Incorporating stakeholders' values into environmental decision support: A Bayesian Belief Network approach

Towards a 'One quality' approach of pork: A perspective on the challenges and opportunities in the context of the farm-to-fork continuum - Invited review

A substantial amount of research on pork production and consumption highlights an interplay between the intrinsic qualities that are inherent to the product and the extrinsic qualities related to how it is produced, which together contribute to the perception and evaluation of fresh pork. However, studies have emphasised difficulties in defining their relative importance depending on the countries, consumers' knowledge, experience and personal beliefs, as well as their dynamic changes over time. A joint and multidimensional consideration of the intrinsic and extrinsic quality dimensions is critical to achieve sustainable development goals that ensure healthy, nutritious, fair and environmentally friendly pork produced in a profitable manner. However, very few studies have investigated the synergies and antagonisms between the multiple dimensions of intrinsic and extrinsic qualities of pork. This perspective aims to define and promote the concept of 'One Quality' pork, as an approach to meeting the multiple and divergent expectations of stakeholders in the pork value chain, while jointly considering pork quality and sustainability. It aims to discuss how the changing expectations of consumers, citizens and public action including policy makers are currently promoting a holistic definition and evaluation of pork quality. It also seeks to explore how the multiple dimensions of pork quality, including their intrinsic and extrinsic dimensions, can be considered simultaneously. The opportunities and challenges of implementing a 'One Quality' approach to pork for an integrated sustainability assessment of the farming systems, i.e., by jointly addressing the intrinsic quality attributes, ensuring sustainable farming practices, economic viability for stakeholders, and alignment with consumer and citizen expectations, are then discussed along the farm-to-fork continuum.

Identifying NH3 emission mitigation techniques from farm to field using a Bayesian network

This study addresses the challenge of reducing ammonia (NH3) emissions from agriculture by evaluating various mitigation techniques. The research utilized a Bayesian Belief Network (BBN) to integrate quantitative data on NH3 volatilization reduction with qualitative stakeholder perceptions, aiming to identify the best available techniques (BATs) that balance environmental, economic, and socio-cultural factors for farmers in the Veneto region of Italy. The BBN framework established probabilistic dependencies between variables related to livestock, crop type, manure storage, fertilization management, and pedo-climatic conditions. Stakeholder opinions were quantified through a value elicitation process and combined with the BBN to create an integrated Influence Diagram (ID). Results indicated that effective NH3 reduction requires a comprehensive approach across the entire agri-livestock supply chain. Based on the results obtained, no single technique clearly emerged as the primary focus, rather various areas would require improvement across the agri-livestock supply chain. However, if prioritizing techniques were necessary, efforts should concentrate on stable management of infirmary animals (HCInf), overcrowding reduction by decreasing the number of animals on densely populated farms (OC-Animal), and optimization of protein in animal ration (FDProt). These measures should be combined with effective manure application through slurry injection (INJSlu) in the field. Stakeholders showed reluctance towards more expensive or innovative methods, indicating that socio-cultural perceptions and economic feasibility can heavily influence the adoption of new technologies although they proved to be among the most environmentally effective. The primary insight from applying the BBNs was that selecting effective techniques necessitates a multi-perspective approach to foster consensus among stakeholders throughout the agri-livestock supply chain.

Capturing and quantifying the aggregate effects of multi-source factors affecting miners' health and well-being: Construction of Bayesian belief networks

Factors originating at the organizational, work, and individual levels are closely interrelated and intricately intertwined, affecting health rates. There was limited research on the interdependence and aggregate effects between multi-source factors and occupational health and well-being (OHW). It is challenging to achieve management goals. Therefore, considering cross-level factors and across the "work environment-stress-exposure-OHW" chain, individual vulnerability was considered. A Fuzzy Bayesian Belief Network (FBBN) driven by both domain knowledge and data was constructed to carve out the logic between multi-source factors and OHW. Workers from four coal mines were surveyed twice in 6 months. 714 valid samples were included in the analysis. The interdependencies among multi-source factors were identified by the Interpretive Structure Modeling method and the visual probability estimation was achieved based on FBBN. It revealed that the work and the organizational level were the root factors. Eight factors involved in work stress were mainly mediating, and actual exposure and individual vulnerability were direct factors. Pathway interventions and joint interventions were proposed. The prediction ability and scheme feasibility of FBBN were verified. The approach developed allows robust assessments of aggregate effects and obtains multi-source factor importance. This study provides vital insights and evaluation tools for understanding workplace stress and OHW management.

