Abstract: Companies dealing with chemical products have to cope with large amounts of waste and environmental risk due to the use and production of toxic substances. Against this background, increasing attention is being paid to green chemistry and the translation of this concept into biobased chemicals. Given the multitude of economic, environmental and societal impacts that the production and use of biobased chemicals have on sustainability, assessment approaches need to be developed that allow for measurement and comparison of these impacts. To evaluate sustainability in the context of policy and decision-making, indicators are generally accepted means. However, sustainability indicators currently predominantly exist for low-value applications in the bioeconomy, like bioenergy and biofuels. In this paper, a review of the state-of-the-art sustainability indicators for biobased chemicals is conducted and a gap analysis is performed to identify indicator development needs. Based on the analysis, a clear hierarchy within the concept of sustainability is found where the environmental aspect dominates over economic and social indicators. All one-dimensional indicator-sets account for environmental impacts (50%), whereas two-dimensional sets complement the environmental issues with economic indicators (34%). Moreover, even the sets encompassing all three sustainability dimensions (16%) do not account for the dynamics and interlinkages between the environment, economy and society. Using results from the literature review, an indicator list is presented that captures all indicators currently used within sustainability assessment of biobased chemicals. Finally, a framework is proposed for future indicator selection using a stakeholder survey to obtain a prioritized list of sustainability indicators for biobased chemicals.

Abstract: Production of food crops on trace element-contaminated agricultural lands in the Campine region (Belgium) can be problematic as legal threshold values for safe use of these crops can be exceeded. Conventional sanitation of vast areas is too expensive and alternatives need to be investigated. Zea mays on a trace element-contaminated soil in the region showed an average yield of 53 ± 10 Mg fresh or 20 ± 3 Mg dry biomass ha−1. Whole plant Cd concentrations complied with legal threshold values for animal feed. Moreover, threshold values for use in anaerobic digestion were met. Biogas production potential did not differ between maize grown on contaminated and non-contaminated soils. Results suggested favorable perspectives for farmers to generate non-food crops profitably, although effective soil cleaning would be very slow. This demonstrates that a valuable and sustainable alternative use can be generated for moderately contaminated soils on which conventional agriculture is impaired.

Abstract: Assumptions on the perceptions of risks, made in agricultural economics literature, are recognized to be over-simplistic. For example most studies assume that risks are independent and static, while in reality most risks are interlinked and dynamic. We propose an alternative method to identify and present risk perception, closer to the actual comprehension of risk by farmers. Grounded theory is used to investigate the perceptions of risk by farmers while avoiding prior assumptions. Main findings are: (i) farmers have difficulty to rank or score probability and impact of risks in a (semi)quantitative manner; (ii) farmers attach different meanings to risk, when the focus shifts between, uncertain event, probability or value at stake and; (iii) farmers perceive risks as being interrelated. Based on these findings, we propose that farmers' risk perception can be best understood as a network of interrelated notions of uncertain events, their effects and uncertain outcomes. Furthermore, cognitive mapping is suggested to elucidate and present these networks. We test cognitive mapping, exploring dairy farmers' risk perception, and demonstrate the appropriateness of this methodology for capturing the complexity and context of perceived risk. Advantages are: (i) the qualitative approach, (ii) the focus on interrelations and context, (iii) the applicability at farm level, (iv) the farmer-driven rather than researcher-driven perspective, and (v) the elucidation of the polyvalent use of the risk concept. Cognitive maps can be used as a communication tool, a risk management tool, and a tool to stimulate bi-directional learning amongst farmers, policy makers, researchers and extension agents. (C) 2013 Elsevier Ltd. All rights reserved.

