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Author Lotze-Campen, H.; von Witzke, H.; Noleppa, S.; Schwarz, G. url  doi
openurl 
  Title Science for food, climate protection and welfare: An economic analysis of plant breeding research in Germany Type Journal Article
  Year 2015 Publication Agricultural Systems Abbreviated Journal Agric. Syst.  
  Volume 136 Issue Pages 79-84  
  Keywords Plant breeding; CO2 emissions; Cost–benefit analysis; Social rate of return; Agricultural research policy  
  Abstract Highlights • We analyze the economic effects of plant breeding research in Germany. • Effects of reduced CO2 emissions due to productivity increases are being quantified. • Expansion of global agricultural area has been reduced by 1–1.5 million ha. • CO2 emissions have been reduced by 160–235 million tons. • German plant breeding research has an economic value of 10.8–15.6 billion EUR. Abstract We analyze the economic effects of plant breeding research in Germany. In addition to market effects, for the first time also effects of reduced CO2 emissions due to productivity increases are being quantified. The analysis shows that investments in German plant breeding research in the period 1991–2010 have reduced the global expansion of agricultural area by 1–1.5 million hectares. This has led to reduced CO2 emissions of 160–235 million tons. The economic value generated by plant breeding research, through increased production and reduced greenhouse gas emissions, is estimated at 10.8–15.6 billion EUR in the same period. This can be translated into a social rate of return on research investment in the range of 40–80% per year. Projections for the period 2011–2030 generate a return rate in the range of 65–140% per year. Investments into plant breeding research in Germany are highly profitable from a societal point of view. At the same time, our results show significant under-investments in agricultural research in Germany. These results provide a good justification for policy-makers to reverse funding cuts for public agricultural research over the last decades and to improve institutional conditions for private research, e.g. through better protection of intellectual property rights.  
  Address  
  Corporate Author Thesis  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0308521x ISBN Medium  
  Area Expedition Conference (up)  
  Notes TradeM, ftnotmacsur Approved no  
  Call Number MA @ admin @ Serial 4999  
Permanent link to this record
 

 
Author Makowski, D. doi  openurl
  Title A simple Bayesian method for adjusting ensemble of crop model outputs to yield observations Type Journal Article
  Year 2017 Publication European Journal of Agronomy Abbreviated Journal Europ. J. Agron.  
  Volume 88 Issue Pages 76-83  
  Keywords Bayesian method; Climate change; Ensemble modelling; Uncertainty; Yield; Linear-Approach; Climate-Change; CO2  
  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.  
  Address 2017-08-07  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1161-0301 ISBN Medium Article  
  Area Expedition Conference (up)  
  Notes CropM, ft_macsur Approved no  
  Call Number MA @ admin @ Serial 5171  
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Author Lorite, I.J.; Gabaldon-Leal, C.; Ruiz-Ramos, M.; Belaj, A.; de la Rosa, R.; Leon, L.; Santos, C. doi  openurl
  Title Evaluation of olive response and adaptation strategies to climate change under semi-arid conditions Type Journal Article
  Year 2018 Publication Agricultural Water Management Abbreviated Journal Agric. Water Manage.  
  Volume 204 Issue Pages 247-261  
  Keywords Irrigation requirements; Yield; Irrigation water productivity; Olive; Climate change; Olea-Europaea L.; Different Irrigation Regimes; Water Deficits; Iberian; Peninsula; CO2 Concentration; Potential Growth; Atmospheric CO2; Southern Spain; Change Impacts; River-Basin  
  Abstract AdaptaOlive is a simplified physically-based model that has been developed to assess the behavior of olive under future climate conditions in Andalusia, southern Spain. The integration of different approaches based on experimental data from previous studies, combined with weather data from 11 climate models, is aimed at overcoming the high degree of uncertainty in the simulation of the response of agricultural systems under predicted climate conditions. The AdaptaOlive model was applied in a representative olive orchard in the Baeza area, one of the main producer zone in Spain, with the cultivar ‘Picual’. Simulations for the end of the 21st century showed olive oil yield increases of 7.1 and 28.9% under rainfed and full irrigated conditions, respectively, while irrigation requirements decreased between 0.5 and 6.2% for full irrigation and regulated deficit irrigation, respectively. These effects were caused by the positive impact of the increase in atmospheric CO2 that counterbalanced the negative impacts of the reduction in rainfall. The high degree of uncertainty associated with climate projections translated into a high range of yield and irrigation requirement projections, confirming the need for an ensemble of climate models in climate change impact assessment. The AdaptaOlive model also was applied for evaluating adaptation strategies related to cultivars, irrigation strategies and locations. The best performance was registered for cultivars with early flowering dates and regulated deficit irrigation. Thus, in the Baeza area full irrigation requirements were reduced by 12% and the yield in rainfed conditions increased by 7% compared with late flowering cultivars. Similarly, regulated deficit irrigation requirements and yield were reduced by 46% and 18%, respectively, compared with full irrigation. The results confirm the promise offered by these strategies as adaptation measures for managing an olive crop under semi-arid conditions in a changing climate.  
