• Energy Research

AbstractIn the past, food-based dietary guidelines (FBDGs) were derived nearly exclusively by using systematic reviews on diet–health relationships and translating dietary reference values for nutrient intake into foods. This approach neglects many other implications that dietary recommendations have on society, the economy and environment. In view of pressing challenges, such as climate change and the rising burden of diet-related diseases, the simultaneous integration of evidence-based findings from different dimensions into FBDGs is required. Consequently, mathematical methods and data processing are evolving as powerful tools in nutritional sciences. The possibilities and reasons for the derivation of FBDGs via mathematical approaches were the subject of a joint workshop hosted by the German Nutrition Society (DGE) and the Federation of European Nutrition Societies (FENS) in September 2019 in Bonn, Germany. European scientists were invited to discuss and exchange on the topics of mathematical optimisation for the development of FBDGs and different approaches to integrate various dimensions into FBDGs. We concluded that mathematical optimisation is a suitable tool to formulate FBDGs finding trade-offs between conflicting goals and taking several dimensions into account. We identified a lack of evidence for the extent to which constraints and weights for different dimensions are set and the challenge to compile diverse data that suit the demands of optimisation models. We also found that individualisation via mathematical optimisation is one perspective of FBDGs to increase consumer acceptance, but the application of mathematical optimisation for population-based and individual FBDGs requires more experience and evaluation for further improvements.

Recognizing the diversity of farmers is crucial for the success of agricultural, rural, or environmental programs and policies aimed at the sustainable use of natural resources. In this study, based on survey data collected in the Kilombero Valley Floodplain (KVF) in Tanzania, we design a typology of farmers to describe the range of farm types and farming systems systematically, and to understand their livelihood and land use behavior. The KVF is the largest, low-altitude, seasonally-flooded, freshwater wetland in East Africa. Despite its values, KVF is a very fragile ecosystem threatened by current and future human interventions. We apply multivariate statistical analysis (a combination of principal component analysis and cluster analysis) to identify farm groups that are homogenous within and heterogeneous between groups. Three farm types were identified: “Monocrop rice producer”, “Diversifier”, and “Agropastoralist”. Monocrop rice producers are the dominant farm types, accounting for 65 percent of the farm households in the valley, characterized by more than 80 percent of the land allocated to rice, showing strong market participation and high utilization of labor. Diversifiers, on the other hand, allocate more land to maize and vegetables. Agropastoralists account for 7 percent of the surveyed farmers and differ from the other two groups by, on average, larger land ownership, a combination of livestock and crop production, and larger household sizes. This typology represents the diversity of farmers in KVF concerning their land use and livelihood strategy, and will allow to target policy interventions. Besides, it may also inform further research about the diverse landscape of floodplain farming, through the classification and interpretation of different socio-economic positions of farm households.

Agricultural systems and problems of sustainability are complex, covering a range of organisational levels and spatial and temporal scales. Integrated assessment (IA) and modelling (IAM) is an attempt to capture complex multi-scale problems. Scale changes and model linking methods (referred to as scaling methods) are important in dealing with these problems but they are often not well understood. The present study aims to analyse scaling methods used in the recently developed multi-scale IA model SEAMLESS-IF which is applied to two case studies of complex agri-environmental problems. The analysis is based on a classification of up- and down-scaling methods which is extended for the purpose of this study. Our analysis shows that scale changes refer to different spatial, temporal and functional scales with changes in extent, resolution, and coverage rate. Accordingly, SEAMLESS-IF uses a number of different scaling methods including data extrapolation, aggregation and disaggregation, sampling, nested simulation and employs descriptive response functions and technical coefficients derived from explanatory models. Despite the satisfactory results obtained from SEAMLESS-IF, a comparative quantitative analysis of alternative scaling methods is still pending and requires further attention. Improved integration of scaling methods may also help to overcome limitations of IA models related to high data demand, complexity of models and scaling methods considered, and the accumulation of uncertainty due to the use of multiple models. In the case studies, the most challenging scaling problem refers to the appropriate consideration of the farm level as intermediate level between the field and market levels. Among the scaling methods analysed, summary models are hardly applied. This is because they are often unavailable due to limited systems understanding and because they may differ depending on the question at stake. The classification of scaling methods used has been helpful to structure this analysis.

The aim of this chapter is to present a bio-economic modelling framework established to provide insight into the complex nature of agricultural systems and to assess the impacts of agricultural and environmental policies and technological innovations. This framework consists of a Farm System Simulator (FSSIM) using mathematical programming that can be linked to a cropping system model to estimate at field level the engineering production and environmental functions. FSSIM includes a module for agricultural management (FSSIM-AM) and a mathematical programming model (FSSIM-MP). FSSIM-AM aims to define current and alternative activities and to quantify their input output coefficients (both yields and environmental effects) using a cropping system model, such as APES (Agricultural Production and Externalities Simulator) and other sources (expert knowledge, surveys, etc.). FSSIM-MP seeks to describe the behaviour of the farmer given a set of biophysical, socio-economic and policy constraints and to predict its reactions under new technologies, policy and market changes. The communication between these different tools and models is based on explicit definitions of spatial scales and software for model integration.

The term “conventionalization” of organic agriculture was created to depict the controversially discussed phenomenon that organic agriculture departs from the core organic principles on which it is based. We present an empirical, index-based approach to investigate developments of organic farming practices towards conventionalization. An index of conventionalization can be used as a monitoring tool to support policymakers to further develop agricultural regulations. We calculate composite indicators for three farm types: farms specialized on crop production, farms specialized on animal husbandry, and mixed farms. Principal component analysis serves to derive objective weights based on the correlations between indicators which then allow a linear aggregation to the composite indicator. Results show that developments towards conventionalization of the whole organic farming sector cannot be detected for German farms between 2000 and 2009. Therefore, we do not see the necessity for changes in regulation of the organic sector with regard to conventionalization.

Steering the EU food system towards a sustainability transformation requires a vast and actionable knowledge base available to a range of public and private actors. Few have captured this complexity by assessing food systems from a multi-dimensional and multi-level perspective, which would include (1) nutrition and diet, environmental and economic outcomes together with social equity dimensions and (2) system interactions across country, EU and global scales. This paper addresses this gap in food systems research and science communication by providing an integrated analytical approach and new ways to communicate this complexity outside science. Based on a transdisciplinary science approach with continuous stakeholder input, the EU Horizon2020 project ‘Metrics, Models and Foresight for European SUStainable Food And Nutrition Security’ (SUSFANS) developed a five-step process: Creating a participatory space; designing a conceptual framework of the EU food system; developing food system performance metrics; designing a modelling toolbox and developing a visualization tool. The Sustainable Food and Nutrition-Visualizer, designed to communicate complex policy change-impacts and trade-off questions, enables an informed debate about trade-offs associated with options for change among food system actors as well as in the policy making arena. The discussion highlights points for further research related to indicator development, reach of assessment models, participatory processes and obstacles in science communication.