Plastic Film Mulching Regulates Soil Respiration and Temperature Sensitivity in Maize Farming Across Diverse Hydrothermal Conditions

Abstract

Soil respiration (Rt), consisting of heterotrophic (Rh) and autotrophic respiration (Ra), plays a vital role in terrestrial carbon cycling and is sensitive to soil temperature and moisture. In dryland agriculture, plastic film mulching (PM) is widely used to regulate soil hydrothermal conditions, but its effects on Rt components and their temperature sensitivity (Q₁₀) across regions remain unclear. A two-year field study was conducted at two rain-fed maize sites: Anding (warmer, semi-arid) and Yuzhong (colder, drier). PM significantly increased Rt, Rh, and Ra, especially Ra, due to enhanced root biomass and improved microclimate. Yield increased by 33.6–165%. Peak respiration occurred earlier in Anding, aligned with maize growth and soil temperature. PM reduced Q₁₀ of Rt and Ra in Anding, but only Ra in Yuzhong. Rh Q₁₀ remained stable, indicating microbial respiration was less sensitive to temperature changes. Structural equation modeling revealed that Rt and Ra were mainly driven by soil temperature and root biomass, while Rh was more influenced by microbial biomass carbon (MBC) and dissolved organic carbon (DOC). Despite increased CO₂ emissions, PM improved carbon emission efficiency (CEE), particularly in Yuzhong (+67%). The application of PM is recommended to enhance yield while optimizing carbon efficiency in dryland farming systems.

1. Introduction

Soil respiration (Rt) is a critical component of the carbon cycle, representing the total CO₂ flux from soil to the atmosphere [1]. Globally, Rt releases an estimated 78–100 Pg C y⁻¹ of CO₂, accounting for a major portion of the terrestrial carbon budget [2]. As the second-largest carbon flux after photosynthetic carbon uptake, Rt comprises two components: heterotrophic respiration (Rh), which is driven by soil microorganisms decomposing organic matter, and autotrophic respiration (Ra), which originates from plant roots and their associated rhizosphere organisms [3]. Root respiration, as a subset of Ra, involves CO₂ released through root metabolic processes fueled by photosynthetically fixed carbon [4,5,6], while Rh reflects the microbial response to environmental changes and is often evaluated via proxies such as microbial biomass carbon (MBC) and soil organic carbon (SOC) [7,8]. The balance between these processes determines the magnitude and sensitivity of soil CO₂ emissions, which are strongly influenced by environmental factors such as soil temperature, moisture, and substrate availability [9].

In agricultural ecosystems, Rt is not only central to carbon-climate feedback but also closely linked to crop productivity and soil carbon sequestration. Understanding the environmental and biological drivers of Rt and its components is particularly important in dryland agriculture, where soil temperature, moisture, and resource availability are highly variable [10].

Arid and semi-arid regions, which make up approximately 40% of the earth’s land area, are characterized by severe water scarcity, imbalanced hydrothermal conditions—where water availability and thermal energy are often mismatched—and inherently fragile ecosystems [11]. These regions are highly sensitive to climate variability and land management practices, with even minor changes in temperature or precipitation potentially causing significant alterations in carbon cycling processes [12]. Rain-fed agriculture, which predominates in these water-limited environments, faces unique challenges where water scarcity and temperature fluctuations not only constrain crop yields but also significantly influence soil carbon dynamics [13]. The combination of low precipitation and high evapotranspiration rates in these regions creates conditions where soil carbon stocks are particularly vulnerable to management-induced changes.

Plastic film mulching (PM) has emerged as a widely adopted agricultural practice in arid and semi-arid regions to address these environmental constraints [14]. By creating a physical barrier between the soil and atmosphere, PM effectively reduces soil water evaporation, increases soil temperature, and improves water use efficiency [15,16]. Some studies have shown that PM can increase crop yields by 20–50% in arid and semi-arid environments [17,18]. However, while the agronomic benefits of PM are well-documented, its effects on Rt and its components (Rh and Ra) under varying hydrothermal conditions remain poorly understood, particularly regarding the underlying mechanisms’ implications for carbon cycling.

