Search

Soil quality is an important determinant of agricultural productivity and environmental quality. Despite its importance, few economic models incorporate sustainable soil management. The objective of this study is to develop and illustrate FARManalytics: a bio-economic model to gain quantitative insight in the economic value of sustainable soil management. First, we defined a comprehensive set of chemical, physical and biological soil quality indicators and quantitative rules on how these indicators respond to farmers' production management over time. Second, we introduce an economic calculation framework that enables accurate calculation of the contribution of different production management decisions towards farm income using Activity-Based-Costing. The set of soil quality indicators and economic calculations serve as the basis for the bio-economic model FARManalytics, which consists of two modules: (1) the PM calculator , a module that calculates the impact of current production management on soil quality and farm economics and (2) the PM optimizer , a module that uses Mixed-Integer-Linear-Programming to maximize farm incomewithin predefined soil quality indicator constraints. The decision variables are the crop rotation, cover crops, manure & fertilizer application and crop residue management. We illustrate the added value of the model by applying it to an extensive and intensive farm type, both on clay and sandy soil. These farm types are derived from the Farm Accountancy Data Network (FADN) in the Netherlands. FARManalytics demonstrates that it is possible to increase farm income with up to €940 ha−1 year−1 on clay soil and up to €683 ha−1 year−1 on sandy soil, while meeting all soil quality targets except subsoil compaction vulnerability. The latter was among the most limiting soil quality indicators for the farm types in this study, together with soil organic matter input, wind erosion vulnerability and plant-parasitic nematodes. FARManalytics integrates the impact of production management decisions on soil quality and economics at farm level. Combined with representative farm types, the bio-economic modeling approach of FARManalytics can provide useful information for policy support. FARManalytics can also be tailored to provide decision support for individual farms, based on data that is commonly available on arable farms at low cost. [Display omitted] • Sustainable soil management is important for agricultural productivity but often lacking in economic models. • The FARManalytics model integrates soil quality, farm economics and farmers' production management. • The objective is to gain insight in the economic value of sustainable soil management via bio-economic modeling. • Farm income can be raised up to €940 ha−1 year−1 while meeting all soil quality target values except subsoil compaction. • FARManalytics can be used to inform policy decisions and can be tailored at individual farm level using existing data. [ABSTRACT FROM AUTHOR]

Abstract: Economic analysis of hazard surveillance in livestock production chains is essential for surveillance organizations (such as food safety authorities) when making scientifically based decisions on optimization of resource allocation. To enable this, quantitative decision support tools are required at two levels of analysis: (1) single-hazard surveillance system and (2) surveillance portfolio. This paper addresses the first level by presenting a conceptual approach for the economic analysis of single-hazard surveillance systems. The concept includes objective and subjective aspects of single-hazard surveillance system analysis: (1) a simulation part to derive an efficient set of surveillance setups based on the technical surveillance performance parameters (TSPPs) and the corresponding surveillance costs, i.e., objective analysis, and (2) a multi-criteria decision making model to evaluate the impacts of the hazard surveillance, i.e., subjective analysis. The conceptual approach was checked for (1) conceptual validity and (2) data validity. Issues regarding the practical use of the approach, particularly the data requirement, were discussed. We concluded that the conceptual approach is scientifically credible for economic analysis of single-hazard surveillance systems and that the practicability of the approach depends on data availability. [Copyright &y& Elsevier]

[Display omitted] • Economic conceptualisation of sustainable soil management. • Definition of Economic Value of Land (E V L) as the basis for financial returns. • E V L based on agricultural productivity and soil-based ecosystem services. • Land use activities and inputs determine current returns and future soil quality. • Management of soil quality and crop production towards maximizing long-term E V L. Soil quality is an important determinant of agricultural productivity, farm resilience and environmental quality. Despite its importance, the incorporation of sustainable soil management in economic models is lacking. This study approaches farmers as decision makers on soil management. Sustainable soil management may be an investment that goes at the expense of short-term returns but increases future soil quality. Hence, the key problem is economic: establishing long-term sustainable soil management at a minimized loss of income. In this study, we define the Economic Value of Land (E V L) as the cumulative returns of a piece of land over a period in time. Maximum long-term E V L is obtained if a soil's potential is maximally utilized in a sustainable way. From this follows that the Economic Value of Sustainable soil Management (E V S M) is defined as the difference between a sustainable and unsustainable E V L. To acquire a fundamental understanding of E V S M , agronomic and technical factors must be integrated with economics. Production management, the complete set of physical and non-physical inputs is the primary determinant of future soil quality and hence E V L. Maximizing E V L first requires a fundamental understanding of soil quality management: What are the properties of soil quality and how are these influenced by crop production? Subsequently, production management has to be organized in such a way E V L is maximized. This study provides an overview of soil quality management and crop production management linked to economics. The framework provides a qualitative blueprint for bio-economic modelling and a basis for policies to enhance sustainable soil management. [ABSTRACT FROM AUTHOR]

Examines connectivity of elements in landscape networks. Extension of existing parameters; Testing of the relationships between the connectivity of locations and their function in the region; Effects of the size of graphs on the measure of parameters; Concerns in spatial analysis; Definition of connectivity.

