Assessing the Potential of Biodegradable Film as an Alternative to Traditional Plastic Film—A Comprehensive Analysis from Yield Enhancement Perspective in China

Abstract

Biodegradable film (BM) presents a promising replacement for plastic film (PM). However, the ability of BM to substitute PM across different crops and regions remains unclear. This study compared the effects of BM and PM on cotton, maize, potato, and wheat by conducting a meta-analysis of yield responses and employing a random forest model to identify key drivers under film mulching. Furthermore, an XGBoost model was applied to simulate spatial yield changes under current and future climate conditions across China’s agro-regions. The meta-analysis indicated that the overall yield enhancement under PM (30.0%) was significantly higher than under BM (26.5%), with a relatively small difference between the two films. The Northern Arid and Semi-Arid Region showed a significant difference, with yield increases of 37.8% under PM and 28.5% under BM. BM resulted in an 18.7% increase in economic return, whereas PM led to a 32% increase. Both BM and PM enhanced the yield of all four crops, but PM had a greater effect on maize (31.8% vs. 28.0%). Random forest identified mean annual temperature (MAT), precipitation (MAP) and nitrogen application rate (N) as the main drivers influencing yield responses under film mulching. Under conditions of MAT < 10 °C, MAP < 450 mm and N ≥ 200 kg ha⁻¹, PM outperformed BM. However, the yield benefits associated with BM will strengthen over time under future climate scenarios. Future scenarios simulations suggested that BM’s relative advantage may increase, particularly for maize, across certain regions. These findings offer region-specific mulching strategies that promote a sustainable agricultural environment.

1. Introduction

A substantial increase in the demand for food security has been driven by the rising global population. Ensuring food security has been recognized as an imperative responsibility of every nation. In China, dryland agriculture accounts for roughly one-third of the total cultivated land area [1]. However, the development of dryland agriculture is constrained by the uneven distribution of water and thermal resources. Under future climate change, this issue may become even more severe.

The impact of limited water availability and low temperature can be mitigated by film mulching technology, thereby enhancing water use efficiency and agricultural productivity [2]. Plastic mulch (PM) consumption is dominated by China, which is estimated to represent 60–80% of the global total [3]. In addition to improving water use efficiency, PM helps maintain soil temperature, control weeds, and enhance effective radiation, all of which contribute to increased crop yields [4,5,6]. Consequently, PM has been widely adopted across China, particularly in the northwestern region. The primary material used for mulching film is polyethylene, which is characterized by a stable structure and resistance to natural degradation [7]. Residual PM in the field, resulting from inadequate recovery efforts, may adversely affect soil health and impede crop growth [8]. Moreover, improper post-treatment methods, such as landfilling or incineration, can lead to environmental pollution [9]. Therefore, while PM offers significant benefits for agricultural production, the environmental burden it imposes must be considered. Biodegradable film (BM) is currently regarded as a viable alternative to PM because it delivers comparable benefits [10,11]. BM does not require field recovery and ultimately degrades into CO₂ and H₂O. This characteristic not only reduces the costs associated with field recovery and post-treatment but also minimizes the negative impacts on soil quality and crop growth [12]. As a result, BM is considered a more ecologically sustainable option than PM [13,14]. However, BM still falls short of PM in terms of cost-effectiveness and mechanical performance, which limit its large-scale adoption [15,16]. Therefore, a comparative evaluation of BM and PM is vital for ensuring the sustainable development of mulching film technology.

Yield serves as a critical indicator for agricultural producers. The potential of BM to replace PM in enhancing crop yield has been a topic of concern among researchers. However, research findings on the yield performance of PM and BM have been inconsistent [10,17,18]. Overall, PM has been found to enhance yield more than BM. A meta-analysis conducted by Gu et al. showed that the yield increase associated with BM was 2.8% lower than that associated with PM [19]. Agricultural production is influenced by climatic factors. It was revealed by a long-term experiment on the Loess Plateau that PM outperformed BM [17]. Similarly, Yin et al. observed that the yield-enhancing effect of BM was less pronounced than that of PM in semi-arid regions [18]. In regions with adequate moisture, yields achieved with BM were comparable to those obtained with PM [10,20]. Field management practices can also influence the effectiveness of film mulching. Under conditions of 173 kg ha⁻¹ nitrogen application and ridge tillage, BM was found to effectively replace PM [21]. Chen et al. recommended the use of BM in combination with 22.5 mm of irrigation and 280 kg ha⁻¹ of nitrogen fertilizer for maize cultivation in arid regions [22]. In addition, yield performance under film mulching was found to vary among different crop types [19,23]. The influence of climatic conditions, soil characteristics, and agronomic practices on the efficacy of film mulching has gained growing attention, frequently employing meta-analysis method [2,24]. A large number of meta-analyses about the effects of film mulching, but existing studies have largely concentrated on PM. In addition, the comparison between PM and BM is still limited, especially across different levels of subgroup analyses. Therefore, systematically evaluating the impacts of both PM and BM on yield, with consideration of key factors including climate, soil characteristics, and agronomic practices, will contribute to a deeper understanding of their differential applications.

