Predicting Long-Term Maize Straw Decomposition from Incorporation Amount and Depth in the Black Soil Region of Northeast China

Abstract

Straw incorporation, as a widely recommended agronomic practice, has been continuously enhancing global crop production and soil–water conservation. However, the absence of a direct predictive capability for the long-term residual biomass of incorporated straw, based on management practices, constrains an accurate assessment of its effectiveness for soil conservation. To address these knowledge gaps, this study conducted systematic 4-year in situ monitoring of decomposition pits with varying incorporation amounts (A6 with 6 kg ha⁻¹, A8 with 8 kg ha⁻¹, A10 with 10 kg ha⁻¹, A12 with 12 kg ha⁻¹, and A14 with 14 kg ha⁻¹) and burial depths (D1 with 0–10 cm, D2 with 10–20 cm, D3 with 20–30 cm, D4 with 30–40 cm, D5 with 40–50 cm) to analyze long-term decomposition dynamics. Furthermore, time-dependent equations for post-incorporation residual biomass were developed based on management variables (incorporation amount and burial depth) to enhance the accuracy of soil loss prediction. The results showed that the higher incorporation amounts accelerated decomposition, with the residual straw ratios (RSRs) reduced by 27.4–62.2% compared to lower amounts at equivalent burial depths. Decomposition slowed with depth, and the RSR increased significantly with greater burial depth, rising at rates of 0.2–1.2% cm⁻¹ (p < 0.05). The RSR decreased significantly with longer incorporation duration at rates of 6.9–18.6% a⁻¹ (p < 0.05), with deeper soil layers exhibiting greater decline rates than shallower depths. The relationship between RSR and landfill amount (m), burial depth (d), and landfill years (a) is represented as follows: RSR = 10^1.62 a⁻¹ m^−0.54 d^0.45 (R² = 0.76). Based on this equation, the soil loss ratios (SLRs) under continuous straw incorporation for 4 years were estimated, and the results suggest that constant straw incorporation exerts cumulative effects, progressively reducing the SLR. This study provides the theoretical foundation for promoting and managing straw incorporation practices.

1. Introduction

Straw incorporation, a traditional land conservation measure, involves decomposing straw to increase soil carbon content, thereby improving soil fertility [1]. It also effectively reduces soil erosion [2], enhancing soil infiltration capacity and reducing runoff [3,4]. In the soil erosion model, the inhibitory effect of straw returning on soil erosion is reflected in the form of the cover and management factor (C factor), which refers to the ratio between soil loss from land cropped under specified conditions and the corresponding loss from clean-tilled, continuous fallow [5]. In the revised Universal Soil Loss Equation (RUSLE), quantifying the impact of residues on soil erosion requires considering their biomass [6]. However, the incorporated straws gradually decompose over time, and some of them still remain within the tillage layer. The decomposition rate of straw under various conditions inevitably varies [7], resulting in differences in the residual amount of straw in the tillage layer over time and response to environmental changes [8]. Therefore, predicting the dynamic changes in the remaining amount of returning straw is essential for accurately quantifying the value of the C factor, thereby improving the accuracy of soil erosion prediction.

