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International audience; The chapter aims to re-contextualize the polarity between the commodification and decommodification of seeds, highlighting reactions and resistances to market pressures. It begins by discussing the emergence of the seed market and its main drivers, such as the biotechnological revolution, intellectual property rights, and the consolidation of the seed industry. The chapter then emphasizes that discontent with changes in agricultural production and economic integration has primarily led to discussions on so-called market failures rather than restricting market transactions. The preservation of public goods, specifically innovation and crop diversity, through the handling of purportedly market failures, has remained a key focus.Additionally, the chapter explores proposals advocating for the radical decommodification of seeds. This perspective aligns with research in ethics and economics on the concept of noxious markets. By examining the ethical and economic aspects of seeds as commodities, the exploration of decommodification (through “open source” and commons-based initiatives) aims to challenge the prevailing market-driven approach to seed production and seed provisioning. The chapter closes with a discussion on the coexistence between market society and peasant farming, examining the feasibility and desirability of maintaining control over innovations and agricultural production models while resisting the complete commodification of seeds.

International audience; The purpose of this paper is to identify and analyze the contributions and barriers of digitalization and industry 4.0 within supply chain (SC). The paper performs a systematic literature review through the management research data bases published between the period of 2012 and January 2021. The paper shows that digitalization and Industry 4.0 improve firm information system, enhance management processes and insure competitiveness. However, it also found that the stigmas left by previous technologies, the lack of industry specific guidelines, lack of digital skills and talents, or lack of top management commitment prevent the adoption of these technologies in the SC. The choice made by limiting analysis on the HCERES journals restricts the scope and future analysis should enlarge the area.

International audience; Agricultural drought is a complex phenomenon with numerous consequences and negative implications for agriculture and food systems. The Sahel is frequently affected by severe droughts, leading to significant losses in agricultural yields. Consequently, assessing vulnerability to agricultural drought is essential for strengthening early warning systems. The aim of this study is to develop a new multivariate agricultural drought vulnerability index (MADVI) that combines static and dynamic factors extracted from satellite data. First, pixel temporal regression from 1981 to 2021 was applied to climatic and biophysical covariates to determine the gradients of trend magnitudes. Second, principal component analysis was applied to groups of factors that indicate the same type of vulnerability to configure the basic equation of vulnerability to agricultural drought. Then, random forest (RF), K-nearest neighbours (KNN), support vector machine (SVM) and naïve Bayes (NB) were used to predict drought vulnerability classes using the 28 factors as inputs and 708 pts of randomly distributed class labels. The results showed statistical agreement between the predicted MADVI spatial variability and the reference model (R=0.86 for RF) and its statistical relationships with the vulnerability subcomponents, with an R=0.73 with exposure to climate risk, R=0.64 with the socioeconomic sensitivity index, R=0.6 with the biophysical sensitivity index and a relatively weak correlation (R=0.21) with the physiographic sensitivity index. The overall vulnerability situation in the watershed is 21.8% extreme, 10% very high, 16.8% high, 27.7% moderate, 22.2% low and 1.5% relatively low considering the cartographic results of the predicted vulnerability classes with SVM having the best performance (accuracy=0.96, Kappa=0.95). The study is the first approach that uses the gradients of magnitudes of satellite covariate anomaly trends in multivariate modelling of vulnerability to agricultural drought. It can be easily scaled up across the Sahel region to improve early warning measures related to the impacts of agricultural drought.

International audience; Land cover (LC) classification remains a challenging task due to the diversity of terrain and topography, limited prior knowledge, and complex data sets. These variations can lead to differences in illumination and shading, which can affect the appearance of objects in the image. The local binary pattern (LBP) model is an effective technique for capturing local texture information in an image, which can help to overcome the effects of topography diversity by analysing patterns in different image regions, even if the illumination or shading conditions are different. However, LBP alone is inadequate for characterizing high-resolution remotely sensed images with complex semantic content as it only utilizes sign information in the local region. In this paper, a new texture characterization descriptor, known as completed homogeneous LBP (CHLBP), is proposed as an improved version of homogeneous LBP (HLBP) for LC classification of remotely sensed images. The CHLBP method mainly involves the following steps: first, sign and magnitude information from the HLBP descriptor is extracted, providing an effective alternative to the HLBP complementary contrast measure. Second, for each sign and magnitude function, a new splitting factor δ is used to obtain a depth relationship between the centre and its neighbouring pixels and to enhance noise robustness. Finally, the centre pixels representing the image grey level are also considered to contain discriminative information. The performance of our descriptor is evaluated using four challenging texture databases: Outex (TC10, TC12), Geofen Image Dataset (GID), and large-scale aerial (AID). Extensive experiments were performed using four classifiers: Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Random Forest (RF), and Multi-Layer Perceptron (MLP), demonstrating the effectiveness and robustness of our descriptor against noise and free noise conditions in public remote sensing datasets.

