Residue Management and Nutrient Stoichiometry Control Greenhouse Gas and Global Warming Potential Responses in Alfisols

Abstract

Although crop residue returns are extensively practiced in agriculture, large uncertainties remain about greenhouse gas (GHG) emissions and global warming potential (GWP) responses to residue return (RR) rates under different residue placements and nutrient supplements. We conducted a laboratory mesocosm experiment in Alfisol in central India to investigate the responses of soil GHG emissions (CO₂, N₂O, and CH₄) and the global warming potential to four wheat RR rates (R0: no residue; R5: 5 Mg/ha; R10: 10 Mg/ha; R15: 15 Mg/ha) and two placements (surface [Rsur] and incorporated [Rinc]) under three nutrient supplement levels (NSLs) (NS0: no nutrients, NS1: nutrients (N and P) added to balance the stoichiometry of C:N:P to achieve 30% humification in RR at 5 t/ha, NS2: 3 × NS1). The results demonstrated a significant (p < 0.05) interaction effect of RR × NSL × residue placement on N₂O emission. However, CH₄ and GWP responses to the RR rate were independent of NSL. N₂O fluxes ranged from −2.3 µg N₂O-N kg⁻¹ soil (R5 NS0 Rsur) to 43.8 µg N₂O-N kg⁻¹ soil (R10 NS2 Rinc). A non-linear quadratic model yielded the best fit for N₂O emissions with RR rate (R² ranging from 0.55 to 0.99) in all NSLs and residue placements. Co-applying wheat residue at 10 and 15 Mg/ha at NS1 reduced CH₄ and N₂O emissions (cf. R0 at NS1). However, increasing NSLs in NS2 reduced the nutrient stoichiometry to < 12:1 (C:N) and < 50:1 (C:P), which increased N₂O emissions in all RR rates (cf. R0) across all residue placements. Averaged across nutrient levels and residue placements, the order of the effects of RR rates on CH₄ emissions (µg C kg⁻¹ soil) was R10 (5.5) > R5 (3.8) > R15 (2.6) > R0 (1.6). Our results demonstrated a significant linear response of total GWP to RR rates R15 > R10 > R5 > R0, ranging from 201.4 to 1563.6 mg CO₂ eq kg⁻¹ soil. In conclusion, quadratic/linear responses of GHGs to RR rates underscore the need to optimize RR rates with nutrient supplements and residue placement to reduce GHG emissions and GWP while ensuring optimal soil health and crop productivity.

1. Introduction

The reintegration of crop residue, a predominant agro waste, into agricultural soils is a sustainable management strategy. This approach aligns with the principles of circular agriculture, promoting soil health and fertility and minimizing environmental impacts by harnessing the inherent potential of organic residues within the farming system [1]. Agricultural practices significantly contribute to greenhouse gas emissions, prompting the urgent need for sustainable management strategies to mitigate climate change impacts [2,3,4]. While the role of crop residue in improving soil organic carbon sequestration and soil health is recognized [5,6], a comprehensive understanding of its impact on different greenhouse gases, specifically soil CO₂, N₂O, and CH₄, and global warming potential, remains elusive [7]. Existing research primarily focuses on individual gases, and a holistic examination of their interplay, especially under varying residue levels, placements, and nutrient conditions, is lacking. Addressing this research gap is crucial for optimizing agricultural practices that balance soil fertility management with climate change mitigation.

Previous studies reported that changes in crop RR rates impact GHG emissions due to the resulting variation in soil respiration or CO₂ emissions [8,9]. The crop RR rate significantly impacts the availability of organic C in the soil, which is a critical factor in regulating soil microbial processes. By supplying energy and electron donors to denitrifiers and methanotrophs, crop residue reduces N₂O to N₂ and methane to CO₂ [10,11,12,13]. A meta-analysis by Wang et al. [14] demonstrated that the inconsistent effect of RR on GHG emissions (CO₂, N₂O, and CH₄) can be modified by multiple management factors, such as soil nutrients and residue placement in the soil. In the field, crop residue placement can vary depending on the nature of the tillage practice, with no residue used in conventional tillage, surface placement of residues in no-tillage practices, and the incorporation in minimum tillage in other methods [15]. The placement of crop residue in the soil can modify physical, chemical, and biological decomposition and soil conditions, which can affect the magnitude and direction of GHG emissions [16]. While some studies have found that crop residues placed on the soil surface decompose more slowly than incorporated residues, the effect is mainly attributed to changes in conditions such as soil–residue contact and soil water content, which control decomposition. The decomposition of residues contributes to nitrogen availability in the soil through 1) mineralization or 2) immobilization, influencing N₂O emissions [9]. Additionally, the placement of residues, whether surface applied or incorporated into the soil, affects microbial processes and nitrogen dynamics. Research by Sainju et al. [17] and Wang et al. [18] explores the influence of residue management on CH₄ emissions. The decomposition of organic residues creates anaerobic conditions conducive to methanogenic microbial activity. The quantity and placement of residues influence the magnitude and direction of CH₄ fluxes [10,11]. Notably, studies emphasize the need to consider residue management as part of an integrated approach to mitigate CH₄ emissions from agricultural soils [19,20]. Maize residue incorporation at four N rates (N0, N200, N250, and N300 kg N ha⁻¹) resulted in significantly lower GWP in the N200 treatment than the N250 and N300 treatments of the traditional planting system [11].

