Impacts of Crop-Specific Agricultural Practices on the Accumulation of Heavy Metals in Soil and Their Potential Availability to Plants

1. Introduction

Agricultural production heavily depends on the soil fertility level and balanced nutrient supply of crops, affecting the yield [1,2]. In agricultural production, sustainability can be achieved through the introduction of sustainable agricultural practices [3], enabling farmers to maintain adequate soil fertility and improve the crop yield or, at least, ensure stability in production in the long term without negatively impacting the quality traits [4] and safety of a product [5]. Therefore, selecting proper agricultural practices is crucial for achieving sustainable development in agricultural production systems [6]. The main challenge that agricultural systems face today is determining the advantages and disadvantages of a particular agricultural practice, as their study requires years and sometimes decades. At the same time, a great deal of scientific knowledge has been accumulated from long-term experiments [7,8] and farm-level studies [9,10], demonstrating the impacts of specific agricultural practices on plant nutrients in soil. Numerous studies have shown that soil macro- and micronutrient composition changes are associated with agricultural practices [11,12,13]. Those changes in the nutrient concentrations can be temporal but, in some cases, last longer and often remain detectable even after changes in land use. Therefore, the historical use of the soil and its related management practices are important predictors of its elemental composition.

Currently, many developments are taking place in the agricultural sector in Georgia [14] as it becomes more market-oriented, which is also causing changes in the types of crops and land. These changes are evident in the regions where agriculture plays an important economic role. Our study was conducted in one of the most active agricultural production regions of Georgia, namely Kvemo Kartli, and we attempted to assess the impacts of the agricultural practices established by farmers and their relationships with the soil micronutrient and heavy metals composition considering the impact of metal-enriched irrigation water in the past. The research was conducted in close cooperation with the farmers to understand their crop-specific management practices and to predict their sustainability in the long term based on the inputs of macro- (N, P, K) and micronutrients (Fe, Cu, Mn, Ni, Zn) and toxic heavy metals (Cd, Pb).

2. Materials and Methods

2.1. Study Area

The study was conducted in the Kvemo Kartli region in eastern Georgia. The region is known for producing vegetables, although the lands occupied by cereals and perennial crops represent a considerable share of the total area. Diverse agricultural production is supported by the region’s favourable soil and climatic conditions. The foothills of the region, at 500-600 m above sea level, have a moderately humid climate, with moderately cold winters (3-5 °C) and hot summers (23-28 °C). The average annual temperature is 12 °C, and the average annual precipitation is 572 mm. The agricultural lands on the foothills are mainly occupied by cereals (wheat, barley, maize), perennial crops (vine, fruits and nuts), and pastures.

The lowland area has a moderately warm steppe climate, with moderately cold winters and hot summers. The average annual temperature is 12 °C, and the absolute maximum is 40 °C. The average annual precipitation is 400-500 mm. The precipitation is unequally distributed throughout the year, with the maximum precipitation observed in spring, as May is the wettest month, and the minimum in winter, with the driest month being December. Due to this, agriculture, particularly vegetable production, heavily depends on irrigation supplied by the rivers Mashavera and Khrami.

The dominant soils belong to the soil reference group [15] of calcic and calcaric Kastanozems. The slopes at higher elevations are characterized by rendzic Leptosols, and the lowest plains near the rivers are characterized by calcaric Fluvisols.

2.2. Soil sampling

The soil sampling was conducted in two stages; initial sampling was done in 2021 for recognizance purposes, based on which the sampling scheme was elaborated, and final sampling was conducted in 2022, which is presented in the paper. Soil samples were taken from plots occupied by crops that were dominant in each selected area, and additional sampling was performed in the neighbouring plots in which annuals and perennials were cultivated, as well as pastures, which mainly served as a local reference, assumed to be the least disturbed soil, as there are no inputs from farmers, as they are used as communal natural pastures.

The soil samples were taken after the harvest of the main crop at a fixed depth between 0-30 cm to ensure the comparability of the results. In each case, five points were sampled using a spade and a shovel, one core sample and four subsamples located 2 m away from the core sample were taken to obtain a composite sample [16], which was used for the laboratory analysis. Samples were collected in plastic bags with appropriate labelling and transported to the laboratory the same day. The full dataset of analytical results is given as supplementary (Table S1, Table S2, Table S3, Table S4).

2.3. Farm Survey

The farm survey was conducted in the study area and covered all the fields of agriculture represented in the region. We used questionaries prepared before the fieldwork and conducted personal interviews with the farmers managing the agricultural lands where soil sampling was performed. The survey was done using a mobile application, Collect [17]. In total, 83 sites were assessed, from which 5 orchards, 9 vineyards, 10 former vineyards, 14 arable intensive, 20 arable extensive, and 25 pasturelands. The survey aimed to understand the agricultural management practices established by a given farmer to better interpret the results obtained. In total, 16 farmers managing arable and perennial croplands were interviewed, covering 37 sites we assessed where farm management practices are established, which allowed us to survey with a margin error of 12 % at a 95% confidence level.

2.4. Soil Analysis

The soil samples were analysed based on the following parameters: the pH; electric conductivity (EC); calcium carbonate; organic matter (OM); bulk density; texture; cation exchange capacity (CEC); mobile phosphorous (P); exchangeable potassium (K); and potentially plant-available and aqua regia extractable forms of copper, cadmium, iron, lead, manganese, nickel, and zinc.

The soil pH was measured in aqueous solution using a 1:2.5 ratio by the potentiometric method [18] with pH/ion meter (InoLab Multi 9310, WTW, Germany); EC was determined in 1:5 ratio extract (InoLab Multi 9310, WTW, Germany) [19]; calcium carbonate was determined volumetrically using Scheibler calcimeter [20]; the OM was measured colorimetrically using spectrophotometer (Specord 210 plus, Analytik Jena AG, Germany) after digestion with potassium dichromate, according to the Walkley–Black method [21]; the soil bulk density was determined in undisturbed soil cores taken using stainless steel cylinders with volume of 100 ml and dried to constant weight using drying oven (UF160, Memmert, Germany); the texture was determined using a pipette method according Kachinskii system [22] and then transferred to USDA textural classes [23] using the conversion method proposed by Shein [24]; The mobile phosphorous were determined colorimetrically after extraction by sodium bicarbonate, according to the Olsen method [25]; the exchangeable potassium was measured using an atomic absorption spectrometer (AAS) (ZEEnit 700P, Analytik Jena AG, Germany) after extraction by 1M ammonium nitrate [26]; cation exchange capacity was measured in 0.01M barium chloride extract [27] using AAS; the potentially plant-available pool of elements were measured using an AAS after extraction using DTPA [28]; and the total forms of the studied elements that can be extracted with aqua regia [29] were measured using an AAS.

