Biogeochemistry (2016) 127:255–272 DOI 10.1007/s10533-015-0178-0
Soil properties controlling inorganic phosphorus availability: general results from a national forest network and a global compilation of the literature David Ludovick Achat . Noe´mie Pousse . Manuel Nicolas . Fe´lix Bre´doire . Laurent Augusto
Received: 23 September 2015 / Accepted: 22 December 2015 / Published online: 7 January 2016 Ó Springer International Publishing Switzerland 2016
Abstract Incorporating the phosphorus (P) cycle in climate-carbon cycle models—or calibrating pedotransfer functions to predict available soil P—are important issues. To achieve them we need to improve our understanding of the P cycle by focusing on processes and on the factors which control P dynamics in soils. The aim of the present study was to evaluate the generality of the relationships between physical– chemical soil properties and the availability of inorganic P (i.e. the dynamics of phosphate ions at the solid-to-solution interface), and to test the predictive capacity of these relationships. We used the French permanent network of forest monitoring (102 forests with contrasting soil properties, called network dataset) and a global compilation of published data from different ecosystems (60 studies, mainly in
forests, grasslands, or croplands, called compilation dataset). All studies used an isotopic dilution method to quantify the availability of inorganic P. Results showed generality of the dominant role of aluminum and iron oxides and organic carbon in controlling the dynamics of phosphate ions in acidic and non-acidic soils. Inversely, soil texture, pH and CaCO3 generally had no or only little effect. The control of inorganic P availability by oxides and organic carbon was confirmed by the compilation dataset, even in non-forest soils. Relationships obtained with the network dataset correctly predicted available soil inorganic P, suggesting that the dynamics of phosphate ions in soils could be simulated by including the main controlling soil properties in models. Our study provides predictive tools which could be included in diagnostic systems for the long-term management of soil fertility.
Electronic supplementary material The online version of this article (doi:10.1007/s10533-015-0178-0) contains supplementary material, which is available to authorized users.
Keywords Al and Fe oxides Diffusive phosphate ions Isotopic dilution Isotopically exchangeable phosphate ions Organic C Pedotransfer functions
Responsible Editor: Steven Perakis. D. L. Achat (&) F. Bre´doire L. Augusto INRA, Bordeaux Sciences Agro, UMR 1391 ISPA, Villenave d’Ornon 33140, France e-mail:
[email protected] N. Pousse ONF, De´partement RDI, Nancy 54000, France M. Nicolas ONF, De´partement RDI, Fontainebleau 77300, France
Introduction Earth system models which combine physical, chemical, and biological components, are useful tools to assess interactions between climate and biogeochemical cycles and to predict how ecosystems will respond to global changes (Reed et al. 2015). The nitrogen
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(N) and phosphorus (P) cycles have to be taken into account in such models (Reed et al. 2015; Thomas et al. 2015) because nutrients control the carbon (C) cycle and its response to climate change (Elser et al. 2007; Harpole et al. 2011). As reviewed by Reed et al. (2015), considering C-N interactions in previous climate-C cycle models improved a model’s capacity to predict ecosystem C sequestration, for instance, but incorporating the P cycle is still a challenge as it requires P-specific drivers and mechanisms (Finzi et al. 2011). Therefore, before incorporating P into models, we need to improve our understanding of the P cycle by focusing on processes and on the general factors (e.g. soil properties) which control P availability in soils. In contrast to soil N, which is mostly present in soil organic matter, soil P occurs as organic and inorganic fractions (Achat et al. 2013a). Consequently, the processes controlling P availability in soils (i.e. replenishing the soil solution with phosphate ions after its impoverishment due to biological consumption) include not only mineralisation of soil organic P and microbial P turnover, but also physical–chemical processes (e.g. diffusion at the solid-to-solution interface, dissolution of phosphate minerals; Achat et al. 2013a; Ziadi et al. 2013; Morel et al. 2014). Physical– chemical processes at the solid-to-solution interface depend to a great extent on soil properties, particularly on the amount of soil surfaces which react to phosphate ions (Hinsinger 2001). Several case studies have shown that the main soil surfaces usually involved in the reactivity of phosphate ions are aluminum (Al) and iron (Fe) oxides (particularly in acidic soils; e.g. Tran et al. 1988; Walbridge et al. 1991; doCarmoHorta and Torrent 2007). Other reactive surfaces include carbonates, clay minerals and soil organic matter (see for instance the review written by Hinsinger (2001) and references therein). Because reactive surface charges, such as those of Al and Fe oxides, and speciation of phosphate ions (H2 PO 4, ) depend on soil acidity, soil pH also affects the HPO2 4 dynamics of phosphate ions at the solid-to-solution interface (Strauss et al. 1997a, b; Hinsinger 2001; Ziadi et al. 2013; Barrow 2015). Consequently many factors potentially determine the availability of soil inorganic P and their relative importance may depend on local conditions: any possible general factor controlling inorganic P dynamics thus needs to be
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tested using data including wide ranges of soil and vegetation characteristics. To our knowledge, such a global approach has never been carried out so far. The first objective of the present study was to test the generality of the main factors controlling inorganic P availability in soils. We assessed relationships between soil properties and the dynamics of phosphate ions at the solid-to-solution interface inferred with an isotopic dilution method, using topsoils from a national monitoring network of forests with contrasting tree species and soil conditions, yet with a majority of acidic soil conditions (hereafter called ‘network dataset’) and a compilation of isotopic dilution data from worldwide published studies (hereafter called ‘compilation dataset’). The network dataset made it possible to assess the relationships accurately because we obtained isotopic dilution data using homogeneous methodological conditions and a large number of soil properties were included and could thus be tested. In contrast, there were methodological variants in the isotopic dilution procedure and the number of soil properties was limited in several published studies. The compilation dataset was consequently slightly less appropriate for the calibration of relationships; however, it made it possible to test the generality of the main factors controlling P availability in a wider range of contexts (including forests, grasslands, and croplands) and materials (organic layers, top and deep mineral soils). Our second objective was to test whether pedotransfer functions—or the inclusion of the most influential soil properties in models—would enable accurate prediction of inorganic P dynamics. Predictions were evaluated using the network dataset.
