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Article

The Content of Soil Glomalin Concerning Selected Indicators of Soil Fertility

by
Jindřich Černý
,
Jiří Balík
*,
Pavel Suran
,
Ondřej Sedlář
,
Simona Procházková
and
Martin Kulhánek
Department of Agro-Environmental Chemistry and Plant Nutrition, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, 16500 Prague, Czech Republic
*
Author to whom correspondence should be addressed.
Agronomy 2024, 14(8), 1731; https://doi.org/10.3390/agronomy14081731 (registering DOI)
Submission received: 6 June 2024 / Revised: 31 July 2024 / Accepted: 5 August 2024 / Published: 6 August 2024
(This article belongs to the Special Issue Soil Organic Matter Contributes to Soil Health)
Browse Figure
Versions Notes

Abstract

:
The glomalin content is generally considered an indicator of the soil organic matter (SOM) quality. The content of easily extractable glomalin (EEG) and the total glomalin (TG) content was investigated across 71 different sites in the Czech Republic with arable soil and crop production (12 chernozems, 30 luvisols, 17 cambisols, and 12 fluvisols). The majority of the crops in the crop rotation were cereals (45.5%—mainly winter wheat, winter barley, and spring barley). The proportion of winter canola within the crop rotation was 15.9%. The contribution of other crops was substantially smaller (alfalfa, clover, potatoes, beet, silage maize, grain maize). The representation of crops in the crop rotation is standard for conventional farming in the Czech Republic. Based on the results of long-term field monitoring at 71 sites in different soil–climate conditions, we can state the following. The TG content was significantly correlated with the soil organic matter carbon content (CSOM), as well as another important indicator of SOM quality (humic and fulvic acid carbon content ratio—CHA/CFA). A significant and positive correlation was also determined for the TG and clay content (size < 0.002 mm), as well as particles smaller than 0.01 mm. The easily extractable glomalin content (EEG) did not differ based on the reference soil group (RSG). On the other hand, the total glomalin content (TG) was significantly higher in the chernozem RSG in comparison with other RSGs (luvisols, cambisols, fluvisols). There was no relationship between the pHCaCl2 and glomalin (EEG; TG). The same can be said about the relationship between glomalin (EEG; TG) and the bulk density and porosity. No link was established between the glomalin content (EEG; TG) and phosphorus plant-available content. There was no relationship between the amount of applied organic matter (carbon inputs) and the soil glomalin content (EEG; TG). This relationship was not influenced by the type of applied organic fertilizer. No significant relationship was found for either straw, manure, or compost. The data on the glomalin content are significantly influenced by the site (soil type and soil texture).

1. Introduction

The increased demand for food, fodder, and fuel has caused increasing concern about the sustainability of farming systems and their impact on the environment, especially concerning climate change and its impacts [1]. Special attention has been given to research on soil degradation prevention [2,3,4,5]. The content and quality of soil organic matter (SOM) play a key role in soil fertility.
Arbuscular mycorrhizal fungi (AMF), like Glomeromycota, are a critical soil biota that can support sustainable crop production strategies [6]. About 72% of all terrestrial plant species have a symbiotic relationship with AMF. The contribution of AMF to soil carbon (C) sequestration and aggregation was attributed to the production of a glycoprotein originally called “glomalin”. This glycoprotein is released into the soil during the decomposition of AMF hyphae [7,8,9,10]. Glomalin is regarded as an indicator of the soil organic matter (SOM) quality and has garnered significant attention in recent decades [8,11]. The precise molecular structure of glomalin remains undiscovered [12], because it is impossible to obtain pure glomalin molecules during extraction [13]. As a result, the scientific literature often refers to it as “glomalin-related soil protein” (GRSP) [14].
In their pioneering work, Wright and Upadhyaya [7] define two fractions of glomalin, easily extractable glomalin (EEG) and total glomalin (TG). The more recent literature also mentions difficulty extractable glomalin (DEG) [12,15]. The DEG content is calculated as the difference between the TG and EEG content. EEG is considered to be the more recent fraction of the TG content. Studies employing the Bradford assay for glomalin determination have shown very strong positive correlations between AMF biomass indicators and either EEG or DEG [16,17,18]. The effectiveness of the Bradford assay in identifying glomalin has been confirmed by Koide and Peoples [19].
The production and accumulation of glomalin in soil vary depending on the plant species, associated mycorrhizal fungi, and soil properties [12,20,21]. In essence, glomalin accumulation is the result of the interaction between plants, mycorrhizae, and the soil. Autotrophic plants supply the C necessary for AMF to synthesize glomalin within their structural components (such as hyphal and spore walls), while the soil provides the habitat, encompassing its physical and chemical properties, as well as microbial decomposers, for glomalin accumulation [14,22,23]. Soil properties and farming systems can modify glomalin production, not only due to the impact on AMF production but also because it influences the glomalin P (phosphorus) turnover and dynamics [24]. Therefore, it is important to identify the soil properties that can influence AMF and glomalin. Soils with relatively higher clay content tend to show a decrease in glomalin production due to the solidification and restriction of AMF hyphal growth [25,26,27,28]. The soil water dynamics also affect the glomalin content. Slightly drier conditions are believed to enhance glomalin’s accumulation due to the increased translocation of photosynthetic products to the rhizosphere and the greater support of AMF by plants [24,28,29]. Various soil physical and chemical factors, such as the pH, nutrient content, and presence of toxic contaminants, can reduce the AMF activity, thereby decreasing glomalin production. AMF’s functional properties (such as the colonization intensity and hyphal length) and the glomalin content tend to decline with increasing soil pH [12,30], although this can vary based on the soil type and land use [31]. Higher soil nitrogen (N) and P content has been associated with increased glomalin levels [32,33]. Conversely, many studies report reduced AMF activity and glomalin content in nutrient-rich soils compared to poorer ones [34,35,36]. Specifically, increased P content in soil can reduce the AMF activity [37] and EEG content [23]. The glomalin content is generally increased in soil with increased AMF and microbial activity [12,31].
The glomalin content in soil is influenced by the soil management practices. Tillage, for instance, can lead to the increased mineralization of glomalin and reduced soil content, as it also diminishes the AMF activity [12,38,39]. Glomalin, a naturally adhesive or sticky compound, binds soil particles and organic matter, thereby promoting soil aggregation. This improves the physical properties of the soil, such as the bulk density and porosity [35], and water retention and reduces erosion [32]. Generally, the glomalin content is higher in soils treated with mineral and organic fertilizers compared to unfertilized soils [40,41,42,43]. Long-term manure application [41,42,44,45,46], compost [42,45,46,47], and straw [48,49] also lead to an increase in glomalin content. A positive influence on the glomalin content was exerted by the combination of mineral fertilizers and straw [48] and by animal litter [45,46]. The total glomalin content positively correlates with the soil organic matter carbon (CSOM) content [16]. All glomalin fractions correlate with the CSOM content [12]. Interestingly, in Li et al.’s study [16], there was no relationship between EEG and CSOM. This was probably caused by the fact that EEG is more influenced by the AMF activity [19,21]. In their work with AMF-dependent plant species, Bedini et al. [50] found over a 50% increase in soil EEG after inoculation with Glomus species. The glomalin content indicates soil fertility changes due to its positive correlations with the CSOM content [8,41,51]. Positive correlations of glomalin and other indicators of the SOM quality were also described (for example, the soil N and P content [12,22] and the plant-available P, total soil N, and plant-available K (potassium) content [33].
In our previous works, we established a robust correlation between the TG content and indicators of both the quality as well as the quantity of soil organic matter, such as the CSOM, humic and fulvic acid content, hot water extractable carbon content, and soil organic matter carbon content/total nitrogen content ratio (CSOM/NT), in our long-term experiments with crop rotation at different sites across the Czech Republic. On the other hand, the correlation between EEG and the same indicators mentioned above was weaker [52,53]. Contrary to this, we did not find any correlations between the EEG (or TG) content and soil organic matter quality indicators (hot water extractable carbon, humic acid/fulvic acid ratio) in our experiments with maize monoculture. Positive correlations were found between the EEG (or TG) content and CSOM/NT ratio [54]. Furthermore, in long-term experiments involving simple crop rotations (maize, winter wheat, spring barley) with various organic fertilizers on luvisol, we also established a positive correlation between the glomalin content and CSOM, as well as carbon in humic acids (CHA) [55]. Similarly, positive correlations between glomalin and CSOM, as well as CHA, were observed in long-term maize monoculture on luvisol [54].
Based on the presented literature and our results, it is possible to assume that the glomalin content can be a significant element of the SOM quality. This realization has prompted us to direct our research efforts towards soil fertility monitoring at the site network of the Czech Institute for Soil Testing in Agriculture. These sites offer several advantages, including the representation of major soil types and textures across various climate conditions. Additionally, the farming systems implemented at these sites mirror the prevailing trends in conventional crop production (regarding the used crops, fertilizer intensity, and soil management) in the Czech Republic.
The aim of this work was to determine (a) the influence of the soil type and soil texture on the individual glomalin fractions’ content and other soil organic matter quality properties; (b) the relationship among selected agrochemical properties (pHCaCl2, CEC, plant-available P, K, Ca, content) and the content of individual glomalin fractions; (c) the relationship between the bulk density, porosity, and content of individual glomalin fractions; and (d) the influence of organic fertilizer application (straw, manure, compost) on individual glomalin fractions.

