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Article

Residues of Symbiont Cover Crops Improving Corn Growth and Soil-Dependent Health Parameters

by
Sundoss Kabalan
1,
Flórián Kovács
1,2,
Enikő Papdi
1,
Eszter Tóth
3,
Katalin Juhos
1 and
Borbála Biró
1,*
1
Department of Agro-Environmental Studies, Hungarian University of Agriculture and Life Sciences, Villányi Str. 29-43, H-1118 Budapest, Hungary
2
Institute of Plant Sciences and Environmental Protection, Faculty of Agriculture, University of Szeged, Dugonics tér 13, H-6720 Szeged, Hungary
3
Department of Agroecology and Organic Farming, Hungarian University of Agriculture and Life Sciences, Villányi Str. 29-43, H-1118 Budapest, Hungary
*
Author to whom correspondence should be addressed.
Agriculture 2024, 14(9), 1601; https://doi.org/10.3390/agriculture14091601
Submission received: 3 August 2024 / Revised: 29 August 2024 / Accepted: 10 September 2024 / Published: 13 September 2024
(This article belongs to the Section Crop Production)
Browse Figures
Versions Notes

Abstract

:
Cover crops have emerged as a crucial tool in promoting sustainable agricultural practices, particularly in improving soil quality and soil–plant health. This study investigates the impact of single cover crop plants each with varying fungal and/or bacterial symbiosis capacities in a pot experiment. The growth of non-symbiont Ethiopian mustard (Brassica carinata), the associative bacterium symbiont black oat (Avena strigosa) and the double (fungus–bacterium) endosymbiont broad bean (Vicia faba) was studied on three distinct soil types, namely a less-fertile sandy soil (Arenosol), an average value of loam soil (Luvisol) and a more productive chernozem soil (Chernozem). Beside the biomass production, nitrogen content and frequency of AM fungi symbiosis (MYCO%) of cover crops, the main soil health characteristics of electrical conductivity (EC), labile carbon (POXC) and fluorescein diacetate enzyme activity (FDA) were assessed and evaluated by detailed statistical analysis. Among the used soil types, the greatest biomass production was found on Chernozem soil with the relatively highest soil organic matter (2.81%) content and productivity. Double symbiotic activity, assessed by soil nitrogen content and mycorrhiza frequency (MYCO%), were significantly improved on the lowest-quality Arenosols (SOM 1.16%). In that slightly humous sandy soil, MYCO% was enhanced by 45%, indicating that symbiosis was crucial for plant growth in the less-fertile soil investigated. After the initial cover crop phase, the accumulated biomass was incorporated into the Luvisol (SOM 1.64%) soil, followed by the cultivation of corn (Zea mays, DK 3972) as the main crop. The results indicate that incorporating cover crop residues enhanced labile carbon (POXC) by 20% and significantly increased the FDA microbial activity in the soil, which positively correlated with the nutrient availability and growth of the maize crop. This study emphasizes the importance of selecting suitable cover crops based on their symbiotic characteristics to improve soil quality and enhance soil–plant health in sustainable agricultural systems.

1. Introduction

Farming practices have a significant influence on soil health and crop growth. As the world population continues to grow, so does the demand for food. With limited arable land and climate change affecting crop yields, farmers are constantly seeking innovative ways to maximize their harvests and minimize their costs. One technique gaining popularity is the use of cover crops [1,2], which are grown between main crop cycles. These cover crops have shown promise in improving soil quality, preventing soil degradation, and enhancing soil organic matter (SOM), known as a key indicator of soil health [3,4]. Cover crops offer multiple benefits for soil quality, enhancing its physical, chemical and biological properties [5,6], and they are also considered to reduce soil degradation potential [7,8,9]. When using cover crops, the need for synthetic fertilizers is lessened, thereby mitigating the negative environmental impacts of agriculture [10,11]. Additionally, cover crops enhance water infiltration and minimize runoff, leading to improved water management [12]. The selection of cover crops and their duration in the crop rotation system significantly influence soil health and main crop growth [13,14]. Therefore, choosing suitable plant species for specific soil–crop systems is crucial to maximizing these benefits. Cover crops can be categorized into three main groups: (i) legumes (e.g., broad bean and vetches), (ii) non-legumes and brassicas (e.g., spinach, mustards and radishes) and (iii) grasses (e.g., oat and wheat) [15]. Each category provides distinct benefits. Legumes, for example, fix atmospheric nitrogen through several symbiotic relationships with nitrogen-fixing rhizobia, plant-growth-promoting rhizobacteria (PGPR) and the phosphorus-mobilizing mycorrhizal fungi, which support plant growth and development, regulating plant health characteristics in one step [16,17]. Non-legumes and brassicas improve soil structure with their dense root systems [18], while grasses are effective in reducing soil erosion and increasing soil organic matter (SOM) content. Brassicas also suppress soil-borne pathogens, contributing to overall soil health. Despite the frequent recommendation of legumes due to their nitrogen-fixing abilities, non-leguminous species have also been studied for their potential to improve plant growth through indirect effects [19,20].
Research has highlighted the importance of arbuscular mycorrhizal (AM) fungi in enhancing plant nutrition and growth. These fungi form symbiotic relationships with the roots of most plants, improving the uptake of essential nutrients such as phosphorus and micronutrients [21]. The mutualistic relationship between AM fungi and plant hosts significantly impacts soil structure and fertility [22]. AM fungi also improve water-holding capacity, reduce soil erosion and increase water infiltration [23]. They can enhance plant growth and productivity, leading to improved crop yields [24,25], and bolster plant tolerance to abiotic stressors such as drought and heavy metals [26,27]. Factors such as land use and soil management affect the abundance and diversity of AM fungi [28,29]. Evidence shows that cover crops and reduced tillage increase AM fungi populations, enhancing soil health and fertility [30]. Winter legume cover crops, in particular, provide fixed nitrogen, conserve resources and sustain soil productivity, making them a valuable nitrogen source for summer crops [31]. By fixing atmospheric nitrogen, leguminous cover crops have the potential to partially replace commercial fertilizers. Their inclusion in conservation tillage systems also helps minimize erosion [32]. Cover crops enhance soil quality and act as stable carbon sinks with a half-life of several years to decades. They increase permanganate-oxidizable carbon content (the POXC, called labile carbon) in the soil by adding easily decomposable plant residues. In general, cover crops that produce high biomass and have a high C ratio can lead to an increase in easily accessible carbon forms in the soil. This increase in labile carbon can, in turn, improve soil health and fertility, as well as improve nutrient availability for subsequent crops [33,34]. This review evaluates main soil health indicators to predict sustainability in production systems, focusing on how common cover crops affect soil microbiological activity, particularly arbuscular mycorrhizal fungi (AMF), and the growth of corn, as main crop plants. This study aims to assess the influence of specific selected cover crops on some soil health and fertility parameters, identifying optimal cover crop categories. The hypothesis is that strategically managed cover crops with powerful symbiosis capacities might enhance soil health, nutrient availability and reduce agriculture’s environmental footprint. Three single cover crops, namely non-symbiont mustard, double fungus–bacterium symbiont bean and simple, associative symbiont oat will be studied for their effect in three different representative soils on the growth of a secondary main crop of maize, supported by proper statistical evaluation.