Film mulching is particularly important in regions limited by low temperatures and water scarcity. However, under future climate scenarios, increases in temperature and precipitation are expected, which may crop yield responses to film mulching [25]. Currently, most simulation studies of film mulching under future climate change have focused on PM, particularly in dry and semi-arid locations such as the Loess Plateau [25,26,27]. However, the environmental impacts and agronomic performance of different mulch types vary widely, making it essential to explore and assess the adaptability of various mulching materials under future climatic conditions. Process-based models are frequently applied to simulate the impacts of film mulching in future scenarios, including DeNitrification-DeComposition model [25,27,28,29], the Decision Support System for Agrotechnology Transfer model [30], and the Soil–Plant–Atmosphere Continuum System model [31,32]. In recent years, machine learning has been increasingly applied in agricultural prediction. Machine learning effectively addresses nonlinear, multivariable functions and has been shown to provide better predictive accuracy [33,34]. Under extreme climate scenarios, machine learning models require fewer parameters and more effectively represent the intricate, nonlinear links between climate change and crop yield, demonstrating substantial potential for application [35,36]. By synergistically integrating meta-analysis with machine learning, existing data can be consolidated and key influencing factors uncovered, thus providing a robust data foundation for machine learning models. In turn, machine learning can explore complex nonlinear relationships and interaction effects among these factors to generate accurate predictions [37].

By combining meta-analysis with machine learning to systematically synthesize multi-source data, we quantified the differential yield changes in BM versus PM under current and future climate scenarios. Therefore, this study: (1) utilized meta-analysis to assess the influence of BM and PM on yield in different crops and regions; (2) analyzed the factors influencing BM and PM use; and (3) utilized machine learning for spatial prediction of yield changes under BM and PM, further incorporating future climate scenarios to predict long-term effects.

2. Materials and Methods

2.1. Data Source

A systematic search was conducted of the Web of Science (https://www.webofscience.com/) and China National Knowledge Infrastructure (CNKI) databases (http://www.cnki.net/) for peer-reviewed articles published up to July 2024. The following keywords were used: TS = ((‘polyethylene film’ OR ‘agricultural mulching’ OR ‘film mulching’ OR ‘plastic mulching’ OR ‘plastic mulch’ OR ‘plastic film mulching’ OR ‘biodegradable film mulching’) AND (‘yield’)). Articles were included if they met the following criteria: (1) experimental designs that compared PM treatments with non-mulching treatments (NM); (2) field experiments conducted in China; (3) reports with clearly defined test sites; (4) crops limited to cotton, maize, potato, and wheat; (5) exclusion of duplicate test data. Ultimately, 503 articles comprising 2521 data pairs were retained for analysis. The references for the included studies can be found in the Supplementary Material. The sampling points are shown in Figure 1. Based on major agricultural regions in China, the study area was divided into nine regions: (1) Northern Arid and Semi-Arid (NASA): Xinjiang, Gansu, Ningxia, and Inner Mongolia; (2) Northeast Plain (NP): Liaoning, Jilin, Heilongjiang; (3) South China (SC): Fujian, Guangdong, Hainan; (4) North China Plain (NCP): Beijing, Tianjin, Hebei, Shandong, Henan; (5) Loess Plateau (LP): Shanxi, Shaanxi; (6) Qinghai–Tibet Plateau (QTP): Tibet and Qinghai; (7) Sichuan Basin and Surrounding Areas (SBSA): Sichuan, Chongqing; (8) Yunnan-Guizhou Plateau (YGP): Guizhou, Yunnan, Guangxi; (9) Middle and Lower Reaches of the Yangtze River (MLYP): Shanghai, Jiangsu, Zhejiang, Anhui, Hubei, Hunan, and Jiangxi. Additionally, the following factors were considered: (1) climatic factors, including mean annual precipitation (MAP) and mean annual temperature (MAT); (2) soil factors, including initial pH, bulk density (BD), soil organic matter (SOM), and total nitrogen (TN); and (3) agronomic practices, including nitrogen fertilization (N), irrigation (I), and cultivation method (CM), categorized as ridge (R) or flat (F). The detailed subgroups for each factor are presented in Table 1.

