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A cradle-to-gate life cycle assessment of polyamide-starch biocomposites: carbon footprint as an indicator of sustainability
Get PDFAccelerating climate change poses an alarming global issue, demanding a range of prompt and effective solutions. In response, bio-based plastics and biocomposites have emerged as extensively researched alternatives to combat the environmental threats posed by a warming climate. In this context, the present paper presents a cradle-to-gate life cycle assessment of a newly developed polyamide-starch biocomposite, with varying content of potato starch as the biofiller (ranging from 0 to 70 wt%). The primary aim was to quantitatively measure the total carbon footprint of the selected biocomposite. The results indicated that the progressive addition of potato starch as the biofiller into the copolyamide matrix significantly reduced the total carbon footprint of the biocomposite, achieving a maximum reduction of 42–43% with the highest starch content of 70 wt%. Moreover, the newly developed polyamide-starch biocomposite demonstrated excellent performance compared to reference fossil-based polyamides of polyamide 6 (PA6), polyamide 12 (PA12), and polyamide 6.6 (PA6.6), as well as composites of PA610/80 wt% polylactic acid modified by reactive extrusion (REX-PLA) and PA40/30 wt% glass fibers, with carbon footprint reductions of 29, 39, 42, 59, and 79%, respectively. Based on these findings, the polyamide-starch biocomposite, especially with the highest content of potato starch (70 wt%), exhibits significant potential as a new material solution to reduce the carbon footprint of several existing fossil- and bio-based polyamides together with polyamide-based composites. In doing so, it contributes to advancing the development of a more climate-friendly future for plastics through reductions in their carbon footprints.202
Synteesiraportti: Metsien kasvun lisäämisen keinot ja vaikutukset
Get PDFSuomen monipuoliset ja runsaat metsävarat ovat perusta metsien kestävälle hyödyntämiselle. Viime vuosina ilmastonmuutos, metsien käytön muutokset ja biologisen monimuotoisuuden yksipuolistuminen ovat heikentäneet metsien elinvoimaisuutta. Kotimaisen puun kysynnän kasvu, metsien kasvun notkahdus ja hiilinielujen heikkeneminen edellyttävät uusia keinoja metsien kasvun ja kestävyyden parantamiseksi.
Puuntuotannon metsissä kokonaiskestävä käsittely edellyttää metsien hiilensidontaa vähentävän puunkäytön ja hiilensidontaa lisäävän metsien kasvun yhteensovittamista. Puun kysynnän säilyessä korkealla tasolla ainoa keino ylläpitää hiilinieluja on lisätä puuston kasvua ja vähentää metsämaan hiilipäästöjä metsänhoidon avulla.
Keskeiset ratkaisut ovat jalostetun metsänviljelymateriaalin käyttö, tehokas metsän uudista-minen, varhaishoito, voimakkaiden harvennusten välttäminen sekä kiertoajan pidentäminen. Myös kasvatuslannoitus voi merkittävästi lisätä kasvua. Näillä toimilla voitaisiin saavuttaa vuosittain lähimmän 10 vuoden aikana arviolta jopa noin kolmen, 10–20 vuoden kuluttua noin seitsemän ja 20–30 vuoden kuluttua noin 10 miljoonan kuutiometrin kasvunlisäys.