2. Materials and Methods

2.1. Experimental Design and Growth Condition

Several soil health indicators were assessed, such as nutrient cycling, crop growth, soil pH, available nitrogen content, catabolic enzyme activity and the labile carbon (POXC) content, using three different and partly contrasting soil types, with lower and higher organic matter content. The cover crop study was conducted in two (preliminary and secondary) phases. In the first phase, three single cover crops, namely non-symbiont Ethiopian mustard (Brassica carinata), double fungal–bacterial symbiont broad bean (Vicia faba) and single associative bacterial symbiont black oat (Avena strigosa) were used, to select one type of plant species from the most abundant cover crop categories. Each cover crop treatment was set up on 3 representative and distinct soil types: Arenosols, Chernozems and Luvisols [35]. The slightly alkaline Arenosols (pH = 7.46) were characterized by a sandy texture, which had a low soil organic matter content (SOM = 1.16%). The loamy Chernozem (pH = 7.35) soil was a fertile, dark-colored soil which had a 2.81% organic matter content. Luvisol soil was rather acidic (pH = 4.91) and characterized by its clay-rich texture (clay loam), which typically has good nutrient-holding capacity and moderate drainage properties but relatively low soil organic matter content (SOM = 1.64%). Further information regarding the main soil characteristics can be seen in Table 1.
In each 10 L of plastic pots, 15 seeds (plants) were placed, set up in four replications in the first and 8 replications in the second phase of the experiments. In the greenhouse, we considered the ridge effect and randomized the pots regularly. The cover crop plants were grown in a heated greenhouse with an average temperature of 19 °C during the day and 10 °C at night, with 52% humidity. After eight weeks of growth in the first experiment, the plants were harvested, and their biomass production (fresh and dry weight of roots and shoot biomass content) was assessed. The cover crop biomass was cut and blended with the soil as green manure in the second phase of the experiment. During the cover crop phase, 4 unplanted pots were also set up for Luvisols as control for the second phase. In the pots filled with Luvisols, corn (Decalb DKC3972) was sown, with one seed per pot, four weeks after the termination of the cover crops. Irrigation was performed twice a week, with no fertilizer added. In the second experiment, the manure, originating from the cover crop biomass phase (first phase) was used and the residues were incorporated into the soil. Detailed arrangement of the experiments is shown in Appendix A.
Luvisols were selected for planting corn in the second phase of the experiment due to their unique characteristics, particularly their low organic phosphorus content in Hungary. The high pH acidity of Luvisols leads to the fixation and immobilization of inorganic phosphorus and phosphorus fertilizers, which complicates the maintenance of adequate phosphorus availability for crops. This characteristic made Luvisols an ideal choice for evaluating the effects of cover crops (with different symbiosis capacities) on soil health and nutrient availability.

2.2. Determination of Plant Biomass and Mycorrhiza Colonization

To assess the growth and development of cover crops and corn plants, fresh and dry biomass was measured at the end of their growth periods. After harvest, dry biomass was obtained after drying the fresh samples in an oven at 70 °C for 48 h. The colonization assay of arbuscular mycorrhiza fungi was conducted to measure the root colonization frequency by the fungi (MYCO%) in the roots of three single cover crops, as in the main crop (corn). The mycorrhizal development was estimated using a modified method of [36]. The roots were carefully washed with tap water and softened in a 7% KOH solution for 24 h, followed by washing in water, acidification in 5% lactic acid for 1–24 h, and staining with 0.01% aniline blue in 5% lactic acid for 24 h at room temperature. The stained roots were stored in lactoglycerol until slide preparation. Mycorrhizal colonization parameters, including frequency of mycorrhization, were determined using thirty 1 cm root fragments per sample. These fragments were examined under a dissecting microscope at X10 magnification, and the frequency of mycorrhization (MYCO%) was calculated as the percentage of root segments showing mycorrhizal fungi presence.

2.3. Determination of N Content in Leaf

The total N content of corn leaves was measured using the Kjeldahl method [37]. A 0.25 g ground leaf sample was digested with 5 mL of concentrated H2SO4 at 380 °C, using Na2SO4 and Se as catalysts until the solution was clear. The volume was adjusted to 50 mL with distilled water. A 10 mL aliquot was distilled after adding 1 mL of 40% NaOH solution. The distillate was titrated with 0.02 N H2SO4 to the endpoint.