Materials and methods Network dataset Soils in the study forests We used samples collected between 2007 and 2012 from the top mineral soil layer (0–10 cm) at the 102 permanent forest sites of the French national network for the long term monitoring of forest ecosystems (‘‘RENECOFOR’’ network established in 1992 by the National Forest Service (ONF)). This monitoring network is the French part of the ICP Forests intensive
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monitoring programme (Ulrich 1995; see location of the study sites in Supplemental Fig. S1a). The RENECOFOR network covers a wide range of forest and soil conditions. The main tree species are oak at 30 sites (Quercus petraea, Quercus robur or a mixture of both; the names of these sites begin with CHS, CHP or CPS, respectively), beech at 20 sites (Fagus sylvatica; HET), pines at 23 sites (Pinus pinaster, Pinus sylvestris, or Pinus nigra laricio Corsican; PM, PS or PL, respectively), Norway spruce at 11 sites (Picea abies; EPC), fir at 11 sites (Abies alba; SP), Douglas fir at six sites (Pseudotsuga menziesii; DOU) and larch at one site (Larix decidua; MEL).The most widely represented soil types are Podzols, Cambisols and Luvisols (FAO/IUSS 2006), but Planosols, Leptosols, Calcisols, Arenosols, Regosols and Andosols are also found at some sites. The main parent materials are calcareous formations, eruptive and metamorphic rocks, sandstone, detritic and weathered formations (Breˆthes and Ulrich 1997). At each site, we used one composite soil sample obtained by pooling five replicate samples distributed through out a 13.5 m 9 13.5 m subplot. Composite soil samples were dried at 40 °C and sieved to 2 mm before analysis of the physical–chemical soil properties and P status. Soil properties Soil characteristics were determined according to French standards (AFNOR 1999). For soil texture, the five size fractions for clay (\2 lm), fine silt (2–20 lm), coarse silt (20–63 lm), fine sand (63–200 lm) and coarse sand (200–2000 lm) were used. Total C and N were measured by dry combustion (NF ISO 10694 and 13878) and organic C was subsequently calculated as the difference between total C minus CaCO3-C (CaCO3 calculated from the volume of CO2 released after acid (HCl) attack). Loss on ignition (LOI) was calculated as the difference in sample weight after calcination at 550 °C. Soil pH was determined in water and 0.01 M CaCl2 solution (10ml-soil: 50-ml-solution; NF ISO 10390). Exchangeable cations were determined after BaCl2 extractions; total exchangeable acidity (Al3? ? H? ? Mn2?? Fe2? ? Fe3? in cmol? kg-1), cationic exchange capacity (effective CEC = K? ? Ca2?? Mg2?? Na? ? Al3? ? H? ? Mn2?? Fe2? ? Fe3? in cmol? kg-1) and base saturation (sum of base cations
257
as a % of effective CEC) were then calculated. K, Ca, Mg and Mn contents were also measured after aqua regia extraction (concentrated HNO3 ? HCl; NF ISO 22036). Poorly crystalline Al and Fe oxides (Alox and Feox) were extracted with an ammonium oxalate solution (McKeague and Day 1966) and the ratio of organic C to Alox ? Feox was then calculated (Achat et al. 2012, 2013a). General soil P status Total P was quantified after ignition at 450 °C and wet digestion with concentrated fluoric (HF) and perchloric (HClO4) acids, using a normalised procedure (NF X 31–147; AFNOR 1999). HF is generally more appropriate than other reagents for the extraction of total P after soil ignition (e.g. Achat et al. 2013b). Accordingly, in the present study, the amount of P extracted with HF after ignition was higher than that of P extracted with sulphuric acid (P-H2SO4) after ignition or P extracted by digestion with aqua regia (P-AR: HNO3 ? HCl (NF ISO 22036)): total P-HF = 1.39 9 P-H2SO4 ? 78.74 (r2 = 0.90; P \ 0.0001) and total P-HF = 1.22 9 P-AR ? 61.28 (r2 = 0.95; P \ 0.0001). Total soil organic P was determined as the difference between ignited and nonignited soil samples in H2SO4-extracted P (2 g of dry soil for 70 mL of 0.2 N H2SO4; 16 h of extraction; Saunders and Williams 1955). Total inorganic P was subsequently calculated as total P-HF minus total organic P. Two different extraction methods were used as proxies of ‘‘available’’ soil P: Dyer P (P extracted with citric acid, NF X 31-160, AFNOR 1999; Dyer 1894) and Duchaufour P (P extracted with diluted H2SO4 ? P extracted with 0.1 N NaOH; Duchaufour and Bonneau 1959). Dynamics of phosphate ions assessed with an isotopic dilution method When assessing relationships between P dynamics and factors with the aim of calibrating pedotransfer functions or including P cycle in models, one issue to take into consideration is the choice of soil P assay on which to base available P fractions and parameters (Achat et al. 2011; Reed et al. 2015). Available soil P can be quantified using various chemical extraction methods (single or sequential extractions; Ziadi et al. 2013). Even though widely used and useful, chemical
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extractions produce data which may be difficult to interpret because they are not entirely based on processes. Indeed, extraction methods do not provide quantitative information on the ability of soils to replenish the soil solution with phosphate ions, and chemical reagents extract undetermined forms of P without taking the kinetics of reactions into account (Fardeau et al. 1988; Demaria et al. 2005; Morel et al. 2014). In contrast, sorption–desorption (Barrow 2008, 2015) and isotopic labelling (32P) and dilution methods (Fardeau 1993; Morel et al. 2014) are based on processes and enable quantification of the amount of phosphate ions in the soil solution and diffusive phosphate ions as a function of time. Phosphate ions in the soil solution are immediately available to plants, whereas diffusion refers to the transfer of phosphate ions under a gradient of concentration from the solid constituents of the soil into the soil solution (Jungk and Claassen 1997). Isotopic labelling and dilution procedures have consequently been found to be more accurate than extraction methods to quantify plantavailable soil P (Fardeau et al. 1988; Morel et al. 2014). Process-based approaches such as isotopic labelling and dilution procedures are also the most appropriate when calibrating pedotransfer functions or when including the P cycle in models. Phosphate ions in soil solution (CP in mg-P l-1 or PW in mg-P kg-1), diffusive phosphate ions at the solid-to-solution interface (Pr in mg-P kg-1), and the isotopically exchangeable phosphate ions (E in mg-P kg-1; E = PW ? Pr) were quantified using a 32Plabeling and isotopic dilution procedure (Fardeau 1993, 1996; Morel et al. 2014). This procedure was carried out in soil suspensions at steady state (e.g. CP values remained constant during the isotopic dilution period; see Supplemental Fig. S2). Five soil suspensions (1 g of soil (dry-soil-basis, M) for 9.9 mL of distilled water) were prepared for each of the 102 mineral soil samples. A biocide (0.1 mL of toluene) was added to prevent microbial activity in the soil suspensions during analysis of isotopic dilution. The soil suspensions were then gently equilibrated for 16 h on a roller at 20 °C. A 0.1-mL aliquot of solution containing a known amount of radioactivity (R) as 32P-labeled phosphate ions was introduced in the soil solution of the pre-equilibrated suspensions. The final volume of solution (V) was 10 mL. The soil solution was then sampled with a plastic syringe after 4, 10, 40, 100 and 400 min of
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isotopic dilution (one replicate or soil suspension for each isotopic dilution period) and filtered through 0.2 lm membrane filters, giving a total of 510 soil solution samples analyzed (102 soil samples 9 5 isotopic dilution periods; additional soil suspensions and soil solution analyses were performed on some heterogeneous soil samples). The filtered solutions were used to determine the radioactivity remaining in the soil solution (r) and CP values for each isotopic dilution period. Radioactivity r was counted in a counter (Packard TR1100) using a liquid scintillation cocktail. CP was determined using a malachite green colorimetric method (van Veldhoven and Mannaerts 1987), and PW was calculated as follows: PW ¼
CP V M
ð1Þ
The Pr- and E-values were calculated after the five elapsed times of isotopic dilution, assuming that R is diluted in E in which all the P fractions have the same isotopic composition (Eq. (2)). R r Rr ¼ ¼ E PW Pr