2.5. Statistical Analysis

Descriptive statistical parameters, including mean, median, standard deviation, and coefficient of variation, were calculated using MS Excel Data Analysis Tool. Analysis of variance (ANOVA) was used to evaluate the impact of crop-specific agricultural practices on changes in studied soil macro- and micronutrients and toxic heavy metals consecrations in soils. The Spearman correlation coefficient was used to determine the correlation between studied elements and other properties of soil (Table S5). Spearman correlation coefficient was preferred as some studied parameters were not normally distributed. In such a case, Spearman’s rank correlation coefficient is recommended as being more robust than the Pearson product-moment correlation coefficient [30], which is also widely used. Principal component analysis (PCA) was used to identify the primary sources of metal inputs, focusing on those affecting their availability to plants and categorized according to farmers’ interviews and previous studies conducted in the region.

2.6. Baseline Content

The regional baseline contents of studied elements were estimated based on median values ± one standard deviation, as proposed by Salminen and Tarvainen [31]. A similar approach was practiced in many studies worldwide where median or mean values were used [32,33,34] and applied in Georgia [35]. Median values were taken from soils studied on pastures, as it was considered a land use type with minimum anthropogenic impact compared to other agricultural land use forms. Median values were preferred over the mean, as the median is less influenced by extremely high or low values, which was also avoided by eliminating outliers from baseline content calculations. Instead of defining baseline as a range, as suggested by Salminen and Tarvainen [31], we reported it as an upper limit of that range to get a single maximum baseline concertation, which could be easily used in calculations.

2.7. Soil Contamination Assessment

Most micronutrients (such as Cu, Mn, Ni, Zn) measured in soils in our study belong to heavy metals like Cd and Pb. Therefore, in parallel to evaluating the impact of crop-specific agricultural practices, we assessed the degree of heavy metals accumulation in soil using various indices frequently applied in monitoring and assessing harmful contaminants in different environmental media. In this study, we used Contamination Factor (CF), Pollution Load Index (PLI) [36,37,38], single-factor Pollution Index (PI), Nemerow’s comprehensive pollution index (PIN) [39,40], Enrichment Factor (EF), and Geo-Accumulation Index (Igeo) [37,41,42], which are described in details in the corresponding subchapters.

2.7.1. Contamination Factor (CF) and Pollution Load Index (PLI)

The contamination factor (CF) is often used to express soil contamination level with some aspects in combination with the Pollution load index (PLI) [36,37,38]. CF is calculated using a ratio of the measured concertation of an element (C_i) and its baseline content (C_b) using the following equation:

CF = Ci /Cb

(1)

PLI represents the total contamination level by considering the CFs of all observed elements [37,38].

PLI = (CF1 × CF2 × CF3 · · · CFn)^1/n

(2)

The scale of the classes for CF and PLI are shown in Table 1.

2.7.2. Nemerow’s Comprehensive Pollution Index Method

The comprehensive pollution index method developed by Nemerow (PIN) is widely applied to assess pollution caused by multiple elements [39,40] based on compositing results obtained from single-factor pollution index (PI) assessments. The corresponding equations to calculate PI (3) and PIN (4) are as follows:

PI_i = C_i /S_i

(3)

PIN = {[(PI_mean)²+(PI_max)²]/2}^0.5

(4)

C_i is the observed concentration of element i; Si is the standard referential value for evaluating this element. In our study, we used threshold values established in Georgia based on a legislative norm to evaluate soil quality on agricultural land [43]. Threshold values are established for Cu, Zn, Mn, Ni, Cd, and Pb. Therefore, PI and PIN were calculated only for these elements.

In the equation for estimating PIN (4), PI_mean corresponds to the single-factor pollution index and PI_max is the maximum single-factor pollution index observed. The scale of the classes for PI and PI_N are shown in Table 2.

2.7.3. Enrichment Factor (EF)

The Enrichment Factor (EF) is another commonly applied index to assess the degree of contamination in different environmental media [37,41,42]. It is used to determine the trend of geochemical characteristics at a local or regional scale. EF index can be used to see whether certain elements in soils or sediments are related to anthropogenic activities or have a natural origin [44]. The equation for estimating EF (5) relies on the local reference element for normalization, which is essential to distinguish anthropogenic impact from natural origin [37,44]. In this research, we selected Fe, as it has the highest baseline content and is the most uniformly distributed element in the study area with the lowest coefficient of variation of 34% among studied elements (Table S1) and potentially less affected by anthropogenic activities.

EF = (C_i /C_ref) / (B_i /B_ref)

(5)

where C_i is the concentration of a measured element, C_ref is the concentration of the measuring element in the reference environment, B_i is the concentration of the reference element in the study area, and B_ref is the concentration of the reference element in the reference environment. The scale of the classes for EF is shown in Table 3.

2.7.4. Geoaccumulation Index (Igeo)

The geoaccumulation index (Igeo) method was initially developed to assess the contamination level of river sediments by Muller [45], but it was widely used to evaluate urban [32,33] and agricultural soils [46]. It is calculated with the following equation:

Igeo = log₂ [C_i/1.5 × C_b]

(6)

where, C_i is the concentration of a measured element i in soil, C_b is a baseline value of a measured element in the study area, and 1.5 is a correction factor for the background matrix [37] to normalize the fluctuation of the element in the baseline value as well as possible minimal anthropogenic influences and/or inputs [44]. The scale of the classes for Igeo is shown in Table 3.

2.7.5. Maximum Permissible Total Addition

According to the legislative act [43] setting environmental quality criteria for soils in Georgia, it is necessary to estimate the maximum permissible total addition of fertilizer or soil improver based on the equation (7), where the concentration of specific element in a material to be added and baseline content and threshold value of respective element are used. Equation (7) calculates the maximum permissible amount of material added per hectare of agricultural land. It is calculated with the following equation:

D = [(0.8×MPC-BS)×3000]/[C]

(7)

where D is the maximum permissible amount of material in t/ha; MPC is the maximum permissible concentration or threshold value of an element in soil, in mg/kg; BS is the actual content of an element in the soil of agricultural land where the material is to be applied, in mg/kg; 0.8 is a coefficient limiting the increase of concertation of an element above 80% of its threshold value; 3000 is an average mass of soil of ploughed layer per hectare, in tones; C is a concentration of an element in material applied to soil.

