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The estimates reveal that hourly earnings of men and women in Ghana are similar and respond positively to food consumption and, to a lesser extent, to nutritional status and negatively to additional hours worked. The last effect is strongest for women, who work fewer hours but have higher workloads during hours not allocated to income earning. The effects of nutrition are strongest for men, reflecting a higher workload during working hours. (DSE/DÜI)

Het 25-jarig bestaan van de Kring Corstens is de aanleiding om Geert Corstens op bijzondere wijze in de vorm van dit boek te bedanken voor alles wat hij voor zijn promotie heeft betekend. Kringgedachten is een samenstel van bijdragen dat de brede belangstelling van Geert goed illustreert: materieel strafrecht, boeterecht, strafprocesrecht, financieel en economisch strafrecht, penitentiair recht en Europees, supranationaal en internationaal strafrecht

AbstractEfficient food safety monitoring should achieve optimal resource allocation. In this article, a methodology is presented to optimize the use of resources for food safety monitoring aimed at identifying noncompliant samples and estimating background level of hazards in food products. A Bayesian network (BN) model and an optimization model were combined in a single framework. The framework was applied to monitoring dioxins and dioxin‐like polychlorinated biphenyls (DL‐PCBs) in primary animal‐derived food products in the Netherlands. The BN model was built using a national dataset with monitoring results of dioxins and DL‐PCBs in animal‐derived food products over a 10‐year period (2008–2017). These data were used to estimate the probability of detecting suspect samples with dioxins and DL‐PCBs levels above preset thresholds, given certain sample conditions. The results of the BN model were then inserted into the optimization model to compute an optimal monitoring scheme. Model estimates showed that the probability of dioxins and DL‐PCBs exceeding threshold limits was higher in laying hen eggs and sheep meat than in other animal‐derived food (except deer meat). Compared with the monitoring scheme used in the Netherlands in 2018, the optimal monitoring scheme would save around 10,000 EUR per year. This could be obtained by reallocating monitoring resources from products with lower probability of dioxin and DL‐PCBs exceeding threshold limits (e.g., pig meat) to products with higher probability (e.g., bovine animal meat), and by shifting sample collection from the last quarter of the year toward the first three quarters of the year.

AbstractFood safety monitoring faces the challenge of tackling multiple chemicals along the various stages of the food supply chain. Our study developed a methodology for optimizing sampling for monitoring multiple chemicals along the dairy supply chain. We used a mixed integer nonlinear programming approach to maximize the performance of the sampling in terms of reducing the risk of the potential disability adjusted life years (DALYs) in the population. Decision variables are the number of samples collected and analyzed at each stage of the food chain (feed mills, dairy farms, milk trucks, and dairy processing plants) for each chemical, given a predefined budget. The model was applied to the case of monitoring for aflatoxin B1/M1(AFB1/M1) and dioxins in a hypothetical Dutch dairy supply chain, and results were calculated for various contamination scenarios defined in terms of contamination fraction and concentrations. Considering various monitoring budgets for both chemicals, monitoring for AFB1/M1 showed to be more effective than for dioxins in most of the considered scenarios, because AFB1/M1 could result into more DALYs than dioxins when both chemicals are in same contamination fraction, and costs for analyzing one AFB1/M1 sample are lower than for one dioxins sample. The results suggest that relatively more resources be spent on monitoring AFB1/M1 when both chemicals' contamination fractions are low; when both contamination fractions are higher, relatively more budget should be addressed to monitoring dioxins.