Impact of soil and cropping management on soil quality bioindicators and soybean yield

ABSTRACT

The no-till system provides an environment for improved soil life and higher crop production. However, disregarding its fundamental principles can compromise its effectiveness. This study evaluated the effects of soil management practices (disc plow, moldboard plow, chisel plow, and no-till) and cropping systems (maize monocropping, maize intercropped with Urochloa ruziziensis, U. ruziziensis intercropped with Crotalaria ochroleuca, and a mixed cover crops) on soil quality bioindicators and soybean production. The hypothesis was that the quantity and quality of plant residues, combined with reduced soil disturbance, would enhance biological activity and crop yield. The experiment was arranged in a split-plot factorial design. Soil samples were collected from the 0–10-cm layer before sowing and at soybean flowering to evaluate soil microbiological components such as microbial biomass carbon, metabolic quotient, and microbial respiration. Soybean production components following the soil management and cropping systems were analyzed. Data were subjected to analysis of variance (p ≤ 0.05), means were compared using Tukey’s test, and Pearson’s correlation was performed among the analyzed variables. Soil management practices with less soil disturbance, such as moldboard plowing, scarification, and no-till, exhibited lower biological soil disturbance and higher soybean yield. Cropping systems with quantity and quality of residues, such as the intercropping of U. ruziziensis with C. ochroleuca, stood out for their positive effects on soybean yield.

Key words
mixed cover crops; microbial biomass carbon; metabolic quotient

INTRODUCTION

The ideal soil management strategy aims to provide favorable plant growth and development conditions by addressing chemical, physical, and biological soil attributes (Proner Júnior et al. 2022). It is possible to reverse soil degradation and promote conservation through conservation practices, thus improving cropping systems. To achieve this, it is essential to identify the specific characteristics of each region and apply management practices appropriately.

In the Cerrado, grain production is primarily based on soybean cultivation during the summer and maize and cotton during the autumn–winter, using direct seeding with low soil disturbance. In the southern region of Mato Grosso do Sul, Brazil, where this study was conducted, the autumn–winter period is characterized by prolonged droughts and low temperatures, including occasional frosts (Fietz et al. 2017). In this context, cultivating green manures and cover crops, either as monocrops or in intercropping systems, plays a crucial role in no-tillage systems, enhancing the sustainability of cropping systems.

The large amount of plant residue produced by species such as Urochloa ruziziensis and Crotalaria provides effective soil cover, reducing erosion, temperature variations, and the emergence of weed species. These residues also contribute to nutrient cycling and nitrogen input via biological nitrogen fixation by leguminous plants (Silva et al. 2021), ultimately leading to increased crop production.

The association of different root systems, as in the intercropping and mixed cover crops, allows for varied exploration of the soil profile. This promotes aggregate formation, enhances water percolation, and increases the production of rhizosphere exudates, thereby providing a greater supply of substrates for microorganisms and supporting the diversity of soil biota (Balota 2018).

In addition, after decomposition and mineralization, root biomass can enhance soil fertility by redistributing nutrients to deeper layers. In no-tillage systems, the absence of soil disturbance, combined with repeated chemical fertilization and the surface accumulation of plant residues, promotes the mineralization of organic materials, leading to a nutrient concentration gradient. This process results in nutrient accumulation within the top 10 cm of soil (Kurihara et al. 2014). In this context, mechanical intervention combined with cover crops may represent a strategy to improve nutrient distribution within the soil profile.

Therefore, interventions in no-tillage systems aim to overcome challenges related to nutrient stratification within the soil profile. Therefore, this study aimed to evaluate the effects of different soil management practices with cover crops on soil quality bioindicators and soybean yield. The goal was to identify practices that minimize negative impacts on the soil. The hypothesis was that greater cover crop diversity and reduced soil disturbance would promote higher biological activity and, consequently, greater crop production.

MATERIAL AND METHODS

Site characterization and experimental design

The experiment was conducted at the experimental station of Embrapa Western-Region Agriculture, located in the municipality of Dourados, Mato Grosso do Sul state, at 22°13’ S and 54°48’ W, and altitude of 408 m. According to Köppen’s classification, the climate of the region is Cwa, with hot summers and dry winters (Fietz et al. 2017). The soil is classified as Latossolo Vermelho Eutroférrico, with a very clayey texture. For site characterization, a soil chemical analysis was performed in October 2019 (Table 1) before the implementation of treatments. Because of the phosphorus gradient, another chemical analysis was conducted in October 2022 after the implementation of soil management treatments (Table 2).

Table 1
Chemical attributes and particle size distribution of the soil in the experimental area, assessed in the 0–0.10, 0.10–0.20, and 0.20–0.30 m soil layers, Dourados, MS, Brazil, 2020.

