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Asseng, S.; Ewert, F.; Martre, P.; Rötter, R.P.; Lobell, D.B.; Cammarano, D.; Kimball, B.A.; Ottman, M.J.; Wall, G.W.; White, J.W.; Reynolds, M.P.; Alderman, P.D.; Prasad, P.V.V.; Aggarwal, P.K.; Anothai, J.; Basso, B.; Biernath, C.; Challinor, A.J.; De Sanctis, G.; Doltra, J.; Fereres, E.; Garcia-Vila, M.; Gayler, S.; Hoogenboom, G.; Hunt, L.A.; Izaurralde, R.C.; Jabloun, M.; Jones, C.D.; Kersebaum, K.C.; Koehler, A.-K.; Müller, C.; Naresh Kumar, S.; Nendel, C.; O’Leary, G.; Olesen, J.E.; Palosuo, T.; Priesack, E.; Eyshi Rezaei, E.; Ruane, A.C.; Semenov, M.A.; Shcherbak, I.; Stöckle, C.; Stratonovitch, P.; Streck, T.; Supit, I.; Tao, F.; Thorburn, P.J.; Waha, K.; Wang, E.; Wallach, D.; Wolf, J.; Zhao, Z.; Zhu, Y. |
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Title |
Rising temperatures reduce global wheat production |
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Year |
2014 |
Publication |
Nature Climate Change |
Abbreviated Journal |
Nat. Clim. Change |
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Volume |
5 |
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2 |
Pages |
143-147 |
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Keywords |
climate-change; spring wheat; dryland wheat; yield; growth; drought; heat; CO2; agriculture; adaptation |
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Abstract |
Crop models are essential tools for assessing the threat of climate change to local and global food production1. Present models used to predict wheat grain yield are highly uncertain when simulating how crops respond to temperature2. Here we systematically tested 30 different wheat crop models of the Agricultural Model Intercomparison and Improvement Project against field experiments in which growing season mean temperatures ranged from 15 °C to 32 °C, including experiments with artificial heating. Many models simulated yields well, but were less accurate at higher temperatures. The model ensemble median was consistently more accurate in simulating the crop temperature response than any single model, regardless of the input information used. Extrapolating the model ensemble temperature response indicates that warming is already slowing yield gains at a majority of wheat-growing locations. Global wheat production is estimated to fall by 6% for each °C of further temperature increase and become more variable over space and time. |
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1758-678x |
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CropM, ft_macsur |
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no |
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MA @ admin @ |
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4550 |
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Author |
Müller, C.; Robertson, R.D. |
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Title |
Projecting future crop productivity for global economic modeling |
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Journal Article |
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Year |
2014 |
Publication |
Agricultural Economics |
Abbreviated Journal |
Agric. Econ. |
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45 |
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1 |
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37-50 |
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climate change; crop modeling; agricultural productivity; land use; greenhouse-gas emissions; soil organic-carbon; sub-saharan africa; climate-change; elevated co2; land-use; system model; wheat yields; maize yields; agriculture |
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Assessments of climate change impacts on agricultural markets and land-use patterns rely on quantification of climate change impacts on the spatial patterns of land productivity. We supply a set of climate impact scenarios on agricultural land productivity derived from two climate models and two biophysical crop growth models to account for some of the uncertainty inherent in climate and impact models. Aggregation in space and time leads to information losses that can determine climate change impacts on agricultural markets and land-use patterns because often aggregation is across steep gradients from low to high impacts or from increases to decreases. The four climate change impact scenarios supplied here were designed to represent the most significant impacts (high emission scenario only, assumed ineffectiveness of carbon dioxide fertilization on agricultural yields, no adjustments in management) but are consistent with the assumption that changes in agricultural practices are covered in the economic models. Globally, production of individual crops decrease by 10-38% under these climate change scenarios, with large uncertainties in spatial patterns that are determined by both the uncertainty in climate projections and the choice of impact model. This uncertainty in climate impact on crop productivity needs to be considered by economic assessments of climate change. |
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0169-5150 |
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CropM, TradeM, ft_macsur |
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MA @ admin @ |
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4533 |
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Klein, D.; Luderer, G.; Kriegler, E.; Strefler, J.; Bauer, N.; Leimbach, M.; Popp, A.; Dietrich, J.P.; Humpenöder, F.; Lotze-Campen, H.; Edenhofer, O. |
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Title |
The value of bioenergy in low stabilization scenarios: an assessment using REMIND-MAgPIE |
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Journal Article |
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Year |
2014 |
Publication |
Climatic Change |
Abbreviated Journal |
Clim. Change |
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123 |
Issue |
3-4 |
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705-718 |
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land-use change; bio-energy; greenhouse gases; carbon-dioxide; climate-change; constraints; emissions; economics; storage; costs |
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This study investigates the use of bioenergy for achieving stringent climate stabilization targets and it analyzes the economic drivers behind the choice of bioenergy technologies. We apply the integrated assessment framework REMIND-MAgPIE to show that bioenergy, particularly if combined with carbon capture and storage (CCS) is a crucial mitigation option with high deployment levels and high technology value. If CCS is available, bioenergy is exclusively used with CCS. We find that the ability of bioenergy to provide negative emissions gives rise to a strong nexus between biomass prices and carbon prices. Ambitious climate policy could result in bioenergy prices of 70 $/GJ (or even 430 $/GJ if bioenergy potential is limited to 100 EJ/year), which indicates a strong demand for bioenergy. For low stabilization scenarios with BECCS availability, we find that the carbon value of biomass tends to exceed its pure energy value. Therefore, the driving factor behind investments into bioenergy conversion capacities for electricity and hydrogen production are the revenues generated from negative emissions, rather than from energy production. However, in REMIND modern bioenergy is predominantly used to produce low-carbon fuels, since the transport sector has significantly fewer low-carbon alternatives to biofuels than the power sector. Since negative emissions increase the amount of permissible emissions from fossil fuels, given a climate target, bioenergy acts as a complement to fossils rather than a substitute. This makes the short-term and long-term deployment of fossil fuels dependent on the long-term availability of BECCS. |