The current food system, with its consumption and production activities, threatens our environment and depletes the resources of future generations. Changing the food system, by consuming foods with lower environmental footprint and choosing more environmental friendly production and distribution alternatives, holds potential to reduce the environmental impact. The aim of this research is to propose healthy and sustainable alternatives to the current consumption of cow's meat and dairy products in the Netherlands, under consideration of the underlying production system. Thus, the study applies linear programming techniques to construct consumption alternatives, taking into account the underlying production and sourcing of products. In this context, different environmental objectives are investigated and compared, namely climate change, land use, water use and fossil fuel depletion. Comparisons are made between the different alternatives with respect to their effect on the overall dietary intake. Four consumption alternatives are proposed, varying with respect to their environmental footprint, food composition and underlying food system. The results show that shifting towards a more plant-based consumption holds both an improvement potential in terms of the environmental impact as well as benefits from a health perspective. Moreover, trade-offs exist between the different environmental indicators, and the choice of environmental objective impacts the solution with respect to the consumption and production of foods. The research demonstrates the importance of taking production relationships into account and shows, that it is possible to propose healthy and environmental friendly alternatives for the future. [ABSTRACT FROM AUTHOR]

• New membrane cascade configurations improved the fractionation while keeping the stage number at 3. • Selecting the best set up with many performance indicators was performed using a multicriteria decision making approach. • Backward analysis was able to pinpoint critical parameters. • This design procedure is applicable for other integrated processes with multiple performance indicators. Membrane cascades can fractionate fructooligosaccharides into 3 different fractions with varying degrees of polymerization (DP). In contrast to the traditional membrane system, membrane cascades have flexibility in configuration and setup for each stage. Apart from the improvement flexibility of the cascades provides, it raises problems related to multiple performance indicators and multiple process parameters. Therefore, new design criteria are required. We have designed an optimization approach for this multi-criteria problem. Eight configurations of cascaded membranes were built, measured and simulated to develop a design strategy. The performance of the separation process was evaluated by 10 different indicators: purities and yields for 3 different fractions and 4 separation factors between molecules with an adjacent DP. We found that the proposed configurations exceeded the performance of the previously reported 3-stage membrane cascade. Within those configurations, the cascade designs were able to increase the purity of (1) monosaccharides to 47% from 9%, (2) DP3 to 34% from 24% and (3) DP ≥ 5 to 77% from 34%. We also report a procedure to select a single optimum combination that compromises all performance indicators. This procedure systematically calculated the weights, which were then used to rank all feasible combinations and select the best one. In addition, a backward analysis using sensitivity coefficients was performed to pinpoint critical process parameters. Knowing these parameters, more targeted and more efficient improvements could be made. This approach is applicable for most integrated systems with multi-process variables and multi-performance indicators combining process modelling and multi-criteria decision making. [ABSTRACT FROM AUTHOR]

Abstract Due to the increasing awareness of climate change, depletion of natural resources, and increasing world population, companies in the agri-food sector need to redesign their existing supply chains and take into account both the economic and environmental impact of their operations. In practice not all the required information is available in advance due to various sources of uncertainty in agri-food supply chains. In this research a multi-objective two-stage stochastic programming model is proposed to analyse and evaluate the economic and environmental impacts to account for uncertainty in agri-food supply chains. A mushroom supply chain in the Netherlands is presented as an illustrative case study. Optimal production planning decisions calculated with a two-stage stochastic programming model are compared with the results of an equivalent deterministic model. The results of the optimizations show that accounting for stochasticity in important model parameters can reduce the difference between expected and realized economic performance by approximately 4% on average. Moreover, this paper demonstrates that including stochastic model parameters can reduce the environmental impact without compromising the current economic performance. Given the assumptions in the setup of the case study and the available information, it is concluded that applying a 2-stage stochastic programming approach for production planning decisions can lead to improved economic and environmental performance in an agri-food supply chain. New findings in real-life case studies are needed to get profound insights and understanding on the impact of uncertainty on production planning decisions in sustainable agri-food supply chains. Highlights • A bi-objective stochastic programming model is implemented for a real-life case. • Trade-offs between environmental and economic indicators are quantified. • Solutions of deterministic models overestimate the actual trade-off. • Accounting for uncertainty improves the environmental performance. [ABSTRACT FROM AUTHOR]