The decomposition of straw is influenced by various factors [2,9]. Firstly, climate conditions such as temperature and precipitation are critical factors that control straw decomposition on a large geographical scale [10]. Cai et al. [11] found that the remaining amount of a specific type of straw in mild regions is higher than in warm and subtropical regions in a calendar year. Secondly, the straw physical traits (type, size, and amount) can affect straw decomposition by altering microbial growth and action [7,12]. In addition to the physical traits, returning crop straw can be incorporated into the soils of different depths via practices like plowing, rotary, or disc-tillages that change the interaction area between soil and straw to affect its decomposition rate [7,13]. Furthermore, differences in soil properties, such as texture, nutrients, and porosity, indirectly affect straw decomposition by influencing microbial activity [14,15]. Although predicting straw decomposition is difficult due to the influence of multiple factors, many scholars have proposed empirical equations to estimate straw decomposition [11,16,17,18,19,20,21]. An overview of recent studies reveals that these equations are primarily categorized into two types. One is primarily based on the “intrinsic properties” of the straw, that is, its inherent physicochemical composition. These models treat the decomposition process as a kinetic process dominated by the material’s own characteristics [18]. For instance, Liang et al. [19] calibrated the parameters of a bi-exponential decay model by quantifying the organic components in various crop straws; similarly, Ding et al. [20] also demonstrated that the straw decomposition rate can be simulated using intrinsic indicators such as its carbon and nitrogen content, lignin concentration, and C/N ratio. The others focused on emphasizing the crucial regulatory role of environmental conditions on the decomposition process. For example, Cai et al. [11] demonstrated that integrating thermal time with a tri-exponential decay equation and soil nutrient data provides optimal accuracy for long-term predictions and Yang et al. [21] showed that a convolutional neural network utilizing spectral features possesses a higher predictive ability for the decomposition rate. However, current equations have not accounted for decomposition variations induced by anthropogenic factors, namely differences in field management practices such as returning depths and amounts. This aspect is intrinsically linked to the effectiveness of straw incorporation in controlling soil erosion [13]. Quantifying the relationships between remaining straw mass and these key management variables would enable the rapid prediction of residual straw under diverse incorporation scenarios, thereby enhancing the assessment of its potential to mitigate soil erosion.

Northeast China is one of the world’s major black soil regions and has a substantial grain production base in China. This area contributes 34% of China’s total corn yield, generating significant volumes of maize residue annually [22]. Historically, farmers practiced complete post-harvest residue removal due to concerns that crop residue would impede seed germination under the region’s cool climatic conditions characterized by high latitude and low mean annual temperatures. However, sloping croplands, long-term cultivation, and frequent extreme rainfall have caused severe soil erosion in this area [2,23]. In recent years, growing awareness of black soil conservation has prompted local governments and farmers to implement straw incorporation policies. Utilizing maize straw for incorporation not only addresses residue surplus but also provides a viable solution to mitigate erosion on sloping farmland [22]. These maize residues are returned to fields via surface mulching, incorporation, and deep burying [13], with straw application amounts varying across farmlands by maize yield differences [2]. Given the challenges of controlling experiments in complex field environments, a four-year controlled experiment was implemented using decomposition pits in Northeast China’s black soil region, incorporating five straw returning amounts and five burial depths to observe the decomposition dynamics of a single straw addition. The primary objective is to establish an equation for describing remaining straw amount dynamics as a function of decomposition time, burial depth, and application amounts, thereby enhancing soil loss prediction accuracy post-incorporation and establishing a theoretical basis for optimizing regional straw management practices.

2. Materials and Methods

2.1. Study Area

This study was conducted at Jiusan Soil and Water Conservation Research Station of Beijing Normal University (48°59′55″ N, 125°17′35″ E) (Figure 1a). The climate of this area is a semi-humid continental cold-temperate climate with an average annual temperature of 0 °C and precipitation of 546 mm (1971–2018). The lowest temperature in January is about −22.5 °C, and the highest temperature in July is about 20.8 °C. Precipitation has an uneven seasonal distribution, with over 67% of rainfall occurring from June to September [13]. The area is a hilly region with long (2000–3000 m) and gentle slopes (1–5°). The dominant soil is black soil in the Chinese Soil Taxonomy, Udic Argiboroll in the US Soil Taxonomy, and Luvic Phaeozem in the FAO/UNESCO system. At present, cropland is the dominant land use type in the local area, with soybean (Glycine max (Linn.) Merr.) and maize (Zea mays L.) primarily cultivated under rain-fed conditions in the up–down ridge system [24].

