Zinc lability and solubility in soils of Ethiopia – an isotopic dilution study

Abstract. Zinc (Zn) deficiency is a widespread nutritional problem in human populations, especially in sub-Saharan Africa (SSA). The Zn concentration of crops consumed depends in part on the Zn status of soil. Improved understanding of factors controlling the phyto-availability of Zn in soils can contribute to potential agronomic interventions to tackle Zn deficiency, although there are major knowledge gaps for many soil types in SSA. Soil samples (n = 475) were collected from a large part of the Amhara Region of Ethiopia where there is widespread Zn deficiency. Zinc status was quantified by measuring several fractions: pseudo-total (Aqua-Regia digestion; ZnTot), available (DTPA-extractable; ZnDTPA), soluble (dissolved in 0.01 M Ca(NO3); ZnSoln) and isotopically exchangeable Zn using the enriched stable Zn isotope 70Zn (ZnE). Soil geochemical properties were assessed for their influence on Zn lability and solubility. ZnTot ranged from 14.1 to 291 mg kg−1 (median = 100 mg kg−1) whereas ZnDTPA in the majority of soil samples was less than 0.5 mg kg−1 indicating widespread phytoavailable Zn deficiency in these soils. The labile fraction of Zn in soil (ZnE as %ZnTot) was low, with median and mean values of 4.7 % and 8.0 % respectively. Labile Zn partitioning between the solid and the solution phases of soil was highly pH-dependent where 94 % of the variation in the partitioning coefficient of 70Zn was explained by soil pH. Similarly, 86 % of the variation in ZnSoln was explained by soil pH. Zinc distribution between adsorbed ZnE and ZnSoln was pH controlled. Notably, Zn isotopic exchangeability increased with soil pH. This contrasts with literature on contaminated and urban soils and may arise from covarying factors such as contrasting soil clay mineralogy across the pH range of the soils used in the current study. These results could be used to improve agronomic interventions to tackle Zn deficiency in SSA.



Introduction
Zinc deficiency is a widespread nutritional disorder affecting ~17% of the global population, and rising to 25% of the population in countries within sub-Saharan Africa (SSA) (Kumssa et al., 2015;Wessells and Brown, 2012). Several interlinked causes contribute to the prevalence of Zn deficiency issues in 35 SSA, including lack of access to animal source foods. This can lead to inadequate Zn intake if the diet is heavily reliant on staple crops which are inherently low in mineral micronutrients (Joy et al., 2014;Kumssa et al., 2015). Soil degradation and a lack of access to micronutrient fertilizers can contribute to the production of staple crops with poor nutritional quality (Kihara et al., 2020). Three-quarters of the arable land in SSA is reported to be depleted in plant nutrients and low in fertility (Toenniessen et 40 al., 2008).
Phyto-availability of Zn in soil is largely controlled by a dynamic equilibrium between the solid phase and pore water and the absorption mechanisms of plant roots (Groenenberg et al., 2010;Menzies et al., 2007;Peng et al., 2020). Traditionally, chemical extraction procedures used to estimate an assumed 'phyto-available' pool of soil Zn have included reagents which vary widely in extraction 45 power such as water, neutral salt solutions, dilute strong acids and chelating agents such as ethylenediaminetetraacetic acid (EDTA) and diethylenetriaminepentaacetic acid (DTPA; Kim et al., 2015). However, these approaches cannot fully characterise both the 'quantity' of potentially available Zn in the soil solid phase and its 'intensity' in the soil solution phase-both of which contribute to the phyto-availability of Zn over the course of a growing season. Isotopic dilution assays may provide a 50 more mechanistically-based characterization of the geochemically reactive fraction of Zn in soils which buffers the free ion activity in the soil solution phase (Guzman-Rangel et al., 2020;Hamon et al., 2008;Young et al., 2005). This approach has been extensively used to study contaminated soils (Degryse et al., 2011;Izquierdo et al., 2013;Mossa et al., 2020;Nolan et al., 2005) but its application to Zn in agricultural soils generally, and especially in the soils of SSA countries, is very limited. 55 The aim of the present study was to investigate the status of Zn lability in soils from a large part of the Amhara Region of Ethiopia which represent a diverse range of soil types from SSA (Gashu et al., 2020), in which Zn deficiency is thought to be widespread (Hengl et al., 2017). The study used several assays of soil Zn status including an isotopic dilution assay, employing enriched 70 Zn, to examine the soil properties that control Zn phyto-availability. The primary objectives were: (i) to determine isotopically 60 exchangeable Zn in soils from the Amhara Region; (ii) to compare different assays of Zn status; (iii) to https://doi.org/10.5194/soil-2020-81 Preprint. Discussion started: 13 January 2021 c Author(s) 2021. CC BY 4.0 License. examine the influence of soil properties on Zn partitioning between the solid and solution phases of these soils.