Ilmastonmuutos ja sen aiheuttamat sään ääri-ilmiöt lisäävät metsätuhoja. Metsien monimuo-toisuutta ja vastustuskykyä voidaan kuitenkin vahvistaa esimerkiksi kasvattamalla lehtipuiden osuutta ja varmistamalla metsänviljelymateriaalin kestävyys ja sopeutumiskyky. Tämä voi auttaa metsien sopeutumisessa. Kullekin kohteelle soveltuvien puulajien ja metsänhoitomenetelmien valitseminen ovat avain metsien kasvun ja elinvoimaisuuden turvaamiseen.202
Ahmakanta Suomessa 2024
Get PDFVuoden 2024 helmikuussa ahmojen lukumäärän Suomessa arvioitiin olleen noin 415 (95 % todennäköisyysväli 348–495). Arvio pohjautuu riistakolmiolaskentoihin ja kolmen pohjoisim-man kunnan (Enontekiö, Inari, Utsjoki) osalta Metsähallituksen koordinoimiin ja yhdessä paliskuntien kanssa suorittamiin aluelaskentoihin vuosina 2020–2023. Arvion pohjana on ahmojen todennäköisin yksilömäärä riistakolmioilla (375 yks., 95 %:n todennäköisyysväli 313–450 ah-maa) ja arvioon pohjoisimpien kuntien ahmakannasta (40 yks. 95 %:n todennäköisyysväli 35–45). Poronhoitoalueen riistakolmioilla liikkui 95 %:n todennäköisyydellä 61–147 ahmaa, todennäköisimmän yksilömäärän ollessa 100. Suomen ahmakannan yksilömäärä on kasvanut 1990-luvun alkuun verrattuna noin kymmenkertaiseksi.
Vuoden 2024 kanta-arvio on noin 7 % pienempi kuin vuoden 2023 arvio (447 ahmaa). Yksilö-määrän pieneneminen näkyy selvemmin poronhoitoalueen kolmioaineistossa, jossa ahman ylitysjälkien määrä putosi noin puoleen verrattuna vuoteen 2023. Muun Suomen alueella muutos oli pienempi. Satunnaisuus vaikuttaa vähälukuisen eläimen jälkien frekvenssiin riistakolmioilla. Siksi kannan kehityssuunnasta voidaan tehdä arvio vasta useamman vuoden aineiston pohjalta.202
Sensor data cleaning for applications in dairy herd management and breeding
Get PDFData cleaning is a core process when it comes to using data from dairy sensor technologies. This article presents guidelines for sensor data cleaning with a specific focus on dairy herd management and breeding applications. Prior to any data cleaning steps, context and purpose of the data use must be considered. Recommendations for data cleaning are provided in five distinct steps: 1) validate
the data merging process, 2) get to know the data, 3) check completeness of the data, 4) evaluate the plausibility of sensor measures and detect outliers, and 5) check for technology related noise. Whenever necessary, the recommendations are supported by examples of different sensor types (bolus, accelerometer) collected in an international project (D4Dairy) or supported by relevant literature. To ensure quality and reproducibility, data users are required to document their approach throughout the process. The target group for these guidelines are professionals involved in the process of collecting, managing, and analyzing sensor data from dairy herds. Providing guidelines for data cleaning could help to ensure that the data used for analysis is accurate, consistent, and reliable, ultimately leading to more informed management decisions and better breeding outcomes for dairy herds.202
Seal of approval : Public preferences for the conservation of endangered Saimaa ringed seal
Get PDFIn species conservation, various options for conservation measures typically exist, yet their implementation may lead to conflicts among different population groups. Heterogenous preferences toward conservation measures often stem from the utilization of natural resources, whether for livelihood or recreational purposes. This study, focusing on the Saimaa ringed seal, a symbol of nature conservation in Finland, examines both population size and conservation measures. We distinguish the stated preferences between recreational visitors to Lake Saimaa, fishers at the lake, and individuals residing in other parts of Finland without direct use of the lake. To measure preferences, we utilize a choice experiment that incorporates both population size and the most promising conservation measures as attributes. The findings reveal significant variations in willingness-to-pay estimates between visitors and non-visitors, as well as between fishers and non-fishers. Interestingly, all population groups expressed a preference for a moderate increase in the seal population and a small extension of conservation measures, rather than opting for a substantial extension of measures. This insight emphasizes the importance of considering diverse stakeholder perspectives when designing and implementing species conservation strategies.202
Root puppet masters: Infauna shift trait‐productivity relationships in submerged aquatic vegetation communities