2.2. Experimental Design

Following the local agricultural practice, the straw was mechanically incorporated by first crushing it with a straw crusher after harvest and then mixing the crushed material into the tillage layer using a rotary tiller [25]. Given the local maize yield range of 10–12 t ha⁻¹ and a grain-to-straw ratio of 1:1, five straw application amount levels were established: 6, 8, 10, 12, and 14 kg ha⁻¹ (denoted A6–A14). To characterize decomposition–depth relationships, five burial depth levels were designed: 0–10 cm (D1), 10–20 cm (D2), 20–30 cm (D3), 30–40 cm (D4), and 40–50 cm (D5). This generated 25 treatments with three replicates each.

Four concrete decomposition pits (2 m length × 2 m width × 1.5 m depth) were constructed in 2018 for the four-year experiment. Each pit featured integral waterproof construction with impermeable concrete walls and base. Stainless steel panels subdivided pits into 16 cells (0.5 m length × 0.5 m width × 1.5 m depth), with each cell accommodating all five depth levels at the same application amount in a randomized complete block design with three replicates (Figure 1b). The black soil and maize straw used in the decomposition pits were collected from local farmland within the Hebei small watershed. The chemical characterization of soil from this specific area, based on the average values from 18 sampling plots across the watershed, has been reported by Li and Duan, with key properties including a soil pH of 6.37 and an organic matter content of 2.04% [26]. To isolate the effect of continuous straw input on total residue quantity and enable clear observation of the decomposition process after incorporation, the straw was applied in a single, one-time application during the first year. Soil bulk density (BD) measured by the cutting ring method at D1–D5 depths was used to calculate the required soil mass per cell (

Table 1. Soil bulk density of each layer and fill soil weight before the experiment.

Depth (cm)	BD (g cm−3)
0–10	0.85
10–20	1.05
20–30	1.05
30–40	1.14
40–50	1.10
Average BD (g cm−3)	1.04
10 cm depth required fill soil weight for each cell (kg)	127.92

). Straw per cell was divided equally into five portions. From D1 to D5, each layer received a homogenized soil–straw mixture at the designated straw landfill amount level (

Table 2. The required straw air-dry weight under different landfill amount treatments.

Landfill amounts (t ha−1)	6	8	10	12	14
10 cm depth required straw air-dry weight for each cell (g)	54	72	90	108	126

).

Meteorological data, including annual precipitation, average temperature, and average maximum and minimum temperatures, were obtained from a local weather station to characterize the climate conditions during the experimental period (Figure 2). The climatic year was defined from September to the following August to align with the post-harvest straw incorporation cycle. The data reveal interannual variability in both temperature and precipitation regimes across the four years of the study. This variation underscores that our decomposition experiment and the subsequent model development were conducted under a range of realistic climatic conditions, thereby incorporating the natural environmental ‘noise’ that influences straw decomposition processes, particularly at shallower soil depths.

Soil temperature and volumetric water content were monitored using HydraProbe soil sensors (Stevens Water Monitoring Systems, Portland, OR, USA). The probes were installed horizontally at three depths (10, 30, and 50 cm) within the soil profile of both the decomposition pits and open field to enable a direct, depth-resolved comparison. Unfortunately, due to technical malfunctions and data logger failures over the multi-year study period, a complete dataset for the entire experimental duration is unavailable. However, the collected measurements from June 2021 to May 2022 provided representative data covering a full seasonal cycle for a robust comparative analysis. The close agreement between the pit and field environments, as demonstrated in Figure 3a,b, ensures that the controlled experiments in the decomposition pits effectively replicated the core environmental drivers of decomposition, thereby supporting the ecological relevance of our findings.