The complexity of PM’s impact on Rt stems from its multifaceted effects on soil’s physical, chemical, and biological properties [19,20]. Previous studies have demonstrated that PM can significantly alter soil microenvironments by modifying temperature and moisture regimes, thereby influencing microbial community composition, enzyme activities, and root growth patterns [19]. These changes can affect the temperature sensitivity (Q₁₀) of Rt, a key parameter that quantifies the change in respiration per 10 °C temperature increase [21,22]. Previous research suggests that the Q₁₀ of Rt may vary substantially between different ecosystem types and management practices, with reported values ranging from 1.3 to 3.3 across global terrestrial ecosystems [23,24]. Recent research suggests that PM may alter Q₁₀ by buffering temperature fluctuations, thus changing the temperature dependency of Rt [20]. However, the contribution of each respiration component to these changes remains unclear.

This study investigates the effects of PM on Rt and its components across different hydrothermal conditions in arid and semi-arid regions. A comprehensive approach combining field measurements, laboratory analyses, and advanced statistical modeling was employed to address the following specific objectives: (1) Quantify the seasonal dynamics and magnitude of PM effects on Rt, Rh, and Ra under different hydrothermal regimes. (2) Evaluate how PM modifies the Q₁₀ of different respiration components and assess the underlying mechanisms. (3) Identify the dominant environmental and biological drivers of Rt variation. By addressing these objectives, this study provides valuable insights into the role of PM in regulating carbon dynamics and its potential implications for carbon-climate feedback, particularly in arid and semi-arid agricultural systems.

2. Materials and Methods

2.1. Experimental Sites

The field experiments were conducted in two different limited hydrothermal areas on the Loess Plateau (Figure S1). The experimental sites are located in Anding District, Dingxi (35°33′ N, 104°37′ E, altitude approximately 1900 m) and Yuzhong County, Lanzhou, Gansu Province (36°02′ N, 104°25′ E, altitude approximately 2400 m). The experiments were carried out from April 2019 to October 2020 at these two sites.

At the Anding site, the experiment was conducted at the Dingxi Arid Meteorology and Ecological Environment Experimental Base of the Lanzhou Institute of Arid Meteorology, China Meteorological Administration. This site is located in a semi-arid region, with precipitation mainly concentrated between July and September. This area is a typical rain-fed agricultural zone with yellow loess soil, a bulk density of 1.38 g·cm^–3, a field capacity of 26.6%, and a permanent wilting point of 6.7%. In the 0–20 cm soil layer, the organic carbon content was 8.9 g·kg⁻¹, the total nitrogen content was 1.3 g·kg⁻¹, the mineral nitrogen content was 19.1 mg·kg⁻¹, and the soil pH was 8.4.

At the Yuzhong site, the experiment was conducted at the Arid Land Agricultural Ecological Experimental Station of Lanzhou University, located in Zhonglianchuan Village, Yuzhong County, Gansu Province. The climate is classified as a temperate arid climate, with precipitation primarily concentrated from July to September. The groundwater level exceeds the crop’s utilization range. This area is a typical rain-fed agricultural zone, with black loess soil, an average bulk density of 1.33 g·cm^–3, a field capacity of 22.9%, and a wilting coefficient of 6.2%. In the 0–20 cm soil layer, the organic carbon content was 5.8 g·kg⁻¹, total nitrogen content was 0.9 g·kg⁻¹, mineral nitrogen content was 9.7 mg·kg⁻¹, and the pH was 8.3.

2.2. Meteorological Conditions During the Experimental Period

The annual average precipitation and temperature in Anding and Yuzhong in 2019 and 2020 were higher in Anding than in Yuzhong, with average precipitation of 486 mm and 420 mm, respectively (Figure S2). Precipitation during the maize growing season was 423 mm in Anding and 379 mm in Yuzhong. The annual average temperature was 7.4 °C in Anding and 5.4 °C in Yuzhong, while the growing season temperature was 15.6 °C in Anding and 13.1 °C in Yuzhong (Figure S2). During the maize growing period (late April to mid-October), precipitation showed a trend of increasing followed by decreasing, with the peak rainfall generally occurring from July to August. Temperature during the maize growing season also followed a similar trend of increasing then decreasing, with the peak temperature generally occurring in July.

2.3. Experimental Design and Field Management

At the Anding and Yuzhong experimental sites, two treatments were established: PM and no mulching (CK). Each treatment was replicated three times, resulting in a total of six plots per site (Figure S3A), and the layout at Yuzhong was similar. The experiment was conducted over two consecutive years, 2019 and 2020. Details regarding the maize varieties and fertilization used at each site are provided in

Table 1. Maize varieties and fertilization in the experimental sites.

