Assessing farmers’ objectives to participate in short food supply chains in Spain, France And Morocco

The promotion of Short Food Supply Chains (SFSC) is an issue that is becoming more relevant to both the public and research agenda, aiming to build more sustainable agri-food supply chains and empower smallholder farmers. This research aims to determine the willingness of small farmers to adopt SFSCs as an alternative to conventional distribution. The Analytical Hierarchy Process (AHP) methodology was used to assess farmers’ objectives of their agricultural activity in Spain, France, and Morocco. For the selection of objectives, deep interviews (DI) and a literature review were carried out. Data were collected from a total of 180 farmers carried out between May and October 2022. Results showed that regardless of the stated interest of farmers in promoting SFSCs, the production-related objectives, especially “Increase productivity” and “Invest in knowledge and machinery”, received the highest priority to distribution-related objectives. Moreover, objectives concerning social responsibility received the lowest relative importance, while environmental preservation concerns outweighed social objectives (especially in the French case). The economic performance of the farm plays a decisive role in the farmers' decision-making as expected in the three cases of the study analyzed. This is important when exploring mechanisms to incentivize farmers to adopt SFSCs where economic sustainability and efficiency are needed. More research is needed to determine the relationship between the choice of supply chain alternatives and the objectives of the farming activity. This knowledge may help in providing alternatives and adapted solutions that are more sustainable regarding farmer’s preferences.Objectius de Desenvolupament Sostenible::12 - Producció i Consum ResponsablesPostprint (published version

Preferences analysis of restaurants, industry and retailers for selecting fruits and vegetables suppliers in Spain, France and Morocco

This study focuses on evaluating the criteria used by stakeholders (restaurants, industry, and small-scale retailers) to select fruits and vegetable suppliers in Spain, France, and Morocco targeting three selected countries’ specific products and supply chains (fresh tomatoes in Spain, Chestnuts in France, Carob in Morocco). The research consisted firstly of conducting deep interviews (DI) with the main stakeholders in the added-value chain of fruits and vegetables in order to understand factors affecting their decision when selecting suppliers. A special interest was drawn on the criteria “produced by local farmers” in order to determine its relative importance within the stakeholder’s decision-making. Secondly, semi-structured questionnaires were carried out by using the analytic hierarchy process (AHP) to estimate the relative importance of each criterion and to evaluate the weight of the sustainable factors. Data were collected from stakeholders in the food-added value chain with a total sample, equally distributed across countries, of 180 restaurants, 30 industries, and 180 small-scale retailers. The survey was applied during the months of May to October 2022. Research results revealed the most important supplier selection attributes vary according to each country and product category. The role of sustainable criteria in selecting suppliers played an important role in particular in France. Local small farmers were important for local industry as sustainable suppliers but less relevant for restaurants and retailers respectively. Improving the sustainability of the supply chain should focus more on retailers and restaurant marketing strategies when purchasing fruits and vegetables in order to set quotas for locally produced products. This outcome highlights the need for a new and optimized business model in which small local farmers can directly supply local restaurants and retailers and contribute to improving sustainability and ensuring reasonable profit for farmers.This study belongs to the project Lab4Supply “Multi-agent Agri-food living labs for new supply chain Mediterranean systems. Towards more sustainable and competitive farming addressing consumers’ preferences and market changes”. Lab4supply received funding from the European Union under PRIMA-S.2 programme (Partnership for Research and Innovation in the Mediterranean Area) and approved by the National Agencies in Spain “Agencia Estatal de Investigación (AEI)”, in Morroco “Ministère de l’Enseignement Supérieur, de la Recherche Scientifique et de l’Innovation (MESRSFC)” and in France “Agence Nationale de la Recherche (ANR)”. The content of this study reflects only the author’s view and the European Union Agency and national agencies involved are not responsible for any use that may be made of the information it contains.Peer ReviewedObjectius de Desenvolupament Sostenible::12 - Producció i Consum ResponsablesObjectius de Desenvolupament Sostenible::12 - Producció i Consum ResponsablesPostprint (published version

Innovative Models Built Based on Image Textures Using Traditional Machine Learning Algorithms for Distinguishing Different Varieties of Moroccan Date Palm Fruit (<i>Phoenix dactylifera</i> L.)