Abstract: Current cross-sectional methodologies measuring climate change impacts assume that regions at the same latitude face a similar climate response and therefore have the same adaptive capacity. This paper proves that assumption to be erroneous in the European Union. It does so by ameliorating the Ricardian methodology by restricting which farmers (and therefore which adaptation options) are allowed in the dataset. In doing so, a comparative Ricardian methodology is suggested that makes it possible to examine, for the first time, how the climate responsiveness of a region changes if adaptive capacity changes. The paper combines climate, soil, geographic, socio-economic, and farm-level data in a linear mixed-effect model and examines whether Eastern and Western Europe have the same climate responses and how these responses change if regional adaptive capacity increases. The paper concludes that both regions currently have a significantly different climate response, but that if Eastern Europe were to implement the same adaptation options as Western Europe, it could avoid a large decrease in land value and even benefit from climate change depending on the climate scenario.

Abstract: Researchers who do not take into account farm heterogeneity in implementing specific climate change adaptation options might significantly bias their findings. To prove this point, this paper focusses on irrigation as an adaptation option to climate change and highlights the fact that there is no such thing as “irrigation.” Instead, different farms consider water management options across a spectrum that ranges from purely rainfed farms to purely irrigated farms with in between the extreme practices such as supplemental irrigation, water conservation practices, and different irrigation techniques. Accounting for such differences is necessary, yet difficult due to a lack of farm-specific data on water management and irrigation. This paper uses unique Farm Accountancy Data Network data of Western European farmers on the proportion of farmland that each farm irrigates. Unlike previous work, this allows taking into account some within-irrigation heterogeneity instead of simply categorizing farms as being “irrigated.” We estimate and compare climate response models based on the Ricardian cross-sectional method for a large range of irrigation categories. The results give insights into how the farm irrigation climate response can be significantly different depending on how irrigation is defined. This proves that ignoring within-adaptation differences when comparing non-adaptation with adaptation (in this case, rainfed versus irrigated agriculture) might lead to biased conclusions with regard to effectiveness of adaptation strategies. We therefore argue that it might be more relevant to understand at which point and under which circumstances irrigated agriculture is more or less beneficial than rainfed agriculture.

Abstract: Multi-objective optimization is an important decision-making tool for energy processes, as multiple targets need to be achieved. These objectives are usually conflicting since a single solution cannot be optimal for all objectives, resulting in a set of Pareto-optimal solutions. Multiple indicators might be available to describe a sustainability objective, such as the environmental impact which is commonly evaluated by performing a life cycle assessment. In this study, Pareto aggregation is proposed as a method which employs a novel multi-objective optimization-based approach as an alternative to the classically used aggregation in life cycle assessment. This method identifies conflicting environmental indicators and performs an aggregation among those that require a trade-off. An environmental-economic optimization of a second-generation bioethanol plant is used to illustrate and evaluate the proposed method. Process parameters from a biochemical conversion pathway flowsheet simulation model are chosen as optimization variables. To reduce the computational time, surrogate models, based on artificial neural networks, are used. Out of the eighteen ReCiPe Midpoint environmental indicators, five were identified as conflicting, resulting in an aggregated environmental objective, which was then traded off with the economic objective function, chosen as the levelized cost of ethanol. Comparison with the widely used single-score EcoIndicator99 showed that the Pareto aggregation method can reduce most of the environmental indicators by up to 6.5%. This research provides an insight on non-redundant objective functions, aiming at reducing the dimensionality of multi-objective optimization problems, while taking into consideration decision-makers’ preferences.

Abstract: Lignocellulosic biomass is the most abundant source of renewable biomass and is seen as a high-potential replacement for petroleum-based resources. The conversion technologies to advanced biofuels are still at a low maturity level, thus allowing for future cost reductions through technological learning. This fact is barely considered in state-of-the-art techno-economic assessments and a structured approach to account for technological learning in techno-economic assessments is needed. In this study, a framework for techno-economic assessments of advanced biofuels, integrating learning curves, is proposed. As a validation of this framework, the economic feasibility of the valorization of corn stover for the production of second-generation bioethanol in Belgium is studied. Process flowsheet simulations in Aspen Plus are developed, with an emphasis on the comparison of four different pretreatment technologies and two plant capacities at 156 dry kt biomass/y and 667 dry kt/y. The dilute acid pretreatment model of the large-scale biorefinery required the lowest minimum learning rate to reach an economically feasible biorefinery by 2030, being 3.9%, almost half as the one calculated for the smaller scale plant. This learning rate seems to be achievable based on learning rates commonly estimated in literature. We conclude that there is a potential for advanced ethanol production in Belgium under the current state of technology for large-scale biorefineries, which require additional biomass imports, when accounting for future cost reductions through learning