2.4. Soil Sampling and Analysis

After the experiment concluded, soil samples were collected from the pots at a depth of 0–10 cm to evaluate soil biological activity. The freshly collected soil samples were preserved at +4 °C and subsequently used for enzyme activity and microbiological assays.
Four weeks after the termination of cover crops but before corn sowing, soil samples were taken from the planted pots. Electrical conductivity (EC), fluorescein diacetate enzyme activity (FDA) and permanganate oxidizable, “labile” carbon content (POXC) was measured to compare the effect of cover crop species on the fertility of the three soil types. In the case of the Luvisols, after harvesting the corn plants, soil samples were taken from the pots, including pots not previously treated with cover crops. In this case, nitrate + ammonium content of soil (Nmin), FDA and POXC were measured.
The EC measurements were obtained by mixing 10 g of soil with 50 mL of distilled water and stirring the mixture for 1 h. The electrical conductivity was then measured using a conductivity meter (AD 8000 pH/MV/EC/TDS and T 0 Bench Meter, Romania). This provided an estimate of the soluble nutrient content in the soil, which can affect plant growth.
To determine Nmin concentration in soil, a 1:5 soil–0.1 M CaCl2 suspension was prepared and shaken for 60 min. After settling, the suspensions were filtered to separate the liquid phase. To determine ammonium concentration, 10 mL of soil extract was mixed with 1 mL of oxidizing reagent (NaOH + dichlorocyanuric acid sodium salt) and 1 mL of salicylate reagent (Na-salicylate + tri-sodium citrate + sodium nitroprusside). Ammonium concentration was measured from the substrate using a spectrophotometer at 655 nm wavelength. To determine nitrate concentration, 5 mL of the filtered soil extract was mixed with 1 mL of C7H5NaO3 and evaporated until a precipitate formed. The precipitate was dissolved in 1 mL of concentrated H2SO4. Then, a beaker glass was filled with 25 mL of distilled water and 5 mL of 10 M NaOH was added, followed by adding the dissolved substrate to the mixture. The volume was then filled to 50 mL with distilled water. Nitrate concentration was measured from the substrate using a spectrophotometer at 410 nm wavelength [37]. The Nmin concentration was expressed as the sum of the nitrate and ammonium-N concentrations.
Fluorescein diacetate (FDA) enzyme activity was used for estimating microbial activity in the soil. Soil samples were taken from each pot and incubated with FDA solution (2 mg/mL). The enzymatic activity of microorganisms in the soil was estimated based on the fluorescence intensity measurements. The experiment was conducted with four replicates per treatment.
POXC indicates the “labile” carbon in the soil that is easily oxidized by potassium permanganate [38]. Soil samples were collected from each pot and air-dried at room temperature. The POXC test was performed according to [39] with the change in the concentration of 0.02 M KMnO4 used to estimate the amount of carbon oxidized. The method was modified by shaking 1 g of air-dried soil in 10 mL KMnO4 solution for 5 min, followed by centrifugation. Then, 0.2 mL of the supernatant was carefully transferred to a 15 mL tube and mixed with 10 mL of deionized water. Finally, the sample absorbance was measured at 565 nm wavelength using a Biochrom Libra S22 spectrophotometer.

2.5. Statistical Analysis

The statistical analysis was performed using R version 4.2.1 [40]. A GLM (general linear model) was used to examine the impact of the treatments. The GLM included correlation analysis and multifactor ANOVA (analysis of variance) to examine the impact of the treatments. The effectiveness of the treatments was assessed using Wilk’s lambda. Given the significance of the overall test, univariate main effects were tested using Bonferroni correction. We evaluated the effect size partial eta squared (η2) value, which gives the effect size as well as the observed power; moreover, this indicates the likelihood of detecting the correct significant effect. The value of the indicator can range from 0 to 1. Zero indicates independence and 1.00 indicates a deterministic relationship. The normality of the GLM for the residuals was assessed using the Shapiro–Wilk test, and peak skewness was also tested. A ratio of kurtosis to the standard error and skewness to the standard error less than 3.3 was used to verify the normality assumption [41]. The linear relationship between measured soil and plant parameters was calculated using Pearson’s correlation analysis.