In our study, we slightly modified the equation (7) and used it to estimate possible inputs of the elements on polluted soils. The modified equation is as follows:

D = [(Cmean-Cmin)×3780]/[C]

(8)

where D is the maximum amount of material in t/ha, which could be applied based on existing agricultural practices; Cmean is an average concertation of an element in soil where the material was applied, in mg/kg; Cmin is a minimum content of an element in soil of agricultural land where the material is applied, in mg/kg; 3780 is an average mass of 0-30 cm soil layer per hectare, in tones, based on an average bulk density 1.26 g/cm³, measured in soils formerly used for vineyards (Table 4), which were used for calculations in our study; C is a concentration of an element in material applied to soil. The modified equation (8) gives an understanding of whether inputs made in the past could contribute to the accumulation of certain elements in soil and that a mean concertation of a certain element observed under different land use types could be reached from its minimum value, used as an initial condition, found under the same land use.

3. Results

3.1. Land Use Types

The farm survey results and analysis of the old topographic maps were used to group the agricultural plots based on the agricultural practices established for the dominant crops, as a crop rotation system was not in place. Usually, the farmers focus on a single crop in a specific field, considering the practice that would best suit that crop. Therefore, we defined six land use forms with similar management practices and cultivated crop types, as the survey showed that crop-specific agricultural practices were the main drivers of farm-to-farm differences. In particular, wheat, barley, and oats were grouped in the category of extensive arable farms; fruit (stone and seed fruits) and nut crops were grouped in the category of orchards; Legumes (alfalfa and bean), vegetables (potato and onion), maize and sunflower were combined in intensive arable farms category. Grapevine was separated as vineyards, and pastures and haylands were separated as pastures, the last serving as a reference to estimate the impact of agricultural practices as the primary source of anthropogenic impact on agricultural lands. Besides that, we separated an additional group of former vineyards, which are currently under different land use forms, including intensive and extensive arable farming and pastures. Previous land use forms were identified on the topographic maps created in the late 60-ies and early 70-ies of the twentieth century, meaning that during that period on those plots, vineyards already existed, mainly as state-owned farms, which remained unchanged at least till 1996, when land privatization took place [47] and some land owners decided to replace vine with more profitable crops, although not all plots were privatized and there are still some plots with abandoned vineyards used currently as pastures.

Consequently, here, we present our results on a land use category basis, providing a better understanding of crop-specific agricultural practices and their impact on the concentration of plant nutrients and toxic elements in soil. The following subchapters discuss the results for each nutrient and toxic element for the abovementioned land use categories. The attempt is made to assess the degree of impact of agricultural practices on the changes of concentrations of heavy metals and their availability to plants, as well as potential risks to human health and the environment, comparing to regional baseline and existing threshold values [43] using statistical methods and various indices.

3.2. Organic Matter

Although organic matter (OM) is not included among the plant nutrients, it plays an essential role in soil fertility. It represents the primary nitrogen source available to the plant [48]. Therefore, we assessed the OM distribution through a method similar to that used for nutrient and toxic elements to determine its changes in relation to agricultural practices and land use. Table 4 clearly shows that the pasture soils had the highest potential fertility. The average concentration of OM in their soils was 6.12%, which was expected because soils under pasture are not tilled, no fertilizers were applied, and, therefore, the conditions required to accelerate OM mineralization are absent, except on several occasions with excessive grazing on specific areas and the development of erosion.

After the pastures, the highest content of OM was found in the soils occupied by orchards, which equalled 5.73% on average, followed by the soils in vineyards, which amounted to 5.31%. In the case of the orchards and vineyards, the relatively high levels of organic matter were probably related to less intensive tillage and, in some cases, the maintenance of inter-row grass cover, which is an effective means of erosion protection. The lowest concentration was recorded in soils under intensive arable farming, amounting to 3.74%, which indicates the intensification of the production of these crops and the combination of factors contributing to the mineralization of OM, whereby farmers ploughed the soil several times per year to obtain two or three yields from the crops per plot by cultivating crops with shorter vegetation that can be gown after harvesting the main crop. OM mineralization can develop more intensively in such cases, as it benefits from the warm season when microbial activity is high [49].

The impact of agricultural practices on OM concentration in soil was assessed using single-factor ANOVA, which showed that the difference between land use types was statistically significant (F= 4.89, F crit=2.33), confirming substantial influence from crop-specific management.

3.3. Phosphorous

The results showed that the plant-available pool of one of the major nutrients, phosphorous (P), was highest in the soils under orchards (Table 4), amounting to 40.98 mg/kg on average, which was caused by single high concentration found in one orchard soil (Table S1), due to the consistent input of synthetic mineral fertilizers, as confirmed by our interview with the farmer, and relatively low removal compared to other crops, as the orchard is still young and the optimal yields are not yet reached. These differences are expressed in a high standard deviation (±52.48), numerically exceeding the mean value. Vineyard was the second land use type to which P-containing fertilizers were applied, and its average content in the soil reached 38.94 ±21.82 mg/kg, which is very similar to soils under intensive arable farming – 38.13 ±25.30 mg/kg. The lowest values, as expected, were found in pasture soils, as the current nutrient management practices established by the farmers do not consider any fertilizer application for pastures, and its use is also minimal under extensive arable farming conditions, showing the second lowest content of P in soils.

According to single factor ANOVA, the impact of agricultural practices on P concentration under different land use types was significant (F=3.19, F crit=2.33), showing that variations between land uses are greater than within each land use type.

P has a weak but statistically significant (p<0.01) negative correlation (ρ=-0.322) with calcium carbonate content in soil (Table S5), which often restricts its availability to plants.

3.4. Potassium

A different pattern was exhibited by the exchangeable potassium (K), where the highest values were found in vineyards, reaching 955.09 mg/kg on average (Table 4). In two vineyards, the level of exchangeable K exceeded 2000 mg/kg (Table S1), which could also result from high organic and inorganic fertilizer input. However, as farmers do not usually have records of the exact amount of each type of fertilizer they applied more than five years ago, it is difficult to estimate their contributions precisely.

The soils under former vineyards had the highest concentrations of K with a mean value of 736.85 ±768.54 mg/kg, showing a legacy of high K inputs in the past. Similar high K content was observed in orchards with an average of 661.30 ±386.82 mg/kg, which is also considered very high. Despite the vital role of K in plant growth and development, its high concentration in soil can inhibit the uptake of calcium [50] and magnesium [51]. High K availability can result from the continued application of complex NPK fertilizers containing equal amounts of the three major nutrients (N, P, K). Complex NPK fertilizers are often applied based on the soil P levels, normally deficient in these soils, resulting in the accumulation of K in excess quantities. Despite this, most of the farmers interviewed during the field studies stated that they prefer to use one type of fertilizer containing all the major nutrients (N, P, K), as they have high nutrient content, and this practice reduces transportation and application costs.