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0165-0009 |
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CropM, ftnotmacsur |
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MA @ admin @ |
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4529 |
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Kahiluoto, H.; Kaseva, J.; Hakala, K.; Himanen, S.J.; Jauhiainen, L.; Rötter, R.P.; Salo, T.; Trnka, M. |
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Cultivating resilience by empirically revealing response diversity |
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Journal Article |
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2014 |
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Global Environmental Change |
Abbreviated Journal |
Glob. Environ. Change |
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25 |
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186-193 |
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generic approach; climate change; food security; agrifood systems; cultivars; adaptive capacity; climate-change; functional diversity; plant-communities; genetic diversity; biodiversity; ecosystems; management; redundancy; evenness; weather |
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Intensified climate and market turbulence requires resilience to a multitude of changes. Diversity reduces the sensitivity to disturbance and fosters the capacity to adapt to various future scenarios. What really matters is diversity of responses. Despite appeals to manage resilience, conceptual developments have not yet yielded a break-through in empirical applications. Here, we present an approach to empirically reveal the ‘response diversity’: the factors of change that are critical to a system are identified, and the response diversity is determined based on the documented component responses to these factors. We illustrate this approach and its added value using an example of securing food supply in the face of climate variability and change. This example demonstrates that quantifying response diversity allows for a new perspective: despite continued increase in cultivar diversity of barley, the diversity in responses to weather declined during the last decade in the regions where most of the barley is grown in Finland. This was due to greater homogeneity in responses among new cultivars than among older ones. Such a decline in the response diversity indicates increased vulnerability and reduced resilience. The assessment serves adaptive management in the face of both ecological and socioeconomic drivers. Supplier diversity in the food retail industry in order to secure affordable food in spite of global price volatility could represent another application. The approach is, indeed, applicable to any system for which it is possible to adopt empirical information regarding the response by its components to the critical factors of variability and change. Targeting diversification in response to critical change brings efficiency into diversity. We propose the generic procedure that is demonstrated in this study as a means to efficiently enhance resilience at multiple levels of agrifood systems and beyond. (C) 2014 The Authors. Published by Elsevier Ltd. |
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0959-3780 |
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CropM |
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MA @ admin @ |
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4525 |
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Author |
Angulo, C.; Gaiser, T.; Rötter, R.P.; Børgesen, C.D.; Hlavinka, P.; Trnka, M.; Ewert, F. |
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Title |
‘Fingerprints’ of four crop models as affected by soil input data aggregation |
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Journal Article |
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Year |
2014 |
Publication |
European Journal of Agronomy |
Abbreviated Journal |
European Journal of Agronomy |
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61 |
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35-48 |
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crop model; soil data; spatial resolution; yield distribution; aggregation; us great-plains; climate-change; integrated assessment; simulating wheat; yields; scale; productivity; uncertainty; variability; responses |
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• Systematic analysis of the influence of spatial soil data resolution on simulated regional yields and total growing season evapotranspiration. • The responses of four crop models of different complexity are compared. • Differences between models are larger than the effect of the chosen spatial soil data resolution. • Low influence of soil data resolution due to: high precipitation amount, methods for calculating water retention and method of data aggregation. The spatial variability of soil properties is an important driver of yield variability at both field and regional scale. Thus, when using crop growth simulation models, the choice of spatial resolution of soil input data might be key in order to accurately reproduce observed yield variability. In this study we used four crop models (SIMPLACE<LINTUL-SLIM>, DSSAT-CSM, EPIC and DAISY) differing in the detail of modeling above-ground biomass and yield as well as of modeling soil water dynamics, water uptake and drought effects on plants to simulate winter wheat in two (agro-climatologically and geo-morphologically) contrasting regions of the federal state of North-Rhine-Westphalia (Germany) for the period from 1995 to 2008. Three spatial resolutions of soil input data were taken into consideration, corresponding to the following map scales: 1:50 000, 1:300 000 and 1:1 000 000. The four crop models were run for water-limited production conditions and model results were evaluated in the form of frequency distributions, depicted by bean-plots. In both regions, soil data aggregation had very small influence on the shape and range of frequency distributions of simulated yield and simulated total growing season evapotranspiration for all models. Further analysis revealed that the small influence of spatial resolution of soil input data might be related to: (a) the high precipitation amount in the region which partly masked differences in soil characteristics for water holding capacity, (b) the loss of variability in hydraulic soil properties due to the methods applied to calculate water retention properties of the used soil profiles, and (c) the method of soil data aggregation. No characteristic “fingerprint” between sites, years and resolutions could be found for any of the models. Our results support earlier recommendation to evaluate model results on the basis of frequency distributions since these offer quick and better insight into the distribution of simulation results as compared to summary statistics only. Finally, our results support conclusions from other studies about the usefulness of considering a multi-model approach to quantify the uncertainty in simulated yields introduced by the crop growth simulation approach when exploring the effects of scaling for regional yield impact assessments. |
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1161-0301 |
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CropM, ft_macsur |
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MA @ admin @ |
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4511 |
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