The effect of the placement and rate of crop RR on GHG emissions has also been shown to depend on nutrient stoichiometry and supplement [21,22,23]. The stoichiometry of C and N affects N availability to decomposers, as influenced by soil–residue contact; moreover, this stoichiometry is involved in the interaction between crop residue and placement and thus regulates GHG emissions in conservation tillage [24]. Optimum nutrient stoichiometry is essential to maintain soil enzyme activity in the aggregates [25] and impacts the abundance of N₂O-reducing phylogroups [26]. Crop RR with excessive nitrogen fertilizer causes imbalances in nutrient stoichiometry, which can negatively affect microbial biomass and diversity [27]. Further, crop RR disturbs the soil nutrient stoichiometry (C:N and C:P ratio) with associated effects on GHG emissions. Crop RR with a high C:N ratio, like wheat, immobilizes soil nitrogen to meet the narrow C:N ratio of 7:1. Commonly observed stoichiometries essential for biological growth and humification include a C:N ratio of 12:1, C:P of 50:1, and C:S of 70:1 [28,29]. Integrated application of wheat residue and phosphorus, improved N retention, and reduced N₂O emissions [28]. In contrast, co-application of C and P to the soil caused enhanced soil carbon priming and overall GHG loss of N₂O and CO₂ [30]. Datta et al. [31] reported greater CH₄ emissions from paddy fields receiving only N fertilizer than N+P+K fertilizer. Therefore, the effect of the co-application of crop residue with nutrients (N and P) is inconsistent and may vary with residue placement and return rate.

Previous studies have shown the responses of GHGs, mainly CH₄ and N₂O, to N rates or N rates with straw or without straw return; however, responses involving nutrient stoichiometry are limited [30]. A non-linear response of N₂O emissions to N application rates was noted at a single level of rice/wheat straw [32]. In addition, a quadratic increase in N₂O with N rates occurred without crop residue [33,34,35,36], and CH₄ emissions increased, exhibiting a quadratic response with increasing N rates in rice paddy [37,38]. The CH₄ flux showed a significant and positive curvilinear quadratic relationship with water-extractable soil organic C content under straw management [39]. In a meta-analysis, Abalos et al. [7] reported that the biochemical nature of crop residue governed the direction of N₂O emissions from RR.

While these studies offer valuable insights, a critical research gap exists concerning the simultaneous examination of residue quantity, placement, and nutrient supplement on greenhouse gas responses. The current study aims to address this gap by providing a holistic assessment of these factors, contributing to a more comprehensive understanding of sustainable agricultural practices. We conducted a laboratory mesocosm experiment under controlled conditions in Alfisol in central India (1) to investigate the responses of soil GHG emissions (CO₂, N₂O, and CH₄) and the global warming potential to different wheat RR rates and placements under three levels of nutrient supplement and (2) to identify the optimum combination of residue placement and nutrient supplementation for each of the residue rates that can result in lower GWP and GHG emissions from Alfisol. The best treatment combination could be further tested and validated in the field experiment. By elucidating the interrelated effects of residue management on soil CO₂, N₂O, and CH₄ emissions and GWP, this research seeks to inform practical strategies for mitigating the climate impact of crop residue in agricultural systems.

2. Materials and Methods

2.1. Study Site

The study was conducted using a laboratory-based soil mesocosm setup at the ICAR-Indian Institute of Soil Science, Bhopal, India. The soil used in this study was collected from a farmer’s field under a maize–fallow cropping system and characterized as Alfisol. The soil sampling site is located at 21.96° N latitude and 77.74° E longitude and is characterized by a humid subtropical climate with mild, dry winters and hot summers followed by a humid monsoon season. The basic soil properties were determined following a standard protocol and characterized as having a sandy loam texture. The following properties were noted: 0.22 ds/m EC (1:2.5), 5.69 pH 1:2.5), soil organic carbon (0.38%), NO₃-N (14.1 mg kg⁻¹ soil), NH₄-N (43.7 mg kg⁻¹ soil), available P (8.9 mg kg⁻¹ soil), available K (61.0 mg kg⁻¹ soil), and a C:N ratio of 11.87.

2.2. Incubation Experiment Details

Surface soil samples (0–15 cm depth) were collected from a farmer’s field under a soybean–wheat cropping system. The collected soil samples were carefully sieved to remove visible roots and large residue fragments and were subsequently stored at 4 °C until further analysis. The wheat crop residues used in the study were air-dried, ground, and sieved to 2 mm for the treatment of soil. Briefly, the treatments consisted (a) 40 g of soils (dry weight basis) treated with 90, 180, and 270 mg 40 g⁻¹ soil equivalents of wheat residue at 5 (R5), 10 (R10), and 15 (R15) Mg/ha residue with surface return (Rsur) and incorporation (Rinc), respectively, in 461 ml glass jars and (b) soil without crop residue (R0, control). A blank glass jar without soil and residue was included to account for the atmospheric GHG concentration present in the headspace of the incubation jars. All treatments were replicated three times and incubated at a moisture content of 80% FC at 30 °C.