International audience; People can now monitor and quantify their daily life activities through quantified-self products, such as self-tracking devices. These types of products are invading the market, and the reasons for their adoption must be understood. Word of mouth (WOM) plays a crucial role in the adoption of disruptive innovations. In line with the literature, this article demonstrates that quantified-self products are disruptive innovations that have distinct antecedents explaining why individuals use WOM. Hence, this article examines the need for uniqueness (NFU) and perceived values as drivers of satisfaction that in turn impact WOM. Data from a sample of 496 actual users of self-tracking devices were collected through an online survey. The results emphasize the positive impact of the NFU on satisfaction through utilitarian and hedonic values. We propose theoretical and managerial insights and suggest that companies must adopt a new perspective on disruptive innovations, particularly related to satisfaction and WOM, by considering the variables proposed in the research model.

In Portugal, almonds are a very important crop, due to their nutritional properties. In the northeastern part of the country, the almond sector has endured over time, with strong cultural traditions and key economic significance. In these areas, several cultivars are used. In effect, the presence of various almond cultivars implies differentiated management in irrigation, disease control, pruning system, and harvest planning. Therefore, cultivar classification is essential over large agricultural areas. Over the last decades, remote-sensing data have led to important breakthroughs in the classification of different cultivars for several crops. Nonetheless, for almonds, studies are incipient. Thus, this study aims to fill this knowledge gap and explore the classification of almond cultivars in an almond orchard. High-resolution multispectral data were acquired by an unmanned aerial vehicle (UAV). Vegetation indices (VIs) and tree structural parameters were, subsequently, estimated. To obtain an accurate cultivar identification, four machine learning classifiers, such as K-nearest neighbour (kNN), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost), were applied and optimized through the fine-tuning process. The accuracy of machine learning classifiers was analysed. SVM and RF performed best with OAs of 76% and 74% using VIs and spectral bands (GREEN, GRVI, GN, REN, ClRE). Adding the canopy height model (CHM) improved performance, with RF and XGBoost having OAs of 88% and 84%. kNN performed worst with an OA of 73% using only VIs and spectral bands, 80% with VIs, spectral bands and CHM, and 93% with VIs, CHM, and tree crown area (TCA). The best performance was achieved by RF and XGBoost with OAs of 99% using VIs, CHM, and TCA. These results demonstrate the importance of the feature selection process. Moreover, this study reveals the feasibility of remote-sensing data and machine learning classifiers in the classification of almond cultivars. info:eu-repo/semantics/publishedVersion

This article aims to characterise the visions of ecologisation found within scientific approaches embraced by different epistemic communities, and which have inspired empirical work and public action on agrifood system transitions. Based on comparative readings of works anchored in our two disciplinary fields (ecology and sociology), we identified six large ensembles of epistemic communities as well as their points of convergence and divergence. We identify six ideotypical visions of ecologisation based on the types of ‘relationships to nature’ embedded in these large sets of epistemic communities: protectionism, functionalism, structuralism, post-structuralism, relational and pragmatist-experience-based. We suggest that pragmatist-experience-based approaches allow us to transcend two classical oppositions: between realism and constructivism, and between a conception of nature as passive and external as opposed to active and relational. Without claiming to offer a detailed analysis of these approaches, we hope that our work can be used as a tool to support reflection among scientists and other actors involved in agrifood system transitions. International audience

This chapter proposes an original analysis of the subtle reconciliation of digital transformation and responsible management within a French family-owned small business named "Les Bougies de Charroux". The research aims to understand better how digital transformation and CSR practices can be reconciled in artisanal family SMEs to work as levers of competitiveness. To achieve this aim, a systemic approach to strategic alignment resulting from the management of Information Systems is followed. The authors formulate a few key points to better support artisanal family SMEs in their digital transformation without denying their values and the specifics of their corporate culture. The study's findings relate to the issue of time. Another interesting conclusion refers to the need to involve all the actors in strategic change induced by digital transformation. International audience