Soil sampling 65
Field sampling is described in detail in Gashu et al., (2020). Briefly, topsoil was collected from a target of 475 locations in the Amhara Region of Ethiopia according to a geospatial design intended to explore spatial variation in soil and crop properties. The sample frame was constrained to sites where the probability that the land was in agricultural use was ≥0.9. At each sampling location, five sub-samples of topsoil were collected from a 100 m 2 circular plot using a Dutch auger with a flight length of 150 70 mm and a diameter of 50 mm. Plant material was removed and the five sub-samples were combined, oven-dried at 40 °C for 24-48 hours, sieved to <2 mm and homogenised prior to analysis.

Geochemical analysis
Soluble trace and major metallic elements (MSoln) were determined in the solution phase of soil suspensions in 0.01 M Ca(NO3)2 (1:10 soil:solution ratio) following equilibration for four days on an 75 end-over-end shaker. The pH of this soil suspension (pHCa) was determined then solutions were isolated by centrifugation and filtration (<0.22 μm) prior to elemental analysis by ICP-MS (iCapQ; Thermo Fisher Scientific, Bremen, Germany). Total carbon content was determined by dry combustion (Tiessen et al., 1981) using a Leco TruMac CN Combustion analyser and inorganic C was measured using an Inorganic Carbon Analyser-Skalar Primacs (Skalar Analytical BV, Breda, Netherlands). 80 Dissolved organic carbon (DOC) was estimated by measuring non-purgeable organic carbon using a Shimadzu TOC-VWP analyzer (Shimadzu Corporation, Kyoto, Japan). Estimates of amorphous and poorly crystalline oxides were obtained following extraction with a mixture of ammonium oxalate and oxalic acid at a 1:100 of soil:solution suspension (Schwertmann, 1964). Samples were shaken in the dark at 20 °C for 4 hours on a reciprocal shaker (120 rpm), then filtered (Whatman No 42), diluted and 85 acidified to 5% HNO3, and analysed using inductively coupled plasma optical emission spectrometry (ICP-OES; Perkin Elmer Life and Analytical, Shelton, USA). The effective cation exchange capacity (eCEC) was determined using the cobalt hexamine (Cohex) method (ISO 23470;. DTPAextractable zinc (ZnDTPA) was solubilized by shaking 5 g soil with 10 mL of 0.005 M DTPA, 0.1 M triethanolamine (TEA) and 0.01 M CaCl2 at pH = 7.3 for 2 h on an end-over-end shaker (Lindsay and 90 Norvell, 1978). The soil suspensions were then centrifuged and filtered (<0.22 µm) prior to analysis using ICP-MS (iCAP-Q; Thermo Fisher Scientific, Bremen, Germany). The pseudo-total Zn concentration in soil (ZnTot) was determined after digesting finely ground soil sample with aqua-regia (Crosland et al., 1995) and analysis using ICP-OES. https://doi.org/10.5194/soil-2020-81 Preprint. Discussion started: 13 January 2021 c Author(s) 2021. CC BY 4.0 License.