Site	Varieties	Fertilization(kg·ha−1)		Row × Plant Spacing (cm × cm)	Density (Plants·ha−1)	Plot Area (m2)
		N	P2O5
Anding	Longyuan 3	150	81	55 × 40	47,500	9 × 9
Yuzhong	Longyuan 3	150	81	55 × 40	47,500	10 × 10

. At the tillage stage, all plots received a basal application of phosphorus fertilizer at a rate of 81 kg·P₂O₅·ha⁻¹. In addition, nitrogen fertilizer was applied uniformly across all treatments at a rate of 150 kg·N·ha⁻¹. Ridges and furrows were then established in each plot, with ridge dimensions of 55 cm in width and 15 cm in height. A plastic film with a thickness of 0.01 mm was subsequently applied to the PM plots. Spring maize was sown at a density of 47,500 plants·ha⁻¹ approximately one week after mulching, in late April or early May. At harvest in October, the aboveground biomass was removed, while the roots were left undisturbed in the soil. Throughout the growing seasons, no artificial irrigation was applied at any of the sites. Manual weeding was carried out following local standard management practices.

2.4. Sample Collection and Measurement

2.4.1. Root Biomass and Grain Yield

Three random samples of maize were selected in each plot once a month during the growing season. All the plants’ root biomass in the laboratory and the roots were washed with water to remove any soil particles. All samples were oven-dried at 70 °C until they reached a constant weight. At maturity, grain yield was taken by harvesting 100 maize plants from the plot’s center. The grain was air-dried for about a month and then threshed. The threshed grain was weighed after oven-drying at 70 °C to a constant weight.

2.4.2. Measurement of Soil Environmental Factors

Soil samples from the 0–20 cm layer were collected twice each year at both experi-mental sites, corresponding to the sowing stage and the harvest stage. At the Anding site, sampling was conducted on 3 May 2019, 17 October 2019, 1 May 2020, and 14 October 2020. At the Yuzhong site, samples were taken on 28 April 2019, 14 October 2019, 24 April 2020, and 13 October 2020. At each point, five composite samples were collected per plot to ensure data representativeness. The field-moist samples were homogenized, sieved through a 2 mm mesh, and divided into two subsamples. One subsample set was stored at 4 °C and used to determine MBC and dissolved organic C (DOC). The other set was air-dried to a constant weight and used to measure SOC. MBC was determined using the chloroform fumigation–extraction method [25]. The determination of DOC in soil was performed according to the method of Jones and Willett [26]. Measurements were conducted with a Multi3100 N/C TOC analyzer (Analytik Jena, Jena, Germany), using an extraction coefficient of 0.45 [27]. Following the removal of carbonate with HCl, the C contents of the SOC was analyzed using an Elemental Analyzer (Vario MacroCube, Langenselbold, Germany). Soil temperature and moisture in the upper 10 cm layer near the soil respiration analysis rings were recorded using a 1000 series Micro station-T/RH (Yimen TM-100Y, Handan, China) at 1-hour intervals in each plot.

2.4.3. Measurement of Rt

During the experiment, Rt was measured in situ throughout the maize growing season using a portable closed dynamic chamber system (LI-8100, LI-COR, Lincoln, NE, USA). Rt and its components were measured using the collar technique [28,29]. PVC collars of different heights (15 cm for Rt and 50 cm for Rh) were inserted between pairs of maize plants in each plot, approximately 10 cm away from each plant, to represent the respective respiration components, also with a compensation height of 3 cm (Figure S3B) [30]. Given that the majority of maize roots are concentrated within the 0–30 cm soil layer [31], the 50 cm collar used for Rh was designed to exclude roots effectively. The collar was fitted with small surface holes and lined with a 100 μm nylon mesh, which prevented root intrusion while still permitting water and gas exchange with the surrounding soil. Autotrophic respiration (Ra) was then calculated as the difference between Rt and Rh. Both Rt and Rh were measured once a month, between 9:00 and 11:00 AM on the day of measurement, and during this period were regarded as representative of daily average fluxes [30,32]. Twenty-four hours before each measurement, all aboveground plant material inside the PVC rings was clipped at ground level and left in place to decompose naturally.