2.3. Soil Sampling and Analysis

Samples were collected after 1 year (2019), 2 years (2020), 3 years (2021), and 4 years (2022) of straw burial. Annually, 75 soil samples (25 treatments × 3 replicates) were retrieved from one decomposition pit. Soil–straw mixtures were separated via immersion–rinsing protocol, with residual straw recovered by filtration. The residual straw ratios after returning were determined by weighing method [12]. The calculation formula is as follows:

$$RSR ={\displaystyle \frac{RSW}{ISW}}\times 100\%$$

(1)

where RSR is the residual straw ratio (%); RSW is the residual straw weight (t ha⁻¹); and ISW is the initial straw weight (t ha⁻¹).

2.4. Data Statistical Analysis, Development and Validation of a Predictive Equation for RSR

The data from these three-factor, multi-level experimental results were analyzed using a three-way analysis of variance (ANOVA). All reported means are estimated marginal means derived from the full factorial model and are presented with their 95% confidence intervals (95% CI). Where significant interactions were detected, simple effect analyses were conducted, with multiple comparisons performed using Duncan’s method at p < 0.05 among treatment levels within a fixed factor.

Based on the experimental data, our study developed a predictive model for the RSR. A power-law model was selected to describe the relationship between the RSR and the independent variables, as it effectively captures the non-linear decay dynamics typical of decomposition processes [11]. The dataset was randomly divided into a training set (60% for model development) and a testing set (40% for model validation). The coefficient of determination (R²), root mean square error (RMSE), and NSE were used to assess the agreement between observed and predicted values. These indices were calculated as follows [27]:

$$R^2={\displaystyle \frac{{[ \sum _{i=1}^N{(O}_i-\overline{O})(P_i-\overline{P})]}^2}{{\sum _{i=1}^N{(O}_i-\overline{O})}^2{\sum _{i=1}^N(P_i-\overline{P})}^2}}$$

(2)

$$RMSE=\sqrt{{\displaystyle \frac{\sum _{i=1}^N{(P_i-O_i)}^2}{N}}}$$

(3)

$$NSE=1-{\displaystyle \frac{\sum _{i=1}^N{(P_i-O_i)}^2}{\sum _{i=1}^N{(O_i-\overline{O})}^2}}$$

(4)

where N is the number of data points, O_i and P_i are the ith observed and predicted values (i varies from 1 to N), and $\overline{\mathrm{O}}$ and $\overline{\mathrm{S}}$ are the mean values of the observed and simulated series, respectively.

All statistical analyses were conducted using SPSS 26.0 [28].

2.5. Scenario Analysis of Soil Loss Ratios Under Slope Cropland

To illustrate the impact of straw residue, this study conducted a scenario analysis according to the Agriculture Handbook about Universal Soil Loss Equation (USLE) published by Wischmeier and Smith [5] to estimate the soil loss ratio (SLR) under four years of continuous straw incorporation. The USLE, an empirical model for predicting soil erosion, is expressed as follows: A = R × K × L × S × C × P [5]. Here, A is the mean annual soil loss per unit area (t ha⁻¹a⁻¹); R is the rainfall and runoff factor (MJ mm ha⁻¹ h⁻¹ a⁻¹); K is the soil erodibility factor (t MJ⁻¹mm⁻¹h); L is the slope-length factor (dimensionless); S is the slope-steepness factor (dimensionless); C is the cover and management factor (dimensionless); and P is the support practice factor (dimensionless).

The SLR, defined as the ratio of soil loss from land under specific management to that from clean-tilled continuous fallow, isolates individual factors to characterize soil loss extent under different scenarios, thus serving as a key metric for quantifying the reduction effect of crop residue cover on soil erosion [5,6]. As SLR decreases and approaches zero, it indicates diminished soil loss and enhanced soil and water conservation effectiveness. According to the USLE, determining the SLR under varying straw residue scenarios requires corresponding residue cover values (

Table 3. Soil loss ratios for the fallow period when corn straws are chopped and distributed.

Residue Cover (%)	Soil Loss Ratios (%)
	Tilled Seedbed 1	No-Till
20	48	34
30	37	26
40	30	21
50	22	15
60	17	12
70	12	8
80	7	5
90	4	3
95	3	2

¹ This column applies for all systems other than no-till.