Isotopic dilution assays 95
To determine the concentration of isotopically exchangeable Zn, 2.0 g of soil was equilibrated with 20 mL of 0.01 M Ca(NO3)2 on an end-over-end shaker for 24 h. This was followed by addition of 0.4 mL solution with a 70 Zn concentration of 11.9 mg L -1 of 70 Zn which provided an amount of 70 Zn equivalent to 2.3% of the average ZnTot (104 mg kg -1 ) concentration in soil. The isotopic tracer solution was prepared from a stock solution enriched with 70 Zn (250 mg L −1 ; isotopic abundance (IA) = 95.47%). To 100 avoid acidifying the soil suspension, the pH of the spiking solution was adjusted to pH 4.0-4.5 using an ammonium acetate buffer immediately before use. After spiking with the isotope, samples were re-equilibrated for a further 3 days, then centrifuged (3500 rpm for 15 min), filtered (< 0.22 µm), and acidified to 2% HNO3 prior to isotopic analysis using ICP-MS (iCAP-Q; Thermo Fisher Scientific, Bremen, Germany). The instrument was operated in 'collision cell mode' using He with kinetic energy 105 discrimination (KED). Rhodium ( 103 Rh; 10 µg L -1 ) was used as an internal standard to correct for instrumental drift. The ICP-MS was calibrated for individual Zn isotopes ( 66 Zn and 70 Zn) using a multiisotope Zn standard (CLMS2; SPEX CertiPrep). In practice, it was found that the measurement of 70 Zn in the supernatant solution of the soil suspensions required two corrections due to significant, and variable, concentrations of soil-derived 70 Ge + and (plasma-generated) doubly-charged 140 Ce ++ (m/z = 110 70). The correction for 70 Ge (IA = 69.9%) was achieved by determining the intensity (count per second (CPS)) of 72 Ge in samples and using the measured CPS ratio 72/70 for Ge standards to infer the proportion of the intensity at m/z 70 arising from 70 Ge. The universal isotopic ratio 72 Ge/ 70 Ge is 1.34; the measured intensity ratio in a Ge standard (including error due to mass discrimination) was typically 1.53. The correction for doubly-charged 140 Ce ++ was implemented by running Ce standards, which 115 typically produced a 70/140 intensity ratio of 0.025, and measuring m/z 140 on samples. The Ce standards were analysed in three concentrations of NaCl (0, 1, and 10 mg L -1 ) to confirm minimal change in the generation of doubly-charged Ce in the plasma with alkali cation concentration. The correction for 70 Ge produced a change in ZnE that ranged from 0.027 mg kg -1 to 0.976 mg kg -1 (median = 0.253 mg kg -1 ) while for 140 Ce ++ the change was 0.024 mg kg -1 to 0.973 mg kg -1 (median = 0.747 mg 120 kg -1 ).
The E-value of Zn (ZnE; mg kg -1 ) was calculated from Eq. 1: Where ZnSoln is the measured concentration of Zn of an equilibrated soil suspension; the liquid to solid ratio (L kg -1 ); KdLab is the distribution coefficient (L kg -1 ) of the added 70 Zn isotope spike between 125 a weight of soil (S; kg) and volume of liquid (L) and is calculated as shown in Eq. 2. https://doi.org/10.5194/soil-2020-81 Preprint. Discussion started: 13 January 2021 c Author(s) 2021. CC BY 4.0 License.

=
(2) The variable 70 Znads is the adsorbed 70 Zn spike (µmol kg -1 ) and was calculated as the difference between the total 70 Zn added to the soil suspension and the amount of 70 Zn spike remaining in the solution after equilibration; 70 ZnSoln is the measured concentration (µmol L -1 ) of 70 Zn spike in solution after 130 equilibration. Crucially, the value of 70 ZnSoln was corrected for the presence of native 70 Zn in solution which was estimated from the measured concentration of 66 Zn and the known isotopic ratio 70 Zn: 66 Zn; this was implemented after all analytical corrections ( 70 Ge and 140 Ce ++ ) and calculation of the Zn isotope concentrations (µmol L -1 ) from isotopic calibration. The measured 70 Zn was overwhelmingly (97.7% ± 2.20%) dominated by the added spike. Therefore, any deviation, in the native soil Zn of 135 individual samples, from the expected isotopic ratio of 70 Zn: 66 Zn would incur a negligible error.

Geochemical modelling using WHAM7
The geochemical model WHAM7 (Tipping, 1994) was used to speciate Zn in the solution phase of the 0.01 M Ca(NO3)2 suspensions. Inputs to the model included cation and anion concentrations, colloidal (dissolved) fulvic acid, pH, temperature and partial pressure of CO2. Colloidal fulvic acid (FA) was 140 estimated from NPOC assuming (i) a carbon content in FA of 50% and (ii) that 65% was 'active' (Lofts et al., 2008). Partial pressure of CO2 (PCO2) and the temperature were set to 0.004 atm and 25°C respectively. WHAM7 was also used to predict the labile pool of Zn (ZnE) within the soil particulate phases. This required inclusion of suspended particulates, calculated from 2 g solid in 20 mL of electrolyte, and included Fe, Al and Mn oxides (estimated by oxalate extraction) and particulate humic 145 acid which was estimated from soil organic C assuming 50% is 'active' humic acid (Buekers et al., 2008;Marzouk et al., 2013b).

