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Abstract: Worldwide biofuel products have rapidly entered the market and consequently so did the availability of their by-products for feed production. A pro-active emerging risk system for biofuel by-products is essential in order to prevent the occurrence of emerging hazards in feed and livestock production. In the current study, two biofuel by-products were elaborated upon, namely Dried Distillers Grains with Solubles (DDGS) resulting from bioethanol production and microalgae slurry resulting from biodiesel production. A holistic approach was used in which both factors from within the feed chain as well as outside the feed chain were incorporated. A list of critical factors linking dynamics in the biofuel production chain with possible feed safety risks was established after consultation of experts in in-depth interviews. Subsequently, a workshop was organized to determine the most important critical factors. Indicators (signals that indicate a change in critical factors) and information sources were linked to these factors for the individual cases. Based on expert judgment, the most important critical factors identified for the two selected cases were: chain complexity, composition of the feed product, origin of raw materials and climate change. Ranking of the indicators showed that in both cases the number of chain stages was the most important general indicator for feed safety risks. The study showed that following this approach resulted in the selection of critical factors and indicators for emerging feed safety risks related to the use of biofuel by-products. Once critical values (upper and/or lower limits) are assigned to the indicators, the system can support decisions to take actions in an early stage in order to prevent emerging risks in feed due to the use of DDGS or microalgae slurry. [Copyright &y& Elsevier]

Abstract: A pro-active emerging risk identification system starts with the selection of critical factors related to the occurrence of emerging hazards. This paper describes a method to derive the most important factors in dynamic production chains starting from a gross list of critical factors. The method comprised the semi-quantitative evaluation of the critical factors for a relatively novel product on the Dutch market and a related traditional product. This method was tested in an expert study with three case studies. The use of group discussion followed by individual ranking in an expert study proved to be a powerful tool in identifying the most important factors for each case. Human behaviour (either producers’ behaviour or human knowledge) was the most important factor for all three cases. The expert study showed that further generalization of critical factors based on product characteristics may be possible. [Copyright &y& Elsevier]

Resilience is the ability to deal with shocks and stresses, including the unknown and previously unimaginable, such as the Covid-19 crisis. This paper assesses (i) how different farming systems were exposed to the crisis, (ii) which resilience capacities were revealed and (iii) how resilience was enabled or constrained by the farming systems' social and institutional environment. The 11 farming systems included have been analysed since 2017. This allows a comparison of pre-Covid-19 findings and the Covid-19 crisis. Pre-Covid findings are from the SURE-Farm systematic sustainability and resilience assessment. For Covid-19 a special data collection was carried out during the early stage of lockdowns. Our case studies found limited impact of Covid-19 on the production and delivery of food and other agricultural products. This was due to either little exposure or the agile activation of robustness capacities of the farming systems in combination with an enabling institutional environment. Revealed capacities were mainly based on already existing connectedness among farmers and more broadly in value chains. Across cases, the experience of the crisis triggered reflexivity about the operation of the farming systems. Recurring topics were the need for shorter chains, more fairness towards farmers, and less dependence on migrant workers. However, actors in the farming systems and the enabling environment generally focused on the immediate issues and gave little real consideration to long-term implications and challenges. Hence, adaptive or transformative capacities were much less on display than coping capacities. The comparison with pre-Covid findings mostly showed similarities. If challenges, such as shortage of labour, already loomed before, they persisted during the crisis. Furthermore, the eminent role of resilience attributes was confirmed. In cases with high connectedness and diversity we found that these system characteristics contributed significantly to dealing with the crisis. Also the focus on coping capacities was already visible before the crisis. We are not sure yet whether the focus on short-term robustness just reflects the higher visibility and urgency of shocks compared to slow processes that undermine or threaten important system functions, or whether they betray an imbalance in resilience capacities at the expense of adaptability and transformability. Our analysis indicates that if transformations are required, e.g. to respond to concerns about transnational value chains and future pandemics from zoonosis, the transformative capacity of many farming systems needs to be actively enhanced through an enabling environment. [Display omitted] • Comparison of pre-Covid-19 findings and Covid-19 crisis • All systems displayed adequate agility to activate coping capacities. • The crisis triggered reflexivity about the operation of the farming systems. • Transformative capacities were not observed. • The systematic resilience assessment revealed system vulnerabilities. [ABSTRACT FROM AUTHOR]

After the abolition of the milk quota in the European Union, milk price volatility is expected to increase because of the liberalized market conditions. At the same time, investment appraisal methods have not been updated to capture the increased uncertainty. Therefore, the objective of this paper is to assess the effect of changing price volatility due to quota abolition on investment decisions at the dairy farm level. To contribute to the objective and to approximate milk price volatility after the European milk quota abolition, the risk-adjusted discount rate for risk-averse dairy farmers is derived based on the milk price volatility of a milk price series from New Zealand. New Zealand dairy farmers have faced liberalized market conditions for more than 3 decades. Afterward, the risk-adjusted discount rate is applied to appraise milking technology investments for an average German dairy farmer. The results show that it is still more reasonable to invest in a parlor system than an automated milking system, although the net present value of the parlor system investment varies between €191,723 for risk-neutral dairy farmers and €100,094 for modestly risk-averse dairy farmers. For the automated milking system investment, the same calculations lead to €132,702 for risk-neutral dairy farmers and €31,635 for risk-averse dairy farmers. According to higher levels of milk price volatility after milk quota abolition, the reduction of the expected utility of the underlying investment decision for modest risk-averse dairy farmers is almost similar to a milk price decrease of 5% for risk-neutral dairy farmers. Therefore, the findings urge finance providers and extension services to consider the change of increasing milk price volatility after dairy quota abolition when giving dairy farmers financial advice. The risk-adjusted discount rate is a flexible tool to do so. [ABSTRACT FROM AUTHOR]

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]

[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]