3. Results

3.1. Growth Capacity of Cover Crops in the Used Soils

The effects of the single cover crops’ growth were examined on five dependent variables of the three used soils. These variables were the dry biomass production of cover crops, the colonization of arbuscular mycorrhiza fungi (MYCO%), Fluorescein diacetate enzyme analysis (FDA), permanganate oxidizable carbon (POXC) and the electric conductivity (EC) of soils.
Biomass production of cover crops: The cover crops, as a dependence of their symbiosis status and other ecophysiology, could result in a significant effect on their biomass production (F(2,27) = 65.94; p < 0.01; partial η2 = 0.83). The types of soils used, however, had no significant effect (F(2,27) = 0.74; p > 0.05; partial η2 = 0.052), and also the interaction between cover crops and soils was found to not be significant (F(4,27) = 0.40; p > 0.05; partial η2 = 0.056) on the biomass production. Figure 1 shows the result of Tukey’s post hoc test, which found significant differences between cover crops and soils with respect to the biomass production. Figure 2 shows the effect of cover crops as a dependence of the three different soil types. The η2 value shows that, whilst the soil (η2 = 0.052) had a considerably smaller influence, the cover crops had a significant impact on the amount of dry biomass (η2 = 0.83). Between the three soils, there was no discernible statistically significant difference in biomass. This was reflected in the η2 squared value, where it was shown that soil slightly contributed to only 5.2% of the biomass production. According to Figure 1A, the soil type with the highest biomass was Chernozem (0.80 ± 0.59), followed by Luvisol (0.73 ± 0.59) and Arenosol (0.65 ± 0.46). Among the cover crops, a notable difference in biomass production was found. In relation to the η2 value of the soil, more than 83% of biomass production was influenced by the used cover crops. The biomass production of the double symbiosis broad bean cover crop was the highest at 1.51 ± 0.45, significantly and statistically different from that of the black oat (0.23 ± 0.11) and Ethiopian mustard (0.44 ± 0.10) cover crops (p < 0.001). The least amount of biomass was produced by the black oat cover crop. There was no significant difference between Ethiopian mustard and black oats (p > 0.05) (Figure 1B). In summary, the influence exerted by cover crops on the production of plant biomass is approximately sixteen-fold greater in magnitude compared to that of soil characteristics.
Regarding the differences among the used soil types, Figure 2 illustrates the effect of different cover crops on biomass production and total leaf N% as a dependence of the three soil types used. For the double symbiont broad bean cover crop, the highest dry biomass weight was found in Chernozem soil (1.7 ± 0.29), followed by Arenosols (1.47 ± 0.76) and Luvisols (1.36 ± 0.14). Considering the Chernozem soil, the broad bean cover crop could result in a 25% higher biomass production than the Luvisol soil. Still, in this pot experiment, no statistically significant difference was detected between the three soil types for the production of broad bean (Vicia faba) cover crop (F(2,9) = 0.50; p > 0.05; partial η2 = 0.10). There was no statistically significant difference for Ethiopian mustard (Brassica carinata) (F(2,9) = 1.71; p > 0.05; partial η2 = 0.27), while a similar trend was also observed, considering the three soil types: Chernozem soil had the highest biomass weight (0.48 ± 0.12) and the lowest biomass was found for Arenosols (0.47 ± 0.09). In the case of black oats (Avena strigosa), the Luvisol soil showed the highest biomass weight (0.24 ± 0.13), followed by Arenosol (0.24 ± 0.07) and the smallest biomass weight was found in the Chernozem soil type (0.22 ± 0.08), but there was no statistically significant difference between the three soil types (F(2,9) = 0.03; p > 0.05; partial η2 = 0.007). Overall, the biomass weight of the cover crops was highest in the Chernozem soil type, where the biomass was 20–30% higher than in the Arenosol and Luvisol soil (Figure 2). For the double symbiont broad bean cover crop, the highest total leaf N content was found in Chernozem soil (4.58 ± 2.05), followed by Arenosol (2.93 ± 0.3) and Luvisol (1.82 ± 0.3). The leaf N content in beans was significantly higher in Chernozem soil compared to the other two soil types. For Ethiopian mustard, there was no significant difference in total leaf N content among the three soil types. The values were similar across Arenosol (1.80 ± 0.04), Chernozem (1.74 ± 0.04) and Luvisol (1.74 ± 0.03) soils. In the case of oats, there was also no significant difference in total leaf N content among the soil types. Similar leaf N contents were measured in Arenosol (1.87 ± 0.09), Chernozem (2.63 ± 1.50), and Luvisol (1.87 ± 0.08) soils (Figure 2).
Summarizing Figure 1 and Figure 2, it can be concluded that soil type did not have a significant effect on the biomass production of the used cover crops. However, different types of cover crops had a significant effect on their biomass production, regardless of soil type in this study.

3.2. Potentials of Soil Biological Quality Indicators

Regarding the colonization of Mycorrhiza fungi, the soil’s quality is a highly determining factor for the growth and development of cover crops. The quality of the soil can be assessed by several soil biological indicators. Mycorrhiza symbiosis might provide better plant development, but differences can be found among the three examined soils and the three different hostplants. Significant differences in the colonization of arbuscular mycorrhiza fungi (MYCO%) between the three soil types are shown in Figure 3, with respect to the presence of cover crops. In the case of broad beans (Vicia faba), statistically significant differences were found between soil types with respect to mycorrhizal activity (F(2,9) = 11.27; p < 0.05; partial η2 = 0.71). The highest mycorrhizal colonization was obtained for the Arenosol soil (0.52 ± 0.02), which was statistically significantly different from the Chernozem soil (p < 0.05). Mycorrhizal colonization was lowest for the Chernozem soil (0.34 ± 0. 06), followed by the Luvisol soil (0.41 ± 0.05), for which it was much larger but not statistically different (p > 0.05). The highest MICO% was obtained in the Arenosol soil (0.50 ± 0.03), which was significantly different (p < 0.05) from that of the Luvisol soil (0.36 ± 0.08). The Arenosol soil was not significantly different (p > 0.05) from either soil type, but on average showed a much higher MICO% (0.47 ± 0.09) than Luvisol soil. In the case of black oats (Avena strigosa), no significant difference was found between the three soil types (F(2,9) = 1.95; p > 0.05; partial η2 = 0.30). The Chernozem (0.42 ± 0.11) and Luvisol (0.42 ± 0.12) soils showed almost equal levels of MICO%, but the Arenosol soil showed much lower levels (0.30 ± 0.03) for the black oat cover crop.
Enzyme activity and labile carbon: For the (Vicia faba) broad bean cover crop, FDA activity exhibited notable variations, with the highest value recorded in Chernozem soil (12.29 ± 1.22), indicating a superior microbial activity compared to Arenosol and Luvisol soils. The electrical conductivity was significantly higher in Luvisol soil (202.00 ± 82.51) for broad beans, suggesting varied ionic transport and nutrient availability in the soil. Additionally, POXC values, indicative of soil organic carbon levels, were highest in Arenosol soil (461.11 ± 107.29) for broad beans, reflecting differences in soil carbon sequestration capabilities across soil types. Mustard, as a cover crop displayed uniform FDA activity across all soils, signifying consistent microbial activity. However, it presented a stark contrast in POXC, with Chernozem (373.28 ± 34.92) and Luvisol (254.73 ± 31.74) soils showing significantly higher organic carbon content than Arenosol soil; this illustrates the impact of soil types on the carbon storage efficiency. Black oats (Avena strigosa), on the other hand, showed a significant increase in FDA activity in Chernozem soil (16.27 ± 3.79), underscoring the soil’s ability to support higher microbial life. Electrical conductivity values for black oats were notably higher in Chernozem and Luvisol soils, indicative of enhanced mineral ion concentration. POXC levels were markedly elevated in Chernozem soil (459.27 ± 94.99) for black oats, highlighting its exceptional capacity for organic carbon accumulation (Table 2).
In the context of our study, the Pearson’s correlation coefficients illuminated valuable insights into the relationships between the studied key variables seen in Figure 4. The analysis demonstrates that the mean percentage of the colonization by arbuscular mycorrhiza fungi (MYCO%) exhibits substantial positive correlations with both FDA and EC values, with correlation coefficients of 0.31 and 0.36, respectively. Additionally, the POXC content showed noteworthy negative correlations with FDA activity (r = −0.48) and EC (r = −0.25).

