Can Precise Irrigation Support the Sustainability of Protected Cultivation? A Life-Cycle Assessment and Life-Cycle Cost Analysis

Internet of Things-Based Automated Solutions Utilizing Machine Learning for Smart and Real-Time Irrigation Management: A Review

This systematic review critically evaluates the current state and future potential of real-time, end-to-end smart, and automated irrigation management systems, focusing on integrating the Internet of Things (IoTs) and machine learning technologies for enhanced agricultural water use efficiency and crop productivity. In this review, the automation of each component is examined in the irrigation management pipeline from data collection to application while analyzing its effectiveness, efficiency, and integration with various precision agriculture technologies. It also investigates the role of the interoperability, standardization, and cybersecurity of IoT-based automated solutions for irrigation applications. Furthermore, in this review, the existing gaps are identified and solutions are proposed for seamless integration across multiple sensor suites for automated systems, aiming to achieve fully autonomous and scalable irrigation management. The findings highlight the transformative potential of automated irrigation systems to address global food challenges by optimizing water use and maximizing crop yields.

Evaluating the environmental impacts of smart vineyards through the Life Cycle Assessment

This study aimed to assess the environmental effectiveness of vineyards utilising on-site weather stations integrated with a decision support system (DSS), and to identify the critical hotspots in smart farms that have already obtained integrated or organic certification. For this purpose, Life Cycle Assessment (LCA) methodology was applied. The research comprised three smart farms employing on-site weather stations and a traditional farm without advanced technologies, which served as a benchmark. The analysis revealed variations in environmental footprints driven by differences in farm management practices and soil characteristics. The results highlighted that smart farms, in compliance with integrated or organic certifications, focus on reducing inputs such as agrochemicals or water consumption. However, these reductions could shift the environmental burden to other impacts, such as those related to machinery use, which remained the most critical aspect across all vineyards considered. In some smart farms, critical issues involve other aspects, such as irrigation and fertilisation. The lack of awareness about the potential environmental impacts of the adopted technical options could make smart farms more impactful than traditional farms. Interestingly, this study found that solely implementing advanced technologies could fall short of achieving ecological objectives. This study emphasises the significance of utilising LCA as a valuable tool to support farmers in making informed decisions while adopting technological strategies to achieve environmentally sustainable goals.

Decarbonization of Agriculture: The Greenhouse Gas Impacts and Economics of Existing and Emerging Climate-Smart Practices

The worldwide emphasis on reducing greenhouse gas (GHG) emissions has increased focus on the potential to mitigate emissions through climate-smart agricultural practices, including regenerative, digital, and controlled environment farming systems. The effectiveness of these solutions largely depends on their ability to address environmental concerns, generate economic returns, and meet supply chain needs. In this Review, we summarize the state of knowledge on the GHG impacts and profitability of these three existing and emerging farming systems. Although we find potential for CO2 mitigation in all three approaches (depending on site-specific and climatic factors), we point to the greater level of research covering the efficacy of regenerative and digital agriculture in tackling non-CO2 emissions (i.e., N2O and CH4), which account for the majority of agriculture's GHG footprint. Despite this greater research coverage, we still find significant methodological and data limitations in accounting for the major GHG fluxes of these practices, especially the lifetime CH4 footprint of more nascent climate-smart regenerative agriculture practices. Across the approaches explored, uncertainties remain about the overall efficacy and persistence of mitigation-particularly with respect to the offsetting of soil carbon sequestration gains by N2O emissions and the lifecycle emissions of controlled environment agriculture systems compared to traditional systems. We find that the economic feasibility of these practices is also system-specific, although regenerative agriculture is generally the most accessible climate-smart approach. Robust incentives (including carbon credit considerations), investments, and policy changes would make these practices more financially accessible to farmers.