Long Term Effects of Forest Liming on the Acid-Base Budget

applied sciences

Article Long Term Effects of Forest Liming on the Acid-Base Budget

Martin Greve 1,* , Joachim Block 1, Gebhard Schüler 1 and Willy Werner 2

1 Research Institute for Forest Ecology and Forestry (Rhineland-Palatinate), Hauptstrasse 16, 67705 Trippstadt, Germany; [email protected] (J.B.); [email protected] (G.S.) 2 Department of Geobotany, Regional and Environmental Sciences, Trier University, Behringstraße 21, 54296 Trier, Germany; [email protected] * Correspondence: [email protected]

Abstract: In Rhineland-Palatinate (Germany), a high percentage of the forest area is located on poor soils with low buffering capacity. Extensive liming applications were performed to compensate for the negative consequences of acid deposition. In 1988, three experimental sites with untreated control plots and different liming treatments were established in coniferous stands to investigate the effectiveness of liming on acidiﬁcation and its effect on forest ecosystems. Measuring deposition and seepage waters for 24 years allowed for calculating long-term acid-base budgets. The original approach was expanded by data from a detailed sampling of the forest stand and mineral weathering rates. Without liming, the acid load exceeded the buffer capacity by base cation release from silicate weathering during the whole observation period. As a result, there was a high release of aluminum. After liming seepage water output of organic anions, nitrate and sulfate increased in some cases, leading to a higher acid load. However, the carbonates of dolomitic limestone compensated for a higher acid load, resulting in less aluminum released compared to the control plots. Until sulfate output by seepage water declines and nitrogen emissions are reduced, liming and restricted biomass harvesting are required for forest stands on base poor soils to prevent further acidiﬁcation, decline of

nutrient stocks, and the destruction of clay minerals. Keywords: Citation: Greve, M.; Block, J.; liming; acidification; acid base budget; base cations; nitrogen; biomass increment; Schüler, G.; Werner, W. Long Term air pollution Effects of Forest Liming on the Acid- Base Budget. Appl. Sci. 2021, 11, 955. https://doi.org/10.3390/app11030955 1. Introduction Academic Editor: Stefan Fleck Since the late 19th century, an increased acid input from anthropogenic activities was Received: 31 October 2020 observed in Europe and North America [1]. Forest ecosystems were especially affected Accepted: 13 January 2021 when their position was exposed and because of their large intercepting canopy surface [2]. Published: 21 January 2021 Since the 1980s, acid atmospheric deposition has been decreasing, mainly due to reduced sulfur dioxide emissions [3–5]. However, the input of nitrogen components still remains at Publisher’s Note: MDPI stays neutral a high level (cf. [6]) and is currently the main source for the acid load entering the forest with regard to jurisdictional claims in ecosystems [7,8]. published maps and institutional affil- In addition, centuries of intensive litter and timber harvesting contributed to large iations. scale soil acidification [9,10]. In Rhineland-Palatinate, which is located in southwestern Germany (Figure 1), a high percentage of forested areas are located on base-poor soils with small nutrient reserves and low buffer capacity against acidity [11]. As a result, mobilization of aluminum and heavy metals, reduction of base cation reserves, and destabilization of clay Copyright: © 2021 by the authors. minerals could, and still can, be observed for many forest sites [12–15]. To compensate for Licensee MDPI, Basel, Switzerland. the negative consequences of the acid deposition, extensive liming actions with dolomitic This article is an open access article lime were performed at an early stage [16,17]. The application of 3 to 4 tons per hectare distributed under the terms and every 10 years was recommended [2,18]. conditions of the Creative Commons In 1988, three experimental sites (Figure 1) with different liming treatments were Attribution (CC BY) license (https:// established. The base-poor forest sites were treated once to evaluate the forest management creativecommons.org/licenses/by/ 4.0/). practice of liming and to investigate its effectiveness and impacts on forest ecosystems [19].

Appl. Sci. 2021, 11, 955. https://doi.org/10.3390/app11030955 https://www.mdpi.com/journal/applsci Appl. Sci. 2021, 10, x FOR PEER REVIEW 2 of 14

Appl. Sci. 2021, 11, 955 2 of 14 ecosystems [19]. Long term input-output element and acid-base budgets were calculated (cf. [14]) to characterize the forest ecosystems of the three experimental sites by the different processes causing the fluxes of acidity and to quantify soil acidification without andLong with term different input-output dosages element of dolomitic and acid-base limestone. budgets Therefore, were calculated we investigated (cf. [14]) to if char- the mobilizationacterize the forestof aluminum ecosystems could of be the observed three experimental in the control sites plots by thewithout different liming, processes which wouldcausing indicate the fluxes that of proton acidity production and to quantify processes soil acidification exceeded the without proton and consumption with different by dosages of dolomitic limestone. Therefore, we investigated if the mobilization of aluminum reaction associated with Mb cations (=Ca, K, Mg, Na). In this case, proton consumption is could be observed in the control plots without liming, which would indicate that proton carried out partially by the weathering of Al, Mn and Fe (Ma cations) oxides causing the production processes exceeded the proton consumption by reaction associated with M destabilization of clay minerals [20]. Further, important questions include did the limingb cations (=Ca, K, Mg, Na). In this case, proton consumption is carried out partially by treatments counter the effects of acidification in the long term without the negative the weathering of Al, Mn and Fe (M cations) oxides causing the destabilization of clay consequences of nitrate mobilization?a These questions will be answered by input-output minerals [20]. Further, important questions include did the liming treatments counter budgeting. the effects of acidification in the long term without the negative consequences of nitrate mobilization? These questions will be answered by input-output budgeting. 2. Materials and Methods 2.1.2. Materials Sites and Methods 2.1. SitesEach of the three experimental sites, Adenau (AD), Idar-Oberstein (IO), and HochspeyerEach of the(HS) three (Table experimental 1), was subjected sites, Adenau to five (AD), liming Idar-Oberstein treatments, ranging (IO), and from Hochspeyer 3 to 15 t(HS) ha−1 (Tabledolomitic1), was limestone subjected with to five untreated liming treatments,control plots ranging (Table from 2). The 3 to area 15 t haof −each1 dolomitic liming treatmentlimestone is with 2000 untreated m2 separated control into plots two (Table subplots2). The of area1000of m each2 (Figure liming 1). treatmentThe control is treatment2000 m2 separated has three into subplots two subplots of 2125 of m 10002 each. m2 (Figure The experimental1). The control sites treatment are fenced, has three and managedsubplots offorest 2125 stands m2 each. are thinned The experimental regularly. sitesLime are and fenced, fertilizers and were managed spread forest by hand stands in Decemberare thinned 1988. regularly. Lime and fertilizers were spread by hand in December 1988.

Figure 1. Location of the three experimental sites Adenau (AD), Idar-Oberstein (IO) and Hochspeyer (HS) in Rhineland- Figure 1. Location of the three experimental sites Adenau (AD), Idar-Oberstein (IO) and Hochspeyer (HS) in Rhineland- Palatinate over a map of the forested area (dark gray). The location of the state Rhineland-Palatinate is shown on the Palatinate over a map of the forested area (dark gray). The location of the state Rhineland-Palatinate is shown on the map map of Germany in the down right corner. In the upper right is a site plan with plot arrangement of the different liming of Germany in the down right corner. In the upper right is a site plan with plot arrangement of the different liming treatments (LT) of the experimental site HS. For more InformationInformation about the numbered treatments, which are part of this article, see Table 2 2.. MoreMore informationinformation aboutabout thethe experimentalexperimental sites sites is is documented documented in in Table Table1 .1. Appl. Sci. 2021, 11, 955 3 of 14

Table 1. Information about the three experimental sites. More detailed Information is available in Greve [22].

