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Sakschewski, B.; von Bloh, W.; Huber, V.; Müller, C.; Bondeau, A. |
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Title |
Feeding 10 billion people under climate change: How large is the production gap of current agricultural systems |
Type |
Journal Article |
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Year |
2014 |
Publication |
Ecological Modelling |
Abbreviated Journal |
Ecol. Model. |
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Volume  |
288 |
Issue |
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Pages |
103-111 |
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Keywords |
Population growth; Food production; Dynamic global vegetation model; Climate change; LPJmL; stomatal conductance; population-growth; food-production; co2; enrichment; model; photosynthesis; scenarios; leaves; plants; yield |
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Abstract |
The human population is projected to reach more than 10 billion in the year 2100. Together with changing consumption pattern, population growth will lead to increasing food demand. The question arises whether or not the Earth is capable of fulfilling this demand. In this study, we approach this question by estimating the carrying capacity of current agricultural systems (K-C), which does not measure the maximum number of people the Earth is likely to feed in the future, but rather allows for an indirect assessment of the increases in agricultural productivity required to meet demands. We project agricultural food production under progressing climate change using the state-of-the-art dynamic global vegetation model LPJmL, and input data of 3 climate models. For 1990 to 2100 the worldwide annual caloric yield of the most important 11 crop types is simulated. Model runs with and without elevated atmospheric CO2 concentrations are performed in order to investigate CO2 fertilization effects. Country-specific per-capita caloric demands fixed at current levels and changing demands based on future GDP projections are considered to assess the role of future dietary shifts. Our results indicate that current population projections may considerably exceed the maximum number of people that can be fed globally if climate change is not accompanied by significant changes in land use, agricultural efficiencies and/or consumption pathways. We estimate the gap between projected population size and K-C to reach 2 to 6.8 billion people by 2100. We also present possible caloric self-supply changes between 2000 and 2100 for all countries included in this study. The results show that predominantly developing countries in tropical and subtropical regions will experience vast decreases of self-supply. Therefore, this study is important for planning future large-scale agricultural management, as well as the critical assessment of population projections, which should take food-mediated climate change feedbacks into account |
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2016-10-31 |
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English |
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ISSN |
0304-3800 |
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Article |
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CropM |
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no |
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Call Number |
MA @ admin @ |
Serial |
4806 |
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Author |
Makowski, D.; Asseng, S.; Ewert, F.; Bassu, S.; Durand, J.L.; Li, T.; Martre, P.; Adam, M.; Aggarwal, P.K.; Angulo, C.; Baron, C.; Basso, B.; Bertuzzi, P.; Biernath, C.; Boogaard, H.; Boote, K.J.; Bouman, B.; Bregaglio, S.; Brisson, N.; Buis, S.; Cammarano, D.; Challinor, A.J.; Confalonieri, R.; Conijn, J.G.; Corbeels, M.; Deryng, D.; De Sanctis, G.; Doltra, J.; Fumoto, T.; Gaydon, D.; Gayler, S.; Goldberg, R.; Grant, R.F.; Grassini, P.; Hatfield, J.L.; Hasegawa, T.; Heng, L.; Hoek, S.; Hooker, J.; Hunt, L.A.; Ingwersen, J.; Izaurralde, R.C.; Jongschaap, R.E.E.; Jones, J.W.; Kemanian, R.A.; Kersebaum, K.C.; Kim, S.-H.; Lizaso, J.; Marcaida, M.; Müller, C.; Nakagawa, H.; Naresh Kumar, S.; Nendel, C.; O’Leary, G.J.; Olesen, J.E.; Oriol, P.; Osborne, T.M.; Palosuo, T.; Pravia, M.V.; Priesack, E.; Ripoche, D.; Rosenzweig, C.; Ruane, A.C.; Ruget, F.; Sau, F.; Semenov, M.A.; Shcherbak, I.; Singh, B.; Singh, U.; Soo, H.K.; Steduto, P.; Stöckle, C.; Stratonovitch, P.; Streck, T.; Supit, I.; Tang, L.; Tao, F.; Teixeira, E.I.; Thorburn, P.; Timlin, D.; Travasso, M.; Rötter, R.P.; Waha, K.; Wallach, D.; White, J.W.; Wilkens, P.; Williams, J.R.; Wolf, J.; Yin, X.; Yoshida, H.; Zhang, Z.; Zhu, Y. |
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Title |
A statistical analysis of three ensembles of crop model responses to temperature and CO2 concentration |
Type |
Journal Article |
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Year |
2015 |
Publication |
Agricultural and Forest Meteorology |
Abbreviated Journal |
Agricultural and Forest Meteorology |
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Volume |
214-215 |
Issue |
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Pages |
483-493 |
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Keywords |