2.4.4. Cumulative CO₂ Emissions and Carbon Emission Efficiency

Cumulative soil CO₂ emissions during the maize growing season were calculated using the following equation:

$$\mathrm{Cumulative}\mathrm{soil}{\mathrm{CO}}_2 \mathrm{emission}\left(\mathrm{g}\mathrm{C}{\mathrm{m}}^{-2}\right)={\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{R}}_{\mathrm{i}}\times \mathrm{t}\times \mathrm{M}\times {10}^{-6}$$

(1)

where n is the number of months during the maize growing season, Ri is the average Rt rate for each month (μmol m⁻² s⁻¹), t is the number of seconds in the corresponding month (s), and M is the molar mass of carbon (g mol⁻¹).

To quantify the relationship between grain yield and carbon emissions, carbon emission efficiency (CEE, kg grain kg⁻¹ C) was calculated. CEE is defined as the ratio of crop yield to total carbon emissions [33].

2.4.5. Temperature Sensitivity of Rt (Q₁₀)

Based on the relationship between Rt and soil temperature [34], an exponential model was used to describe the response of Rt and its components (Rh and Ra) to soil temperature:

R = ae^bT

(2)

Q₁₀ = e^10b

(3)

where R is the Rt rate (μmol m⁻² s⁻¹), T is the soil temperature at a depth of 10 cm (°C), a is the basal respiration rate at 0 °C, b is a fitted parameter, and Q₁₀ represents the factor by which the respiration rate increases with a 10 °C rise in temperature.

2.5. Statistical Analysis

An independent sample t-test was conducted to assess significant differences in MBC, DOC, SOC, grain yield, root biomass, soil temperature, soil moisture, and Q₁₀ among different treatments. Time and mulching type were treated as fixed factors, and a repeated-measures analysis of variance (ANOVA) was performed to evaluate the effects of PM on Rt, Rh, and Ra. All statistical analyses were conducted using GenStat (18th Edition, VSN International Ltd., Rothamsted, UK). Statistical significance levels were denoted as follows: ns (p > 0.05), * (p < 0.05), ** (p < 0.01), and *** (p < 0.001). Piecewise structural equation modelling (SEM) was used to quantify the direct and indirect factors governing soil respiration, using the psem function in the “piecewiseSEM” package in R (version 4.4.2) for SEM [35]. The measured variables were soil temperature, soil moisture, root biomass, MBC, and DOC. In addition, hierarchical partitioning was performed with the “rdacca.hp” package in R (version 4.4.2) to quantify the relative contributions of soil factors to the variations in Rt, Rh, and Ra [36].

3. Results

3.1. Maize Grain Yield and Soil Properties

In 2019, maize grain yield under CK and PM treatments was 6636 and 8089 kg·ha⁻¹ in Anding, and 1599 and 5584 kg·ha⁻¹ in Yuzhong, respectively. In 2020, corresponding yields were 6837 and 9939 kg·ha⁻¹ in Anding, and 3239 and 5880 kg·ha⁻¹ in Yuzhong (Figure 1A). Compared to CK, PM increased the average grain yield by 34% (22–46%) and 165% (82–249%), respectively; PM significantly increased average root biomass by 91% (90.8–91.4%), and 53% (46–60%), respectively (Figure 1B); PM significantly increased soil temperature by 9% and 14%, respectively (Figure 1C); and PM significantly increased soil moisture by 18% and 46%, respectively (Figure 1D). Compared to CK, PM decreased the average MBC by 20% (19–22%) and 53% (49–56%), respectively (Figure 1E). In general, compared with CK, PM had no significant effect on the content of DOC (Figure 1F). There was no significant difference in SOC content between PM and CK (Figure 1G).

3.2. Rt and Its Components

During the experimental period, we observed that the Rt, Rh, and Ra rates exhibited dynamic patterns similar to those of air temperature and precipitation, showing a unimodal curve throughout the maize growing season. PM did not alter the seasonal pattern of Rt, Rh, and Ra rates (Figure 2). The peak Rt in Anding occurred in late June, with average peak values of 6.86 and 4.28 μmol m⁻² s⁻¹ under PM and CK treatments, respectively. In Yuzhong, the peak Rt rates under PM and CK occurred in mid-July and late July, with peak values of 5.00 and 3.09 μmol m⁻² s⁻¹, respectively. Repeated-measure ANOVA showed that, at both sites, PM significantly increased Rt rates compared to CK, and that measurement time also had a significant effect on Rt (Figure 2).