) [5].

Since the predictive equation for RSR developed in this study provides an estimate of straw amount, our study converted the straw amount to residue cover, based on the relationship between residue cover and residue dry weight established by Xin et al. [29] in the black soil region of Northeast China:

$$RC=\left[1-\exp \left(-0.00024\times B_s\right)\right]\times 100\%$$

(5)

where RC is the percent residue cover (%) and B_s is the residue dry weight (kg ha⁻¹). Then, the SLR corresponding to different residue cover levels was obtained by consulting Table 3, with interpolation applied as needed.

The experiment employed a single one-time application of straw in the first year, rather than continuous annual incorporation. This design was chosen to isolate the effect of the incorporation rate and clearly observe the decomposition process of the initial straw input. To illustrate the practical scenario of continuous annual straw return, the decomposition model was extended to multi-year application. The cumulative residue retention after n years of continuous incorporation was calculated as follows:

$$S(n)=M{\sum }_{k=1}^{n}RSR\left(k\right)$$

(6)

where S(n) is the total residual straw amount after n years of continuous incorporation; M is the annual incorporation amount (kg ha⁻¹); RSR(k) is the residual proportion after k years of decomposition (as predicted by the equation for residue straw ratio established in this study); and n is the number of years of continuous incorporation.

3. Results

3.1. Results of Statistical Analysis

The three-way ANOVA results revealed that the landfill year, landfill amount, and burial depth all had highly significant main effects on the RSR (p < 0.01), with large effect sizes (partial η² = 0.42–0.90,

Table 4. Three-way ANOVA of factors affecting residual straw ratio.

Source of Variation	df	F-Value	p-Value	Partial η2
Landfill year	3	609.34	0.00	0.90
Landfill amount	4	36.01	0.00	0.42
Burial depth	4	146.51	0.00	0.75
Landfill year × Landfill amount	12	2.93	0.00	0.15
Landfill year × Burial depth	12	8.93	0.00	0.35
Landfill amount × Burial depth	16	1.31	0.20	0.10
Landfill year × Landfill amount × Burial depth	48	1.56	0.02	0.27

). Furthermore, the two-way interactions of landfill year × landfill amount and landfill year × burial depth, as well as the three-way interaction, were also significant (p < 0.05) with large effect sizes (Partial η² = 0.15–0.35). In contrast, the landfill amount × burial depth interaction was not significant. Due to these significant interactions, indicating that the factor effects are not independent, we proceeded with simple effects analyses to examine the effect of one factor at specific levels of the others.

3.2. RSR Under Different Landfill Amounts, Burial Depths and Landfill Years

Except for the first year after straw returning, the RSRs were significantly lower at the same depth for high compared to low return amounts, and this difference gradually increased with longer incorporation duration (Figure 4). Figure 4a showed that after one year of straw incorporation, there was no significant difference in the RSR with different returning amounts in all soil layers except for the surface soil. By the second year, however, the RSRs in the same depth were significantly reduced by 32.9%, 37.2%, 39.7%, 35.5%, and 27.4% for the high amount compared to the low amount of straw returning from D1 to D5, respectively (Figure 4b). Similarly, three and four years after straw incorporation, the RSRs under higher landfill amounts were lower than those under lower landfill amounts at the same soil depths, with the reductions reaching 48.9% (D1), 55.5% (D2), 48.2% (D3), 36.3% (D4), and 34.1% (D5) after three years, and 62.2% (D1), 47.6% (D2), 49.1% (D3), 36.2% (D4), and 39.1% (D5) after four years, respectively (Figure 4c,d).