Figure 1. Sample points map. Note: abbreviations: NP, Northeast Plain; YGP, Yunnan-Guizhou Plateau; NASA, Northern Arid and Semi-Arid Region; SC, South China; SBSA, Sichuan Basin and Surrounding Areas; MLYP, Middle and Lower Reaches of the Yangtze River; QTP, Qinghai–Tibet Plateau; LP, Loess Plateau; NCP, North China Plain.

Table 1. Subgroups.

Subgroups	Categories
Crops	cotton, maize, potato, wheat
Treatment	BM, PM
MAP	<450 mm, 450–600 mm, ≥600 mm
MAT	<10 °C, ≥10 °C
pH	<6.5, 6.5–7.5, ≥7.5
BD	<1.3 g cm−3, ≥1.3 g cm−3
SOM	<12.5 g kg−1, 12.5–15 g kg−1, ≥15 g kg−1
TN	<1 g kg−1, ≥1 g kg−1
N	<100 kg ha−1, 100–200 kg ha−1, ≥200 kg ha−1
I	no, yes
CM	R vs. F, R vs. R, F vs. F

Note: For irrigation (I), “no” signifies non-irrigated conditions, while “yes” signifies irrigated conditions. For “CM” comparisons (e.g., “R vs. F”), the first listed practice (e.g., “R”) refers to the film mulching treatment, whereas the second practice (e.g., “F”) refers to the non-mulched treatment.

2.2. Data Analysis

2.2.1. Meta-Analysis

This study utilized the natural logarithmic response ratio (lnRR) to assess yield variation resulting from the application of film mulching [38],

$$lnRR=\mathit{ln}\left(X_{FM}\right)-ln\left(X_{NM}\right)$$

(1)

where X_FM denotes the mean yield of the BM or PM treatment, and X_NM denotes the mean yield of the no mulching.

The variance (V) and weighting (W) are calculated using the following formulas:

$$V(RR)=\frac{{SD}_{FM}^2}{n_{FM}{X}_{FM}^2}+\frac{{SD}_{NM}^2}{n_{NM}{X}_{NM}^2}$$

(2)

where SD_FM and SD_NM represent the standard deviations (SD) of the mulching and the no mulching treatments, respectively, while n_FM and n_NM indicate the number of replications for the mulching and the no mulching treatments, respectively. To include additional data, the missing SD is supplemented by the formula $SD=X\times CV$, where X is the mean of the studies with missing SD, and CV is the coefficient of variation. When only standard errors (SE) were reported, SD was calculated as $SD=SE\times \sqrt{n}$. In total, SD was imputed for 1614 data pairs, accounting for 64.0% of the dataset. The Fail-Safe Numbers for the film mulching, PM, and BM datasets were 272,278,773, 232,192,072, and 1,594,898, respectively, all of which far exceeded the critical threshold of 5n + 10. Figure S1 presents the funnel plots for the three datasets. Egger’s regression tests for film mulching and PM indicated no significant publication bias (p > 0.05). Although the test for BM was significant (p < 0.05), the adjusted effect size obtained using the trim-and-fill method showed a negligible difference from the original estimate. Therefore, the Fail-Safe Numbers and funnel plot analyses demonstrate the reliability of results.