Study Areas Adenau (AD) Idar-Oberstein (IO) Hochspeyer (HS) Elevation above mean sea level 580–630 m 540–550 m 385–400 m Coordinates (ETRS 1989 UTM32N) X: 364340 Y: 5588220 X: 371450 Y: 5512000 X: 421560 Y: 5476010 Slope (Degree) 7◦ 4◦ 3◦ Mean annual temperature 7.6 ◦C 8.3 ◦C 8.7 ◦C Mean annual temperature of the 12.6 ◦C 13.3 ◦C 14.5 ◦C vegetation period Mean annual precipitation 850 mm 1065 mm 770 mm Seepage (60 cm) 275 mm 310 mm 180 mm Diluvial loam above devonic Diluvial loam above devonic Sandstone of the bunter Parent material quarzite quarzite sandstone Soil Taxonomy (WRB) Cambisol Stagnic cambisol Podzol Humus form Mor humus Mor humus Raw humus Soil texture Clay loam Clay loam Loamy Sand pH(CaCl2): 0–10/20–30 cm 2.9/3.9 3.0/4.0 2.9/3.6 Base saturation: 0–10/20–30 cm 7.2%/2.8% 6.3%/4.6% 10.3%/5.5% Cation exchange capacity [µeq g−1]: 139/46 137/47 103/19 0–10/20–30 cm C content [g kg−1]: 0–10/20–30 cm 65.8/12.7 75.0/12.1 72.8/10.7 N content [g kg−1]: 0–10/20–30 cm 3.1/1.3 3.7/1.1 2.7/0.5 S content [g kg−1]: 0–10/20–30 cm 1.49/0.38 1.21/0.46 0.58/0.19 Pinus sylvestris mixed with Tree species Picea abies Picea abies Fagus sylvatica from natural regeneration Stand age (2016) 81 97 90/96 (Pinus sylvestris)

Table 2. Liming treatments (LT) of the three study areas. Lime and fertilizer were applied in December 1988. Patentkali is a potash fertilizer containing potassium sulfate and magnesium sulfate.

Lime and Fertilizer Mg Ca K P S ANC Additional LT Application [kg ha−1] [kg ha−1] [kg ha−1] [kg ha−1] [kg ha−1] [keq ha−1] Informations control plot, no 0 - - - treatment 1 Dolomite: 3000 kg ha−1 349 603 51 particle size 0–2 mm Dolomite: 3000 kg ha−1 349 603 particle size 0–2 mm 3 53 Soft ground rock Hyperphos: 330 kg ha−1 6 96 6 37 phosphate 6 Dolomite: 5000 kg ha−1 582 1005 85 particle size 0–2 mm Dolomite: 9000 kg ha− 1048 1809 particle size 0–2 mm −1 7 Patentkali: 340 kg ha 21 85 58 153 as K2SO4 and MgSO4 −1 Kieserite: 660 kg ha 107 145 as MgSO4 particle size 0–0.09 Mixture of Dolomite and mm mixed with soft 8 1441 3804 25 145 304 Hyperphos: 15,000 kg ha−1 ground rock phosphate

Since the establishment of the study, the sampling of seepage water occurred at depths of 60 cm and 10 cm using suction cups and directly below the humus layer by funnel lysimeters. Four suction cups per depth and ﬁve funnel lysimeters were installed on each subplot. Six continuously open bulk samplers with a total collection area of 1885 cm2 were used to collect the bulk deposition at a nearby clearing and the throughfall on two of the control subplots. The water samples were collected every two weeks and kept in cold storage. For each subplot, the collected water was analyzed once every three months as a mixed sample for each of the different sampling types. The analyses were performed according to the methods of the Handbuch Forstliche Analytik [21].

2.2. Element Fluxes The soil water fluxes for each experimental site were calculated by a calibrated COUP- MODEL, as described in Karl et al. [23]. To calculate the element fluxes, the element concentrations in the seepage water are multiplied with the soil water fluxes, for each depth (cf. [24,25]). The total deposition (TD) was calculated for each experimental site Appl. Sci. 2021, 11, 955 4 of 14

using the bulk deposition and throughfall measurements as inputs into the canopy budget model after Ulrich [26] and Draaijers and Erisman [27]. For the total annual N deposition, the maximum from both canopy budget models plus the input of organic N of the bulk deposition was used, because total N deposition derived from canopy budget models is typically underestimated [8,28]. Additionally, weathering rates, calculated by PROFILE (cf. [29]), were used to derive the proton consumption from base cation release for each experimental site. The updated PROFILE version 4.4 was used, which includes typical minerals found in German soils [30]. A quantitative mineral analysis was performed in 1997 and the surface area of the mineral soil was calculated based on the soil texture, the coarse soil, and the dry bulk density (DBD) after Becker [30]. The long term mean air temperature was used for the soil temperature (cf. [31]). The plant available soil water content (water content reduced by the non-plant available water content below the permanent wilting point) was calculated by the COUP- MODEL and used for soil moisture input to the PROFILE model. The influence of CO2 was removed from the calculations for all minerals by setting the “Rate Constant_CO2” to 30, because of its weak effect on mineral dissolution (cf. [32–34]). The acid load by biomass increment was also calculated. The single tree-based stand simulator SILVA [35], which was adjusted with allometric relations for Rhineland- Palatinate [36,37], was used to calculate the above ground biomass of the different tree compartments for the years 1988 and 2011 (IO, HS), respectively 1988 and 2013 (AD) for each subplot. As input data, the diameter at breast height (DBH) was measured for each tree and the height for every third tree on the experimental site in winter 1988/89 and 2011/12 (IO, HS) respectively 1988/89 and 2013/14 (AD). In the winter of 2011/12, nine trees of the control plots (treatment 0) and six trees per liming treatment 1 (3 t ha−1), 3 (3 t ha−1 +P), and 8 (15 t ha−1 +P) were felled on each experimental site to obtain information about the incorporated nutrients. These trees were divided into needles, twigs, branches, bark, and wood according to the method described by Pretzsch et al. [37]. Additionally, the wood of the Scots pine trees was separated into sapwood and heartwood. A mixed sample of each compartment per tree was analyzed separately. Under the assumption, the element concentrations gained of the trees from the control plot were identical to the element concentrations at the beginning of the experiment, they were used to calculate the above ground element stocks for all liming treatments and the control at the start of the liming trial in 1988. The above ground element stocks of the years 2011 (IO, HS) respectively 2013 (AD) were calculated by the element concentrations of the trees felled from the according liming treatments. The element concentrations of the liming treatments 6 and 7 were interpolated by linear regression, because the needle and litterfall samples of all liming treatments indicate a significant correlation between liming dosage and element concentration [22]. The differences of the element stocks between these two dates represent the amount of elements incorporated since the beginning of the liming trial. The results were adjusted to account for the period of the other element fluxes by calculating the mean annual incorporation and multiplying it by 24 years.

2.3. Calculation of Acid-Base Budgets The input-output budgets for the forest soil were calculated based on element ﬂuxes (cf. [14,26]) on a yearly basis for 24 years, from 1989 to 2012. The inputs to the soil- internal element cycle are total deposition and elements released by mineral weathering. The element loss by seepage water is an output from the forest soil. Lime and fertilizers are treated as part of the soil and not as an additional input. + + 2+ 3+ 3+ 2− − The acid load based on the budgets of NH4 ,H , Mn , Al , Fe , SO4 , NO3 , organic anions (Org−), Ca2+,K+, Mg2+, and Na+ was calculated for each subplot after Ulrich [26,38]. Dissolved organic carbon (DOC) was measured in the water samples and converted to Org−, as described in Mosello et al. [39] with the updated conversion factor of ICP Forests [40]. P was not included because of uncertainties in the input-output Appl. Sci. 2021, 11, 955 5 of 14

budgeting. The element ﬂuxes at 60 cm depth were taken as an output from the ecosystem and subtracted from the accordant element input by TD. In the original approach by Ulrich [26], mineral weathering and the acid load by biomass increment are not calculated separately but instead included in the element + + 2+ 3+ 3+ budgets. Positive budgets of NH4 ,H , Mn , Al , or Fe account for a net input of acidity into the ecosystem because these ions count as potential proton donors [14]. Moreover, positive budgets of Ca2+,K+, Mg2+, and Na+ add to the acid load, because it is assumed that these Mb cations are incorporated into the biomass increment or are bound to exchange sites of the mineral soil and releasing Ma cations that lead to proton production. 2− − − Negative budgets of SO4 , NO3 or Org represent the dissolution of aluminum sulfates, nitriﬁcation, or the dissociation of dissolved organic acids and contribute also to the acid load. Opposing results of the element budgets lead to proton consumption by acid base reactions like dissolution of Mb and Ma oxides, cation exchange, mineralization, or the formation of aluminum sulfates [26]. Because of the decision to treat the added lime and fertilizer as part of the soil, the Mb cation budgets of the liming treatments are more negative in comparison to the control plots. Lime and fertilizer could also be treated as an additional input, which would lead to positive Mb cation budgets and therefore adding to the acid load for all liming treatments. This acid load originating from the Mb cation input by liming would have to be balanced by − protonation of HCO3 , which would complicate the calculations as well as the presented tables, without adding additional insights.