Prioritizing decision-making of health and well-being response tactics: Incorporating organizational and individual shared demands

As a major energy source in China, the occupational health and well-being (OHW) of miners is a priority. Various statistical techniques have been used to identify factors or assess OHW to provide valuable information for the implementation of health promotion activities. The main bottleneck is the limited focus on solutions that address the demands of both organizations and individuals, and scientific and effective decision-making is pending. Therefore, this study describes the OHW mechanism covering both antecedents and consequences through the driving force-pressure-state-impact-response model. A probabilistic model of management tradeoff analysis was established by using a Bayesian decision network. Causal relationships and dependencies between multiple factors are captured visually. The model was verified and applied with samples of miners (N = 816). The results showed that the comprehensive strategy (R5) was the best tactic, and the management effect of stress (R2) and vulnerability (R3) was prominent. This study provides a valuable tool for managers to identify priority management factors. Prioritizing tactics formulated from dual demands of organizational and individual can ensure project feasibility, operability, and effectiveness. This study is a novel attempt to combine theory with practice, which is timely and necessary for management.

Modelling framework to evaluate societal effects of ecosystem management

The ecosystem effects of different management options can be predicted through models that simulate the ecosystem functioning under different management scenarios. Optimal management strategies are searched by simulating different management (and other, such as climate) scenarios and finding the management measures that produce desirable results. The desirability of results is often defined through the attainment of policy objectives such as good environmental/ecological status. However, this often does not account for societal consequences of the environmental status even though the consequences can be different for different stakeholder groups. In this work we introduce a method to evaluate management alternatives in the light of the experiential value of stakeholder groups, using a case study in the Baltic Sea. We use an Ecopath with Ecosim model to simulate the ecosystem responses to management and climate scenarios, and the results are judged based on objectives defined based on a stakeholder questionnaire on what aspects of the ecosystem they value or detest. The ecosystem responses and the stakeholder values are combined in a Bayesian decision support model to illustrate which management options bring the highest benefits to stakeholders, and whether different stakeholder groups benefit from different management choices. In the case study, the more moderate climate scenario and strict fisheries and nutrient loading management brought the highest benefits to all stakeholders. The method can be used to evaluate and compare the effects of different management alternatives to various stakeholder groups, if their preferences are known.

Intelligent lung cancer MRI prediction analysis based on cluster prominence and posterior probabilities utilizing intelligent Bayesian methods on extracted gray-level co-occurrence (GLCM) features

Lung cancer is the second foremost cause of cancer due to which millions of deaths occur worldwide. Developing automated tools is still a challenging task to improve the prediction. This study is specifically conducted for detailed posterior probabilities analysis to unfold the network associations among the gray-level co-occurrence matrix (GLCM) features. We then ranked the features based on t-test. The Cluster Prominence is selected as target node. The association and arc analysis were determined based on mutual information. The occurrence and reliability of selected cluster states were computed. The Cluster Prominence at state ≤330.85 yielded ROC index of 100%, relative Gini index of 99.98%, and relative Gini index of 100%. The proposed method further unfolds the dynamics and to detailed analysis of computed features based on GLCM features for better understanding of the hidden dynamics for proper diagnosis and prognosis of lung cancer.

Bayesian dynamic profiling and optimization of important ranked energy from gray level co-occurrence (GLCM) features for empirical analysis of brain MRI

Accurate classification of brain tumor subtypes is important for prognosis and treatment. Researchers are developing tools based on static and dynamic feature extraction and applying machine learning and deep learning. However, static feature requires further analysis to compute the relevance, strength, and types of association. Recently Bayesian inference approach gains attraction for deeper analysis of static (hand-crafted) features to unfold hidden dynamics and relationships among features. We computed the gray level co-occurrence (GLCM) features from brain tumor meningioma and pituitary MRIs and then ranked based on entropy methods. The highly ranked Energy feature was chosen as our target variable for further empirical analysis of dynamic profiling and optimization to unfold the nonlinear intrinsic dynamics of GLCM features extracted from brain MRIs. The proposed method further unfolds the dynamics and to detailed analysis of computed features based on GLCM features for better understanding of the hidden dynamics for proper diagnosis and prognosis of tumor types leading to brain stroke.

A Bayesian Dynamic Inference Approach Based on Extracted Gray Level Co-Occurrence (GLCM) Features for the Dynamical Analysis of Congestive Heart Failure