ð2Þ
Re-arranging Eq. 2 gives: R PW E ¼ PW ¼ r r=R Pr ¼ PW
Rr r
ð3Þ
¼ PW
1 1 r=R
ð4Þ
The theoretical Eq. (5) was used to closely fit the experimental values of the isotopic dilution ratio (r/R) as a function of isotopic dilution time, i.e. over the period from 4 to 400 min (Fardeau 1993, 1996; see Supplemental Fig. S3). n r r req ¼ m time þ m1=n ð5Þ for R R R where req/R corresponds to the maximum possible dilution of the isotope considering that all inorganic P maybe involved in the isotopic dilution (req/R & PW/total inorganic P; Fardeau 1993). The m and n parameters (dimensionless) account for the immediate and the slow physical–chemical reactions, respectively. The parameter n theoretically ranges from 0 to 0.5 (Fardeau 1993). When n is zero, diffusion at the solid-to-solution interface does not depend on time, whereas when 0.5 is reached, diffusion is at its
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theoretical maximum. Isotopic dilution parameters enable assessment of phosphate mobility at the solidto-solution interface or phosphate reactivity with the solid constituents: reactivity increases with increasing values of parameter n and/or decreasing values of parameter m (Frossard et al. 1989; Achat et al. 2011). Adjusted m and n parameters were finally used to extrapolate r/R over longer periods (i.e. 1 day (1440 min) in the present study). Extrapolated r/R values and Eqs. (3)and (4) were subsequently used to calculate E1day and Pr1day. Compilation dataset Data acquisition Using the ISI Web of Science database and other sources (mainly Google scholar), we identified studies with keywords related to soil P and isotopic labelling and dilution methods. We selected 60 peer-reviewed articles or published reports (see list of references in supplementary information) from which primary information on isotopic dilution could be extracted. We also added personal unpublished data. The selected studies were distributed worldwide: America (Canada, USA, Colombia, Venezuela and Brazil), Europe (Albania, Germany, France, Italy, Spain, Switzerland and the UK), Asia (Siberia), Oceania (Australia and New Zealand) and Africa (Burkina Faso, Cameroon, Congo, Kenya, Madagascar, Mali, Morocco, Nigeria, Senegal and Togo). All countries were not represented equally in the compilation dataset (France, Switzerland, Russia (Siberia) and Canada were the subject of more studies than the other countries; Supplemental Fig. S1b). The primary information collected on isotopic dilution included isotopic dilution parameters, CP, volume: mass ratio, and models used to describe r/R as a function of time. We also collected data on soil P status (total P, total organic and inorganic P), as well as soil properties. Because the number of soil properties studied greatly varied among the references, we selected the most frequently reported ones: soil texture (clay, silt and sand fractions), pHH2O, organic C, CaCO3, Al and Fe oxides extracted with ammonium oxalate solution (Alox and Feox; McKeague and Day 1966) or citrate-dithionite-bicarbonate (Alcdb and Fecdb; Mehra and Jackson 1960). However, it should be noted that the concentrations of CaCO3, Alcdb and
259
Fecdb were not reported in several studies. Finally, we collected information concerning land use (mainly forests, grasslands, or croplands; Supplemental Fig. S1b) and material (organic layer, top mineral soil: sampled layers with a mean soil depth B20 cm, deep soil: sampled layers with a mean soil depth[20 cm). All publications in which the isotopic dilution procedures used resembled the procedure used in our network dataset were selected (i.e. Fardeau’s procedure 1993, 1996), although there were some methodological variants among the case studies (the isotopic dilution period and the model used to determine parameters, the volume to mass ratio of soil suspensions). The volume to mass ratio was generally 1 g-dry-soil: 10 ml-water, but differed in some case studies. Equations used to fit the experimental values of the isotopic dilution ratio (r/R) as a function of isotopic dilution time also varied among published studies (Eq. (5) or simplified Eq. (6) with parameters r1min/R and n; Fardeau 1993). However, preliminary investigations led us to conclude that this methodological variant did not hamper our overall assessment of the relationships between immediate (m or r1min/R parameter) and slow physical–chemical reactions (n parameter) and soil properties. r r1min ¼ timen R R
for
r req R R
ð6Þ
Benefits and limits of the compilation dataset Our compilation dataset was useful to test the generality of the main factors which control the dynamics of phosphate ions in a wide range of contexts (e.g. land uses) and materials (different soil depths). However it should be noted that the compilation dataset is slightly less appropriate than the network dataset for accurate calibration of relationships with soil properties. Indeed, published case studies displayed methodological variants and limited information about the soil properties, which could bias parameters informing on relationships. For all these reasons, the compilation dataset was used to assess overall relationships between isotopic dilution parameters, CP and soil properties, but not to test whether pedotransfer functions—or including the most influential soil properties in models—would enable accurate prediction of P dynamics. Predictions were evaluated using the network dataset only.
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Data handling and statistics The strength of the associations between isotopic dilution data (isotopic dilution parameters, CP, E1day and Pr1day) and all physical–chemical soil properties was first explored using correlation coefficients. Spearman’s rank order correlation was the most appropriate for our datasets because the relationships were generally non-linear. Because our compilation dataset included missing data, we used all cases with valid data for each pairwise comparison. We also used Breiman’s random forest algorithm (random Forest Package, version 4.6-10; Liaw and Wiener 2002) in R software (R version 3.0.1; R Core Team 2013) to rank the importance of soil properties. We then selected the soil properties which were the most closely and significantly correlated with the isotopic dilution data, and assessed relationships with isotopic dilution data using regressions. We tested linear (after log10 transformations of the data) and different non-linear equations; the latter were of the power, logarithm or Mitscherlich type (Alivelu et al. 2003). For the compilation dataset, we tested two non-linear regression approaches: with or without random effects due to a possible ‘‘site’’ effect (tests carried out for regressions between the m parameter and the ratio of organic C to Alox ? Feox). The outcome of both procedures were fairly similar and we concluded that data dependency in the compilation dataset (e.g. dependence among soil layers of a given site) did not seriously affect the relationships. Therefore, random effects were neglected in regressions. Regressions (i.e. pedotransfer functions) adjusted based on the network dataset were used to predict values of the parameters of isotopic dilution and CP, which were subsequently used to predict the isotopic dilution ratio (r/R) for different periods (see Eq. (5)). Finally, E and Pr were predicted as a function of time using the predicted values of r/R and CP (see Eq. (1), (3) and (4)). Values of r/R, E and Pr were predicted for different periods corresponding to the measurements (4 and 400 min) and one extrapolation (1 day, 1440 min). The reliability and the accuracy of the pedotransfer functions in the estimation of isotopic dilution data were tested as follows. Measured values were plotted against predicted values and linear regression analysis applied (Pin˜eiro et al. 2008). In addition, the mean error of prediction (MEP; Wallach and Goffinet 1987)
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and the modeling efficiency (ME, Mayer and Butler 1993) were calculated (Eqs. 7 and 8). rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 Xn MEP ¼ ðy y^Þ2 ð7Þ 1 n1 where y is the measured value and yˆ is the predicted value. P ðyi y^i Þ2 ME ¼ 1 P ð8Þ ðyi yÞ2 where ¯y is the mean of observed values. The ME ranges from 0 (model not better than ¯y) to 1 (perfect relationship). Data handling and statistics were carried out using R software (R version 3.0.1; R Core Team 2013).