Layer (m)	pH (CaCl2)	K	Ca	Mg		P (Mehlich-1)		Cu	Fe	Mn	Zn
		----- cmol·dm-3 ----				---------- mg·dm-3 -------------
0–0.10	5.35	0.47	4.92	1.62		52.65		10.00	28.24	62.28	4.98
0.10–0.20	4.69	0.29	2.47	0.91		9.58		10.51	33.65	30.58	0.71
0.20–0.30	4.71	0.20	2.20	0.85		4.48		9.94	37.31	26.64	0.36
	C	m	BS	CEC		ECEC		Sand	Silt	Clay
	g•kg	(%)	(%)	----mmol·dm-3 -----				--------- g·kg-1 ----------
0–0.10	19.61	1.94	57.79	12.13		7.21		140	364	496
0.10–0.20		1.93	36.51	10.05		4.15		114	222	663
0.20–0.30		1.26	35.95	9.04		3.67		113	234	652

• C: organic carbon content; m: aluminum saturation; BS: base saturation; CEC: cation exchange capacity; ECEC: effective cation exchange capacity.

Table 2
Soil phosphorus content in the 0–0.10, 0.10–0.20, 0.20–0.30, and 0.30–0.40 m soil layers after soil management practices, October 2022, Dourados, MS, Brazil*.

Management		Disc plow	Moldboard plow	Chisel plow	No-tillage
Layer		------------------------------------ mg·dm-3 ----------------------------------
0.10		41.3 AB	34.6 B	34.6 B	42.8 A
0.20		21.3 A	22.3 A	20.1 A	16.1 A
0.30		11.8 A	10.7 A	10.8 A	8.8 A
0.40		5.9 A	3.8 A	5.6 A	4.6 A
Average		20.1	17.9	17.8	18.1

• ^*
  Means followed by the same letter in the row do not differ according to Tukey’s test (p < 0.05).

Precipitation and temperature data for the experimental period, from October 2022 to March 2023, were obtained from the meteorological station of Embrapa Western-Region Agriculture (Fig. 1).

Figure 1
Precipitation (bars) and daily maximum and minimum temperatures (lines) averaged over 10-day intervals during the 2022/2023 soybean season. Soil sampling was carried out at pre-sowing and during soybean flowering.

The experimental design was a randomized complete block with a split-plot scheme and three replications. Each plot measured 27 meters in length and 12 meters in width. The plots were assigned to the following soil management practices:

• Disc plow;

• Moldboard plow;

• Chisel plow;

• No-tillage.

The subplots were assigned to four autumn–winter cropping systems:

• Maize monocropping;

• Maize intercropped with Urochloa ruziziensis;

• U. ruziziensis intercropped with Crotalaria ochroleuca;

• Mixed cover crops (maize + U. ruziziensis + C. ochroleuca + C. spectabilis + Raphanus sativus).

Area history

The area has been under a no-tillage system since 2010, with soybean grown in the summer and maize in the autumn–winter season. In March 2020, detecting a phosphorus gradient in the soil (Table 1) motivated mechanical intervention.

On March 5, 2020, 4 tons per hectare (t.ha^-1) of dry dolomitic lime were applied to the experimental area to raise base saturation to 70% in the 0–0.20-m soil layer, along with an additional 3 t.ha^-11 of gypsum. These limestone and gypsum were incorporated using the three soil management practices evaluated in this study. The moldboard plow, equipped with two moldboards, operated at a depth of 0.25 m; the disc plow with three discs at 0.17 m; and the chisel plow with seven shanks at 0.27 m. All implementations were pulled by a New Holland TM 7010 tractor with 139 hp. In no-tillage, fertilizers were left on the soil surface.

During the autumn–winter of 2020 and 2021, maize was cultivated intercropped with Urochloa, and soybean was grown during the 2020/2021 and 2021/2022 summer seasons. The study was initiated in March 2022, 24 months after the soil tillage operations. A soil chemical analysis assessed the phosphorus gradient (Table 1), which showed reduced differences among soil management practices. Differences remained only in the 0–0.10-m layer (Table 2), although all treatments exhibited high phosphorus levels within the 0–0.30-m soil layer.

Cover crops were established in the 2022 season, and soybean was sown in the 2022/2023 season. The hybrid maize cultivar K7500VIP3 was sown on February 23, 2022, at the density of 60,000 plants.ha^-1, without fertilization. Emergence occurred on March 4, and pest control was conducted with two applications of acephate (0.8 L.ha^-1 of active ingredient) 15 days after emergence.

In the maize + U. ruziziensis intercropping system, U. ruziziensis seeds were distributed using the third seed box of the planter, adjusted to a target density of 10 plants per square meter. The C. ochroleuca and U. ruziziensis seeds were mixed and distributed via the third seed box under an intercropping system. In the mixed cover crops treatment, maize was sown using its designated seed compartment, while forage radish seeds were added to the third box. All species were sown in a single operation along with maize, using seeding rates of 4 kg.ha^-1 for U. ruziziensis, and 2 kg.ha^-1 for C. ochroleuca and R. sativus.

Row spacing for all treatments was 0.45 m. Maize seeds were sown at a depth of 0.05 m, while the other species were broadcast in front of the cutting disc and partially incorporated into the soil by the planter mechanism.

In the treatments with maize monocropping and maize intercropped with U. ruziziensis, atrazine (1.5 L.ha^-1 of active ingredient) was applied at the V5 growth stage for weed control. In June 2022, a 1-m² sample of the autumn–winter crops (maize, U. ruziziensis, C. ochroleuca, and R. sativus) was collected to determine the dry matter.