Abstract: Due to climate change, the frequency and intensity of droughts are expected to increase. To improve resilience to droughts, proactive drought management is essential. Economic assessments are typically included to decide on the drought risk-reducing investments to make. The choice of both methods and scope of economic assessments influences the outcome, and thus the investment choice. This paper aims to identify how comprehensively economic assessments are applied in practice. Through a systematic literature review, 14 actual economic assessments are identified and their methods are evaluated based on seven criteria for economic assessments as derived from the United Nations Framework Convention on Climate Change (UNFCCC). The results show that in practice, economic assessments rarely address all criteria. Applying a limited number of criteria reduces the scope and narrows the approach, possibly leading to the underestimation of drought risk reduction approaches’ related benefits. Applying the seven criteria in practice will improve the results of economic assessments of drought risk reduction measures, allowing for optimal investment selection. Based on the different criteria, a Framework for Economic Assessments of Drought Risk-Reducing Applications (FEADRRA) is proposed. Applying the criteria of the framework can support decision-makers in drought risk management and in carrying out the most fitting drought interventions.

Abstract: Canola production is an important alternative for agricultural policy-makers in Iran to reduce dependency on the imported vegetable oils. Nevertheless, the canola planted area is only increasing at a slow pace, indicating a low willingness-to-accept of farmers. The general aim of this study was to determine the factors influencing the canola adoption in the Kermanshah Province in Western Iran. Employing stratified random sampling method, 106 farmers from each adopter and non-adopter group were selected. Helping to reach a suitable extensional program, two main categories of variables were defined; i.e. farmers personal characteristics and extension parameters. The analysis of farmers personal characteristics variables revealed that the adopters had larger farms and were younger. The results also show that 80% of the adopters were highly to very highly willing to cultivate canola. Furthermore, a logistic regression model estimated the influence of extensional parameters variables on the canola adoption. According to the regression model, the most effective factors are contact with extension agents and participating in extension classes. As a conclusion, it is suggested that the focus of extension services should be to reduce the distance to agricultural service centers in combination with more contact with extension agents and classes.

Abstract: The relationship between irrigation water availability and crop revenue is multifaceted. However, most of the previous studies focused only on the direct effect of irrigation water on crop revenue or considered that the indirect effect passes only through the farmers’ improved farm inputs usage. Nevertheless, unlike previous studies, this study argues that a one-sided argument that irrigation water directly causes high crop revenue or indirectly affects crop revenue only via the farmers’ improved farm inputs usage is incomplete, as irrigation water not only directly contributes to crop revenue but also indirectly conduces to crop revenue via both the type of crops produced and the farmers’ improved farm inputs usage. Considering the previous studies’ limitations, this study investigates pathways how small-scale irrigation water affects crop revenue and identifies challenges of small-scale irrigation farming in Fogera district, Ethiopia. Results endorsed that irrigation water has both direct and indirect effects on crop revenue. The indirect effect is 67 percent of the total effect and it is mediated by both the type of crops produced and farmers’ improved farm inputs usage. The result also indicated that irrigation user farmers have a higher income, more livestock assets and resources and better food, housing, and cloths than the non-users. Moreover, challenges related to agricultural output and input market were identified as the most severe problem followed by crop disease. The findings of our study suggest that to utilize the benefits of irrigation water properly, it is crucial to encourage farmers to use more improved farm inputs and to shift from staple to cash crop production. Moreover, farmers are frequently exposed to cheating by illegal brokers in the output market, therefore it is also important to increase farmers’ accessibility to output and input markets, the quality of improved farm inputs, and the bargaining power of farmers with market information.