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Article

Enhancing Soil Organic Matter Transformation through Sustainable Farming Practices: Evaluating Labile Soil Organic Matter Fraction Dynamics and Identifying Potential Early Indicators

by
Yves Theoneste Murindangabo
1,*,
Marek Kopecký
1,
Kristýna Perná
1,
Thi Giang Nguyen
1,
Mohammad Ghorbani
1,
Petr Konvalina
1,
Andrea Bohatá
1,
Miloslava Kavková
2,
Trong Nghia Hoang
1,
David Kabelka
1 and
Eva Klenotová
1
1
Faculty of Agriculture and Technology, University of South Bohemia in České Budějovice, Branišovská 1645/31A, 370 05 České Budějovice, Czech Republic
2
Dairy Research Institute Ltd., Ke Dvoru 21a, 160 00 Prague, Czech Republic
*
Author to whom correspondence should be addressed.
Agriculture 2023, 13(7), 1314; https://doi.org/10.3390/agriculture13071314
Submission received: 19 May 2023 / Revised: 21 June 2023 / Accepted: 26 June 2023 / Published: 27 June 2023
(This article belongs to the Section Agricultural Soils)
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Versions Notes

Abstract

:
The growing global population and increasing demand for agricultural products have exerted significant pressure on agricultural systems. As a result, soil organic matter depletion and degradation have become prevalent issues, including in regions such as South Bohemia, Czech Republic, where conventional farming practices are predominant. Soil organic matter (SOM) plays a critical role in soil health, crop productivity, and the sustainability of agricultural systems, with changes occurring in both the total and labile fractions of the organic matter pools. However, changes in the total soil organic matter carbon pool (TOC) resulting from agricultural practices occur gradually and may become evident only after several years, posing challenges for timely management adjustments. Therefore, the identification of early indicators of SOM dynamics is crucial for implementing prompt corrective actions. The aim of this study was to evaluate the effects of sustainable management practices, such as cultivated crops (Pisum sativum and Lupinus albus), selected entomopathogenic and myco parasitic fungi (MEHA) (Trichoderma virens and Metarhizium brunneum), and lactic acid bacteria (LAB) on the labile fraction of the SOM pool (CLSOM) and identify potential early indicators. Our findings demonstrated that the type of crop and applied microorganisms (treatments) significantly affected the CLSOM in peas, and the crop growth stages affected the TOC in both pea and lupin. Growth stages also showed an impact on the CLSOM in lupin. Moreover, in both crops, the change in CLSOM correlated with changes in the SOM oxidation rate constant (k), carbon lability index (LI), carbon management index (CMI), and carbon enrichment ratio (ER). Conversely, changes in the TOC did not exhibit significant correlations, except for LI and CMI, which showed a positive correlation with the TOC in peas (p < 0.05). Furthermore, the separate application of MEHA and LAB on seeds or leaves resulted in increased SOM carbon pools compared with the combined application. The application of these beneficial microorganisms in pea and lupin crops showed potential in maintaining or increasing CLSOM, which can be assessed early through indicators such as k, LI, CMI, and ER. Contributing to the development of sustainable soil management strategies, future research should further investigate different crops and microorganisms—and the mechanisms underlying their observed relationships—and explore additional early indicators to refine and optimize sustainable agricultural practices.

1. Introduction

Soil is the thin layer of the Earth’s crust, consisting of organic and mineral components, which is influenced by bio-geophysical and chemical processes [1]. From a physical standpoint, soil is a three-phase system, consisting of solid components (organic, inorganic, and biota), liquid components (soil water and a nutrient solution), and gaseous components (soil and air), providing support for plants [2]. On a human timescale, soil is a vital, living, dynamic, nonrenewable natural resource that plays many critical functions in terrestrial ecosystems [3,4]. Recent studies have demonstrated that land use management practices have a significant impact on the soil quality and greenhouse gas emission into the atmosphere. This is particularly evident in agricultural fields, where the soil nutrients are utilized for crop growth [5,6]. Soil organic matter serves as the primary reservoir of nutrients and plays a crucial role in maintaining soil quality and health [7]. It influences the chemical, physical, and biological properties of the soil, which are essential for maintaining soil structure, fertility, water flow, microorganisms, carbon storage, and overall productivity [8]. The organic matter content of the soil is a balance between the addition and decomposition rates (turnover rates) and, as such, changes in management, land use, and land cover, can result in significant alterations in both turnover rates and the size of the organic matter pool, affecting carbon, nutrients, and agricultural productivity [9,10,11,12,13]. Additionally, studies have shown that the size and management outcomes of the soil organic matter pool depend on the activities of the soil microorganisms [14,15]. Although soil microorganisms differ in their cellular capabilities and metabolic requirements as decomposers, bacteria and fungi heterotrophs dominate the rest in their ability to decompose organic material in the soil [16,17,18]. During decomposition, fungi act as primary degraders with high enzymatic capabilities and low metabolic nutrient requirements [19,20,21,22], while bacteria have a short turnover and high metabolic activities and act as rapid recyclers of simply structured molecules that are rich in organic matter compounds [23,24]. The abundance of both microbial groups and soil physical–chemical properties significantly impacts organic matter and the carbon pool, as well as ecosystem functioning [25,26]. Furthermore, legume-based cropping systems, particularly rotations involving legumes, have been found to have a significant impact on the SOM carbon levels over both short and long durations. Legumes, characterized by their low carbon-to-nitrogen ratio, contribute easily degradable residues that enhance the soil organic matter pools [27,28]. They also stimulate soil biological activity, improve soil structure, enhance soil aeration, and increase the soil’s water-holding capacity [29,30,31]. Among legumes, lupin (Lupinus albus) has demonstrated remarkable capabilities in influencing soil organic matter and microbial activity [32,33,34,35]. Lupin is well suited for maintaining soil nitrogen and organic matter contents [36], establishing symbiotic relationships with important soil microorganisms, such as bacteria and fungi, and exuding important phenolic compounds and organic acids [37]. Lupin thrives in acidic soils where most crops have difficulties growing [33], performs well in cold climates, and has a high protein content [38]. Lupin also exhibits resilience to various stresses and aids in the recovery of degraded and polluted soils [34]. Pea (Pisum sativum), another important legume, has shown the ability to establish beneficial relationships with lactic acid bacteria and MEHA [39,40,41,42]. It contributes to increasing SOM and maintaining yields, and it address the soil organic carbon dilemma [43].
In general, detecting small changes in the total soil organic matter carbon, especially in the short term, is difficult. Therefore, it has been proposed that sub-pools of SOM (carbon) should be used as more sensitive indicators of change [44,45]. SOM can be divided into two main sub-pools or fractions according to their level and ability to decompose, i.e., the labile and the stable fractions, although the boundaries between them are not easy to detect [46,47]. The labile fraction, also known as primary or readily oxidizable organic matter, is composed of plant and soil organisms that are partly decomposed but have not yet undergone humification. This fraction is particularly significant due to its high turnover rate, high oxidation rate, positive impact on soil fertility, and susceptibility to management practices [48]. The second sub-pool is a stable fraction, characterized by resistance to oxidation, high cation exchange capacity, and due to its recalcitrant nature, it is not affected by land use and management as a labile fraction [49,50]. These sub-pools are commonly used to study organic matter dynamics and cycling models, as decomposition rates can vary greatly [51].
Although total SOM varies depending on the cultivated crop and management practices, it is not as sensitive as its fractions, particularly the labile fraction, and especially over short durations [52,53,54]. Therefore, determining the most sensitive soil organic carbon pools and their early indicators in regions such as South Bohemia where SOM keeps declining due to management practices, would lead to better management choices, (re)building up the soil health, fertility, and productivity. Hence, this study investigates early indicators of changes in the soil organic matter stock using the approach of rotating crops involving pea and lupin legumes, as well as microorganisms, such as lactic acid bacteria and entomopathogenic and mycoparasitic fungi, to be able to make early management decisions and take quick remedial or corrective actions whenever needed.