The weighted response ratio (RR₊₊) is computed as follows:

$$W_{ij}=\frac{1}{V\left(RR\right)}$$

(3)

$${RR}_{++}=\frac{{\sum }_{i=1}^k{\sum }_{j=1}^m{w}_{ij}{RR}_{ij}}{{\sum }_{i=1}^k{\sum }_{j=1}^m{w}_{ij}}$$

(4)

where the weighted factor is represented by w_ij, k and m denote the number of studies and the number of experimental replications within the subgroup, respectively. For easier understanding, the results were expressed as the percentage change $\left(e^{{RR}_{++}}-1\right)\times 100\%$. If the confidence interval includes zero, the effect is not statistically significant; if entirely above zero, the effect is significantly positive; if entirely below zero, the effect is significantly negative. To address the lack of independence in data arising from multiple data points coming from the same study, a hierarchical nested model (random = ~ID|Reference) was used in this study. The model was implemented in R (v2024) using the “rma.mv” function [39]. In addition, we used “anova” to statistically analyze the significant differences in BM and PM levels among different subgroups.

Linear regression was employed to analyze relationships between numerical variables (MAP, MAT, pH, BD, SOM, TN, N, I) and crop yield. Finally, variable importance was ranked using “metaforest” package in R (v2024) based on random forest analysis [40].

2.2.2. Economic Benefit Analysis

This study further assessed the economic benefits associated with BM and PM. The percentage change in economic benefit (EBC%) resulting from film mulching was calculated using the following formula:

$$EBC=\frac{{EB}_{FM}-{EB}_{NM}}{{EB}_{NM}}\times 100\%$$

(5)

where EB_FM (CNY ha⁻¹) is the economic benefit under either BM or PM. EB_NM (CNY ha⁻¹) is the economic benefit without mulching. The data were extracted from the peer-reviewed literature included in our meta-analysis.

2.2.3. Spatial Prediction of Yield Change Under BM and PM in Current and Future Climate

As future climate may affect BM and PM performance, this study employed XGBoost to spatially predict yield changes under both current and future climate. XGBoost is an optimized model based on traditional Gradient Boosting [41]. The optimization of XGBoost enhanced model training speed and helped prevent overfitting. Detailed methodology regarding the modeling framework is presented in Supplementary Text S1. In addition, the selected hyperparameters are listed in Table S2, and the model performance on the test set is shown in Table S3. The XGBoost model was implemented using Python 3.12 (v2023) with the libraries “xgboost,” “pandas,” “sklearn,” and “numpy.” The input variables of the model included MAP, MAT, pH, BD, SOM, TN, N, I, and CM, while the dependent variable was yield change (YC%). The calculation formula was as follows:

$$YC=\frac{Y_{FM}-Y_{NM}}{Y_{NM}}\times 100\%$$

(6)

where Y_FM represents the yield of BM or PM (kg ha⁻¹), and Y_NM represents the yield without film mulching (kg ha⁻¹).

The Coupled Model Intercomparison Project 6 (CMIP6) provides crucial data for future climate simulations. We evaluated two future scenarios from 2041 to 2060. The combination of Shared Socioeconomic Pathway 2 (SSP2) and Representative Concentration Pathway 4.5 (RCP4.5), referred to as SSP2-4.5, represents a medium future scenario. The combination of SSP5 and RCP 8.5 represents a pessimistic future scenario [42]. These scenarios are among the most extensively studied to date [31,43,44]. The source of simulated climatic and soil data are listed in Table S1, while the agronomic practices simulated data are the mode of the meta-analysis database.

3. Results

3.1. Effects of BM and PM on Yield and Economic Benefit

Film mulching significantly enhanced crop yield, with an overall increase of 29.6% (Figure 2a). Both BM and PM led to significant yield increases of 26.5% and 30.0%, respectively (Figure 2b). While PM showed a statistically higher effect than BM (p < 0.0001), BM still achieved a substantial yield improvement comparable to that of PM. The economic benefit analysis revealed that BM increased economic return by 18.7%, whereas PM resulted in a 32.0% increase (Figure 2c). When crop-specific effects were examined, BM significantly boosted the yields of cotton (22.0%), maize (28.0%), potato (16.7%), and wheat (27.0%) (Figure 3a). Similarly, PM significantly enhanced yields of these four crops: cotton (22.0%), maize (31.8%), potato (28.3%), and wheat (27.6%) (Figure 3a). In maize, the yield improvement under PM was significantly greater than that under BM (p < 0.01). Regionally, BM significantly enhanced yields in only five areas—LP, MLYP, NASA, NCP, and NP—by 25.8%, 29.9%, 28.5%, 23.4%, and 23.5%, respectively (Figure 3b). No data were available for BM in QTP and SBSA. PM had no notable effect on yield enhancement in SBSA and SC, but demonstrated significant increases in the other seven regions, reaching a maximum of 37.8% in NASA. In NASA, the yield enhancement potential of PM was significantly greater than that of BM (p < 0.0001) (Figure 3b).

