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Yrittäjämäiset taidot ja strategiat suomalaisissa kotieläinyrityksissä: dynaamisten kyvykkyyksien näkökulma
Isomäki, Riina; Pyysiäinen, Jarkko; Rantamäki-Lahtinen, Leena; Mattila, Tiina; Väre, Minna; Niemi, Jarkko
Maaseutututkimus : 1 (Maaseudun uusi aika ry, 2026)
Tutkimuksessa tarkastellaan, miten suomalaiset kotieläinyrittäjät kehittävät ja hyödyntävät keskeisiä yrittäjämäisiä kykyjä ja taitoja, niin sanottuja dynaamisia kyvykkyyksiä. Näillä tarkoitetaan mahdollisuuksien tunnistamista, niihin tarttumista ja resurssien uudelleenjärjestelyä vastauksena toimintaympäristön muutoksiin. Vuonna 2024 toteutettuihin haastatteluihin osallistui 18 nauta-, sika- ja siipikarjatilaa, jotka jakautuivat analyysin perusteella neljään dynaamisten kyvykkyyksien pääryhmään: lisäarvon tavoittelijoihin, ketjuohjauksen alaisiin, kumppanuuksia hyödyntäviin ja tasapainoisiin perheviljelmiin. Ryhmät erosivat toisistaan siinä, miten yrittäjät tunnistivat uusia mahdollisuuksia, tarttuivat niihin ja järjestelivät resurssejaan. Tulosten perusteella dynaamiset kyvykkyydet ovat keskeisiä myös kotieläinyritysten menestyksekkään kehittämisen kannalta, mutta niiden soveltamisen ja kehittämisen mahdollisuudet riippuvat yrityksen strategiasta ja rakenteesta, toimintaympäristöstä, verkostoista sekä yrittäjän arkipäiväisistä oppimismahdollisuuksista.
Excretion calculations of sheep and goats in Finland
Nousiainen, Jouni; Vattulainen, Jenni; Kuoppala, Kaisa; Rinne, Marketta
Luonnonvara- ja biotalouden tutkimus : 51/2026 (Luonnonvarakeskus, 2026)
The official excretion calculations provide the Finnish national annual amounts of dry matter, organic matter and nutrients excreted by different livestock into faeces and urine. Natural Resources Institute Finland (Luke) is responsible for the national excretion calculations in Finland, and the results are used e.g. in estimating emissions during manure management and use into air (greenhouse gases, ammonia) and waters [phosphorus (P), nitrogen (N), potassium (K)], in estimating the quantity and composition of manure produced in Finland and in setting the targets to improve its utilization (nutrient balances, circular economy). The amounts of nutrients excreted are calculated as the difference between nutrient intake in feed and the nutrients retained in the animals as well as in their products, i.e. as "nutrient input – nutrient retained= nutrient excretion" The components included in the calculations are dry matter, organic matter, N, P and K. Excretion calculations of sheep and goats are conducted annually for different animal groups based on their gender and age. The feeds consumed by the animals is one of the most critical aspects of excretion calculations. In this report, the consumption of feeds was evaluated based on energy requirements of animals at different production levels. The composition of diets was based on data gathered from stakeholders and on expert evaluations. The highest uncertainty lies in the actual amount of nutrients consumed by the animals. The detailed steps of calculations are described in this report. Based on the results with year 2025, the annual N and P excretion of one adult ewe were 9.09 and 1.21 kg, respectively. Similar excretions for nine-month-old lamb raised for slaughter were 10.2 and 1.37 kg (when estimated for a whole year), respectively.
Valkokukkainen koirapuu Aasiasta
Nuorteva, Heikki
Metsälehti : 13/2026 (Tapio, 2026)
Environmental variables improve remote sensing-based water table monitoring in peatlands
Christiani, Priscillia; Räsänen, Aleksi; Kuzmin, Anton; Ojanen, Paavo; Minkkinen, Kari; Korpelainen, Pasi; Rana, Parvez; Kumpula, Timo; Isoaho, Aleksi
Science of the total environment (Elsevier, 2026)
Water table (WT) is a key indicator of peatland ecosystem functioning, but its spatiotemporal monitoring is challenging. Optical remote sensing has been used in peatland WT monitoring with varying success, but few studies have tested whether environmental variables—particularly topographic and tree stand structure variables derived from LiDAR—improve modelling performance. We tested whether environmental variables improve (1) uncrewed aerial vehicle-derived spatial WT models in two northern boreal, partly drained aapa mires and (2) satellite image-derived spatiotemporal WT models in a southern boreal drained peatland forest in Finland. We employed random forest regression and variable selection techniques to model WT, using optical remote sensing and environmental variables as predictors. Our results showed that environmental variables related to topography and tree stand structure improve modelling performance, with R2 increasing by 0.01–0.19 compared to optical-only models. Our findings support the integration of optical and environmental data for spatial and spatiotemporal WT monitoring in boreal peatlands.
Federated learning in forest resource modelling and monitoring: Bridging data confidentiality and collaborative research
Schumacher, Johannes; Cescatti, Alessandro; Chirici, Gherardo; D’Amico, Giovanni; Francini, Saverio; Hertzler, Johannes; Mehtätalo, Lauri; Nabuurs, Gert-Jan; Nilsson, Mats; Pitkänen, Juho; Breidenbach, Johannes
International journal of applied earth observation and geoinformation (Elsevier, 2026)
The availability of reliable ground-truth data is one of the main bottlenecks for improving high-resolution forest attribute maps from Earth observation data. This is underpinned by the European Union (EU) Forest Strategy for 2030 that underscores the need for harmonized, cross-border forest resource assessments that integrate both remote sensing and field-based National Forest Inventory (NFI) data. However, confidentiality constraints on NFI plot coordinates present a significant barrier to aligning these datasets, thereby limiting the development of unified forest monitoring systems that can fully leverage the potential of Earth Observation data. To overcome these data-sharing limitations we explored the effectiveness of a privacy-enhancing technique, known as Federated Learning (FL), that is a form of distributed computing aimed at preserving the privacy and confidentiality of data owned by different organizations. This methodology has been tested for the collaborative modelling and mapping of forest timber volume across four European countries: Norway, Sweden, Finland, and Italy. We employed a time-series convolutional neural network (CNN) architecture tailored to integrate 40 years of Landsat or 7 years of Sentinel imagery and terrain variables with harmonized NFI data from more than 85,000 sample plots. This model architecture was used for the FL approach and compared to traditional country-specific and centralized modelling strategies. FL models achieved predictive performances comparable to the traditional models, which proofs the effectiveness of the proposed approach. Centralized or global models showed slightly reduced performance compared to the national models, highlighting the value of fine-tuning with local ground-truth data. By aligning with the EU’s forest monitoring objectives, FL facilitates the generation of harmonized models and maps of forest features, like timber volume and biomass, that are critical to support evidence-based forest policy and management. The findings underscore the potential of FL to transform collaborative environmental monitoring, particularly in domains where data confidentiality and interoperability are critical.