2. Materials and Methods

2.1. Site Description and Experimental Method

This study was conducted as part of an ongoing experiment at the Zvíkov organic certified field (according to EU law, regularly monitored by KEZ, o.p.s. controlling organization) in České Budějovice, Czech Republic. The field is located at coordinates 48.974013 N, 14.609433 E at an altitude of 460 m above sea level. The climate in the area is characterized by comfortably warm summers and cold windy winters. It is generally partly cloudy and humid throughout the year. The temperature typically ranges from −4 °C to 24 °C, rarely dropping below −12 °C or exceeding 31 °C (Figure 1). The average long-term total annual rainfall is 623 mm, with the majority occurring during the summer months [55,56].
The soil at the experimental site is typical loamy soil (orthic Luvisol, a loamy soil with a medium particle size texture) (Table 1). The field has been under organic farming practices since 2015, spanning a period of seven years. During this time, it has been utilized for cultivating wheat (Triticum spelta), oats, and barley cereals rotated with clover legume. Land management and planting were carried out using conventional methods, while weeding was performed using mechanical techniques. Minimum tillage and no fertilizer were used once when only clover was planted. For this specific experiment, which took place from September 2021 to July 2022, the field was initially covered with clover (bonus variety). During the experiment, we rotated it with pea (Pisum sativum) and lupin (Lupinus albus), and these crops were treated with Trichoderma virens, Metarhizium brunneum, and lactic acid bacteria microorganisms. Soil management practices involved methods such as mechanical and minimum tillage, weeding, and the application of composted sheep manure. As it is an organic farm, no synthetic inputs were used. The experiment followed a randomized complete block design with three replications. The factors considered were the type of crop, with two levels (peas and lupin), and the type of treatment, with nine levels (T1: control, T2: seeds treated with bacteria, T3: seeds treated with fungi, T4: leaves treated with bacteria, T5: leaves treated with fungi, T6: seeds and leaves treated with bacteria, T7: seeds and leaves treated with fungi, T8: seeds treated with bacteria and leaves treated with fungi, and T9: seeds treated with fungi and leaves treated with bacteria). This resulted in a total of 54 plots, each measuring 25 square meters.

2.2. Fungi and Bacteria

Trichoderma virens and Metarhizium brunneum, obtained from the collection of the Department of Crop Production of the Faculty of Agriculture and Technology at the University of South Bohemia in České Budějovice [57], and a consortium of lactic acid bacteria from the Culture Collection of Dairy Microorganisms of Dairy Research Institute Ltd., Prague [58], were used in this experiment. All isolates were stored as live cultures on agar slants and in cryopreservatives as frozen cultures. The seeds of selected crops were coated with a suspension of these microorganisms. A suspension of Trichoderma virens, Metarhizium brunneum, and lactic acid bacteria was prepared either from an experimental batch of the preparation or from a pure culture of these microorganisms cultivated on an artificial nutrient medium. The spores were washed into an adhesive solution with which they were easily attached to the surface of the seeds. The suspension was adjusted to a standard titer of 1 × 106 spores for T. virens and M. brunneum and 1 × 107 lactic acid bacteria spores in 1 mL of suspension. The seeds were transferred to a container, and an adequate amount of suspension was added so that each seed was thoroughly coated with the prepared suspension.

2.3. Soil Sampling

Soil sampling was conducted at three different times: before sowing (after land preparation, during sowing time), during the vegetative growth stage, and at the harvest stage (before starting the harvesting activities). In each plot, soil samples were collected using an auger from the center and near the four corners of the plot, at a depth of 20 cm. The core samples from each plot (treatment) were mixed together thoroughly and combined into one composite sample, weighing approximately 600 g. This resulted in a total of 54 composite samples. The composite samples were then dried at a constant temperature of 60 °C until they reached a stable weight. The subsequent analyses were performed using soil samples obtained from this process [59].