The plants were separated by species, identified, oven-dried at 60°C until constant weight, and then weighed to determine dry matter. The mixed cover crops and Crotalaria + U. ruziziensis intercropping treatments were mowed and left on the soil surface within the experimental plots.

The experimental area was desiccated with glyphosate at a rate of 1.44 kg.ha^-1 of acid equivalent on October 4, 2022, and soybean cultivar BRS 1061IPRO was sown on October 24 using an SHM 15/17 planter (Semeato brand) at a planting density of 288,888 plants.ha^-1. The seeds were inoculated with Bradyrhizobium japonicum at a rate of 0.12 L of commercial product per 50 kg of soybean seeds. Fertilization at sowing consisted of 300 kg.ha^-1 of the NPK 00-20-20 formula.

Two insecticide applications were performed for insect pest control. The first used lambda-cyhalothrin at 0.35 L.ha^-1 with 0.25% natural oil, and the second used imidacloprid and bifenthrin at a 0.4 L.ha^-1 rate. For disease control, fluxapyroxad and pyraclostrobin were applied at a rate of 0.35 L.ha^-1 with 0.25% vegetable oil.

Microbiological attributes evaluations

Soil samples for evaluating microbiological attributes were collected at the pre-sowing stage, representing a relatively dry environment. A second sampling was performed at soybean flowering (R2 stage), when microbial activity was expected to be higher in the presence of the crop, using a Dutch auger at the 0–10-cm soil layer. Four subsamples were collected per plot, alternating between the central planting rows. The four subsamples were homogenized to form one composite sample per plot. The samples were sieved through a 2-mm mesh in the laboratory and stored in a cold chamber at approximately 7°C. Analyses were performed the following day at the Soil Microbiology Laboratory of Embrapa Western-Region Agriculture.

Soil microbial biomass carbon (MBC) was analyzed using the fumigation–extraction method proposed by Vance et al. (1987) and Tate et al. (1988), with a correction factor for extraction efficiency K(ec) = 0.33. Basal soil respiration, or microbial activity (C-CO₂), was determined using the respirometry method. The metabolic quotient (qCO₂) was calculated as the ratio of soil C-CO₂ flux to MBC content (Anderson and Domsch 1993), and the microbial quotient (qMIC) was calculated as the ratio of MBC to total organic carbon.

Phytotechnical evaluations in soybean

Three soybean plants were collected from the usable area of each plot at the beginning of grain filling (R5 stage), using a spade to ensure retrieval of the root system down to a depth of 0.10 m. Plant height, number of leaves + petioles, and stems + pods were measured. Nodules were removed with tweezers, counted, and placed in a forced-air oven at 60°C for 72 hours to determine dry matter.

At soybean maturity, two rows measuring three meters in length and spaced 0.45-m apart were manually harvested from the usable area of each plot and threshed using a Wintersteiger plot combine. The 100-grain mass was measured, and grain yield was calculated with moisture content adjusted to 13%. Yield components were evaluated in 10 plants per plot, recording the number of grains per pod and plant.

Data were subjected to analysis of variance (p ≤ 0.05), and means were compared using the Tukey’s test with the SISVAR statistical software (Ferreira 2011). Pearson correlation analyses were performed among the variables using the SAEG software (SAEG 2007).

RESULTS AND DISCUSSION

Dry matter of cover crops

The highest total dry matter was produced by the maize + U. ruziziensis intercropping, which outperformed both the mixed cover crops and the U. ruziziensis + Crotalaria intercropping, but it did not differ significantly from maize monocropping (Table 3), highlighting the effectiveness of maize as a cover crop. No significant effect of the soil management system was observed on the dry matter of cover crops.

Table 3
Dry matter production of cover crops in June 2022, Dourados, MS, Brazil*.

Soil management	Maize	Urochloa ruziziensis	Crotalaria	Others dm	Total
	----------------------------- kg·ha-1 -----------------------------
Moldboard plow	6,943	753	699	51	8,446ns
Disc plow	5,754	706	713	74	7,246
Chisel plow	7,317	415	782	85	8,600
No-tillage	6,760	894	513	66	8,232
Crop management
Maize	10,08	-	-	703.5	10,785 ab
Maize + Brachiaria	11,36	939	-	16.3	12,316 a
U. ruziziensis + Crotalaria	-	1,06	1,14	50.2	2,249 c
Mixed cover crops	5,342	979	64	789.2dm	7,174 b
Average					8,131.0
C. V. (%)					43.26

• ^*
  Means followed by the same letter in the row do not differ according to Tukey’s test (p ≤ 0.05);
• ^ns
  not significant;
• ^dm
  dry matter Raphanus sativus + weeds, C. V.: coefficient of variation.

The high dry matter production in the maize + U. ruziziensis intercropping system, combined with the high C/N ratio of plant residues from both species, contributes to the slower decomposition and mineralization of these residues. This process results in more persistent soil cover (Silveira et al. 2020), which helps retain moisture, promotes the formation of stable aggregates, and consequently enhances yield.

