation (10th to 90th model ensemble percentile range) of grain yields simulated by the MME on average by 39% in the calibration data set and by 26% in the independent evaluation data set for crops grown in mean seasonal temperatures >24 °C. MME mean squared error in simulating grain yield decreased by 37%. A reduction in MME uncertainty range by 27% increased MME prediction skills by 47%. Results suggest that the mean level of variation observed in field experiments and used as a benchmark can be reached with half the number of models in the MME. Improving crop models is therefore important to increase the certainty of model-based impact assessments and allow more practical, i.e. smaller MMEs to be used effectively. |
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2016-09-13 |
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Newsletter July 2016 |
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0378-4290 |
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CropM |
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CropMwp;wos; ft=macsur; wsnot_yet; |
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Call Number |
MA @ admin @ |
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4776 |
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Author |
Liu, X.; Lehtonen, H.; Purola, T.; Pavlova, Y.; Rötter, R.; Palosuo, T. |
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Title |
Dynamic economic modelling of crop rotations with farm management practices under future pest pressure |
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Journal Article |
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Year |
2016 |
Publication |
Agricultural Systems |
Abbreviated Journal |
Agricultural Systems |
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Volume |
144 |
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Pages |
65-76 |
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Keywords |
Farm management; Dynamic optimization; Crop rotation; Risk aversion; Climate change; Prices; climate-change; sequester carbon; changing climate; food security; challenge; Finland; ensembles; systems; europe; tool |
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Abstract |
Agricultural practice is facing multiple challenges under volatile commodity markets, inevitable climate change, mounting pest pressure and various other environment-related constraints. The objective of this research is to present a dynamic optimization model of crop rotations and farm management and show its suitability for economic analysis over a 30 year time period. In this model, we include management practices such as fertilization, fungicide treatment and liming, and apply it in a region in Southwestern Finland. Results show that (i) growing pest pressure favours the cultivation of wheat-oats and wheat-oilseeds combinations, while (ii) market prices largely determine the crops in the rotation plan and the specific management practices adopted. The flexibility of our model can also be utilized in evaluating the value of other management options such as new cultivars under different projections of future climate and market conditions. |
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0308521x |
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Notes |
CropM, TradeM, ftnotmacsur |
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Call Number |
MA @ admin @ |
Serial |
4719 |
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Author |
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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ISSN |
1354-1013 |
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Notes |
CropM, ft_macsur |
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no |
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Call Number |
MA @ admin @ |
Serial |
5219 |
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Author |
Lehtonen, H. |
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Title |
Evaluating adaptation and the production development of Finnish agriculture in climate and global change |
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Journal Article |
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Year |
2015 |
Publication |
Agricultural and Food Science |
Abbreviated Journal |
Agricultural and Food Science |
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Volume |
24 |
Issue |
3 |
Pages |
219-234 |
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Keywords |
agricultural sector modelling; economic adjustment; global prices; climate change; finnish agriculture; crop production; land-use; challenge; ensembles; Finland; Europe; policy |
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Abstract |
Agricultural product prices and policies influence the development of crop yields under climate change through farm level management decisions. On this basis, five main scenarios were specified for agricultural commodity prices and crop yields. An economic agricultural sector model was used in order to assess the impacts of the scenarios on production, land use and farm income in Finland. The results suggest that falling crop yields, if realized due to low prices and restrictive policies, will result in decreasing crop and livestock production and increasing nutrient surplus. Slowly increasing crop yields could stabilise production and increase farm income. Significantly higher crop prices and yields are required, however, for any marked increase in production in Finland. Cereals production would increase relatively more than livestock production, if there were high prices for agricultural products. This is explained by abundant land resources, a high opportunity cost of labour and policies maintaining current dairy and beef production. |
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2016-07-22 |
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ISSN |
1459-6067 |
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TradeM, ft_macsur |
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Call Number |
MA @ admin @ |
Serial |
4750 |
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