2.4. Modification of the Original Approach In our modified approach, we use the biomass sampling of forest stand and the PRO- FILE calculations to get a detailed look at the budget of Mb cations in the original calculation. Mb cations are also taken up by the forest stand and are incorporated into the biomass, which leads to proton production. For the calculation of the acid load, the incorporation of elements in the aboveground biomass (net element uptake by the forest stand that is needed for long-term growth) is taken into account. The element uptake to supply roots, leaves, and needles, which is not removed by harvesting, or returned to the forest floor by fine root turnover or litterfall, is part of the ecosystem-internal element cycle and was not included in the input-output budgets. The cation excess equals the proton production by biomass increment [26] and was calculated based on the analyzed elements:

+ + 2+ 2+ + 2+ 3+ 3+ 2− − h −1i H = K + Ca + Mg + Na + Mn + Fe + Al − SO4 − H2PO4 keq ha

In this calculation, the inﬂuence of the utilized N form is not taken into account, because of the uncertainty of their availability to and uptake by the forest stand. It is + assumed that both N forms are taken up in equal shares. A higher NH4 uptake would − increase the acid load whereas a higher NO3 uptake would lead to a lower acid load by biomass increment [41]. To calculate the Mb cation exchange and dissolution of calcium and magnesium carbonates included in the dolomite, the amount of Mb cations incorporated into the biomass and lost by seepage water output are subtracted from Mb cation release by mineral weathering and deposition input. For the control plots, this difference represents the proton consumption or production by Mb cation exchange processes. On the liming trials, the dissolution of carbonates is also included in this difference. Differences between the budgets of the subplots within an experimental site are the result of differences in element loss by seepage, as well as the incorporation of elements in the forest stand, which were measured separately for each subplot. Deposition and mineral weathering were assumed to be identical for all subplots of an experimental site. Appl. Sci. 2021, 11, 955 6 of 14

3. Results 3.1. Site Characteristics We use the original approach to characterize the three sites because the complex processes are summarized to a greater extent. The acid load and the acid-base reactions, based on the element budgets of the control plots, show clear differences in their composition between the three experimental sites (Table3). At AD, the main source of proton production + - is the N budget because of high NH4 input. In some years, the NO3 output exceeds the − + NO3 input, also leading to proton production. In IO, the input of NH4 is lower and + almost all the N is retained in the ecosystem so that the combined N budget of NH4 and − 2− NO3 still adds to the proton consumption. Instead of N, the release of stored SO4 and the retention of H+ contribute primarily to the acid load of this forest site. In HS, the main source of proton production is the loss of organic acids in combination with the input of + − NH4 . Like in IO, almost all NO3 is retained in the ecosystem, noticeably contributing to the proton consumption. The proton consumption for all control plots of the three study areas occurs mostly by Ma cation exchange or weathering, especially of aluminum. Except for subplot 2 in AD, the − accumulation of NO3 contributes noticeably to the proton consumption. In IO and HS, higher proton consumption is calculated compared to the proton production (Difference unequal zero). This could be caused by an underestimation of the ﬂux of organic acids (cf. [26]). Additionally, natural variation inside the experimental plots may contribute to the errors. Appl. Sci. 2021, 11, 955 7 of 14

Table 3. Total proton production and proton consumption by the different element budgets accumulated over 24 years (1989 to 2012) for the control subplots and liming treatments of the three experimental sites.

Proton Production [keq ha−1 24a−1] Proton Consumption [keq ha−1 24a−1] Portion Liming Site Subplot H+ NH + NO − SO 2− Org− M M Diff. H+ NH + NO − SO 2− Org− M M M M Treatment 4 3 4 a b ∑ ∑ 4 3 4 a b a b 0 1 5.4 31.1 0.2 5.8 6.6 0.1 5.5 54.6 0.6 54.0 1.2 0.0 18.5 2.0 0.0 29.9 2.5 55% 5% 0 2 6.5 31.1 1.8 4.0 4.3 0.1 5.6 53.4 −0.7 54.1 0.7 0.0 6.5 3.6 0.0 40.1 3.2 74% 6% 1 1 6.0 31.4 4.5 13.8 7.2 0.1 4.5 67.5 −1.2 68.7 0.7 0.0 8.6 0.4 0.0 34.0 24.9 50% 36% 1 2 6.3 31.0 7.9 22.3 6.0 0.1 3.9 77.6 −2.5 80.1 0.8 0.0 1.7 0.5 0.0 39.3 37.7 49% 47% 3 1 6.4 30.7 2.4 9.5 6.9 0.1 4.4 60.3 0.1 60.2 1.6 0.0 7.7 1.2 0.0 35.9 13.9 60% 23% 3 2 5.6 31.4 1.5 3.7 10.2 0.0 3.7 56.1 −0.5 56.5 1.0 0.0 7.5 3.4 0.0 29.3 15.4 52% 27% AD 6 1 6.3 31.5 9.2 11.3 7.1 0.1 4.2 69.7 0.2 69.5 0.6 0.0 1.3 1.9 0.0 35.8 29.9 52% 43% 6 2 5.0 31.0 22.3 15.5 11.0 0.0 3.2 88.0 −2.3 90.4 1.2 0.0 1.3 0.9 0.0 38.6 48.4 43% 54% 7 1 6.0 31.4 26.0 26.6 11.1 0.0 3.6 104.8 −5.5 110.3 0.9 0.0 0.0 0.6 0.0 44.5 64.3 40% 58% 7 2 6.7 31.3 10.9 29.1 9.4 0.0 5.1 92.5 −4.8 97.2 0.9 0.0 5.9 1.0 0.0 47.0 42.5 48% 44% 8 1 7.1 31.6 7.1 14.7 10.0 0.0 3.5 74.0 −2.6 76.6 0.4 0.0 1.9 1.7 0.0 26.0 46.6 34% 61% 8 2 6.5 31.3 9.9 14.2 9.0 0.1 3.3 74.2 −1.7 75.9 0.5 0.0 3.7 1.2 0.0 25.7 44.7 34% 59% 0 1 13.8 13.7 0.0 18.3 2.5 0.2 5.7 54.1 −5.7 59.8 0.0 0.0 20.8 0.3 0.0 35.0 3.7 58% 6% 0 2 13.8 13.7 0.0 34.7 3.3 0.2 3.0 68.7 −6.7 75.4 0.0 0.0 17.6 0.0 0.0 46.5 11.3 62% 15% 1 1 15.0 13.7 0.0 18.9 2.9 0.1 3.9 54.6 −6.5 61.1 0.0 0.0 21.4 0.5 0.0 30.2 9.1 49% 15% 1 2 14.9 13.7 0.0 32.2 3.6 0.1 2.3 66.8 −6.2 73.0 0.0 0.0 14.3 0.0 0.0 31.6 27.2 43% 37% 3 1 13.8 13.7 7.3 34.0 3.7 0.1 1.6 74.2 −7.7 81.9 0.0 0.0 14.7 0.1 0.0 39.8 27.3 49% 33% 3 2 15.8 13.7 0.0 13.8 1.6 0.1 4.1 49.1 −6.9 56.0 0.0 0.0 21.1 0.6 0.0 27.7 6.6 49% 12% IO 6 1 14.7 13.5 2.2 29.6 9.7 0.1 0.8 70.6 −10.4 81.0 0.0 0.0 12.4 0.0 0.0 27.3 41.3 34% 51% 6 2 15.3 13.7 0.0 30.6 3.1 0.1 1.9 64.7 −7.1 71.8 0.0 0.0 17.3 0.3 0.0 26.9 27.3 38% 38% 7 1 15.2 13.8 0.1 29.1 2.5 0.1 2.3 63.2 −6.9 70.2 0.0 0.0 17.4 0.0 0.0 30.9 21.9 44% 31% 7 2 15.4 13.5 0.3 38.3 6.7 0.1 0.7 75.1 −6.1 81.1 0.0 0.0 13.0 0.0 0.0 25.4 42.7 31% 53% 8 1 15.8 13.8 0.1 25.7 2.7 0.2 2.5 60.8 −6.5 67.3 0.0 0.0 15.3 0.2 0.0 23.0 28.8 34% 43% 8 2 15.9 13.8 0.0 31.0 4.0 0.1 1.4 66.2 −5.5 71.8 0.0 0.0 18.7 0.6 0.0 20.5 31.9 29% 45% 0 1 2.9 11.1 0.0 0.5 16.5 0.1 2.8 33.8 −3.8 37.6 0.5 0.0 14.3 2.0 0.0 18.2 2.6 48% 7% 0 2 5.9 11.2 0.0 3.3 3.1 0.2 3.3 27.0 −2.8 29.7 0.0 0.0 14.3 0.1 0.0 13.4 1.9 45% 6% 1 1 5.1 11.2 0.0 1.0 16.4 0.1 1.5 35.2 −6.3 41.5 0.0 0.0 14.3 1.6 0.0 20.0 5.6 48% 13% 1 2 4.6 11.1 0.0 2.8 16.4 0.1 2.4 37.4 −8.0 45.4 0.2 0.0 14.3 1.1 0.0 22.8 7.0 50% 15% 3 1 5.9 11.2 0.0 8.4 6.9 0.1 1.5 33.9 −3.3 37.2 0.0 0.0 14.2 0.4 0.0 9.2 13.5 25% 36% 3 2 5.6 11.2 0.0 2.9 9.3 0.1 3.5 32.7 −5.0 37.7 0.0 0.0 14.3 1.5 0.0 16.4 5.5 44% 15% HS 6 1 5.5 11.2 0.0 2.2 12.2 0.1 2.2 33.3 −10.6 43.9 0.0 0.0 14.1 0.5 0.0 20.0 9.3 46% 21% 6 2 6.0 11.2 0.0 5.0 11.3 0.1 2.5 36.0 −4.3 40.3 0.0 0.0 14.1 0.2 0.0 12.7 13.3 32% 33% 7 1 5.5 11.2 0.0 9.4 14.6 0.1 2.1 42.9 −6.6 49.5 0.0 0.0 14.3 1.5 0.0 19.8 13.9 40% 28% 7 2 4.2 11.1 0.0 14.8 10.0 0.1 1.8 42.0 −11.7 53.8 0.2 0.0 14.3 0.3 0.0 21.9 17.0 41% 32% 8 1 5.6 11.2 0.0 5.6 14.2 0.1 2.5 39.2 −4.9 44.1 0.0 0.0 14.2 0.4 0.0 16.0 13.4 36% 30% 8 2 6.1 11.1 1.3 2.8 25.9 0.1 1.7 49.1 −9.6 58.6 0.0 0.0 11.9 1.7 0.0 22.9 22.1 39% 38% Appl. Sci. 2021, 11, 955 8 of 14