2. Materials and Methods

The land monitored by the Central Institute for Supervising and Testing in Agriculture from 2012 to 2023 was carefully selected for evaluation, comprising 71 observation plots situated on arable land with diverse soil–climatic conditions. At each site, three soil samples were collected from a depth of 0–30 cm in 2023. These plots were situated on land conventionally farmed by local farmers.
Soil samples were collected in 2023. A central point onsite was selected and three additional sampling points (distance 20 m from the central point and equidistant from each other) were chosen. Each of the three sampling points was drilled seven times. These were pooled together, resulting in three soil samples from three sampling points (3 samples from each of the 71 sites, for 213 samples in total). Each mixed sample was later air-dried in a forced-air oven until a constant weight was reached at 40 °C; then, the mixed samples were ground and sieved for particles < 2 mm. For organic C and total N determination, the mixed samples were further sieved for <0.4 mm particles.
Figure 1 provides an overview of the texture and types of the soils studied, while Table 1 presents the characteristics of the plots. The majority of the crops in the crop rotation were cereals (45.5%—mainly winter wheat, winter barley, and spring barley). Winter canola constituted 15.9% of the crop rotation, with other crops, such as alfalfa, clover, potatoes, beet, silage maize, and grain maize, making up a smaller proportion. This crop distribution is consistent with the conventional farming practices in the Czech Republic. The average dose of mineral N fertilizer was also consistent with the conventional farming practices (80–110 kg N.ha−1.year−1). The P and K fertilizers were applied in conventional doses (5–7 kg P.ha−1.year−1 and 7–9 kg K.ha−1.year−1, respectively). Notably, our dataset did not include any outlier sites concerning the soil fertility indicators, (CSOM and NT content, pHCaCl2, bulk density, plant-available nutrient content) (Table 1).
In this study, the individual soil types were grouped into reference soil groups (RSGs) based on their similarity, aiming to streamline the categorization of the soil types. These reference soil groups were designated according to the dominant soil type, adhering to the World Reference Base for Soil Resources [56]. For the purpose of this study, the RSGs were named based on the dominant soil type: (a) chernozems (combining chernozem and phaeozem); (b) luvisols (luvisol, albeluvisol, planosol); (c) cambisols (cambisol, leptosol); (d) fluvisols (fluvisol, gleysol, regosol). Four soil texture groups were also created based on the textures [57] of the individual sites to further streamline the results. The resulting soil texture groups (TXGs) were (a) Clay + Silty Clay + Clay Loam (Cy + SiC + CL); (b) Sandy Clay Loam + Loam (SCL + L); (c) Silt Loam (SiL); and (d) Sandy Loam (SL). Since clay is generally abbreviated as C, which was in conflict with carbon in this study, we decided to use Cy for clay.
Due to missing data related to the composition of the organic fertilizers applied in all years at specific sites, the following properties in the fresh matter of the organic fertilizers were used to calculate the C input during the 2012–2022 period: cereal and canola straw 42% C, farmyard manure 9% C, digestate from biogas stations 2.3% C, slurry 0.03% C, sewage sludge 32% C, and compost 12.5% C. The table of the used abbreviations and their meanings is presented in Appendix A at the end of this manuscript.

2.1. Sample Analysis

The soil particle size distribution was determined using a dispersion of soil particles by boiling with an alkaline (NaPO3)6 solution. After dilution to a uniform volume of 1000 mL, the resulting suspension was mixed and allowed to sediment freely. The pipetting method was used for the actual determination. After an appropriate time, equal to the settling time of particles of a certain maximum size, a constant volume of suspension was pipetted from a given depth into a weighed container. After evaporation and drying, the weight of the relevant particle size fraction was determined by new weighing [58].
The bulk density and porosity were measured using Kopecky’s cylinders as described in Spasić et al. [59]. Undisturbed soil samples were used for the determination of the soil’s basic physical properties. Samples were taken in Kopecký steel cylinders (V = 100 cm3). Undisturbed soil samples were used to determine the actual humidity (of naturally wet soil), capillary saturation, maximum capillary capacity, overall porosity, and maximum water capacity.
The soil organic carbon (CSOM) and total nitrogen (NT) content in air-dried samples of soil were determined using oxidation on the CNS Analyzer Elementar Vario Macro (Elementar Analysensysteme, Hanau-Frankfurt am Main, Germany).
The fractionation of humic substances (CHS) was performed according to Kononova [60] to obtain the pyrophosphate extractable fraction, which represents the sum of the carbon in humic acids (CHA) and fulvic acids (CFA). The following is a summary of the procedure. A 5 g soil sample was subjected to extraction with a mixed solution of 0.10 mol.L−1 NaOH and 0.10 mol.L−1 Na4P2O7 (1:20 w/v). This extraction process isolated the CHA and CFA fractions. CFA was obtained from the solution by acidifying it with dilute H2SO4 to achieve a pH of 1.0–1.5. The resulting solution was left undisturbed for 24 h. CHA was obtained by dissolving the precipitate formed during acidification in a hot 0.05 mol.L−1 NaOH solution. Before iodometric titration, the dry matter from each sample, formed through vaporization, was dissolved in a mixture of 0.067 mol.L−1 K2Cr2O7 and concentrated H2SO4 under elevated temperature conditions.
Extractable organic carbon was determined using CaCl2 and hot water extraction.
For CaCl2 extraction (CDOC), the extraction was performed according to Houba et al. [61]. The CDOC content was determined in the soil samples using segmental flow analysis with infrared detection on a Skalarplus System (Skalar, Breda, The Netherlands).
Hot water extraction was used to assess the hot water extractable soil organic carbon content (CHWC) [62] and hot water extractable nitrogen content (NHWN). The content of CHWC in the extract was determined using segmental flow analysis with infrared detection on a Skalarplus System (Skalar, Breda, The Netherlands). Part of the extract was further digested in the presence of ultraviolet light according to Walinga et al. [63]. After the Griess reaction, the N content in the digest was measured colorimetrically using the segmental flow analyzer (Skalar, Breda, The Netherlands).
The easily extractable glomalin (EEG) and total glomalin (TG) were analyzed according to Wright and Upadhyaya [8]. In summary, 1.00 g of air-dried soil (<2 mm) was mixed with 8 mL of sodium citrate (20 mmol.L−1 at pH 7.0 for EEG and 50 mmol.L−1 at pH 8.0 for TG). The mixture underwent autoclaving at 121 °C for 30 min for EEG and 60 min for TG, followed by cooling and centrifugation at 5000 rpm for 10 min for EEG and 15 min for TG. For TG, the supernatant of the same sample was centrifuged five times until the characteristic red-brown color, typical of glomalin, was no longer observed. Both forms of glomalin were quantified colorimetrically using bovine albumin (BSA) as a standard for quantification and the Bradford protein assay (both from Bio-Rad, Hercules, CA, USA) to detect the color change. The glomalin content in the extracts was determined using the Tecan Infinite M Plex multimode microplate reader (Männedorf, Switzerland) at 595 nm. The difficultly extractable glomalin (DEG) content was calculated as the difference between the TG and EEG.
The pHCaCl2 value was determined in the 0.01 mol.L−1 CaCl2 solution according to ISO 10390 [64].
The available phosphorus (PM3), potassium (KM3), calcium (CaM3), and magnesium (MgM3) content in the soil sample was determined using the Mehlich 3 extractant [65]. The sample was extracted for 1 h on a shaker (1:10 w/v ratio). After this, the solution was filtered and a clear extract was obtained. The Mehlich 3 solution was produced from 0.015 mol.L−1 NH4F, 0.013 mol.L−1 HNO3, 0.25 mol.L−1 HN4NO3, 0.001 mol.L−1 EDTA, and 0.20 mol.L−1 acetic acid. The nutrient content in the extracts was determined using optical emission spectroscopy with inductively coupled plasma (ICP-OES) with an axial plasma configuration, on the Varian VistaPro, equipped with an SPS-5 autosampler (Mulgrave, Australia).