The adoptability of the heart to external and internal stimuli is reflected by heart rate variability (HRV). Reduced HRV can be a predictor of post-infarction mortality. In this study, we propose an automated system to predict and diagnose congestive heart failure using short-term heart rate variability analysis. Based on the nonlinear, nonstationary, and highly complex dynamics of congestive heart failure, we extracted multimodal features to capture the temporal, spectral, and complex dynamics. Recently, the Bayesian inference approach has been recognized as an attractive option for the deeper analysis of static features, in order to perform a comprehensive analysis of extracted nodes (features). We computed the gray level co-occurrence (GLCM) features from congestive heart failure signals and then ranked them based on ROC methods. This study focused on utilizing the dissimilarity feature, which is ranked as highly important, as a target node for the empirical analysis of dynamic profiling and optimization, in order to explain the nonlinear dynamics of GLCM features extracted from heart failure signals, and distinguishing CHF from NSR. We applied Bayesian inference and Pearson’s correlation (PC). The association, in terms of node force and mapping, was computed. The higher-ranking target node was used to compute the posterior probability, total effect, arc contribution, network profile, and compression. The highest value of ROC was obtained for dissimilarity, at 0.3589. Based on the information-gain algorithm, the highest strength of the relationship was obtained between nodes “dissimilarity” and “cluster performance” (1.0146), relative to mutual information (81.33%). Moreover, the highest relative binary significance was yielded for dissimilarity for 1/3rd (80.19%), 2/3rd (74.95%) and 3/3rd (100%). The results revealed that the proposed methodology can provide further in-depth insights for the early diagnosis and prognosis of congestive heart failure.

Prioritizing risk mitigation measures for binary heavy metal contamination emergencies at the watershed scale using bayesian decision networks

Water pollution accidents have the characteristics of high uncertainty, rapid evolution and are difficult to control, thus posing great threats to human health, ecological security, and social stability. During the last 10 years, China has faced the occurrence of six extraordinarily serious heavy metal contamination pollution events at the watershed scale. This has alerted governments and enterprises of the significance of emergency decision-making. To quantitatively prioritize risk mitigation strategies for heavy metal emergencies, a Bayesian Decision Network-based probabilistic model is proposed under the Drivers-Pressures-States-Impacts-Responses (DPSIR) framework. A Copula-based exposure risk model is embedded to simulate the fate of heavy metal ions for each risk reduction option, whose joint probability distributions can then be used as input parameters in the Bayesian Decision Network. This method was applied to the emergency response prioritization for acute Cr(VI)-Hg(II) contamination accidents in the Danshui River watershed. The results indicated that comprehensive measure (M5) was the best option for decreasing ecological and human health risks. As for a single risk mitigation strategy, risk source prevention (M1) was the best alternative compared to exposure pathway interruption (M2) and human/ecological receptor protection (M3-M4). This probabilistic method can not only address the uncertainties between certain risk sources and receptors in the BDN structure, but also realize the risk system optimization in a satisfactory/preferred mode under the DPSIR framework. Overall, it provides the probabilistic risk estimates for watershed-scale risk management and policy making for local risk managers and stakeholders.

The biodiversity-wind energy-land use nexus in a global biodiversity hotspot

Wind energy is the leading renewable technology towards achieving climate goals, yet biodiversity trade-offs via land take are emerging. Thus, we are facing the paradox of impacting on biodiversity to combat climate change. We suggest a novel method of spatial planning that enhances windfarm sustainability: investments are prioritized in the most fragmented zones that lie outside the Natura 2000 network of protected areas. We showcase it in Greece, a biodiversity hotspot with a strong climate policy and land conflict between conservation and wind energy schemes. The analysis indicates that the suggested investment zone supports wind harnessing 1.5 times higher than the 2030 national goal, having only marginally lower (4%) wind speed. It performs well for the conservation of the annexed habitats and species of the two Nature Directives and it greatly overlaps with the Important Bird Areas (93%) and the roadless areas (80%) of Greece. It also greatly overlaps (82%-91%) with the exclusion zones suggested according to three sensitivity maps for bird conservation. Since land use change triggers biodiversity decline, we underline the necessity of such approaches for meeting both climate and biodiversity goals and call for a greater environmental policy convergence towards biodiversity conservation and no net land take.

An application of Bayesian Belief Networks to assess management scenarios for aquaculture in a complex tropical lake system in Indonesia

A Bayesian Belief Network, validated using past observational data, is applied to conceptualize the ecological response of Lake Maninjau, a tropical lake ecosystem in Indonesia, to tilapia cage farms operating on the lake and to quantify its impacts to assist decision making. The model captures ecosystem services trade-offs between cage farming and native fish loss. It is used to appraise options for lake management related to the minimization of the impacts of the cage farms. The constructed model overcomes difficulties with limited data availability to illustrate the complex physical and biogeochemical interactions contributing to triggering mass fish kills due to upwelling and the loss in the production of native fish related to the operation of cage farming. The model highlights existing information gaps in the research related to the management of the farms in the study area, which is applicable to other tropical lakes in general. Model results suggest that internal phosphorous loading (IPL) should be recognized as one of the primary targets of the deep eutrophic tropical lake restoration efforts. Theoretical and practical contributions of the model and model expansions are discussed. Short- and longer-term actions to contribute to a more sustainable management are recommended and include epilimnion aeration and sediment capping.