Figure 2. Overall effect of film mulching on yield: (a) overall mulching effect and (b) effects of different film types; (c) effects of BM and PM on economic benefits. Note: “n” and displayed values denote the sample size, and “CI” denotes the confidence intervals. The red “*” denotes the significant levels for differences between BM and PM within each subgroup. “***”: p < 0.0001. Abbreviations: BM, biodegradable film; PM, plastic film.

Figure 3. Effects of BM and PM on yield in (a) different crops and (b) regions. Note: Displayed values denote the sample size. The red “*” denotes the significant levels for differences between BM and PM within each subgroup. “***”: p < 0.0001, “*”: p < 0.01. Abbreviations: BM, biodegradable film; PM, plastic film; LP, Loess Plateau; MLYP, Middle and Lower Reaches of the Yangtze River; NASA, Northern Arid and Semi-Arid Region; NCP, North China Plain; NP, Northeast Plain; QTP, Qinghai–Tibet Plateau; SBSA, Sichuan Basin and Surrounding areas; SC, South China; YGP, Yunnan-Guizhou Plateau.

3.2. Effects of BM and PM on Yield Under Different Climatic Factors

Notable differences across categories of MAP and MAT were observed between BM and PM. In regions with less than 450 mm of precipitation, the yield increase under BM (28.4%) was significantly lower than that under PM (37.2%) (p < 0.0001) (Figure 4a). However, in areas with precipitation levels of 450–600 mm and ≥600 mm, no significant differences between BM and PM were detected. Specifically, at 450–600 mm, yield increases were 25.0% with BM and 27.5% with PM; at MAP ≥ 600 mm, the increases were 21.3% and 17.9%, respectively. When MAT is below 10 °C, the yield increase under BM (28.5%) was significantly lower than that under PM (35.7%) (p < 0.001) (Figure 4b). However, at MAT ≥ 10 °C, no significant difference was observed between the two mulching films, with yield increases of 22.1% and 22.2%, respectively. Under both BM and PM treatments, MAP and MAT exhibited significant negative correlations with yield increases (p < 0.001) (Figure 5a,b). In the ranking of importance, MAT and MAP were ranked first and second, respectively, indicating that climatic factors predominantly influenced the efficacy of film mulching (Figure 6).

Figure 4. Effects of BM and PM on yield in different (a) MAP and (b) MAT. Note: Displayed values denote the sample size. The red “*” denotes the significant levels for differences between BM and PM within each subgroup. “***”: p < 0.0001, “**”: p < 0.001. Abbreviations: BM, biodegradable film; PM, plastic film; MAP, mean annual precipitation; MAT, mean annual temperature.

Figure 5. The relationships of (a) MAP, (b) MAT, (c) BD, (d) pH, (e) SOM, (f) TN, and (g) N with LnRR under BM and PM. Note: Abbreviations: MAP, mean annual precipitation; MAT, mean annual temperature; BD, bulk density; SOM, soil organic matter; TN, total nitrogen; N, nitrogen fertilization.

Figure 6. The ranking of relative importance of factors. Note: Abbreviations: MAP, mean annual precipitation; MAT, mean annual temperature; BD, bulk density; SOM, soil organic matter; TN, total nitrogen; CM, cultivation method; I, irrigation; N, nitrogen fertilization.

3.3. Effects of BM and PM on Yield Under Different Soil Factors

Among the soil factors evaluated, significant differences between the effects of BM and PM on yield were observed only for BD. Specifically, in soil with BD ≥ 1.3 g cm⁻³, the yield increases associated with BM and PM were 22.9% and 28.5%, respectively, and this difference was statistically significant (p < 0.01) (Figure 7a). For the BM treatment, BD showed a significant negative correlation with yield increases (p < 0.05) (Figure 5c). In terms of pH, BM significantly enhanced yield under pH 6.5–7.5 and ≥7.5, with respective increases of 31.3% and 29.3% (Figure 7b). Conversely, PM led to a significant yield increase across all three pH levels, with respective values of 25.4%, 19.2%, and 33.6%, respectively. Furthermore, under PM, soil pH was observed to be significantly and positively correlated with the yield increases (p < 0.001) (Figure 5d). Across all levels of SOM and TN, both BM and PM resulted in significant yield increases. For PM, TN content was significantly correlated with yield increases (p < 0.05) (Figure 5f). The relative importance of these soil factors in influencing yield was ranked as follows: pH, SOM, BD, and TN (Figure 6).