To calculate the amount of supplementary nutrients (N and P) needed, we assumed that the stoichiometry of the more stabilized soil organic matter was the same as that suggested by Himes (1998) with a C:N:P ratio of 10,000:833:200. The carbon, nitrogen, and P contents in the wheat straw were determined using a standard protocol and were 44.78%, 0.56%, and 0.013%, respectively. Using these ratios and assuming that only nutrients already present in the straw are available for the humification process, it can be shown that P is the limiting element, and 1.5% of the straw C can be humified. Additional nutrients are thus needed to achieve a higher level of humification. The three nutrient treatments included for all residue levels and control soil were (i) soils with zero nutrient supplement (NS0), (ii) soils receiving nutrient supplement to achieve 30% humification with a residue level of 5 Mg/ha (NS1), and (iii) soils receiving a nutrient supplement three times that of NS1 (NS2). For example, for NS1 treatment, this was done by adding 1 mL 40 g⁻¹ soil of a nutrient solution containing 0.5 and 0.20 g per L of N and P using AR grade urea and potassium dihydrogen phosphate, respectively. The pH of the nutrient solution was adjusted to pH 7 using a 10 M sodium hydroxide solution. Therefore, the achieved nutrient stoichiometry for the three residue levels at NS1 was 12:1 (C:N) and 50:1 (C:P) in R5, 24:1 (C:N) and 100:1 (C:P) in R10, and 36:1 (C:N) and 150:1 (C:P) in R15. Similarly, the nutrient stoichiometry at NS2 was 4:1 (C:N) and 17:1 (C:P) in R5, 8:1 (C:N) and 33:1 (C:P) in R10, and 12:1 (C:N) and 50:1 (C:P) in R15.

2.3. GHG Sampling and Measurements

Headspace gases were sampled at regular intervals on fixed days (0, 1, 3, 7, 11, 18, 25, 32, 39, 46, 56, 66, 76, 86, 96, 106, and 116 days of incubation). The gas samples were drawn from the incubation jars using a syringe and immediately transferred to a 10 ml evacuated glass vial. The unequal interval is designed to capture the asymptotic decrease commonly observed in incubation experiments. Total headspace CO₂, N₂O, and CH₄ concentrations were measured using gas chromatography (Agilent Technologies model 7890A). The CH₄/CO₂ flux rate was calculated as the change in headspace CH₄/ CO₂ concentration per kg soil (dry weight equivalent) per unit incubation time (day). Cumulative N₂O, CH₄, and CO₂ emissions were determined by summing the fluxes from each measurement [9].

The global warming potential expressed as the CO₂ equivalent was calculated by multiplying the cumulative N₂O and CH₄ emissions by their respective radiative forcing potentials using the following equation [40]:

GWP (mg CO₂ eq. kg⁻¹ soil) = CH₄ (mg kg⁻¹ soil) × 27.2 + N₂O (mg kg⁻¹ soil) × 273 + CO₂ (mg kg⁻¹ soil) × 1

$\mathrm{E}\mathrm{m}\mathrm{i}\mathrm{s}\mathrm{s}\mathrm{i}\mathrm{o}\mathrm{n} \mathrm{f}\mathrm{a}\mathrm{c}\mathrm{t}\mathrm{o}\mathrm{r} {\mathrm{N}}_2\mathrm{O} (\mathrm{E}\mathrm{F},\mathrm{\%})=\frac{({\mathrm{N}}_2\mathrm{O}-\mathrm{N} \mathrm{t}\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{t}\mathrm{m}\mathrm{e}\mathrm{n}\mathrm{t})-({\mathrm{N}}_2\mathrm{O}-\mathrm{N} \mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l})}{\mathrm{i}\mathrm{n}\mathrm{o}\mathrm{r}/\mathrm{o}\mathrm{r}\mathrm{g} \mathrm{N} \mathrm{i}\mathrm{n}\mathrm{p}\mathrm{u}\mathrm{t}}\times 100$

2.4. Statistical Analysis

All data were subjected to normality and homogeneity of variance tests. Log transformation was applied when necessary. Statistical analysis was conducted using SPSS software, and a significance level of p = 0.05 was set. General linear model univariate ANOVA was used, followed by the Tukey’s HSD multiple comparison method for mean comparisons. We performed linear, quadratic, and exponential curve fittings to simulate the response of N₂O, CH₄, and CO₂ emissions to residue rates and EF N₂O to inorganic/organic N rates. Then, we used the coefficient of determination (R²) and variance (sum of squares for total [SST], sum of squares for regression [SSR}, and sum of squares for error [SSE]) to evaluate the confidence levels. We first selected the function with the highest R² value at p < 0.05 for the least significant differences (LSD) test. When R² values were the same among these fittings, a lower SSE value (meaning a lower systematic error) was used as the best GHG responding function.