Results Network dataset Soil properties and P status: ranges of values As expected, there were major differences among forest soils in physical–chemical properties which may affect the dynamics of phosphate ions (soil texture, pH, CaCO3 content, organic C and Al and Fe oxides; Table 1). Almost all textural classes, ranging from clayey to sandy soils, were well represented. Soil pHH2O ranged from 3.6 to 8.3, but almost all the study soils were acidic (only five alkaline soils displayed pHH2O values from 7.7 to 8.3). Similarly, only five soils contained CaCO3 (15131 g kg-1; CaCO3 content = 0 g kg-1 for 97 soils). Study soils also differed markedly in their P status (Table 1). In general, there were two to three orders of magnitude of difference in total P (ranging from 19 to 1824 mg kg-1), organic, and inorganic P. Available soil P assessed using extraction methods (Dyer and Duchaufour P) or isotopic dilution (CP, E and Pr) also displayed marked heterogeneity. Analysing isotopic dilution parameters revealed soil samples with very small amounts of reactive surfaces (with little dilution of the 32 P isotope; e.g. minimum value of parameter n = 0.001) as well as strongly reactive soils (e.g. maximum value of parameter n = 0.81, which is greater than the theoretical maximum; Table 1; see kinetics of the isotopic dilution ratio used to determine parameters m and n in Supplemental Fig. S3).
Biogeochemistry (2016) 127:255–272 Table 1 Selected soil properties and phosphorus status of the French forest soils (network dataset)
261
Min
a
Textural classes: clay (N = 3 soil samples); silty clay (N = 8); silty clay loam (N = 8); silty loam (N = 24); clay loam (N = 5); loam (N = 12); sandy clay loam (N = 5); sandy loam (N = 21); loamy sand (N = 8); sand (N = 8) b
Al and Fe extracted with oxalate ammonium
Median
Mean
Quartile Q3
Max
Selected soil properties Clay (%)a
0.1
10.9
18.8
20.5
25.2
59.7
Silt (%)a
0.1
18.5
39.4
38.4
56.1
80.4
Sand (%)a
0.6
14.6
38.1
41.1
66.0
99.8
pHH2O
3.6
4.1
4.4
4.7
5.0
8.3
-1
N = 102 soil samples (0–10 cm)
Quartile Q1
CaCO3 (g kg )
0.0
0.0
0.0
4.5
0.0
131.0
Organic C (g kg-1)
8.0
32.2
46.4
59.1
68.1
458.0
4.5
78.7
149.0
178.2
238.4
1157.7
18.5
236.8
413.3
483.6
606.6
1824.2 1074.1
Alox ? Feox (mmol kg-1)b Soil P status Total P (mg kg-1) -1
Inorganic P (mg kg ) Organic P (mg kg-1) P
Duchaufour
P
Dyer
(mg kg-1)
(mg kg-1)
CP (mg l-1) E Pr
1day 1day
5.8
116.6
203.8
250.1
319.3
12.7
115.8
195.4
233.6
275.7
833.1
4.4
31.7
58.3
81.7
88.7
685.2
16.4
16.0
250.9
2.2
6.2
9.6
0.02
0.06
0.10
0.24
0.23
5.01
(mg kg-1)
1.7
18.2
27.9
48.6
48.1
487.6
(mg kg-1)
0.2
15.9
26.7
46.2
47.4
485.2
Parameter m
0.09
0.33
0.54
0.63
0.96
1.82
Parameter n
0.001
0.31
0.37
0.36
0.44
0.81
Correlations between isotopic dilution data and soil properties The most closely correlated physical–chemical soil property was Alox ? Feox for parameter n (correlation coefficient = 0.79) and the ratio of organic C to Alox ? Feox for CP and parameter m (correlation coefficient = 0.74–0.78; Table 2). Isotopic dilution parameters were also strongly correlated with other soil properties: clay content, organic C content or the ratio of organic C to clay (absolute values of the correlation coefficient = 0.50–0.57). Isotopic dilution parameters were also correlated with CP: parameter m increased and parameter n decreased with increasing CP values. Surprisingly, there were no statistically significant (P [ 0.05) or no close correlations with soil properties related to soil acidity: total CaCO3 and soil pH (only a correlation between parameter n and total exchangeable acidity was found: correlation coefficient = 0.50; Table 2). Similar results were found with the random Forest test. Ranked in decreasing order of importance, the main predictors of parameter m were CP, the ratio of organic C to Alox ? Feox and Alox ? Feox. The main predictors of parameter n were Alox ? Feox, the ratio
of organic C to Alox ? Feox, and CP. Finally, the main predictors of CP were the ratio of organic C to Alox ? Feox, and Alox ? Feox. In addition to the correlations between isotopic dilution parameters and soil properties, results also showed close relationships between E and Pr in one day and other P status variables (e.g. total P, Duchaufour P), Alox and Feox, organic C, total N, and clay content (Table 2; see more details in Supplemental Tables S1 and S2 and Supplemental Figs. S4 to S6). Calibration of the relationships Regressions were performed between CP, isotopic dilution parameters and the most closely correlated physical-chemical soil properties (Fig. 1; Eqs. (9) to (11)). A linear regression was performed between log10-transformed values of CP and log10-transformed values of the ratio of organic C to Alox ? Feox, and a Mitscherlich equation was used to describe parameter m as a function of the same ratio. For the relationship between parameter n and Alox ? Feox, we tested different possible equations (power, logarithm and Mitscherlich forms; Supplemental Fig. S7). Mitscherlich regressions made it possible to account