Figure 7. Effects of BM and PM on yield in different (a) BD, (b) pH, (c) SOM and (d) TN. Note: Displayed values denote the sample size. The red “*” denotes the significant levels for differences between BM and PM within each subgroup. “*”: p < 0.01. Abbreviations: BM, biodegradable film; PM, plastic film; BD, bulk density; SOM, soil organic matter; TN, total nitrogen.

3.4. Effects of BM and PM on Yield Under Different Agronomic Practices

Regarding the agronomic practices evaluated, a significant difference in yield enhancement between BM and PM was observed only in relation to N. This analysis combined the results for CM, where R was compared with F. The combination of PM with R was found to be more effective in enhancing crop yield. BM performed in the R vs. F with a yield increase of 32.8%. PM performed best in R vs. R, with a yield increase of 36.7%. Under non-irrigation conditions, both BM and PM resulted in greater yield increases compared to irrigated conditions (Figure 8b). Specifically, under non-irrigated conditions, PM resulted in a yield increase of 31.3%, while BM achieved a 26.0% increase (Figure 8b). BM and PM exhibited consistent performance across varying levels of nitrogen application. At N ≥ 200 kg·ha⁻¹, the yield increase for PM (33.5%) was significantly higher than that for BM (27.2%) (p < 0.01) (Figure 8c). For both treatments, the rate of N application was significantly correlated with yield increases (p < 0.05) (Figure 5g). In the importance ranking, while cultivation management practices had less influence than climatic and soil conditions, nitrogen fertilization ranked immediately after MAP, indicating its considerable impact on crop yield (Figure 6).

Figure 8. Effects of BM and PM on yield in different (a) CM, (b) I and (c) N. Note: Displayed values denote the sample size. The red “*” denotes the significant levels for differences between BM and PM within each subgroup. “*”: p < 0.01. Abbreviations: BM, biodegradable film; PM, plastic film; CM, cultivation method; I, irrigation; N, nitrogen fertilization; R, ridge; F, flat.

3.5. Spatial Prediction of Crop Yield Change Under BM and PM

The findings revealed substantial differences in BM and PM performance across spatial scales (Figure S3). Overall, both films showed improvement effects in NASA (Figure 9). Significant variances were observed between the two film mulching types in this region. YC under PM exceeded that under BM by 0.6%, 2.3%, 1.5%, and 0.4% for cotton, maize, potato, and wheat, respectively. Crop performances also varied across regions. In addition to NASA, maize performance was particularly pronounced in LP. Potato showed a favorable result in NCP, while wheat performed most effectively in QTP (Figure S2). Under future scenarios, the differences between BM and PM varied among crops relative to current conditions (Figure 10 and Figure 11). Overall, except for maize, the spatial distribution of YC differences between BM and PM in the other three crops exhibited limited variation, with PM generally outperforming BM. An increasing trend in cotton was observed in LP and QTP (Figures S4a and S6a). Potato (Figures S4c and S6c) and wheat (Figures S4d and S6d) generally exhibited a downward trend in YC. For maize under both future scenarios, YC under BM exceeded that under PM in all regions except NCP (Figure 10b and Figure 11b). Except for maize, YC variation between BM and PM across most regions ranged from 0% to 1%.

Figure 9. Spatial distribution of the percentage difference in yield for major crops in China under current conditions: (a) cotton, (b) maize, (c) potato, and (d) wheat.

Figure 10. Spatial visualization of the difference in YC between BM and PM under SSP2-4.5: (a) cotton, (b) maize, (c) potato, and (d) wheat.

Figure 11. Spatial visualization of the difference in YC between BM and PM under SSP5-8.5: (a) cotton, (b) maize, (c) potato, and (d) wheat.