3. Results

3.1. CO₂ Fluxes

The cumulative soil CO₂ fluxes ranged from 52.80 (R0 NS2) to 424.39 (R15 NS2) mg C kg⁻¹ soil across treatments over the incubation period. The main effect of residue levels was significant (Table 1). The mean cumulative CO₂ fluxes exhibited the highest values with significance at the residue level of 15 Mg ha⁻¹ (381.88 mg C kg⁻¹ soil), which is 1.55-fold greater than that noted for R10, 2.59-fold greater that noted for R5, and 6.58-fold greater that noted for R0. Soil CO₂ fluxes showed a linear response to increasing levels of wheat residue inputs (R² > 0.95, Figure 1), and the R² varied with the NSL and residue placement. The residue incorporation at both nutrient levels (NS1 and NS2) increased the soil CO₂ fluxes compared to the surface placement of residue and no nutrient (N0), and the effects were significant at 46 DAI and insignificant at 116 DAI. However, at 46 DAI, the main effect of residue and nutrient levels with residue placement showed a significant effect on soil CO₂ fluxes; however, the interaction effects were nonsignificant (Table 1). Repeated measures ANOVA indicated a significant effect of time of incubation and interaction effects of time × residue level, time × residue placement, and time × nutrient level on soil CO₂ fluxes (Table 2).

3.2. N₂O Fluxes

The interaction effect of residue placement and wheat residue and nutrient levels was significant for cumulative N₂O fluxes at 46 and 116 DAI, with the exception of the interaction effect of residue level × placement × nutrient at 116 DAI. The cumulative N₂O fluxes increased with increasing residue and nutrient application rates. However, the magnitude was less than that of the control soil without residue at all nutrient levels (Figure 2) except in the soil treatment of +R at 5, 10, and 15 Mg/ha at nutrient level NS2 with wheat residue incorporated. The cumulative N₂O fluxes ranged from −2.31 µg N kg⁻¹ soil (soil + Rsur at 5 Mg/ha surface placement at N0) to 43.82 µg N kg⁻¹ soil (soil +Rinc at 10 Mg/ha at NS2).

The cumulative N₂O flux exhibited a non-linear response to increasing residue levels at all nutrient rates (Figure 2), and the magnitude of the response was affected by the residue placement. Incorporating residues increased N₂O emissions compared with surface placement across all residue and nutrient levels. A non-linear quadratic model yielded the best fit for N₂O emissions with RR (R² ranging from 0.55 to 0.99) at all NSLs and residue placements. We explored different linear and non-linear models for establishing the relationship between N₂O emissions and increasing N (organic and inorganic) rates in residue-treated soil and inorganic N rates in control soil. The non-linear quadratic model had the highest R² and exhibited a better fit than other models at all residue levels and for the control soil (Figure 2). N₂O emissions increased quadratically as the N rates increased from 0 to 12.5 mg kg⁻¹ soil and then decreased nonlinearly afterward (12.5 to 37.5 mg N kg⁻¹ soil) in control soil without residue.

The residue levels and placement significantly affected the magnitude and direction of cumulative N₂O emissions from increasing rates of inorganic N. The N₂O emissions factor (EF) from residue N increased with nutrient and RR rates (Figure 3). Negative EF values ranging from −0.257 to −0.008 were observed for all treatments except for treatments at the higher nutrient level N2 at all residue levels (5, 10, and 15 Mg/ha) with residue incorporation (i.e., surface placement). The overall response of residue N was modified by residue placement and the amount of inorganic nutrients. Nutrient stoichiometry and residue placement affected the relationship between the N₂O EF from residue N and the crop residue N rate. The emission factor from inorganic N was higher with Rinc and at higher residue levels (Figure 3). The results showed that irrespective of residue placement, co-application of wheat residue at 10 and 15 Mg/ha with nutrients (NS1) at stoichiometries greater than 12:1 (C:N) and 50:1 (C:P) and wheat residue at 5 Mg/ha with (NS1) at stoichiometries of 12:1 (C:N) and 50:1 (C:P) decreased the N₂O EF from inorganic N compared to control soil without residue (R0) at NS1. In contrast, NS2 treatment where the stoichiometries were less than 12:1 (C:N) and 50:1 (C:P) in R5 and R10 and equal to 12:1 (C:N) and 50:1 (C:P) in R15 significantly enhanced cumulative N₂O emissions compared to control soil without residue at NS2 (Figure 2). Repeated measures ANOVA indicated a significant effect of incubation time and interaction effects of time × RR rate × residue placement × nutrient on soil N₂O fluxes (Table 2).