2.4. Analyses

2.4.1. Total Organic Carbon

The total organic carbon (TOC) content of all samples was determined using the PrimacsSLC TOC analyser (SKALAR, Netherlands) with a dual oven design, allowing separate analyses of total carbon (TC) and inorganic carbon (IC). Total carbon was determined by catalytic oxidation of the sample at 1100 °C, converting the carbon present in the sample to CO2, which was detected by the non-dispersive infrared detector. Inorganic carbon was determined by acidification of the sample in the IC reactor, which converted inorganic carbon to CO2. TC − IC = TOC [46].

2.4.2. Labile Organic Matter

The labile fraction of soil organic matter was determined by the kinetics of the oxidation reaction of soil carbon [48,60]. Soil samples (5 flasks for 1 soil sample) were dispersed in a solution of 0.07 mol/L of K2Cr2O7 in 12 M of H2SO4. Their organic compounds were oxidized at a temperature of 60 °C in a water bath. During this time, four partial samples were gradually removed from the water bath at 10 min, 20 min, 30 min, and 40 min. The amount of oxidizable carbon (COX) was then determined in the samples (automatic DL 50 Mettler-Toledo titrator, Greifensee, Switzerland). From the measured values, we calculated the reaction speed (rate) constant of oxidation (it was a first-order reaction). The temperature was then raised to 90 °C for 30 min, and then COX was determined in the sample from the last flask and designated as the labile fraction of soil organic matter (CLSOM).

2.4.3. The Labile Fraction of the Soil Organic Matter Oxidation Reaction Rate (Speed) Constant (k)

As described, each sample was divided into five subsamples and oxidized at intervals of 10 min each, and the oxidizable carbon (COX), which can be designated as COX1, COX2, COX3, and COX4, was determined. This oxidizable carbon was determined during oxidation at 60 °C in a solution of 0.07 mol/L of K2Cr2O7 in 12 M of H2SO4. The labile fraction of soil organic matter (CLSOM) value was also determined at the end of the process at 90 °C (30 min). From the aspect of reaction kinetics, oxidation is a first-order reaction, and its rate is proportionate to the concentration of not-yet-oxidized organic matter [51]:
d y d t = K L − y = K . L z
L—total organic matter, y—oxidized portion of organic matter at time t, K—rate constant, and Lz—not-yet-oxidized organic matter at time t.
Using the integration from 0 to t, it is possible to write this equation as:
L z = L . e − k t
and after conversion to decadic logarithms, K will change to k:
L z = L .10 k
For the oxidizable portion of organic matter at time t, this equation can be written as:
y = L 1 − 10 k
The values Cox1, Cox2, Cox3, and Cox4 and the differences CLSOM—Cox1, CLSOM—Cox2, CLSOM—Cox3, and CLSOM—Cox4 were calculated, and the logarithms of these differences were determined. In an orthogonal coordinate system, these logarithms were plotted on the y-axis against time t in minutes; on the x-axis, the rate constant “k” is the slope of the plotted straight line and was calculated from the relation:
k = 2.303. tg α
Because tgα is the ratio of the opposite to the adjacent legs of a triangle of right angles whose hypotenuse is the plotted straight line, the calculation of the “k” is the 2.303 multiple of this ratio, and its dimension is in min. The higher the value, not only the greater the lability of CLSOM and the better the quality in terms of functions in the soil (source of energy and nutrients for crops) but also the more reduced carbon and organic matter stocks in the soil.

2.4.4. Non-labile Organic Matter

A non-labile fraction of soil organic matter is composed of resistant organic compounds that do not oxidize in the solution of K2Cr2O7 and H2SO4 mentioned above under the given conditions. As the value CLSOM is known and TOC is known, the difference between them gives the non-labile organic matter fraction (CN-LSOM): TOC– CLSOM = CN-LSOM [48].

2.5. Carbon Management Index, Lability Index, and Enrichment Ratio

Based on the fact that a continuous carbon supply is a function of the total stock and lability (turnover rate), these two should be taken into consideration when developing a carbon management index. The carbon management index, lability index, and enrichment ratio were determined according to Blair et al. and Sainepo et al. [45,54], and they were formulated as follows:
C a r b o n   m a n a g e m e n t   i n d e x   CMI = CPI ∗ LI ∗ 100
CPI is the carbon pool index, and LI is the soil carbon lability index under a particular use (treatment).
C a r b o n   p o o l   i n d e x   CPI = T o t a l   c a r b o n   i n   t h e   t r e a t m e n t T o t a l   c a r b o n   i n   t h e   r e f e r e n c e
It should be noted that the decline in carbon from a large carbon stock soil has no serious consequences compared with the loss of the same amount of carbon in a soil already depleted of carbon. The more soil that has been depleted of carbon, the more difficult it is to rehabilitate.
L a b i l i t y   i n d e x   LI = L   i n   t h e   t r e a t m e n t L   i n   t h e   r e f e r e n c e
C a r b o n   l a b i l i t y   o f   t h e   s o i l   L = C o n t e n t   o f   l a b i l e   c a r b o n C o n t e n t   o f   n o n   l a b i l e   c a r b o n
The loss of labile carbon is of greater consequence than the loss of stable carbon.
The enrichment ratio of the labile carbon was calculated by dividing it by the total organic carbon of the same treatment (ER).
ER = l a b i l e   f r a c t i o n   o f   s o i l   o r g a n i c   m a t t e r   c a r b o n T o t a l   o r g a n i c   c a r b o n ∗ 100
In our experiment, CMI was used to monitor differences in soil carbon stock dynamics between treatments and control (reference), and it should be noted that there is no “ideal” value of CMI. The index provides a sensitive measure of the rate of change in the C dynamics of soil systems relative to the reference.
The accuracy and precision of this analytical method was proven and defined by Blair et al., Sainepo et al., and Kopecký et al. [46,54]. It also addresses the quality control (QC), instrument accuracy, recovery rates, limit of detection (LOD), and limit of quantification (LOQ) of the method.