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262 Table 2 Cp, isotopic dilution parameters, E and Pr in one day in relation to other P status variables and soil properties (Spearman’s correlation coefficients; network dataset)
Biogeochemistry (2016) 127:255–272
Cp CP
–
PDuchaufour
-0.26
PDyer
0.09
Inorganic P Total P
Parameter m
Parameter n
E1
day
Pr1
day
0.83
-0.54
-0.22
-0.37
0.62
0.74
0.74
0.02
0.35
0.50
0.48
-0.29
-0.42
0.62
0.76
0.77
-0.25
-0.40
0.64
0.81
0.82
Clay
-0.38
-0.56
0.50
0.65
0.66
Fine silt
-0.36
-0.48
0.26
0.35
0.37
Coarse silt Silt
-0.28 -0.33
-0.33 -0.45
0.12 0.18
0.13 0.24
0.14 0.24
Fine sand
0.13
0.28
-0.18
-0.36
-0.36
Coarse sand
0.30
0.40
-0.13
-0.20
-0.21
Sand
0.32
0.49
-0.26
-0.40
-0.40
Organic C
-0.04
-0.23
0.57
0.78
0.75
LOIa
-0.13
-0.32
0.62
0.82
0.80
Organic C/clay
0.52
0.51
-0.13
-0.05
-0.08
Total N
-0.13
-0.34
0.64
0.87
0.85
Albox Febox
-0.50
-0.63
0.74
0.79
0.82
-0.48
-0.59
0.76
0.74
0.76
-0.53
-0.64
0.79
0.80
0.82
0.74
-0.50
-0.31
-0.36 -0.06
(Alox + Feox Þ
b
Organic C/(Alox + Feox Þ Total CaCO3
b
0.78
0.02
-0.22
-0.08
H2O
-0.19
-0.18
-0.03
0.01
0.03
CaCl2
-0.28
-0.27
0.09
0.12
0.16
Total exchangeable acidity
-0.21
-0.25
0.50
0.45
0.45
Exchangeable Al
-0.28
-0.30
0.54
0.46
0.47
Exchangeable Ca
0.08
-0.01
0.11
0.28
0.27
Exchangeable Fe
0.10
0.08
0.26
0.21
0.20
Exchangeable H
0.39
0.37
-0.07
-0.04
-0.06
Exchangeable K
-0.14
-0.27
0.33
0.44
0.43
Al and Fe extracted with oxalate ammonium
Exchangeable Mg
0.01
-0.13
0.24
0.40
0.39
Exchangeable Mn
-0.33
-0.27
0.24
0.13
0.15
c
Exchangeable Na
-0.10
-0.19
0.27
0.36
0.35
CECc
-0.04
-0.23
0.42
0.66
0.66
Base saturation
0.15
0.10
-0.17
-0.04
-0.05
KdAR CadAR MgdAR MndAR
-0.39
-0.47
0.46
0.53
0.54
-0.06
-0.17
0.13
0.28
0.29
-0.39
-0.52
0.49
0.59
0.60
-0.46
-0.48
0.29
0.28
0.30
pH pH
N = 102 soil samples (0–10 cm) a
Loss on ignition
b
Effective cation exchange capacity
d
Aqua regia extraction
Correlations between all P status variables are given in Table S1 Bold values indicate significant correlations (P \ 0.05)
for a maximum value of parameter n (0.487–0.537), which was close to the theoretical maximum of 0.5. On the other hand, Mitscherlich regressions had the lowest modelling efficiency. Although power and logarithm functions had high modelling efficiency,
123
0.13
-0.28
they were not entirely appropriate as they would lead to negative predicted values of parameter n. In the end, we found that the most appropriate function was a modified Mitscherlich equation (Fig. 1 and Supplemental Fig. S7).
Biogeochemistry (2016) 127:255–272
263
Fig. 1 Relationships between the concentration of phosphate ions in soil solution (CP), isotopic dilution parameters and the most important soil properties in a, c and e. Comparisons between measured and predicted values in b, d and f (network dataset). CP and parameter m were predicted using the ratio between organic C and Al and Fe oxides extracted with ammonium oxalate. Soil properties used to predict parameter n include Al and Fe oxides extracted with ammonium oxalate and
total exchangeable acidity (see relationships between residual values of parameter n and total exchangeable acidity in Supplemental Fig. S8). The solid lines in (a), (c) and (e) represent linear or non-linear regressions (see Eqs. (9) to (11) in the text). The solid lines in (b), (d) and (f) represent linear regressions (y = ax; see details in Table 3). The dashed lines in (b), (d) and (f) represent the 1:1 lines
Using the relationships with Alox ? Feox and the ratio of organic C to Alox ? Feox, we calculated the residual values (measured–predicted values) of CP and the parameters m and n; and correlations between residual values and soil properties were assessed (Supplemental Table S3). Correlation coefficients were generally low (B0.41). Few soil properties were significantly (P \ 0.05) correlated with the residual values of CP and relationships with these soil properties were not statistically confirmed by linear regressions. For residual values of isotopic dilution parameters, we only used soil properties when the relationships (linear regressions) were statistically
significant and when they could be explained by processes. Finally, it was only possible to use one relationship between residual values of parameter n and total exchangeable acidity (see linear regression in Supplemental Fig. S8 and explanation in the discussion). Combining the different relationships led to the following equations: Log10 ðCP Þ ¼ 1:129 Log10 ðorganic C=ðAlox þ Feox ÞÞ 0:445 $ CP ¼ 10ð1:129Log10 ðorganic C=ðAlox þFeox ÞÞ0:445Þ ð9Þ
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Biogeochemistry (2016) 127:255–272
of the linear regression between measured and predicted data was not statistically significant for r/R; but this was not always the case for E and Pr (Table 3). In general, the predictions of r/R, E and Pr as a function of time (i.e. by taking all times in the analysis; r2: 68–92 % of the variability explained by the model; ME = 0.59–0.86) were correct. However, the prediction of E and Pr in 400 min and 1 day (ME = 0.51–0.59) was better than the prediction of E and Pr in 4 min (ME = 0.37–0.39).