3.3. CH₄ Fluxes

Cumulative CH₄ emissions responded significantly to the main effect of residue placement and rates, but nutrient stoichiometry and the interactive effect of factors had no significant effect (Table 1). The surface return of residue showed significantly higher CH₄ emissions with levels 1.7-fold greater than that noted for incorporation. CH₄ emissions increased, exhibiting a quadratic response to increasing crop residue rates (Figure 4). The order of the effects of RR rates on CH₄ emission (µg C kg⁻¹ soil) was R10 (5.5) > R5 (3.8) > R15 (2.6) > R0 (1.6) across nutrient levels and residue placements. The range of cumulative CH₄ emission was −3.5 to 9.8 µg C kg⁻¹ soil. Nutrient application generally increased the CH₄ fluxes by 1.4-fold compared with the control soil; however, the finding was not statistically significant. Averaged across nutrient levels and residue placements, the cumulative CH₄ emission of R10 was 1.4-fold greater than that of R0, and the cumulative CH₄ emissions of R15 were reduced by 0.7-fold compared with R0. Repeated measures ANOVA indicated a significant effect of time of incubation and interaction effects of time × RR rate and time × residue placement on soil CH₄ fluxes (Table 2).

3.4. Global Warming Potential (GWP)

The GWP showed significant variation among residue rates only (Table 1). However, the main effects of residue placement, nutrient levels, and their interaction had no significant effect (Figure 4). Averaged across nutrient levels and residue placements, the effects of residue rates on total GWP exhibited the order of R15 > R10 > R5 > R0. The magnitude ranged from 201.4 to 1563.6 mg CO₂ eq. kg⁻¹ soil. The GWP increased linearly with increasing residue rates (R² varying from 0.96 to 0.99 at p < 0.05) (Figure 5).

4. Discussion

4.1. CO₂ Emissions

The observed results demonstrate a significant influence of crop residue levels on cumulative soil CO₂ fluxes, with a notable linear response to increasing wheat residue inputs. The highest mean cumulative CO₂ fluxes were recorded at the residue level of 15 Mg ha⁻¹, showing a clear positive correlation between residue quantity and CO₂ emissions. This finding aligns with the existing literature suggesting that higher organic matter inputs into the soil can stimulate microbial activity, leading to increased carbon mineralization and subsequent CO₂ release [12,41,42]. The significant main effect of residue levels further underscores the importance of residue management practices in regulating soil CO₂ dynamics. The linear response observed in Figure 1 suggests that as wheat residue inputs increase, so does the release of CO₂. However, it is noteworthy that nutrient levels and residue placement modulate the response, which is comparable to findings from earlier studies [11,12].

The interaction among residue incorporation, nutrient addition (NS1 and NS2), and residue placement substantially impacted soil CO₂ fluxes. The combined effect of residue and nutrient levels was significant at 46 DAI, indicating that the early stages of the incubation period are crucial in determining CO₂ emissions. This could be attributed to the initial microbial decomposition of organic matter, which is influenced by both residue quality and nutrient availability [43]. The nonsignificant interaction effects at 116 DAI may suggest that over a more extended incubation period, the initial effects of residue and nutrient levels diminish, highlighting the importance of considering the temporal aspect in assessing soil CO₂ dynamics. The significant effects of incubation time and their interactions with residue level, placement, and nutrient level, as indicated by repeated measures ANOVA, emphasize the dynamic nature of the microbial processes involved in carbon mineralization [44]. The increased soil CO₂ fluxes with residue incorporation and nutrient addition, particularly at 46 DAI, suggest that microbial activity is stimulated under conditions of higher nutrient availability and enhanced organic matter decomposition [45,46]. The insignificant effect at 116 DAI might be indicative of a saturation point or a slowdown in microbial activity as the incubation progresses. Our results underscore the importance of nutrient stoichiometry in the microbial decomposition of residue carbon [8,9].

4.2. N₂O Emission

Cumulative N₂O fluxes exhibited a non-linear response to increasing residue levels at all nutrient rates. This suggests an optimal range of residue input beyond which N₂O emissions increase at a diminishing rate. This observation aligns with studies highlighting the nonlinearity of N₂O emissions in response to organic matter inputs [7,39,47].This finding is consistent with previous studies indicating a threshold effect of N₂O emissions in response to nitrogen inputs [33,39]. Similar to residue levels, a quadratic increase in N₂O emissions with increasing nitrogen rates from 0 to 12.5 mg kg⁻¹ soil, followed by a non-linear decrease afterward (12.5 to 37.5 mg N kg⁻1 soil) in control soil without residue, highlights the intricate relationship between nitrogen availability and N₂O emissions. As nitrogen inputs increase, there is a point at which the soil’s capacity to utilize and assimilate nitrogen becomes saturated. Beyond this threshold, additional nitrogen may not contribute proportionately to increased microbial activity, diminishing the return on N₂O emissions. This is supported by the insignificant effect of nutrients observed on soil CO₂ emissions at 116 DAI compared to 46 DAI (Table 1). Further, beyond a certain nitrogen input level, there can be a shift in the composition of the microbial community. Certain microbial populations may become dominant, leading to changes in nitrogen cycling processes. This shift may alter N₂O production dynamics, contributing to the observed quadratic response [48,49].

