Crop production constitutes a significant portion of the EU’s agricultural output and influences land use decisions. Various elements within the crop production system can significantly impact its outcomes. This paper aims to evaluate the environmental and economic performance of crop rotation, catch crops, and different tillage practices in Latvia by analyzing data from case studies, field trials, and field monitoring to identify the potential for improvement towards a more sustainable utilization of agricultural land. Environmental performance was evaluated by focusing on nitrogen use efficiency (NUE), as it is likely to play a significant role in assessing the environmental suitability of crop production according to the Platform on Sustainable Finance. For economic performance, gross margins were calculated. Crop rotation in Latvia tends to be monotonous, with wheat and oilseed rape dominating over 60% of the cultivated area due to their profitability. The findings of this study indicate that achieving a minimum NUE of 70% is challenging. Crop rotations including oilseed rape, particularly the common wheat–oilseed rape rotation, have an average NUE below the threshold, while proper use of catch crops may increase NUE by 7–9%. The three-year field trials on commercial farms yielded divergent findings about the impact of various tillage practices on NUE and gross margin. However, the field trials conducted on the farm practicing reduced tillage for over ten years show higher NUE compared to ploughing. The advantage of reduced tillage was supported by the obtained results indicating lower costs of agrotechnical operations, including less diesel consumption.

Today’s global food system (including production, transportation, processing, packing, storage, retail sale, consumption, losses and waste) provides income to more than a billion people all over the world and makes up a significant part of many countries’ economies. The 21st century’s food systems that bring food from “farm to fork” face various challenges, including a shortage of agricultural land and water, competition with the energy industry, changes in consumption preferences, a rising global population, negative effects of climate change, etc. Therefore, many countries are working on creating various models to function as an important decision support system tool for policymakers, farmers and other stakeholders. Various agricultural sector models see particularly extensive use in the European Union (EU), determining the impact of the Common Agricultural Policy (CAP) and helping to create future development scenarios. This is why a special model adapted to the national conditions, called LASAM (Latvian Agricultural Sector Analysis Model), was created in Latvia, making it possible to use historical data on the development of agricultural sectors, medium-term price projections for agricultural products in the EU, changes in support policy, as well as the necessity for the resources used to project the long-term (up to 2050) development of agriculture. The LASAM model covers the crop sector, the animal sector and the overall socioeconomic development, as well as the growth of organic farming and greenhouse gas (GHG) emissions. This paper discusses the main objectives achieved in developing a decision support tool and presenting the research results: LASAM was used to prepare projections of the possible development of Latvia’s principal sectors of agriculture until 2050, considering the necessity to reduce GHG emissions, made available through the LASAM web application. Given that the projection data obtained by LASAM are public, they can be used (1) for national policy making in rural business development, which affects the development of the economy as a whole; and (2) internationally, to compare the projections made in Latvia with those obtained through various agricultural sector models and projected development trends.

Manure is a valuable organic resource for sustainable agriculture, enhancing soil fertility, and is crucial for nutrient cycling; however, it also contributes significantly to methane and nitrous oxide emissions. The European Green Deal and Latvia's National Energy and Climate Plan have set targets for reducing agricultural greenhouse gas (GHG) emissions, including those related to improved manure management. Therefore, this research aims to estimate the future manure production in Latvian agriculture to assess the potential for reducing GHG emissions by 2050. Using the LASAM model developed in Latvia, the number of farm animals, the amount of manure, and the associated GHG emissions were projected for the period until 2050. The calculations used the Intergovernmental Panel on Climate Change (IPCC) methodology and were based on national indicators and current national GHG inventory data to cover the years 2021–2050. The study predicts significant changes in the structure of manure in Latvia by 2050, as the proportion of liquid manure is expected to increase, while the amount of solid and manure deposited by grazing animals is expected to decrease. The results of the GHG emission projection showed that by 2050, total emissions from manure management would decrease by approximately 5%, primarily due to a decline in the number of farm animals and, consequently, a reduction in the amount of manure. In contrast, methane emissions are expected to increase by approximately 5% due to production intensification. The research results emphasise the need to introduce more effective methane emission reduction technologies and improve projection approaches.

Precision beekeeping focuses on ICT approaches to collect data through various IoT solutions and systems, providing detailed information about individual bee colonies and apiaries at a local scale. Since the flight radius of honeybees is equal to several kilometers, it is essential to explore the specific conditions of the selected area. To address this, the aim of this study was to explore the potential of using crowdsourced data combined with geographic information system (GIS) solutions to support beekeepers’ decision-making on a larger scale. This study investigated possible methods for processing open geospatial data from the OpenStreetMap (OSM) database for the environmental analysis and assessment of the suitability of selected areas. The research included developing methods for obtaining, classifying, and analyzing OSM data. As a result, the structure of OSM data and data retrieval methods were studied. Subsequently, an experimental spatial data classifier was developed and applied to evaluate the suitability of territories for beekeeping. For demonstration purposes, an experimental prototype of a web-based GIS application was developed to showcase the results and illustrate the general concept of this solution. In conclusion, the main goals for further research development were identified, along with potential scenarios for applying this approach in real-world conditions.