3.2. Effect of Liming Treatments The liming treatments in AD show a higher acid load compared to the control plots. The low-dose as well as the high-dose liming treatments increased the dissolution of sulfates and the nitrification, leading to a greater loss of S and N by seepage water flux [42]. − Especially the lower retention of NO3 reduces the proton consumption by the acid-base reactions of the N budget in comparison to the control plot. The high acid load of treatment 7 is caused by the additional application of sulfur bound K and Mg fertilizers (cf. Table2), which leads to a high sulfate flux by seepage water accompanied by cation loss. The forest ecosystem in IO shows a different reaction to the liming treatment. Acid load does not increase on the different liming treatments in general. There are plots of the liming treatments with similar, higher, or lower acid load compared to the control plots. In HS, some liming treatments show higher acid load due to higher flux of organic acids with the seepage water, though the acid load in HS for all treatments is lower than even the acid load of the control plots in AD and IO. For all three experimental sites, the bigger part of the acid load on the control plots is compensated by Ma cation release. Although the acid load increases for most liming treatments, the proportion of proton consumption by Ma cations decreases with increasing dosage for all study areas. In AD and HS, the absolute portion [keq ha−1] remains on a similar level to the control plots and in IO both absolute portion and relative proportions decrease. This shows clearly that the additional acid load of reactions triggered by the liming is compensated by the dissolution of calcium and magnesium carbonates included in the dolomite. The contribution of the different element budgets to H+ buffering or production is shown more clearly when proton production and consumption cations (which are shown separately for the control plots in Table3) are summed up (Table4). In this table, we also included the additional data of mineral weathering and biomass production to examine the effects of the liming treatments and the involved processes on the Mb budget in more detail. For the liming treatments, the acid load through biomass increment increases compared to the control plot because of higher incorporation of Ca and Mg in all biomass compartments and because of increase in biomass increment [22]. With regard to this + − calculation, it is assumed that NH4 and NO3 are taken up with the same proportion + of 50%. In HS and AD an uptake of N only as NH4 could almost double the acid load + compared to a proportion of 50% NH4 . In the case of IO an uptake of N only in the form − of NO3 would not only lead to a lower acid load, but result in proton consumption by biomass increment for the liming treatments (Figure2). The Mb cation input by mineral weathering and deposition is on all control plots not high enough to compensate the loss by seepage water and uptake by the forest stand. This indicates that whole-tree harvesting is not nutrient sustainable for these forest stands without any nutrient return. HS has an especially low mineral weathering rate because of the nutrient and base poor parent material. The input of Mb cation depends almost solely on deposition rates. Appl. Sci. 2021, 11, 955 9 of 14

Table 4. Columns a-f and h contain the net proton production (>0)/proton consumption (<0) accumulated over for 24 years (1989 to 2012) [keq ha−1 24a−1] which is shown separately in

Table3. Column g contains remaining acid load when the M b budget (h) is not included. Columns i-n give a detailed look at the processes involved in the Mb budget. Mb accumulation or release (n): k + l − j − m; Acid/base reactions through Mb budget (h): j + n − k.

