The FOODRUS index: Assessing suitability for effective food loss and waste prevention management under an integral perspective.

The impact of food loss and waste (FLW) generation on food supply chains' (FSC) sustainability represents a challenge embodied in the Sustainable Development Goal (SDG) 12.3. This problem requires a methodology to measure such an impact in a rigorous, holistic, and standardized way that can be applied to any FSC. This paper aims to develop and validate a single index to assess the readiness of FSCs to implement FLW prevention strategies and measure their impact: the so-called FOODRUS index. The co-creation methodology followed incorporates experts and FSC stakeholders feedback. The index has been validated in 3 FSCs: The Slovak pilot scored 74.35%, the Spanish pilot reached 68.79%, and the Danish pilot was rated 61.14%. Its calculation, eased by the FOODRUS index self-assessment tool (described in the Appendix), allows quick diagnosis of the FSC capability to implement FLW prevention strategies considering both the knowledge provided by experts and the experience of the FSC stakeholders that participated in its co-creation process. In this way the FSC can assess its FLW prevention performance at a strategic and management level, with the aim of improving its sustainability impact.

A Taxonomy of Food Supply Chain Problems from a Computational Intelligence Perspective.

In the last few years, the Internet of Things, and other enabling technologies, have been progressively used for digitizing Food Supply Chains (FSC). These and other digitalization-enabling technologies are generating a massive amount of data with enormous potential to manage supply chains more efficiently and sustainably. Nevertheless, the intricate patterns and complexity embedded in large volumes of data present a challenge for systematic human expert analysis. In such a data-driven context, Computational Intelligence (CI) has achieved significant momentum to analyze, mine, and extract the underlying data information, or solve complex optimization problems, striking a balance between productive efficiency and sustainability of food supply systems. Although some recent studies have sorted the CI literature in this field, they are mainly oriented towards a single family of CI methods (a group of methods that share common characteristics) and review their application in specific FSC stages. As such, there is a gap in identifying and classifying FSC problems from a broader perspective, encompassing the various families of CI methods that can be applied in different stages (from production to retailing) and identifying the problems that arise in these stages from a CI perspective. This paper presents a new and comprehensive taxonomy of FSC problems (associated with agriculture, fish farming, and livestock) from a CI approach; that is, it defines FSC problems (from production to retail) and categorizes them based on how they can be modeled from a CI point of view. Furthermore, we review the CI approaches that are more commonly used in each stage of the FSC and in their corresponding categories of problems. We also introduce a set of guidelines to help FSC researchers and practitioners to decide on suitable families of methods when addressing any particular problems they might encounter. Finally, based on the proposed taxonomy, we identify and discuss challenges and research opportunities that the community should explore to enhance the contributions that CI can bring to the digitization of the FSC.

New methodology for facilitating food wastage quantification. Identifying gaps and data inconsistencies.

The Food and Agriculture Organization of the United Nations estimated that about 1.3 billion tons of food produced for human consumption was lost or wasted globally. Thus, the reduction of the current food loss and waste along the agrifood chain is becoming a priority, both for optimization of resources and reduction waste generation costs. For this purpose, the first step is to quantify the food wastage generation to be able to identify corrective measures. However, in spite of the considerable efforts already undertaken to establish common methodologies to measure the food wastage at different geographical scales, there are still some gaps and inconsistencies. In this regard, the information gathering is labour-intensive because of the different actors involved. The creation of new methodologies and tools capable of automatically identifying these agents would be of great value so as to subsequently apply the more appropriates quantification methodologies. This work aims at providing a new methodology to facilitate this process thanks to the previous identification and classification of the potential food wastage generators. As a result, it provides baseline information for one of the earliest steps of the food wastage quantification process, which is the establishment of the scope of the food wastage inventory. The baseline data needed is taken from the Statistical classification of economic activities in the European Community (NACE), particularly from the most disaggregated level called "classes". This information has been combined with data from the trading income tax at municipal scale thanks to the use of Geographic Information Systems (GIS) and the common codes for NACE classes, generating a visual tool for the localization of points with potential of food-wastage generation and their weight of each economic activity over the agrifood chain. The proposed methodology has been implemented for the real case of the municipality of Zamudio (Spain) and it has allowed the identification of the different entities linked with economic activities that are potential generators of food wastage, the weight of each activity over the entire agrifood chain, and the geographical location of these entities in the territory. Furthermore, this methodology was used to compare the nature and number of these activities in another municipality (Karrantza, Spain) and it has also been applied to the entire region of the Basque Country (Spain).

Identification of influencing municipal characteristics regarding household waste generation and their forecasting ability in Biscay.

The planning of waste management strategies needs tools to support decisions at all stages of the process. Accurate quantification of the waste to be generated is essential for both the daily management (short-term) and proper design of facilities (long-term). Designing without rigorous knowledge may have serious economic and environmental consequences. The present works aims at identifying relevant socio-economic features of municipalities regarding Household Waste (HW) generation by means of factor models. Factor models face two main drawbacks, data collection and identifying relevant explanatory variables within a heterogeneous group. Grouping similar characteristics observations within a group may favour the deduction of more robust models. The methodology followed has been tested with Biscay Province because it stands out for having very different municipalities ranging from very rural to urban ones. Two main models are developed, one for the overall province and a second one after clustering the municipalities. The results prove that relating municipalities with specific characteristics, improves the results in a very heterogeneous situation. The methodology has identified urban morphology, tourism activity, level of education and economic situation as the most influencing characteristics in HW generation.