Systematic use of complex NPK fertilizers can be a cause of a statistically significant (p<0.01) moderate positive correlation (ρ=0.456) between P and K (Table S5), meaning that concertation changes of those macronutrients are partially interdependent.

The lowest K content was observed in soils under extensive arable farming (404.45 mg/kg on average), which can be influenced by the low input production system established for winter cereals, the dominant crop types in these farms.

The impact of agricultural practices on K concentration in soil was confirmed using single-factor ANOVA, showing a statistically significant difference (F=3.53, F crit=2.33) between land use types.

3.5. Copper

The level of potentially plant-available copper (Cu) was observed to be highest in the former vineyard and current vineyard soils, reaching on average 46.95 and 41.66 mg/kg, respectively (Table 5), which can be considered as a high value and indicates an excessive availability of Cu to plants. Cu is an essential micronutrient for plants [52], but it is needed in small quantities [53], and its optimal range for most crops in the soil varies from 0.9 to 2.5 mg/kg extracted by DTPA [54]. It is reported that DTPA extractable Cu content higher than 20 mg/kg in soil can be toxic to crops [55] and cause considerable yield reduction [53]. This is the case for all soils from former vineyards and nearly 67% of soils analysed under current vineyards (Table S3). Cu availability sharply decreases in soils of other land use types and exceeds the abovementioned toxic level (20 mg/kg) only in one sample taken from the pastures. Cu is generally a less mobile nutrient due to its strong fixation in soil, although metals of an anthropogenic origin have higher mobility in soil than those of a natural origin [56]. Statistically significant differences between land use forms in potential plant-available Cu content are confirmed by ANOVA test results (F=18.39, F crit=2.33) and its very high variability in studied soils (CV=111%).

The accumulation of copper in vineyard soils is a well-known issue often reported worldwide [55,56,57,58], mainly associated with the long-term application of copper-based fungicides [56]. It could be a primary source of Cu input to vineyard soils in our study sites also, although several studies conducted in Kvemo Kartli region showed the impact of contaminated irrigation water [54,55,56,62,63], which is taken into account in our study, considering irrigation as an integral part of agricultural practice, as an amount of water and frequency depends on crop water demand and method of irrigation. To estimate the possible contribution from irrigation water, we used the maximum concentration of Cu found in samples (0.1076 mg/l) taken from irrigation canals reported by Withanachchi et al. [37] from the study region. The amount of irrigation water applied we estimated using FAO-CROPWAT model [74] as it is often used to estimate irrigation requirements of grapevine [75,76], and climate data of the region we extracted from the TerraClimate high-resolution global dataset [77]. As the CROPWAT model uses average annual data and rainfall distribution can vary from year to year during vegetation season, we did not consider rainfall in calculations, and total crop water requirements were taken as irrigation water. According to the results, the grapevine in the study requires 3860 m³ of water per year, which was used to calculate the possible input of Cu through irrigation water using equation #8. The calculation was done for former vineyard soils, where minimum Cu concentration (138.77 mg/kg) was taken as a baseline (Table S4) and mean Cu concentration (300.01 mg/kg) as a target to be reached (Table 6). According to the results, it would require more than 1400 years to increase Cu concentration using irrigation water only by applying an equal amount of irrigation water with the same quality. In addition, we made calculations including sediments, usually containing a higher concentration of metals, which can be delivered to soil with irrigation water.

Similarly, we took average values of the maximum concentration of Cu (410.03 mg/kg) found in irrigation water sediments reported by Withanachchi et al. [37]. We estimated an average of 1000 mg sediments per litter, corresponding to high turbidity, which is rarely observed in the region. Based on the results, it would require about 240 years to bring a sufficient amount of Cu to reach 300.0 mg/kg concentration in soil from the baseline value - 138.77 mg/kg. The calculation showed that Cu addition from the irrigation water and its sediments is obvious. However, there were other sources of Cu, as the Cu mining site located in the region is the source of water contamination and has operated since 1975 [78]. Time was insufficient to raise Cu concentration to the level found in this study.

Therefore, at the beginning of our research, we hypothesized that the influence of crop-specific agricultural practice plays a major role in soil nutrient concentrations, which is evident based on the historical land use and the statistical analysis of the results. Furthermore, in our study, total Cu contents in existing and former vineyards soils vary from 67.35 to 582.97 mg/kg (Table 6) and are in the same range reported from different countries having no issues with irrigation water quality, indicating that such accumulation can be reached based on copper-based fungicides only [51,53,57,58,59]. Cu-based fungicides have been used in vineyards for more than 150 years, and in the 50-ies of the twentieth century, their annual application amounted to 20-30 kg/ha, sometimes reaching 80 kg/ha [67,68]. Nowadays, due to raised concerns about Cu accumulation in soils [50], its application is limited to 6 kg/ha/year in organic agriculture, which mainly relies on Cu-based fungicides, and in some countries, down to 3-4 kg/ha/year [60,61].

Dependency of Cu accumulation in soil on application rate of Cu-based fungicides and thus its crop-specific character is confirmed by ANOVA test result showing statistically significant difference between land use forms (F=22.91, F crit=2.33). Those differences are also indicated by very high variation of Cu content in studied soil samples (CV=105%).

Based on factorial analysis using PCA, Cu accumulation is mainly influenced by a single factor, which we assume to be a combined effect of irrigation water and fungicides (Table S6).

According to the CF index, 77.8% of the existing vineyard (Figure S1.2) and 20.0% of the former vineyard (Figure S1.3) soils are categorized as moderately contaminated (CF value=1-3). Contamination is considerable for 11.1% of the vineyard and 50.0% of the former vineyard soils (CF value=3-6). In 11.1% of cases of the vineyard and 33.0% of the former vineyard soils, contamination is very high (CF value>6). CF index is not exceeding the value 3 for all remaining land use types (Figure S1.1, S1.4, S1.5, S1.6) and has the following order based on moderately contaminated category: arable, intensive (78.6%)>arable, extensive (60%)>orchards (40%)>pastures (20%).