a b c d e f g h i j k l m n Liming Seepage Site Acid Budget Acid Load by above Mineral Deposition Treatment Subplot Acid/Base Reactions through Budget of Load of Ground Biomass Increment Weathering Water Accum./Release + + − 2− − H NH4 NO3 SO4 Org Ma ∑ Mb Total Only Mb 0 1 4.1 31.1 −18.3 3.8 6.6 −29.9 −2.5 3.1 17.5 15.9 8.9 29.1 26.0 −4.0 0 2 5.8 31.1 −4.8 0.4 4.3 −40.0 −3.1 2.4 16.8 15.3 8.9 29.1 26.6 −4.0 1 1 5.3 31.4 −4.1 13.4 7.2 −33.9 19.3 −20.5 24.6 24.6 8.9 29.1 49.5 −36.2 1 2 5.5 31.0 6.2 21.8 6.0 −39.2 31.3 −33.8 21.9 22.0 8.9 29.1 62.9 −46.9 3 1 4.8 30.7 −5.3 8.3 6.9 −35.8 9.5 −9.5 25.9 24.9 8.9 29.1 38.5 −25.4 AD 3 2 4.5 31.4 −5.9 0.3 10.2 −29.2 11.2 −11.6 26.9 26.0 8.9 29.1 40.7 −28.8 6 1 5.7 31.5 7.9 9.4 7.1 −35.7 25.9 −25.7 23.9 24.4 8.9 29.1 54.7 −41.2 6 2 3.9 31.0 21.0 14.5 11.0 −38.5 42.9 −45.2 23.4 23.9 8.9 29.1 74.3 −60.2 7 1 5.2 31.4 26.0 26.0 11.1 −44.5 55.2 −60.7 25.0 25.9 8.9 29.1 89.8 −77.7 7 2 5.8 31.3 5.0 28.0 9.4 −47.0 32.6 −37.4 28.9 30.2 8.9 29.1 66.5 −58.7 8 1 6.8 31.6 5.2 13.0 10.0 −26.0 40.6 −43.2 24.7 26.5 8.9 29.1 72.3 −60.8 8 2 6.0 31.3 6.2 13.0 9.0 −25.7 39.7 −41.4 30.1 32.0 8.9 29.1 70.5 −64.5 0 1 13.8 13.7 −20.8 18.0 2.5 −34.8 −7.7 1.9 17.2 13.7 11.4 18.2 16.2 −0.3 0 2 13.8 13.7 −17.6 34.7 3.3 −46.3 1.6 −8.3 20.5 16.3 11.4 18.2 26.5 −13.3 1 1 15.0 13.7 −21.4 18.3 2.9 −30.0 −1.4 −5.1 16.4 15.5 11.4 18.2 23.3 −9.2 1 2 14.9 13.7 −14.3 32.2 3.6 −31.5 18.6 −24.8 24.0 22.6 11.4 18.2 43.0 −36.1 3 1 13.8 13.7 −7.3 33.9 3.7 −39.7 18.0 −25.6 17.3 17.2 11.4 18.2 43.8 −31.4 IO 3 2 15.8 13.7 −21.1 13.2 1.6 −27.6 −4.4 −2.5 22.9 22.3 11.4 18.2 20.7 −13.4 6 1 14.7 13.5 −10.3 29.6 9.7 −27.3 30.0 −40.5 25.9 26.2 11.4 18.2 58.6 −55.3 6 2 15.3 13.7 −17.3 30.3 3.1 −26.8 18.3 −25.4 22.1 22.8 11.4 18.2 43.6 −36.8 7 1 15.2 13.8 −17.2 29.1 2.5 −30.7 12.7 −19.6 28.3 29.3 11.4 18.2 37.8 −37.5 7 2 15.4 13.5 −12.7 38.3 6.7 −25.3 35.9 −42.0 25.0 26.4 11.4 18.2 60.1 −57.0 8 1 15.8 13.8 −15.2 25.5 2.7 −22.8 19.7 −26.3 26.1 28.1 11.4 18.2 44.4 −43.0 8 2 15.9 13.8 −18.7 30.4 4.0 −20.4 25.0 −30.5 26.2 29.0 11.4 18.2 48.7 −48.1 0 1 2.4 11.1 −14.3 −1.5 16.5 −18.1 −3.9 0.1 10.9 11.1 2.7 11.6 11.5 −8.3 0 2 5.9 11.2 −14.3 3.2 3.1 −13.2 −4.1 1.3 12.5 12.6 2.7 11.6 10.3 −8.6 1 1 5.0 11.2 −14.3 −0.6 16.4 −19.9 −2.2 −4.1 18.5 18.9 2.7 11.6 15.7 −20.3 1 2 4.4 11.1 −14.3 1.7 16.4 −22.7 −3.4 −4.6 13.1 13.5 2.7 11.6 16.2 −15.4 3 1 5.9 11.2 −14.2 8.1 6.9 −9.0 8.8 −12.0 15.6 15.9 2.7 11.6 23.6 −25.2 HS 3 2 5.6 11.2 −14.3 1.5 9.3 −16.3 −3.1 −1.9 11.3 11.3 2.7 11.6 13.5 −10.5 6 1 5.5 11.2 −14.1 1.7 12.2 −19.9 −3.5 −7.1 11.4 11.7 2.7 11.6 18.7 −16.1 6 2 6.0 11.2 −14.1 4.8 11.3 −12.6 6.6 −10.8 14.1 14.5 2.7 11.6 22.4 −22.6 7 1 5.5 11.2 −14.3 7.9 14.6 −19.6 5.2 −11.8 18.3 19.1 2.7 11.6 23.4 −28.3 7 2 3.9 11.1 −14.3 14.5 10.0 −21.7 3.5 −15.3 16.6 17.1 2.7 11.6 26.8 −29.6 8 1 5.6 11.2 −14.2 5.2 14.2 −15.9 6.0 −10.9 16.5 17.1 2.7 11.6 22.5 −25.3 8 2 6.0 11.1 −10.6 1.1 25.9 −22.7 10.8 −20.4 19.1 20.1 2.7 11.6 32.0 −37.8 Appl. Sci. 2021, 10, x FOR PEER REVIEW 9 of 14

1 2 4.4 11.1 −14.3 1.7 16.4 −22.7 −3.4 −4.6 13.1 13.5 2.7 11.6 16.2 −15.4 3 1 5.9 11.2 −14.2 8.1 6.9 −9.0 8.8 −12.0 15.6 15.9 2.7 11.6 23.6 −25.2 HS 3 2 5.6 11.2 −14.3 1.5 9.3 −16.3 −3.1 −1.9 11.3 11.3 2.7 11.6 13.5 −10.5 6 1 5.5 11.2 −14.1 1.7 12.2 −19.9 −3.5 −7.1 11.4 11.7 2.7 11.6 18.7 −16.1 6 2 6.0 11.2 −14.1 4.8 11.3 −12.6 6.6 −10.8 14.1 14.5 2.7 11.6 22.4 −22.6 7 1 5.5 11.2 −14.3 7.9 14.6 −19.6 5.2 −11.8 18.3 19.1 2.7 11.6 23.4 −28.3 7 2 3.9 11.1 −14.3 14.5 10.0 −21.7 3.5 −15.3 16.6 17.1 2.7 11.6 26.8 −29.6 8 1 5.6 11.2 −14.2 5.2 14.2 −15.9 6.0 −10.9 16.5 17.1 2.7 11.6 22.5 −25.3 8 2 6.0 11.1 −10.6 1.1 25.9 −22.7 10.8 −20.4 19.1 20.1 2.7 11.6 32.0 −37.8

For the liming treatments, the acid load through biomass increment increases compared to the control plot because of higher incorporation of Ca and Mg in all biomass compartments and because of increase in biomass increment [22]. With regard to this calculation, it is assumed that NH4+ and NO3− are taken up with the same proportion of 50%. In HS and AD an uptake of N only as NH4+ could almost double the acid load Appl. Sci. 2021, 11, 955 10 of 14 compared to a proportion of 50% NH4+. In the case of IO an uptake of N only in the form of NO3− would not only lead to a lower acid load, but result in proton consumption by biomass increment for the liming treatments (Figure 2).

− + Figure 2. Acid load through biomass increment when NO − and NH + are taken up with different proportions for the Figure 2. Acid load through biomass increment when NO33 and NH44 are taken up with different proportions for the −1 − control plots andand thethe 33 andand 1515 tt haha−1 limingliming treatments ofof thethe threethree studystudyareas. areas. AnAn uptake uptake of of N N only only as as NO NO3 3− (100/0)(100/0) leads leads + to a lower acid load,load, anan uptakeuptake ofof NN onlyonly asas NHNH44+ (0/100) leads leads to to a a higher higher acid acid load. load. For For the the following following calculations, calculations, it is ++ −− assumed that NH4 andand NO NO33 areare taken taken up up with with the the same same proportion proportion (50/50, (50/50, highlighted highlighted in in the the figure ﬁgure by by the the grey grey area). area). Negative values in IO stand for proton consumption by biomass increment.