Incorporating residues into the soil increased N₂O emissions compared to surface placement across residue and nutrient levels. This result emphasizes the role of residue placement in influencing nitrogen transformation processes [39]. Residue incorporation likely creates microsites with enhanced microbial activity, increasing N₂O production [45]. Incorporation of residues into soil may enhance nitrogen availability from decomposing organic matter. This increased nitrogen availability provides more substrates for denitrification, resulting in higher N₂O emissions [12,14].

N₂O Emission Factor (EF)

Our study demonstrated that the N₂O EF from residue N was negative across residue levels at nutrient levels NS0 and NS1 and across residue placements. The EF was positive only at the highest level of residue R15 under the highest nutrient level NS2, and with residue incorporation. The negative emission factors in these treatment combinations suggest potential N₂O consumption or mitigation. This phenomenon underscores the importance of considering nutrient stoichiometry and residue placement in determining the net effect of residue N on N₂O emissions. The results also indicate that a minimum nutrient stoichiometry is required to get a positive N₂O EF from residue N. Wang et al. [23] also reported optimal fertilizer N doses in combination with full straw return in calcareous soil promoted a 57% reduction of soil N₂O to N₂.

However, the N₂O EF values from inorganic N at nutrient levels NS1 and NS2 were positive across residue placements and levels, including control soil, except R10 (surface and incorporated) at NS1 and R15 (incorporated) at NS1. All residue levels witnessed lower N₂O EF values from inorganic N compared with control soil without residue at NS1. In contrast, the highest nutrient level NS2 (3 times NS1) increased the N₂O EF values from inorganic N across residue levels and placements. This implies that adding residues, regardless of level, mitigated N₂O emissions at the lower nutrient level. The results demonstrated that a balanced nutrient stoichiometry is crucial in managing N₂O emissions from residue-returned soils, and excessive nutrient availability may contribute to higher emissions. The findings highlight the complex interactions among nutrient levels, residue placement, and N₂O emissions in residue-returned soils. The responses indicated the importance of considering multiple factors in agricultural practices to optimize nutrient management and mitigate greenhouse gas emissions, particularly nitrous oxide. Our results agree with previous studies highlighting the necessity of optimizing fertilizer applications in combination with crop straw return to reduce N₂O emissions and net GWP [23,39,45,50].

4.3. CH₄ Emissions

The cumulative methane emissions responded significantly to the main effect of residue placement and rates, indicating that these factors play a pivotal role in regulating CH₄ dynamics. Our results aligned with existing literature highlighting the importance of organic matter management in influencing anaerobic microbial processes that produce methane [10,14]. Cumulative CH₄ emissions were significantly positively correlated with the rice straw application rate in China [18], corn stover, and wheat straw in the Mid-Atlantic USA [41]. In contrast to the previous works of Battaglia et al. [41] and Wang et al. [18], both linear and non-linear (quadratic) responses of cumulative CH₄ emissions to RR rates observed in this study were modified by residue placement and nutrient levels. For example, a linear response was observed for RR rates in NS0 Rsur, and a non-linear was noted for RR rates in NS0 Rinc and NS2 Rsur. However, no response was observed for other treatment combinations (NS1 Rsur and Rinc, NS2 Rinc). The results indicated that irrespective of residue placement, co-application of wheat residue at 10 and 15 Mg/ha with nutrients (NS1) at stoichiometries greater than 12:1 (C:N) and 50:1 (C:P) except wheat residue at 5 Mg/ha with (NS1) at stoichiometries of 12:1 (C:N) and 50:1 (C:P) helps mitigate CH₄ emissions compared to control soil without residue at NS1. Similar to NS1, a nutrient stoichiometry of NS2 less than 12:1 (C:N) and 50:1 (C:P) in R5 and R10 and equal to 12:1 (C:N) and 50:1 (C:P) in R15 resulted in lower cumulative CH₄ emissions compared to control soil without residue at NS2 (Figure 3). However, irrespective of residue placement, all residue levels (R5, R10, and R15) showed higher cumulative methane emissions at NS0 (no nutrient) compared with control soil at NS0. Therefore, the stoichiometry of nutrient application balancing residue C input mitigated CH₄ emissions compared with control soil without residue. The linear response of CH₄ emissions to increasing residue input (i.e., control soil) under no nutrient (NS0) conditions was probably due to enhanced methanogenic activity [51] from improvements in available C substrates, microbial biomass, and soil respiration (Figure 1), leading to soil anaerobic conditions and highly reducing conditions in the absence of other potential electron acceptors like NO₃⁻ and SO₄⁻ consequently offering favorable conditions to methanogenesis and also inhibiting CH₄ oxidation [52]. However, nutrient addition in NS1 and NS2 treatments decreased methanogenesis and enhanced CH₄ oxidation due to the availability of electron acceptors like NO₃⁻ and SO₄⁻ for methanotrophy [53]. Further, across residues and nutrients, the surface return of residues resulted in significantly higher CH₄ emissions than incorporation (Figure 1). This finding is likely attributed to the aerobic interfaces of methanogenic environments in residue-incorporated soil providing habitats for methanotrophs, where methanotrophs can oxidize CH₄ [54]. This observation is consistent with studies suggesting that surface-applied residues create anaerobic microsites conducive to methane-producing microbial activity [50,55]. The higher emissions may be attributed to the increased accessibility of organic matter to methanogenic microbes at the soil surface [16].

