| Records |
| 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. |
| Title |
Global wheat production with 1.5 and 2.0 degrees C above pre-industrial warming |
Type |
Journal Article |
| Year |
2019 |
Publication |
Global Change Biology |
Abbreviated Journal |
Glob. Chang. Biol. |
| Volume |
25 |
Issue |
4 |
Pages |
1428-1444 |
| 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 |
| 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. |
| Address |
2019-04-27 |
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English |
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Edition |
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ISSN  |
1354-1013 |
ISBN |
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Article |
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| Notes |
CropM, ft_macsur |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
5219 |
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| Author |
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. |
| Title |
Multi-wheat-model ensemble responses to interannual climate variability |
Type |
Journal Article |
| Year |
2016 |
Publication |
Environmental Modelling & Software |
Abbreviated Journal |
Env. Model. Softw. |
| Volume |
81 |
Issue |
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Pages |
86-101 |
| 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 |
| 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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English |
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| ISSN |
1364-8152 |
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Article |
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| Notes |
CropM, ft_macsur |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4769 |
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| Author |
Refsgaard, J.C.; Arnbjerg-Nielsen, K.; Drews, M.; Halsnaes, K.; Jeppesen, E.; Madsen, H.; Markandya, A.; Olesen, J.E.; Porter, J.R.; Christensen, J.H. |
| Title |
The role of uncertainty in climate change adaptation strategies – a Danish water management example |
Type |
Journal Article |
| Year |
2013 |
Publication |
Mitigation and Adaptation Strategies for Global Change |
Abbreviated Journal |
Mitig. Adapt. Strateg. Glob. Change |
| Volume |
18 |
Issue |
3 |
Pages |
337-359 |
| Keywords |
Climate change; Adaptation; Uncertainty; Risk; Water sectors; Multi-disciplinary; change impacts; global change; winter-wheat; models; scenarios; ensembles; denmark; vulnerability; community; knowledge |
| Abstract |
We propose a generic framework to characterize climate change adaptation uncertainty according to three dimensions: level, source and nature. Our framework is different, and in this respect more comprehensive, than the present UN Intergovernmental Panel on Climate Change (IPCC) approach and could be used to address concerns that the IPCC approach is oversimplified. We have studied the role of uncertainty in climate change adaptation planning using examples from four Danish water related sectors. The dominating sources of uncertainty differ greatly among issues; most uncertainties on impacts are epistemic (reducible) by nature but uncertainties on adaptation measures are complex, with ambiguity often being added to impact uncertainties. Strategies to deal with uncertainty in climate change adaptation should reflect the nature of the uncertainty sources and how they interact with risk level and decision making: (i) epistemic uncertainties can be reduced by gaining more knowledge; (ii) uncertainties related to ambiguity can be reduced by dialogue and knowledge sharing between the different stakeholders; and (iii) aleatory uncertainty is, by its nature, non-reducible. The uncertainty cascade includes many sources and their propagation through technical and socio-economic models may add substantially to prediction uncertainties, but they may also cancel each other. Thus, even large uncertainties may have small consequences for decision making, because multiple sources of information provide sufficient knowledge to justify action in climate change adaptation. |
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English |
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| ISSN |
1381-2386 1573-1596 |
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Article |
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| Notes |
CropM, ftnotmacsur |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4613 |
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| Author |
Dumont, B.; Leemans, V.; Ferrandis, S.; Bodson, B.; Destain, J.-P.; Destain, M.-F. |
| Title |
Assessing the potential of an algorithm based on mean climatic data to predict wheat yield |
Type |
Journal Article |
| Year |
2014 |
Publication |
Precision Agriculture |
Abbreviated Journal |
Precision Agric. |
| Volume |
15 |
Issue |
3 |
Pages |
255-272 |
| Keywords |
stics model; yield prediction; real-time; proxy-sensing; stochastic weather generator; crop yield; mediterranean environment; simulation-model; variability; nitrogen; ensembles; forecasts; demeter; europe |
| Abstract |
The real-time non-invasive determination of crop biomass and yield prediction is one of the major challenges in agriculture. An interesting approach lies in using process-based crop yield models in combination with real-time monitoring of the input climatic data of these models, but unknown future weather remains the main obstacle to reliable yield prediction. Since accurate weather forecasts can be made only a short time in advance, much information can be derived from analyzing past weather data. This paper presents a methodology that addresses the problem of unknown future weather by using a daily mean climatic database, based exclusively on available past measurements. It involves building climate matrix ensembles, combining different time ranges of projected mean climate data and real measured weather data originating from the historical database or from real-time measurements performed in the field. Used as an input for the STICS crop model, the datasets thus computed were used to perform statistical within-season biomass and yield prediction. This work demonstrated that a reliable predictive delay of 3-4 weeks could be obtained. In combination with a local micrometeorological station that monitors climate data in real-time, the approach also enabled us to (i) predict potential yield at the local level, (ii) detect stress occurrence and (iii) quantify yield loss (or gain) drawing on real monitored climatic conditions of the previous few days. |
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English |
Summary Language |
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1385-2256 1573-1618 |
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CropM |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4621 |
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| Author |
Mansouri, M.; Destain, M.-F. |
| Title |
Predicting biomass and grain protein content using Bayesian methods |
Type |
Journal Article |
| Year |
2015 |
Publication |
Stochastic Environmental Research and Risk Assessment |
Abbreviated Journal |
Stoch. Environ. Res. Risk Assess. |
| Volume |
29 |
Issue |
4 |
Pages |
1167-1177 |
| Keywords |
crop model; particle filter; prediction; ensemble kalman filter; parameter-estimation; particle filters; decision-support; state estimation; model; nitrogen; navigation; tracking; systems |
| Abstract |
This paper deals with the problem of predicting biomass and grain protein content using improved particle filtering (IPF) based on minimizing the Kullback-Leibler divergence. The performances of IPF are compared with those of the conventional particle filtering (PF) in two comparative studies. In the first one, we apply IPF and PF at a simple dynamic crop model with the aim to predict a single state variable, namely the winter wheat biomass, and to estimate several model parameters. In the second study, the proposed IPF and the PF are applied to a complex crop model (AZODYN) to predict a winter-wheat quality criterion, namely the grain protein content. The results of both comparative studies reveal that the IPF method provides a better estimation accuracy than the PF method. The benefit of the IPF method lies in its ability to provide accuracy related advantages over the PF method since, unlike the PF which depends on the choice of the sampling distribution used to estimate the posterior distribution, the IPF yields an optimum choice of this sampling distribution, which also utilizes the observed data. The performance of the proposed method is evaluated in terms of estimation accuracy, root mean square error, mean absolute error and execution times. |
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English |
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1436-3240 1436-3259 |
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CropM |
Approved |
no |
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MA @ admin @ |
Serial |
4664 |
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