2.2. Statistical Evaluation

In order to identify the most important variables, a factor analysis was performed. Since certain variables were dependent on each other, the equamax normalized rotation was also performed. To extract the correct number of factors, we used the following criteria: eigenvalue > 1.0 and cumulative variance over 70%.
The normality of distribution regarding our data was tested using the Shapiro–Wilk test. Once the normality of the data was confirmed, an analysis of variance (ANOVA) was conducted with a follow-up Scheffe’s test (p < 0.05). This test was selected for its strength to reduce the type I error probability, as well as its applicability in situations where the number of cases is not equal (the soil pHCaCl2 and soil P, K, Ca were measured only at 41 out of 71 sites).
In order to evaluate the relationships among the individual variables, Pearson’s correlation coefficients (p < 0.05; 0.01; 0.001) were calculated. All statistical analyses were performed using the Statistica software ver. 12 (TIBCO, Paolo Alto, CA, USA).

3. Results

A total of four reference soil groups (RSGs) were created based on the set of 213 soil samples from 71 sites. Factor analysis was performed (Table 2) to determine the most influential components in our study. Four factors were extracted using this analysis, which collectively explained 71.6% of the total variance. The first factor (PC1) is mostly related to soil-type-specific and soil texture characteristics. The second factor (PC2) is mostly related to the glomalin content of the soil (both EEG and TG). The third factor (PC3) is mostly related to the labile fractions of carbon in the soil. The fourth factor (PC4) seems to be mostly related to the properties typical for the soil texture. The influence of these components was further investigated.
The statistical evaluation of the CSOM and NT content and their fractions based on the soil type is displayed in Table 3. It is clear that the highest content and quality of CSOM was found in the chernozem RSG. The SOM’s stability emerges from the high CHA/CFA ratio (significantly higher than other RSGs). The CHWC content is not significantly different from that of other RSGs. The high stability of the SOM in the chernozem RSG is visible in the CHWC/CSOM ratio. The relative content of CHWC extracted from the CSOM was 2.87%. This was the lowest of all RSGs. The lowest CSOM content (1.43%) was present in the luvisol RSG. This RSG also had a low CHA/CFA ratio (0.831), indicating low SOM stability. Hot water extraction released a relatively small number of compounds (CHWC = 496 mg.kg−1), but it represented a larger proportion of the CSOM content (CHWC/CSOM = 3.47%). This ratio is significantly higher than the one of the chernozem RSG. The CSOM content for the cambisol RSG was 1.68%. The CHA/CFA ratio was 0.927, implying smaller stability in the SOM content. In line with this, the CHWC and CHWC/CSOM reached higher values. The CSOM content for the fluvisol RSG was 1.60%. The SOM of this RSG is relatively stable with a CHA/CFA ratio of 1.08. The higher SOM stability is also supported by the lower values of CHWC and the CHWC/CSOM ratio (3.07%).
There were no significant differences in the CSOM/NT ratio among the evaluated RSGs. The lowest ratio (10.0) was found in the luvisol RSG. More N-containing organic compounds are released using the hot water extraction method given the comparison of the CHWC/NHWC ratio and CSOM/NT ratio values. Table 2 further presents the results for the extraction using a weak reagent (0.01 mol.L−1 CaCl2). The C content extracted via this method (CDOC) is independent of the RSG.
The content of glomalin fractions (EEG, TG) based on the RSG is presented in Table 4. The easily extractable glomalin content (EEG) did not differ based on the RSG. On the other hand, the total glomalin content (TG) was significantly higher in the chernozem RSG. The EEG/TG ratio in the chernozem RSG was also the lowest and significantly different compared to the other RSGs. The EEG/TG ratio in the chernozem and luvisol RSGs was 14.7% and 27.3%, respectively. The content of TG in the chernozem RSG led to the largest proportion of TG in CSOM (TG/CSOM = 27.0%). In comparison, the ratio was 16.9% in the cambisol RSG. The EEG/CSOM ratio was not statistically different based on the RSG; this ratio was between 3.5% and 4.0%.
Table 5 displays the results of the SOM quality indicators based on the soil texture. Four texture groups (TXGs) were created for the statistical evaluation. Soils with higher clay content contained more SOM of higher quality. The CSOM content and the CHA/CFA ratio of the “Clay + Silty Clay + Clay Loam” TXG were 1.82% and 1.05, respectively. The CSOM content and the CHA/CFA ratio of the “Sandy Loam” TXG were 1.45% and 0.793, respectively. In line with this, changes in the CHWC/CSOM and NT values were also present. In relation to the CSOM content, the highest content of organic compounds was extracted in this TXG (“Sandy Loam”) using hot water extraction (CHWC/CSOM = 3.77%).
Table 6 presents the changes in the glomalin content based on the TXG. The EEG content showed no significant differences. Significantly higher content of TG was measured in the “C + SiC + CL” and “SiL” TXGs in comparison with the other two groups. Based on the EEG/TG ratio, it is clear that it is possible to extract more EEG in comparison to TG from soil with lower clay content (Sandy Loam).
The division of the samples into the soil texture groups shown in Table 5 and Table 6 led to certain methodological errors. The first TXG was composed of a combination of the C + SiC + CL textures. Similarly, the second group was composed of a combination of the Sandy Clay Loam + Loam textures. In addition, soils with higher fine particle content (clay or very fine silt) can envelop the SOM content and protect it from extraction.
Therefore, we present the results of our survey based only on the clay content (Table 7). The criteria were selected so that the resulting groups were of a similar size (similar number of cases). There was significantly higher content of CSOM and NT in soils with “higher” clay content (clay content > 24.5%) (Table 7). This group also typically exhibits higher stability of the SOM (CHA/CFA ratio = 1.23). The highest content of organic compounds extracted by the hot water method, expressed as a proportion of CSOM, was determined in soils with “smaller” clay content (<18%). It is possible to state that these findings are in line with the CSOM quality (CHA/CFA ratio = 0.900). The content of dissolved organic C (CDOC) extracted with 0.01 mol.L−1 CaCl2 is independent of the soil texture.
Likewise, the EEG content is not dependent on the clay content (Table 8). On the other hand, the TG content increases with increasing clay content. A significant influence of the clay content was discovered in relation to the EEG/TG ratio. The ratio was 29.8% in soils with “smaller” clay content and 18.1% in soils with higher clay content. The smaller proportion of TG in the CSOM content was also determined for the soils with “smaller” clay content (<18%).
Considering the assumption that the glomalin content is a certain indicator of the soil organic matter quality, it is necessary to define its relationship with other standard indicators—like the CHA/CFA ratio. The Pearson’s correlation coefficient values for the content of glomalin and other indicators of soil fertility are presented in Table 9. We determined the correlations for the easily extractable glomalin (EEG), difficultly extractable glomalin (DEG), and total glomalin (TG) content. There are significant correlations between EEG and TG. The TG content significantly correlates with CSOM as well as other indicators of the SOM quality (CHA/CFA). In accordance with these correlations, there is also a significant and negative correlation of TG with the CHWC/CSOM ratio. A significant and positive correlation was also determined for the TG and clay content (size < 0.002 mm), as well as particles smaller than 0.01 mm. The SOM quality and clay content is reflected in the cation exchange capacity (CEC). The TG content is also positively correlated with the CHA/CFA as well as the clay content. This is the reason for the positive correlations of TG and CEC. There was no relationship between the pHCaCl2 and glomalin fractions (Table 9). The same can be said about the relationship between TG and the bulk density and porosity. The correlation coefficients of the glomalin content and plant-available nutrient content (measured in the Mehlich 3 extract) is also shown in the same table. No link was established between the glomalin content and P content. On the other hand, there was a weak correlation of TG with K and Ca. This is the result of the generally higher glomalin, calcium, and K content in the chernozems.
Simple linear regression was used to test whether some soil properties significantly predicted the TG content. The fitted regression model is shown in Table 9. The overall regression was statistically significant for the clay content and clay particle content, CSOM, CHA/CFA, and CEC.
Correlations were calculated between the glomalin content and applied C content given the fact that the long-term sites had well-known crop rotations, intensity of fertilization, and organic fertilizer quality. There was no relationship between the amount of applied organic matter (C inputs) and the soil glomalin content (Table 10). This relationship was not influenced by the type of applied organic fertilizer. No significant relationship was found for either straw, manure, or compost.

