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Reidsma, P.; Janssen, S.; Jansen, J.; van Ittersum, M. |
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Title |
On the development and use of farm models for policy impact assessment in the European Union – A review |
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Journal Article |
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Year |
2017 |
Publication |
Agricultural Systems |
Abbreviated Journal |
Agric. Syst. |
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159 |
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111-125 |
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• Evidence use in EU Impact Assessment reports is limited. • Many scientific studies used farm models for assessment of policies in the EU. • Scientific challenges include understanding farmer decision-making and interactions. • Model codes and data should be published, including evaluation. • Stronger science-policy interaction is required. Farm models are potentially relevant tools for policy impact assessment. Governments and international organizations use impact assessment (IA) as an ex-ante policy process and procedure to evaluate impacts of policy options as part of the introduction of new policies. IA is increasingly used. This paper reviews both the use of farm models in such policy IAs in the European Commission, and the development and use of farm models for policy IA by the scientific community over the past decade. A systematic review was performed, based on 202 studies from the period 2007–2015 and results were discussed in a science-policy workshop. Based on the literature review and the workshop, this paper describes progress in the development of farm models, challenges in their use in policy processes and a research and cooperation agenda. We conclude that main issues for a research agenda include: 1) better understanding of farmer decision-making and effects of the social milieu, with increased focus on the interactions between farmers and other actors, the link to the value chain, and farm structural change; 2) thorough and consistent model evaluation and model comparison, with increased attention for model sensitivity and uncertainty, and 3) the organization of a network of farm modellers. In addition, the agenda for science-policy cooperation emphasizes the need for: 4) synthesizing research evidence into systematic reviews as an institutional element in the existing science-policy-interfaces for agricultural systems, 5) improved and timely data collection, allowing to assess heterogeneity in farm objectives, management and indicators, and 6) stronger science-policy interaction, moving from a research-driven to a user-driven approach. |
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LiveM, ftnotmacsur |
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MA @ admin @ |
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5179 |
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Zimmermann, A.; Webber, H.; Zhao, G.; Ewert, F.; Kros, J.; Wolf, J.; Britz, W.; de Vries, W. |
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Title |
Climate change impacts on crop yields, land use and environment in response to crop sowing dates and thermal time requirements |
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Journal Article |
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Year |
2017 |
Publication |
Agricultural Systems |
Abbreviated Journal |
Agric. Syst. |
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157 |
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81-92 |
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Keywords |
Integrated assessment; Crop management; Climate change; Europe; INTEGRATED ASSESSMENT; EUROPEAN AGRICULTURE; FOOD SECURITY; HEAT-STRESS; ADAPTATION; SYSTEMS; TEMPERATURE; SCENARIOS; WHEAT; PRODUCTIVITY; Vries W., 2011, ENVIRONMENTAL POLLUTION, V159, P3254 |
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Impacts of climate change on European agricultural production, land use and the environment depend on its impact on crop yields. However, many impact studies assume that crop management remains unchanged in future scenarios, while farmers may adapt their sowing dates and cultivar thermal time requirements to minimize yield losses or realize yield gains. The main objective of this study was to investigate the sensitivity of climate change impacts on European crop yields, land use, production and environmental variables to adaptations in crops sowing dates and varieties’ thermal time requirements. A crop, economic and environmental model were coupled in an integrated assessment modelling approach for six important crops, for 27 countries of the European Union (EU27) to assess results of three SRES climate change scenarios to 2050. Crop yields under climate change were simulated considering three different management cases; (i) no change in crop management from baseline conditions (NoAd), (ii) adaptation of sowing date and thermal time requirements to give highest yields to 2050 (Opt) and (iii) a more conservative adaptation of sowing date and thermal time requirements (Act). Averaged across EU27, relative changes in water-limited crop yields due to climate change and increased CO2 varied between -6 and + 21% considering NoAd management, whereas impacts with Opt management varied between + 12 and + 53%, and those under Act management between 2 and + 27%. However, relative yield increases under climate change increased to + 17 and + 51% when technology progress was also considered. Importantly, the sensitivity to crop management assumptions of land use, production and environmental impacts were less pronounced than for crop yields due to the influence of corresponding market, farm resource and land allocation adjustments along the model chain acting via economic optimization of yields. We conclude that assumptions about crop sowing dates and thermal time requirements affect impact variables but to a different extent and generally decreasing for variables affected by economic drivers. |
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2017-11-02 |
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0308-521x |
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CropM, TradeM, ft_macsur |
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MA @ admin @ |
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5178 |
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Zhai, R.; Tao, F. |
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Contributions of climate change and human activities to runoff change in seven typical catchments across China |