Data analysis
Data analysis was carried out using R (version 4.0.2) software (R Core Team, 2020). Measured soil properties were related to Zn lability (ZnE) and the labile distribution coefficient of 70 Zn (KdLab) using 150 standard least squares regression. Soil variables used in the regression were: soil pH (measured in the Ca(NO3)2 suspensions), organic C (%), sum of the concentration of Al, Fe and Mn in the oxalate extractions (mol kg -1 ), dissolved organic C (mg L -1 ) and the effective cation exchange capacity (eCEC; cmolC kg -1 ). All data were checked for normality using the Shapiro-Wilk normality test and log- Most measured soil properties varied widely (Table 1). Soil pH ranged from 4.2-7.5 with ca. 70% soils having pH values below 6.0. The organic carbon (COrg) also varied widely with a median value of 1.72% (Table 1). There was a 10-fold variation in eCEC, potentially indicating a large range of Zn binding 160 strength within the studied soils. mg kg -1 . The median value was 100 mg kg -1 (Fig. 1A and Table 1), which is at the top of the range suggested for uncontaminated soils: 10-100 mg kg -1 (Mertens and Smolders, 2013). Values of ZnE ranged from 0.44 to 57.7 mg kg -1 (median: 4.82 mg kg -1 ) ( Table 1 and Fig 1B). The labile fraction (ZnE as % of ZnTot) ranged from 0.75% to 69.7% with median and mean values of 4.66% and 8.00% respectively. These values (%ZnE) are lower than those reported for both contaminated and 170 uncontaminated soils (Degryse et al., 2011;Izquierdo et al., 2013;Marzouk et al., 2013b). The distribution of ZnDTPA concentrations were positively skewed (Fig. 1C) with a variation of 0.01-5.25 mg kg -1 (median = 0.69 mg kg -1 ). Only 31.4% of samples had ZnDTPA less than 0.5 mg kg -1 , indicating that they are potentially Zn deficient (Mertens and Smolders, 2013). Values of ZnDTPA as % of ZnTot ranged from 0.013% to 3.82% with a median and mean of 0.690% and 0.871% respectively. There was a 175 https://doi.org/10.5194/soil-2020-81 Preprint. Discussion started: 13 January 2021 c Author(s) 2021. CC BY 4.0 License. significant positive correlation (r = 0.25) between COrg and %ZnDTPA possibly indicating that Zn bound to soil organic matter is in a form accessible to DTPA extraction. The Zn concentration in 0.01 M Ca(NO3)2, ZnSoln, varied by more than two orders of magnitude (0.001-0.789 mg kg -1 ). Values of ZnSoln showed a unimodal and positively skewed distribution (Fig 1D), indicating predominately small concentrations (<0.1 mg kg -1 in 72% of soils studied). A maximum of 0.96% of ZnTot was extracted in Ca(NO3)2 (median = 0.027%). 185 To evaluate the correlation between soil variables, principal component analysis (PCA) was employed (Fig. 2). The first two principal components (PCA 1 and 2) explained 58.7 % of the variation in the datasets; 41.1% was explained by PCA 1. PCA 1 was strongly correlated with KdLab and soil properties https://doi.org/10.5194/soil-2020-81 Preprint. Discussion started: 13 January 2021 c Author(s) 2021. CC BY 4.0 License.
that are likely to affect KdLab, such as pH and eCEC. PCA 2 correlated with ZnDTPA, Corg, and mineral oxides (Fig. 2). PCA analysis also shows that ZnSoln and ZnE react in opposite ways. 190

Method assessment and validation
In principle, E-value determination is based on the premise that an added isotope is reversibly 195 adsorbed and is in a dynamic equilibrium between the solid and solution phases (Hamon et al., 2008;Young et al., 2005). Therefore, the reliability of the determined E-value rests on an accurate measurement of the distribution coefficient of the added 70 Zn (KdLab) and Zn concentration in the soil solution ZnSoln (Eq. 1). For an accurate measurement of KdLab, the added 70 Zn must produce a change in the isotopic ratio ( 70 Zn/ 66 Zn) that can be reliably quantified while still reflecting the native Zn 200 equilibrium in the soil. As illustrated in Fig. 3A, there was a clear distinction between the natural isotopic ratio (0.02) and measured ratios, with a minimum 70 Zn/ 66 Zn ratio of 0.15 which is almost 8 times the natural ratio. At the same time, the amount of the added isotope was small compared to https://doi.org/10.5194/soil-2020-81 Preprint. Discussion started: 13 January 2021 c Author(s) 2021. CC BY 4.0 License.
ZnTot and amounted to 2.1% of ZnTot on average (< 5% in 94% of the samples). To confirm the consistency of ZnSoln, an inter-laboratory comparison was undertaken. Figure