The soils in the existing and the former vineyards are the most heavily enriched with Cu, based on EF index assessment (Figure S2.1-2.6). Only those two land use forms showed significant enrichment (EF value=5-20) with Cu, corresponding to 40.0% of the former vineyards (Figure S2.3) and 11.1% of the existing vineyards (Figure S2.2). The soils under other land use types are moderately (EF value=2-5) or minimally enriched (EF value=0-2) with Cu. Typically, EF values from 0.05 to 1.5 are considered to be a result of natural processes or have a crustal origin, while EF >1.5 is related to anthropogenic activities [32]. Based on this consideration, we can conclude that Cu in all orchard soils (Figure S2.1), the absolute majority of pastures (Figure S2.6), and intensive (Figure S2.4) and extensive arable farms (Figure S2.5) in our study area can be mainly of natural origin, whereas the soils in the existing and the former vineyards experience significant enrichment from anthropogenic activities.

The metal enrichment with Cu, based on Igeo index, showed the highest values (Igeo value=2-3) in soils of the former (25.0%) and existing vineyards (11.1%) only, classified as moderately to strongly polluted (Figure S3.2, S3.3). 41.7% of the soils of the former and 11.1% of the existing vineyards are moderately polluted (Igeo value=1-2). Igeo index values remain below 1 on all other land use types soils corresponding to unpolluted to moderately polluted category (Figure S3.1, S3.4, S3.5, S3.6) and have the following order according to the percentage of occurrence: existing vineyards (66.7%)>former vineyards (33.3%)>arable, intensive (28.6%)>arable, extensive (20%)>pastures (4%). The orchards were categorized as unpolluted with Cu (Figure S3.1).

The PI index was used to evaluate the risk associated with environmental and human health threats; the total Cu concentrations found in the soils were compared to the guide values established by the legislative norms in Georgia [43]. PI values showed that the Cu concentrations exceeded the established threshold (132 mg/kg) in soils under the existing and the former vineyards more than three times. Consequently, 11.1% of vineyards (Figure S4.2) and 30.0% of former vineyards (Figure S4.3) are classified as heavily polluted with Cu according to the PI index (PI value >3), while orchards (Figure S4.1), extensive arable farms (Figure S4.5) and pastures soils (Figure S4.6) as pollution free (PI value <1). The soils under intensive arable farms also remain pollution-free (Figure S4.4) in the case of 88.9% of farms, and only 11.1% have light pollution (PI value=1-2).

Such elevated Cu concentrations are mainly a consequence of the use of copper-based fungicides, which persist for decades, and the time required to reach critical levels depends on the initial Cu content in the soil and annual Cu input based on the farmer-established practices. According to the farmers interviewed during our study, the Cu input as a fungicide in the vineyards varied from 2 to 5 kg/ha/year.

Cu accumulation is also typical in orchard soils due to using Cu-based fungicides [69,70], but their input is relatively lower than in vineyards. It should be noted that most fruit orchards studied were relatively young and were established on less intensive arable lands or former pasturelands. Therefore, these soils had not been subject to substantial anthropogenic influence.

Cu exhibited a statistically significant (p<0.01) strong positive correlation (Table S5) with Zn (ρ=0.732) and a moderate correlation with Cd (ρ=0.465), which potentially had a common source of origin that, based on our studies, may have included both Zn- and Cu-based fungicides and fertilizers. The statistically significant moderate correlation of Cu with K (ρ=0.418) and a weak but still significant correlation with P (ρ=0.345) can be related to Cu input through K and P-containing fertilizers. Potentially plant available, DTPA extractable, Cu level is highly dependent on total Cu level in soil expressed in a very strong correlation (p<0.01, ρ=0.885). Furthermore, the soil enrichment with total Cu increases the availability of DTPA extractable Zn. This phenomenon is observed in previous studies [71] and confirmed with a statistically significant strong correlation (p<0.01, ρ=0.709) in our study.

3.6. Zinc

Zn followed a similar pattern as that observed for Cu, and the highest values were found in soils of the former and existing vineyards. The mean value of the potentially plant-available concentration of Zn equalled 18.17±17.85 mg/kg in the soils of the former vineyards and 11.23±19.89 mg/kg in the existing vineyards (Table 5). These concentrations of DTPA extractable Zn indicate a very high availability [54] and can be toxic to plants. Zn toxicity was observed in wheat at 7 mg/kg concertation and in maize at 11 mg/kg [72]. The lowest concentrations were found in extensive arable farms soils – 0.97±1.24 mg/kg, which can be deficient to crops [54], especially on calcareous and alkaline soils, which are dominant in the study region, where Zn bioavailability is reduced due to increased sorption in soils [53]. Significant variation in Zn availability is evident though ANOVA test results (F=6.14, F crit=2.35) showing statistically significant differences between crop-specific management practices under different land use forms and very high variability of DTPA extractable Zn in studied soils (CV=244%).

Likewise of DTPA extractable Zn, the highest concentrations of total Zn were observed in soils under the existing and former vineyards, 324.31±353.34 and 310.86±166.27 mg/kg, respectively (Table 6). The variability of total Zn in studied soils is also very high (CV=109%), and it also shows larger variability between studied land use forms based on the ANOVA test (F=8.48, F crit=2.33).

Based on factorial analysis using PCA, Zn accumulation has a pattern similar to Cu and is mainly influenced by irrigation water and fungicides (Table S6).

CF index shows a similar distribution of contamination classes among land use forms as observed in the case of Cu. 33.3% of vineyards (Figure S1.2) and 30.0% of former vineyards (Figure S1.3) have very high contamination levels (CF value>6) with Zn. CF index is below the value 3 for all remaining land use types (Figure S1.1, S1.4, S1.5, S1.6) and has the following order based on moderately contaminated category: arable, intensive (71.4%)>arable, extensive (70%)>orchards (40%)>pastures (16%).

According to EF, only soils under the existing and former vineyards soils have significant enrichment (EF value=5-20) with Zn, corresponding to 33.3% of vineyards (Figure S2.2) and 10.0% of former vineyards (Figure S2.3). The soils under extensive (18.2%) and intensive arable (7.1%) lands are moderately enriched (EF value=2-5) with Zn. All soils under orchards (Figure S2.1), nearly all of the pastures (Figure S2.6), and majority of the intensive (Figure S2.4) and extensive arable lands (Figure S2.5) in our study area have EF<1.5, which can be of natural origin. In contrast, soils in the existing and former vineyards soils experience significant enrichment from anthropogenic sources.