Although the RSR under the two landfill amount treatments (A6, A12) after one year of straw incorporation did not show a significant increasing trend with depth, significant increasing trends occurred in all other landfill amount treatments (A8, A10, A14) (Figure 5a) with trends for A8, A10, and A14 of 0.6% cm⁻¹, 0.8% cm⁻¹, and 1.2% cm⁻¹, respectively (p < 0.05). After 2, 3, and 4 years of straw returning, the RSR under all landfill amount treatments had a significant increasing trend with increasing depth (Figure 5b–d), with trends of 0.6–0.8% cm⁻¹, 0.5–0.6% cm⁻¹ and 0.2% cm⁻¹ (p < 0.05), respectively. In short, RSR increased significantly with depth, and this increasing trend became more gradual with longer landfill years.

With increasing landfill year, RSR decreased significantly, and the downward trend was more pronounced in the deeper layers than in the shallower layers (Figure 6). Except for the A6 treatment at the D2 depth, the RSR significantly decreased with increasing landfill year in all landfill amount treatments at all depths. The decreasing trends of A6, A8, A10, A12, and A14 treatments were 10.6–15.3% a⁻¹, 10.6–16.4% a⁻¹, 8.7–15.9% a⁻¹, 9.4–18.0% a⁻¹, and 6.9–18.6% a⁻¹, respectively (p < 0.05). For a given returning treatment, the smallest decline rate occurred at depth D1, while the largest occurred at depth D5. Four years after straw incorporation, the differences in RSRs among different depths under the same landfill amount treatment were substantially reduced compared to those one year after incorporation (Figure 6). For example, after one year straw returning in the A8 treatment, the RSR at depth D5 was higher than that at depths D1, D2, D3, and D4 by 24.1%, 19.4%, 15.3%, and 11.4%, respectively, compared to only 8.6%, 6.4%, 4.6%, and 3.1% higher four years after return.

3.3. Development and Validation of a Predictive Equation for RSR

Multiple regression analysis on the training set yielded a significant predictive model (p < 0.05), from which the final power-law equation was derived by back-transforming the linear regression coefficients:

RSR = 10^1.62 a⁻¹ m^−0.54 d^0.45 (R² = 0.76).

where RSR is the residual straw ratio (%); a is the landfill year (a); m is the landfill amount (t ha⁻¹); and d is the burial depth (cm).

The model explained a high proportion of variance in the training data (R² = 0.76), suggesting a good fit. Its predictive performance was further evaluated on an independent test set. Results showed close agreement between predicted and observed values, with R² = 0.75, NSE = 0.71, and RMSE = 5.36% in testing (Figure 7). This confirms the model’s good reliability for data not used in its development.

3.4. Soil Loss Assessment for Slope Cropland Under Straw Incorporation

This study calculated the topsoil RSR at varying incorporation amounts over four consecutive years. The RSR values were then converted to SLR through residue coverage-based transformation. It showed that continuous straw incorporation progressively reduced SLR over time, with the reduction attributed to cumulative residual effects (Figure 8).

4. Discussion

4.1. Effects of Different Landfill Amounts, Burial Depths, and Durations on Straw Decomposition

Higher straw landfill amounts led to faster straw decomposition. Our study demonstrated that, except for the first year after returning, RSR was significantly lower under higher return amounts compared to lower amounts at the same depth (Figure 4). The degradation process of returned straw is a mineralization and putrefaction process involving both microorganisms and enzymes [30]. Liu et al. [12] demonstrated a positive correlation between straw returning amount and bacterial species richness during decomposition. Latifmanesh et al. [31] further indicated that higher straw input amounts increased total soil porosity, which facilitates aerobic microbial activity. Consequently, microorganisms utilize energy and nutrients from returned straw to enhance their biomass and respiration, with this enhancement being more pronounced at elevated straw levels [32]. Concurrently, enhanced soil microbial activity promotes increased production of exudates, including soil enzymes [33], with enzyme activities rising proportionally with straw landfill amounts [34]. Therefore, higher microbial and enzymatic activities under elevated landfill amounts contribute to accelerated straw decomposition, as reported in previous studies [31,32,33,34,35]. This mechanism explains why RSR was significantly smaller in high landfill amount treatments compared to low amount treatments in our study.