4. Discussion

The production and accumulation of glomalin in soil can vary depending on the plant species and related factors, as well as the soil properties [12,20,21]. On the monitored sites, the crop rotation remained consistent across all sites, primarily comprising cereals such as winter wheat, spring barley, winter barley, and winter canola. However, notable differences were observed in the soil–climatic conditions. The highest content and quality of soil organic matter was determined for the chernozem soil type [66], evidenced by the lowest proportion of CHWC relative to CSOM, indicating high CSOM quality. This is in accordance with the highest content of TG, as well as the highest proportion of TG relative to CSOM (TG/CSOM = 27.0%). Relatively stable SOM is present in the fluvisol RSG, with a high CHA/CFA ratio (1.08) as well as a low CHWC/CSOM ratio (3.07%). The lowest CSOM content (1.43%) and the lowest SOM quality were determined in the luvisol RSG. The CHA/CFA and CHWC/CSOM ratios were 0.831 and 3.47%, respectively. The cambisol RSG showed lower SOM quality and higher CSOM content [66]. Based on the results, the CHA/CFA ratio is a viable indicator of the SOM quality [55,60].
A good indicator of the SOM quality is also the hot water extractable C (CHWC) content [62]. It is possible to recommend the CHWC/CSOM ratio as a SOM quality indicator as well. Based on our results, it can be stated that with increasing SOM quality, the CHWC/CSOM ratio decreases. A similar conclusion emerged from our previous experiments, where the CHWC/CSOM ratios were 2.97% on luvisols [52], 3.04% on cambisols [53], and 1.21% on chernozems [54]. It is worth noting that hot water extraction tends to release more N-containing compounds. Therefore, the CHWC/NHWC ratio is typically lower than the CSOM/NT ratio. Our results did not indicate a significant influence of the soil type on the EEG content. This variability could be attributed to differences among the individual sites. Furthermore, it can be inferred that soil types with higher SOM quality exhibit a smaller proportion of EEG relative to TG. For example, the EEG/TG ratios on the chernozem and luvisol RSGs were 14.7% and 27.3%, respectively. A comparable ratio to that observed on the luvisol RSG in the current work was also found in an experiment on the luvisol soil type across five different sites (EEG/TG = 27.9%) [52]. No significant differences were noted between the EEG/CSOM ratios across the different RSGs, ranging from 3.5% to 4.0%, consistent with our previous research. Specifically, the EEG/CSOM value was 5.0% on luvisol [52] and 4.9% on cambisol [56]. Our results indicate a larger proportion of glomalin relative to the CSOM content on higher-quality soils (chernozems) in the current work. The TG/CSOM ratios align with those observed in our previous works on luvisol and cambisol soil types [52,53].
The influence of the soil texture on the content and quality of the CSOM is documented in Table 5 and Table 6. A similar type of evaluation is presented in Table 7 and Table 8. There is significantly higher CSOM and NT content on soils with higher clay content (>24.5%). There is also the highest SOM quality and stability (CHA/CFA = 1.23). The ratio of CSOM and clay content is considered as a certain criterion for soil degradation evaluation [3]. Clay contributes positively to the binding of organic compounds in soil, resulting in a reduced mineralization rate [4]. This aligns with our findings, as the CSOM content is highest in soils with high clay content in the current work. Interestingly, the clay content does not significantly influence the easily extractable glomalin (EEG) content. On the other hand, the TG content increases with increasing soil clay content. The soils with lower clay content are typically associated with relatively higher content of extracted EEG in comparison with CSOM.
Our previous research emphasized glomalin as a potential criterion for the assessment of the soil SOM quality [55]. Therefore, it is imperative to define the relationship between glomalin and standard SOM quality indicators. Table 9 presents the Pearson’s correlation coefficients between key soil fertility factors and the EEG, DEG, and TG content. Significant correlations were observed for EEG and TG, consistent with our prior findings [52,53]. Moreover, the current study reaffirms our earlier conclusions that the TG content significantly correlates with the CSOM content and other SOM quality indicators [52,53,54,55]. However, no significant relationship was observed between EEG and CSOM, likely due to EEG being more influenced by the AMF activity [19,21].
As previously noted, the CHWC content is increased with decreasing CSOM quality. Therefore, there was an increase in the CHWC/CSOM ratio. On the other hand, the TG content is higher with higher CSOM quality. For these reasons, the correlation coefficient between TG and the CHWC/CSOM ratio is negative.
The activity of AMF increases and the concentration of glomalin is higher in soils with relatively lower clay content; however, soils with relatively higher clay content typically show a decreasing trend. This is caused by a reduction in extracellular mycorrhizal fungi activity and increased solidification [25,26,27,28]. Our work did not confirm these conclusions. On the contrary, we found a positive association between the TG content and clay content (<0.002 mm), as well as the clay particle content (<0.01 mm). Higher CSOM, TG, and clay content were determined in fertile soils (chernozems). This was the cause of the positive correlation between the TG and clay content. The cation exchange capacity (CEC) is basically influenced by the quality and quantity of SOM and the clay content or the pHCaCl2 values [67]. There is a positive correlation between the TG content and CEC value as a result of the aforementioned correlations among the TG, CSOM, CHA/CFA, and clay content.
Although glomalin is known to serve as a binding agent, enhancing the soil structure and potentially reducing the bulk density while increasing the porosity [35], the present study did not validate these findings. Our results, as indicated in Table 9, suggest that the glomalin content on the monitored sites was primarily influenced by other dominant factors, such as the CSOM content, the CHA/CFA ratio, and the clay content. Consequently, the expected effects of glomalin in terms of bulk density reduction and porosity improvement were not evident in our observations. This underscores the nuanced interplay of various soil properties and highlights the need for further investigation to clarify these relationships.
Certain physical–chemical soil properties, like the pH value, or the nutrient content can influence the AMF activity and, consequently, the glomalin content. For instance, a reduction in glomalin content can be observed with an increase in the soil pH value [11,30]. However, no significant relationship was detected between the soil pH value and glomalin content in our current work. The cause was the relatively even distribution of the soil pHCaCl2 across the entire data set (acidic to basic range). The pH values created relatively good conditions for the produced crops. These conditions, apparently, were not influential factors in the growth of the produced crops.
A high soil P concentration reduces the development of AMF [37] and the EEG content [23]. On the other hand, Gispert et al. [32] mentioned increased glomalin content along with increased soil N and P content. We determined a weak positive correlation between the plant-available P content (in Mehlich 3) and EEG content [54] in our long-term experiments with maize monoculture fertilized intensively by cattle manure and sewage sludge. However, in our current work, no relationship was observed between the glomalin content and plant-available P content, likely due to the relatively good, even content of plant-available P across the entire data set. The P content was not a limiting factor in crop growth. At the same time, the intensity of P fertilization in the mineral treatments was small (5–7 kg P.ha−1.year−1) and not enough to influence AMF development. We have not confirmed the results of Šarapatka et al. [33], who determined a positive correlation between the glomalin content and plant-available P. On the other hand, we determined a weak and positive correlation between other plant-available nutrients (Ca and K, in Mehlich 3) and the TG content. This was most likely caused by the fact that the TG content increased along with the Ca and K content in the chernozem RSG. The K fertilizer dose at our evaluated sites was also very small (7–9 kg K.ha−1.year−1) and did not influence the AMF development.
It is possible to state that the glomalin content increases for mineral and organic fertilizer treatments in comparison with non-fertilized systems [40,41,42,43,52,53]. The organic fertilizer doses were recorded and the amount of applied C to the soil was calculated. Straw application did not influence the glomalin content, contrary to the findings of Nie et al. [48] and Liang et al. [49], who reported a positive influence of straw application on the glomalin content. The literature extensively documents the positive influence of farmyard manure on the glomalin content [41,42,44,45,46]. We can conclude the same based on our results from long-term experiments with crop rotation [55]. However, our current work did not confirm these conclusions regarding the influence of manure. Similarly, the combination of compost and manure application did not increase the glomalin content, and we could not support the conclusions of Řezáčová et al. [47] regarding the positive influence of compost. Generally, the current data set did not show any significant influence of organic fertilizers. Our conclusions are influenced by the fact that the intensity of organic fertilizer application was low, and other factors, such as the soil type, soil texture, and clay content, were more pronounced.

5. Conclusions

Based on the results of long-term field monitoring at 71 sites in different soil–climate conditions, we can state the following.
(a)
The total glomalin content (TG) significantly correlates with CSOM, as well as another important indicator of SOM quality (CHA/CFA). A significant and positive correlation was also determined for the TG and clay content (size < 0.002 mm), as well as particles smaller than 0.01 mm.
(b)
The easily extractable glomalin content (EEG) did not differ based on the reference soil group (RSG). On the other hand, the TG content was significantly higher in the chernozem RSG compared to other RSGs (luvisols, cambisols, fluvisols).
(c)
There was no relationship between the pHCaCl2 and glomalin (EEG; TG). The same can be said about the relationship between the glomalin content (EEG; TG) and the bulk density and porosity. No link was established between the glomalin content (EEG; TG) and plant-available P content.
(d)
There was no relationship between the amount of applied organic matter (C inputs) and the soil glomalin content (EEG; TG). This relationship was not influenced by the type of applied organic fertilizer. No significant relationship was found for either straw, manure, or compost.
(e)
It is important to focus future research on the chemical composition of glomalin (C and N content) and its mineralization dynamics in the soil, especially in regard to the trend of C sequestration and promising soil cultivation technologies (no tillage).