4.4. Global Warming Potential (GWP)

The overall environmental risk of non-CO₂ emissions (N₂O and CH₄) is usually expressed as GWP (CO₂ eq) to help us understand the benefits of best management practices [16]. The GWP showed significant variation among residue rates independent of nutrient levels and residue placements, emphasizing the role of residue quantity in influencing the overall global warming potential. Averaged across nutrient levels and residue placements, the GWP values of R15 and R10 were increased by 1.7- and 2.6-fold, respectively, compared to R5. The linear increase in GWP with rising residue rates implies that higher residue inputs lead to greater greenhouse gas warming potential. This is consistent with studies emphasizing the significance of organic matter inputs in shaping the overall climate impact of agricultural practices [10,14,18]. Further, the linear response of GWP to residue rate is probably due to the linear response of CO₂ emissions to residue inputs, and the magnitude of CO₂ emissions is manifold greater than that of non-CO₂ emissions (CH₄ and N₂O). Additionally, residue rates had a more substantial impact on GWP than residue placement or nutrient levels, as indicated by the lack of significant main effects or interactions for these factors. This suggests that in the context of GWP, the quantity of residue input is a primary determinant. The order of the effects of residue rates on total GWP was R15 > R10 > R5 > R0, highlighting the importance of considering residue quantity in assessing the overall environmental impact. The magnitude of the GWP increased linearly with increasing residue rates, underlining the need for careful management of residue inputs to mitigate potential climate impacts. In summary, the observed results underscore the importance of residue management practices in influencing N₂O, CH₄, and CO₂ emissions and GWP. These findings provide valuable information for sustainable agricultural practices that balance soil fertility management with climate impact mitigation.

5. Conclusions

In conclusion, the study investigated the impact of wheat residue management (rate and placement) and nutrient addition on soil greenhouse gas fluxes (CO₂, N₂O, and CH₄) and the overall GWP. The results revealed that increasing wheat residue levels led to a linear increase in cumulative soil CO₂ fluxes, with the highest (p < 0.05) emissions observed at a residue level of 15 Mg ha⁻¹. Furthermore, incorporating residues and nutrient addition significantly enhanced CO₂ fluxes compared to the surface placement and no nutrient treatments, indicating the interactive effects of residue management and nutrient availability. The results showed that irrespective of residue placement, co-application of wheat residue at 10 and 15 Mg/ha with nutrients (NS1) at stoichiometries greater than 12:1 (C:N) and 50:1 (C:P) and wheat residue at 5 Mg/ha with (NS1) stoichiometries of 12:1 (C:N) and 50:1 (C:P) decreased N₂O EF from inorganic N and residue N compared to control soil without residue (R0) at NS1. The N₂O fluxes exhibited a non-linear response to increasing residue levels, with the magnitude influenced by both residue placement and nutrient stoichiometry. However, CH₄ emissions responded significantly to residue placement and rates, with the surface return of residues resulting in higher CH₄ emissions than incorporation. The GWP exhibited a linear increase with higher residue rates, emphasizing the importance of residue management practices in influencing overall soil greenhouse gas emissions. The study highlights the intricate relationships among residue management, nutrient stoichiometry, and greenhouse gas emissions in agricultural soils. The findings underscore the need for a comprehensive field study to understand these interactions to develop sustainable crop residue management that mitigates greenhouse gas emissions and contributes to climate change mitigation efforts.

Author Contributions

Conceptualization, S.L. and S.S.Y.; Data curation, D.S., D.K.Y. and S.S.Y.; Formal analysis, D.S., S.L., N.K.L., D.K.Y., A.S. and J.K.; Funding acquisition, S.L.; Investigation, D.S., S.L., N.K.L. and J.K.; Methodology, D.S., S.L., D.K.Y., S.S.Y., A.S. and J.K.; Project administration, S.L.; Software, D.S., N.K.L. and A.S.; Supervision, S.L., N.K.L., S.S.Y. and A.S.; Validation, N.K.L., D.K.Y. and R.S.K.; Visualization, S.L., D.K.Y., S.S.Y. and R.S.K.; Writing—original draft, D.S. and S.L.; Writing—review & editing, N.K.L. and R.S.K. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data are contained within the article.

Acknowledgments

The corresponding authors (S.L. and R.S.K.) express sincere gratitude to USDA-Foreign Agricultural Service for granting the Scientific Exchange Program Fellowship on Climate Smart Agriculture to S.L. We gratefully acknowledge the editor’s and reviewers’ invaluable contributions in shaping this research.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

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Figures and Tables

Figure 1. The response of cumulative CO2 emissions (mg C kg−1 soil) to residue return (RR) rates under different residue placements (surface [Rsur] and incorporated [Rinc]) and nutrient supplement levels (NS0, NS1, and NS2) after 116 days of incubation. Details of the statistical analysis are provided in Table 1. The plotted values are averages of three replicates, and error bars represent standard errors.