3.3. Secondary Effect of Cover Crops on Corn Growth in Luvisols

In the second phase of our study, we explored the impact of cover crops on various soil and corn growth parameters, including soil FDA, POXC, MYCO%, Nmin and corn biomass weight (Figure 5). The statistical analysis revealed a significant effect of cover crops on these variables (F(20,37) = 7.92; p < 0.05; Wilk’s λ = 0.004; partial η2 = 0.74), with a high partial η2 indicating a strong influence of cover crops.
Tukey’s post hoc test results, illustrated in Figure 5, show that FDA activity was highest with the broad beans (Vicia faba) catch crop (1.67 ± 0.23), significantly differing from the other cover crop treatments (p < 0.01). The Ethiopian mustard (Brassica carinata) (0.39 ± 0.59), black oats (Avena strigosa) (0.22 ± 0.34) and non-treated control (0.08 ± 0.21) followed. Similarly, the highest POXC level (412.00 ± 280.04) was observed in corn after broad beans, significantly different from the control plants but not from broad bean, Ethiopian mustard and black oat cover crops (p < 0.01). The lowest POXC value was found in the untreated control plants (50.23 ± 39.25). Then, after the control, the lowest POXC value was found in the black oat plants (69.92 ± 87.64). The average POXC value of the Ethiopian mustard cover crop was (246.70 ± 140.00).
Cover crops also influenced mycorrhizal colonization differently. The non-treated control corn exhibited the lowest MICO% (5.25 ± 2.87). No significant difference was found between board bean, Ethiopian mustard and black oat treatments (p > 0.05). Nmin content showed significant difference between cover crop treatments (p < 0.05), with higher values observed after corn harvest for broad bean (25.55 ± 4.22), black oat (15.70 ± 0.69) and Ethiopian mustard (11.50 ± 5.18). The lowest Nmin value compared to the other treatments was observed in the untreated control plants (8.45 ± 4.22). The control had the lowest biomass (11.05 ± 2.91), followed by black oat (13.32 ± 1.78), broad bean (13.96 ± 1.83) and Ethiopian mustard (14.43 ± 0.78) treatments. The broad bean (6.86 ± 1.43) had the highest total N content, while the control (2.01 ± 0.59) had the lowest. No significant difference was detected between mustard (5.11 ± 0.62) and oats (4.62 ± 1.43). Overall, the highest levels of FDA, POXC, Nmin and biomass for the maize main crop were found in the broad bean cover crop, which was statistically significantly different from the control.
Our analysis focused on how cover crops influenced six dependent variables, as measured by partial η2 values. The most significant impacts of cover crops were observed on FDA (η2 = 0.78) and Nmin (η2 = 0.77), indicating that cover crops contributed to 77–78% of the variations in FDA and Nmin. Lesser effects were seen in POXC (η2 = 0.41), MYCO% (η2 = 0.66), total leaf N% (η2 = 0.36) and biomass, experiencing the least influence (η2 = 0.12) from cover crops (Figure 6).
Figure 7 shows the strong positive correlation between corn dry biomass and MYCO% (r = 0.69), as well as the noteworthy positive correlation between corn dry biomass and Nmin (r = 0.67). A negative significant correlation was found between EC and dry biomass (r = −0.82).