The average peak Rh rates in Anding were 4.17 and 3.38 μmol m⁻² s⁻¹ under PM and CK treatments, respectively. In Yuzhong, during July 2019, Rh under the CK treatment decreased sharply, and the differences between PM and CK treatments gradually narrowed thereafter. The average peak rates under PM and CK in Yuzhong were 1.71 and 2.82 μmol m⁻² s⁻¹, respectively. Repeated-measure ANOVA indicated that PM significantly increased Rh rates compared to CK at both sites, and that measurement time had a significant effect on Rh (Figure S4).

The average peak Ra rates in Anding were 2.70 and 1.15 μmol m⁻² s⁻¹ under PM and CK treatments, respectively. In Yuzhong, the average peak Ra rates were 2.51 and 1.60 μmol m⁻² s⁻¹ under PM and CK treatments, respectively. Repeated-measure ANOVA indicated that PM significantly increased Rh rates compared to CK at both sites, and that measurement time had a significant effect on Rh (Figure S5).

3.3. Cumulative Rt and Its Components

At the Anding and Yuzhong sites, compared to CK, the PM treatment significantly increased the cumulative emissions of Rt and Ra. However, in Anding, there was no significant difference in the cumulative Rh emissions between PM and CK treatments, whereas in Yuzhong, PM significantly increased cumulative Rh compared to CK.

In 2019, compared to CK, PM increased cumulative Rt, Rh, and Ra by 36%, 23%, and 75%, respectively, in Anding and by 36%, 40%, and 31%, respectively, in Yuzhong. In 2020, compared to CK, cumulative emissions under PM increased by 24%, 0.5%, and 98% for Rt, Rh, and Ra, respectively, in Anding and by 43%, 47%, and 33%, respectively, in Yuzhong (Figure 3).

However, compared to CK, PM significantly increased the CEE (Figure 4). During the experimental period, in Anding, the average CEE under PM was 10% higher than that under CK. In Yuzhong, the average CEE under PM was 67% higher than that under CK. Furthermore, the CEE was significantly higher in Anding than in Yuzhong. Under the CK, CEE in Anding was 128% higher than in Yuzhong, while under PM, CEE in Anding was 50% higher than in Yuzhong.

3.4. Changes in Q₁₀ Induced by PM

In Anding, PM significantly reduced the Q₁₀ values for Rt and Ra by 20.1% and 32.1%, respectively, compared to CK (Figure 5A); however, PM did not significantly reduce the Q₁₀ value for Rh (Figure 5B). In Yuzhong, PM significantly reduced the Q₁₀ value for Ra by 18.4%, compared to CK (Figure 5C). However, PM did not significantly affect the Q₁₀ values for Rt and Rh (Figure 5A,B).

3.5. Factors Affecting Soil Respiration

A SEM was constructed to explore the causal pathways among temperature (TEM), moisture (MOIS), root biomass (ROOT), MBC, DOC, Rh, Ra, and Rt. The model exhibited good fit with the data, as indicated by χ² = 13.548 (p = 0.139), Fisher’s C = 21.002 (p = 0.2279), and AIC = 221.006, indicating strong consistency between the observed data and the model structure, and it explained 98% of the observed variability in Rt (Figure 6A). Both TEM and MOIS exerted strong direct positive effects on Ra by increasing root biomass (ROOT) (standardized path coefficients, SPC = 0.978 ***). Ra subsequently showed a direct positive effect on Rt (SPC = 0.468 ***). Similarly, TEM and MBC demonstrated direct positive effects on Rh, which in turn had a direct positive effect on Rt (SPC = 0.780 ***).

Relative importance analysis indicated that in Anding and Yuzhong, TEM was the most important driver affecting Rt, explaining 53.2% and 47.8% of the variability in the five variable groups, respectively (Figure 6B,C). For Rh, in Anding, TEM and DOC were the two most important drivers, explaining 78.4% and 10.2%, respectively, while in Yuzhong, DOC and soil temperature were the two most important drivers, explaining 39.2% and 34.1%, respectively (Figure 6D,E). For Ra, in both Anding and Yuzhong, soil temperature and root biomass were the two most important driving factors (Figure 6F,G).