4. Discussion

4.1. Analysis of Influencing Factors of Film Mulching

The yield effects of film mulching were comprehensively affected by climate, soil, and agronomic practices. In the random forest importance ranking, climatic factors such as MAT and MAP emerged as the most significant influencing variables (Figure 8). Meta-analysis results indicated that film mulching achieved optimal performance when MAP was below 450 mm (Figure 4a) and MAT was below 10 °C (Figure 4b). These findings suggested that the effectiveness of film mulching technology was strongly influenced by climatic factors. It also implied that in areas with a humid climate or high temperature, film mulching usage could be reduced and yield could be improved through other measures. In addition to climatic, soil factors also ranked high overall. Previous studies have also emphasized that soil factors could significantly affect the effectiveness of film mulching [24]. The application efficacy of film mulching in China exhibited notable regional variation owing to uneven climate and soil distributions. Spatial simulations based on machine learning demonstrated that PM and BM both exhibited spatial variation and performed well in NASA. Chen et al. employed an enhanced crop growth model to simulate BM’s impact on production in Inner Mongolia, revealing significant spatial variability in yield [45]. Cultivation management measures investigated in this study included N, I, and CM. Among these practices, N ranked immediately after MAT and MAP in the importance ranking, indicating its substantial impact on film mulching effectiveness. Ma et al. concluded that the yield improvement under PM at 200–300 kg ha⁻¹ was most effective in the northwest region [1]. Furthermore, PM can enhance nitrogen fertilizer utilization rate. A meta-analysis further indicated that PM could increase nitrogen partial factor productivity by 26% [23]. However, increased N application may not only constrain further yield gains but also exacerbate greenhouse gas emissions [46]. Under non-irrigation conditions, film mulching significantly boosted yield, further demonstrating its potential under water-scarce environments. Moreover, combining irrigation with PM could further increase irrigation use efficiency [47]. Our results align with Zhang et al. [48], who found that ridge-furrow planting with film mulching outperformed both ridge-furrow planting without film mulching and flat planting in terms of yield, due to its superior heat and water retention capacity [49,50]. However, Liu et al. suggested that furrow planting with mulching planting may not be advisable under future climate scenarios [26].

4.2. Comparison Between BM and PM

While PM achieved a significantly higher yield increase (30.0%) compared to BM (26.5%), the overall performance gap between the two mulches was relatively narrow (Figure 2b). In low MAT and MAP, a significant difference was found between BM and PM (Figure 4). At the regional scale, a significant difference was observed in NASA, a region characterized by low temperature and low precipitation, where PM outperformed BM (Figure 3b and Figure 4). The effect of both PM and BM on soil was similar during the initial phases of crop growth. However, as BM began to degrade, its impact on soil moisture and temperature declined [10,11,51]. Moreover, the degradation process caused fluctuations in soil moisture, which reduced water use efficiency [18]. A continuous nine-year experiment of film mulching clearly indicated that PM provided greater yield increases and enhanced stability over an extended period [17]. Despite PM’s overall advantage, evidence also suggests that BM can match or even surpass PM under specific environmental and management conditions. For example, Huang et al. found that BM outperformed PM in areas with 600–800 mm of precipitation [16]. The spatial modeling results revealed that the yield enhancement under PM exceeded that under BM in most regions (Figure 10). Yang et al. noted that BM could replace PM for short-season crops [52]. Even if the water retention capacity of BM decreases in the later stage, its water-preserving effect during the critical growth period is still significant. Management practices can also narrow the yield enhancement gap. Fang et al. estimated using machine learning that under 180 kg·ha⁻¹ of N fertilizer, BM could replace PM [53]. In semi-humid and semi-arid areas, the preferred degradation rate of BM applied to summer maize was 60 days, resulting in enhanced production [54]. In dry and semi-arid environments, appropriate irrigation combined with BM that had a longer degradation period could effectively replace PM [55]. Although BM’s primary benefit lies in its ability to mitigate agricultural “white pollution” from residual conventional film and microplastic accumulation, it still confronts various economic and environmental challenges. The higher cost of BM than PM may limit its adoption, as farmers tend to choose PM to maximize short-term economic benefits [56]. Although BM can reduce the environmental impact at the disposal, its production may have a greater environmental burden due to more complicated manufacturing processes. Life-cycle assessment studies have shown that BM may have a higher global warming potential than PM [57,58]. In addition, BM can break down into microplastic particles in soil, which may adversely affect soil ecosystems [59]. To truly promote the sustainable development of BM in agricultural production, it is essential to integrate it with effective field management practices, increase subsidies, and further develop biodegradable film manufacturing technology.