2.6. Statistical Analyses

Analysis of variance (ANOVA) and post hoc Tukey’s honestly significant difference test for multiple comparisons of means were performed using Statistica 14.0 software, TIBCO Inc., Palo Alto, CA, USA, 2021. Statistical significance was tested at p < 0.05. To analyze the existing correlation between the change of organic matter carbon stock in soil and selected indicators, a linear regression analysis was used as follows: (a) mean predicted values and residuals, (b) normality of unstandardized residue values (p > 0.05) by the Shapiro–Wilk test, (c) the existence of potential outliers by the Cook–Weisberg test, (d) the presence of autocorrelation among regression variables by the Durbin–Watson test, and (e) the significance of the regression model by the Fisher–Snedecor test.

3. Results

3.1. Fractions and Corresponding Carbon Content (%)

In general, the stages had statistically significant effects on the TOC (p < 0.05) (Figure 2) in both crops. Overall, the TOC in pea appeared with an average of 1.52a, 1.49a, and 1.41b for the pre-seeding, harvesting, and vegetation stages, respectively. The averages for lupin were 1.54a, 1.50a, and 1.42b TOC for the pre-seeding, harvesting, and vegetation stages, respectively. The treatment with the highest TOC during pre-seeding was T2, TOC = 1.58; harvesting T5, TOC = 1.55; and vegetation T5, TOC = 1.52. In addition, in lupin, the treatments with the highest TOC during pre-seeding were T2, TOC = 1.58; harvesting T5, TOC = 1.58; and vegetation T9, TOC = 1.52.
The results also showed that the treatments had a significant effect (p < 0.05) on the labile fraction of soil organic matter (CLSOM) in pea, with an average of 0.87a, 0.87a, 0.90ab, 0.89ab, and 0.89a CLSOM for T2, T4, T6, T7, and T8, respectively (Figure 3). The lowest CLSOM recorded during the harvest was T2, CLSOM = 0.79; pre-seeding T7, CLSOM = 0.88; and vegetation T6, CLSOM = 0.83. The stages also had a significant effect on CLSOM in lupin, with an average of 1.03b, 0.88a, 0.87a CLSOM for the pre-seeing, harvesting, and vegetation stages, respectively. The lowest CLSOM recorded during harvest was T9, CLSOM = 0.71; pre-seeding T5, CLSOM = 0.99; and vegetation T2, CLSOM = 0.76.
In pea, treatments significantly affected the oxidation reaction rate constant of soil organic matter (k) (p < 0.05), with an average of 3.11b, 3.01ab, 2.96ab, 2.87ab, and 2.59abd k for T2, T7, T8, T3, and T5, respectively (Figure 4). The highest oxidation reaction rate constant (k) recorded during the harvesting stage was T2, k = 3.56; vegetation T7, k = 2.97; and pre-seeding T7, k = 3.01, while in lupin, the highest k was obtained with values of 3.65 (T2), 3.45 (T7), and 3.51 (T6), respectively, for the pre-seeding, vegetation, and harvesting stages.

3.2. Carbon Management Index (CMI), Carbon Lability Index (LI), and Carbon Enrichment Ratio (ER)

As shown in Figure 5, the highest CPI in pea was obtained from the pre-seeding and vegetation stages, with a value of approximately 1.01 without significant difference from each other. However, the harvesting stage in lupin showed the highest CPI, significantly higher than the two other stages. Regarding LI, the pre-seeding stage had the highest amounts in pea and lupin crops (0.97 and 1.19, respectively). Similar to LI, the highest values for CPI were also obtained in pre-seeding in both crops. The treatments and stages showed different effects on the enrichment ratio (ER) in pea; however, they were not statistically significant. In lupin, the ER was statistically different at the three stages (p < 0.05): pre-sowing (0.67b), vegetation (0.66a), and harvesting (0.58a).

3.3. Regression Analysis between Soil Organic Matter Carbon Stocks and Selected Indicators

The results of the regression analysis in Table 2 and Figure 6 show an apparent statistically significant correlation between the labile fraction of soil organic matter carbon (CLSOM) and the four indicators (the oxidation reaction rate constant (k), carbon lability index (LI), carbon management index (CMI), and the enrichment ratio (ER)) in both crops. In addition, except for the lability index (LI) and the carbon management index (CMI), the total organic carbon (TOC) did not correlate with these indicators. A negative correlation for the oxidation rate constant was observed, but the rest of the indicators were positively correlated with the labile fraction of organic matter. The statistical characteristics of the regressions are presented in Table 2. The residuals of all models passed the Cook–Weisberg test of heteroscedasticity for the existence of potential outliers. The results of the Shapiro–Wilk test significantly showed that the residuals followed a normal distribution (p < 0.05). Additionally, autocorrelation among residuals was not observed with the Durbin–Watson test. The regression model is significant according to the Fisher–Snedecor model significance test. Because the mean of the residuals was close to zero, the residuals were normally distributed, and the model was better fitted.