4. Discussion

4.1. Effects of PM on Maize Agronomic Performance Under Different Hydrothermal Conditions

PM consistently increased maize yield compared to CK. In Anding, maize yield under PM increased by 22–46% in 2019 and 2020, whereas in Yuzhong, yield increased by 82–249% during the same period (Figure 1). These yield increases were attributed to the enhanced soil temperature and moisture retention under PM, which promoted better root development and nutrient uptake [18,37]. The root biomass in PM treatments was 53–91% higher than that in the CK, reflecting the increased root growth due to more favorable microclimatic conditions [38]. The enhancement in root biomass is particularly notable, as it directly contributes to both Ra and Rh [30]. The increase in root biomass suggests that PM not only promoted root growth but also improved the efficiency of carbon allocation to the roots, which is essential for maintaining high levels of plant productivity [39]. The positive correlation between root biomass and grain yield highlights the importance of root vigor in maize productivity under PM [40].

This study showed that compared with CK, PM could significantly increase grain yield under different hydrothermal conditions, and the yield increase effect of PM was better in Yuzhong with more limited hydrothermal conditions, which was consistent with the results of previous studies. For instance, Zhang et al. [41] showed using model simulation that in the dry farming area of the Loess Plateau with annual precipitation of 300–600 mm and an annual average temperature of 3–9 °C, PM had the best yield increase and stable yield. In this range, the region with more restrictions on hydrothermal conditions had a better yield increase. Similarly, Ye and Liu [42] analyzed 29 sets of maize yield data under different precipitation gradients on the Loess Plateau, further supporting the notion that PM is particularly effective in areas with more restrictive water and temperature conditions.

However, the increase in maize yield due to PM diminished with the improvement of hydrothermal conditions. While maize yields in Anding (with more favorable hydrothermal conditions) were consistently higher than in Yuzhong, the relative benefit of PM was more pronounced in Yuzhong, where the environmental constraints were greater. These findings further confirm that the yield-enhancing effect of PM exhibits regional differentiation, with PM providing more significant benefits in areas with more severe water and temperature limitations.

4.2. Effects of PM on Rt and Its Components Under Different Hydrothermal Conditions

Our findings demonstrate that PM significantly increased Rt, Rh, and Ra at both experimental sites, Anding and Yuzhong, with a particularly pronounced effect on Ra. This observation aligns with previous studies, indicating that PM enhances microbial activity and root metabolism by improving soil hydrothermal conditions, thereby intensifying CO₂ emissions [30]. Additionally, the peak of Rt, Rh, and Ra occurred earlier in Anding than in Yuzhong, which may be attributed to the growth stages of maize and soil temperature in different regions. We observed that the peak of soil respiration rates (Rt, Rh, and Ra) all occurred during the V12 stage of maize growth, which was earlier in Anding than in Yuzhong, and the soil temperature was also the highest at the V12 stage of maize (Figure S6), and the soil respiration rate and soil temperature had an exponential function relationship (Figure S7).

The unimodal seasonal variation patterns observed for Rt, Rh, and Ra are consistent with previous research [30,43], highlighting the strong coupling between soil respiration and seasonal temperature and moisture changes. Notably, the promoting effect of PM on Ra was stronger than on Rh, especially in Anding, where the cumulative flux of Ra nearly doubled, suggesting that PM primarily enhances soil respiration by stimulating root activity, closely tied to plant photosynthesis [44]. In contrast, the effect of PM on Rh was more variable; it increased Rh in Yuzhong but had no significant impact in Anding. This discrepancy may relate to changes in MBC, which declined in PM-treated soils at Anding but remained unchanged in Yuzhong. The reduction in MBC could result from altered microbial community structure or decreased substrate availability due to accelerated decomposition of organic matter [45,46,47,48].

4.3. Effects of PM on Q₁₀ Under Different Hydrothermal Conditions

Q₁₀ is considered an important parameter for the temperature sensitivity of Rt. Lower Q₁₀ values imply a reduced increase in Rt with rising temperature, potentially mitigating the positive feedback of soil CO₂ emissions to climate warming [49]. Previous studies have indicated that this temperature adaptation could be due to the consumption of active carbon, or the physiological adaptation of microorganisms or plants [34,50].