Author Contributions

Conceptualization, J.Č.; Data curation, S.P. and M.K.; Methodology, P.S., O.S., J.Č., M.K. and S.P.; Validation, J.Č. and M.K.; Writing—original draft, J.Č., J.B. and P.S. All authors have read and agreed to the published version of the manuscript.

Funding

This manuscript was funded by the following sources: Ministerstvo Zemědělství (the Ministry of Agriculture of the Czech Republic), grant numbers QK21010124 and QK23020056.

Data Availability Statement

All data are available from the corresponding author.

Acknowledgments

We would like to thank the team at the Central Institute for Supervising and Testing in Agriculture in Brno, especially Šárka Poláková, for their help with and coordination of soil sampling.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. Table of abbreviations.
Table A1. Table of abbreviations.
DescriptionAbbreviationUnit/Scale
CarbonC-
PotassiumK-
CalciumCa-
PhosphorusP-
NitrogenN-
Soil organic matterSOM%
Carbon in soil organic matterCSOM%
Total nitrogenNT%
Humic/fulvic acid C content ratioCHA/CFA-
Hot water extractable carbonCHWCmg.kg−1
Dissolved organic carbonCDOCmg.kg−1
Hot water extractable nitrogenNHWNmg.kg−1
Easily extractable glomalinEEGmg.kg−1
Difficulty extractable glomalinDEGmg.kg−1
Total glomalinTGmg.kg−1
ClayCy%
LoamL%
Clay loamCL%
Sandy clay loamSCL%
Silt loamSiL%
Sandy loamSL%
Silty claySiC%
Plant-available potassium, calcium, phosphorusKM3; CaM3; PM3mg.kg−1
Cation exchange capacityCECmmol+.kg−1

