Alfons Oude Lansink

1. The emission of nitrogen has a negative impact on the sustainability goals of the Dutch government (Stokstad, 2019). In response to critical deposition values set by the European Union (EU), the...

Contracts and ownership structures play a critical role in fostering innovation in the bioeconomy. However, identifying which designs best promote bioresource circularity and collaboration remains challenging, as existing studies use diverse frameworks and are often limited to specific contexts or regions. Therefore, we conducted a review to clarify the types of contracts and ownership structures within bio‐based value chains and evaluate their roles in fostering innovation. To this end, we followed PRISMA‐P guidelines when collecting studies and organised them based on methodologies to facilitate the synthesis. Using a framework of the ownership distribution along the value chain between primary producer and biorefinery, we categorise three main categories of ownership structures: vertical integration, individual ownership and joint ownership. Some literature argues that primary producers prefer low‐risk attributes and therefore shift much of the responsibility onto biorefineries. While such structures can encourage participation, they hinder innovation by concentrating power within the biorefinery. Joint ownership structures, such as cooperatives, emerge as promising alternatives, offering a more balanced and collaborative approach to sharing risks, costs and profits. Our findings highlight the need for future research to explore joint ownership structures and apply different methods to support substantial advancement.

Lower manure application limits increase off-farm manure disposal costs for Dutch dairy farmers and are expected to raise the use of energy-intensive chemical fertilizers, increasing fossil fuel dependency and exposure to energy price volatility. This study uses a bio-economic farm-level model, extended with an energy module, to assess the economic and environmental effects of four manure processing techniques for conventional dairy farms in the Netherlands, using data from the Dutch Farm Accountancy Data Network. A regression-based meta-model is applied to identify key farm characteristics explaining variation in impacts. Results show that manure processing reduces greenhouse gas emissions by 1-13% and ammonia emissions by 16-39% at the farm level. Energy use declines by 2-33%, with the largest reductions observed for mono-digestion due to substitution of chemical nitrogen fertilizer and on-farm energy production. However, most technologies reduce farm income by up to 35%, primarily due to high investment and operational costs. Impacts vary substantially across farms, with larger and more intensive farms benefiting more due to economies of scale and higher substitution potential of chemical nitrogen fertilizers, while increased grazing reduces the effectiveness of these technologies. Manure processing offers considerable potential to improve environmental performance and reduce fossil energy use, but adoption is unlikely as it remains economically unviable for most Dutch dairy farmers. Targeted policies are therefore likely to be more cost-effective than uniform policies.

Lower manure application limits increase off-farm manure disposal costs for Dutch dairy farmers and are expected to raise the use of energy-intensive chemical fertilizers, increasing fossil fuel dependency and exposure to energy price volatility. This study uses a bio-economic farm-level model, extended with an energy module, to assess the economic and environmental effects of four manure processing techniques for conventional dairy farms in the Netherlands, using data from the Dutch Farm Accountancy Data Network. A regression-based meta-model is applied to identify key farm characteristics explaining variation in impacts. Results show that manure processing reduces greenhouse gas emissions by 1-13% and ammonia emissions by 16-39% at the farm level. Energy use declines by 2-33%, with the largest reductions observed for mono-digestion due to substitution of chemical nitrogen fertilizer and on-farm energy production. However, most technologies reduce farm income by up to 35%, primarily due to high investment and operational costs. Impacts vary substantially across farms, with larger and more intensive farms benefiting more due to economies of scale and higher substitution potential of chemical nitrogen fertilizers, while increased grazing reduces the effectiveness of these technologies. Manure processing offers considerable potential to improve environmental performance and reduce fossil energy use, but adoption is unlikely as it remains economically unviable for most Dutch dairy farmers. Targeted policies are therefore likely to be more cost-effective than uniform policies.

Designing effective food safety monitoring schemes is a complex task involving multiple, often conflicting, criteria. This study applied Multi-Criteria Decision Analysis (MCDA) to evaluate and identify optimal aflatoxin monitoring schemes along a Dutch dairy supply chain-a critical context where aflatoxin B1 (AFB1) contamination in feed can lead to aflatoxin M1 (AFM1) in milk, potentially posing public health concerns and economic losses. Monitoring schemes differed in detection intensity at feed mills and dairy farms, defined as the probability of identifying contaminated batches (low: 50%, medium: 80%, high: 90%) and determined by the number of monitoring batches and corresponding sample sizes used for AFB1/AFM1 sampling and analysis. Performance scores for each monitoring scheme were derived from quantitative models, scientific evidence, and expert consultation, while preference weights for criteria were elicited separately from representatives of the feed industry, dairy industry, and from a combined supply chain perspective. Results revealed that all stakeholder groups prioritized public health, but differed in their weighting of monitoring costs, production losses, customer trust, and implementation complexity. The feed industry preferred high-intensity detection at both control points, while the dairy industry preferred medium-intensity at feed mills and high-intensity at farms. Overall, the MCDA framework facilitated a transparent and evidence-based approach to identify an optimal monitoring scheme, highlighting the importance of stakeholder engagement in designing programs that are not only scientifically robust but also socially responsive and aligned with the WHO Global Strategy for Food Safety and the Sustainable Development Goals.