The Mb cation input by mineral weathering and deposition is on all control plots not high4. Discussion enough to compensate the loss by seepage water and uptake by the forest stand. This indicatesLiming that forests whole-tree in Rhineland-Palatinate harvesting is not was nutrient performed sustainable under thefor assumptionthese forest of stands rapid withoutacidification any nutrient of soils taking return. place HS has by airan pollutantsespecially fromlow mineral anthropogenic weathering sources rate [43 because]. During of thethe nutrient 24 years, and the base control poor plotsparent show material. that theThe acidinput load of M noticeablyb cation depends exceeds almost the proton solely onconsumption deposition byrates. Mb cations. Without liming, Mb cations compensated for less than 15% of the acid load (Table3). As a result M a cations, especially Al and Mn, are released, 4.which Discussion can destabilize clay minerals [15,44], disturb plant nutrition [45–47], damage fine rootLiming systems forests [48], and in reduceRhineland-Palatinate the activity of soil was biota performed [49]. With under regard the to theassumption three study of rapidplots, acidification liming partially of soils increased taking place the acid by air load pollutants because from of (1) anthropogenic a higher output sources of anions [43]. (SO 2−, NO −, Org−) with the seepage water and (2) a higher proton production through During4 the 324 years, the control plots show that the acid load noticeably exceeds the the incorporation of more cations into the biomass. However, the reduction of Ma cation proton consumption by Mb cations. Without liming, Mb cations compensated for less than release below the level of the control plot shows that the higher acid load was compensated 15% of the acid load (Table 3). As a result Ma cations, especially Al and Mn, are released, by the carbonates of the dolomitic limestone. The clay minerals were stabilized on the plots which can destabilize clay minerals [15,44], disturb plant nutrition [45–47], damage fine of the liming treatments whereas on the control plots an ongoing destabilization could be observed. However, this stabilization effect started to diminish for the low-dose treatments after approximately 20 years and was limited to the upper 5 cm of the mineral soil [50–52]. On the three sites, the loss of anions is linked with a loss of metal cations, which is equivalent to quantitative acidification of the ecosystem [14]. Negative consequences are the depletion of the soil nutrient reserves if Mb cations are leached together with anions, or the pollution of spring water and other adjacent fresh water if the anions are relocated 3+ 2− together with Ma cations like Al . The high loss of SO4 and aluminum in AD caused by the liming treatments indicates dissolution of aluminum sulfates due to changed pH value (cf. [53]). To a lower extent, this was also observed in IO at a soil depth of 10 cm [22]. In IO 2− the SO4 output of the control plot is already on a high level whereby liming induced none 2− or only a slight increase. In HS, the missing effect of the liming treatments on the SO4 2− output is caused by the soil type. Sandy soils have a lower storage capacity for SO4 than more cohesive soils with a higher percentage of clay [54]. Because of the lower S pools in 2− HS [22], there is a smaller potential of a high long-term SO4 release, as observed in AD and IO. Appl. Sci. 2021, 11, 955 11 of 14

The liming treatment 7 with the additional sulfur bound K and Mg fertilizers (Table2) 2− 2− has the highest acid load due to the high SO4 output. The loss of SO4 led to a coupled output of Mg and Al. On the other hand, K was retained in the ecosystem. Higher K fluxes compared to the control plots could only be observed in soil depth of 10 cm, but not below the rooting zone in 60 cm soil depth. Also, the forest stands show a tendency for higher K concentrations in older needles and in needle litter compared to the other high-dose liming treatment 5 and 8 [22]. The N budget in AD is the main source for the proton production on the control plot − −1 −1 (Table4). The loss of more than 5 kg NO 3 ha a [22] indicates N saturation on a low − level (cf. [55]). Liming increases the leaching of NO3 and therefore the acid load. This is an example of the risks which are associated with liming of N saturated forest areas. − However, on the control plot, NO3 is for the most part accompanied by Al which could be a risk for biota living in adjacent freshwater ecosystems and can cause higher costs and increased technical effort to remove aluminum from drinking water [56]. After liming the absolute portion and relative proportion of Al is reduced and instead of Al more Mb cations − (especially Mg) are transported together with NO3 to the outside of the ecosystem. To improve the accuracy of the acid load by biomass increment it is important to know in which form N is taken up by the forest stand. The sample calculations for the three study − areas show that uptake of N only in the form of NO3 reduces the acid load considerably, in the case of IO even below zero, which means net proton consumption. On the other + hand, a high proportion of NH4 could almost double the acid load. Experiments indicate + for Picea abies that a large proportion of its N uptake from the soil is NH4 [57–59]. Based on the literature, it is more likely that there is an underestimate in the acid load by biomass increment when the influence of the utilized N form is not taken into account. For the long-term, it is unknown if liming leads to a loss of a greater amount of N if a reduction of N deposition occurs. It could be hypothesized, that the amount of N lost with seepage water is the same. Under current deposition, limed ecosystems could show a more rapid increase of N in seepage waters. However, with reduced N deposition the limed areas may begin to store N and evade N saturation at an earlier point in time. Liming increases tree growth and additional N can be retained by the vegetation or removed by harvesting. In IO and HS, liming leads also to a slight increase of the acid load from the N budget but is less important to the other proton sources. Most of the N input is retained in the ecosystem of these two study areas even on the high-dose treatment 8 with 15 t/ha. In the case of IO liming could prevent the ecosystem to reach N saturation as there was significantly more N taken up and stored in the stem wood of the trees on the liming trials [22]. Possible reasons for this could be a higher number of living cells (cf. [60]) or − + that N is taken up to a greater extent as NO3 instead of NH4 , which is stored in the wood (cf. [61]). Why this effect could not be observed for the spruce forest stand in AD is not clear. This study clearly shows the importance of minimizing the input of N into forest ecosystems as long as most of the N is still retained and not lost as nitrate by seepage water flux. Otherwise, nitrate loss would likely continue over decades similar to the sulfate outputs (cf. [3,62]). The lower amount of anions in the seepage water in HS compared to AD and IO leads to stronger retention of the applied Mb cations (Mg and Ca). Therefore, sandy soils should not be excluded automatically from liming treatments. Instead, the period between liming treatments can be longer for sandy soils with similar conditions to HS. The lower cation exchange capacity in HS [22] does not cause higher outputs of Mg and Ca by the seepage water.

Author Contributions: Conceptualization, M.G., J.B., G.S. and W.W.; methodology, M.G., J.B., G.S. and W.W.; validation, M.G., J.B. and W.W.; formal analysis, M.G.; investigation, M.G.; resources, M.G. and W.W.; data curation, M.G.; writing—original draft preparation, M.G.; visualization, M.G.; supervision, M.G., J.B., G.S. and W.W.; project administration, M.G. and G.S. All authors have read and agreed to the published version of the manuscript. Appl. Sci. 2021, 11, 955 12 of 14

Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The data presented in this study are available on request from the corresponding author. Acknowledgments: We thank the numerous colleagues and students involved in field sampling, laboratory analyses and maintenance of the plots of the Kompensationsversuch since 1988. Conflicts of Interest: The authors declare no conflict of interest.