  Address 2018-06-28  
  Corporate Author Thesis  
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  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0378-3774 ISBN Medium  
  Area Expedition Conference (up)  
  Notes CropM, ft_macsur Approved no  
  Call Number MA @ admin @ Serial 5204  
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Author Webber, H.; Ewert, F.; Olesen, J.E.; Müller, C.; Fronzek, S.; Ruane, A.C.; Bourgault, M.; Martre, P.; Ababaei, B.; Bindi, M.; Ferrise, R.; Finger, R.; Fodor, N.; Gabaldón-Leal, C.; Gaiser, T.; Jabloun, M.; Kersebaum, K.-C.; Lizaso, J.I.; Lorite, I.J.; Manceau, L.; Moriondo, M.; Nendel, C.; Rodríguez, A.; Ruiz-Ramos, M.; Semenov, M.A.; Siebert, S.; Stella, T.; Stratonovitch, P.; Trombi, G.; Wallach, D. doi  openurl
  Title Diverging importance of drought stress for maize and winter wheat in Europe Type Journal Article
  Year 2018 Publication Nature Communications Abbreviated Journal Nat. Comm.  
  Volume 9 Issue Pages 4249  
  Keywords Climate-Change Impacts; Air CO2 Enrichment; Food Security; Heat-Stress; Nitrogen Dynamics; Semiarid Environments; Canopy Temperature; Simulation-Model; Crop Production; Elevated CO2  
  Abstract Understanding the drivers of yield levels under climate change is required to support adaptation planning and respond to changing production risks. This study uses an ensemble of crop models applied on a spatial grid to quantify the contributions of various climatic drivers to past yield variability in grain maize and winter wheat of European cropping systems (1984-2009) and drivers of climate change impacts to 2050. Results reveal that for the current genotypes and mix of irrigated and rainfed production, climate change would lead to yield losses for grain maize and gains for winter wheat. Across Europe, on average heat stress does not increase for either crop in rainfed systems, while drought stress intensifies for maize only. In low-yielding years, drought stress persists as the main driver of losses for both crops, with elevated CO2 offering no yield benefit in these years.  
  Address 2018-10-25  
  Corporate Author Thesis  
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  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2041-1723 ISBN Medium  
  Area Expedition Conference (up)  
  Notes CropM, ft_macsur Approved no  
  Call Number MA @ admin @ Serial 5211  
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Author Foyer, C.H.; Siddique, K.H.M.; Tai, A.P.K.; Anders, S.; Fodor, N.; Wong, F.-L.; Ludidi, N.; Chapman, M.A.; Ferguson, B.J.; Considine, M.J.; Zabel, F.; Prasad, P.V.V.; Varshney, R.K.; Nguyen, H.T.; Lam, H.-M. doi  openurl
  Title Modelling predicts that soybean is poised to dominate crop production across Africa Type Journal Article
  Year 2019 Publication Plant Cell and Environment Abbreviated Journal Plant Cell Environ.  
  Volume 42 Issue 1 Pages 373-385  
  Keywords Climate-Change; Food Security; Sustainable Intensification; Smallholder; Farmers; Nitrogen-Fixation; Yield; Adaptation; Diversity; Impact; CO2  
  Abstract The superior agronomic and human nutritional properties of grain legumes (pulses) make them an ideal foundation for future sustainable agriculture. Legume-based farming is particularly important in Africa, where small-scale agricultural systems dominate the food production landscape. Legumes provide an inexpensive source of protein and nutrients to African households as well as natural fertilization for the soil. Although the consumption of traditionally grown legumes has started to decline, the production of soybeans (Glycine max Merr.) is spreading fast, especially across southern Africa. Predictions of future land-use allocation and production show that the soybean is poised to dominate future production across Africa. Land use models project an expansion of harvest area, whereas crop models project possible yield increases. Moreover, a seed change in farming strategy is underway. This is being driven largely by the combined cash crop value of products such as oils and the high nutritional benefits of soybean as an animal feed. Intensification of soybean production has the potential to reduce the dependence of Africa on soybean imports. However, a successful “soybean bonanza” across Africa necessitates an intensive research, development, extension, and policy agenda to ensure that soybean genetic improvements and production technology meet future demands for sustainable production.  
  Address 2019-01-10  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0140-7791 ISBN Medium  
  Area Expedition Conference (up)  
  Notes CropM, ft_macsur Approved no  
  Call Number MA @ admin @ Serial 5215  
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