33.3% of the existing and 25.0% of the former vineyards (Figure S3.2, S3.3) belong to the moderately to strongly polluted category based on the Igeo index (Igeo value=2-3). 33.3% of the soils of the former vineyards and 5.0% of the extensive arable lands (Figure S3.5) are moderately polluted (Igeo value=1-2). Igeo values remain below 1 in soils of all other land use types, except one site from extensive arable farms, corresponding to 5% of the total number of this land use form is classified as moderately polluted (Igeo value=1-2). Based on Igeo index, the least Zn accumulation is observed under orchards (Igeo value ≤0), followed by pastures (Figure S3.6) with 4% in the unpolluted to moderately polluted category (Igeo value=0-1), the former vineyards with 8.3% and intensive and extensive arable farms, 28.6% and 35%, respectively (Figure S3.4, S3.5).

PI index were used to evaluate the risk associated with environmental and human health threats; the total Zn concentrations found in the soils were compared to the guide values established by the legislative thresholds [43]. PI values showed that the Zn concentrations exceeded the established threshold (220 mg/kg) more than three times (PI value >3) in soils of 33.3% of the vineyards and are classified as heavily polluted according to PI index (Figure S4.2). The soils under 30.0% of the former vineyards (Figure S4.3) have medium pollution levels (PI value=2-3) with Zn. Single cases of light pollution are observed in soils under intensive and extensive arable farms (Figure S4.4, S4.5), corresponding to 5.0 and 5.6%, according to PI index. In contrast, soils under orchards (Figure S4.1) and pastures (Figure S4.6) are classified as pollution-free (PI value <1) with Zn.

High accumulation of Zn in soils can result from the active use of Zn-based plant protection products to control fungal diseases [73], organic and mineral fertilizers, and polluted irrigation water, as reported by previous studies [59,60,61].

We evaluated possible contributions from irrigation water using the same method as for Cu. We used the maximum concertation of Zn found in irrigation water (0.1218 mg/l) and the average value of maximum concentrations of Zn (863.7 mg/kg) found in irrigation water sediments by Withanachchi et al. [37] from the study region. The calculation was done using equation #8 for the soil of the former vineyards, where minimum Zn concertation (104.9 mg/kg) was taken as a baseline (Table S4) and mean Zn concertation (310.9 mg/kg) as a target to be reached (Table 6). According to the results it would require more than 1650 years to increase Zn concentration using irrigation water and more than 160 years to increase Zn concentration from the baseline value - 104.9 mg/kg to 310.9 mg/kg through the sediments delivered with irrigation water. As in the case of Cu, Zn addition from the irrigation water and its sediments is significant, but it could only enrich soils to such a level with the existence of other sources.

3.7. Iron

The total iron content is usually never deficient in soil, although there are frequent cases of inadequate supply in the case of the plant-available forms [79]. The average values of the concentrations of the DTPA extractable Fe in the soils subjected to the studied agricultural practices were the highest in soils under the orchards, on average 59.43±88.85 mg/kg (Table 5). It should be noted that high Fe availability is not characteristic for all orchards but is a result of the single highest concentration found in one orchard soil, under slightly acidic (pH=6.5) conditions free from carbonates, which probably is the leading cause of greater availability of Fe. The dependency of DTPA extractable Fe on soil pH and carbonate content is also expressed in statistically significant (p<0.01) moderate negative correlations with pH (ρ=-0.432) and CaCO₃ (ρ=-0.404). The vineyards are the next richest land use form in plant available Fe with an average of 37.89±23.54 mg/kg. The given values, in both cases, correspond to a very high content of iron in the soil, which can be explained by relatively higher inputs of organic fertilizers supporting higher extractability of Fe in alkaline calcareous soils [80]. The lowest iron content was recorded in soils of extensively used arable lands, on average 12.35±7.33 mg/kg, considered a moderate level of Fe [54].

Total Fe has the lowest degree of variation among studied elements (CV=34%) and was considered a reference element in this study. Nevertheless, according to ANOVA test results, both DTPA extractable and total forms of Fe show statistically significant differences between crop-specific management practices. The difference is more noticeable in the case of total Fe (F=9.67, F crit=2.33) than in the case of DTPA-Fe (F=3.36, F crit=2.33).

Based on factorial analysis using PCA, total Fe content is mainly a result of soil mineralogical composition, and DTPA extractable Fe is more affected by organic fertilizer inputs (Table S6).

3.8. Manganese

The situation was different from that of iron in the case of manganese, for which the highest concentrations were recorded in soils of the extensive arable farms, where the average plant-available Mn concentration is 86.65±72.23 mg/kg (Table 5) and 1645.57±748.74 mg/kg for total Mn forms (Table 6). In the case of the mobile forms, the lowest rate was recorded in vineyards, on average 28.68±20.89 mg/kg, and the lowest total content was in orchard soils, 671.40±157.58 mg/kg.

According to the CF index, 65.0% of the soils of the extensive arable lands (Figure S1.5), 30.0% of the former vineyards (Figure S1.3), 28.6% of the intensive arable lands (Figure S1.4), and 12.0% of the pastures (Figure S1.6) are categorized as moderately contaminated (CF value=1-3). Soils in the orchards (Figure S1.1) and vineyards (Figure S1.2) have low contamination level with Mn (CF value<1).

22.7% of soils under the intensive arable lands (Figure S2.4), 7.1% under the extensive arable lands (Figure S2.5), and 4% under the pastures (Figure S2.6) are moderately enriched with Mn based on EF index. Soils of all other land use forms show minimal enrichment (EF value=0-2), where the absolute majority have EF value <1.5 (Figure S2.1-2.3) and can be the result of natural processes or have crustal origin.

Based on the assessment with Igeo index, 65.0% of the soils of the extensive arable lands (Figure S3.5), 25.0% of the former vineyards (Figure S3.3), 21.4% of the intensive arable lands (Figure S3.4), and 12% of pastures (Figure S3.6) correspond to unpolluted to moderately polluted category (Igeo value=0-1). All other land use forms are categorized as unpolluted (Figure S3.1, S3.2) with Mn (Igeo value<0).

Despite the low toxicity of Mn in soils under near natural and alkaline conditions, we have assessed the risk related to its accumulation according to PI index (Figure S4.1-4.6). The total Mn concentrations found in the soils were compared to the maximum permissible concentration values established by the legislative norm [43]. PI values showed that the Mn concentrations exceeded the established threshold (1500 mg/kg) more than two times in 4% of pastures classified as moderately polluted (Figure S4.6) according to PI index (PI value=2-3). 65% of the soils of the extensive arable farms (Figure S4.5), 30% of the former vineyards (Figure S4.3), 16.7% of the intensive arable farms (Figure S4.4), and 8.0% of the pastures are lightly polluted (PI value=1-2). In contrast, the orchards (Figure S4.1) and the vineyards (Figure S4.2) remain free from Mn pollution (PI value <1).