These characteristics, such as high dry matter production, were evident in the mixed cover crops, in which maize accounted for 74% of the total dry matter. The lower dry matter of C. ochroleuca may be attributed to interspecies competition in mixed cropping systems, which can limit its growth. Additionally, the mixed cover crops and the U. ruziziensis + C. ochroleuca intercropping were mowed before flowering, preventing the plants from fully developing. Nevertheless, the amount of residue produced, especially by U. ruziziensis, followed early management (although not quantified in this study). This likely reduced weed incidence (Silva et al. 2023) and promoted nutrient cycling due to the diversity of plant residues and the early mowing conducted in May.

The early mowing to remove volunteer soybean plants and weeds in the mixed cover crops and the U. ruziziensis + C. ochroleuca intercropping system reduced the dry matter produced by the intercropping. However, the legume remains an important cover crop option due to its efficiency in biological nitrogen fixation, which results in rapid decomposition and mineralization in the soil environment (Silva et al. 2021).

Even when producing less dry matter, the association of Crotalaria with a grass species can still benefit the production system. After mowing at flowering, high-quality residues are deposited on the soil surface, which may enhance biological activity (Araújo et al. 2019). Meanwhile, U. ruziziensis continues root growth, promoting more efficient water infiltration as its residues function as soil cover.

Microbiological characteristics

The evaluated variables were influenced only by soil management practices, with no effect of cropping systems at either pre-sowing or flowering (Tables 4, 5 and 6).

Table 4
Mean square of the residual for microbial biomass carbon (C-MBC), microbial respiration (C-CO₂), metabolic quotient (qCO₂), and microbial quotient (qMIC), at pre-sowing and soybean flowering (R2).

Sources of variation	Pre-sowing
	DF	C-MBC	C-CO2	qCO2	qMIC (%)
		(µg·C·g-1)	(µg·g-1·day-1)	(µ·CO2/µg·C-mic·h-1)
Soil (S)	3	2,197ns	39.02ns	224.25*	0.052ns
Crop (C)	3	5,156ns	12.57ns	74.69ns	0.174*
S × C	3	1,960ns	33.6*	80.68ns	0.096ns
Residue	30	2,743	14.35	27.41	0.063
Average		229.9	13.75	29.29	1.181
C. V. (%)		227.8	27.55	25.86	21.40
Sources of variation	Flowering (R2)
	DF	C-MBC	C-CO2	qCO2	qMIC
Soil (S)	3	4,573ns	111.91*	139.86ns	0.129*
Crop (C)	3	1,664ns	25.18ns	330.1ns	0.004ns
S × C	3	1,055ns	50.78ns	251.15ns	0.036ns
Residue	30	1,341	32.1	264.8	0.027
Average		197.2	31.07	41.15	16.49
C. V. (%)		18.57	18.23	39.54	16.49

• ^ns
  not significant, according to the F-test;
• ^*
  significant at p ≤ 0.05, according to the F-test; DF: degrees of freedom; C. V.: coefficient of variation.

Table 5
Mean square of the residue for plant height (PH), dry mass of stems (DMS), dry mass of leaves (DML), total dry mass (TDM), number of nodules (NN), nodule mass (NM), pods per plant (PPP), number of grains per plant (NGP), internode length (IL), hundred-grain mass (HGM), and soybean yield (YLD).

Sources of variation	DF	PH	DMS	DML	TDM
Soil (S)	3	29.16ns	783,093.18ns	1,246,772.72ns	3,974,949.23ns
Crop (C)	3	71.83*	859,159.52ns	816,185.46ns	3,202,419.84ns
S × C	3	7.07ns	969,220.02ns	499,387.7ns	2,680,323.7ns
Residue	30	16.77	579,280.64	666,551.82	2,297,824.99
Average		0.981	3,310	3,130	6,440
C. V. (%)		4.17	22.99	26.08	23.54
Sources of variation	DF	NN	NM	PPP	NGP
Soil (S)	3	1,314.24ns	14,420.17ns	2.52ns	50.02ns
Crop (C)	3	457.87ns	14,392.97ns	125.96ns	1,053.35ns
S × C	3	345.47ns	12,403.43ns	132.89ns	696.17ns
Residue	30	689.74	20,697.43	81.88	491.28
Average		73.77	415.7	45.71	112.4
C. V. (%)		35.6	34.61	19.79	19.72
Sources of variation	DF	IL	HGM	YLD
Soil (S)	3	0.05ns	1.16ns	131,050
Crop (C)	3	0.25ns	4.46ns	172,238
S × C	3	0.12ns	0.27ns	64,752
Residue	30	0.12	0.37	50,337
Average		0.075	0.015	3,589
C. V. (%)		13.9	3.86	6.25

• ^ns
  not significant, according to the F-test;
• ^*
  significant at p ≤ 0.05, according to the F-test; DF: degrees of freedom; C. V.: coefficient of variation.

Table 6
Microbiological attributes in the 0–0.10-m soil layer after soil and autumn–winter crop management, assessed at soybean pre-sowing (PS) and flowering (R2) stages, Dourados, MS, Brazil, 2023*.