References 1. Ulrich, B. Die Rolle der Bodenversauerung beim Waldsterben: Langfristige Konsequenzen und forstliche Möglichkeiten. Forstwiss. Cent. 1986, 105, 421–435. [CrossRef] 2. Ulrich, B.; Mayer, R.; Khanna, P.K. Deposition von Luftverunreinigungen und Ihre Auswirkungen in Waldökosystemen im Solling; Schriftenreihe der Forstlichen Fakultät der Universität Göttingen: Göttingen, Germany, 1979; Volume 58. 3. Alewell, C.; Manderscheid, B.; Gerstberger, P.; Matzner, E. Effects of reduced atmospheric deposition on soil solution chemistry and elemental contents of spruce needles in NE-Bavaria, Germany. J. Plant Nutr. Soil Sci. 2000, 163, 509–516. [CrossRef] 4. Waldner, P.; Marchetto, A.; Thimonier, A.; Schmitt, M.; Rogora, M.; Granke, O.; Mues, V.; Hansen, K.; Pihl Karlsson, G.; Žlindra, D.; et al. Detection of temporal trends in atmospheric deposition of inorganic nitrogen and sulphate to forests in Europe. Atmos. Environ. 2014, 95, 363–374. [CrossRef] 5. Stoddard, J.; Jeffries, D.; Lükewille, A.; Clair, T.; Dillon, P.; Driscoll, C.T.; Forsius, M.; Johannessen, M.; Kahl, J.S.; Kellogg, J.; et al. Regional trends in aquatic recovery from acidification in North America and Europe. Nature 1999, 401, 575–578. [CrossRef] 6. European Environment Agency Air Pollution Fact Sheet 2014: Germany. Available online: http://www.eea.europa.eu/themes/ air/air-pollution-country-fact-sheets-2014/germany-air-pollutant-emissions-country-factsheet (accessed on 21 January 2021). 7. Umweltbundesamt Ammoniak-Emissionen—Entwicklung Seit 1990. Available online: http://www.umweltbundesamt.de/ daten/luftbelastung/luftschadstoff-emissionen-in-deutschland/ammoniak-emissionen (accessed on 21 January 2021). 8. Meesenburg, H.; Eichhorn, J.; Meiwes, K.J. Atmospheric Deposition and Canopy Interactions. In Functioning and Management of European Beech Ecosystems; Brumme, R., Khanna, P.K., Eds.; Ecological Studies 208; Springer: Berlin/Heidelberg, Germany, 2009; pp. 265–302. 9. Meiwes, K.J. Application of lime and wood ash to decrease acidification of forest soils. Water Air Soil Pollut. 1995, 85, 143–152. [CrossRef] 10. Ebermayer, E. Die Gesammte Lehre der Waldstreu Mit Rücksicht auf die Chemische Statik des Waldbaues; Julius Springer: Berlin/Heidelberg, Germany, 1876. 11. Block, J.; Gauer, J. Waldbodenzustand in Rheinland-Pfalz; Mitteilungen aus der Forschungsanstalt für Waldökologie und Forstwirtschaft Rheinland-Pfalz: Trippstadt, Germany, 2012; Volume 70. 12. Dise, N.B.; Matzner, E.; Armbruster, M.; MacDonald, J. Aluminum output fluxes from forest ecosystems in Europe: A regional assessment. J. Environ. Qual. 1994, 30, 1747–1756. [CrossRef] 13. Ulrich, B. Natural and anthropogenic components of soil acidification. Z. Pflanzenernähr. Bodenkd. 1986, 149, 702–717. [CrossRef] 14. Van Breemen, N.; Mulder, J.; Driscoll, C.T. Acidification and alkalinization of soils. Plant Soil 1983, 75, 283–308. [CrossRef] 15. Butz-Braun, R. Einfluss der Bodenschutzkalkung auf den Entwicklungszustand der Tonminerale. Forstarchiv 2014, 85, 65–67. [CrossRef] 16. Hüttl, R.F.; Zöttl, H.W. Liming as a mitigation tool in Germany’s declining forests—Reviewing results from former and recent trials. For. Ecol. Manag. 1993, 61, 325–338. [CrossRef] 17. Schüler, G. Schutz versauerter Böden in nachhaltig bewirtschafteten Wäldern-Ergebnisse aus 10-jähriger interdisziplinärer Forschung. Allg. Forst Jagdztg. 2002, 173, 1–7. 18. Gussone, H.A. Empfehlungen zur Kompensationsdüngung. Forst Holzwirt 1984, 6, 154–160. 19. Schüler, G. Der Vergleichende Kompensationsversuch mit Verschiedenen Puffersubstanzen zur Minderung der Auswirkungen von Luftschad- stoffeinträgen in Waldökosystemen; Mitteilungen aus der Forstlichen Versuchsanstalt Rheinland-Pfalz: Trippstadt, Germany, 1992; Volume 21. 20. Veerhoff, M.; Brümmer, G.W. Silicatverwitlerung und -zerstörung in Waldböden als Folge von Versauerungsprozessen und deren ökologische Konsequenzen. Nat. Landsch. 1992, 28, 25–32. 21. Gutachterausschuss Forstliche Analytik. Handbuch Forstliche Analytik; Gutachterausschuss Forstliche Analytik: Göttingen, Germany, 2009. 22. Greve, M. Langfristige Auswirkungen der Waldkalkung auf den Stoffhaushalt; Mitteilungen aus der Forschungsanstalt für Waldökologie und Forstwirtschaft Rheinland-Pfalz: Trippstadt, Germany, 2015; Volume 73. Appl. Sci. 2021, 11, 955 13 of 14

23. Karl, S.; Block, J.; Schüler, G.; Schultze, B.; Scherzer, J. Wasserhaushaltsuntersuchungen im Rahmen des Forstlichen Umweltmonitorings und bei Waldbaulichen Versuchen in Rheinland-Pfalz; Mitteilungen aus der Forschungsanstalt für Waldökologie und Forstwirtschaft Rheinland-Pfalz: Trippstadt, Germany, 2012; Volume 71. 24. Ellenberg, H.; Mayer, R.; Schauermann, J. Ökosystemforschung. Ergebnisse des Sollingprojekts 1966–1986; Ulmer Eugen Verlag: Stuttgart, Germany, 1986; ISBN 978-3800134311. 25. Berger, T.W.; Untersteiner, H.; Toplitzer, M.; Neubauer, C. Nutrient fluxes in pure and mixed stands of spruce (Picea abies) and beech (Fagus sylvatica). Plant Soil 2009, 322, 317–342. [CrossRef] 26. Ulrich, B. Nutrient and acid-base budget of central European forest ecosystems. In Effects of Acid Rain on Forest Processes; Godbold, D.L., Hüttermann, A., Eds.; Wiley-Liss: New York, NY, USA, 1994; pp. 1–50. ISBN 9780471517689. 27. Draaijers, G.P.J.; Erisman, J.W. A canopy budget model to assess atmospheric deposition from throughfall measurements. Water Air Soil Pollut. 1995, 85, 2253–2258. [CrossRef] 28. Meesenburg, H.; Mohr, K.; Dämmgen, U.; Schaaf, S.; Meiwes, K.J.; Horváth, B. Stickstoff-Einträge und -Bilanzen in den Wäldern des ANSWER-Projektes: Eine Synthese. Landbauforsch. Völkenrode Sonderh. 2005, 279, 95–108. 29. Sverdrup, H.; Warfvinge, P.E.R. Calculating field weathering rates using a mechanistic geochemical model PROFILE. Appl. Geochem. 1993, 8, 273–283. [CrossRef] 30. Becker, R. Critical Load-PROFILE 4.4 (Manual); ÖKO-DATA Strausberg: Brandenburg, Germany, 2002. 31. Watson, C. Seasonal soil temperature regimes in south-eastern Australia. Aust. J. Soil Res. 1980, 18, 325. [CrossRef] 32. Stephens, J.C. Response of Soil Mineral Weathering to Elevated Carbon Dioxide. Ph.D. Thesis, California Institute of Technology, Pasadena, CA, USA, 2002. 33. Golubev, S.V.; Pokrovsky, O.S.; Schott, J. Experimental determination of the effect of dissolved CO2 on the dissolution kinetics of Mg and Ca silicates at 25 ◦C. Chem. Geol. 2005, 217, 227–238. [CrossRef] 34. Brantley, S.L. Kinetics of Water-Rock Interaction. In Kinetics of Water-Rock Interaction; Brantley, S.L., Kubicki, J.D., White, A.F., Eds.; Springer: New York, NY, USA, 2008; pp. 151–210. ISBN 978-0-387-73562-7. 35. Pretzsch, H.; Biber, P.; Durskˇ ý, J. The single tree-based stand simulator SILVA: Construction, application and evaluation. For. Ecol. Manag. 2002, 162, 3–21. [CrossRef] 36. Pretzsch, H.; Block, J.; Dieler, J.; Schuck, J.; Gauer, J.; Göttlein, A.; Moshammer, R.; Weis, W.; Wunn, U. Nährstoffentzüge durch die Holz- und Biomassenutzung in Wäldern. Teil 1: Schätzfunktionen für Biomasse und Nährelemente und ihre Anwendung in Szenariorechnungen. Allg. Forst Jagdztg. 2014, 185, 261–285. 37. Pretzsch, H.; Block, J.; Böttcher, M.; Dieler, J.; Gauer, J.; Göttlein, A.; Moshammer, R.; Schuck, J.; Weis, W.; Wunn, U. Entscheidungsstützungssystem zum Nährstoffentzug im Rahmen der Holzernte: Nährstoffbilanzen Wichtiger Waldstan- dorte in Bayern und Rheinland-Pfalz. Abschlussbericht zum DBU-Projekt Az. 25966-33/0. 2013. Available online: https: //fawf.wald-rlp.de/de/veroeffentlichungen/projektberichte/ (accessed on 21 January 2021). 38. Ulrich, B. Rechenweg zur Schätzung der Flüsse in Waldökosystemen: Identifizierung der sie Bedingenden Prozesse; Berichte des Forschungszentrums Waldökosysteme Göttingen, Reihe B; Berichte des Forschungszentrums Waldökosysteme Göttingen: Göttingen, Germany, 1991; Volume 24. 39. Mosello, R.; Amoriello, T.; Benham, S.; Clarke, N.; Derome, J.; Derome, K.; Genouw, G.; Koenig, N.; Orrù, A.; Tartari, G.; et al. Validation of chemical analyses of atmospheric deposition on forested sites in Europe: 2. DOC concentration as an estimator of the organic ion charge. J. Limnol. 2008, 67, 1–14. [CrossRef] 40. ICP Forests New Excel File for Analytical Data Validation (with DOC). Available online: http://storage.ning.com/topology/ rest/1.0/file/get/118302774?profile=original (accessed on 21 January 2021). 41. Van Breemen, N.; Driscoll, C.T.; Mulder, J. Acidic deposition and internal proton sources in acidification of soils and waters. Nature 1984, 307, 599–604. [CrossRef] 42. Greve, M. Langfristige Auswirkungen der Waldkalkung auf Bodenzustand, Sickerwasser und Nadelspiegelwerte von drei Versuchsanlagen in Rheinland-Pfalz. Forstarchiv 2014, 46, 35–46. [CrossRef] 43. Block, J.; Roeder, A.; Schüler, G. Waldbodenrestauration durch Aktivierung ökosystemarer Nährstoffkreisläufe. Allg. For. Z. 1997, 52, 29–33. 44. Veerhoff, M.; Brümmer, G. Silicatverwitterung und Tonmineralumwandlung in Waldböden als Folge von Versauerungsprozessen. Mitt. Deutsch. Bodenkd. Gese. 1989, 59, 1203–1207. 45. Gjengedal, E.; Martinsen, T.; Steinnes, E. Background levels of some major, trace, and rare earth elements in indigenous plant species growing in Norway and the influence of soil acidification, soil parent material, and seasonal variation on these levels. Environ. Monit. Assess. 2015, 187.[CrossRef] 46. Ducić,T.; Parladé, J.; Polle, A. The influence of the ectomycorrhizal fungus Rhizopogon subareolatus on growth and nutrient element localisation in two varieties of Douglas fir (Pseudotsuga menziesii var. menziesii and var. glauca) in response to manganese stress. Mycorrhiza 2008, 18, 227–239. [CrossRef] 47. De Wit, H.A.; Eldhuset, T.D.; Mulder, J. Dissolved Al reduces Mg uptake in Norway spruce forest: Results from a long-term field manipulation experiment in Norway. For. Ecol. Manag. 2010, 259, 2072–2082. [CrossRef] 48. Poschenrieder, C.; Gunsé, B.; Corrales, I.; Barceló, J. A glance into aluminum toxicity and resistance in plants. Sci. Total Environ. 2008, 400, 356–368. [CrossRef] Appl. Sci. 2021, 11, 955 14 of 14

