1. Introduction
The agriculture, forestry, and other land use (AFOLU) sector accounts for 74% of all Brazilian greenhouse gas (GHG) emissions [
1]. This exemplifies how strongly agricultural land-use systems influence soil carbon stocks in tropical regions. The global SOC pool to 1-m depth is estimated at about 1500 Pg C, of which more than 140 Pg C is found in the top 30 cm of cropland soils [
2]. In this context, capture and storage of soil organic carbon (SOC) in croplands came to be recognized as a nature-based solution for carbon dioxide (CO
2) removal.
As one of the world’s leading agricultural exporters in tropical regions, Brazil depends critically on soybean and maize production [
3,
4,
5]. According to the Brazilian National Supply Company (CONAB), Brazil remains the world’s leading soybean producer. For the 2024/25 growing season, soybean acreage was projected at 47.35 million hectares, with an expected grain output of 171.5 million tons, while maize was forecast to cover 21.86 million hectares and yield 139.7 million tons, consolidating Brazil as the third-largest maize producer globally [
6]. Approximately 42% of the country’s agricultural land, particularly for grain production, is located in the Cerrado biome [
7]. However, Cerrado soils are highly weathered, nutrient-poor, with low natural fertility. Consequently, crop production in this region depends heavily on external inputs, particularly nitrogen (N) fertilization, which is intensively applied to maximize yields and, consequently, increase crop biomass returns to the soil [
8].
One widely adopted strategy to increase soil nitrogen (N) is the use of cover crops (CCs), a practice that has expanded rapidly in Brazilian agriculture, especially across the Cerrado biome. Cover crops can also contribute to building soil organic carbon (C) stocks [
9,
10]. When combined with N fertilization of the cash crop, CCs deliver multiple benefits: enhanced functional properties of the cover crop itself, higher yields of the commercial crop, and increased C sequestration in the soil. However, the magnitude of these benefits strongly depends on cover-crop species traits, particularly the plant chemical composition and the resulting effects on soil N cycling and availability [
11].
Cover crops contribute to soil carbon (C) accumulation through the various functions they perform in the soil. They improve soil health and quality and, consequently, enhance the productivity of subsequent cash crops [
12]. In addition, CCs help prevent soil degradation and protect soil organic matter (SOM) [
13], stimulate soil biological activity [
14,
15], and enhance N supply [
11,
16,
17]. Consequently, they increase soil C stocks [
18,
19,
20] through biomass inputs from both shoot and root systems, which often improves even deeper soil layers [
15,
21].
Cover crops are also used to enhance nutrient availability [
22], particularly of N [
23,
24], for subsequent cash crops. This improvement is a result of biological N fixation (BNF) and the decomposition of crop residues, resulting in the release of essential nutrients [
19,
25]. All these functions of CCs collectively contribute to SOM accumulation, which occurs in different fractions with distinct functions and residence times in primarily the soil, ranging from days to centuries. Chemical fractionation is a widely utilized technique to assess the impact of cover crops on SOM accumulation. This method analyzes three main fractions: fulvic acids (FAa), humic acids (HAa), and humin (HUM) [
22], which are chemically distinguished by their solubility. Humin (HUM) is classified as an insoluble humic substance (HS); humic acids (HAs) are soluble under alkaline conditions; fulvic acids (FAs) are soluble in both acidic and alkaline environments [
26]. These specific SOM fractions serve as key, more sensitive indicators of soil quality than total carbon stocks, especially in highly weathered soils such as Oxisols [
18].
Humic substances (HSs) are understood as multifunctional natural catalysts in sustainable agriculture. They interact with ions to form complexes of varying stability and structural characteristics, creating new opportunities to improve soil health, crop yield, and environmental resilience [
27,
28]. Due to these properties, HSs can indicate the adequacy of soil management in agricultural areas [
29]. Their complex molecular structure constitutes an abundant C and energy source for beneficial soil microorganisms such as bacteria, fungi, and actinomycetes [
30]. In addition, humic fractions enhance particle cohesion, stabilize soil aggregates, influence aggregate-size distribution, and are closely linked to soil C conservation [
31,
32].
Soil C contents and stocks vary depending on the crop rotation sequence and successive cover crops. In this experiment, soil C stocks increased during the maize phase but declined significantly after the transition to soybean in 2021, except in the sorghum (
Sorghum bicolor) and wheat (
Triticum aestivum) treatments [
33]. We hypothesized that maize–soybean rotations combined with CCs improve N availability and biomass production, thereby increasing soil C stocks. To this end, this study analyzed soil C contents and stocks in 2018 and 2024, assessed changes in soil C delta (ΔC), and determined maize and soybean yields alongside CC biomass/dry matter production in the same years. Additionally, humic fractions of SOM in a long-term experiment, under maize treated with and without N topdressing, were compared in 2024.