References

  1. Food Agriculture Organization of United Nations. FAO’S Work on Climate Change. 2018. Available online: https://openknowledge.fao.org/server/api/core/bitstreams/7b68d197-abcf-49a2-ad5a-48e7b9141d86/content (accessed on 31 May 2024).
  2. Lal, R. Restoring soil quality to mitigate soil degradation. Sustainability 2015, 7, 5875–5895. [Google Scholar] [CrossRef]
  3. Johannes, A.; Matter, A.; Schulin, R.; Weisskopf, P.; Baveye, P.C.; Boivin, P. Optimal organic carbon values for soil structure quality of arable soils. Does clay content matter? Geoderma 2017, 302, 14–21. [Google Scholar] [CrossRef]
  4. Prout, J.M.; Shepherd, K.D.; McGrath, S.P.; Kirk, G.J.D.; Haefele, S.M. What is a good level of soil organic matter? An index based on organic carbon to clay ratio. Eur. J. Soil Sci. 2021, 72, 2493–2503. [Google Scholar] [CrossRef]
  5. Keestra, S.; Mol, G.; De Leeuw, J.; Okx, J.; Molenaar, C.; De Cleen, M.; Visser, S. Soil-related sustainable development goals: Four concepts to make land degradation neutrality and restoration work. Land 2018, 7, 133. [Google Scholar] [CrossRef]
  6. Gianinazzi, S.; Gollotte, A.; Binet, M.N.; van Tuinen, D.; Redecker, D.; Wipf, D. Agroecology: The key role of arbuscular mycorrhizas in ecosystem services. Mycorrhiza 2010, 20, 519–530. [Google Scholar] [CrossRef] [PubMed]
  7. Wright, S.F.; Upadhyaya, A. Extraction of an abundant and unusual protein from soil and comparison with hyphal protein of arbuscular mycorrhizal fungi. Soil Sci. 1996, 161, 575–586. [Google Scholar] [CrossRef]
  8. Wright, S.F.; Upadhyaya, A. A survey of soils for aggregate stability and glomalin, a glycoprotein produced by hyphae of arbuscular mycorrhizal fungi. Plant Soil 1998, 198, 97–107. [Google Scholar] [CrossRef]
  9. Rillig, M.C.; Wright, S.F.; Nichols, K.A.; Schmidt, W.F.; Tor, M.S. Large contribution of arbuscular mycorrhizal fungi to soil carbon pools in tropical forest soils. Plant Soil 2001, 233, 167–177. [Google Scholar] [CrossRef]
  10. Driver, J.D.; Holben, W.E.; Rillig, M.C. Characterization of glomalin as a hyphal wall component of arbuscular mycorrhizal fungi. Soil Biol. Biochem. 2005, 37, 101–106. [Google Scholar] [CrossRef]
  11. Bedini, S.; Avio, L.; Sbrana, C.; Turrini, A.; Migliorini, P.; Vazzana, C.; Giovanneti, M. Mycorrhizal activity and diversity in a long-term organic Mediterranean agroecosystem. Biol. Fertil. Soils 2013, 49, 781–790. [Google Scholar] [CrossRef]
  12. Singh, A.K.; Rai, A.; Singh, N. Effect of long-term land use systems on fractions of glomalin and soil organic carbon in the Indo-Gangetic plain. Geoderma 2016, 277, 41–50. [Google Scholar] [CrossRef]
  13. Schindler, F.V.; Mercer, E.J.; Ricc, A.J. Chemical characteristics of glomalin-related soil protein (GRSP) extracted from soil of varying organic matter. Soil Biol. Biochem. 2007, 39, 320–329. [Google Scholar] [CrossRef]
  14. Rillig, M.C. Arbuscular mycorrhizae, glomalin, and soil aggregation. Can. J. Soil Sci. 2004, 80, 355–363. [Google Scholar] [CrossRef]
  15. Liu, R.C.; Gao, W.Q.; Srivastava, A.K.; Zou, Y.N.; Kuča, K.; Hashem, A.; Abd-Allah, E.F.; Wu, Q.S. Differential effects of exogenous glomalin-related soil proteins on plant growth of trifoliate orange through regulating auxin changes. Front. Plant Sci. 2021, 12, 745402. [Google Scholar] [CrossRef] [PubMed]
  16. Li, X.; Han, S.; Luo, X.S.; Chen, W.L.; Huang, Q.Y. Arbuscular mycorrhizal-like fungi and glomalin-related soil protein drive the distributions of carbon and nitrogen in a large scale. J. Soil Sediments 2020, 20, 963–972. [Google Scholar] [CrossRef]
  17. Agnihotri, R.; Bharti, A.; Ramesh, A.; Prakash, A.; Sharma, M.P. Glomalin related protein and C16:1ω5 PLFA associated with AM fungi as potential signatures for assessing the soil C sequestration under contrasting soil management practices. Eur. J. Soil Biol. 2021, 103, 103286. [Google Scholar] [CrossRef]
  18. Wang, Y.; Luo, W.; Xu, J.; Guan, P.; Chang, L.; Wu, X.; Wu, D. Fallow Land Enhances Carbon Sequestration in Glomalin and Soil Aggregates Through Regulating Diversity and Network Complexity of Arbuscular Mycorrhizal Fungi Under Climate Change in Relatively High-Latitude Regions. Front. Microbiol. 2022, 13, 930622. [Google Scholar] [CrossRef]
  19. Koide, R.T.; Peoples, M.S. Behavior of Bradford-reactive substances is consistent with predictions for glomalin. Appl. Soil Ecol. 2013, 63, 8–14. [Google Scholar] [CrossRef]
  20. Staunton, S.; Saby, N.P.A.; Arrouays, D.; Quiqampoix, H. Can soil properties and land use explain glomalin-related soil protein (GRSP) accumulation? A nationwide survey in France. Catena 2020, 193, 104620. [Google Scholar] [CrossRef]
  21. Wu, Q.S.; Cao, M.Q.; Zou, Y.N.; He, X.H. Direct and indirect effects of glomalin, mycorrhizal hyphae, and roots on aggregate stability in rhizosphere of trifoliate orange. Sci. Rep. 2014, 4, 5823. [Google Scholar] [CrossRef]
  22. Singh, A.K.; Zhu, X.; Chen, C.; Wu, J.; Yang, B.; Zakari, S.; Jiang, X.J.; Singh, N.; Lie, W. The role of glomalin in mitigation of multiple soil degradation problems. Crit. Rev. Environ. Sci. Technol. 2020, 52, 1604–1638. [Google Scholar] [CrossRef]
  23. Lovelock, C.; Wright, S.F.; Clark, D.A.; Ruess, R.W. Soil stocks of glomalin produced by arbuscular mycorrhizal fungi across a tropical rain forest landscape. J. Ecol. 2004, 92, 278–287. [Google Scholar] [CrossRef]
  24. Singh, A.K.; Rai, A.; Pandley, V.; Singh, N. Contribution of glomalin to dissolve organic carbon under different land uses and seasonality in dry tropics. J. Environ. Manag. 2017, 192, 142–149. [Google Scholar] [CrossRef] [PubMed]
  25. Adame, M.F.; Wright, S.F.; Grinham, A.; Lobb, K.; Reymond, C.E.; Lovelock, C.E. Terrestrial-marine connectivity: Patterns of terrestrial soil carbon deposition in coastal sediments determined by analysis of glomalin related soil protein. Limnol. Oceranogr. 2012, 57, 1492–1502. [Google Scholar] [CrossRef]
  26. Aliasgharzad, N.; Rastin, S.N.; Towfighi, H.; Alizadeh, A. Occurrence of arbuscular mycorrhizal fungi in saline soils of the Tabriz Plain of Iran in relation to some physical and chemical properties of soil. Mycorrhiza 2001, 11, 119–122. [Google Scholar] [CrossRef] [PubMed]
  27. Oehl, F.; Laczko, E.; Bogenrieder, A.; Stahr, K.; Bosch, R.; van der Heijden, M.; Sieverding, E. Soil type and land use intensity determine the composition of arbuscular mycorrhizal fungal communities. Soil Biol. Biochem. 2010, 42, 724–738. [Google Scholar] [CrossRef]
  28. Treseder, K.K.; Turner, K.M. Glomalin in ecosystems. Soil Sci. Soc. Am. J. 2007, 71, 1257–1266. [Google Scholar] [CrossRef]
  29. Williams, A.; de Vries, F.T. Plant root exudation under drought: Implications for ecosystem functioning. New Phytol. 2020, 225, 1899–1905. [Google Scholar] [CrossRef] [PubMed]
  30. Zhu, H.H.; Yao, Q.; Sun, X.T.; Hu, Y.L. Colonization, ALP activity and plant growth promotion of native and exotic arbuscular mycorrhizal fungi at low pH. Soil Biol. Biochem. 2007, 39, 942–950. [Google Scholar] [CrossRef]
  31. Wu, F.; Dong, M.; Liu, Y.; Ma, X.; An, L.; Young, J.P.W.; Feng, H. Effects of long-term fertilization on AM fungal community structure and Glomalin-related soil protein in the Loess Plateau of China. Plant Soil 2011, 342, 233–247. [Google Scholar] [CrossRef]
  32. Gispert, M.; Emran, M.; Pardini, G.; Doni, S.; Ceccanti, B. The impact of land management and abandonment on soil enzymatic activity, glomalin content and aggregate stability. Geoderma 2013, 202–203, 51–61. [Google Scholar] [CrossRef]
  33. Šarapatka, B.; Alvarado-Solano, D.P.; Čižmár, D. Can glomalin content be used as an indicator for erosion damage to soil and related changes in organic matter characteristics and nutrients? Catena 2019, 181, 104078. [Google Scholar] [CrossRef]
  34. Singh, P.K.; Singh, M.; Tripathi, B.N. Glomalin: An arbuscular mycorrhizal fungal soil protein. Protoplasma 2013, 250, 663–669. [Google Scholar] [CrossRef] [PubMed]
  35. Singh, G.; Bhattacharyya, R.; Das, T.K.; Sharma, A.R.; Ghosh, A.; Das, S.; Jha, P. Crop rotation and residue management effects on soil enzyme activities, glomalin and aggregate stability under zero tillage in the Indo-Gangetic Plains. Soil Till Res. 2018, 184, 291–300. [Google Scholar] [CrossRef]
  36. Verbruggen, E.; van der Heijden, M.G.A.; Rillig, M.C.; Kiers, E.T. Mycorrhizal fungal establishment in agricultural soils: Factors determining inoculation success. New Phytol. 2013, 197, 1104–1109. [Google Scholar] [CrossRef] [PubMed]
  37. Sharma, M.P.; Adholeya, A. Parameters for Selecting Efficient Arbuscular Mycorrhizal Fungi for Plants Under Microcosm Conditions. Proc. Natl. Acad. Sci. India Sect. B Biol. Sci. 2015, 85, 77–83. [Google Scholar] [CrossRef]