4. Discussion

4.1. Cover Crop Species, Symbiosis Capacity and the Monitored Soil Characteristics

Regarding the suggestions of potential applicable cover crop species, focus is given to those that have the ability of biological nitrogen fixation and good phosphorous use efficiency. Nitrogen is the only essential element for plant growth that can be introduced into the soil–plant system from external sources, in addition to being present in the soil in varying amounts. Unlike other elements, nitrogen can be fixed from the atmosphere through biological nitrogen fixation by legumes. However, the amount of nitrogen in the soil is often highly variable and can be easily lost through leaching or volatilization. It is the leguminous plants and their symbionts which have the ability of potentially reducing or replacing inorganic additional fertilizers in sustainable agricultural practices. Considering phosphorous, it represents about 30% of the European soils, which are known to have of their own P-containing minerals. The keys to its availability, however, is highly dependent on soil microorganisms. As a function of the soil–plant microbiological status, both the nitrogen and the phosphorous elements can become available even when replacing the artificial inorganic fertilizers. Leguminous plants are known to have bacterial and fungal symbionts, providing the most important N, P macro-nutrients and contributing to greater biomass production.
Broad beans exhibited higher production of the above and below-ground biomass content, compared to the other non-leguminous plants, examined in the first phase of the experiment. This is attributed to their intricate root system, which potentially facilitates efficient water and mineral absorption from the soil. This observation aligns with prior research [42]. Notably, broad beans showed the highest biomass generation in Chernozem soil (Figure 1). In general, the prevalence of dual symbiotic systems in the broad bean leguminous crop could contribute to enhanced biomass production. Chernozem soil is known for its high fertility and favorable properties for plant growth, which may have resulted in the best performance of broad beans. This is in contrast to mustard, which lacks the capacity to sustain either nitrogen-fixing bacteria or the soil’s symbiotic arbuscular mycorrhizal fungi [43]. Black oats might allocate more resources to above-ground growth, such as leaves and stems, which are easily observed as biomass, while legumes often allocate more resources below-ground to roots and nodules [44].
Mycorrhizal association contributes to the augmentation of cover crop yields [45]. The kind of cover crop plays a pivotal role in determining arbuscular mycorrhizal (AM) fungal colonization. Among these, broad beans, displaying a dual symbiotic relationship, exhibit the highest mycorrhizal activity, which is consistent with the findings of [46]. Conversely, mustard, devoid of a symbiotic connection, demonstrates a mycorrhizal percentage of 0%, a result shown by the study of [47]. Our study reveals that the Arenosol soil exhibits a significantly higher mycorrhizal percentage compared to other soil types (Figure 3), and the black oats display elevated mycorrhizal percentages, with black oats being a single symbiotic plant [48].
Enzymes also play a role in soil mineralization processes [49] and have been linked to other soil biological properties [49,50]. The variability in FDA hydrolysis activity among cover crops in different soil types (Table 2) can be attributed to complex soil–plant–microbe interactions. Chernozem soil likely promotes higher microbial activity and FDA hydrolysis due to factors like nutrient availability, root exudates, microbial diversity and pH-dependent enzymes [17,51]. Contrasting conditions in Luvisol soil may limit microbial activity and lower FDA hydrolysis [52]. pH, microbial composition, root development and microorganism efficiency in utilizing FDA contribute to the observed variation. In the context of soil carbon dynamics, the measurement of easily oxidizable organic carbon (POXC) provides insight into the active fraction of soil organic matter (Table 2). Our study revealed a clear trend in the POXC levels across different cover crops and soil types. Particularly noteworthy were the higher values observed in both broad bean in Arenosol soil, and black oat in Chernozem soil. This outcome suggests that these leguminous crops, known for their root exudates and symbiotic relationships, may contribute to enhanced organic carbon input into the soil [53], consequently elevating POXC levels. These findings shed light on the intricate dynamics of soil carbon enrichment, underlining the significance of both crop selection and soil type in influencing POXC levels. Considering the soil health assessment, the electrical conductivity (EC) assesses the concentration of salts (predominantly negatively charged ions like -NO3 and -SO4) in the soil–plant systems. EC is suggested as an appropriate indicator of soil fertility by [54], indicating a positive correlation with soil salinity and measurable ion content. Our investigation into EC levels (Table 2), among different cover crops and within the distinct context of Luvisol soil, supported those observations. Conversely, Ethiopian mustard crops displayed the lowest EC levels within the same Luvisol soil. This divergence could be attributed to mustard’s inherent characteristics, potentially involving its root system architecture and nutrient utilization strategies. Our results emphasize the capacity of various cover crops to influence soil salinity dynamics, with broad beans potentially contributing to elevated EC values; while Ethiopian mustard appears to have a mitigating effect, the grower can benefit from a thoughtful selection of cover crops based on their specific attributes.
In the context of our study, the Pearson’s correlation coefficients illuminated valuable insights into the relationships between various key variables in (Figure 4). Notably, the analysis demonstrates that the mean percentage of mycorrhizal colonization (MYCO%) exhibits substantial positive but low correlations with both FDA hydrolysis and electrical conductivity (EC), with correlation coefficients of 0.31 and 0.36, respectively [55,56]. These correlations suggest that higher mycorrhizal colonization levels indicate increased microbial activity, as measured by FDA hydrolysis and higher soil salinity and as indicated by elevated EC values. This is in line with the results of previous studies, such as [57], who observed similar relationships between mycorrhizal colonization and microbial activity. However, further studies are needed to confirm this.
Additionally, the mean permanganate oxidizable organic carbon (POXC) content showcases noteworthy negative correlations with FDA activity (r = −0.48) and EC (r = −0.25). This indicates that, as POXC content decreases, microbial activity tends to rise, as evidenced by higher FDA hydrolysis values. Furthermore, a higher level of electrical conductivity corresponds to lower levels of POXC, a trend consistent with the work of [58].