Furthermore, straw decomposition rates decreased significantly with increasing soil depths. In this study, RSR exhibited similar depth-dependent trends across different landfill amounts (Figure 5). Except for two treatments (A6 and A12) during the first year after return, all other treatments showed significantly increased RSR values from depth D1 to D5 at equivalent landfill ages (Figure 5). The impact of burial depth on straw decomposition constitutes a complex process involving soil environment, microbial activity, and physicochemical conditions [7]. On the one hand, microbial biomass directly governs straw decomposition rates, with higher decomposition rates being attributed to greater microbial biomass, and topsoil layers typically exhibit significantly higher microbial biomass than deeper soil layers [36,37]. On the other hand, differences in soil environmental factors—such as temperature and oxygen availability across depths—indirectly affect decomposition rates by altering microbial activity and diversity [15]. Latifmanesh et al. [31] reported higher total porosity in shallow burial depth treatments, indicating greater oxygen availability in topsoil layers. Their study also documented elevated rates of straw decomposition under higher soil temperatures. Thus, microbial activity in shallow soil exhibits higher sensitivity, which benefits improved decomposition conditions [7], resulting in accelerated straw decomposition.

Meanwhile, the RSR significantly decreased over time; however, the downward trend of RSR was smaller in the shallow layers and larger in the deep layers (Figure 6). After straw returning, although decomposition was slower in deeper layers, the RSR difference between depths gradually diminished over time. One contributing factor is that all straw was filled in 2018, not continuously, causing RSR in all treatments to approach zero over time. Additionally, as straw decomposes, the lower residual amount in surface layers reduces available energy sources, leading to the consequent reduction in microbial growth and enzyme activity [38]. As mentioned earlier, higher residue amounts enhance microbial and enzymatic activities, accelerating straw decomposition [32]. This mechanism is corroborated by our observation that the decline rate at depth D5 was greater under high landfill amounts than under low amounts (Figure 6).

It is noteworthy that the meteorological data from the experimental site reveal distinct interannual variations during the four-year study (Figure 2). The relatively higher temperature in the first year likely accelerated the initial straw decomposition, due to the positive correlation between temperature and decomposition rates [9]. Furthermore, the notably higher precipitation in the third year, followed by drier conditions in the fourth, would have directly modulated soil moisture. According to recent findings, while straw decomposition is generally proportional to soil moisture within an appropriate range, excessive water content can inhibit the activity of key enzymes such as urease and catalase, thereby restricting decomposition [39,40]. Although such climatic differences are known to directly affect decomposition rates, particularly in surface layers [9,41], our experiment and model were conducted under this realistic spectrum of conditions. Thus, the effects of climate were not omitted but were incorporated as natural environmental “noise” within the dataset used to build our model.

4.2. Effect of Continuous Straw Incorporation on Soil Loss

Crop residue cover reduces soil erosion by providing a protective mulch layer and enhancing soil consolidation [2]. Models like the Universal Soil Loss Equation (USLE) and its revised version (RUSLE), used to estimate erosion potential, require reliable estimation of crop residue decomposition to assess changes in soil surface cover [42]. This study established an equation relating the RSR after straw incorporation to field management practices, including straw incorporation rate and burial depth. To assess the soil conservation efficacy of straw incorporation, our study calculated the topsoil RSR at varying incorporation amounts over four consecutive years. The RSR values were then converted to SLR through residue coverage-based transformation. The results showed that continuous straw incorporation exerts cumulative effects, progressively reducing the SLR (Figure 5).

On the one hand, as fundamental units of soil structure, the distribution and abundance of soil aggregates govern pore characteristics and regulate water retention and aeration, which play an important role in soil fertility and erosion resistance [43]. Continuous straw incorporation amplified surface cover and aboveground biomass, which in turn promoted the formation and stabilization of soil aggregates. On the other hand, continuous straw incorporation sustains soil organic matter accumulation [44], and erosion resistance increases with higher soil organic matter content [45]. Consequently, continuous straw incorporation concurrently augments surface coverage and improves soil physicochemical properties, thereby progressively diminishing the SLR.