  38. Dai, J.; Hu, J.; Zhu, A.; Lin, X. No-tillage with half-amount residue retention enhances microbial functional diversity, enzyme activity and glomalin-related soil protein content within soil aggregates. Soil Use Manag. 2017, 33, 153–162. [Google Scholar] [CrossRef]
  39. Saikia, R.; Sharma, S.; Thind, H.S.; Sidhu, H.S.; Yadvinder, S. Temporal changes in biochemical indicators of soil quality in response to tillage, crop residue and green manure management in a rice-wheat system. Ecol. Indic. 2019, 103, 383–394. [Google Scholar] [CrossRef]
  40. Curaqueo, G.; Barea, J.M.; Acevedo, E.; Rubio, R.; Cornejo, P.; Borie, F. Effects of different tillage system on arbuscular mycorrhizal fungal propagules and physical properties in a Mediterranean agro-ecosystem in central Chile. Soil Till. Res. 2011, 113, 11–18. [Google Scholar] [CrossRef]
  41. Dai, J.; Hu, J.L.; Lin, X.G.; Yang, A.; Wang, R.; Zhang, J.B.; Wong, M.H. Arbuscular mycorrhizal fungal diversity, external mycelium length, and glomalin-related soil protein content in response to long-term fertilizer management. J. Soils Sediments 2013, 13, 1–11. [Google Scholar] [CrossRef]
  42. Turgay, O.C.; Buchan, D.; Moeskops, B.; De Gusseme, B.; Ortas, I.; De Neve, S. Changes in soil ergosterol content, glomalin-related soil protein, and phospholipid fatty acid profile as affected by long-term organic and chemical fertilization practices in Mediterranean Turkey. Arid Land Res. Manag. 2015, 29, 180–198. [Google Scholar] [CrossRef]
  43. Sandeep, S.; Manjaiah, K.M.; Pal, S.; Singh, A.K. Soil carbon fractions under maize-wheat system: Effect of tillage and nutrient management. Environ. Monit. Assess. 2016, 188, 14. [Google Scholar] [CrossRef]
  44. Bertagnoli, B.G.; Oliveira, J.F.; Barbosa, G.M.; Colozzi Filho, A. Poultry Litter and Liquid Swine Slurry Applications Stimulate Glomalin, Extraradicular Mycelium Production, and Aggregation in Soils. Soil Till. Res. 2020, 202, 104657. [Google Scholar] [CrossRef]
  45. Zhang, X.; Wu, X.; Zhang, S.; Xing, Y.; Wang, R.; Liang, W. Organic amendment effects on aggregate-associated organic C, microbial biomass C and glomalin in agricultural soils. Catena 2014, 123, 188–194. [Google Scholar] [CrossRef]
  46. Valarini, P.J.; Curaqueo, G.; Seguel, A.; Manzano, K.; Rubio, R.; Cornejo, P.; Borie, F. Effect of compost application on some properties of a volcanic soil from central south Chile. Chil. J. Agric. Res. 2009, 69, 416–425. [Google Scholar] [CrossRef]
  47. Řezáčová, V.; Czakó, A.; Stehlík, M.; Mayerová, M.; Šimon, T.; Smatanová, M.; Madaras, M. Organic fertilization improves soil aggregation through increases in abundance of eubacteria and products of arbuscular mycorrhizal fungi. Sci. Rep. 2021, 11, 12548. [Google Scholar] [CrossRef] [PubMed]
  48. Nie, J.; Zhou, J.M.; Wang, H.Y.; Chen, X.Q.; Du, C.W. Effect of long-term rice straw return on soil glomalin, carbon and nitrogen. Pedosphere 2007, 17, 295–302. [Google Scholar] [CrossRef]
  49. Liang, G.; Wu, H.; Houssou, A.A.; Cai, D.; Wu, X.; Gao, L.; Wang, B.; Li, S. Soil respiration, glomalin content, and enzymatic activity response to straw application in a wheat-maize rotation system. J. Soil. Sediment. 2017, 18, 697–707. [Google Scholar] [CrossRef]
  50. Bedini, S.; Pellegrino, E.; Avio, L.; Pellegrini, S.; Bazzoffi, P.; Argese, E.; Giovannetti, M. Changes in soil aggregation and glomalin-related soil protein content as affected by the arbuscular mycorrhizal fungal species Glomus mosseae and Glomus intraradices. Soil Biol. Biochem. 2009, 41, 1491–1496. [Google Scholar] [CrossRef]
  51. Xie, H.T.; Li, J.W.; Zhang, B.; Wang, L.F.; He, H.B.; Yhang, X.D. Long-term manure amendments reduced soil aggregate stability via redistribution of the glomalin-related soil protein in macroaggregates. Sci. Rep. 2015, 5, 14687. [Google Scholar] [CrossRef]
  52. Balík, J.; Suran, P.; Sedlář, O.; Černý, J.; Kulhánek, M.; Procházková, S.; Asrade, D.A.; Smatanová, M. Long-term application of manure and different mineral fertilization in relation to the soil organic matter quality of luvisols. Agronomy 2023, 13, 2678. [Google Scholar] [CrossRef]
  53. Balík, J.; Suran, P.; Sedlář, O.; Černý, J.; Kulhánek, M.; Procházková, S.; Asrade, D.A.; Smatanová, M. The effect of long-term farmyard manure and mineral fertilizer application on the increase in soil organic matter quality of Cambisols. Agronomy 2023, 13, 2960. [Google Scholar] [CrossRef]
  54. Balík, J.; Kulhánek, M.; Černý, J.; Sedlář, O.; Suran, P.; Asrade, D.A. The Influence of organic and mineral fertilizers on the quality of soil organic matter and glomalin content. Agronomy 2022, 12, 1375. [Google Scholar] [CrossRef]
  55. Balík, J.; Kulhánek, M.; Černý, J.; Sedlář, O.; Suran, P. Soil organic matter degradation in long-term maize cultivation and insufficient organic fertilization. Plants 2020, 9, 1217. [Google Scholar] [CrossRef]
  56. Food and Agriculture Organization of the United Nations; World Reference Base for Soil Resources. International Soil Classification System for Naming and Creating Legends for Soil Maps; Food and Agriculture Organization of the United Nations: Rome, Italy, 2015; Available online: http://www.fao.org/3/i3794en/I3794en.pdf (accessed on 1 September 2020).
  57. National Resource Conservation Service United States Department of Agriculture. Soil Taxonomy. 1999. Available online: https://www.nrcs.usda.gov/sites/default/files/2022-06/Soil%20Taxonomy.pdf (accessed on 16th December 2023).
  58. Zbiral, J.; Cizmarova, E.; Obdrzalkova, E.; Rychly, M.; Vilamova, V.; Srnkova, J.; Zalmanova, A. Uniform Working Procedures—Soil Analysis I; Central Institute for Supervising and Testing in Agriculture.: Brno, Czech Republic, 2016; ISBN 978-80-7401-123-8. [Google Scholar]
  59. Spasić, M.; Vacek, O.; Vejvodová, K.; Tejnecký, V.; Polák, F.; Borůvka, L.; Drábek, O. Determination of physical properties of undisturbed soil samples according to V. Novák. MethodsX 2023, 102133. [Google Scholar] [CrossRef] [PubMed]
  60. Kononova, M.M. Soil Organic Matter: Nature, Properties and Methods of Study; Pergamon Press Ltd.: Oxford, England, 1966. [Google Scholar]
  61. Houba, V.J.G.; Temminghoff, E.J.M.; Gaikhorst, G.A.; van Vark, W. Soil analysis procedures using 0.01 M calcium chloride as extraction reagent. Commun. Soil Sci. Plant Anal. 2008, 31, 1299–1396. [Google Scholar] [CrossRef]
  62. Körschens, M.; Albert, E.; Armbruster, M.; Barkusky, D.; Baumecker, M.; Behle-Schalk, L.; Bischoff, R.; Čergan, Z.; Ellmer, F.; Herbst, F. Effect of mineral and organic fertilization on crop yield, nitrogen uptake, carbon and nitrogen balances, as well as soil organic carbon content and dynamics: Results from 20 European long-term field experiments of the twenty-first century. Arch. Agron. Soil Sci. 2013, 59, 1017–1040. [Google Scholar] [CrossRef]
  63. Wallinga, L.; van Vark, W.; Houba, V.J.G.; van der Lee, J.J. Plant Analysis Procedures, Part 7; Department of Soil Science and Plant Nutrition, Wageningen Agricultural University: Wageningen, Holland, 1989; pp. 138–141. [Google Scholar]
  64. International Organization for Standardization. Soil, treated biowaste and sludge – Determination of pH (ISO Standard No. 10390:2021). 2021. Available online: https://www.iso.org/standard/75243.html (accessed on 31 May 2024).
  65. Mehlich, A. Mehlich 3 soil test extractant: A modification of Mehlich 2 extractant. Commun. Soil Sci. Plant Anal. 1984, 15, 1409–1416. [Google Scholar] [CrossRef]
  66. Pavlů, L.; Balík, J.; Procházková, S.; Vokurková, P.; Falušková, I.; Sedlář, O. Soil organic matter quality of variously managed agricultural soil in the Czech Republic evaluated using DRIFT spectroscopy. Soil Water Res. 2023, 18, 281–291. [Google Scholar] [CrossRef]
  67. Balík, J. Ion Interaction in Soil Solution Related to the Yields and Quality of Oat Hay and Silage Maize. Habilitation Thesis, Agricultural University of Praze, Prague, Czech Republic, 1994; p. 192. (In Czech). [Google Scholar]
Agronomy 14 01731 g001
Figure 1. Number of individual soil types (A) and textures (B) of the studied soils.
Figure 1. Number of individual soil types (A) and textures (B) of the studied soils.
Agronomy 14 01731 g001
Table 1. Characteristics of the study sites (n = 71).
Table 1. Characteristics of the study sites (n = 71).
VariableQuartile 25%Quartile 75%AverageMedian
pH (CaCl2)5.757.106.346.20
Altitude (m.a.s.l.)237406295321
Average daily air temperature (°C)9.0810.129.579.65
Average annual precipitation (mm)512622569564
Average grain yield of cereals (t.ha−1) 5.006.895.906.02
Cereals in the crop rotation (%)36.454.547.145.5
C input in organic fertilizer (t.ha−1)3.2212.608.978.10
PM3 (mg.kg−1)37.0106.597.083.4
KM3 (mg.kg−1)149262242195
