The Complex Pathway towards Farm-Level Sustainable Intensification: An Exploratory Network Analysis of Stakeholders’ Knowledge and Perception

Analysis of Casual Relationships between Social Determinants of Health in Iran: Using Fuzzy Cognitive Map

BACKGROUND

Health is a fundamental issue in recent years, highlighting the importance of harmonizing the policies of any sector with health strategies. The present study aims to identify the socio-economic factors affecting health and to provide a cognitive map of the social determinants of health in Iran.

STUDY DESIGN

A retrospective cohort study.

METHODS

This study follows a developmental process with an exploratory sequential mixed methods approach. First, a meta-synthesis qualitative method determines the most critical health determinants by reviewing 54 studies from 2000 to 2019. Then, the fuzzy cognitive map (FCM) is drawn based on interviews with six experts to derive the causal relationships among the social determinants of population health in Iran.

RESULTS

Stage 1 introduces 170 factors as the health determinants, classified into four levels: individual, local, national, and global levels, with 7, 4, 13, and 3 branches, respectively. According to the causal relationships and the out-degree (od) index, the most effective factors are the economic system (18.24), governance and policy-making (17.13), and national policies (16.93). According to the degree of centrality, these factors are the economic system (33.27), health system (30.37), and governance and policy-making (30.15).

CONCLUSION

Considering health as a complex and comprehensive system, the resulting FCM displays that the policies developed in other sectors than health are profoundly affecting population's health in Iran. Specifically, the comparative analysis of this research shows that policies regarding the economic system and people's livelihood are more effective than the policies regarding the health system itself on the population's health in Iran.

Trust Versus Content in Multi-functional Land Management: Assessing Soil Function Messaging in Agricultural Networks

Growing sustainability demands on land have a high knowledge requirement across multiple scientific domains. Exploring networks can expose opportunities for targeting. Using mixed-methods combining social network analysis (SNA) and surveys, networks for key soil functions in case studies in Germany, Ireland and the Netherlands are explored. We find a diversity of contrasting networks that reflect local conditions, sustainability challenges and governance structure. Farmers were found to occupy a central role in the agri-environmental governance network. A comparison of the SNA and survey results indicate low acceptance of messages from many central actors indicating scope to better harness the network for sustainable land management. The source of the messages was important when it came to the implementation of farm management actions. Two pathways for enhanced farmer uptake of multi-functionality are proposed that have wider application are; to increase trust between farmers and actors that are agents of multi-functional messages and/or to increase the bundling or multi-functionality of messages (mandate) of actors trusted by farmers.

Measuring Sustainable Intensification Using Satellite Remote Sensing Data

Farm-level sustainable intensification metrics are needed to evaluate farm performance and support policy-making processes aimed at enhancing sustainable production. Farm-level sustainable intensification metrics require environmental impacts associated with agricultural production to be accounted for. However, it is common that such indicators are not available. We show how satellite-based remote sensing information can be used in combination with farm efficiency analysis to obtain a sustainable intensification (SI) indicator, which can serve as a sustainability benchmarking tool for farmers and policy makers. We obtained an SI indicator for 114 maize farms in Yangxin County, located in the Shandong Province in China, by combining information on maize output and inputs with satellite information on the leaf area index (from which a nitrogen environmental damage indicator is derived) into a farm technical efficiency analysis using a stochastic frontier approach. We compare farm-level efficiency scores between models that incorporate environmental damage indicators based on satellite-based remote sensing information and models that do not account for environmental impact. The results demonstrate that (a) satellite-based information can be used to account for environmental impacts associated with agriculture production and (b) how the environmental impact metrics derived from satellite-based information combined with farm efficiency analysis can be used to obtain a farm-level sustainable intensification indicator. The approach can be used to obtain tools for farmers and policy makers aiming at improving SI.

An approach for comparing agricultural development to societal visions

There is broad agreement that agriculture has to become more sustainable in order to provide enough affordable, healthy food at minimal environmental and social costs. But what is "more sustainable"? More often than not, different stakeholders have opposing opinions on what a more sustainable future should look like. This normative dimension is rarely explicitly addressed in sustainability assessments. In this study, we present an approach to assess the sustainability of agricultural development that explicitly accounts for the normative dimension by comparing observed development with various societal visions. We illustrate the approach by analyzing farm- and landscape-scale development as well as sustainability outcomes in a Swiss case study landscape. Observed changes were juxtaposed with desired changes by Avenir Suisse, a liberal think tank representing free-market interests; the Swiss Farmers Association, representing a conservative force; and Landwirtschaft mit Zukunft, an exponent of the Swiss agroecological movement. Overall, the observed developments aligned most closely with desired developments of the liberal think-tank (72%). Farmer interviews revealed that in the case study area farms increased in size (+ 57%) and became more specialized and more productive (+ 223%) over the past 20 years. In addition, interpretation of aerial photographs indicated that farming became more rationalized at the landscape level, with increasing field sizes (+ 34%) and removal of solitary field trees (- 18%). The case study example highlights the varying degrees to which current developments in agriculture align with societal visions. By using societal visions as benchmarks to track the progress of agricultural development, while explicitly addressing their narratives and respective systems of values and norms, this approach offers opportunities to inform also the wider public on the extent to which current developments are consistent with different visions. This could help identify mismatches between desired and actual development and pave the way for designing new policies.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13593-021-00739-3.