climate change; crop model; emulator; meta-model; statistical model; yield; climate-change; wheat yields; metaanalysis; uncertainty; simulation; impacts |
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Abstract |
Ensembles of process-based crop models are increasingly used to simulate crop growth for scenarios of temperature and/or precipitation changes corresponding to different projections of atmospheric CO2 concentrations. This approach generates large datasets with thousands of simulated crop yield data. Such datasets potentially provide new information but it is difficult to summarize them in a useful way due to their structural complexities. An associated issue is that it is not straightforward to compare crops and to interpolate the results to alternative climate scenarios not initially included in the simulation protocols. Here we demonstrate that statistical models based on random-coefficient regressions are able to emulate ensembles of process-based crop models. An important advantage of the proposed statistical models is that they can interpolate between temperature levels and between CO2 concentration levels, and can thus be used to calculate temperature and [CO2] thresholds leading to yield loss or yield gain, without rerunning the original complex crop models. Our approach is illustrated with three yield datasets simulated by 19 maize models, 26 wheat models, and 13 rice models. Several statistical models are fitted to these datasets, and are then used to analyze the variability of the yield response to [CO2] and temperature. Based on our results, we show that, for wheat, a [CO2] increase is likely to outweigh the negative effect of a temperature increase of +2 degrees C in the considered sites. Compared to wheat, required levels of [CO2] increase are much higher for maize, and intermediate for rice. For all crops, uncertainties in simulating climate change impacts increase more with temperature than with elevated [CO2]. |
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English |
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ISSN |
0168-1923 |
ISBN |
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Article |
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Notes |
CropM, ft_macsur |
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no |
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Call Number |
MA @ admin @ |
Serial |
4714 |
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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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Address |
2016-09-13 |
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Summary Language |
Newsletter July 2016 |
Original Title |
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ISSN |
0378-4290 |
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Article |
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CropM |
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Notes |
CropMwp;wos; ft=macsur; wsnot_yet; |
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no |
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Call Number |
MA @ admin @ |
Serial |
4776 |
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Author |
Cammarano, D.; Rötter, R.P.; Asseng, S.; Ewert, F.; Wallach, D.; Martre, P.; Hatfield, J.L.; Jones, J.W.; Rosenzweig, C.; Ruane, A.C.; Boote, K.J.; Thorburn, P.J.; Kersebaum, K.C.; Aggarwal, P.K.; Angulo, C.; Basso, B.; Bertuzzi, P.; Biernath, C.; Brisson, N.; Challinor, A.J.; Doltra, J.; Gayler, S.; Goldberg, R.; Heng, L.; Hooker, J.E.; Hunt, L.A.; Ingwersen, J.; Izaurralde, R.C.; Müller, C.; Kumar, S.N.; Nendel, C.; O’Leary, G.; Olesen, J.E.; Osborne, T.M.; Priesack, E.; Ripoche, D.; Steduto, P.; Stöckle, C.O.; Stratonovitch, P.; Streck, T.; Supit, I.; Tao, F.; Travasso, M.; Waha, K.; White, J.W.; Wolf, J. |
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Title |
Uncertainty of wheat water use: Simulated patterns and sensitivity to temperature and CO2 |
Type |
Journal Article |
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Year |
2016 |
Publication |
Field Crops Research |
Abbreviated Journal |
Field Crops Research |
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Volume |
198 |
Issue |
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Pages |
80-92 |
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Keywords |
Multi-model simulation; Transpiration efficiency; Water use; Uncertainty; Sensitivity |
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Abstract |
