Climate change, agriculture and food security: a comparative review of global modelling approaches

The dual relationship existing between land-based activities and climate change has long been established. Land-based activities are responsible for about 30% (IPCC) of global GHG emissions and are at the same time particularly impacted by climate change as they are strongly dependent on weather patterns. Although physical and technical considerations may help to investigate these two kinds of issues, economic considerations are crucial to understand how agricultural producers react to climate change and to climate policies. Quantitative economic models are appropriate tools to examine these interactions and to understand how they influence human activities and ecosystems. However, there are many different economic models with different characteristics regarding the way economies are modelled, the way climate change is considered in the models and the way GHG emissions are accounted for. All these specificities determine the type of uses that each model can be employed for. This paper describes the different characteristics and uses of 13 economic models that are currently used to investigate issues concerning land-based activities and climate change

Dynamic Merge of the Global and Local Models for Sustainable Land Use Planning with Regard for Global Projections from GLOBIOM and Local Technical–Economic Feasibility and Resource Constraints

In order to conduct research at required spatial resolution, we propose a model fusion involving interlinked calculations of regional projections by the global dynamic model GLOBIOM (Global Biosphere Management Model) and robust dynamic downscaling model, based on cross-entropy principle, for deriving spatially resolved projections. The proposed procedure allows incorporating data from satellite images, statistics, expert opinions, as well as data from global land use models. In numerous case studies in China and Ukraine, the approach allowed to estimate local land use and land use change projections corresponding to real trends and expectations. The disaggregated data and projections were used in national models for planning sustainable land use and agricultural development

The future of food security, environments and livelihoods in Eastern Africa: four socio-economic scenarios

This report presents 4 scenarios for the future of food security, agriculture, livelihoods and environments in East Africa. These scenarios were developed by the CGIAR Research Program on Climate Change, Agriculture and Food Security in collaboration with a wide range of regional stakeholders. The report discusses the theory and development process of the scenarios, then presents detailed scenario narratives, semi-quantitative assumptions for a range of indicators, and finally outputs generated by 2 agricultural economic models, IMPACT and GLOBIOM. The report goes on to discuss the key results from the scenarios and then to describe the use of the scenarios in processes to guide decision-making in the context of East African food security and climate adaptation

Greenhouse gas mitigation potentials in the livestock sector

Acknowledgements This paper constitutes an output of the Belmont Forum/FACCE-JPI funded DEVIL project (NE/M021327/1). Financial support from the CGIAR Program on Climate Change, Agriculture and Food Security (CCAFS) and the EU-FP7 AnimalChange project is also recognized. P.K.T. acknowledges the support of a CSIRO McMaster Research Fellowship.Peer reviewedPostprin

Land-based climate change mitigation potentials within the agenda for sustainable development

Even though enormous expectations for greenhouse gas mitigation in the land use sector exist at the same time worries about potential implications for sustainable development have been raised as many Sustainable Development Goals (SDGs) are closely tied to developments in the sector. Here we assess the implications of achieving selected key SDG indicators for Zero Hunger, Clean Water and Sanitation, Responsible Consumption and Production, and Life on Land on the land-based climate change mitigation potential. We find that protecting highly biodiverse ecosystems has profound impacts on biomass potentials (−30% at >12 US dollar per gigajoule) while other SDGs mainly affect greenhouse gas abatement potentials. Achieving SDGs delivers synergies with greenhouse gas abatement and may even in the absence of additional mitigation policies allow to realize up to 25% of the expected greenhouse gas abatement from land use required to stay on track with the 1.5 °C target until 2050. Future land use mitigation policies should consider and take advantage of these synergies across SDGs

Integrated Management of Land Use Systems under Systemic Risks and Security Targets: A Stochastic Global Biosphere Management Model

Interdependencies among land use systems resemble a complex network connected through demand–supply relationships. Disruption of this network may catalyse systemic risks affecting food, energy, water and environmental security (FEWES) worldwide. We describe the conceptual development, expansion and practical application of a stochastic version of the Global Biosphere Management Model (GLOBIOM), used to assess competition for land use between agriculture, bioenergy and forestry at regional and global scales. In the stochastic version of the model, systemic risks of various kinds are explicitly covered and can be analysed and mitigated in all their interactions. While traditional deterministic scenario analysis produces sets of scenario-dependent outcomes, stochastic GLOBIOM explicitly derives robust outcomes that leave the systems better-off, independently of which scenario applies. Stochastic GLOBIOM is formulated as a stochastic optimisation model that is critical for evaluating portfolios of robust interdependent decisions: ex-ante strategic decisions (production allocation, storage capacities) and ex-post adaptive (demand, trading, storage control) decisions. As an example, the model is applied to the question of optimal storage facilities, as buffers for production shortfalls, to meet regional and global FEWES requirements when extreme events occur. Expected shortfalls and storage capacities have a close relationship with Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR) risk measures. A Value of Stochastic Solutions is calculated to illustrate the benefits of the stochastic over the deterministic model approach

Global hunger and climate change adaptation through international trade

International trade enables us to exploit regional differences in climate change impacts and is increasingly regarded as a potential adaptation mechanism. Here, we focus on hunger reduction through international trade under alternative trade scenarios for a wide range of climate futures. Under the current level of trade integration, climate change would lead to up to 55 million people who are undernourished in 2050. Without adaptation through trade, the impacts of global climate change would increase to 73 million people who are undernourished (+33%). Reduction in tariffs as well as institutional and infrastructural barriers would decrease the negative impact to 20 million (−64%) people. We assess the adaptation effect of trade and climate-induced specialization patterns. The adaptation effect is strongest for hunger-affected import-dependent regions. However, in hunger-affected export-oriented regions, partial trade integration can lead to increased exports at the expense of domestic food availability. Although trade integration is a key component of adaptation, it needs sensitive implementation to benefit all regions

Quantification of uncertainties in global grazing systems assessments

Livestock systems play a key role in global sustainability challenges like food security and climate change, yet, many unknowns and large uncertainties prevail. We present a systematic, spatially explicit assessment of uncertainties related to grazing intensity (GI), a key metric for assessing ecological impacts of grazing, by combining existing datasets on a) grazing feed intake, b) the spatial distribution of livestock, c) the extent of grazing land, and d) its net primary productivity (NPP). An analysis of the resulting 96 maps implies that on average 15% of the grazing land NPP is consumed by livestock. GI is low in most of worlds grazing lands but hotspots of very high GI prevail in 1% of the total grazing area. The agreement between GI maps is good on one fifth of the world's grazing area, while on the remainder it is low to very low. Largest uncertainties are found in global drylands and where grazing land bears trees (e.g., the Amazon basin or the Taiga belt). In some regions like India or Western Europe massive uncertainties even result in GI > 100% estimates. Our sensitivity analysis indicates that the input-data for NPP, animal distribution and grazing area contribute about equally to the total variability in GI maps, while grazing feed intake is a less critical variable. We argue that a general improvement in quality of the available global level datasets is a precondition for improving the understanding of the role of livestock systems in the context of global environmental change or food security