Soil factors determining Zn lability (ZnE) 215
Previous studies, mainly on contaminated soils, have reported that the labile fraction of metals tends to decline with rising pH in response to increased adsorption strength (Degryse et al., 2004;Tye et al., 2003); data in the current study showed the opposite trend ( Fig. 4A-B). However, with contaminated soils the behaviour of trace metals often partly reflects the properties of the source of metal (Mao et al., 2014;Marzouk et al., 2013b). For example, contamination with calcareous materials in the case of 220 soils contaminated with mine spoil produces co-variation of total Zn concentration with pH.
Furthermore, there is usually a restricted pH range in the case of urban soils and temperate agricultural soils. The current study deals with soils that have comparatively low %ZnE and ZnTot and a wide range of pH values (c. 4.0 -7.5) which are likely to include substantial changes in geocolloidal mineralogy (e.g. oxide-based vs alumina-silicate clays) (Fig. 4C). Thus the trend depicted in Fig. 4A  225 probably reflects a combination of different factors. For example, in soils with higher pH values it is possible that Zn adsorption is on surfaces which are more likely to retain Zn in a labile form -e.g. humic acid and 2:1 alumino-silicate clays. Similarly, there may be greater Zn fixation under acid conditions because of the greater incidence of oxide-rich mineralogy in highly weathered soils with a https://doi.org/10.5194/soil-2020-81 Preprint. Discussion started: 13 January 2021 c Author(s) 2021. CC BY 4.0 License. low pH (Fig 4C). A significant negative correlation (r = -0.26; p < 10 -8 ) between ZnE and the sum of the 230 concentration of mineral oxides in soil may support that hypothesis. Furthermore, solution phase speciation (from WHAM7) suggested that the proportion of Zn bound to dissolved organic carbon increased with pH (Fig. 5A). At very low Zn concentrations in solution (ZnSoln c. 1 µg L -1 above pH 6.5) it is possible that the fulvic-bound Zn was sufficiently strongly bound to be non-labile -i.e. excluded from isotopic equilibrium with the added 70 Zn. In the calculation of E-value (Eq. 1) this would 235 (erroneously) inflate the apparent ZnE. A similar outcome would occur if there were significant amounts of non-labile Zn held in particulate form as part of the measurement of ZnSoln at higher pH values. Non-labile particulate metal was first demonstrated by Lombi et al., (2003) who used chelating resin in E-values measurements (Er) to quantify the fraction of the colloidal metal that was not isotopically exchangeable. They reported that Er values were generally less than E values based on 240 equilibrated solution measurements (E) and that the ratio E/Er increased with pH. Use of the resin method has produced variable results. Marzouk et al., (2013b) also reported metals associated with sub-micrometre colloidal particles in the solution phase, based on resin phase measurements. They found this association to be positively correlated with soil humus content and pH. However, for their dataset, they found that the presence of nano-particulate non-labile Zn in solution produced, on 245 average, less than 2% difference in the determination of E-values (E vs Er). Mao et al., (2017) also investigated the presence of non-labile metal fractions of Ni, Cu, Zn, Cd, and Pb in suspended colloidal particles. They also found an average of only 2% difference between Er and E for all five metals and the difference was only significant for Cu with an increased presence of non-labile colloidal particles at high pH -probably organically bound Cu. 250 If the trend in ZnE with soil pH (Fig. 4A-B) was affected by interferences from particulate materials in the soil solution then the source of the error would either be in KdLab or ZnSoln-the two variables in 255 the calculation of ZnE (Eq. 1). However, the presence of non-labile Zn within particulate matter in the https://doi.org/10.5194/soil-2020-81 Preprint. Discussion started: 13 January 2021 c Author(s) 2021. CC BY 4.0 License. isolated soil suspension supernatant would not contribute to an error in KdLab. This is because, by definition, the labile spiked isotope is excluded from mixing with the particulate Zn phase. However, the measurement of 66 Zn would include Zn in solution and any particulate-bound Zn (< 0.22 µm), with which the 70 Zn would not have mixed. Thus it is the determination of ZnSoln (Eq. 1) that may produce 260 an error in ZnE. Tavakkoli et al., (2013) investigated the possible occurance of non-isotopically exchangable Zn in sub-micron sized colloids in filtered soil extraction at high soil pH. They found no non-exchangeable Zn when filtering to <0.1 µm to remove particles but gradually increasing proportions of isotopically non-exchangable Zn where solutions had been filtered using progressively larger filter pore sizes (0.22, 0.45, and 0.7 µm). In the present study soil extraction solutions were 265 filtered to <0.22 µm.
The possible presence of non-labile nanoparticulate Zn in the soil solution was investigated using a resin (Chelex-100) purfication step (Marzouk et al., 2013b) in the determination of ZnE. A comparison was made of 70 Zn/ 66 Zn ratios in the centrifuged, filtered solution and in a resin extraction of that solution. No evidence of non-labile nanoparticulate Zn below pH 5.5 was found; the isotopic ratios 270 70 Zn/ 66 Zn in the solution and resin phases were equal. Unfortunately, at higher soil pH (>5.5), our investigation was confounded due to resin Zn contamination that compromised the measurement of low soluble Zn concentrations in soils with high pH. However, considering the magnitude of the trend depicted in Fig. 4A, the majority of Zn in the filtered soil solution would have to be present as nonlabile particulate matter for the trend shown to be due to non-labile particulate Zn contributing to 275 ZnSoln. This seems unlikely and so we therefore suggest that the increase in ZnE values with soil pH in the soils studied is probably a genuine trend.
The WHAM7 predictions of labile Zn distribution among different soil surfaces are presented in Fig.   5B. At low soil pH, the WHAM7 model predicted the sorption to be overwhelmingly onto Mn oxide and humic acids, whereas at intermediate and high pH, humic acid-bound Zn became dominant. 280 WHAM7 predicts a negligible role for Fe oxide in adsorbing Zn but at pH > 6.5 sorption onto Al oxides was important. The fractionation suggested by the WHAM7 model relates only to labile Zn and does not predict the location of the 'fixed' (non-labile) Zn in the soils. The speciation of Zn in the soil solution, as calculated by WHAM, is presented in Fig. 5A. It was predicted that the free Zn ion activity (Zn 2+ ) constituted 36.1% to 99.2% (median = 77.1%) of the total ZnSoln and was highly correlated with 285 pH. At pH < 5.5, the majority of ZnSoln was present as the free Zn 2+ ion (> 61%). This percentage decreased to an average of 49% at soil pH > 7. Previous studies have also shown minimal complexation of Zn in the soil solution at pH < 6.5 (Catlett et al., 2002;Rutkowska et al., 2015). The proportion of the total Zn present as dissolved fulvic acid complexes ranged from 0.81% to 61.2.% (median 22.7%).