Figure 1. The response of cumulative CO2 emissions (mg C kg−1 soil) to residue return (RR) rates under different residue placements (surface [Rsur] and incorporated [Rinc]) and nutrient supplement levels (NS0, NS1, and NS2) after 116 days of incubation. Details of the statistical analysis are provided in Table 1. The plotted values are averages of three replicates, and error bars represent standard errors.

Figure 2. The response of cumulative N2O emissions (µg N kg−1 soil) to residue return (RR) rates under different residue placements (surface [Rsur] and incorporated [Rinc]) and nutrient supplement levels (NS0, NS1, and NS2) after 116 days of incubation. Details of the statistical analysis are provided in Table 1. The plotted values are averages of three replicates, and error bars represent standard errors.

Figure 3. The effect of residue return (RR) rate under different residue placements (surface [Rsur] and incorporated [Rinc]) and nutrient supplement levels (NS0, NS1, and NS2) on the percent emission factor (EF) from residue N (A–C) and inorganic N (D) after 116 days of incubation.

Figure 3. The effect of residue return (RR) rate under different residue placements (surface [Rsur] and incorporated [Rinc]) and nutrient supplement levels (NS0, NS1, and NS2) on the percent emission factor (EF) from residue N (A–C) and inorganic N (D) after 116 days of incubation.

Figure 4. The response of cumulative CH4 emissions (µg C kg−1 soil) to residue return (RR) rates under different residue placements (surface [Rsur] and incorporated [Rinc]) and nutrient supplement levels (NS0, NS1, and NS2) after 116 days of incubation. Details of the statistical analysis are provided in Table 1. The plotted values are averages of three replicates, and error bars represent standard errors.

Figure 4. The response of cumulative CH4 emissions (µg C kg−1 soil) to residue return (RR) rates under different residue placements (surface [Rsur] and incorporated [Rinc]) and nutrient supplement levels (NS0, NS1, and NS2) after 116 days of incubation. Details of the statistical analysis are provided in Table 1. The plotted values are averages of three replicates, and error bars represent standard errors.

Figure 5. The response of the global warming potential (GWP, mg CO2 eq kg−1 soil) to residue return (RR) rates under different residue placements (surface [Rsur] and incorporated [Rinc]) and nutrient supplement levels (NS0, NS1, and NS2) after 116 days of incubation. Details of the statistical analysis are provided in Table 1. The plotted values are averages of three replicates, and error bars represent standard errors.

Figure 5. The response of the global warming potential (GWP, mg CO2 eq kg−1 soil) to residue return (RR) rates under different residue placements (surface [Rsur] and incorporated [Rinc]) and nutrient supplement levels (NS0, NS1, and NS2) after 116 days of incubation. Details of the statistical analysis are provided in Table 1. The plotted values are averages of three replicates, and error bars represent standard errors.

Table 1

Summary of ANOVA results indicating source effects on soil CO₂, N₂O, and CH₄ emissions and the GWP at 46 and 116 days after incubation (DAI).

Source of Variation	Degrees of Freedom	CO2(46 DAI)	CO2(116 DAI)	N2O(46 DAI)	N2O(116 DAI)	CH4(46 DAI)	CH4(116 DAI)	GWP
RR rate	3	<0.001	<0.001	0.001	<0.001	0.044	0.019	<0.001
Residue placement	1	0.008	0.802	<0.001	<0.001	0.051	0.045	0.778
NSL	2	0.035	0.288	<0.001	<0.001	0.653	0.573	0.152
RR rate × Residue placement	2	0.130	0.910	0.028	0.028	0.717	0.602	0.841
RR rate × NSL	6	0.387	0.163	0.035	0.017	0.629	0.414	0.195
Residue placement × NSL	2	0.649	0.179	<0.001	<0.001	0.580	0.427	0.247
RR rate × Residue placement × NSL	4	0.880	0.929	0.107	0.030	0.983	0.588	0.976

Note: Residue return rate: RR, Nutrient supplement levels: NSL.

Table 2

Summary of repeated measures ANOVA results indicating source effects on soil CO₂, N₂O, and CH₄ emissions during the incubation period.

Source of Variation	CO2	N2O	CH4
Time	<0.001	<0.001	<0.001
Time × RR rate	<0.001	<0.001	0.069
Time × residue placement	<0.001	<0.001	0.091
Time × NSL	0.020	<0.001	0.474
Time × RR rate × residue placement	0.967	0.045	0.387
Time × RR rate × NSL	0.334	0.008	0.397
Time × residue placement × NSL	0.306	0.001	0.819
Time × RR rate × residue placement × NSL	0.944	0.055	0.210

Note: Residue return rate: RR, Nutrient supplement level: NSL.

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