Our study revealed spatial differences in Q₁₀ values among Rt components and between sites. The differences in Q₁₀ response between Anding and Yuzhong highlight the critical role of local hydrothermal backgrounds in modulating soil respiration sensitivity. In Anding, where soil temperature was higher and crop development more advanced, PM significantly reduced the Q₁₀ of both Rt and Ra, indicating a stronger buffering effect on temperature-induced respiration [20]. This suggests that, under favorable thermal conditions, PM may reduce the Q₁₀ by stabilizing root metabolic activity or enhancing rhizosphere microbial functions. In contrast, the cooler, drier conditions in Yuzhong limited this effect to Ra alone. The Q₁₀ of Rh remained unaffected in both environments, reinforcing the idea that Rh is less influenced by short-term temperature changes and more constrained by substrate availability and microbial dynamics [20,48,51]. These findings also confirm previous research, which shows that Ra is more sensitive to temperature changes than Rh [28,52]. Given that PM modifies soil microenvironments, it is likely that these changes in temperature and moisture availability influence microbial community composition, potentially favoring microbial groups with greater resilience to temperature stress [19]. This shift in microbial community structure, while not directly quantified in this study, provides a plausible explanation for the observed patterns and should be investigated in future studies using community-level analysis and metagenomic approaches.

Overall, our results underscore the need to consider site-specific hydrothermal conditions when assessing the ecological impacts of PM. Understanding how PM influences the microbial and enzymatic mechanisms driving soil respiration will improve our ability to predict the long-term effects of mulching on soil carbon dynamics and enhance the sustainability of dryland farming systems.

4.4. Primary Drivers of PM Effects on Rt and Its Components

SEM and relative importance analysis indicated that soil temperature was the dominant driver of Rt at both sites, explaining 53.2% and 47.8% of the variation in Anding and Yuzhong, respectively. These research results indicate that despite PM-induced increases in soil moisture, temperature remains the core variable regulating Rt, which is consistent with previous studies [20,53,54]. Ra was primarily driven by soil temperature and root biomass, reinforcing its direct linkage with plant physiological activity. In contrast, Rh was more strongly influenced by MBC and DOC, highlighting its dependence on carbon substrates.

The ranking of dominant factors influencing Rh varied between sites, indicating significant spatial heterogeneity in the response of Rh to environmental changes [55]. Overall, PM affects soil respiration by altering the relative contributions of environmental and biological factors, which have critical implications for carbon management in agricultural systems.

4.5. Implications for Agricultural Carbon Management

Although PM significantly increased the total carbon flux of Rt and its components at both sites, it also substantially improved CEE, particularly in Yuzhong, where CEE increased by 67%. This indicates that PM can enhance crop yield without a proportional rise in carbon emissions, thereby improving the CEE of agroecosystems [56], which is a key metric for sustainable agriculture. PM’s ability to optimize soil temperature and moisture retention plays a crucial role in enhancing productivity while maintaining or even reducing carbon emissions per unit of output.

However, caution is needed when assessing the trade-off between increased Rt and yield gains, especially concerning long-term soil carbon storage. While PM may boost short-term productivity, its potential to reduce MBC and alter Q₁₀ values warrants further investigation to evaluate its long-term impacts on soil health and carbon sequestration capacity. Microbial responses, as a critical aspect of soil carbon cycling, need to be carefully considered [57], as PM may alter microbial community dynamics, potentially reducing soil microbial diversity and affecting the microbial-driven processes that contribute to carbon storage [19,58]. Moreover, although PM enhances yield and carbon efficiency in the short term, its long-term sustainability is challenged by plastic residue accumulation, which can harm soil structure and biota. To address this, biodegradable alternatives are gaining attention [59,60]. Therefore, while PM presents significant short-term benefits for agricultural productivity and carbon efficiency, future research should focus on evaluating the long-term impacts of PM on soil health, microbial responses, and the viability of biodegradable mulching alternatives for reducing plastic pollution in agroecosystems.

5. Conclusions

This study demonstrates that PM significantly increases Rt and crop productivity in dryland agriculture, but the magnitude and underlying drivers differ under contrasting hydrothermal conditions. In the warmer and wetter Anding site, PM strongly enhanced Ra, reduced Q₁₀, and shifted control of respiration toward root-driven processes. In contrast, at the colder and drier Yuzhong site, microbial factors such as MBC and DOC played a larger role, and PM had a more moderate effect on Rt and Q₁₀. Although PM increased total CO₂ emissions at both sites, it also greatly improved CEE, especially in Yuzhong, indicating that PM can enhance resource-use efficiency even under harsher conditions. Overall, PM enhances crop yield and CEE, making it a promising tool for sustainable dryland agriculture. However, the observed decline in MBC and alterations in Q₁₀ warrant further investigation into the long-term sustainability of PM, particularly regarding its implications for soil health and carbon sequestration. Sustainable agricultural strategies should balance productivity gains with the maintenance of soil ecological function to ensure long-term resilience in dryland farming systems.