Zinc solubility
The partition coefficient in the current study (KdLab) represents the distribution of the added 70 Zn spike between the isotopically exchangeable Zn on the solid phase and in the solution phase (Eq. 2). Values 300 of Kdlab varied by more than 3 orders of magnitude-ranging from 15.4 to 42600 L kg -1 . As shown in Fig. 6A, values of KdLab were highly pH-dependent, in agreement with increased adsorption strength of cationic trace metals onto soil surfaces with pH. Regression analysis of soil properties (eCEC, COrg, ZnTot, mineral oxides) against KdLab is presented in Table 2. Only significant variables were retained in regression equations and the variables were checked for multicollinearity using variance inflation 305 factors (VIF). Values of VIF for all variables were less than 3. While all variables in Table 2 were significant in the regression analysis, they accounted for a very small proportion of the variation (< 4%) in the data. The majority of the variation in the data (90%) was explained solely by soil pH (Table   2 and Fig. 6A). Despite the fact that soil organic matter is known to be an important sorbent for trace elements (Degryse et al., 2011), COrg had a negligible influence on KdLab. 310 https://doi.org/10.5194/soil-2020-81 Preprint. Discussion started: 13 January 2021 c Author(s) 2021. CC BY 4.0 License. As seen for KdLab, ZnSoln was also mainly controlled by soil pH; 77% of the variation in ZnSoln was explained solely by soil pH (Fig 6B). There was also a weak, but significant, correlation between ZnSoln 315 and soil COrg (r = 0.23, ρ < 5.8 x 10 -7 ); some influence on metal adsorption strength would be expected (Fan et al., 2016). However, the limited effect of soil organic matter may be due to a dual influence on Zn solubility. Soil humus will contribute to Zn adsorption within the soil solid phase but also produce greater DOC (r = 0.47 between Corg and DOC) which will promote dissolved organo-complexation of Zn. 320 The concentration of Zn in soil solutions is largely determined by the combined influence of soil properties which affect the strength of adsorption and the total Zn concentration in soil. Thus, the relationship between KdLab and ZnSoln (Fig. 6C) demonstrates the much greater importance of soil characteristics over the influence of ZnTot in the Amhara soils. In considering the relationship in Fig 6C,  325 it should be emphasised that KdLab and ZnSoln are completely independent of each other. The value of KdLab is the distribution coefficient of the added 70 Zn isotope and ZnSoln is determined from measured values of 66 Zn; this negates the common, and justified, criticism of such relations in which ZnSoln is the denominator of the Kd which would tend to produce a declining trend with ZnSoln. Therefore, the very strong capacity-intensity dependence of the studied soils genuinely reflects control by soil properties 330 over Zn solubility. In particular, for the soils studied, soil pH alone virtually controls the strength of Zn adsorption and ZnSoln (Fig. 6A&B), despite considerable variation in ZnTot (14.1 -291 mg kg -1 ; Table 1).