4.3. The Limitations of the Predictive Equation for RSR

This study conducted a four-year straw decomposition experiment in the Black Soil Region of Northeast China, incorporating five straw incorporation amounts (6–14 t ha⁻¹) and five burial depths (0–50 cm). Based on these factors, a predictive equation for straw decomposition was developed. This model serves as a reasonable preliminary tool that offers a valuable reference for practical agricultural management, while we acknowledge that further model refinement is required in future studies.

The study site is located in the northern part of Northeast China. This area experiences a semi-humid continental cold-temperate climate, and its dominant soil is classified as Black Soil in the Chinese Soil Taxonomy, Udic Argiboroll in the US Soil Taxonomy, and Luvic Phaeozem in the FAO/UNESCO system [24]. However, Northeast China covers a vast territory of approximately 1.2 million km², including the provinces of Jilin, Liaoning, Heilongjiang, and Inner Mongolia (Figure 1). Temperature exhibits a decreasing gradient from south to north, with average temperatures in the hottest month of July ranging from 20 °C to 24 °C. Precipitation decreases from southeast to northwest, with an mean annual precipitation between 400 and 1200 mm [46]. Consequently, due to the significant spatial variations in hydrothermal conditions across this extensive region, the predictive equation for straw decomposition developed in this study has limited universal applicability across the entire Northeast China black soil area, thus requiring further validation and refinement over a broader geographical scope.

Furthermore, straw decomposition is a complex and prolonged process. Driven by factors such as soil microorganisms and enzymes, easily decomposable substances are largely broken down initially, leaving behind difficult decomposable substances like cellulose and lignin that require long-term decomposition [47]. This explains the high decomposition rate observed in the first year, followed by a gradual decline in subsequent years [40]. However, the decomposition dynamics captured in this study span only four years, leaving the long-term decomposition behavior of these recalcitrant components beyond this period subject to further experimental confirmation. Therefore, the predictive model developed here requires longer-term validation for projections extending beyond four years of incorporation. Meanwhile, mechanical tillage operations in practice lead to uneven distribution of residue between surface and deeper soil layers. However, to isolate the effect of burial depth on RSR and facilitate model development, this study intentionally excluded variation in incorporation amount. Future research could refine prediction models by incorporating this practical factor into experimental designs.

Although this study established the relationship between the RSR and landfill amounts, burial depth, and landfill years, it primarily addressed the effects of two management practices. Other critical factors—such as fertilization measures and crop rotations—may alter soil properties and influence straw decomposition. Future research should quantify these interactive effects to determine optimal regional straw incorporation strategies.

5. Conclusions

Based on a 4-year straw decomposition experiment, this study analyzed the effects of different landfill amounts, burial depths, and landfill years on straw decomposition, and an equation predicting RSR as a function of these three parameters was established. Using this model, SLR under the 4-year continuous incorporation was estimated, and the following conclusions were drawn: (1) Higher incorporation amounts accelerated straw decomposition, reducing the RSR by 27.4–62.2% compared to lower amounts at equivalent burial depths; Deep decomposition is slower than surface, and RSR increased significantly with greater burial depth, rising at rates of 0.2–1.2% cm⁻¹ (p < 0.05); RSR decreased significantly with longer incorporation duration at rates of 6.9–18.6% a⁻¹ (p < 0.05), with deeper soil layers exhibiting greater decline rates than shallower depths; (2) RSR varied with landfill amount (m), burial depth (d), and landfill years (a) as follows: RSR = 10^1.62 a⁻¹ m^−0.54 d^0.45 (R² = 0.76); (3) Continuous straw incorporation exerts cumulative effects, progressively reducing the SLR.