Get PDFSubmerged aquatic vegetation (SAV) growth can be limited by light and nutrient availability. Infauna are common inhabitants of SAV meadows. Their activity increases nutrient mobility, and they can positively affect plant growth, but we do not know their role in plant trait-biomass production relationships. We approached this problem using a 15-week in situ transplant experiment in the Baltic Sea with experimental additions of Macoma balthica, a sedentary bivalve, to experimental SAV communities. Experimental plant communities were tricultures with varying species composition, compiled from a pool of six different species, to create an experimental gradient of trait community weighted means that allowed us to detect changes more clearly in plant trait-biomass production relationships in response to the M. balthica treatment. We evaluated the relationships between plant height, leaf area, maximum root length (MMRL), specific root length (SRL), and SAV biomass production, then compared M. balthica condition index (soft tissue biomass [WW, mg]/valve length [mm]) to plant community leaf tissue nutrient concentrations (N (%DW), δ15N). Community biomass production was significantly related to plant height in the control treatment, but this relationship was decoupled in the M. balthica treatment, where community biomass production was instead significantly related to MMRL and SRL. This suggested a shift in the predominant SAV growth strategy, from height-related to root-related community biomass production. Leaf tissue δ15N was significantly related to M. balthica condition index. The growth of one species, Potamogeton perfoliatus, was significantly inhibited by the M. balthica treatment. Our results show that infauna have an important role in the plant traits related to community biomass production, and they have the potential to shape plant community structure via selective influences on different plant species.202
Ignition probability and fuel consumption of boreal ground vegetation fuels : an experimental study in Finland
Get PDFIn boreal forests fires often ignite and spread within the dominant moss and lichen cover of the ground layer vegetation, which thus greatly influences fire hazard. We used an experimental set-up in greenhouse conditions to study the differences in how (1) fuel moisture and (2) wind velocity influence the ignition probability and fuel consumption among four common circumboreal ground vegetation fuels, Pleurozium schreberi (Willd. ex Brid.) Mitt., Hylocomium splendens Schimp., Dicranum spp. and Cladonia rangiferina (L.) F. H. Wigg. Our results show that the reindeer lichen C. rangiferina was clearly the most flammable species, with high ignition probability even at high moisture contents and low wind velocities. Of the mosses, Dicranum was the least flammable, with low ignition probability and mass loss at low wind velocities regardless of moisture content. P. schreberi and H. splendens behaved somewhat similarly with wind velocities quickly increasing the initially low ignition probability and mass loss observed in the absence of wind. However, especially for mass loss, among-species differences tended to disappear with stronger winds. The observed differences can be explained by the different structures and growth forms of the studied species and open a potential avenue for improving forest fire risk predictions.202
Machine Learning Applications for Fisheries - At Scales from Genomics to Ecosystems
Get PDFFisheries science aims to understand and manage marine natural resources. It relies on resource-intensive sampling and data analysis. Within this context, the emergence of machine learning (ML) systems holds significant promise for understanding disparate components of these marine ecosystems and gaining a greater understanding of their dynamics. The goal of this paper is to present a review of ML applications in fisheries science. It highlights both their advantages over conventional approaches and their drawbacks, particularly in terms of operationality and possible robustness issues. This review is organized from small to large scales. It begins with genomics and subsequently expands to individuals (catch items), aggregations of different species in situ, on-board processing, stock/populations assessment and dynamics, spatial mapping, fishing-related organizational units, and finally ecosystem dynamics. Each field has its own set of challenges, such as pre-processing steps, the quantity and quality of training data, the necessity of appropriate model validation, and knowing where ML algorithms are more limited, and we discuss some of these discipline-specific challenges. The scope of discussion of applied methods ranges from conventional statistical methods to data-specific approaches that use a higher level of semantics. The paper concludes with the potential implications of ML applications on management decisions and a summary of the benefits and challenges of using these techniques in fisheries