4. Materials and Methods
4.1. Experimental Area—Location and Characteristics
The experiment was conducted at Embrapa Cerrados, in Planaltina, Federal District, Brazil (15°35′50.12″ S, 47°42′26.97″ W; altitude 973 m). It was arranged in a randomized complete block design with split plots and three replications, with 12 × 8 m main plots and 6 × 8 m subplots (
Figure 3). The main plots represented the CCs
Cajanus cajan (L.) Millsp. (Fabaceae),
Crotalaria juncea L. (Fabaceae),
Raphanus sativus L. (Brassicaceae), and
Mucuna aterrima L. (Fabaceae), while the subplots corresponded to the application or absence of N topdressing of maize (WN and NN, respectively).
According to the Köppen–Geiger classification, the regional climate is Aw (tropical savannah), characterized by a distinct dry season (winter) and rainy season (summer) [
70,
71]. During the study period, the average annual rainfall was 1.187 mm and the mean air temperature was 21.73 °C. In 2018, the mean maximum and minimum temperatures were 28.63 and 15.89 °C, respectively, with total annual precipitation of 1349.40 mm. In 2024, the mean maximum temperature reached 29.46 °C and the mean minimum 16.37 °C, while total annual precipitation was 1278.20 mm (
Figure 4).
The soil of the experimental site was classified as Oxisol. Soil chemical properties are summarized in
Table 8. Soil chemical analyses were performed according to the methods described by Sparks [
72].
4.2. History of the Study Area, Experimental Design, and Management Practices
Between 1995 and 2005, the area was left fallow. In 2005, an experiment of an annual maize–CC off-season fallow sequence with maize hybrid Pioneer
® 30F53VYHR was initiated (
Figure 5), which assessed cutting management at flowering and maturity stages [
20,
25].
In the 2010/2011 growing season, the experiment was subdivided according to nitrogen topdressing of maize (treatments with and without N fertilization, WN and NN, respectively). Hybrid maize 30F53VYHR was sown in November in a no-tillage system (directly on CC residues). Plant density was approximately 65,000 plants ha
−1, with a row spacing of 0.75 m. At planting, all plots and subplots received 20 kg N ha
−1, 65.5 kg P ha
−1, 66.4 kg K ha
−1, 2 kg Zn ha
−1 (ZnSO
4 7H
2O), and 10 kg ha
−1 FTE BR 12 as micronutrient source (containing 3.2% S, 1.8% B, 0.8% Cu, 2.0% Mn, 0.1% Mo, and 9.0% Zn). Nitrogen topdressing of maize of 130 kg N ha
−1 was applied as urea in two split doses at the phenological stages V4 and V8. Weed control consisted of glyphosate and 2,4D for pre-planting desiccation, atrazine (pre-emergence herbicide), and glyphosate (post-emergence). In the growing seasons 2021/2022 to 2023/2024, soybeans replaced maize as the main crop. Soybean was sown in November in rows spaced 0.5 m apart, at a density of about 220,000 plants ha
−1. Phosphorus and potassium were applied at 59 and 37.4 kg ha
−1, respectively, at sowing. To control weeds, glyphosate and glufosinate (for pre-planting desiccation), and glufosinate (for post-emergence desiccation) were applied. The land-use sequence from 1995 to 2024 is illustrated in
Figure 5.
After the maize and soybean harvests (March and February, respectively), the CCs were planted in April, without fertilization (under residual effects of commercial crop fertilization) and cut at flowering (between May and August, depending on the plant species). Sowing densities were 40 plants m
−2 for
Cajanus cajan and
Crotalaria juncea; 20 plants m
−2 for
Mucuna aterrima; and 80 plants m
−2 for
Raphanus sativus. Row spacing for CCs was 0.5 m. Vegetative cycles were as follows:
Cajanus cajan (70–90 days),
Crotalaria juncea (90–100 days),
Mucuna aterrima (140–180 days), and
Raphanus sativus (45–60 days) [
37]. No weed control was applied to the cover crops.
4.3. Soil Sampling and Analyses
Soil sampling for carbon stock assessment was carried out in 2018 and 2024, after maize and soybean harvests, respectively, under CCs. Soil was collected from the layers 0–5, 5–10, 10–20, 20–40, 40–60, 60–80, and 80–100 cm. After the soybean harvest in 2024, soil was sampled again to determine SOM fractions (0–10, 10–20, and 20–40 cm deep).