4. Discussion

4.1. Carbon Pool (TOC, CLSOM)

The results of this study demonstrated that cultivated crops (lupin and pea) and management practices (involving microorganisms) had a profound influence on the soil organic matter pool, especially the labile fraction (Figure 2 and Figure 3). This interaction between cultivated crops and inoculated microorganisms was proven to improve soil fertility, nutrient cycling, and overall soil health [41,43,61]. Previous studies by Virk et al. and Van der Pol et al. [43,62] have shown that addressing the soil carbon dilemma through the inclusion of legumes in intensified rotations can enhance soil carbon while maintaining yields through a simultaneous increase in nitrogen (N) and soil organic carbon (SOC) by way of rhizodeposition, root senescence, and decomposition. Additionally, they found that legume cultivation can improve SOC levels by promoting increased microbial activity and enhancing soil structural improvement, particularly through aggregation, which is induced by the addition of organic residues with a favorable carbon-to-nitrogen (C/N) ratio. Compared with other cropping systems, legume inclusion has shown a significant potential for sequestering SOC, and the decomposition rate of legume residues is normally higher than that of cereals, thus resulting in a higher rate of SOC addition. Legume crops, such as peas and lupins, and other green manure crops contribute organic residues and enhance the carbon and nitrogen availability [30,43,63,64], while microorganisms, including entomopathogenic and mycoparasitic fungi, such as Trichoderma virens and Metarhizium brunneum, as well as lactic acid bacteria, aid in the decomposition of organic matter and crop protection, leading to increased soil organic carbon stocks [25,65,66].
The observed effect of treatments on the SOM pools may have also resulted from the soil properties, soil management, and climatic conditions (Table 1) that were conducive to microbial activities. According to Garrido-Jurado et al., Kessler et al., and others [8,67,68], soil properties, such as pH, texture, bulk density, electrical conductivity, temperature, and precipitation, significantly impact the soil microorganisms’ activity, plant colonization, and soil organic matter decomposition. For instance, soil pH plays a significant role in SOM decomposition, as the production of organic acids during the process leads to a decrease in pH, particularly in the topsoil. Acidic soils tend to be dominated by fungal communities, while neutral soils create conditions favoring bacterial communities. Soil pH also influences the diversity and structure of microorganism communities, which, in turn, affect the decomposition and nitrification processes [69,70]. Electrical conductivity also increases with organic matter decomposition [71]. Soil texture influences organic matter decomposition and carbon mineralization, with a higher loam content correlating with increased pores, infiltration rate, and decomposition [72,73]. Moisture content and soil temperature also play a role, with higher levels stimulating decomposition but being dependent on the labile carbon supply and microbial activity [74,75]. The soil architecture, characterized by factors such as bulk density, affects microbial-mediated decomposition processes, as increased bulk density leads to lower soil porosity, aeration, infiltration rate, and organic matter decomposition [73,76].
The application of Trichoderma virens, Metarhizium brunneum, and lactic acid bacteria in pea and lupin crops in this experiment has positively affected the soil organic carbon pools, and specific indicators were used to detect this. For both crops, the stages significantly affected TOC, with the pre-seeding coming first, followed by the harvesting and then the vegetation stages. The dominance of the pre-seeding stage would be attributed to the fact that until harvesting, litter was accumulated on the soil, and there was no soil disturbance, but after harvesting, all straw was returned to the field. The low TOC during the vegetation stage would be due to the increased exposure of soil organic matter to microbial decomposition, accelerated residue decay from the tillage-induced aeration and plant uptake, and the conducive soil temperature, pH, and moisture. These results are similar to those of Chen et al., Li et al., Wang et al., and Zhu et al. [65,66,77,78], which showed that the return of short-term crop straw to the farm and the application of organic fertilizers enhanced the TOC in the upper soil layers. Although some studies did not find a significant effect of returning crop straw to the farm in the short durations of less than 3 years [79,80], many others have reported positive results in the long durations [63,81,82]. Although crop residues, organic fertilizers, and green manure play important roles in increasing the organic C sequestration, reducing the soil temperature fluctuation, and the conserving soil moisture [83], management practices—such as the addition of bacteria, fungi, or other effective microorganisms and minimum soil disturbance coupled with ideal environmental conditions—increase soil organic matter decomposition and mineralization [5,19,24,84,85,86,87,88,89,90,91].
In pea and lupin, the application of lactic acid bacteria, Trichoderma virens, and Metarhizium brunneum to seeds (T2 and T3) showed an increased TOC compared with the other treatments. However, a combined application of the two showed a significant increase in the TOC (T6, T8) as well, but it was less than when they were applied separately. This effect would be attributed to the fact that legume-based cropping systems result in residues that are more readily degraded (low C:N ratio), which influence soil organic matter carbon in short durations [29,30,31]. Furthermore, lactic acid bacteria are effective and dominate other effective microorganisms when applied together [24,84,92]. Various studies have reported increased yields and nutrient uptake where effective microorganisms, comprising lactic acid bacteria, have been applied compared with where they had not [93,94,95]. In addition to lactic acid bacteria, the effect shown by used fungi may be due to their ability to colonize and establish a beneficial symbiosis with many plant species, including beans and forbs [96]; soybean, corn, tobacco, and wheat [97]; and peas [98] as well as acting as primary degraders (including low-quality substrates) with high enzymatic capabilities and requiring low metabolic nutrients [22,99]. Moreover, Romaní et al. [100] studied the interactions of bacteria and fungi in the decomposing litter; although their interactions during the decomposition process were not well documented, synergistic and antagonistic interactions were detected in terms of the growth and patterns of the degradative enzymes expressed by the communities of both bacteria and fungi grown separately and together in phragmites leaves. In their experiment, the bacteria grew well when together with fungi, even though at some point, fungi growth was limited by the bacteria. The fungi performed well when alone.
Even though the TOC showed changes with some treatments, the labile fraction of soil organic matter was affected the most by the management practices. This may be attributed to the fact that the TOC is composed primarily of a stable fraction. The stable fraction is recalcitrant in nature and inaccessible to many decomposing microbes, leading to a humification process [49,101]. Hence, small changes in total soil organic matter or carbon are difficult to detect, and various authors proposed that the SOM sub-pool (labile) may serve as the early sensitive indicators of changes in the pool size [45,54,102].
The treatments in peas and the stages in lupin showed statistically significant effects on the change in the labile fraction of soil organic matter. The stages were as follows: pre-seeding first, harvesting, and then vegetation. At all stages, a single application of Trichoderma virens and Metarhizium brunneum on seeds and leaves (T7) showed a high CLSOM, followed by the single application of lactic acid bacteria on seeds and leaves (T6) and then (T5). Statistically significant treatments were the application of lactic acid bacteria on seeds and leaves (T6), the application of Trichoderma virens and Metarhizium brunneum on seeds and leaves (T7), the application of lactic acid bacteria on seeds and Trichoderma virens and Metarhizium brunneum on leaves (T8), the application of lactic acid bacteria on leaves (T4), and the application of lactic acid bacteria on seeds (T2). The high change in CLSOM would be attributed to the fact that this fraction of soil organic matter is more active, mineral-free, and made up of highly oxidizable, partially decomposed plant and animal residues, with a high turnover [48,60]. The more the soil is disturbed, the more the labile fraction oxidizes [5,103]. Results of this experiment showed that although the TOC in the soil was affected by management practices, CLSOM was the most sensitive and was affected in the short term, as found in other studies [45,54]. Therefore, the CLSOM and its lability within each management or treatment type can be used as an early indicator for soil organic matter stock dynamics [79,104,105,106].