Total forms of Mn are characterized by a moderate degree of variation (CV=66%), indicating some influence from anthropogenic sources. This is confirmed by ANOVA test results, where both DTPA extractable and total forms of Mn show statistically significant differences between crop-specific management practices. The difference is greater in the case of total Mn (F= 5.35, F crit=2.33) than in the case of DTPA-Mn (F=2.53, F crit=2.33), which can be caused by a rapid fixation of Mn in alkaline soils.

Based on factorial analysis using PCA, total Mn content is conditioned by atmospheric deposition from diffuse sources and soil mineralogical composition. In addition to those two factors, organic fertilizer inputs greatly affect DTPA extractable Mn (Table S6).

3.9. Nickel

Nickel is often described as one of the heavy metals and a potential contaminant. However, studies have confirmed the functions of nickel as a micronutrient, and the problems caused by its deficiency have been widely studied in the case of nut crops [81]. Therefore, in this paper, nickel is considered a micronutrient and a heavy metal, similar to copper, zinc, and manganese.

The results showed that the highest average concentration of nickel in the plant-available form was found in soils of the extensive arable farms, on average 3.89±2.02 mg/kg (Table 5), and the highest values of the total forms were observed in soils of the former vineyards, 137.06±103.21mg/kg on average (Table 6). In the case of the mobile forms, the lowest content was found in vineyards soils, 1.72±0.66 mg/kg, and the lowest total content was found in soils under the orchards, 73.64±34.01 mg/kg.

Based on the CF index, 40.0% of the orchards (Figure S1.1) and the former vineyards (Figure S1.3), 33.3% of the vineyards (Figure S1.2), 28.6% of the intensive arable lands (Figure S1.4), 15% of the extensive arable lands (Figure S1.5) and 12% of the pastures (Figure S1.6) are moderately contaminated (CF value=1-3). The remaining sites of the studied land use types show low contamination with Ni (CF value<1).

20% of the soils of the former vineyards (Figure S2.2) and 11.1% of the existing vineyards (Figure S2.3) are moderately enriched with Ni by the evaluation by EF index (EF value=2-5). The soil of all other land use forms and the remaining parts of the existing and the former vineyards are minimally enriched with Ni (EF=0-2). All soils under the orchards (Figure S2.1), the intensive (Figure S2.4) and extensive arable lands (Figure S2.5), and the pastures (Figure S2.6) have EF<1.5, indicating a natural origin of Ni.

According to the Igeo index (Figure S3.1-3.6), 33.3% of soils of the former and existing vineyards (Figure S3.2, S3.3) are classified as unpolluted to moderately polluted (Igeo value=0-1) with Ni. In contrast, the soils of the orchards (Figure S3.1), the extensive and intensive arable lands (Figure S3.4, S3.5), and the pastures (Figure S3.6) are unpolluted (Igeo value ≤0).

In order to assess the risk associated with Ni accumulation in soil, the total Ni concentrations found under different land-use soils were compared to the guide values established by the legislation [43]. PI values showed that the Ni concentrations exceeded the established threshold (120 mg/kg) more than two times (PI value=2-3) in 30.0% of soils of the former vineyards (Figure S4.2) and 22.2% of the existing vineyards (Figure S4.3), having medium pollution level. The light pollution of soils with Ni is observed in 11.1% of the existing vineyards, 10.0% of the former vineyards, and 5.0% of the extensive arable lands (Figure S4.5) (PI value=1-2). In contrast, soils in the orchards (Figure S4.1), the pastures (Figure S4.6), the intensive arable lands (Figure S4.4), and 95.0% of the extensive arable lands are classified as pollution-free (PI value <1) according to PI index (Figure S4.1-4.6).

Notably, the statistical analysis of the data showed a weak but statistically significant (p<0.01) correlation (Table S5) of total nickel with soil clay fraction (ρ=0.286). At the same time, DTPA extractable Ni strongly correlates with soil clay fraction (ρ=0.619). Those relationships of Ni with soil texture indicate its possible crustal origin, as predicted also according to the EF index for most land use forms (Figure S2.1-2.6). Total forms of Ni have one of the lowest degrees of variation after Fe (CV=59%) but still show an anthropogenic impact on its contents in soils under agricultural use, which is confirmed by ANOVA test results, where both DTPA extractable and total forms of Ni show statistically significant differences between crop-specific management practices. The difference is greater in the case of DTPA extractable Ni (F=7.66, F crit=2.33) than in the case of total Ni concentration (F=2.83, F crit=2.33).

Based on factorial analysis using PCA, total Ni content is mainly affected by irrigation water and fungicides. In contrast, DTPA extractable Ni is linked to atmospheric deposition from diffuse sources and soil mineralogical composition and, to a lesser extent, with organic and mineral fertilizer inputs (Table S6).

3.10. Cadmium

High content of the mobile and total forms of cadmium was found in the soils of former vineyards, on average 0.74±0.48 mg/kg (Table 5) and 6.96±9.39 mg/kg (Table 6), respectively. One of the known sources of Cd in agriculture is phosphorous fertilizers, which may contain variable concentrations of Cd, and taking into account the systematic application of these fertilizers, they can be a significant contributor.

Based on the CF index, the highest contamination with Cd (CF value>6) among land use forms is observed in 10.0% of soils of the former vineyards (Figure S1.3). CF index is less than 3 in soils for all of the remaining land use types (Figure S1.1, S1.2, S1.4, S1.5, S1.6) and has the following order based on moderately contaminated category: existing vineyards (77.8%)>former vineyards (70.0%)>orchards (60.0%)>arable, intensive (50.0%)>pastures (24.0%)>arable, extensive (20.0%).

Soils of the former vineyards show a significant enrichment (EF value=5-20) with Cd according to EF index (Figure S2.3), corresponding to 10.0% of this land use form and 11.1% of soils currently used under the vineyards (Figure S2.2) are moderately enriched (EF value=2-5). The soils of other land use forms have only minimal or no enrichment with Cd, and their absolute majority have EF<1.5 (Figure S2.1, S2.4, S2.5, S2.6), indicating the natural origin of Cd without substantial impact from anthropogenic sources. This statement is supported by a moderate (ρ=0.560) but statistically significant (p<0.01) correlation of Cd with Fe, which is the least affected element in our study (Table S5).