Soil management	MBC (µg·C·g-1)		C-CO2 (µg·g-1·day-1)	qCO2 (µ·CO2/µg·C-mic·h-1)		qMIC (%)
	PS	R2	R2	PS	R2	PS	R2
Disc plow	228.3 a	179.6 b	15.30 b	34.52 a	41.57 a	1.26 a	1.03 ab
Moldboard plow	219.9 a	187.0 ab	16.30 ab	24.30 b	36.48 a	1.15 a	0.85 b
Chisel plow	221.9 a	198.0 ab	19.28 ab	30.54 ab	44.69 a	1.11 a	1.09 a
No-tillage	249.5 a	224.2 a	22.04 a	27.81 ab	41.87 a	1.19 a	1.04 a
Crop management
Off-season maize	223.2 a	190.14 a	20.07 a	32.52 a	48.45 a	1.07 a	0.98 a
Maize + U. ruziziensis	260.0 a	193.26 a	17.53 a	27.59 a	37.91 a	1.30 a	1.03 a
U. ruziziensis + Crot-alaria	224.0 a	214.79 a	18.61 a	27.09 a	36.83 a	1.08 a	1.00 a
Mixed cover crops	212.5 a	190.78 a	17.53 a	29.98 a	41.42 a	1.26 a	1.00 a
Average	229.9	197.24	18.23	29.29	41.15	1.18	1.00
C. V. (%)	22.7	18.5	31	25.8	39.5	21.4	16.4

• ^*
  Means followed by the same lowercase letter for each variable do not differ according to Tukey’s test (p ≤ 0.05); MBC: microbial biomass carbon; C-CO₂: microbial respiration; qCO₂: metabolic quotient; qMIC: microbial quotient.

The MBC and microbial respiration (C-CO₂) did not show significant differences between soil management practices in the pre-sowing phase (Table 6). However, at soybean flowering, microbial biomass and the microbial quotient were significantly higher in the no-tillage system compared to the disc plow (Table 6). This difference observed at flowering may be related to a series of factors associated with no-tillage, such as the maintenance of soil structure, greater accumulation of plant residues, and lower temperature oscillations, favoring a more stable environment conducive to microbial development (Ferreira et al. 2013).

Furthermore, flowering is a period with intense release of root exudates, which serve as a source of energy and carbon for soil microorganisms. In less disturbed systems, such as no-tillage, these compounds are better utilized by the soil microbiota (Dennis et al. 2010).

Lourente et al. (2011) reported similar values during the pre-sowing period of winter crops in the Cerrado region, with no significant differences between no-tillage and conventional tillage in areas with 10 years of no-tillage history.

In the present study, microbial community stability under the management conditions may have occurred, as soil tillage was implemented after 10 years of no-tillage, and cover crops were grown following successive cycles of summer soybean and off-season maize. In this context, supporting these findings, Gonçalves et al. (2019), when evaluating microbial biomass under no-tillage and conventional systems with crop successions, also found no statistical differences, suggesting a complex system that requires further investigation.

The metabolic quotient (qCO₂) was influenced by soil management only in the pre-sowing phase of soybean, showing higher values under disc plow than chisel plow. At flowering, the microbial quotient (qMIC) was affected only by soil management, without interaction with the cropping systems, with the chisel plow showing higher values (Tables 4 and 6). Despite these variations, except for the treatment with chisel plow, all qMIC values were above 1%, indicating the availability of organic carbon in the soil and, consequently, an active microbial population (Mendes et al. 2019).

The stability observed in qMIC values between phases can be attributed to adapting the microbiota to established agricultural systems, such as no-tillage and crop succession. Possibly, even in the face of disturbances caused by management practices, the ratio between microbial biomass and total organic carbon tended to remain stable, reflecting a state of microbial equilibrium (Spliethoff et al. 2023). On the contrary, qCO₂, which is more sensitive to immediate environmental changes, indicates higher respiratory activity and lower carbon use efficiency in more disturbed systems.

Ferreira et al. (2017) also observed higher qCO₂ values in conventional management systems, reinforcing that greater levels of soil disturbance promote increased microbial instability, reducing the metabolic efficiency of microorganisms and increasing CO₂ release.

At soybean flowering, the organic residues serving as substrates for microorganisms had declined, and the effects of soil management became more evident, with no-tillage showing superior results compared to disc plowing, although not differing significantly from moldboard plowing or chisel plowing.

Nascimento et al. (2009) showed that no-tillage promoted a greater accumulation of microbial biomass carbon than conventional tillage, a result attributed to the positive effects of different management practices on soil biological activity (Balota 2018). This result is attributed to the accumulation of plant residues, increased organic carbon input, aggregate stability, and moisture preservation factors that favor biological activity (Balota 2018).

For microbial respiration (C-CO₂), there was an interaction between soil management and cropping systems (Tables 4 and 7), but no significant differences were observed among the cropping treatments. Disc plowing in the U. ruziziensis + C. ochroleuca intercropping system resulted in higher values than moldboard plowing and no-tillage under the mixed cover crops treatment.