4.3. The Effect of Film Mulching on Crop Yields in Future Climate

Climatic conditions exert a major influence on agricultural production, and the increasing threat of future climate change will undoubtedly introduce more uncertainties and severe challenges to the stability and sustainability of agricultural systems [60]. Future climate warming combined with increased precipitation is expected to accelerate phenological events and reduce evapotranspiration [61]. As an important strategy for enhancing agricultural production, the adaptability and effectiveness of film mulching technology in future climate scenarios have become central topics of academic research. Yu et al. studied the yield increase in Xinjiang lint under PM in future climate scenarios [28]. Zhang et al. discovered that future climatic change exerted minimal influence on the yield variation in crops protected by PM [27]. However, under future climate scenarios characterized by higher temperatures and precipitation, the advantage of PM was reduced [43]. In addition, Liu et al. noted that crop yields were projected to increase both with and without PM in future scenarios [26]. Research by Zhang et al. indicated that when the temperature rose by 2.85 °C, the output increase from PM would decrease [62]. Quan et al. concluded that rising temperatures shortened the reproductive growth stages of crops, resulting in a decline in maize production [31]. Nevertheless, Quan et al. offered solutions for addressing climate change, suggesting that PM combined with 400 mm of irrigation could effectively mitigate the decrease in maize yield caused by climate change [30]. This study focused on comparing the performance of BM and PM in China under different future climate scenarios. Regarding the response of different film mulching for maize under future climate scenarios, our findings indicated that BM outperformed PM in all regions except for the NCP (Figure 10b and Figure 11b). The findings of Wang et al. demonstrated that BM had a greater advantage, especially in terms of water use efficiency under future climate scenarios [43]. The primary explanation for this is that BM does not significantly elevate soil temperature during the later growth phases, thus reducing the impact of high temperature on maize.

4.4. Limitations and Uncertainties

While this study tried to enhance analytical rigor and reliability through careful data selection and methodological design, certain limitations remain. Firstly, with respect to data sources, our reliance on peer-reviewed publications, while ensuring data quality, introduced reporting inconsistencies among studies, resulting in potential data gaps. Furthermore, the number of studies on BM is substantially lower than that on PM. These factors may have impacted the meta-analysis and model-training processes, thereby influencing the results. Secondly, with regard to methodological design, although meta-analysis and machine learning were utilized for spatial prediction, the interactive effects among climatic conditions, soil properties, and agronomic practices were not fully examined. Additionally, data for spatial modeling were integrated from multiple databases that vary in update frequency, spatial resolution, and variable definitions, potentially reducing model training efficacy. Moreover, a growing body of research has focused on the effects of microplastics in agricultural fields on crop growth. Future studies should incorporate the influence of microplastics to enable a more comprehensive comparison of BM and PM impacts on crop productivity.

5. Conclusions

This study systematically evaluated the effects of BM and PM on the yields of major crops (cotton, maize, potato, and wheat) in China by integrating meta-analysis with machine learning, and further investigated their spatial variability under present and future climate scenarios. The meta-analysis revealed that film mulching markedly enhanced crop yields (29.6%), with PM showing a slightly higher average yield increase (30.0%) compared to BM (26.5%). In terms of economic benefits, PM (32.0%) also outperformed BM (18.7%). A pronounced difference between PM and BM was observed in the NASA region and for maize. Climatic factors, including MAT and MAP, were identified as primary drivers of mulching effects, followed by N. Both BM and PM were strongly negatively correlated with MAT and MAP, and strongly correlated with N. Under conditions of MAT < 10 °C, MAP < 450 mm, and N ≥ 200 kg ha⁻¹, PM significantly outperformed BM in yield enhancement. Soil conditions also significantly influenced these responses. Spatial predictions revealed regional and crop-specific variations in YC for both mulch types, with PM generally performing better under current conditions, particularly in the NASA. Except for maize, the YC difference between BM and PM was minimal. Under future climate change scenarios (SSP2-4.5, SSP5-8.5), results suggested that for crops such as maize, BM might outperform PM in certain regions (excluding the NCP). These findings enhance scientific understanding of the regional impacts of PM and BM applications, offering theoretical insights and practical guidance for formulating targeted regional management strategies and advancing sustainable agriculture.