Parameter m ¼ 1:187 1:456 expð2:705ðorganic C=ðAloxþFeoxÞÞÞ ð10Þ Parameter n ¼ 0:352 2 1 expð2:004 10 ðAlox þ FeoxÞÞ þ 3:465 104 ðAlox þ FeoxÞ þ ð0:009 total exchange acidity 0:034Þ ð11Þ
Compilation dataset
Evaluation of the predictions
Soil properties and P status: ranges of values
Equations (9) to (11) produced reasonably correct predictions of CP and parameters m and n (see Fig. 1 and Table 3, ME = 0.35–0.73).Using Eqs. (1), (3), (4) and (5), we were able to predict r/R, E, and Pr as a function of time (Fig. 2 and Table 3). The constant b
The compilation dataset included wider ranges of values for organic C (0.4–476.7 g kg-1) and Alox ? Feox (4.6–1344.0 mmol kg-1) than those in the network dataset. The compilation dataset also included wide ranges of pH (3.3–9.1) and CaCO3
Table 3 Evaluation of the predictions (network dataset) N
y = ax ? b (measured vs predicted values) a
b
Intercept significance
y = ax (measured vs predicted values) r2
a
Mean error of prediction
Modelling efficiency
r2
Phosphate ions in soil solution and isotopic dilution parameters CP (mg l-1)
102
0.85
0.07
ns
0.37
0.93
0.47
0.43
0.35
Parameter m
102
1.00
0.00
ns
0.62
1.00
0.91
0.22
0.62
Parameter n
102
0.94
0.02
ns
0.74
0.99
0.96
0.08
0.73
All 4 min
306 102
1.02 1.06
0.00 -0.03
ns ns
0.86 0.86
1.03 1.01
0.92 0.95
0.10 0.11
0.86 0.86
400 min
102
1.05
0.01
ns
0.83
1.06
0.88
0.10
0.83
1 day
102
1.04
0.01
ns
0.79
1.06
0.84
0.10
0.79
306
0.94
5.73
**
0.60
1.00
0.69
29.96
0.59
r/R
E (mg kg-1) All 4 min
102
1.01
0.73
ns
0.40
1.11
0.64
4.71
0.39
400 min
102
1.02
3.62
ns
0.61
1.10
0.78
19.11
0.59
1 day
102
0.87
13.24
*
0.54
0.98
0.67
48.20
0.51
All
306
0.93
5.35
**
0.60
0.99
0.68
29.75
0.59
4 min
102
1.23
-0.10
ns
0.43
1.20
0.77
1.59
0.37
400 min
102
1.00
3.76
ns
0.60
1.08
0.76
18.74
0.58
1 day
102
0.86
12.78
*
0.53
0.97
0.66
48.15
0.51
Pr (mg kg-1)
ns not significant (P [ 0.05) * P \ 0.05; ** P \ 0.01
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Biogeochemistry (2016) 127:255–272
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soils and the P status of top soils in other contexts is given in Supplemental Fig. S9. Correlations between isotopic dilution data and soil properties
Fig. 2 Comparison between measured and predicted values of the isotopic dilution ratio (r/R; a), isotopically exchangeable phosphate ions (E value; b) and diffusive phosphate ions (Pr; c) (network dataset). Open symbols, 4 min; grey symbols, 400 min; black symbols, 1 day (1440 min). The dashed lines represent the 1:1 lines. The solid lines represent linear regressions (y = ax; see details in Table 3)
(0–340 g kg-1), even though there were more acidic and/or non-calcareous soils than alkaline and/or calcareous soils, as was also the case in the network dataset. The range of textures in the compilation dataset (clay content = 0.3–62.0 %, silt = 0.2–75.8 %, sand = 1.1–99.3 %) was similar to that in the network dataset. In addition to the marked heterogeneity of the soil properties, the compilation dataset also contained wide ranges of values for total P (16–4925 mg kg-1), CP (0.0008–66.6 mg l-1), parameter m (or r1min/R; 0.002–2.60), and parameter n (0.00–0.76). A detailed comparison between the P status of the French forest
Because the size of the compilation dataset varied with variables, comparing coefficients of correlation directly was not possible. Instead, we used matrix correlation to identify the most prominent relationships among soil properties and P status variables. Like the network dataset, CP and parameter m were well correlated with the ratio of organic C to Alox ? Feox(positive relationships: correlation coefficients = 0.54–0.60; Table 4). The CP and parameter m were also correlated, but to a lesser extent, with other soil properties such as the amount of Alox (negative relationships: correlation coefficients = -0.42 to -0.48). Unlike in the network dataset, parameter n was not strongly correlated with the sum of Alox and Feox (positive relationship: correlation coefficients = 0.33). On the other hand, parameter n was well related to the ratio of organic C to Alox ? Feox (negative relationship: correlation coefficients = -0.42). Although statistically significant (P \ 0.05), correlations between CP and parameters of isotopic dilution and other soil properties including soil organic matter, soil texture or soil acidity (pH and CaCO3) were generally low. The compilation dataset suggested that overall, CP and the isotopic dilution parameters were more correlated with Alox and Feox than with Alcdb and Fecdb (Table 4). Like in the network dataset, E- and Pr-values correlated well with total P, Al, and Fe (extracted with oxalate ammonium or citrate-dithionite-bicarbonate) and clay content (Table 4). Generality of the relationships between isotopic dilution data and soil properties The shape of the relationships between CP, isotopic dilution parameters and soil properties found with the network dataset (e.g. Mitscherlich equation for the relationships between parameter m and the ratio of organic C to Alox ? Feox) was also supported by the compilation dataset in wider ranges of values (Figs. 3, 4), across different soil layers (Supplemental Fig. S10) and land uses (Supplemental Fig. S11). Similar forms of equations were also found between isotopic dilution parameters and CP (Fig. 5).
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266 Table 4 CP, isotopic dilution parameters, E and Pr in one day in relation to soil properties (Spearman’s correlation coefficients): results from the compilation dataset
N = 476–1511 a
Al and Fe extracted with oxalate ammonium
b
Al and Fe extracted with citrate-dithionitebicarbonate Bold values indicate significant correlations (P \ 0.05)
Biogeochemistry (2016) 127:255–272
Parameter n
E1day
Pr1day
CP
–
0.74
-0.76
0.40
0.25
Total P
0.44
0.10
-0.20
0.75
0.75
Clay
0.04
-0.32
-0.11
0.47
0.48
Silt
0.17
-0.09
-0.18
0.39
0.38
Sand
-0.09
0.23
0.16
-0.41
-0.40
pHH2O
0.10
-0.10
-0.29
0.31
0.37
CaCO3
0.00
-0.22
-0.20
0.16
0.23
Organic C Alaox
0.38 -0.42
0.23 -0.48
-0.14 0.43
0.47 0.31
0.34 0.43
-0.17
-0.30
0.20
0.53
0.63
-0.31
-0.42
0.33
0.47
0.59
0.54
-0.42
0.08
-0.08
Feaox (Alox + Feox Þ
a
Organic C/(Alox + Feox Þa
0.60
Albcdb
-0.12
-0.40
0.33
0.63
0.65
0.16
-0.18
-0.09
0.64
0.64
0.02
-0.29
0.09
0.67
0.68
0.18
0.18
0.07
0.02
0.02
Febcdb (Alcdb + Fecdb Þ
b
Organic C/(Alcdb + Fecdb Þ
b
Our results revealed a superimposition of the relationships between parameter m and the ratio of organic C to Alox ? Feoxin both datasets (Fig. 3b). However, this was not strictly the case for other relationships (Figs. 3a, 4, 5), perhaps due to differences in land uses: the relationships we found with the network dataset were comparable to those of other forests, but differed slightly from those of grasslands and croplands (see examples in Supplemental Fig. S11). Our results also suggest that differences among land uses in the relationships may be due to differences in total P content (Supplemental Fig. S12). Importantly, results from the compilation dataset showed that the relationships between CP (and isotopic dilution parameters) and the ratio of organic C to Alox ? Feox did not depend on soil acidity or on CaCO3 content (see an example in Supplemental Fig. S13). In other words, organic C and Al and Fe oxides are explanatory factors not only in acidic soils but also in alkaline and calcareous soils.