Bulk density was measured in undisturbed samples collected from trenches in native Cerrado at a depth of 120 cm, approximately 50 m away from the experimental site [
20]. Duplicate samples per depth were collected from opposite trench walls using stainless-steel rings. Samples were oven-dried at 105 °C and bulk density was calculated based on the ring volume.
Total carbon content was determined in sieved (<2 mm), ground, and re-sieved (<150 µm) soil samples [
73,
74]. Subsequently, total C was determined using a CHNS elemental analyzer (Macro Vario Cube-Elementar, model 2400 Series II CHNS/O). Carbon stocks were calculated via the equivalent soil mass approach [
75], using the following:
where
C stock: in Mg ha−1;
TC: total carbon (g kg−1);
BD: bulk density (g cm−3);
h: soil layer thickness (cm).
The change in soil C stock (ΔC) between 2018 and 2024 was calculated as the difference: 2018–2024.
For SOM chemical fractionation, humic fractions were determined based on the differential solubility method [
76].
One gram of soil sample was placed in a Falcon tube with 20 mL of 0.1 mol L−1 NaOH. The solution was shaken for 4 h at 80 rpm, left to stand for 12 h, and then centrifuged at 3800 rpm for 30 min. The supernatant was collected, and 20 mL of NaOH was added to the residue, followed by agitation for 2 h 30 min and centrifugation for another 30 min at 3800 rpm. The combined supernatants constituted the alkaline extract containing HA and FA. The remaining insoluble precipitate, identified as humin (HUM), was oven-dried at 50–60 °C.
After centrifugation for 20 min at 3800 rpm, the supernatant (FA) was separated from the precipitate (HA), which was treated with 20 mL of 0.5 mol L
−1 NaOH, homogenized, and the volume adjusted to 50 mL with distilled water. The C contents of HA and FA fractions were determined following the method of Yeomans & Bremner [
77].
Samples were digested at 140 °C with potassium dichromate (0.042 mol L
−1 for FA and HA; 0.167 mol L
−1 for HUM) and titrated with ammonium ferrous sulfate (AFS). Humic substances (HSs) were calculated by the following:
where
VB: AFS volume in mL for blank titration;
VS: AFS volume in mL for sample titration;
N: normality of AFS (0.25 N for HUM, 0.033 N for FA and HA);
3: conversion factor of the chemical formula from the dichromate ion (Cr2O72−) to C;
I: aliquot volume for titration (mL);
m: soil sample weight (g).
The equation expresses oxidizable organic carbon in g kg−1 dry soil. The method consists of heating to enhance C recovery.
4.4. Cover Crop Biomass, Contribution to C and N Inputs, and Maize–Soybean Yield
Total N in CC shoots was determined by digesting 0.2 g of dried sample at 350 °C for 1 h with 10 mL of HClO
4:H
2O
2 (2:1,
v/
v). Digests were diluted (1:6, Milli-Q water) and analyzed by flow injection analysis (FIA, Lachat QuikChem Series 2) using the Berthelot colorimetric method. Carbon input was calculated from CC shoot biomass, based on the IPCC default carbon fraction for herbaceous biomass [
78]. Inputs of C and N from cover crop biomass were computed as follows:
where
C or N input: in kg ha−1,
C or N in shoots: in g kg−1,
CC shoot biomass: in kg ha−1.
Maize data correspond to the harvest in March 2018, and soybean yield data to the harvest of February 2024. Two replicates per subplot were sampled, encompassing four 4-m rows for maize and three 4-m rows for soybean. Grain yields were corrected to 13% moisture. Maize–soybean productivity (MSP) was calculated as follows:
where
GY: soybean/maize grain yield (kg ha−1),
W: total weight of harvested soybean/maize grains (kg),
A: subplot area (m2).
To monitor the residual effect of CCs, aboveground biomass samples were collected in the previous years (2017 and 2023, respectively), during full CC flowering. A 1 m
2 steel frame was randomly placed on the soil surface. The plants contained within this 1 m
2 were cut close to the surface and two samples per subplot were taken and oven-dried at 65 °C to constant weight. Dry matter (DM) was calculated as follows:
where
DM is dry matter (kg ha−1),
Wf: sample weight after drying (kg),
Wi: sample weight before drying (kg).
4.5. Statistical Analyses
For C and N inputs from cover crop biomass, means were compared using Tukey’s test (p < 0.05).
