Global High-resolution Land-use Change Projections: A Bayesian Multinomial Logit Downscaling Approach Incorporating Model Uncertainty and Spatial Effects

Using econometric models to estimate land-use change has a long tradition in scientific literature. Recent contributions show the importance of including spatial information and of using a multinomial framework to take into account the interdependencies between the land-use classes. Few studies, however, agree on the relevant determinants of land-use change and there are no contributions so far comparing determinants on a global scale. Using multiple 5 arc minute resolution datasets of land-use change between 2000 and 2010 and taking into account the transitions between forest, cropland, grassland and all other land covers, we estimate a Bayesian multinomial logit model, using the efficient Pólya-Gamma sampling procedure introduced by Polson et al. (2013). To identify and measure the determinants of land-use change and the strength of spatial separation, our model implements Bayesian model selection through stochastic search variable selection (SSVS) priors and spatial information via Gaussian Process (GP) priors. Our results indicate that spatial proximity is of central importance in land-use change, in all regions except the pacific islands. We also show that infrastructure policy, proxied by mean time to market, seems to have a significant impact on deforestation throughout most regions. In a second step we use aggregate, supra national land-use change results from the partial equilibrium agricultural model GLOBIOM as a framework for projecting our model in ten-year intervals up to 2100 on a spatially explicit scale along multiple shared socioeconomic pathways

Towards systematic evaluation of crop model outputs for global land-use models

Land provides vital socioeconomic resources to the society, however at the cost of large environmental degradations. Global integrated models combining high resolution global gridded crop models (GGCMs) and global economic models (GEMs) are increasingly being used to inform sustainable solution for agricultural land-use. However, little effort has yet been done to evaluate and compare the accuracy of GGCM outputs. In addition, GGCM datasets require a large amount of parameters whose values and their variability across space are weakly constrained: increasing the accuracy of such dataset has a very high computing cost. Innovative evaluation methods are required both to ground credibility to the global integrated models, and to allow efficient parameter specification of GGCMs. We propose an evaluation strategy for GGCM datasets in the perspective of use in GEMs, illustrated with preliminary results from a novel dataset (the Hypercube) generated by the EPIC GGCM and used in the GLOBIOM land use GEM to inform on present-day crop yield, water and nutrient input needs for 16 crops x 15 management intensities, at a spatial resolution of 5 arc-minutes. We adopt the following principle: evaluation should provide a transparent diagnosis of model adequacy for its intended use. We briefly describe how the Hypercube data is generated and how it articulates with GLOBIOM in order to transparently identify the performances to be evaluated, as well as the main assumptions and data processing involved. Expected performances include adequately representing the sub-national heterogeneity in crop yield and input needs: i) in space, ii) across crop species, and iii) across management intensities. We will present and discuss measures of these expected performances and weight the relative contribution of crop model, input data and data processing steps in performances. We will also compare obtained yield gaps and main yield-limiting factors against the M3 dataset. Next steps include iterative improvement of parameter assumptions and evaluation of implications of GGCM performances for intended use in the IIASA EPIC-GLOBIOM model cluster. Our approach helps targeting future efforts at improving GGCM accuracy and would achieve highest efficiency if combined with traditional field-scale evaluation and sensitivity analysis

Towards Systematic Evaluation of Crop Model Outputs for Global Land-use Models

Land provides vital socioeconomic resources to the society; however, at the cost of large environmental degradation (Verburg et al., 2013). At the crossroads of these dimensions, agriculture becomes increasingly interconnected to various natural and human systems across various scales. In order to inform the design of policies to navigate land use towards a more sustainable operating space, comprehensive global assessment models are increasingly being used. They rely partly on the loose coupling of biophysical crop models to global economic models, via one-way exchange of output variables (Rosenzweig et al. 2013). Accuracy of variables exchanged strongly influences the outcomes assessed at various scales, and its improvement is likely to require iterative improvements. Yet there has been little effort to document, evaluate and compare these exchange variables across models (Mueller & Robertson et al. 2014). We here present a novel dataset (the Hypercube) generated by the Environmental Policy Integrated Model (EPIC) crop model and providing the Global Biosphere Management Model (GLOBIOM) with high-resolution information at global scale on the yield, water, and nutrient needs of 16 crops for 15 different combinations of management. We present the dataset and its links to the EPIC and GLOBIOM model, and the rationale for developing a systematic evaluation of the data, before illustrating them with preliminary results

Robust Rescaling Methods for Integrated Water, Food, and Energy Security Management under Systemic Risks and Uncertainty