4.2. Cover Crop Biomass Influencing the Growth of Main Crop

Following the cultivation of three individual cover crops, and the subsequent planting of corn, our study investigated various parameters to assess their effects on the subsequent corn growth (Figure 5). Corn biomass exhibited significant variation among the different cover crop treatments. These results align with those of [59], who also observed an impact on the subsequent crop biomass [60], and discovered in their study that the corn yields resulting from the use of sunflower, spectabilis and jungle bean were approximately 10% higher. Additionally, the bean cover crop also led to the highest total leaf N % content in corn (Figure 5), indicating its superior ability to enhance organic nitrogen availability and uptake in the subsequent crop. The significant increase in total leaf N observed with beans as a cover crop is mainly due to their ability to fix atmospheric nitrogen through symbiosis with N-fixing bacteria such as Rhizobium. This process enriches the soil with organic nitrogen, which becomes available for subsequent crops. Similarly, nitrate levels in the soil showed significant differences for the corn planted after broad bean, consistent with the findings by [54,60]. Corn plants have a high demand for nitrogen during their growth. If the cover crops, with higher C/N ratios, immobilized nitrogen in the form of organic matter, the corn might have utilized the available nitrate from the soil to meet its nitrogen requirements [61]. However, a notable divergence emerged in mycorrhizal colonization, as the control pots without prior cover crops displayed significantly lower mycorrhizal activity. In the absence of a cover pre-crop treatment, there might be lower microbial activity and corn plants may face challenges in nutrient availability and uptake [62]. This discrepancy supports the outcomes of [63], underscoring the intricate relationships between plant–microbe associations. Enzyme activity analysis unveiled an intriguing pattern, with the FDA hydrolysis activity significantly elevated in samples previously exposed to broad beans as cover crops. This enhancement in microbial activity corresponds to the findings of [64], emphasizing the role of leguminous crops in fostering microbial processes. Permanganate oxidizable carbon (POXC) analysis revealed a significant increase in POXC levels for the soil of corn planted after broad bean cover crop treatment, distinguishing them from the control treatments This trend resonates with the investigations conducted by [65], highlighting the potential of specific cover crops to influence soil organic carbon content. Collectively, these findings contribute to our understanding of the complex dynamics governing cover crop effects on subsequent corn growth and soil health parameters.
In examining the correlations (Figure 7), a noteworthy negative correlation was identified between electrical conductivity (EC) and soil organic carbon content (POXC). This aligns with the findings of [66]and [67,68], emphasizing the adverse impact of salinity on both crop growth and organic carbon levels in the soil. The observed negative correlation supports the work of [63], underscoring the intricate interplay among mycorrhizal associations, biomass production and soil salinity.
Furthermore, a significant correlation was uncovered between nitrate levels and soil salinity, consistent with the research by [69]. This correlation underscores the influence of nitrate availability on both crop growth and soil salinity dynamics. These findings align with the observations made by [70], elucidating the intricate connections between nitrate-mediated microbial processes, salinity and crop growth.

5. Conclusions

This study highlights the critical role of cover crops and soil treatments in improving soil quality, enhancing plant growth and promoting sustainable agriculture. It shows the impact of specific cover crops on soil properties and subsequent corn growth, revealing that the symbiosis status of cover crops is an efficient tool for improving biomass production and leaf total nitrogen content across different soil types. However, symbiosis was less efficient in low-quality Arenosol soil and highly fertile Chernozem soil but produced the greatest mycorrhiza status in Luvisol soil, reflecting that plant–mycorrhiza interactions depend on mutual demand and soil conditions. The legume cover crop, broad beans (Vicia faba), showed the best biomass production across all soils, emphasizing the need for tailored cover crop selection. Mycorrhizal colonization patterns revealed that broad beans fostered the highest mycorrhizal activity. Soil enzymatic activity analysis, specifically FDA hydrolysis, highlighted enhanced soil microbial activities, suggesting FDA assessment as a parameter of soil microbial status. This study confirms the importance of customizing cover crop selections based on soil types, elucidating direct correlations between cover crop types, soil characteristics and subsequent corn growth. Economic considerations reveal that cover crops can lead to cost savings through reduced synthetic fertilizer use and improved crop yields, enhancing overall agricultural profitability. Future research should further explore these economic aspects to provide a comprehensive understanding of the benefits of cover crops. Additionally, this study did not address all potential variables influencing cover crop effectiveness, such as long-term impacts, pest and disease dynamics, or specific economic analyses. Future research should investigate the long-term effects of cover crops on soil health and crop yields, examine their role in pest and disease management, conduct comprehensive economic analyses to quantify cost savings and profitability and explore the impact of different cover crop mixtures and their interactions with various soil types.

Author Contributions

Conceptualization, B.B. and K.J.; methodology, B.B. and K.J.; software, F.K.; validation, B.B., K.J. and S.K.; formal analysis, F.K. and S.K.; investigation, S.K., K.J., E.P., E.T. and F.K.; resources, K.J.; data curation, F.K., S.K. and B.B.; writing—original draft preparation, S.K.; writing—review and editing, B.B. and K.J.; visualization, F.K.; supervision, B.B. All authors have read and agreed to the published version of the manuscript.

Funding

Support of S.K., as Stipendium Hungaricum Scholarships of Tempus Public Foundation, based on 81/2015. (III. 31.) directive of Hungarian Government are highly acknowledged.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

Data available upon request due to restrictions, e.g., privacy or ethical. The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to the intensive practical application for industrial development and potential support.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

