Phosphorus availability may shape plantmicroorganism-soil interactions in forest ecosystems. Our aim was to quantify the interactions between soil P availability and P nutrition strategies of European beech (Fagus sylvatica) forests. We assumed that plants and microorganisms of P-rich forests carry over mineral-bound P into the biogeochemical P cycle (acquiring strategy). In contrast, P-poor ecosystems establish tight P cycles to sustain their P demand (recycling strategy). We tested if this conceptual model on supply-controlled P nutrition strategies was consistent with data from five European beech forest ecosystems with different parent materials (geosequence), covering a wide range of total soil P stocks (160-900 g P m -2 ; \1 m depth). We analyzed numerous soil chemical and biological properties. Especially P-rich beech ecosystems accumulated P in topsoil horizons in moderately labile forms. Forest floor turnover rates decreased with decreasing total P stocks (from 1/5 to 1/40 per year) while ratios between organic carbon and organic phosphorus (C:P org ) increased from 110 to 984 (A horizons). High proportions of fine-root biomass in forest floors seemed to favor tight P recycling. Phosphorus in fine-root biomass increased relative to microbial P with decreasing P stocks. Concomitantly, phosphodiesterase activity decreased, which might explain increasing proportions of diester-P remaining in the soil organic matter. With decreasing P supply indicator values for P acquisition decreased and those for recycling increased, implying adjustment of plantmicroorganism-soil feedbacks to soil P availability. Intense recycling improves the P use efficiency of beech forests.

Mid-infrared spectroscopy (MIRS) is assumed to be superior to near-infrared spectroscopy (NIRS) for the prediction of soil constituents, but its usefulness is still not sufficiently explored. The objective of this study was to evaluate the ability of MIRS to predict the chemical and biological properties of organic matter in soils and litter. Reflectance spectra of the mid-infrared region including part of the near-infrared region (7000-400 cm -1 ) were recorded for 56 soil and litter samples from agricultural and forest sites. Spectra were used to predict general and biological characteristics of the samples as well as the C composition which was measured by 13 C CPMAS-NMR spectroscopy. A partial least-square method and cross-validation were used to develop equations for the different constituents over selected spectra ranges after several mathematical treatments of the spectra. Mid-infrared spectroscopy predicted well the C : N ratio: the modeling efficiency EF was 0.95, the regression coefficient (a) of a linear regression (measured against predicted values) was 1.0, and the correlation coefficient (r) was 0.98. Satisfactorily (EF ≥ 0.70, 0.8 ≤ a ≤ 1.2, r ≥ 0.80) assessed were the contents of C, N, and lignin, the production of dissolved organic carbon, and the contents of carbonyl C, aromatic C, O-alkyl C, and alkyl C. However, the N mineralization rate, the microbial biomass and the alkyl-to-aromatic C ratio were predicted less satisfactorily (EF < 0.70). Limiting the sample set to mineral soils did generally not result in improved predictions. The good and satisfactory predictions reported above indicate a marked usefulness of MIRS in the assessment of chemical characteristics of soils and litter, but the accuracies of the MIRS predictions in the diffuse-reflectance mode were generally not superior to those of NIRS.

Abstract. The analysis of soil phosphorus (P) in fractions of different plant availability is a common approach to characterize the P status of forest soils. However, quantification of organic and inorganic P fractions in different extracts is labor intensive and therefore rarely applied for large sample numbers. Therefore, we examined whether different P fractions can be predicted using near-infrared spectroscopy (NIRS). We used the Hedley sequential extraction method (modified by Tiessen and Moir, 2008) with increasingly strong extractants to determine P in fractions of different plant availability and measured near-infrared (NIR) spectra for soil samples from sites of the German forest soil inventory and from a nature reserve in southeastern China. The R2 of NIRS calibrations to predict P in individual Hedley fractions ranged between 0.08 and 0.85. When these fractions were combined into labile, moderately labile and stable P pools, R2 of calibration models was between 0.38 and 0.88 (all significant). Model prediction quality was higher for organic than for inorganic P fractions and increased with the homogeneity of soil properties in soil sample sets. Useable models were obtained for samples originating from one soil type in subtropical China, whereas prediction models for sample sets from a range of soil types in Germany were only moderately useable or not useable. Our results indicate that prediction of Hedley P fractions with NIRS can be a promising approach to replace conventional analysis, if models are developed for sets of soil samples with similar physical and chemical properties, e.g., from the same soil type or study site.

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The presence of relatively inert organic materials such as char has to be considered in calibrations of soil C models or when calculating C-turnover times in soils. Rapid and cheap spectroscopic techniques such as near-infrared (NIRS) or mid-infrared spectroscopy (MIRS) may be useful for the determination of the contents of char-derived C in soils. To test the suitability of both spectroscopic techniques for this purpose, artificial mixtures of C-free soil, char (lignite, anthracite, charcoal, or a mixture of the three coals) and forest-floor Oa material were produced. The total C content of these mixtures (432 samples) ranged from 0.5% to 6% with a proportion of char-derived C amounting to 0%, 20%, 40%, 50%, 60%, or 80%. All samples were scanned in the visible and near-IR region (400-2500 nm). Cross-validation equations for total C and N, C and N derived from char (C char , N char ) and Oa material were developed using the whole spectrum (first and second derivative) and a modified partial least-square regression method. Thirtysix samples were additionally scanned in the middle-IR and parts of the near-IR region (7000-400 cm -1 which is 1430-25,000 nm) in the diffuse-reflectance mode. All properties investigated were successfully predicted by NIRS as reflected by RSC values (ratio of standard deviation of the laboratory results to standard error of cross-validation) > 4.3 and modeling efficiencies (EF) ‡ 0.98. Near-infrared spectroscopy was also able to differentiate between the different coals. This was probably due to structural differences as suggested by wavelength assignment. Mid-IR spectroscopy in the diffuse-reflectance mode was also capable to successfully predict the parameters investigated. The EF values were > 0.9 for all constituents. Our results indicated that both spectroscopic techniques applied, NIRS and MIRS, are able to predict C and N derived from different sources in soil, if closed populations are considered.