Table 7
Microbial respiration (C-CO₂) at 0–0.10-m depth in the soybean pre-sowing stage, as affected by soil management and autumn–winter cropping systems, Dourados, MS, Brazil, 2022/2023*.

Soil management	Crops
	MM	M+B	B+C	Mix
	C-CO2 (µg·g-1·day-1)
Disc plow	12.33 aA	13.16 aA	16.71 aA	19.87 aA
Moldboard plow	13.06 aA	15.79 aA	7.40 bA	9.54 bA
Chisel plow	15.47 aA	16.71 aA	11.9 6abA	15.03 abA
No-tillage	16.73 aA	12.94 aA	13.44 abA	9.87 bA

• PS: pre-sowing stage; R2: flowering stage; MM: maize monocropping; M+B: maize + U. ruziziensis; B+C: U. ruziziensis + Crotalaria; Mix: mixed cover crops;
• ^*
  means followed by the same lowercase letter in the column and uppercase letter in the row do not differ according to Tukey’s test (p ≤ 0.05).

The release of C-CO₂ results from the decomposition of organic residues and is influenced by residue quality and soil management (Pulrolnik 2009). This process can indicate efficiency or disturbance in the ecosystem, requiring careful evaluation. High respiratory activity may lead to nutrient loss and lower system stability (Gonçalves et al. 2019).

It is possible that soil management with disc plowing, characterized by fragmentation and removal of a substantial portion of surface plant residues, resulted in increased biological activity due to the smaller size of organic particles, which increased the surface area for microbial colonization and decomposition (Pulrolnik 2009). Despite the lower overall residue production (Table 2), the early mowing of the U. ruziziensis + C. ochroleuca intercropping and the mixed cover crops treatments contributed to the accumulation of high-quality residues on the soil surface. This is likely due to the combination of grasses and legumes, which presented varying C/N ratios (Balota 2018), enhancing residue decomposition and mineralization.

When correlating crop performance variables with soil microbiological attributes, a positive correlation was observed between cover crop residue and the metabolic quotient at the R2 stage (Table 8). Considering that all treatments included at least one grass species (maize or U. ruziziensis), which is characterized by a high C/N ratio and greater lignin content (Ziech et al. 2015), microbial activity was likely reduced (Cardoso and Andreote 2016), leading to limited microbial growth and higher metabolic quotient values (Balota 2018).

However, it may not be the quantity of residues but rather their quality, such as the high C/N ratio in maize and U. ruziziensis cultivation or the low C/N ratio of C. ochroleuca residues in the U. ruziziensis intercropping, that creates a favorable environment for soil microbial activity, ultimately influencing higher soybean yield.

The low correlation values observed are considered to have minimal influence. Therefore, cultivating grasses as cover crops remains an important option for sustaining direct seeding and the no-tillage system.

The qMIC refers to the ratio of microbial biomass carbon to total organic carbon and reflects the quality of organic matter. An increase in this variable indicates the addition of high-quality organic matter (Araújo et al. 2019, Balota 2018), and higher values indicate more biologically active organic matter (Dadalto et al. 2015).

When evaluating microbiological attributes under different management practices and land uses during winter and summer in a Cerrado area, Lourente et al. (2011) found no differences in microbial quotient across treatments in either season. This result was attributed to the short duration of only one year since implementing the different management systems, which may not have been sufficient to produce measurable differences.

Table 8
Pearson correlation coefficients (r) and significance levels between cover crop residues, 100-grain mass (100M), soybean yield, and soil microbiological attributes at pre-sowing (PS) and flowering (R2) stages, as well as the number of grains per plant (NGP), in Dourados, MS, Brazil, 2023.

	Residues			100M			Soybean yield
	r	Significance		r	Significance		r	Significance
Residues	-	-		0.5319	0.0000 *		-0.3068	0.0170 *
C-MBC (PS)	0.2051	0.081 ns		0.2456	0.0462 *		-0.1852	0.1038 ns
C-MBC (R2)	-0.1759	0.1419 ns		0.0954	0.2596 ns		0.3098	0.0160 *
C-CO2 (PS)	0.895	0.2726 ns		0.1291	0.1909 ns		0.0014	0.5000 ns
C-CO2 (R2)	0.1327	0.1844 ns		0.1505	0.1704 ns		0.2016	0.0847 ns
qCO2 (PS)	-0.0029	0.4923 ns		-0.0844	0.2841 ns		-0.1035	0.2420 ns
qCO2 (R2)	0.2408	0.0496 *		0.0986	0.4610 ns		-0.611	0.3399 ns
NGP	-0.3068	0.2242		-0.2946	0.0210 *		0.1719	0.1214 ns

• ^ns
  not significant;
• ^*
  significant at p ≤ 0.05; C-MBC: microbial biomass carbon; C-CO₂: microbial respiration; qCO₂: metabolic quotient.

The chisel plow and, especially, the no-tillage system are considered conservationist practices due to their lower soil disturbance (Conte et al. 2020), and preservation of plant residues showed higher microbial quotient values than moldboard plowing. Moldboard plowing did not differ from disc plowing or no-tillage at soybean flowering.