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Journal Article |
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2017 |
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Science of the Total Environment |
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Sci. Tot. Environ. |
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605 |
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219-229 |
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Catchments; Detection; Attribution; Runoff; VIC; Water resource; Weihe River-Basin; Hydrologic Response; Temporal-Changes; Loess Plateau; United-States; Yellow-River; Streamflow; Impacts; Variability; Model |
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Climate change and human activities are two major factors affecting water resource change. It is important to understand the roles of the major factors in affecting runoff change in different basins for watershed management. Here, we investigated the trends in climate and runoff in seven typical catchments in seven basins across China from 1961 to 2014. Then we attributed the runoff change to climate change and human activities in each catchment and in three time periods (1980s, 1990s and 2000s), using the VIC model and long-term runoff observation data. During 1961-2014, temperature increased significantly, while the trends in precipitation were insignificant in most of the catchments and inconsistent among the catchments. The runoff in most of the catchments showed a decreasing trend except the Yingluoxia catchment in the northwestern China. The contributions of climate change and human activities to runoff change varied in different catchments and time periods. In the 1980s, climate change contributed more to runoff change than human activities, which was 84%, 59%,-66%,-50%, 59%, 94%, and -59% in the Nianzishan, Yingluoxia, Xiahui, Yangjiaping, Sanjiangkou, Xixian, and Changle catchment, respectively. After that, human activities had played a more essential role in runoff change. In the 1990s and 2000s, human activities contributed more to runoff change than in the 1980s. The contribution by human activities accounted for 84%,- 68%, and 67% in the Yingluoxia, Xiahui, and Sanjiangkou catchment, respectively, in the 1990s; and -96%,-67%,-94%, and -142% in the Nianzishan, Yangjiaping, Xixian, and Changle catchment, respectively, in the 2000s. It is also noted that after 2000 human activities caused decrease in runoff in all catchments except the Yingluoxia. Our findings highlight that the effects of human activities, such as increase in water withdrawal, land use/cover change, operation of dams and reservoirs, should be well managed. (C) 2017 Elsevier B.V. All rights reserved. |
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2017-09-14 |
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0048-9697 |
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CropM, ft_macsur |
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MA @ admin @ |
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5177 |
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Grosz, B.; Dechow, R.; Gebbert, S.; Hoffmann, H.; Zhao, G.; Constantin, J.; Raynal, H.; Wallach, D.; Coucheney, E.; Lewan, E.; Eckersten, H.; Specka, X.; Kersebaum, K.-C.; Nendel, C.; Kuhnert, M.; Yeluripati, J.; Haas, E.; Teixeira, E.; Bindi, M.; Trombi, G.; Moriondo, M.; Doro, L.; Roggero, P.P.; Zhao, Z.; Wang, E.; Tao, F.; Roetter, R.; Kassie, B.; Cammarano, D.; Asseng, S.; Weihermueller, L.; Siebert, S.; Gaiser, T.; Ewert, F. |
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The implication of input data aggregation on up-scaling soil organic carbon changes |
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Journal Article |
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2017 |
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Environmental Modelling & Software |
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Env. Model. Softw. |
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96 |
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361-377 |
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Biogeochemical model; Data aggregation; Up-scaling error; Soil organic carbon; DIFFERENT SPATIAL SCALES; NITROUS-OXIDE EMISSIONS; MODELING SYSTEM; DATA; RESOLUTION; CROP MODELS; CLIMATE; LONG; PRODUCTIVITY; CROPLANDS; DAYCENT |
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In up-scaling studies, model input data aggregation is a common method to cope with deficient data availability and limit the computational effort. We analyzed model errors due to soil data aggregation for modeled SOC trends. For a region in North West Germany, gridded soil data of spatial resolutions between 1 km and 100 km has been derived by majority selection. This data was used to simulate changes in SOC for a period of 30 years by 7 biogeochemical models. Soil data aggregation strongly affected modeled SOC trends. Prediction errors of simulated SOC changes decreased with increasing spatial resolution of model output. Output data aggregation only marginally reduced differences of model outputs between models indicating that errors caused by deficient model structure are likely to persist even if requirements on the spatial resolution of model outputs are low. (C)2017 Elsevier Ltd. All rights reserved. |
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2017-09-14 |
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1364-8152 |
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CropM, ft_macsur |
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MA @ admin @ |
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5176 |
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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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Improving the use of crop models for risk assessment and climate change adaptation |
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Journal Article |
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2017 |
Publication |
Agricultural Systems |
Abbreviated Journal |
Agric. Syst. |
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159 |
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296-306 |
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Crop model; Risk assessment; Climate change impacts; Adaptation; Climate models; Uncertainty |
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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, ft_macsur |
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MA @ admin @ |
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5175 |
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