4.2. Organic Matter Pool Change and Indicators (k, ER, LI, CMI)

To analyze the early indicators that can reflect the change in the soil organic matter pool, especially the most active (labile), we evaluated the organic matter oxidation rate constant, the lability index, the carbon management index, and the carbon enrichment ratio (Figure 6, Table 2). In pea, during pre-seeding, the highest lability index, the highest carbon management index, the highest carbon enrichment ratio, and the lowest organic matter oxidation reaction rate constant were found with T9. At the harvesting stage, T5 had a high lability index, a carbon management index, a carbon enrichment ratio, and a lower organic matter oxidation rate constant. T3 had the same trend during the vegetation stage. In lupin, during pre-seeding, the highest lability index, the highest carbon management index, the highest carbon enrichment ratio, and the lowest organic matter oxidation rate constant were found in T7. At the harvesting and vegetation stages, T6 and T4, respectively, had the same trend as the pre-seeding stage. The same trend of results was obtained by Blair et al. [45] while studying soil carbon fractions based on their degree of oxidation and developing a carbon management index for agricultural systems; by Vieira et al. [107], studying the carbon management index based on the physical fractionation of soil organic matter in an acrisol under long-term no-till cropping systems; by Sodhi et al. [108], studying how to use the carbon management index to assess the impact of compost application on changes in soil carbon; and by others [54,109]. The highest CLSOM observed, the highest lability index, the highest carbon management index, the highest carbon enrichment ratio, and the lowest organic matter oxidation rate constant, especially in treatment numbers 9,5,3,7,4, and 6 compared with the control (reference), can be attributed to the increased activities of the applied lactic acid bacteria, Trichoderma virens and Metarhizium brunneum, legumes plants, straw return, and organic fertilizers. The same results were observed by Six et al. [110] while studying the sources and composition of soil organic matter fractions between and within soil aggregates; by Wu et al. [111] while studying microbial interactions with dissolved organic matter and how it drives carbon dynamics and community succession; by Bot and Benites [8] on the importance of soil organic matter (the key to drought-resistant soil and sustained food production); and by others.
Regression analyses were then performed to determine whether there was a significant correlation between the change in the SOM pool and these selected indicators (Table 2 and Figure 6). Apart from the lability index and the carbon management index that showed a positive correlation with TOC in pea, the rest of the indicators did not correlate with the total organic carbon in either crop, while the labile fraction of soil organic matter was correlated with all these indicators in both crops. This shows the ability of CLSOM to predict SOM dynamics in general. For both crops, k was negatively correlated with CLSOM, showing that the increase in the oxidation rate constant reduced the labile fraction of soil organic matter (Kopecký et al., 2021). The rest of the indicators showed a positive correlation with CLSOM in both crops, indicating that the increase in the lability index, carbon management index, and carbon enrichment ratio resulted in an increased labile fraction of soil organic matter. There was a significant correlation between the change in the selected indicators and the changes in the carbon stocks. These indicators were studied and confirmed to be useful in evaluating the soil organic matter dynamics in different agricultural systems by various other researchers [45,48,54,60,112,113,114].
The results of this study provide insights into the influence of crop selection and sustainable management practices involving microorganisms (effective ones) on the soil carbon pool. The legume crops inoculated with microorganisms showed the ability to improve the soil organic matter pool, which may affect the soil fertility, nutrient cycling, and overall soil health. It should be added that the soil properties also played a significant role in the soil microorganism activity and organic matter decomposition. The labile fraction of soil organic matter was found to be the most sensitive to management practices, and indicators such as the lability index, carbon management index, carbon enrichment ratio, and organic matter oxidation rate constant were used to assess the changes in the organic matter pool. Although the effectiveness of these practices may vary in different conditions, it is important to note that in temperate regions like South Bohemia, Czech Republic, the soil organic matter addition and decomposition rates are generally slower compared with tropical regions [8]. Furthermore, considering the declining trend of soil organic matter in this region due to agricultural intensification using conventional methods, there is a need to adopt sustainable management practices that would address this issue [5,115]. The practices implemented in this study can be seen as a step towards addressing this issue and promoting sustainable soil management in the region. Overall, these findings emphasize the importance of crop selection, microbial inoculation, and soil management practices in enhancing the soil carbon pools and improving agricultural sustainability. Specific practices and their effectiveness may vary depending on the local conditions, crops grown, and farm management systems. It is essential to tailor these practices to the specific needs and constraints of a specific region when considering the long-term sustainability of agricultural production.