Designing effective food safety monitoring schemes is a complex task involving multiple, often conflicting, criteria. This study applied Multi‐Criteria Decision Analysis (MCDA) to evaluate and identify optimal aflatoxin monitoring schemes along a Dutch dairy supply chain—a critical context where aflatoxin B1 (AFB1) contamination in feed can lead to aflatoxin M1 (AFM1) in milk, potentially posing public health concerns and economic losses. Monitoring schemes differed in detection intensity at feed mills and dairy farms, defined as the probability of identifying contaminated batches ( low : 50%, medium : 80%, high : 90%) and determined by the number of monitoring batches and corresponding sample sizes used for AFB1/AFM1 sampling and analysis. Performance scores for each monitoring scheme were derived from quantitative models, scientific evidence, and expert consultation, while preference weights for criteria were elicited separately from representatives of the feed industry, dairy industry, and from a combined supply chain perspective. Results revealed that all stakeholder groups prioritized public health, but differed in their weighting of monitoring costs, production losses, customer trust, and implementation complexity. The feed industry preferred high‐intensity detection at both control points, while the dairy industry preferred medium‐intensity at feed mills and high‐intensity at farms. Overall, the MCDA framework facilitated a transparent and evidence‐based approach to identify an optimal monitoring scheme, highlighting the importance of stakeholder engagement in designing programs that are not only scientifically robust but also socially responsive and aligned with the WHO Global Strategy for Food Safety and the Sustainable Development Goals.

Sustainability‐Linked Bonds (SLBs) are a recent phenomenon in debt financial markets that is attracting increasing attention in the literature. Nevertheless, there is still very little evidence on the potential existence of a so‐called interest premium, reflecting the investors' willingness to accept lower expected returns for holding SLBs as compared to conventional bonds. This paper proposes the use of a frontier approach for analyzing the presence of the SLBs premium using Data Envelopment Analysis (DEA). In particular, we develop a two‐stage procedure. In the first stage, DEA is applied to derive inefficiency measures reflecting the distance of the bond to a frontier defined by time to maturity, risk, and interest rate. Inefficiency indicates that the issuer pays a lower interest rate (discount) as compared to that of the highest yielding bonds located at the frontier. In the second stage, OLS regression with robust standard errors is used to analyze the relation between the distance to the frontier derived from the first stage and a set of bond characteristics, including a dummy variable indicating whether the bond is an SLB. The empirical application focuses on data of all bonds issued in Euros observed over the period 2015–2022. The main result shows that SLBs pay higher, though statistically insignificant, interest rates compared to conventional bonds. Hence, we do not find evidence for the SLBs interest premium and cost of capital argument, in which SLBs are issued to benefit from a cheaper source of financing.

Circularity in agriculture regarding the recycling of by-products from one form of production for use as inputs in another has become an urgent initiative as resources become more scarce and valuable. One potential example of circular agriculture is recycling the by-products of insect production (frass) as a crop and soil health promoter. This research investigates the drivers of arable farmers' intentions to trial insect frass as an input on their farms using the Theory of Planned Behaviour and the Innovation Decision Process. In addition, the influence of group discussion participation on the drivers of farmers' intentions is investigated to identify potential opportunities to influence the uptake of frass. Two questionnaires at two time-points (t₁ and t₂) were distributed to forty-six Dutch arable farmers. Between these time-points, half of the farmers participated in group discussions where their first impressions of frass were shared amongst each other. The results from several regression models suggest that in t₁, farmers' attitudes, perceived (descriptive) social norms and perceived behavioural control drive their intentions to trial frass. By t₂, for those not in group discussions, attitudes were the only significant predictors of their intentions. For those who participated in the group discussion, the descriptive norm had a larger association with intentions than for those who did not. The results of this research contribute to an informed discussion on how group discussions, alongside policy-driven approaches, can serve as a mechanism for shaping perceptions and beliefs and influence the adoption of agricultural innovations like frass.

Circular agriculture is vital to achieve a substantial reduction of greenhouse gas (GHG) emissions. Optimizing resources and land use are an essential circularity principle. The objective of this article is to assess the extent to which land optimization can simultaneously reduce GHG emissions and increase production on dairy farms. In addition, we explore the potential reduction of GHG emissions under four different pathways. The empirical application combines the network Data Envelopment Analysis (DEA) with the by‐production approach. This study focuses on a representative sample of Dutch dairy farms over the period of 2010–2019. Our results suggest that farms can simultaneously increase production and reduce GHG emissions by both 5.1%. However, only 0.6% can be attributed to land optimization. The land optimization results show that on average 25.3% of total farm size should be allocated to cropland, which is 6.7% more than the actual land allocation. GHG emissions could be reduced by 11.79% without changing the level of inputs and outputs. This can be achieved by catching up with the mitigation practices of the best performing peers.