Discussion Identifying the main soil properties which control the dynamics of soil inorganic P In agreement with previous case studies (e.g. Tran et al. 1988; do Carmo Horta and Torrent 2007; Achat
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Parameter m (or r1min/R)
Cp
et al. 2011), our network dataset revealed important roles for Al and Fe oxides. However, we found no effect of CaCO3. This result could be explained by the fact that the number of soils containing CaCO3 was very low (N = 5 in the network dataset). Nevertheless, our compilation dataset enabled us to go one step further as it showed that Al and Fe oxides play an important role in controlling soil reactivity for phosphate ions not only in acidic soils but also in soils with an alkaline pH and CaCO3. Indeed, Al and Fe oxides have positive charges over the whole range of pH usually encountered in soils (high point of zero charge: between pH 7 and pH 10) and have large amounts of specific surface area. Consequently, Al and Fe oxides are present as strongly reactive surfaces in most soils, including soils with alkaline pH (e.g. calcareous soils; see also Hinsinger (2001) and Regelink et al. (2015)). Our general results (important role of Al and Fe oxides, no -or only a slight-effect of CaCO3 content) are also in agreement with those of Scha¨rer (2003), who reported a biggest increase in P sorption after Fe/ OH amendment than after CaCO3 amendment in acidic and calcareous soils. We found positive relationships between Al and Fe oxides extracted with ammonium oxalate and Al and Fe oxides extracted with citrate-dithionite-bicarbonate (correlation coefficient = 0.84; compilation dataset), but Alox ? Feox matched 59 % of Alcdb ? Fecdb as a mean value and Alox and Feox were most closely
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Fig. 3 CP and parameter m in relation to the ratio of organic C to Al and Fe oxides extracted with ammonium oxalate: comparison between the network and the compilation datasets. Red symbols, network dataset; grey symbols, compilation dataset. For more details, see Supplemental Figs. S10 to S13. Regressions: r2 = 0.67 and 0.48, respectively for the network dataset and the compilation dataset in a (linear regression after log10-transformations); r2 = 0.62 and 0.36, respectively for the network dataset and the compilation dataset in b (Mitscherlich type equations). (Color figure online)
related to CP and isotopic dilution parameters. These results are probably explained by the fact that Alox and Feox correspond to less crystalline and amorphous fractions (more reactive surfaces) than Alcdb and Fecdb (Duiker et al. 2003; Igwe et al. 2009). Moreover, in agreement with previous observations (Villapando and Graetz 2001; Achat et al. 2011), CP and isotopic dilution parameters were generally better related to Alox than Feox (results using both datasets). In addition to Al and Fe oxides, soil texture also affected phosphate dynamics, but to a lesser extent. Indeed, reactivity inferred with isotopic dilution parameters increased with an increase in the clay fraction. This could be due to the presence of P sorbents in mineral clays (silanol groups bearing positive charges; Hinsinger 2001; Callesen and Raulund-Rasmussen 2004) and/or the fact that fine soil fractions
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Fig. 4 Parameter n in relation to Al and Fe oxides extracted with ammonium oxalate (a) and the ratio of organic C to Al and Fe oxides (b): comparison between the network and the compilation datasets. Red symbols, network dataset; grey symbols, compilation dataset. Regressions: r2 = 0.70 and 0.24, respectively for the network dataset and the compilation dataset in a (modified Mitscherlich equations); r2 = 0.59 and 0.50, respectively for the network dataset and the compilation dataset in b (linear regression after log10-transformations). (Color figure online)
may contain Al and Fe oxides (covariate effect). Indeed, we found positive relationships between the clay fraction and the concentrations of Al and Fe oxides (correlation coefficients = 0.32–0.79; both datasets). Consequently, in agreement with previous studies (Callesen and Raulund-Rasmussen 2004; Pellerin et al. 2006), we found that the capacity of fine-textured soils to release available P (diffusive phosphate ions in the present study) was higher than in coarse-textured soils (Supplemental Figs. S5, S6). Our study also revealed the role of organic C in controlling inorganic P availability. Like earlier observations in one particular forest context (Achat et al. 2012), CP was generally closely and positively correlated with the ratio of organic C to Alox ? Feox.
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268
Fig. 5 Isotopic dilution parameters in relation to the concentration of phosphate ions in soil solution (CP): comparison between the network and the compilation datasets. Red symbols, network dataset; grey symbols, compilation dataset. For more details, see Supplemental Figs. S10 to S13. Non-linear regressions: r2 = 0.67 and 0.38, respectively for the network dataset and the compilation dataset in a (Mitscherlich type equations); r2 = 0.39 and 0.57, respectively for the network dataset and the compilation dataset in b (logarithmic functions). (Color figure online)
This important result is probably due to the fact that organic C and Al and Fe oxides have opposite effects. CP decreases with an increase in reactive surfaces and hence with increasing Al and Fe oxide contents. Conversely, the reactivity of phosphate ions decreases leading to an increase in CP with increasing organic C content, owing to competition between phosphate ions and organic anions for reactive surfaces (Jones and Brassington 1998; Borggaard et al. 2005; de Souza et al. 2014; see also surface complexation modelling study by Regelink et al. (2015). The decreasing reactivity of phosphate ions inferred with isotopic dilution parameters (increasing parameter m and decreasing parameter n) with an increase in the ratio of the organic C to Alox ? Feox also supports these opposing effects. When land uses were analysed
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separately, the compilation dataset revealed significant relationships between CP (or isotopic dilution parameters) and the ratio of organic C to Alox ? Feox in forests and grasslands; however, this was not the case for croplands (Supplemental Fig. S11). One possible explanation is that soil organic matter is less associated with metal oxides in croplands mainly due to PO4 fertilisation and the resulting reduction in adsorption of soil organic matter (Regelink et al. 2015). Another possible explanation is that the range of ratios of organic C to Alox ? Feox in the cultivated soils in our compilation dataset was narrow (Supplemental Fig. S11), mainly due to the lack of deep layers and surface organic layers; our assessment of the relationships between CP (or isotopic dilution parameters) and the ratio of organic C to Alox ? Feox in croplands was consequently hampered. Besides the competition between phosphate ions and organic anions for reactive surfaces, the relationships between organic C and CP could also be partly explained through microbial processes, which occurred before soil sampling. Indeed, immobilisation of phosphate ions in micro-organisms and mineralisation of organic P due to microbial and enzymatic activities could affect the concentration of phosphate ions in the soil solution (Oehl et al. 2001a, b; Achat et al. 2010), and microbial and enzymatic activities are generally correlated to organic C (Bu¨nemann 2015). Given that organic C content is generally lower in croplands, its effect on CP through microbial processes is also expected to be lower in these contexts, compared with forests and grasslands. Positive correlations between E/Pr in one day and soil organic matter were inferred with organic C and N contents. These relationships appear to contradict our general findings concerning the role of soil organic matter, as explained above (increasing organic C decreases soil reactivity to phosphate ions). But, in fact, the positive relationships between E/Pr and organic matter resulted from a covariate effect. Indeed, the finest textured soils generally displayed not only the highest concentrations of Al and Fe oxides (and consequently the highest E- and Prvalues), but also the highest soil organic matter content (correlation coefficients between clay fraction and organic C = 0.27–0.58). In addition to the importance of reactive surfaces, soil acidity also plays a role in the dynamics of phosphate ions at the solid-to-solution interface. The
