Current issue: 53(4)
Under compilation: 54(1)
Forest ecosystems may accumulate large amounts of nitrogen in the biomass and in the soil organic matter. However, there is increasing concern that deposition of inorganic nitrogen compounds from the atmosphere will lead to nitrogen saturation; excess nitrogen input does not increase production. The aim of this study was to determine the long-term changes caused by nitrogen input on accumulation of nitrogen in forest soils and in ground vegetation.
The fertilization experiments used in this study were established during 1958–1962. They were situated on 36- to 63-year-old Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) Karst.) stands of different levels of fertility. The experiments received nitrogen fertilization 5–7 times over a 30-year period, and the total input of nitrogen was 596–926 kg/ha.
Nitrogen input increased the amount of organic matter in the humus layer and the nitrogen concentration in the organic matter. Furthermore, the total amount of nutrients (N, P, K, Ca and Mg) bound by the humus layer increased due to the increase in the amount of organic matter. However, nitrogen input decreased the biomass of ground vegetation. The nitrogen concentration of the plant material on the nitrogen-fertilized plots was higher than on the control plots, but the amount of nutrients bound by ground vegetation decreased owing to the drastic decrease in the biomass of mosses. Ground vegetation does not have the potential to accumulate nitrogen, because vegetation is dominated by slow-growing mosses and dwarf shrubs which do not benefit from nitrogen input.
It was examined whether the present site classification method, and especially its applicability to site productivity estimation, could be improved in upland Scots pine (Pinus sylvestris L.) forests in Southern Finland by developing a classification key based on Two-way Indicator Species Analysis (TWINSPAN), and/or by inclusion of soil texture, stoniness and the humus layer depth more closely in the classification method. TWINSPAN clusters (TW) explained 71%, and forest site types (FST) 64% of the variation in site index (SI) (H100). When soil texture (TEXT) was added to the regression model, the explanatory power increased to 82% (SI = TW + TW * TEXT) and to 80% (SI = FST + FST * TEXT), respectively. Soil texture alone explained 69% of the variation in site index. The influence of stoniness on site index was significant (P <0.05) on sorted medium sand soils and on medium and fine sand moraine soils. The thickness of the humus layer (2–6 cm) was not significantly (P=0.1) related to site index.
It is suggested that the proposed TWINSPAN classification cannot replace the present forest site type system in Scots pine stands in Southern Finland. However, the TWINSPAN key may be used to aid the identification of forest types. The observation of dominant soil texture within each forest type is recommended.
The PDF includes an abstract in Finnish.