The aim of this presentation is to discuss robust, non-Bayesian, probabilistic, cross-entropy-based disaggregation (downscaling) techniques. Systems analysis of global change (including climate) processes requires new approaches to integrating and rescaling of models, data, and decision-making procedures between various scales. For example, in the analysis of water security issues, the hydrological models require inputs that are much finer than the resolution of, say, the economic or climatic models generating those inputs. In relation to food security, aggregate national or regional land-use projections derived with global economic land-use planning models give no insights into potentially critical heterogeneities of local trends. Many practical studies analyzing regional developments use cross-entropy minimization as an underlying principle for estimation of local processes. However, the traditional cross-entropy approach relies on a single prior distribution. In reality, we can identify a set of feasible priors. This is relevant, in particular, for land-cover data. Existing global land cover maps (GLC2000, MODIS2000, GLOBCOVER2000) differ in terms of spatially resolved estimates of land use, (e.g., crop, forest, and grass lands). We present novel general approach to achieving downscaling results that are robust with respect to a set of potential prior distributions reflecting non-Bayesian uncertainties, that is, data that are incomplete or not directly observable. The robust downscaling problem is formulated as a probabilistic inverse problem (from aggregate to local data) generally in the form of a non-convex, cross-entropy minimization model. The approach will be illustrated by sequential downscaling aggregate model projections of land-use changes using the Global Biosphere Management Model, with case studies from Africa, Brazil, China, and Ukraine. The approach is being used to harmonize alternative land-cover maps and to develop hybrid maps

Integrated Management of Land-use Systems under Systemic Risks and Food-(bio)energy-water-environmental Security Targets: A Stochastic Global Biosphere Management Model

Interdependencies among land-use systems resemble a complex network connected through demand–supply relations, and disruption of the network may catalyze systemic risks affecting food, energy, water, and environmental security (FEWES) worldwide. This paper describes the conceptual development, expansion, and practical application of a stochastic version of the Global Biosphere Management Model (GLOBIOM), a model that is 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 analyzed and mitigated in all their interactions. While traditional deterministic scenario analysis produces sets of often contradictory outcomes, stochastic GLOBIOM explicitly derives robust decisions that leave the systems better off, independently of what scenario occurs. Stochastic GLOBIOM is formulated as a stochastic optimization model that is central for evaluating portfolios of robust interdependent decisions: ex ante strategic decisions (production allocation, storage capacities) and ex post adaptive (demand, trading, storage control) decisions. For example, the model is applied to the case of increased storage facilities, which can be viewed as catastrophe pools to buffer production shortfalls and fulfill regional and global FEWES requirements when extreme events occur. Expected shortfalls and storage capacities have a close relation with Value-at-Risk and Conditional Value-at-Risk risk measures. The Value of Stochastic Solutions is calculated to present the benefits of the stochastic over the deterministic model

Climate change induced transformations of agricultural systems: insights from a global model

Climate change might impact crop yields considerably and anticipated transformations of agricultural systems are needed in the coming decades to sustain affordable food provision. However, decision-making on transformational shifts in agricultural systems is plagued by uncertainties concerning the nature and geography of climate change, its impacts, and adequate responses. Locking agricultural systems into inadequate transformations costly to adjust is a significant risk and this acts as an incentive to delay action. It is crucial to gain insight into how much transformation is required from agricultural systems, how robust such strategies are, and how we can defuse the associated challenge for decision-making. While implementing a definition related to large changes in resource use into a global impact assessment modelling framework, we find transformational adaptations to be required of agricultural systems in most regions by 2050s in order to cope with climate change. However, these transformations widely differ across climate change scenarios: uncertainties in large-scale development of irrigation span in all continents from 2030s on, and affect two-thirds of regions by 2050s. Meanwhile, significant but uncertain reduction of major agricultural areas affects the Northern Hemisphere's temperate latitudes, while increases to non-agricultural zones could be large but uncertain in one-third of regions. To help reducing the associated challenge for decision-making, we propose a methodology exploring which, when, where and why transformations could be required and uncertain, by means of scenario analysis

Climate change impacts and mitigation in the developing world: An Integrated Assessment of the Agriculture and Forestry Sectors. Policy Research Working Paper No. WPS 7477

This paper conducts an integrated assessment of climate change impacts and climate mitigation on agricultural commodity markets and food availability in low- and middle-income countries. The analysis uses the partial equilibrium model GLOBIOM to generate scenarios to 2080. The findings show that climate change effects on the agricultural sector will increase progressively over the century. By 2030, the impact of climate change on food consumption is moderate but already twice as large in a world with high inequalities than in a more equal world. In the long run, impacts could be much stronger, with global average calorie losses of 6 percent by 2050 and 14 percent by 2080. A mitigation policy to stabilize climate below 2 degrees C uniformly applied to all regions as a carbon tax would also result in a 6 percent reduction in food availability by 2050 and 12 percent reduction by 2080 compared to the reference scenario. To avoid more severe impacts of climate change mitigation on development than climate change itself, revenue from carbon pricing policies will need to be redistributed appropriately. Overall, the projected effects of climate change and mitigation on agricultural markets raise important issues for food security in the long run, but remain more limited in the medium term horizon of 2030. Thus, there are opportunities for low- and middle- income countries to pursue immediate development needs and thus prepare for later periods when adaptation needs and mitigation efforts will become the greatest

IDCases

Although bacterial vaginosis is the most common and benign vaginal infection worldwide, some cases of severe acute infections have been described in the literature. We report the case of a 57-year-old French female who developed a life-threatening postoperative peritonitis after a total hysterectomy with adnexectomy in the context of the removal of leiomyosarcoma. The microbiological analysis of the peritoneal fluid identified Gardnerella vaginalis and Atobopium vaginae. The final diagnosis was a septic shock induced by an early onset peritonitis caused by Gardnerella vaginalis and Atobopium vaginae. The normal flora of the genital area could lead to a serious life threatening postoperative infection and should always be in the differential diagnosis