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charges of reactive surfaces such as Al and Fe oxides vary with the soil pH (Strauss et al. 1997a; Hinsinger 2001) and an increase in positive charges with decreasing pH results in higher reactivity for phosphate ions. As a result, negative relationships have been found between soil pH and P sorption capacity (Barrow 1987, Barrow 2015; Jones and Brassington 1998; Dubus and Becquer 2001; Nwoke et al. 2003), or reactivity inferred with isotopic dilution parameters (Tran et al. 1988; Achat et al. 2013b). Moreover, Tran et al. (1988) found that parameter n was more closely correlated with soil pHH20 (ranging from 4.4 to 7.9) than with the amounts of Alox and Feox. Conversely, no significant effect of soil acidity was revealed in other studies (Singh and Gilkes 1991, Maguire et al. 2001; Achat et al. 2011) because the ranges of pH values were too narrow. Although our two datasets included wide ranges of pHH20 values (3.6–8.3 in the network dataset, 3.3–9.1 in the compilation dataset), we generally found no strong effect of soil acidity. Only a small, secondary, effect of total exchangeable acidity was found on parameter n in the network dataset. The strong CP-dependence of parameters m and n (the present study, Morel et al. 1994; Demaria et al. 2013) suggests that Al and Fe oxides and organic C could affect isotopic dilution parameters directly and/ or indirectly through their effects on CP. Finally, by combining the ranges of soil conditions in the network dataset with those in the compilation dataset, we demonstrated generality in the dominant role of the ratio of organic C to Alox ? Feox in controlling the dynamics of phosphate ions at the solid-to-solution interface, across different soil layers and land uses. However, our compilation dataset displayed marked heterogeneity in the relationships between CP, isotopic dilution parameters and soil properties, which could be partly due to differences in land uses and total P content, but also to methodological variations in the published studies. Capacity of pedotransfer functions and models to predict P dynamics in soils Besides the importance of incorporating the P cycle in C-N models, improving our understanding of the relationships between the P cycle and factors such as soil properties could also lead to the development of pedotransfer functions, thus enabling estimation of
269
available soil P (Callesenand Raulund-Rasmussen Callesen and Raulund-Rasmussen 2004; Achat et al. 2011; Demaria et al. 2013). These tools could be incorporated in diagnosticsystems for long-term management of chemical soil fertility and site productivity (Achat et al. 2011). In previous studies, relationships with soil properties have been used to develop alternative approaches to predicting the P sorption capacity of soils (based on sorption–desorption methods;Borggaard et al. 2004; Alves and Lavorenti 2006; Burkitt et al. 2006; Regelink et al. 2015). Using the isotopic dilution method, Demaria et al. (2013) calibrated relationships to predict E and Pr in cultivated soils displaying low to medium heterogeneities in several soil properties (e.g. pH = 6.0–7.7, organic C = 12–26 g kg-1, total P = 595–1204 mg kg-1). Similarly, Achat et al. (2011) calibrated pedotransfer functions for the prediction of E and Pr in acidic and sandy podzols under maritime pine plantations, i.e. in specific site conditions. The network dataset used in the present study enabled us to go one step further, as we calibrated pedotransfer functions using soils under different tree species and with wide ranges of several properties. High modeling efficiency values showed that the prediction of available phosphate ions (CP; Pr and E up to one day) was generally good. Our study thus provides appropriate predictive tools that could be included in diagnostic systems for long-term management of forest ecosystems. We propose (i) pedotransfer functions enabling the prediction of CP and E and Pr as a function of time (see Eqs. (9) to (11)) and (ii) simplified pedotransfer functions enabling more direct estimations of E and Pr in one day (see equations in Supplemental Table S2; Figs. S4, S5, S6). More generally, our study should help incorporate the P cycle in C-N models (Reed et al. 2015). Indeed, our results show that it is possible to accurately simulate the dynamics of phosphate ions in soils by including the main controlling soil properties in models. However, in addition to physical–chemical soil properties, the role of soil structure (effects of soil aggregates; see review by Ziadi et al. (2013) and references therein) and water content (effects of the ratio of water to soil mass (Barrow 2015)) on the exchange of phosphate ions between the soil solution and solid constituents have to be taken into account. Finally, further research is also needed to improve our understanding of the processes that control the
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270
availability of organic P fractions (Bu¨nemann 2015; Reed et al. 2015), which include biological and biochemical organic P mineralisation and microbial P turnover (Oehl et al. 2001a, b; Achat et al. 2013a; Spohn and Kuzyakov 2013). Previous case studies suggest that organic P mineralisation depends on soil properties including organic C content, Al and Fe oxides, and phosphatase activity (Achat et al. 2013a; Bu¨nemann 2015). Organic P mineralisation may also be linked to C and N mineralisation (Achat et al. 2010; Spohn and Kuzyakov 2013; Bu¨nemann 2015). For instance, Achat et al. (2010) found similar organic P mineralisation rates and organic C and N mineralisation rates (i.e. C: N: P stoichiometry in mineralisation fluxes) under basal conditions of microbial activity, suggesting that mineralisation of P in dead soil organic matter can be driven by microbial need for C (Spohn and Kuzyakov 2013). However, the generality of the coupling of C, N and P in mineralisation fluxes and of their relationships with soil properties will have to be assessed before they can be incorporated in models.
Conclusion Covering a wide range of soil and vegetation conditions, we underline the general and prominent role of Al–Fe oxides and organic C in controlling the availability of soil inorganic phosphorus. In contrast, soil texture, soil pH, and soil carbonate contents had no -or only a slight- influence. This large-scale study provides appropriate predictive tools (pedotransfer functions) that could be included in diagnostic systems for long-term management of forest ecosystems, as well as in models such as biogeochemical models. Acknowledgments Forest soils of the RENECOFOR network were analysed in the framework of the INSENSE project, supported by the French agency for energy and environment (ADEME).We are grateful to INRA Bordeaux (UMR ISPA), for allowing David Achat to use their laboratory facilities for isotopic dilution analyses and to complete the current study. We thank Nathalie Gallegos and the INRA Soil Analysis Laboratory (Arras, France) for their help in total P, organic P, Dyer P and Duchaufour P quantifications. We thank Christian Morel, Andre´ Schneider, Pascal Denoroy, Arnaud Legout and Bernard Jabiol for helpful discussions about the relationships between soil properties and isotopic dilution data. Finally, we thank editor and two anonymous reviewers for useful comments on the manuscript.
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