These results may be attributed to the accumulation of organic substrates provided by no-tillage and chisel plowing, which create ideal temperature and moisture conditions for microbial activity and result in greater availability of organic carbon. Disc plowing likely led to greater short-term incorporation of organic carbon into microbial biomass tissues (Dadalto et al. 2015). In contrast, moldboard plowing showed values below 1%, indicating a limiting condition. Similarly, Gonçalves et al. (2019) also reported higher qMIC values under no-tillage, highlighting the importance of organic residue accumulation on the soil surface.

Soybean response to soil and cropping management

When evaluating soybean agronomic traits, a significant effect was observed only for cropping systems on plant height. No significant differences were observed in the number or dry mass of nodules, stem, leaf, or total dry matter, and no interaction was detected between soil management and cropping systems (Tables 5 and 9).

Table 9
Plant height (PH), stem dry matter (SDM), leaf dry matter (LDM), total dry matter (TDM), number of nodules (NN), and nodule dry matter (NDM) of soybean grown in succession to autumn–winter crops, 2022/2023 season, in Dourados, MS, Brazil*.

Soil management	PH			SDM		LDM		TDM			NN		NDM
	(m)			------------kg·ha-1-----------------							n		(mg)
Disc plow	0.985 a			3,253 a		2,864 a		6,237 a			70.3 a		385.1
Moldboard plow	0.987 a			3,678 a		2,983 a		7,274 a			80.0 a		452.6
Chisel plow	0.995 a			3,218 a		3,076 a		6,294 a			60.6 a		387.2
No-tillage	0.959 a			3,091 a		3,595 a		5,955 a			84.0 a		437.8
Crop management
Maize	0.970 b			3,443 a		3,106 a		6,55 a			68.5 a		412.0
Maize + U. ruziziensis	0.969 b			2,941 a		2,784 a		5,726 a			78.9 a		430.6
U. ruziziensis + Crotalaria	1,018 a			3,299 a		3,224 a		6,523 a			68.3 a		369.4
Mixed cover crops	0.968 b			3,556 a		3,404 a		6,961 a			79.3a		450.7
Average	0.981			3,31		3,13		6,44			73.77		415.7
C. V. (%)	4.2			22.9		26.1		23.5			35.6		34.6

• ^*
  Means followed by the same letter in the column do not differ by Tukey’s test (p < 0.05); C. V.: coefficient of variation.

The intercropping of U. ruziziensis with Crotalaria, despite producing the lowest quantity of residues (Table 3), resulted in greater plant height, highlighting the importance of the quality of plant residues deposited on the soil surface. The subsequent soybean crop likely benefited from a more favorable growing environment, with faster nutrient release through decomposition and nitrogen input from C. ochroleuca via biological nitrogen fixation (Sousa et al. 2020).

In addition to the nitrogen input from biological fixation, the root system of legumes such as Crotalaria may have contributed to an increase in phosphorus-solubilizing bacteria, as well as higher soil levels of potassium and magnesium, which are essential for plant growth and development (Silva et al. 2022).

The low C/N ratio of C. ochroleuca favored rapid mineralization and nutrient cycling, benefiting the subsequent crop (Cardoso and Andreote 2016). Due to its slower decomposition, the grasses contribute with straw that persists on the soil surface for longer, helping retain moisture, suppress weeds, and support long-term carbon sequestration.

Forte et al. (2018), evaluating crop rotation systems, reported that maize grown after a black oat + common vetch intercropping showed higher ear insertion height, confirming these findings.

Nodule number and mass were not affected by the treatments. Fontaneli et al. (2000) reported similar results when comparing preceding crops and soybean nodulation in succession, with no significant differences in nodule dry matter, which ranged from 0.291 to 0.425 g per plant—values comparable to those found in the present study (0.369 to 0.450 g per plant). These results indicated high nodule dry matter and suggested efficient biological nitrogen fixation. Leaf, stem, and total dry matter (Tables 5 and 9), as well as the number of pods per plant, grains per pod, and internode spacing (Tables 5 and 10), were not influenced by the treatments and are likely more related to the genetic characteristics of the cultivar and the growing environment (Tagliapietra et al. 2022).

The 100-grain mass and soybean yield were influenced independently by soil management and cropping systems. Moldboard plowing and the maize + U. ruziziensis intercropping system showed superior results to disc plowing and to U. ruziziensis + C. ochroleuca intercropping, respectively. By partially inverting the soil, moldboard plowing contributes to greater aggregate stability and improved moisture retention compared to disc plowing. This enhanced moisture retention supports root development, as water availability strongly influences this variable (Tagliapietra et al. 2022).

The maize + U. ruziziensis intercropping system resulted in a higher 100-grain mass than the U. ruziziensis + C. ochroleuca intercropping, with no significant difference from maize monocropping or the mixed cover crops (Table 5 and 10). These results are consistent with the statistically significant correlations observed (Table 8), showing a positive influence of residue quantity (r = 0.53) and MBC at pre-sowing (r = 0.24) and a negative correlation between the number of grains per plant and 100-grain mass.

