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This paper presents an exploratory qualitative Social Life Cycle Analysis (S-LCA) of intensive greenhouse tomato farming in Almeria, Spain, with a specific focus on the social impacts on migrant workers. By setting as a functional unit the production of 10 kg of greenhouse tomato, the study investigates the social dynamics and challenges faced by migrant workers within the industry. The research sheds light on the negative aspects of intensive greenhouse farming for migrants, i.e., labor conditions, health and safety risks, social inequality, and exploitation of migrant labor. Utilizing document analysis, this study contributes to the field of S-LCA by providing a nuanced understanding of the social dimensions surrounding tomato production and migrant workers' experiences. It emphasizes the need for sustainable practices, improved labor conditions, and ethical considerations to ensure a socially responsible tomato farming industry. The findings have implications for policymakers, industry stakeholders, and consumers, providing insights for informed decision-making and the promotion of socially sustainable agricultural practices that prioritize the well-being and rights of migrant workers in the context of intensive greenhouse farming in Almeria. [ABSTRACT FROM AUTHOR]

Plastic covered greenhouse (PCG) mapping via remote sensing has received a great deal of attention over the past decades. The WorldView-3 (WV3) satellite is a very high resolution (VHR) sensor with eight multispectral bands in the visible and near-infrared (VNIR) spectral range, and eight additional bands in the short-wave infrared (SWIR) region. A few studies have already established the importance of indices based on some of these SWIR bands to detect urban plastic materials and hydrocarbons which are also related to plastics. This paper aims to investigate the capability of WV3 (VNIR and SWIR) for direct PCG detection following an object-based image analysis (OBIA) approach. Three strategies were carried out: (i) using object features only derived from VNIR bands (VNIR); (ii) object features only derived from SWIR bands (SWIR), and (iii) object features derived from both VNIR and SWIR bands (All Features). The results showed that the majority of predictive power was attributed to SWIR indices, especially to the Normalized Difference Plastic Index (NDPI). Overall, accuracy values of 90.85%, 96.79% and 97.38% were attained for VNIR, SWIR and All Features strategies, respectively. The main PCG misclassification problem was related to the agricultural practice of greenhouse whitewash (greenhouse shading) that temporally masked the spectral signature of the plastic film. [ABSTRACT FROM AUTHOR]

The current agricultural water panorama in many Mediterranean countries is composed by desalination facilities, wells (frequently overexploited), the water public utility network, and several consumer agents with different water needs. This distributed water network requires centralized management methods for its proper use, which are difficult to implement as the different agents are usually geographically separated. In this sense, the use of enabling technologies such as the Internet of Things can be essential to the proper operation of these agroindustrial systems. In this paper, an Internet of Things cloud architecture based on the FIWARE standard is proposed for interconnecting the several agents that make up the agroindustrial system. In addition, this architecture includes an efficient management method based on a model predictive control technique, which is aimed at minimizing operating costs. A case study inspired by three real facilities located in Almería (southeast of Spain) is used as the simulation test bed. The obtained results show how around 75% of the total operating costs can be saved with the application of the proposed approach, which could be very significant to decrease the costs of desalinated water and, therefore, to maintain the sustainability of the agricultural system. [ABSTRACT FROM AUTHOR]