Carbon content and stock, organic matter fractions, yield, and biomass data were analyzed using linear mixed models to assess fixed effects of treatments (CCs) and maize N topdressing (with and without N) as subplots. The year was treated as a repeated measure within subplots to account for temporal correlations. In long-term field experiments, observations throughout the same plot are correlated over time; treating year as a repeated measure permits modeling this correlation structure, which improves the estimation of standard errors and the inferential power [
79,
80]. To formally represent the experimental design, we used the notation b = 1, …, B for blocks, i = 1, …, I for main plot treatments (CC), j = 1, …, J for subplot treatments (N), and t = 1, …, T for years (repeated measures). The experimental unit “subject” for the temporal repetition was considered the subplot (b,i,j), evaluated in different years. The general linear mixed model can be expressed as:
where μ is the intercept; α
i, γj, and (αγ)
ij, are fixed effects of CCs, N, and their interaction, respectively; τ
t is the fixed effect of year; the term
corresponds to the interaction between CC and year, capturing how the effect of each cover crop treatment varies over time. Interactions with year were included depending on biological plausibility; β
b~N(0, σ
2B) is the random block effect; δ
bi~N(0, σ
2W) the whole-plot error (block × CC); S
bij~N(0, σ
2S) the random subplot intercept, capturing heterogeneity across experimental units over time; and ε
bijt is the within-subject error, assumed to follow a temporal covariance structure.
Model adequacy was checked by graphical inspection of residuals (residuals versus fitted values, Q–Q plots) and formal tests for normality (Shapiro–Wilk), homogeneity of variances (Breusch–Pagan test), linearity, and independence of errors. Estimated marginal means (emmeans) were calculated using the Kenward–Roger method to compute degrees of freedom, enhancing the precision of inferences, especially for small sample sizes [
81].
Multiple comparisons among treatments, N rates (with and without topdressing), and years used Tukey’s adjustment [
82], controlling the familywise error rate at a significance level of α = 0.05. When necessary, the adjusted means obtained from the mixed model were compared using Sidak’s method, which controls Type I error in multiple comparisons more efficiently than Bonferroni, especially in mixed model-based analyses. When applicable, adjusted means were compared using Sidak’s method [
83]. For ΔC, when linear model assumptions were not met, the nonparametric Kruskal–Wallis test (α = 0.05) was applied to compare distributions among treatments. Significant differences were further explored by multiple post hoc comparisons using Dunn’s test or the Kruskal–Wallis multiple comparison procedure (agricolae package).
In addition, a principal component analysis (PCA) examined multivariate patterns and relationships among soil properties (C content and fractions) and crop responses (yield and biomass). The rationale for using PCA was to integrate multiple correlated variables into synthetic axes (principal components), thereby reducing dimensionality and highlighting the main gradients of variation associated with management practices. Prior to the analysis, variables were centered and scaled to unit variance to avoid bias due to differences in measurement units. The components were interpreted based on variable loadings, with PC1 and PC2 representing the main directions of variation. By these multivariate patterns, it was possible to identify which treatments were associated with higher soil C fractions, greater biomass, or improved yield. All analyses were performed in R Core Team, 2025; version 4.3.3 [
84], using the following packages: lme4 [
85] for fitting mixed linear models; emmeans [
86] for adjusted means and multiple comparisons; multcomp [
87] for simultaneous tests and groupings, and FactoMineR for PCA.
5. Conclusions
In successive maize–CCs systems, nitrogen management of the cash crop influences C and N inputs from cover crop biomass. During the soybean phase, C and N inputs were affected by cover crops and N management. Soil C contents were not significantly affected by maize N fertilization in the maize– or soybean–cover crop rotation. Total soil C stocks were only influenced by the exchange of the main crop maize for soybean, with reductions of 1.4 to 10.4% when not fertilized (NN) and 6.7 to 15.7% under N fertilization (WN) management, when maize was replaced by soybean. Cajanus cajan showed the greatest stability in fulvic acid (FA) and humic acid (HA) fractions at all depths in response to N fertilization of the previous maize crop. Under Mucuna aterrima, Raphanus sativus, and Crotalaria juncea, FA fractions without and HA fractions with N fertilization were highest. The lowest soil ΔC loss over five years was observed for Raphanus sativus without (−1.82) and Crotalaria juncea with N fertilization (−9.69). In addition, N fertilization increased maize yields by 28 to 44%. In the maize phase, in response to N fertilization, biomass increased only in plots with Cajanus cajan. In the soybean phase, biomass was influenced only by cover crops; biomass stocks under Cajanus cajan were higher than under Crotalaria juncea. In conclusion, these results highlight the effectiveness of combining cover crops with nitrogen management to promote low-carbon agriculture in tropical regions.