Effect of anionic salts in concentrate mixture on some blood and urine minerals, acid-base balance and feed intake of dry pregnant cows on grass silage based feeding with high calcium intake

Selostus: Kationi-anionitasapaino ummessaolevien lypsylehmien säilörehuruokinnassa kalsiumin saannin ollessa runsas

Effect of anionic salts in concentrate mixture and calcium intake on some blood and urine minerals, acid-base balance and feed intake of dry pregnant cows on grass silage based feeding

Selostus: Kationi-anionitasapaino ja kalsiumin saanti ummessaolevien lypsylehmien säilörehuruokinnassa

Effect of anionic salts in concentrate mixture and magnesium intake on some blood and urine minerals and acid-base balance of dry pregnant cows on grass silage based feeding

Selostus: Kationi-anionitasapaino ja magnesiumin saanti ummessaolevien lypsylehmien säilörehuruokinnassa

Effect of anionic salts on some blood and urine minerals, acid-base balance and udder oedema of dry pregnant cows

Selostus: Anionisten suolojen vaikutus ummessa olevien lehmien veren ja virtsan kivennäisiin, happo-emästasapainoon sekä utarepöhöön

Effects of anthropogenic and natural hydrological changes on the behavior of the acidic metal discharge from acid sulfate soils in a river- and lake system in western Finland

Larsmo-Öjasjön i Österbotten skapades genom invallningar på 1960-talet pga. industrins behov av sötvatten. Sedan dess har vattenområdet drabbats av återkommande försurning och fiskdöd, och invallningen har ofta beskyllts för problemen. Avhandlingen undersöker syrabelastningen i området; bl.a. hur markanvändning, hydrologi och klimatförändringen påverkar belastningen. Konsekvenserna undersöks med fiskyngel som bioindikator, och olika miljömetoder testas och diskuteras. Ökad kunskap om försurningen hjälper oss att tillämpa effektiva miljömetoder och få förbättrad vattenkvalitet i framtiden. Den primära orsaken till den försämrade vattenkvaliteten under de senaste 40 åren är intensiv dikning av svavelrika sediment. Detta leder till oxidering av svavlet till svavelsyra och uppkomst av sura sulfatjordar. Syran löser upp mängder med toxiska metaller som spolas ut i vattendragen. Undersökningen visar att tiotusentals ton svavelsyra tillsammans med stora mängder metaller rinner till Larsmo-Öjasjön per år från sura sulfatjordar. Åarna bidrar med mest belastning, men den sammanlagda belastningen från de otaliga dikena och bäckarna är oväntat stor. Andra potentiella källor till försurningen, t.ex. muddringar och humussyror, beräknas vara obetydliga. Syra- och metallbelastningen varierar kraftigt med hydrologin, dvs. störst belastning sker under vår- och höstflöden. En eventuell klimatförändring kan ändra på avrinningsmönstret och orsaka mera belastning vintertid. Den årligt återkommande syra- och metallbelastningen kan ofta hindra lakens förökning, vilket kan ha större långtgående konsekvenser för fiskpopulationerna än de relativt sällsynta stora surchockerna med synlig fiskdöd. För att förebygga skador på vattendragen bör man undvika att dränera svavelrika sedimenten. På redan existerande sura sulfatjordar visade sig kontroll av grundvattennivån kunna möjliggöra en effektiverad markanvändning utan märkbart ökade miljökonsekvenser.

Spatial modeling techniques for mapping and characterization of acid sulfate soils

Acid sulfate (a.s.) soils constitute a major environmental issue. Severe ecological damage results from the considerable amounts of acidity and metals leached by these soils in the recipient watercourses. As even small hot spots may affect large areas of coastal waters, mapping represents a fundamental step in the management and mitigation of a.s. soil environmental risks (i.e. to target strategic areas). Traditional mapping in the field is time-consuming and therefore expensive. Additional more cost-effective techniques have, thus, to be developed in order to narrow down and define in detail the areas of interest. The primary aim of this thesis was to assess different spatial modeling techniques for a.s. soil mapping, and the characterization of soil properties relevant for a.s. soil environmental risk management, using all available data: soil and water samples, as well as datalayers (e.g. geological and geophysical). Different spatial modeling techniques were applied at catchment or regional scale. Two artificial neural networks were assessed on the Sirppujoki River catchment (c. 440 km2) located in southwestern Finland, while fuzzy logic was assessed on several areas along the Finnish coast. Quaternary geology, aerogeophysics and slope data (derived from a digital elevation model) were utilized as evidential datalayers. The methods also required the use of point datasets (i.e. soil profiles corresponding to known a.s. or non-a.s. soil occurrences) for training and/or validation within the modeling processes. Applying these methods, various maps were generated: probability maps for a.s. soil occurrence, as well as predictive maps for different soil properties (sulfur content, organic matter content and critical sulfide depth). The two assessed artificial neural networks (ANNs) demonstrated good classification abilities for a.s. soil probability mapping at catchment scale. Slightly better results were achieved using a Radial Basis Function (RBF) -based ANN than a Radial Basis Functional Link Net (RBFLN) method, narrowing down more accurately the most probable areas for a.s. soil occurrence and defining more properly the least probable areas. The RBF-based ANN also demonstrated promising results for the characterization of different soil properties in the most probable a.s. soil areas at catchment scale. Since a.s. soil areas constitute highly productive lands for agricultural purpose, the combination of a probability map with more specific soil property predictive maps offers a valuable toolset to more precisely target strategic areas for subsequent environmental risk management. Notably, the use of laser scanning (i.e. Light Detection And Ranging, LiDAR) data enabled a more precise definition of a.s. soil probability areas, as well as the soil property modeling classes for sulfur content and the critical sulfide depth. Given suitable training/validation points, ANNs can be trained to yield a more precise modeling of the occurrence of a.s. soils and their properties. By contrast, fuzzy logic represents a simple, fast and objective alternative to carry out preliminary surveys, at catchment or regional scale, in areas offering a limited amount of data. This method enables delimiting and prioritizing the most probable areas for a.s soil occurrence, which can be particularly useful in the field. Being easily transferable from area to area, fuzzy logic modeling can be carried out at regional scale. Mapping at this scale would be extremely time-consuming through manual assessment. The use of spatial modeling techniques enables the creation of valid and comparable maps, which represents an important development within the a.s. soil mapping process. The a.s. soil mapping was also assessed using water chemistry data for 24 different catchments along the Finnish coast (in all, covering c. 21,300 km2) which were mapped with different methods (i.e. conventional mapping, fuzzy logic and an artificial neural network). Two a.s. soil related indicators measured in the river water (sulfate content and sulfate/chloride ratio) were compared to the extent of the most probable areas for a.s. soils in the surveyed catchments. High sulfate contents and sulfate/chloride ratios measured in most of the rivers demonstrated the presence of a.s. soils in the corresponding catchments. The calculated extent of the most probable a.s. soil areas is supported by independent data on water chemistry, suggesting that the a.s. soil probability maps created with different methods are reliable and comparable.