Projected global warming and population growth will reduce future water availability for agriculture. Thus, it is essential to increase the efficiency in using water to ensure crop productivity. Quantifying crop water use (WU; i.e. actual evapotranspiration) is a critical step towards this goal. Here, sixteen wheat simulation models were used to quantify sources of model uncertainty and to estimate the relative changes and variability between models for simulated WU, water use efficiency (WUE, WU per unit of grain dry mass produced), transpiration efficiency (Teff, transpiration per kg of unit of grain yield dry mass produced), grain yield, crop transpiration and soil evaporation at increased temperatures and elevated atmospheric carbon dioxide concentrations ([CO2]). The greatest uncertainty in simulating water use, potential evapotranspiration, crop transpiration and soil evaporation was due to differences in how crop transpiration was modelled and accounted for 50% of the total variability among models. The simulation results for the sensitivity to temperature indicated that crop WU will decline with increasing temperature due to reduced growing seasons. The uncertainties in simulated crop WU, and in particularly due to uncertainties in simulating crop transpiration, were greater under conditions of increased temperatures and with high temperatures in combination with elevated atmospheric [CO2] concentrations. Hence the simulation of crop WU, and in particularly crop transpiration under higher temperature, needs to be improved and evaluated with field measurements before models can be used to simulate climate change impacts on future crop water demand. |
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2016-10-31 |
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English |
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0378-4290 |
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Notes |
CropM, ft_macsur |
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no |
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Call Number |
MA @ admin @ |
Serial |
4786 |
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Author |
Challinor, A.J.; Müller, C.; Asseng, S.; Deva, C.; Nicklin, K.J.; Wallach, D.; Vanuytrecht, E.; Whitfield, S.; Ramirez-Villegas, J.; Koehler, A.-K. |
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Title |
Improving the use of crop models for risk assessment and climate change adaptation |
Type |
Journal Article |
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Year |
2017 |
Publication |
Agricultural Systems |
Abbreviated Journal |
Agric. Syst. |
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Volume |
159 |
Issue |
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Pages |
296-306 |
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Keywords |
Crop model; Risk assessment; Climate change impacts; Adaptation; Climate models; Uncertainty |
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Abstract |
Highlights
• 14 criteria for use of crop models in assessments of impacts, adaptation and risk • Working with stakeholders to identify timing of risks is key to risk assessments. • Multiple methods needed to critically assess the use of climate model output • Increasing transparency and inter-comparability needed in risk assessments
Abstract
Crop models are used for an increasingly broad range of applications, with a commensurate proliferation of methods. Careful framing of research questions and development of targeted and appropriate methods are therefore increasingly important. In conjunction with the other authors in this special issue, we have developed a set of criteria for use of crop models in assessments of impacts, adaptation and risk. Our analysis drew on the other papers in this special issue, and on our experience in the UK Climate Change Risk Assessment 2017 and the MACSUR, AgMIP and ISIMIP projects. The criteria were used to assess how improvements could be made to the framing of climate change risks, and to outline the good practice and new developments that are needed to improve risk assessment. Key areas of good practice include: i. the development, running and documentation of crop models, with attention given to issues of spatial scale and complexity; ii. the methods used to form crop-climate ensembles, which can be based on model skill and/or spread; iii. the methods used to assess adaptation, which need broadening to account for technological development and to reflect the full range options available. The analysis highlights the limitations of focussing only on projections of future impacts and adaptation options using pre-determined time slices. Whilst this long-standing approach may remain an essential component of risk assessments, we identify three further key components: 1. Working with stakeholders to identify the timing of risks. What are the key vulnerabilities of food systems and what does crop-climate modelling tell us about when those systems are at risk? 2. Use of multiple methods that critically assess the use of climate model output and avoid any presumption that analyses should begin and end with gridded output. 3. Increasing transparency and inter-comparability in risk assessments. Whilst studies frequently produce ranges that quantify uncertainty, the assumptions underlying these ranges are not always clear. We suggest that the contingency of results upon assumptions is made explicit via a common uncertainty reporting format; and/or that studies are assessed against a set of criteria, such as those presented in this paper. |
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phase 2+ |
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0308521x |
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CropM |
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CropM, ft_macsur |
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no |
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MA @ admin @ |
Serial |
5175 |
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