The dynamics of carbon (C) and nitrogen (N), derived from the decomposition of windrowed harvest residues, was examined in the establishment phase of a second rotation (2R) hoop pine (Araucaria cunninghamii Aiton ex A. Cunn) plantation in subtropical Queensland, Australia. Following harvesting and site preparation, when residues were formed into windrows, in situ N mineralisation was measured in positions along the three tree-planting rows formed between the windrows. The position above the windrow had a higher nitrification rate than the other positions, averaging about 18 kg N ha−1/month compared with 12 and 9 Kg N ha−1 for the positions between and below the windrow positions, respectively. This position also had consistently greater soil moisture.
Macroplots were formed extending 5 m above and 10 m below a windrow. Windrowed residues within the macroplots were replaced by 15N-labelled material comprising hoop pine foliage, branch and stem. Hoop pine trees were planted within each macroplot with foliar samples taken at 12 and 24 months. Differences in foliar 15N enrichment between positions within macroplots were <1‰. Soil samples were taken from positions along the macroplots at 6-monthly intervals. Samples revealed an initial release of labile C and N but soil δ15N showed that residue-derived N was largely immobilised within the windrows for the 30-month sampling period. Whilst the use of windrows may act as a barrier to the down-slope movement of water, the residue N within the windrows may not be available to the trees of the following rotation for a considerable period following planting. Trees closest to the windrows may be able to introduce roots under the windrows thereby gaining access to the available N, but trees in the central tree planting row are unlikely to derive any significant benefit from the decomposition of windrowed residues. 相似文献
The fast wavelet transform (FWT) algorithm in wavelet analysis was introduced in the paper. With quadrature mirror filters (QMF) associated with popular wavelet bases, the fast wavelet decomposition and reconstruction for signals were implemented. Combined with virtual instrument technique, the FWT analysis system for signals was successfully developed. The system can break up signal not only into approximations, which are the high-scale and low-frequency components of the signal, but also into details which are the low-scale and high-frequency components. Especially it can identify singularity signal, which contain some important message of equipment condition and fault, and refine signal from noisy signal, which is corrupted by noise. 相似文献