CaM3 (mg.kg−1)1428483133722430
MgM3 (mg.kg−1)152335257229
Porosity (vol %)45.148.945.144.7
Bulk density (g.cm−3)1.331.521.421.45
CSOM (%)1.182.001.591.48
NT (%)0.1200.1700.1770.145
CEC (mmol+.kg−1)162246212192
Table 2. Factor analysis and factor loadings of individual components.
Table 2. Factor analysis and factor loadings of individual components.
VariablePrincipal Component
PC1PC2PC3PC4
CSOM0.9430.095−0.154−0.196
NT0.8820.175−0.066−0.302
CSOM/NT0.536−0.275−0.3830.274
CHWC0.6430.0260.3500.234
CDOC0.412−0.0980.761−0.106
CHWC/CSOM−0.141−0.0320.7050.480
CDOC/CSOM−0.219−0.1140.8320.049
NWHN0.6420.1590.450−0.055
CHWC/NHWN0.454−0.211−0.1200.460
CHA/CFA0.3130.438−0.183−0.292
EEG0.2380.7660.0340.415
TG0.3810.823−0.134−0.262
EEG/TG−0.290−0.5640.2170.611
EEG/CSOM−0.6630.4550.2720.368
TG/CSOM−0.2500.928−0.014−0.139
Bulk density−0.497−0.023−0.0400.303
Porosity0.318−0.1970.222−0.345
Clay (<0.002 mm)0.2510.1160.009−0.790
Clay particles (<0.01 mm)0.3190.032−0.058−0.794
Eigenvalue5.753.312.561.98
Variance (%)30.317.413.510.4
Cumulative variance (%)30.347.761.271.6
Correlations of individual variables and principal components. Values greater than 0.7 are in bold. n = 71.
Table 3. The influence of the soil type on selected properties of the soil organic matter.
Table 3. The influence of the soil type on selected properties of the soil organic matter.
Indicator/
Reference Soil Group		CSOMNTCSOM/NTCHA/CFACHWC
%%--mg.kg−1
Chernozems (1)1.88 b0.180 b10.4 a1.48 c540 ab
Luvisols (2)1.43 a0.143 a10.0 a0.831 a496 a
Cambisols (3)1.68 b0.163 b10.3 a0.927 ab610 b
Fluvisols (4)1.60 ab0.156 ab10.3 a1.08 b491 a
Indicator/
Reference Soil Group		NWHNCHWC/NHWNCDOCCHWC/CSOMCDOC/CSOM
mg.kg−1-mg.kg−1%%
Chernozems (1)64.9 a8.32 a13.1 a2.87 a0.070 a
Luvisols (2)59.4 a8.35 a11.1 a3.47 b0.078 a
Cambisols (3)64.4 a9.47 b13.1 a3.63 b0.078 a
Fluvisols (4)60.4 a8.13 a11.3 a3.07 ab0.071 a
Scheffe’s post hoc test; p < 0.05; n = 213. Different letters describe statistically significant differences. (1) Chernozem + phaeozem (n = 36); (2) luvisol + albeluvisol + planosol (n = 90); (3) cambisol + leptosol (n = 51); (4) fluvisol + gleysol + regosol (n = 36).
Table 4. Glomalin content and glomalin SOM fractions as related to soil types.
Table 4. Glomalin content and glomalin SOM fractions as related to soil types.
Indicator/
Reference Soil Group		EEGTGEEG/TGEEG/CSOMTG/CSOM
mg.kg−1mg.kg−1%%%
Chernozems (1)745 a5078 b14.7 a4.0 a27.0 b
Luvisols (2)663 a2433 a27.3 b3.5 a17.0 a
Cambisols (3)720 a2838 a25.4 b3.8 a16.9 a
Fluvisols (4)675 a2948 a22.9 b3.6 a18.4 a
Scheffe’s post hoc test; p < 0.05; n = 213. Different letters describe statistically significant differences. (1) Chernozem + phaeozem (n = 36); (2) luvisol + albeluvisol + planosol (n = 90); (3) cambisol + leptosol (n = 51); (4) fluvisol + gleysol + regosol (n = 36). Different letters describe statistically significant results.
Table 5. The influence of the soil texture on some SOM properties.
Table 5. The influence of the soil texture on some SOM properties.
Indicator/
Soil Texture		CSOMNTCSOM/NTCHA/CFACHWC
%% mg.kg−1
Cy + SiC + CL (1)1.82 b0.181 b10.1 a1.05 b580 a
SCL + L (2)1.54 a0.153 a10.1 a0.944 ab492 a
SiL (3)1.59 ab0.154 a10.4 a0.896 ab541 a
SL (4)1.45 a0.137 a10.6 a0.793 a547 a
Indicator/
Soil Texture		NWHNCHWC/NHWNCDOCCHWC/CSOMCDOC/CSOM
mg.kg−1 mg.kg−1%%
Cy + SiC + CL (1)66.3 b8.75 a12.8 a3.19 a0.070 a
SCL + L (2)58.7 ab8.38 a10.6 a3.19 a0.068 a
SiL (3)66.5 b8.14 a13.2 a3.40 a0.083 a
SL (4)54.5 a10.0 b12.0 a3.77 a0.083 a
Scheffe’s post hoc test; p < 0.05; n = 213. Different letters describe statistically significant differences. (1) Clay + Silty Clay + Clay Loam (n = 39); (2) Sandy Clay Loam + Loam (n = 84); (3) Silt Loam (n = 60); (4) Sandy Loam (n = 30).
Table 6. Glomalin content and glomalin SOM fractions as related to soil texture.
Table 6. Glomalin content and glomalin SOM fractions as related to soil texture.
Indicator/Soil Texture GroupEEGTGEEG/TGEEG/CSOMTG/CSOM
mg.kg−1mg.kg−1
Cy + SiC + CL (1)666 a3 971 b16.4 a3.7 a21.8 b
SCL + L (2)663 a2 640 a25.1 b4.3 ab17.1 a
SiL (3)728 a3 409 b21.3 ab4.6 ab21.4 b
SL (4)737 a2 382 a30.9 c5.1 b16.4 a
Scheffe’s post hoc test; p < 0.05; n = 213. Different letters describe statistically significant differences. (1) Clay + Silty Clay + Clay Loam (n = 39); (2) Sandy Clay Loam + Loam (n = 84); (3) Silt Loam (n = 60); (4) Sandy Loam (n = 30).
Table 7. The influence of the clay content on some SOM properties.
Table 7. The influence of the clay content on some SOM properties.
Indicator/Clay Category GroupCSOMNTCSOM/NTCHA/CFACHWC
%% mg.kg−1
Low (1)1.56 a0.148 a10.5 b0.900 a571 b
Medium (2)1.45 a0.146 a9.90 a1.03 a474 a
High (3)1.76 b0.173 b10.1 a1.23 b542 b
Indicator/Clay Category GroupCDOCNHWNCHWC/NHWNCHWC/CSOMCDOC/CSOM
mg.kg−1mg.kg−1 %
Low (1)12.4 a60.6 a9.42 b3.75 b0.080 a
Medium (2)10.7 a59.8 a7.92 a3.27 a0.074 a
High (3)12.7 a64.4 a8.41 a3.08 a0.072 a
Scheffe’s post hoc test; p < 0.05; n = 213. Different letters describe statistically significant differences. (1) n = 72; (2) n = 69; (3) n = 72. Low: clay content < 18%; medium: clay content 18–24.5%; high: clay content >24.5%. Min = 6.3%; Max = 54.6%.
Table 8. Glomalin content and glomalin SOM fractions as related to clay content.
Table 8. Glomalin content and glomalin SOM fractions as related to clay content.
Indicator/Clay Group CategoryEEGTGEEG/TGEEG/CSOMTG/CSOM
mg.kg−1mg.kg−1%%%
Low (1)719 a2 412 a29.8 c4.6 b15.5 a
Medium (2)701 a3 151 b22.2 b4.8 b21.7 b
High (3)658 a3 633 b18.1 a3.7 a20.6 b
Scheffe’s post hoc test; p < 0.05; n = 213. Different letters describe statistically significant differences. (1) n = 72; (2) n = 69; (3) n = 72. Low: clay content < 18%; medium: clay content 18–24.5%; high: clay content >24.5%. Min = 6.3%; Max = 54.6%.
Table 9. Correlation of easily extractable glomalin total glomalin content and difficultly extractable glomalin with some soil properties, followed by linear regression of some soil properties and TG content.
Table 9. Correlation of easily extractable glomalin total glomalin content and difficultly extractable glomalin with some soil properties, followed by linear regression of some soil properties and TG content.
IndicatorEEGTGDEG
CSOM0.2300.528 ***0.356 *
NT0.569 ***0.259 *0.435 **
CSOM/NT−0.0520.024−0.028
CHK/CFK0.1770.525 ***0.708 ***
CHWC0.300 *0.208−0.073
CHWC/CSOM0.075−0.314 **−0.413 **
CDOC0.0010.058−0.082
PM3 (a,b)0.159−0.009−0.026
KM3 (a,b)0.1950.377 *0.378 *
CaM3 (a,b)0.1770.387 *0.390 *
Bulk density−0.068−0.186−0.332 *
Porosity (%)−0.0800.0120.021
Clay (<0.002 mm)−0.1480.395 ***0.258
Clay particles (<0.01 mm)−0.1720.364 **0.256
CEC−0.030.400 ***0.073
pHCaCl2−0.1650.2380.269
EEG 0.569 ***0.499 ***
TG 0.996 ***
Linear regressionEquationSignificant
Clay (indep.) and TGy = 66.535x + 1583.4R2 = 0.1557p < 0.001
Clay particles (indep.) and TGy = 51.984x + 1164.9R2 = 0.1323p < 0.002
CSOM (indep.) and TGy = 1859.6x + 100.88R2 = 0.2788p < 0.000
CHA/CFA (indep.) and TGy = 1914.9x + 1041.7R2 = 0.2757p < 0.000
CEC (indep). and TGy = 6.0807x + 1775.4R2 = 0.1602p < 0.001
Pearson’s correlation coefficient; * p < 0.05; ** p < 0.01; *** p < 0.001; n = 71. (a) Incomplete pairs were removed from the calculation, meaning that n = 41 for these variables. (b) Plant-available content of nutrients determined using Mehlich 3 extraction method. Linear regression n = 71.
Table 10. Correlations of glomalin fractions and C inputs from different sources (2012–2022).
Table 10. Correlations of glomalin fractions and C inputs from different sources (2012–2022).
Glomalin/
C Input		Total C Input (a)C from Straw (b)C from Manure (c)C from
Manure + Compost (d)
EEG−0.159−0.1320.012−0.030
DEG−0.0590.058−0.039−0.081
TG−0.0590.058−0.039−0.081
Not significant. Incomplete pairs were removed from the calculation. (a) n = 69; (b) n = 59; (c) n = 26; (d) n = 29. C inputs were calculated as the sum of the inputs during the 2012–2022 period.
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Černý, J.; Balík, J.; Suran, P.; Sedlář, O.; Procházková, S.; Kulhánek, M. The Content of Soil Glomalin Concerning Selected Indicators of Soil Fertility. Agronomy 2024, 14, 1731. https://doi.org/10.3390/agronomy14081731

AMA Style

Černý J, Balík J, Suran P, Sedlář O, Procházková S, Kulhánek M. The Content of Soil Glomalin Concerning Selected Indicators of Soil Fertility. Agronomy. 2024; 14(8):1731. https://doi.org/10.3390/agronomy14081731

Chicago/Turabian Style

Černý, Jindřich, Jiří Balík, Pavel Suran, Ondřej Sedlář, Simona Procházková, and Martin Kulhánek. 2024. "The Content of Soil Glomalin Concerning Selected Indicators of Soil Fertility" Agronomy 14, no. 8: 1731. https://doi.org/10.3390/agronomy14081731

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