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Figure 1. Cumulative effect of three different soil types (A) and three different cover crops (B) on the dry biomass production of used cover crops. Results of the multiple comparisons of the GLM are indicated by the letters above the graph. Statistical significance was determined using Tukey’s post hoc test (p < 0.05).
Figure 1. Cumulative effect of three different soil types (A) and three different cover crops (B) on the dry biomass production of used cover crops. Results of the multiple comparisons of the GLM are indicated by the letters above the graph. Statistical significance was determined using Tukey’s post hoc test (p < 0.05).
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Figure 2. Effect of soil types on dry biomass of cover crops (A) and leaf total N % content (B). Uppercase letters indicate differences between the three soil types for the same cover crop. Lowercase letters indicate differences between different cover crops within the same soil type. Statistical significance was determined using Tukey’s post hoc test (p < 0.05).
Figure 2. Effect of soil types on dry biomass of cover crops (A) and leaf total N % content (B). Uppercase letters indicate differences between the three soil types for the same cover crop. Lowercase letters indicate differences between different cover crops within the same soil type. Statistical significance was determined using Tukey’s post hoc test (p < 0.05).
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Figure 3. Effect of soil types on mycorrhizal colonization (MYCO%) of cover crop species. Results of the multiple comparisons of the GLM are indicated by the letters above the graph. Statistical significance was determined using Tukey’s post hoc test (p < 0.05).
Figure 3. Effect of soil types on mycorrhizal colonization (MYCO%) of cover crop species. Results of the multiple comparisons of the GLM are indicated by the letters above the graph. Statistical significance was determined using Tukey’s post hoc test (p < 0.05).
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Figure 4. Pearson’s correlation matrix between studied soil parameters. Correlations are shown in blue (positive) and red (negative); the intensity of the color is proportional to the correlation coefficient. Statistically significant correlations are indicated as follows: ** p < 0.01, and *** p < 0.001.
Figure 4. Pearson’s correlation matrix between studied soil parameters. Correlations are shown in blue (positive) and red (negative); the intensity of the color is proportional to the correlation coefficient. Statistically significant correlations are indicated as follows: ** p < 0.01, and *** p < 0.001.
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Figure 5. Effects of different cover crop treatments in subsequent corn phase on the fluorescein diacetate (FDA) activity (A), the permanganate oxidizable organic carbon (POXC) level (B) of the soils, the mycorrhizal colonization (MYCO%) of corn (C), the soil mineral nitrogen (Nmin) level (D), corn dry biomass (E) and leaf total N% (F). Results of the multiple comparisons of the GLM are indicated by the letters above the graph. Statistical significance was determined using Tukey’s post hoc test (p < 0.05).
Figure 5. Effects of different cover crop treatments in subsequent corn phase on the fluorescein diacetate (FDA) activity (A), the permanganate oxidizable organic carbon (POXC) level (B) of the soils, the mycorrhizal colonization (MYCO%) of corn (C), the soil mineral nitrogen (Nmin) level (D), corn dry biomass (E) and leaf total N% (F). Results of the multiple comparisons of the GLM are indicated by the letters above the graph. Statistical significance was determined using Tukey’s post hoc test (p < 0.05).
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Figure 6. Effect of cover crops on the partial η2 value of the dependent variables with their 90% confidence interval. Partial η2 is classified and presented in green (low effect size), medium yellow (medium effect size) and red (large effect size).
Figure 6. Effect of cover crops on the partial η2 value of the dependent variables with their 90% confidence interval. Partial η2 is classified and presented in green (low effect size), medium yellow (medium effect size) and red (large effect size).
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Figure 7. Pearson’s correlation matrix between soil parameters. Correlations are shown in blue (positive) and red (negative); the intensity of the color is proportional to the correlation coefficient. Statistically significant correlations are indicated as follows: * p < 0.05 and ** p < 0.01.
Figure 7. Pearson’s correlation matrix between soil parameters. Correlations are shown in blue (positive) and red (negative); the intensity of the color is proportional to the correlation coefficient. Statistically significant correlations are indicated as follows: * p < 0.05 and ** p < 0.01.
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Table 1. Some physico-chemical characteristics of three soil types used in the experiment.
Table 1. Some physico-chemical characteristics of three soil types used in the experiment.
Soil TypesTexturepHSOM (%)Bulk Density (g/cm)Water-Holding Capacity (%)Replication
First/Second Experiment
ArenosolsSand7.461.161.65154/-
ChernozemsLoam7.352.811.35404/-
LuvisolsClay loam4.911.641.25504/8
Table 2. The effect of cover crop and soil type interactions on fluorescein diacetate activity (FDA), permanganate oxidizable carbon (POXC) and electrical conductivity (EC) of soils. Statistically significant differences between treatments shown by the post hoc test.
Table 2. The effect of cover crop and soil type interactions on fluorescein diacetate activity (FDA), permanganate oxidizable carbon (POXC) and electrical conductivity (EC) of soils. Statistically significant differences between treatments shown by the post hoc test.
Cover CropsSoil ParametersType of SoilsTukey’s Post Hoc Test
Broad Bean
(Vicia faba)		FDAArenosol10.68 ± 1.10 ab
Chernozem12.29 ± 1 2.2 b
Luvisol8.87 ± 8.87 a
ECArenosol86.90 ± 6.15 a
Chernozem122.25 ± 17.34 ab
Luvisol202.00 ± 82.51 b
POXCArenosol461.11 ± 107.29 b
Chernozem187.16 ± 72.00 a
Luvisol90.72 ± 35.71 a
Ethiopian Mustard
(Brassica carinata)		FDAArenosol6.79 ± 2.36 a
Chernozem10.64 ± 3.89 a
Luvisol5.84 ± 1.61 a
ECArenosol78.82 ± 7.33 ab
Chernozem85.40 ± 6.69 b
Luvisol66.00 ± 10.62 a
POXCArenosol76.03 ± 56.69 a
Chernozem373.28 ± 34.92 b
Luvisol254.73 ± 31.74 c
Black Oat
(Avena strigosa)		FDAArenosol8.34 ± 2.56 a
Chernozem16.27 ± 3.79 b
Luvisol5.12 ± 0.28 a
ECArenosol70.75 ± 4.69 a
Chernozem103.25 ± 8.14 b
Luvisol111.25 ± 23.22 b
POXCArenosol62.47 ± 31.62 a
Chernozem459.27 ± 94.99 b
Luvisol52.03 ± 32.92 a
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MDPI and ACS Style

Kabalan, S.; Kovács, F.; Papdi, E.; Tóth, E.; Juhos, K.; Biró, B. Residues of Symbiont Cover Crops Improving Corn Growth and Soil-Dependent Health Parameters. Agriculture 2024, 14, 1601. https://doi.org/10.3390/agriculture14091601

AMA Style

Kabalan S, Kovács F, Papdi E, Tóth E, Juhos K, Biró B. Residues of Symbiont Cover Crops Improving Corn Growth and Soil-Dependent Health Parameters. Agriculture. 2024; 14(9):1601. https://doi.org/10.3390/agriculture14091601

Chicago/Turabian Style

Kabalan, Sundoss, Flórián Kovács, Enikő Papdi, Eszter Tóth, Katalin Juhos, and Borbála Biró. 2024. "Residues of Symbiont Cover Crops Improving Corn Growth and Soil-Dependent Health Parameters" Agriculture 14, no. 9: 1601. https://doi.org/10.3390/agriculture14091601

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