The high residue production from the maize + U. ruziziensis intercropping (Table 2) enhanced soil cover, forming a physical barrier against weeds (Araújo et al. 2019), reducing competition and preserving moisture. The fibrous root systems of both species promoted the formation of stable soil aggregates and the release of root exudates (Balota 2018), stimulating microbial activity and increasing MBC. This improved the soybean crop’s ability to utilize the growing environment and increased 100-grain mass.

The significant negative correlation between the number of grains per plant and 100-grain mass (r = -0.29) (Table 8) may be attributed to increased demand for photoassimilates. Plants with higher 100-grain mass tend to produce fewer grains per plant, allowing greater allocation of dry matter and nutrients to grain filling (Tagliapietra 2022).

The treatments influenced soybean yield (Tables 5 and 10). Moldboard plowing outperformed disc plowing, while the maize + U. ruziziensis intercropping was lower than the U. ruziziensis + C. ochroleuca intercropping, with no significant differences among the other treatments. Moldboard plowing causes less soil layer inversion, whereas disc plowing breaks down aggregates and increases disturbance, which increases the metabolic quotient (Tables 4 and 5). The increase in qCO2 reduced microbial efficiency in carbon use (Balota 2018), resulting in lower MBC at soybean flowering (Table 4).

The positive and significant correlation (r = 0.30) between MBC and soybean yield (Table 8) indicated that, during flowering, a larger microbial population enhances the efficiency of organic matter degradation and mineralization, thereby increasing nutrient availability and improving yield.

Supporting these findings, Stone et al. (2013), when evaluating the correlation between common bean yield and soil biological attributes, found a strong positive correlation with MBC (r = 0.44). This confirms that high microbial biomass values are associated with more active organic matter, promoting nutrient cycling and benefiting the succeeding crop.

Table 10
Number of pods per plant (PP), number of grains per pod (NGP), internode length (IL), 100-grain mass (100M), and yield (YLD) of soybean in the 2022/2023 season grown in succession to autumn–winter crops, following soil management in 2020, in Dourados, MS, Brazil*.

Soil management	PP	NGP	IL (m)	100M (kg)	YLD (kg·ha-1)
Disc plow	45.90 a	110.8 a	0.075 a	0.015 b	3,448 b
Moldboard plow	46.19 a	114.4 a	0.073 a	0.016 a	3,700 a
Chisel plow	45.68 a	113.9 a	0.078 a	0.015 ab	3,607 ab
No-tillage	45.08 a	110.5 a	0.075 a	0.016 ab	3,602 ab
Crop management
Maize	44.28 a	108.2 a	0.079 a	0.016 ab	3,531 ab
Maize + U. ruziziensis	42.22 a	102.4 a	0.077 a	0.016 a	3,490 b
U. ruziziensis + Crot-alaria	49.93 a	124.2 a	0.069 a	0.015 c	3,761 a
Mixed cover crops	46.42 a	114.7 a	0.075 a	0.015 bc	3,574 ab
Average	45.71	112.4	0.075	0.015	3,589
C. V. (%)	19.83	19.72	13.9	3.86	6.25

• Means followed by the same letter in the column do not differ according to Tukey’s test (p < 0.05); C. V.: coefficient of variation.

It is likely that the lower microbial population (Table 6) during the period of high nutrient demand by the crop (Tagliapietra 2022) negatively affected nutrient availability, thereby reducing yield. In contrast to disc plowing, moldboard plowing merely cuts through the soil surface, moving some of the organic residues to deeper layers and causing less disturbance to soil microorganisms.

The association between grass and legumes improved the quality of residues left on the soil after mowing. Despite the lower dry matter production (Table 3) and the negative correlation with yield (r = -0.30), mowing C. ochroleuca at flowering enhanced soil cover by U. ruziziensis and accelerated legume mineralization due to its low C/N ratio, creating a nutrient-rich environment that supported soybean development and increased yield.

CONCLUSION

Soil management with lower disturbance, such as moldboard plow, chisel plow, and no-tillage, resulted in higher microbiological activity and greater soybean yield.

Autumn–winter cropping with a high quantity and quality of residues, such as the U. ruziziensis intercropping with C. ochroleuca, enhanced soybean yield in the succeeding crop.

ACKNOWLEDGMENTS

The authors thank the Universidade Federal da Grande Dourados and Embrapa Agropecuária Oeste for their partnership.

• How to cite:
  Branquinho, I.C.F., Ceccon, G. and Garcia, R. (2026). Impact of soil and cropping management on soil quality bioindicators and soybean yield. Bragantia, 85, e20250126. https://doi.org/10.1590/1678-4499.20250126
• FUNDING
  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
  Finance code 001
• DECLARATION OF USE OF ARTIFICIAL INTELLIGENCE TOOLS
  The authors declare that no artificial intelligence tools were used in the preparation, writing, data analysis, or review of this manuscript.

DATA AVAILABILITY STATEMENT

The datasets are available from the corresponding author upon reasonable request.

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Edited by

• Section Editor:
  Aline Renée Coscione https://orcid.org/0000-0002-8331-603X

Publication Dates

• Publication in this collection
  09 Jan 2026
• Date of issue
  2026

History

• Received
  26 June 2025
• Accepted
  21 Oct 2025