Based on the Igeo index, 8.3% of the former vineyards (Figure S3.3) are moderately to strongly polluted (Igeo value=2-3). Igeo values remain below one on all other land use types soils and have the following order in unpolluted to moderately polluted category: former vineyards (16.7%)> existing vineyards (11.1%) (Figure S3.6) (Figure S3.2)> arable, extensive (10.0%) (Figure S3.5)> arable, intensive (7.1%) (Figure S3.4). The pastures (Figure S3.6) and orchards (Figure S3.1) are free from Cd pollution (Igeo value ≤0).

According to the PI index, the risks associated with environmental and human health are the highest in soils of the former and existing vineyards (Figure S4.2, S4.3). Cd concentration is more than three times higher than the maximum permissible concentration (2 mg/kg) established by the legislative norm [43] in 20.0% of the former and 11.1% of the existing vineyards. The land use forms based on the medium pollution level (PI value=2-3) with Cd have the following order: former vineyards (40.0%)>vineyards (22.2%)>orchards (20.0%) (Figure S4.1)>arable, intensive (14.3%) (Figure S4.4)>arable, extensive (10.0%) (Figure S4.5)>pastures (4.0%) (Figure S4.6). All the remaining soils experience light (PI value=1-2) or no pollution (PI value<1) by Cd.

The possible input of Cd through the irrigation water was calculated using the same method as for Cu and Zn. We used the maximum concentration of Cd found in irrigation water (0.0003 mg/l) and the average value of maximum concentrations of Cd (3.29 mg/kg) found in irrigation water sediments by Withanachchi et al. [37] from the study region. The calculation was done using equation #8 for former vineyard soils, where minimum Cd concertation (2.4 mg/kg) was taken as a baseline (Table S4) and mean Cd concertation (6.96 mg/kg) as a target to be reached (Table 6). According to the results, it would require more than 14900 years to increase Cd concentration using irrigation water and more than 940 years to increase Cd concentration from the baseline value – 2.4 mg/kg to 6.96 mg/kg through the sediments delivered with irrigation water. As in the case of Cu and Zn, Cd addition from the irrigation water and its sediments is considerable, but it could not be sufficient to increase Cd concentration without the existence of other anthropogenic sources.

Total Cd content shows the highest variability among studied elements in studied soils (CV=111%). Consequently, there are statistically significant differences between crop-specific management practices under different land use forms, which is more evident for DTPA extractable Cd (F=4.64, F crit=2.34) than for total Cd (F=3.16, F crit=2.33).

Based on factorial analysis using PCA, total and DTPA extractable Cd content is mainly affected by irrigation water and fungicides. An additional factor affecting total Cd concentration is mineral fertilizer inputs, most probably phosphorous fertilizers (Table S6).

3.11. Lead

The highest content of the mobile and total forms of Pb was found in the soils under the extensive arable land use, on average 2.03±0.58 and 30.10±13.91 mg/kg, respectively. The lowest values of the DTPA extractable Pb content were observed in pastures, on average 0.93 ±0.40 mg/kg, and the total Pb content in vineyards was 14.36±11.21 mg/kg.

According to the CF index, soils in the study region do not significantly increase Pb concertation on agricultural lands. The land use types assessed in our study has the following order based on CF index (CF value=1-3) corresponding to moderately contaminated category (Figure S1.1-S1.6), which is the highest contamination level observed for Pb: arable, extensive (55.0%)>former vineyards (30.0%)>arable, intensive (28.6%)>orchards (20.0%)>pastures (16.0%)>vineyards (11.1%). The rest of the investigated soils have low pollution levels (CF value=1).

Only 4.5% of soils of the extensive arable land (Figure S2.5) show a moderate enrichment (EF value=2-5) with Pb. The remaining part of the extensive arable land soils and all the soils of other land use forms have minimal or no enrichment with Pb, and their absolute majority have EF<1.5, indicating a natural origin of Pb (Figure S2.1, S2.2, S2.3, S2.4, S2.6), without significant influence from anthropogenic sources.

The former and existing vineyards (Figure S3.2), as well as orchards (Figure S3.1), are unpolluted with Pb according to the Igeo index (Igeo value<0). Igeo values remain below 1 in soils of all other land use types and have the following order in unpolluted to moderately polluted category: arable, extensive (25.0%) (Figure S3.5)> arable, intensive (11.1%) (Figure S3.4)> pastures (4.0%).

Based on the PI index, the risks associated with environmental and human health are relatively low in all studied soils (Figure S4.1-4.6). Pb concentration in soils exceeds the maximum permissible concentration (32 mg/kg) established by the legislative norm [43] experiencing light pollution (PI value=1-2) and have the following order: arable, extensive (40.0%)>orchards (25.0%)>former vineyards (20.0%)>arable, intensive (11.1%) =vineyards (11.1%)>pastures (4.0%). All the remaining soils are not polluted (PI value<1) by Pb.

The possible addition of Pb through the irrigation water was assessed similarly for Cu, Zn, and Cd. We used the maximum concertation of Pb measured in irrigation water (0.0055 mg/l) and the average value of maximum concentrations of Pb (34.25 mg/kg) found in irrigation water sediments by Withanachchi et al. [37] from the study region. The calculation was done using equation #8 on the example of the former vineyard soils, where minimum Pb concertation (5.5 mg/kg) was taken as a baseline (Table S4) and mean Pb concertation (25.03 mg/kg) as a target to be reached (Table 6). According to the results, it would require more than 3470 years to increase Pb concentration using irrigation water and more than 410 years to increase Pb concentration from the baseline value – 5.5 mg/kg to 25.03 mg/kg through the sediments delivered with irrigation water. Pb addition through the irrigation water and its sediments can contribute to soil enrichment with Pb. However, increasing Pb concentration without substantial addition from other anthropogenic sources could not be sufficient.

The total form of Pb has the slightest degree of variation after Fe (CV=53%) but still shows the anthropogenic impact on its contents in soils under agricultural use, which is confirmed by ANOVA test results, where both DTPA extractable and total forms of Pb show statistically significant differences between crop-specific management practices. The difference is greater in the case of DTPA extractable Pb (F=13.30, F crit=2.33) than in the case of total Pb concentration (F=3.19, F crit=2.33). The behaviour of Pb is quite similar to that of total Ni, which is expressed in their moderate (ρ=0.410) but significant (p<0.01) two-tailed correlation (Table S5).

Based on factorial analysis using PCA, total Pb content is mainly affected by atmospheric deposition from diffuse sources and, to a smaller extent, by irrigation water and fungicides. However, DTPA extractable Pb is nearly equally associated with the application of irrigation water and fungicides and atmospheric deposition from diffuse and relatively less influenced by soil mineralogical composition (Table S6).