5. Conclusions

In conclusion, this study highlights the significant influence of sustainable management practices, including cultivated crops and applied microorganisms, on the dynamics of organic matter. Our results showed that treating legume crops, such as pea and lupin, with entomopathogenic and mycoparasitic fungi (Trichoderma virens and Metarhizium brunneum) and lactic acid bacteria in agricultural farms influences the soil organic matter pools, particularly CLSOM. The labile fraction of the soil organic matter (CLSOM) fluctuated greatly and was high across all treatments as well as during the pre-seeding stage compared with the other stages. This was attributed to the input of crop straw, organic fertilizer application, conducive environment, soil properties, low disturbance in most of the growing season, and the increased activities of applied microorganisms. When assessing the sensitivity of organic matter, especially CLSOM, to the treatments, the indicators CMI, ER, LI, and k showed the capability to serve as early indicators that can alert us to changes in the SOM stocks, which would help in determining the right action to take. This study showed that lactic acid bacteria, entomopathogenic and mycoparasitic fungi particularly Trichoderma virens and Metarhizium brunneum, and returning crop straw to the farm should be practiced in agricultural fields to enhance the status of organic matter, decomposition (labile pool), and mineralization of the nutrients, hence improving the health and fertility of the soils. As all indicators (CMI, ER, LI, and k) clearly showed sensitivity to the treatments, we conclude that CLSOM and selected indicators are promising to monitor the effects of farm management practices on the organic matter dynamics and soil quality in short durations. We, therefore, recommend further testing to evaluate the soil organic matter dynamics across a wide range of soil management practices, including different types of tillage and crops, crop rotation, intercropping, and integration of effective microorganisms.

Author Contributions

Conceptualization, P.K., A.B., M.K. (Miloslava Kavková) and Y.T.M.; data curation, Y.T.M., T.G.N., T.N.H., E.K. and M.G.; formal analysis, Y.T.M. and M.K. (Marek Kopecký); funding acquisition, P.K. and A.B.; investigation, Y.T.M., M.K. (Marek Kopecký), K.P., T.G.N. and D.K.; methodology, M.K. (Marek Kopecký), K.P., A.B. and M.K. (Miloslava Kavková); project administration, P.K., A.B. and M.K. (Miloslava Kavková); resources, P.K.; software, M.K. (Marek Kopecký); supervision, P.K. and A.B.; validation, M.K. (Marek Kopecký) and P.K.; visualization, K.P.; writing—original draft, Y.T.M.; writing—review and editing, Y.T.M. and M.G.. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the University of South Bohemia in Ceske Budejovice (research project No. GAJU 085/2022/Z) and the ministry of Agriculture of the Czech Republic (research project No. NAZV QK22010255).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data will be provided on request.

Conflicts of Interest

The authors declare no conflict of interest.