Multi-surface modelling of soluble Zn concentration
It is widely recognised that while the total concentration of an element in soil is important, it is the chemical speciation that plays a key role in determining availability to plants. Despite that, direct 335 https://doi.org/10.5194/soil-2020-81 Preprint. Discussion started: 13 January 2021 c Author(s) 2021. CC BY 4.0 License. measurement of the chemical forms of an element is limited. Therefore, geochemical modelling offers a feasible alternative and has been widely applied to soil (Bonten et al., 2008;Cui and Weng, 2015;Klinkert and Comans, 2020). An important consideration when using geochemical models is the choice of the 'reactive' pool of metals, which is in equilibrium with the soil solution, as an input variable. It has been well established that the total concentration of metals does not reflect the reactive fraction 340 in soil (Kelepertzis and Argyraki, 2015;Peng et al., 2018;Rodrigues et al., 2013). Extractions with 0.43 M nitric acid (HNO3) and EDTA have been frequently used to approximate the geochemically reactive pool of metals in soil (Groenenberg et al., 2017;Liu et al., 2019;Ren et al., 2017). However, the isotopic dilution method is recognized to be conceptually the most robust and mechanistically based method that reliably quantifies the reactive pool of metals in soil (Groenenberg et al., 2017;Hamon et al., 345 2008;Peng et al., 2018). To assess the capability of WHAM7 to predict Zn solubility, the concentrations in the solution phase (ZnSoln) were compared with the outputs from fractionation of Zn across the whole soil-solution system, using either ZnTot, ZnE, or ZnDTPA concentrations as the fraction of Zn controlling Zn solubility. Results of these simulations are presented in Fig. 7.  Figure 7 clearly illustrates that using ZnE, substantially improves the prediction of Zn solubility compared to using ZnTot, particularly at low soil pH. This reinforces the conclusion that the geochemically reactive metal pool, rather than the total soil Zn concentration, is the most relevant 355 representation of Zn availability in soil. At high pH (>7.5), the model predicts higher ZnSoln than observed concentrations. This may be partly due to limitations in binding surfaces considered in WHAM7. At pH >7, adsorption on calcium carbonate or phosphate minerals may occur which is not accounted for in WHAM7. This was reported by Peng et al., (2018) who excluded data at pH >7 from their results, when using WHAM7 to predict the solid-solution partition and speciation of heavy 360 metals, in response to a lack of consideration of precipitation on carbonates. Izquierdo et al., (2013) listed the failure to include binding to carbonate surfaces as a possible source of error in predicting metal concentration in the soil solution from WHAM7. Mao et al., (2017) also attributed the overestimation of metal concentrations in the soil solution to the exclusion of phases such as calcite and hydroxyapatite as binding phases in WHAM7. Additionally, overestimation of E-values at high pH 365 due to the presence of (non-labile) Zn which has not isotopically mixed with the added 70 Zn spike would also explain the poorer performance of WHAM7 in predicting Zn solubility at high pH.
When ZnDTPA was used as input, prediction of Zn solubility by WHAM7 was apparently improved over that achieved by using ZnE (Fig. 7), particularly at high pH. This may confirm the possible overestimation of ZnE, as discussed above. Alternatively, it may reflect counteracting errors between (i) 370 the inadequacy of DTPA (0.005 M) as an extractant which would decrease modelled ZnSoln and (ii) the underestimation of Zn binding in WHAM7 at high pH which would raise the estimate of ZnSoln. It is recognised, for example, that 0.005 M DTPA extracts less Zn from soil than 0.05 M EDTA and also underestimates ZnE (Marzouk et al. 2013a). To assess whether the binding capacity of the DTPA used was limited, the mole ratios of cations to DTPA in the extracted solutions (excluding alkali/alkali-earth 375 cations) were calculated. The average ratio was only 0.17 ± 0.08, suggesting that the DTPA extractant was probably not capacity-limited. These results suggest, broadly, that both ZnDTPA and ZnE may be reasonable estimates of the 'labile' pool of Zn in soil. DTPA appears to provide a better estimate of ZnSoln using a current geochemical model, especially at high pH. Alternatively, the isotopic dilution method, measured in neutral 0.01 M Ca(NO3)2, probably better reflects variation with pH in labile Zn 380 Kd value, and possibly in the true labile pool of Zn in soil, compared with DTPA, which is buffered at pH 7.3.