49. Chen, D.; Lan, Z.; Hu, S.; Bai, Y. Effects of nitrogen enrichment on belowground communities in grassland: Relative role of soil nitrogen availability vs. soil acidification. Soil Biol. Biochem. 2015, 89, 99–108. [CrossRef] 50. Butz-Braun, R. Beprobung und Mineralogische Analysen an dem Vergleichenden Kompensationsversuch Idar-Oberstein, dem Bodenrestaurationsversuch Pirmasens und der UKS Merzalben. Forschungsbericht für die Forschungsanstalt für Waldökologie und Forstwirtschaft. 2012. Available online: https://fawf.wald-rlp.de/de/veroeffentlichungen/projektberichte/ (accessed on 21 January 2021). 51. Butz-Braun, R. Tonmineralogische Untersuchungen zum Kompensationsversuch 2010 Versuchsanlage Adenau (101)—3. Detail- lierte Wiederholung—Ergänzung um die 15 t Dolomit-Variante. Forschungsbericht für die Forschungsanstalt für Waldökologie und Forstwirtschaft. 2011. Available online: https://fawf.wald-rlp.de/de/veroeffentlichungen/projektberichte/ (accessed on 21 January 2021). 52. Butz-Braun, R. Tonmineralogische Untersuchungen zum Kompensationsversuch 2007 und 2012 Versuchsanlage Hochspeyer (318)—2. und 3. Wiederholung. Forschungsbericht für die Forschungsanstalt für Waldökologie und Forstwirtschaft. 2012. Available online: https://fawf.wald-rlp.de/de/veroeffentlichungen/projektberichte/ (accessed on 21 January 2021). 53. Prenzel, J.; Meiwes, K.J. Sulfate Sorption in Soils under Acid Deposition: Modeling Field Data from Forest Liming. J. Environ. Qual. 1994, 23, 1212–1217. [CrossRef] 54. Alewell, C. Predicting reversibility of acidification: The European sulfur story. Water Air Soil Pollut. 2001, 130, 1271–1276. [CrossRef] 55. Block, J.; Eichhorn, J.; Gehrmann, J.; Kölling, C.; Matzner, E.; Meiwes, K.J.; von Wilpert, K.; Wolff, B. Kennwerte zur Charakter- isierung des Ökochemischen Bodenzustandes und des Gefährdungspotentials Durch Bodenversauerung und Stickstoffsättigung an Level II-Waldökosystem-Dauerbeobachtungsflächen. Arbeitskreis C der Bund-Länder-Arbeitsgruppe Level II; Bundesministerium für Ernährung, Landwirtschaft und Forsten (BMELF): Bonn, Germany, 2000. 56. Bittersohl, J.; Walther, W.; Meesenburg, H. Gewässerversauerung durch Säuredeposition in Deutschland—Entwicklung und aktueller Stand. Hydrol. Wasserbewirtsch. 2014, 58, 260–273. [CrossRef] 57. Marschner, H.; Häussling, M.; George, E. Ammonium and nitrate uptake rates and rhizosphere pH in non-mycorrhizal roots of Norway spruce [Picea abies (L.) Karst.]. Trees 1991, 5, 14–21. [CrossRef] 58. Buchmann, N.; Schulze, E.-D.; Gebauer, G. 15N-ammonium and 15N-nitrate uptake of a 15-year-old Picea abies plantation. Oecologia 1995, 102, 361–370. [CrossRef] 59. Gessler, A.; Schneider, S.; Von Sengbusch, D.; Weber, P.; Hanemann, U.; Huber, C.; Rothe, A.; Kreutzer, K.; Rennenberg, H. Field and laboratory experiments on net uptake of nitrate and ammonium by the roots of spruce (Picea abies) and beech (Fagus sylvatica) trees. New Phytol. 1998, 138, 275–285. [CrossRef] 60. Anttonen, S.; Manninen, A.-M.; Saranpää, P.; Kainulainen, P.; Linder, S.; Vapaavuori, E. Effects of long-term nutrient optimisation on stem wood chemistry in Picea abies. Trees 2002, 16, 386–394. [CrossRef] 61. Feng, Z. Partitioning of Atmospheric Nitrogen under Long-Term Reduced Atmospheric Deposition Conditions in a Norway Spruce Forest Ecosystem; Göttinger Forstwissenschaften; Universitätsverlag Göttingen: Göttingen, Germany, 2010; Volume 4. 62. Kopáˇcek,J.; Hejzlar, J.; Kaˇna,J.; Porcal, P.; Turek, J. The sensitivity of water chemistry to climate in a forested, nitrogen-saturated catchment recovering from acidification. Ecol. Indic. 2016, 63, 196–208. [CrossRef]