Abstract: In South Patagonia, Argentina, sweet cherry is the main fruit-tree crop grown for export, resulting in a highly seasonal labour demand. Managers of deciduous perennial fruit orchards must consider both biological and economic relationships in selecting crop species and orchard design. This makes decisions at the farm-level extremely complex, as especially in such perennial crops, strategic (‘what to plant’, ‘with which technology’ and ‘how much area of each activity’, i.e. the final design) and tactical (‘when, what and how to plant in time’, the pathway to the planned farm) decisions have a long-term effect. The objective of this study was to explore the consequences of different strategic and tactical decisions at farm scale in fruit production systems of South Patagonia, considering the variation in interests and aims of different stakeholders, and using a sensitivity analysis to evaluate the consequences of possible changes in external conditions. A dynamic farm-scale optimization model called OPTIFROP was developed to generate alternative farm development plans, by allocating, in the course of the time horizon of the run, production activities to different land units, while optimising different objective functions, subject to several constraints. Although time-dependent, dynamic, mathematical programming models for analysing farming systems have been described in literature, the dynamic aspects of long-term decision-making in orchard design and their impact on the sequential (annual) nature of orchards in different growth phases (i.e. medium-term decision-making), need a higher time-staged dynamic approach with a staircase matrix structure. The model includes two objective functions at farm level: (1) maximization of the present value of cumulative financial result, which is the main objective for growers, and (2) maximization of cumulative farm labour, which is an objective often mentioned by policy makers. The inter-months deviation for labour demand (during the period of high labour demand, November–April) was included as an upper-bound. Input and output coefficients for the land use options considered in OPTIFROP were quantified using the Technical Coefficient Generator FRUPAT. Model results indicated that the present value of cumulative financial result and the cumulative farm labour are conflicting to a very limited extent. Timing and feasibility of implementing certain combinations of production technologies are affected by resource endowments and initial conditions, but these factors do not influence land use selection in the long term. Land use selection is driven by the objectives of the stakeholders. OPTIFROP showed that, through introduction of alternative crops, substantial reductions in labour peaks in the period November–April could be achieved with a relatively small reduction in farm income. The sensitivity of the model solution to the cherry price suggests that the fruit production sector of South Patagonia should pay more attention to the robustness of their land use plans and take preventive measures to avoid being caught by a possible crisis due to changes in the context. [Copyright &y& Elsevier]

• MINLP is used to optimize a 3-stage nanofiltration cascade for FOS purification. • The model selects the optimum membrane, TMP, temperature and area in each stage. • The solutions are used to map the optimum operating conditions in the cascade. • In a 3-stage cascade, the top stage works as the critical separation stage. Inhomogeneous membrane cascade systems have been utilized to purify fructooligosaccharides (FOS). Such a process allows a different setup at every stage of the cascade. Varying the setup at every stage implies an optimization problem related to the selection of the membrane and combinations of operating conditions. This paper solves the optimization problem for an inhomogeneous 3-stage membrane cascade and uses the solution as a design guideline. The optimization problem in the 3-stage membrane cascade design has been formulated as a mixed integer, non-linear programming model and solved using the global optimization solver, BARON. By maximizing the yield repetitively with varying purity requirements, a frontier curve has been constructed. The frontier curve was mapped showing the window of operation. The map guides towards the setup that promotes higher permeation in the feed stage when we switch from high yield to high purity. On the other hand, the setup selection at the bottom stage does not show a clear switch, which indicates that the selection at this stage is less critical. [ABSTRACT FROM AUTHOR]

The fast-growing practice of e-commerce implies a strong increase in parcel deliveries, which in turn creates significant pressure on last-mile city logistics. Due to the important role the city transportation plays in energy use and greenhouse gas emission, effective last-mile solutions in cities must be developed to contribute to sustainability and a cleaner world economy. Crowdsourced delivery as an emerging "sharing economy" initiative can be an effective tool to mitigate the problems emerging from the last-mile city logistics. To valorise the benefits of crowdsourced delivery, a transition towards a hybrid city logistic system is required where crowdsourced delivery and the conventional delivery networks are closely integrated. Due to the lack of theoretical guidelines for crowdsourced delivery integration, this research develops a conceptual framework to facilitate last-mile city logistics transition adopting the multi-level socio-technical transition theory as the basis. The core of the conceptual framework is the "five basic principles" to be followed by stakeholders when designing intervening niche innovations at the current stage of system transition. To demonstrate the usability of the conceptual framework, an illustrative discrete event simulation study with specific settings that fits in with the current status of last-mile city logistics is conducted. Results show that incorporating crowdsourced delivery as a supplement to the conventional delivery network, following the five basic principles proposed by the conceptual framework can reduce the last-mile logistic costs. Moreover, the offline participation rate plays a key role in ensuring the feasibility of the new hybrid last-mile model. To conclude, the developed conceptual framework has a great potential of improving last-mile delivery in the era of e-commerce and having a critical scale of potential deliverer pool is the prerequisite for the successful application of crowdsourced deliveries. [ABSTRACT FROM AUTHOR]