Kristina Sermuksnyte-Alesiuniene

Digitalization of agriculture is one of the priorities of the EU’s rural development strategy “From Field to Table”, which promotes the creation of more added value and climate change mitigation in agriculture. A growing body of the literature argues that digitalization enables better information management, reduces production costs, and increases the potential for farm income growth, but only a few papers provide empirical studies on how digitalization improves the performance of small farms. To fill this gap in the literature, this paper presents a case study as empirical evidence of the impact of digital innovation on smallholder performance through a sustainable development lens. This paper reports research based on a pilot digitalization project implemented on a small organic farm. It examines the identification of logical links between the digitalization processes introduced and the impact of digitalization on the economic, social, and environmental performance of the small farm. The case study data were collected through semi-structured interviews and based on the results of a pilot project. The findings of this study provide evidence that the introduction of digital technologies has improved the economic performance of the farm, including a reduction in labor costs, improved customer relations, improvements in farmers’ investment planning, and process redesign. Based on this study, recommendations are made to policymakers on how to promote the uptake of digital technologies in smallholder farming.

The EU’s goals by 2050 are to ensure food security, prevent bio-diversity loss, and strengthen the EU food system’s resilience. Recent scientific research and the situation in the global market show that the cultivation and processing of raspberries is currently completely unsustainable. This sector is experiencing a huge decline in Lithuania. Therefore, we chose the sustainability of raspberry growing (from farm) and processing (to fork) as an object. The aim of this article was (i) to analyze the raw material of the raspberry plant for product sustainable processing, (ii) to create a digital sustainability measurement model, and (iii) to present sustainable development solutions for effective raspberry growing and processing on Lithuanian farms using content and descriptive methods. This paper discusses how to help small raspberry growers and processors achieve sustainable economic, environmental, and social performance from field raw material to processed products. Analysis of the scientific literature has revealed qualitative and quantitative sustainability indicators for improving raspberry production. The assessment of the sustainability according to our created model revealed the (un)sustainable factors and the current situation in raspberry farms on a Likert scale from very unsustainable to very sustainable. Based on the evaluation we have determined sustainable development solutions. Raspberry growing and processing in Lithuania can contribute to environmental conservation, economic growth, and social well-being, fostering a more sustainable and resilient agricultural sector by investing in R&D, improving productivity, creating employment opportunities and supporting rural communities, establishing a robust waste management system, and embracing renewable energy sources. Raspberry growers and processors can use the digital model we created for the sustainability, efficiency, and development directions of their farm.

In Europe, the green course is becoming increasingly relevant, and there are more and more suggestions for its improvement. The valorization of food waste attracts increasing attention and is one important current research area. The aim of this study was to examine oils from 16 raspberry variety seeds and to compare their yields and fatty acid contents. The next task was to extract oil from the raspberry variety ‘Polka’ by four different methods and to compare the yield, colors, fatty acids content and composition, and kinematic and dynamic viscosity. The last task was to analyze the economic profitability of oil extraction by different methods. This study demonstrates the potential of different varieties of raspberry by-products and shows the influence of different oil extraction methods on the fatty acid composition of the oil and the economic potential of such products. The analysis revealed that the predominating fatty acid in the raspberry variety ‘Polka’ seed oil was linoleic acid (44.0–44.8%), followed by α-linolenic acid (37.9–38.1%) and oleic acid (10.2–10.6%). Of the 16 raspberry cultivars tested, ‘Polka’ seed oil had the least linoleic (ω-6) (44.79%) and the most α-linolenic (ω-3) fatty acids and the best ratio of ω-6 to ω-3 fatty acids—1.2:1. Raspberry variety ‘Polka’ seed oil contains a lot of carotenoids; their total amount depending on the extraction method varies from 0.81 mg/100 g (extracted with subcritical CO₂) to 3.25 mg/100 g (extracted with supercritical CO₂). The oil yield can be increased by grinding the seeds into a finer fraction. The most expensive method of oil production is supercritical CO₂ extraction, and the cheapest method with the fastest payback of equipment is the cold-pressing method. The results of the research have revealed the influence of different oil recovery methods on the yield of oil, the composition of the fatty acid, colors, and viscosity. The results are very important for producers wishing to commercialize raspberry seed oil.

During the COVID-19 crisis, there were many restrictions to transportation. Due to that, a significant disruption in the food supply chain has emerged. The transportation of the fresh food and maintaining the quality, from farm to the table or distributing and then collecting from the warehouses and delivering to the consumer, has become crucial. Technologies and especially IoT, have become the primary tool to fight it. The research objective is to analyze and create new knowledge about digital technologies used to improve and make more effective the food supply chain processes. An exploratory case study methodology helps to investigate a large consortium based on IoT technologies, implemented in pilot cases on farm level and measuring their performance over the period of four years. This is an interpretative study, and the method of semi-structured interviews and document review for collecting the data was used. The results show IoT-connected sensors and systems in food and beverage supply chain logistics offer real-time visibility and data-driven analytics, allowing stakeholders to improve performance, cut operating costs, conduct predictive maintenance to avoid downtime, and even decrease energy usage or reduce negative environmental impacts.