A Fuzzy Cognitive Map method for integrated and participatory water governance and indicators affecting drinking water supplies

Drinking water governance is challenging with different perceptions and priorities among stakeholders in different countries. To make provision for drinking water protection in agricultural areas, governance systems need to be mapped for bottlenecks to be identified and solutions highlighted. To address this a system thinking approach was used in an explanatory network analysis of Fuzzy Cognitive Maps (FCM) that were created during face to face interviews with stakeholder representative groups (individuals, policy developers, researchers, and regulators). Two exercises were designed and facilitated to obtain stakeholder maps on A) the water governance framework from stakeholders' own perspective with a ranking of actors in terms of their perceived importance and B) a list of importance factors and how these were connected for the provision of good drinking water quality supplies in agricultural areas. Causal relationships were subsequently drawn around each subject allowing mapping. A graph theory Hierarchy Index (h) approach examined if stakeholder groups preferred top down hierarchical governance or a more inclusive democratic governance approach. Finally, an auto-associative neural network method was deployed on group maps for examination during steady-state conditions for three scenarios to be explored i.e. changing "Farmers knowledge", "best management practice (BMP) uptake" and "Farmers behaviour and belief" to the highest level of influence and seeing how the system reacted. Results of Exercise A showed that all stakeholder representative groups had a different perception of the water governance framework. Most stakeholder groups had a democratic point of view regarding water governance structures and the ranking and importance of the actors within the framework. Results of Exercise B demonstrated that most of the groups have similar opinions regarding the highest ranked factors affecting drinking water quality and the possible environmental ecological policy options. In this second exercise, only one representative group showed a democratic outlook whereas all others had a hierarchal outlook. Scenario testing of policy options enabled bottlenecks and possible solutions to be identified. By boosting "Farmers behaviour and belief" to the highest possible level, resulted in a large increase in other factors - a scenario where farmers could benefit from the outcome. This would be achieved by enhancing farmers' willingness and intention to participate and implement BMPs. Better results would be achieved if farmers believed in the method and could benefit from the outcome. Also keeping "Farmers knowledge" at the highest point had a positive influence on the other factors. This can be achieved by enhancing farmers training and knowledge transfer by local and national actors. This method is widely applicable and should be considered for more integrated and participatory approaches to drinking water governance.

Spatial and Ecological Farmer Knowledge and Decision-Making about Ecosystem Services and Biodiversity

Amid climate change, biodiversity loss and food insecurity, there is the growing need to draw synergies between micro-scale environmental processes and practices, and macro-level ecosystem dynamics to facilitate conservation decision-making. Adopting this synergistic approach can improve crop yields and profitability more sustainably, enhance livelihoods and mitigate climate change. Using spatially explicit data generated through a public participatory geographic information system methodology (n = 37), complemented by spatial analysis, interviews (n = 68) and focus group discussions (n = 4), we explored the synergies between participatory farmer-to-farmer agroecology knowledge sharing, farm-level decisions and their links with macro-level prioritization of conservation strategies. We mapped farm conditions and ecosystem services (ES) of two village areas with varying knowledge systems about farming. Results of the farm-level analysis revealed variations in spatial perception among farmers, differences in understanding the dynamics of crop growth and varying priorities for extension services based on agroecological knowledge. The ES use pattern analysis revealed hotspots in the mapped ES indicators with similarities in both village areas. Despite the similarities in ES use, priorities for biodiversity conservation align with farmers’ understanding of farm processes and practices. Farmers with training in agroecology prioritized strategies that are ecologically friendly while farmers with no agroecology training prioritized the use of strict regulations. Importantly, the results show that agroecology can potentially contribute to biodiversity conservation and food security, with climate change mitigation co-benefits. The findings generally contribute to debates on land sparing and land sharing conservation strategies and advance social learning theory as it pertains to acquiring agroecological knowledge for improved yield and a sustainable environment.