Free Zn 2+ activity in soil solution
The free ion activity is considered a key factor controlling plant uptake, although other factors will affect buffering and diffusion rates in the soil (Degryse et al., 2012). The concentration of Zn 2+ activity 385 in the soil solution is effectively an integration of soil properties that govern sorption processes. Data https://doi.org/10.5194/soil-2020-81 Preprint. Discussion started: 13 January 2021 c Author(s) 2021. CC BY 4.0 License. presented in Fig 8 show that the activity of Zn 2+ is highly pH-dependent; 81% of variation in the free Zn 2+ activity was accounted for solely by soil pH in the Ethiopian Amhara soils. The concentration of free Zn 2+ activity varied over 3 orders of magnitude. The range of the free Zn 2+ activity is probably a product of the counteracting effects of ZnE and ZnSoln variation with pH; ZnE increases with pH while 390 ZnSoln falls as pH rises as discussed above (Fig 4B and 6B).

Conclusions
Combining isotopic dilution method with geochemical speciation modelling in this study provides 395 useful insights into the intrinsic reactivity of Zn in soils at a regional scale and elucidates the key variables determining Zn phyto-availability. The results demonstrate that intrinsic soil properties, rather than the variation in ZnTot concentration, determine the adsorption strength (Kd) of labile Zn and dictate Zn solubility. In the Amhara dataset soil pH was the key determining factor. The traditional DTPA extraction method provided a better estimate of ZnSoln, predicted from a geochemical modelling 400 approach, when compared with ZnE as a model input variable. However, reasons for this remain unresolved and may reflect shortcomings in either approach and in model prediction at higher pH values.
These findings may have practical implications for agronomic interventions to improve crop Zn concentrations for they provide a tool for differentiating between soils in terms of the strength with 405 https://doi.org/10.5194/soil-2020-81 Preprint. Discussion started: 13 January 2021 c Author(s) 2021. CC BY 4.0 License. which they adsorb Zn. This is an important consideration for a site-specific strategy to ensure a more effective agronomic biofortification of staple crops with Zn fertilizers Manzeke et al., 2014Manzeke et al., , 2020Zia et al., 2020). For instance, these results indicate that in soils with pH >6.5 foliar fertilisers are most appropriate because Zn is strongly adsorbed, while in soils with low pH applying fertilizers to the soil might be feasible. Furthermore, these findings can be used to identify areas where 410 the use of soil management practices, such as organic matter incorporation, could increase Zn availability in soil-thus improving the Zn concentration of staple crops (Manzeke et al., 2019;Wood et al., 2018).

Data availability
Data used in this study is available for the corresponding author upon a reasonable request as the lab 415 data will be published as part of a national datasets.

Author contribution
Conceptualization of the study for this manuscript was done by AWM, MRB, SPM, and SDY, with input from EHB, GD, and SJD. Data curation and investigation: AWM. Analysis, methodology, and visualization for the manuscript was performed by AWM with substantial input from SJD, MRB, SDY, 420 EHB and feedback from all authors. AWM wrote the initial draft and all authors were involved in the review and editing of the manuscript.

Declaration of competing interest
The authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. 425