Browsing by Subject "Saaristomeri"

Gypsum treatment of fields has proven to be a promising method of agriculture water protection. The purpose of this study is to investigate the drivers and barriers of gypsum treatment adoption among innovator and early adopter farmers. Based on the literature review, the factors affecting adoption of gypsum treatment are divided to farm, farmer, practice, program and context characteristics. The quantitative data of this study was gathered as a part of the KIPSI project from farmers that have or had suitable arable fields for gypsum treatment in the Archipelago Sea catchment area in spring 2020. The research data is analyzed with statistical methods including exploratory factor analysis, one-way analysis of variance, Kruskal-Wallis test, Chi-square test and Mann-Whitney U test. The results show that what determines the applying gypsum treatment of fields are practice, program and context characteristics such as information about the long term effects of gypsum, peer references, gypsum treatment being cost-free to the farmer, practical elements of the program, gypsum treatment’s environmental benefits and improving environmental image of agriculture. Farmer’s attitudes towards gypsum treatment play a role as well. On the other hand, farm or farmer sociodemographic characteristics such as farmer age, education, fulltime farming, share of rented land or total field area of the farm do not affect to the adoption of gypsum treatment. Out of the farm characteristics, only cereal farming is emphasized among the farmers that applied or are likely to apply gypsum treatment in the future in comparison to the farmers that are unlikely to apply gypsum treatment in the future. Exploratory factor analysis revealed four motivation factors to apply gypsum treatment of fields: water protection, fertilization and gaining new experiences, land improvement and protection of local waters and cost-effectiveness and recommendation of acquaintances. Water protection motivated farmers have positive views and attitudes towards gypsum treatment and agriculture water protection. Strong fertilization and gaining new experiences motivation is associated with cereal farming and the strong land improvement and protection of local waters motivation with large total field area of farm and younger age. Exploratory factor analysis revealed five motivation factors to not apply gypsum treatment of fields: doubts about the method, need for peer experiences and information about the long-term effects of gypsum, practical reasons, urgency and a view that the state of the waters is not weak and unsuitability of fields. Farmers having strong doubts about the method motivation have cautious attitude towards gypsum. They have also large field area as like the strong practical reasons motivated, who are also often fulltime farmers. Strong need for peer experiences and information about the long-term effects of gypsum motivation is associated with cereal farming. As this study focused on gypsum treatment, further research could expand to studying the acceptance as well as drivers and barriers of applying other agricultural water protection measures, such as structural lime treatment of fields.

Ecosystem modelling gives us a tool to understand the complicated processes in an ecological system. When studying the changes in an ecosystem, the system health is one of the main characteristics to define. Healthy ecosystem can endure stress and is in stable state. Ecological network analysis and different ecological indices have been used as a basis for measuring the state of an ecosystem, characterizing the dynamics of marine environments, and quantifying the impacts of fishing. The Archipelago Sea, located in Northern Baltic Sea, is characterized by large gradients in salinity and numerous islands. The area is greatly affected by human impact and climate change. However, no broad research on ecosystem changes has been carried out, hence, there is a need for holistic models both scientifically and societally to understanding the changing ecosystem thoroughly and to provide contribution in the decision-making processes of environmental management actions. The aim of this study was to find out how the state of the Archipelago Sea food web has changed from 1990 to 2014. Three steady-state trophic models of the study area for three different years (1990, 2000 and 2014) were constructed using the Ecopath modelling software and approach. The changes in the study area were measured comparing the calculated ecological indices and fishing impact indicators. The models captured changes in the system such as before and after the invasion of non-indigenous species, increase of cormorants, increase of seals, and decrease of cod. The models consist of 23 (1990), 25 (2000) and 27 (2014) different functional groups from predators to producers and detritus. The quality of the models was tested and according to three different approaches, the models can be said to adequately represent the Archipelago Sea food web and ecosystem. The ecosystem indices calculated showed that there had been system wide changes. The state of the Archipelago Sea food web had changed during the study period to a less mature but more resilient condition. This was due to the increase in number of predator species and higher primary production and flow to detritus. The fishing impact on ecosystem changed as fishery practice experienced a change into a more industrialized direction. Changes in trophic levels and ecosystem composition were observed. The invasion of non-indigenous species and the increase in top predators such as seals and the great cormorant affected the structure of the food web. In Addition, the decrease of flounder and unsuccessful recovery of cod have had an impact on the ecosystem and its maturity. Further research on the Archipelago Sea food web is needed. The ecosystem is stressed and does not show recovery; hence, management actions may become necessary. Future simulations based on these Ecopath models would facilitate the selection of the most suitable ecosystem management application. Knowledge of the whole ecosystem and its health is required, and this can be achieved with the help of ecosystem modelling.

The interactions within plankton communities are complex, and realistic modelling of these interactions create a challenge in large-scale environmental models. The objective of this thesis was to evaluate whether Bayesian networks could be a suitable method in the modelling of these communities. Besides observing the interactions between different groups within phyto- and zooplankton communities, another goal was to focus on the potential change on the ecosystem level. To achieve this, dynamic Bayesian networks with hidden variables were used to observe whether structural changes in plankton communities could reveal larger trends in the aquatic ecosystem. To compare performance and accuracy of the model, two Bayesian food webs with differing causal links between observations were built. Of the two models, the simpler construct utilizing hidden Markov model fared better, and a clear trend was detected in the hidden variable. This trend in the time series signify that the relationships between the observed variables have changed during the study period. The plankton data set was collected from the Archipelago Sea between 1991 and 2016 and the results from the model were further analyzed alongside with this observational plankton data. In the samples the total biomass of phytoplankton grew throughout the study period, whereas at the same time the total biomass of zooplankton declined. As the Bayesian network considers the observable variables while maximizing the fit of the hidden variable, the observed trend in the hidden variable indicate that some unobservable variables are affecting both phyto- and zooplankton communities. This clear trend detected by the hidden variable might be related to a trend of increasing eutrophication in the study area, but to better understand the drivers causing this change further research is needed. Besides detecting underlying trends, the dynamic Bayesian networks are a promising method to study the interactions within plankton communities.