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Agriculture 13 01314 g001 550
Figure 1. Monthly average air temperature and precipitation for 2021, 2022, and 30 years at the experimental location.
Figure 1. Monthly average air temperature and precipitation for 2021, 2022, and 30 years at the experimental location.
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Figure 2. Effect of growing stages on total organic carbon (TOC) in two types of crops (pea and lupin) and nine different biological treatments as follows; T1: control, T2: seeds treated with bacteria, T3: seeds treated with fungi, T4: leaves treated with bacteria, T5: leaves treated with fungi, T6: seeds and leaves treated with bacteria, T7: seeds and leaves treated with fungi, T8: seeds treated with bacteria and leaves treated with fungi, and T9: seeds treated with fungi and leaves treated with bacteria. The vertical bars denote 0.95 confidence intervals.
Figure 2. Effect of growing stages on total organic carbon (TOC) in two types of crops (pea and lupin) and nine different biological treatments as follows; T1: control, T2: seeds treated with bacteria, T3: seeds treated with fungi, T4: leaves treated with bacteria, T5: leaves treated with fungi, T6: seeds and leaves treated with bacteria, T7: seeds and leaves treated with fungi, T8: seeds treated with bacteria and leaves treated with fungi, and T9: seeds treated with fungi and leaves treated with bacteria. The vertical bars denote 0.95 confidence intervals.
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Figure 3. Effect of growing stages on labile fraction of soil organic matter (CLSOM) in two types of crops (pea and lupin) and nine different biological treatments as follows; T1: control, T2: seeds treated with bacteria, T3: seeds treated with fungi, T4: leaves treated with bacteria, T5: leaves treated with fungi, T6: seeds and leaves treated with bacteria, T7: seeds and leaves treated with fungi, T8: seeds treated with bacteria and leaves treated with fungi, and T9: seeds treated with fungi and leaves treated with bacteria. The vertical bars denote 0.95 confidence intervals.
Figure 3. Effect of growing stages on labile fraction of soil organic matter (CLSOM) in two types of crops (pea and lupin) and nine different biological treatments as follows; T1: control, T2: seeds treated with bacteria, T3: seeds treated with fungi, T4: leaves treated with bacteria, T5: leaves treated with fungi, T6: seeds and leaves treated with bacteria, T7: seeds and leaves treated with fungi, T8: seeds treated with bacteria and leaves treated with fungi, and T9: seeds treated with fungi and leaves treated with bacteria. The vertical bars denote 0.95 confidence intervals.
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Figure 4. Effect of growing stages on the oxidation reaction rate constant of soil organic matter (k) in two types of crops (pea and lupin) and nine different biological treatments as follows; T1: control, T2: seeds treated with bacteria, T3: seeds treated with fungi, T4: leaves treated with bacteria, T5: leaves treated with fungi, T6: seeds and leaves treated with bacteria, T7: seeds and leaves treated with fungi, T8: seeds treated with bacteria and leaves treated with fungi, and T9: seeds treated with fungi and leaves treated with bacteria. The vertical bars denote 0.95 confidence intervals.
Figure 4. Effect of growing stages on the oxidation reaction rate constant of soil organic matter (k) in two types of crops (pea and lupin) and nine different biological treatments as follows; T1: control, T2: seeds treated with bacteria, T3: seeds treated with fungi, T4: leaves treated with bacteria, T5: leaves treated with fungi, T6: seeds and leaves treated with bacteria, T7: seeds and leaves treated with fungi, T8: seeds treated with bacteria and leaves treated with fungi, and T9: seeds treated with fungi and leaves treated with bacteria. The vertical bars denote 0.95 confidence intervals.
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Figure 5. Effect of growing stages on carbon pool index (CPI), lability index (LI), enrichment ratio (ER), and carbon management index (CMI) in two types of crops (pea and lupin). (a). CPI Vs growth stages, (b). LI Vs growth stages, (c). CMI Vs growth stages, (d). ER Vs growth stages. The vertical bars denote 0.95 confidence intervals. Different lowercase and uppercase letters indicate significant difference between growth stages in pea and lupin, respectively.
Figure 5. Effect of growing stages on carbon pool index (CPI), lability index (LI), enrichment ratio (ER), and carbon management index (CMI) in two types of crops (pea and lupin). (a). CPI Vs growth stages, (b). LI Vs growth stages, (c). CMI Vs growth stages, (d). ER Vs growth stages. The vertical bars denote 0.95 confidence intervals. Different lowercase and uppercase letters indicate significant difference between growth stages in pea and lupin, respectively.
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Agriculture 13 01314 g006a 550Agriculture 13 01314 g006b 550
Figure 6. Regression analysis between carbon stocks and selected indicators. (a). CLsOM Vs K, (b). CLSOM Vs ER, (c). CLsOM Vs LI, (d). CLSOM Vs CMI, (e). LI Vs TOC, (f). CMI Vs TOC. CLSOM: the labile fraction of soil organic matter carbon, TOC: total organic carbon, k: oxidation reaction rate constant, LI: carbon lability index, CMI: carbon management index, and ER: carbon enrichment ratio.
Figure 6. Regression analysis between carbon stocks and selected indicators. (a). CLsOM Vs K, (b). CLSOM Vs ER, (c). CLsOM Vs LI, (d). CLSOM Vs CMI, (e). LI Vs TOC, (f). CMI Vs TOC. CLSOM: the labile fraction of soil organic matter carbon, TOC: total organic carbon, k: oxidation reaction rate constant, LI: carbon lability index, CMI: carbon management index, and ER: carbon enrichment ratio.
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Table
Table 1. The Zvíkov experimental site description.
Table 1. The Zvíkov experimental site description.
ParameterDescription
Soil typeOrthic Luvisol
Soil texture Loamy soil
pH (H2O)5.7
pH (CaCl2)5.1
Electrical conductivity0.0932 dS/m
Bulk density1.3 g/cm3
Soil managementConventional methods with minimum soil disturbance
FertilizationComposted sheep manure, 4 T/ha (8.9 Kg N/t, 5.4 kg P2O5/t, 17.7 kg K2O/t)
No synthetic inputs
Rotation PlanWheat–Oat–Barley/Clover–Peas–Lupin
Table
Table 2. Details of regression analysis between carbon stocks and selected indicators.
Table 2. Details of regression analysis between carbon stocks and selected indicators.
CropsVariablesS-W/K-SD-WMean ResidualF
PeakCLFOM0.9612.2390.000F(1, 79) = 38.80
ERCLFOM0.1391.3450.000F(1, 25) = 54.85
LICLFOM0.2001.6540.000F(1, 25) = 18.73
CMICLFOM0.2001.4920.000F(1, 25) = 27.89
LITOC0.201.6780.000F(1, 25) = 13.80
CMITOC0.21.030.000F(1, 25) = 18.74
LupinkCLFOM0.61.4310.000F(1, 79) = 33.40
ERCLFOM0.4721.1210.000F(1, 25) = 106.24
LICLFOM0.6040.8150.000F(1, 25) = 30.007
CMICLFOM0.8540.690.000F(1, 25) = 41.314
LITOC0.600.9910.000F(1, 25) = 0.116
CMITOC0.8541.030.000F(1, 25) = 0.0274
S-W: Shapiro–Wilk test, K-S: Kolmogorov–Smirnov test, D-W: Durbin–Watson test, F: Fisher–Snedecor test, CLSOM: the labile fraction of soil organic matter carbon, TOC: total organic carbon, k: oxidation reaction rate constant, LI: carbon lability index, CMI: carbon management index, and ER: carbon enrichment ratio.
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Murindangabo, Y.T.; Kopecký, M.; Perná, K.; Nguyen, T.G.; Ghorbani, M.; Konvalina, P.; Bohatá, A.; Kavková, M.; Hoang, T.N.; Kabelka, D.; et al. Enhancing Soil Organic Matter Transformation through Sustainable Farming Practices: Evaluating Labile Soil Organic Matter Fraction Dynamics and Identifying Potential Early Indicators. Agriculture 2023, 13, 1314. https://doi.org/10.3390/agriculture13071314

AMA Style

Murindangabo YT, Kopecký M, Perná K, Nguyen TG, Ghorbani M, Konvalina P, Bohatá A, Kavková M, Hoang TN, Kabelka D, et al. Enhancing Soil Organic Matter Transformation through Sustainable Farming Practices: Evaluating Labile Soil Organic Matter Fraction Dynamics and Identifying Potential Early Indicators. Agriculture. 2023; 13(7):1314. https://doi.org/10.3390/agriculture13071314

Chicago/Turabian Style

Murindangabo, Yves Theoneste, Marek Kopecký, Kristýna Perná, Thi Giang Nguyen, Mohammad Ghorbani, Petr Konvalina, Andrea Bohatá, Miloslava Kavková, Trong Nghia Hoang, David Kabelka, and et al. 2023. "Enhancing Soil Organic Matter Transformation through Sustainable Farming Practices: Evaluating Labile Soil Organic Matter Fraction Dynamics and Identifying Potential Early Indicators" Agriculture 13, no. 7: 1314. https://doi.org/10.3390/agriculture13071314

APA Style

Murindangabo, Y. T., Kopecký, M., Perná, K., Nguyen, T. G., Ghorbani, M., Konvalina, P., Bohatá, A., Kavková, M., Hoang, T. N., Kabelka, D., & Klenotová, E. (2023). Enhancing Soil Organic Matter Transformation through Sustainable Farming Practices: Evaluating Labile Soil Organic Matter Fraction Dynamics and Identifying Potential Early Indicators. Agriculture, 13(7), 1314. https://doi.org/10.3390/agriculture13071314

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