This research explores the prospective implementations of artificial intelligence (AI) algorithms within the agrifood sector, focusing on the Spanish context. AI methodologies, encompassing machine learning, deep learning, and neural networks, are increasingly integrated into various agrifood sectors, including precision farming, crop yield forecasting, disease diagnosis, and resource management. Utilizing a comprehensive bibliometric analysis of scientific literature from 2020 to 2024, this research outlines the increasing incorporation of AI in Spain and identifies the prevailing trends and obstacles associated with it in the agrifood industry. The findings underscore the extensive application of AI in remote sensing, water management, and environmental sustainability. These areas are particularly pertinent to Spain’s diverse agricultural landscapes. Additionally, the study conducts a comparative analysis between Spain and global research outputs, highlighting its distinctive contributions and the unique challenges encountered within its agricultural sector. Despite the considerable opportunities presented by these technologies, the research identifies key limitations, including the need for enhanced digital infrastructure, improved data integration, and increased accessibility for smaller agricultural enterprises. The paper also outlines future research pathways aimed at facilitating the integration of AI in Spain’s agriculture. It addresses cost-effective solutions, data-sharing frameworks, and the ethical and societal implications inherent to AI deployment.

In order to maintain the quality of the potatoes over time, it is necessary to store them under certain storage conditions, which minimize losses both of quality and product, preferably without using chemical treatments. Conservation chambers consume a considerable amount of energy. Between 60% and 70% of the electricity consumed is used in refrigeration. Good insulation reduces the need for cooling the potato since its optimum storage temperature for consumption is around 4–7°C and relative humidity is 85%–90%. This research studies potatoes’ cold storage process to minimize the cost in the product value chain and to ensure its competitiveness in the market. A model is developed to assess energy consumption and propose measures to reduce energy, environmental, and economic costs. All this to reduce their impact within the value chain of potato consumption.Practical Applications: In this case study, different energy efficiency measures applied to the cold storage of potatoes have been implemented: replacement or improvement of the performance of refrigeration equipment, insulation and infiltrations in the refrigeration chamber, control of the product entry temperature, thermal conditioning through free‐cooling, improvements in lighting equipment, technical management, and supervision of facilities, and thermographic control. The set of actions implemented has allowed to obtain a reduction in energy demand, standardized through the developed reference line, by 16.41% compared to previous years.

Six Sigma is a methodology widely used in manufacturing production to guarantee quality and several factors facilitate its success. This paper presents a structural equations model to identify the relationships between managerial commitment, Six Sigma implementation strategy, investments in human resources and the economic benefits obtained; and five hypotheses are proposed. These variables are also related to sustainability, especially human resources, a paradigm very combinable with Six Sigma. The model is evaluated using partial least squares and information obtained from 301 Mexican manufacturing companies from different industrial sectors. The results indicate that managerial commitment is the basis for Six Sigma success, but requires an adequate implementation strategy focused on customers and their needs, which must integrate an investment plan in human resources that is focused on training and teamwork. In addition, managers must have a reward program that encourages motivation and recognizes the achievements of the human resources involved.

Despite its worldwide presence, the characteristics of dairy industry may vary largely from one country to another and also between the different regions of a country. Farmers can form co-operatives or operate individually, while processors may transform very different amounts of milk into a diversity of dairy products. Moreover, the variety of dairy products that can be found in the market, the short-term expiry date of some of them, as well as the strong competition for a saturated market, lead to a complex environment, where the design and operation of the dairy factories require decision support tools for helping the decision makers. In this paper, it is considered the development of a decision support system for improving the survival chances of dairy plants in a competitive worldwide market. This tool is based in the implementation of an optimization process, where the performance evaluation of the feasible decisions is performed by means of simulation. The objective of the decision making covers the complete life cycle of the dairy plant from its design to its operation.