The aim of this study was to develop the procedure for the varietal discrimination of date palm fruit using image analysis and traditional machine learning techniques. The fruit images of ‘Mejhoul’, ‘Boufeggous’, ‘Aziza’, ‘Assiane’, and ‘Bousthammi’ date varieties, converted to individual color channels, were processed to extract the texture parameters. After performing the attribute selection, the textures were used to build models intended for the discrimination of different varieties of date palm fruit using machine learning algorithms from Functions, Bayes, Lazy, Meta, and Trees groups. Models were developed for combining image textures selected from a set of all color channels and for sets of textures selected for individual color spaces and color channels. The models, including combined textures selected from all color channels, distinguished all five varieties with an average accuracy reaching 98%, and ‘Bousthammi’ and ‘Mejhoul’ were completely correctly discriminated for the SMO (Functions) and IBk (Lazy) machine learning algorithms. By reducing the number of varieties, the correctness of the date palm fruit classification increased. The models developed for the three most different date palm fruit varieties ‘Boufeggous’, ‘Bousthammi’, and ‘Mejhoul’ revealed an average discrimination accuracy of 100% for each algorithm used (SMO, Naive Bayes (Bayes), IBk, LogitBoost (Meta), and LMT (Trees)). In the case of individual color spaces and channels, the accuracies were lower, reaching 97.3% for color space RGB and SMO and LMT algorithms for all five varieties and 99.63% for Naive Bayes and IBk for the ‘Boufeggous’, ‘Bousthammi’, and ‘Mejhoul’ date palm fruits. The results can be used in practice to develop vision systems for sorting and distinguishing the varieties of date palm fruit to authenticate the variety of the fruit intended for further processing

Comprehensive Characterization of Date Palm Fruit ‘Mejhoul’ (<i>Phoenix dactylifera</i> L.) Using Image Analysis and Quality Attribute Measurements

An in-depth determination of date fruit properties belonging to a given variety can have an impact on their consumption, processing, and storage. The objective of this study was to characterize date fruits of the ‘Mejhoul’ variety using (i) objective and non-destructive image-analysis features and (ii) measurements of physicochemical parameters. Based on images acquired using a digital camera, more than 1600 texture parameters from the individual color channels L, a, b, R, G, B, X, Y, and Z, and 40 geometric characteristics (including linear dimensions and shape factors for each fruit), were determined. Additionally, pomological features, water content, water activity, color parameters (L*, a*, b*), total soluble solids (TSS), reducing sugars, and total sugars were measured. As a main result, the application of machine vision allowed for the correct detection of ‘Mejhoul’ dates and the determination of the image features. The differences in the values of the histogram’s mean (HMean texture) for individual color channels were determined. The ‘Mejhoul’ date fruit images in color channel a (aHMean equal to 145.88) and color channel b (bHMean: 145.49) were the brightest, and in channel Z they were the darkest (ZHMean: 4.23). Due to the determination of the elliptic shape factor (W1) of 1.000 and the circular shape factor (W2) of 0.110, the elliptical shape of the fruit was confirmed. On the other hand, ‘Mejhoul’ dates were characterized by a length of 47.3 mm, a diameter of 26.4 mm, flesh thickness of 6.25 mm, total soluble solids of 62.1%, water content of 28.0%, water activity of 0.652, hardness of 694 g, reducing sugars of 13.8%, and total sugars of 58.8%. Due to the determination of many image features and other parameters, this paper presents the first comprehensive characterization of ‘Mejhoul’ date fruits using a non-destructive imaging technique linked to some physicochemical quality attributes