On the uncertainty and confidence in decision support tools (DSTs) with insights from the Baltic Sea ecosystem

Ecosystems around the world are increasingly exposed to multiple, often interacting human activities, leading to pressures and possibly environmental state changes. Decision support tools (DSTs) can assist environmental managers and policy makers to evaluate the current status of ecosystems (i.e. assessment tools) and the consequences of alternative policies or management scenarios (i.e. planning tools) to make the best possible decision based on prevailing knowledge and uncertainties. However, to be confident in DST outcomes it is imperative that known sources of uncertainty such as sampling and measurement error, model structure, and parameter use are quantified, documented, and addressed throughout the DST set-up, calibration, and validation processes. Here we provide a brief overview of the main sources of uncertainty and methods currently available to quantify uncertainty in DST input and output. We then review 42 existing DSTs that were designed to manage anthropogenic pressures in the Baltic Sea to summarise how and what sources of uncertainties were addressed within planning and assessment tools. Based on our findings, we recommend future DST development to adhere to good modelling practise principles, and to better document and communicate uncertainty among stakeholders.

Spatial aspects of the dioxin risk formation in the Baltic Sea: A systematic review

Dioxins have been an inconvenience to the Baltic Sea ecosystem for decades. Although the concentrations in the environment and biota have continuously decreased, dioxins still pose a risk to human health. The risk and its formation vary in different parts of the Baltic Sea, due to variability in the environmental and societal factors affecting it. This paper presents a systematic literature review and knowledge synthesis about the regional dioxin risk formation in four sub-areas of the Baltic Sea and evaluates, whether systemic approach changes our thinking about the risk and its effective management. We studied the dioxin flux from atmospheric deposition to the Baltic Sea food webs, accumulation to two commercially and culturally important fish species, Baltic herring (Clupea harengus membras) and Baltic salmon (Salmo salar), and further to risk group members of four Baltic countries. Based on 46 studies, we identified 20 quantifiable variables and indexed them for commensurable regional comparison. Spatial differences in dioxin pollution, environmental conditions, food web dynamics, and the following dioxin concentrations in herring and salmon, together with fishing and fish consumption, affect how the final health risk builds up. In the southern Baltic Sea, atmospheric pollution levels are relatively high and environmental processes to decrease bioavailability of dioxins unfavorable, but the growth is fast, which curb the bioaccumulation of dioxins in the biota. In the North, long-range atmospheric pollution is minor compared to South, but the local pollution and slower growth leads to higher bioaccumulation rates. However, based on our results, the most remarkable differences in the dioxin risk formation between the areas arise from the social sphere: the emissions, origin of national catches, and cultural differences in fish consumption. The article suggests that acknowledging spatial characteristics of socio-ecological systems that generate environmental risks may aid to direct local focus in risk management.

Monitoring social–ecological networks for biodiversity and ecosystem services in human-dominated landscapes

The demand the human population is placing on the environment has triggered accelerated rates of biodiversity change and created trade-offs among the ecosystem services we depend upon. Decisions designed to reverse these trends require the best possible information obtained by monitoring ecological and social dimensions of change. Here, we conceptualize a network framework to monitor change in social–ecological systems. We contextualize our framework within Ostrom’s social–ecological system framework and use it to discuss the challenges of monitoring biodiversity and ecosystem services across spatial and temporal scales. We propose that spatially explicit multilayer and multiscale monitoring can help estimate the range of variability seen in social–ecological systems with varying levels of human modification across the landscape. We illustrate our framework using a conceptual case study on the ecosystem service of maple syrup production. We argue for the use of analytical tools capable of integrating qualitative and quantitative knowledge of social–ecological systems to provide a causal understanding of change across a network. Altogether, our conceptual framework provides a foundation for establishing monitoring systems. Operationalizing our framework will allow for the detection of ecosystem service change and assessment of its drivers across several scales, informing the long-term sustainability of biodiversity and ecosystem services.

Bayesian Networks in Environmental Risk Assessment: A Review

Human activities both depend upon and have consequences on the environment. Environmental risk assessment (ERA) is a process of estimating the probability and consequences of the adverse effects of human activities and other stressors on the environment. Bayesian networks (BNs) can synthesize different types of knowledge and explicitly account for the probabilities of different scenarios, therefore offering a useful tool for ERA. Their use in formal ERA practice has not been evaluated, however, despite their increasing popularity in environmental modeling. This paper reviews the use of BNs in ERA based on peer-reviewed publications. Following a systematic mapping protocol, we identified studies in which BNs have been used in an environmental risk context and evaluated the scope, technical aspects, and use of the models and their results. The review shows that BNs have been applied in ERA, particularly in recent years, and that there is room to develop both the model implementation and participatory modeling practices. Based on this review and the authors' experience, we outline general guidelines and development ideas for using BNs in ERA. Integr Environ Assess Manag 2021;17:62-78. © 2020 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).