Optimizing cover crop practices as a sustainable solution for global agroecosystem services

Domain: earth_science
Published: 2024-12-05
Editorial outcome: major_revision

Abstract
The practice of cover crops has gained popularity as a strategy to improve agricultural sustainability, but its full potential is often limited by environmental trade-offs. Using meta-analytic and data-driven quantifications of 2302 observations, we optimized cover crop practices and evaluated their benefits for global agroecosystems. Cover crops have historically boosted crop yields, soil carbon storage, and stability, but also stimulated greenhouse gas emissions. However, combining them with long-term implementation (five years or more) and climate-smart practices (such as no-tillage) can enhance these services synergistically. A biculture of legume and non-legume cover crops, terminated 25 days before planting the next crop and followed by residue mulching, is the optimal portfolio. Such optimized practices are projected to increase agroecosystem multiservices by 1.25%, equivalent to annual gains of 97.7 million metric tons in crop production, 21.7 billion metric tons in carbon dioxide sequestration, and 2.41 billion metric tons in soil erosion reduction. By 2100, the continued implementation of optimized practices could mitigate climate-related yield losses and contribute to climate neutrality and soil stabilization, especially in harsh and underdeveloped areas. These findings underscore the promising potential of optimized cover crop practices to achieve the synergy in food security and environmental protection.

Introduction
To meet the ever-growing food demand, the globally unprecedented cropland expansion and agricultural intensification have caused a myriad of negative environmental impacts, such as climate change, biodiversity loss, and nitrogen (N) pollution1,2. As these impacts escalate and break through the corresponding planetary boundaries, the sustainability of agroecosystems and their several critical services will be irreversibly undermined3,4. Therefore, achieving higher yields with lower environmental costs—i.e., to synchronously achieve the interlinked sustainable development goals (SDGs)—is an inevitable challenge5. Perceived as one of climate solutions6, cover crops (CCs) represent a potential strategy to safeguard food production while reducing environmental impacts7.

CCs refer to the crops incorporated during the transition period between the growth cycles of two cash crops, offering various benefits to agroecosystem through surface coverage and crop diversification, including provision (e.g., food and feed supply), regulation (e.g., carbon (C) storage and climate mitigation), and support (e.g., soil stabilization and erosion control)7. For example, legume CCs substantially improve soil nutrition through biological N fixation, while some non-legume CCs enhances soil structure and penetration to support well-developed root systems of primary crops8. CCs also accelerate the formation of microbial-derived C by providing diverse residues for microbes9. However, some investigations have demonstrated that CC implementation may interfere with the yield of subsequent crops by competing for soil moisture and nutrients10, and stimulate nitrous oxide (N2O) and methane (CH4) emissions through activated microbial metabolism11,12, leading to an uncertain greenhouse gas (GHG) budget. Although CC practices are generally beneficial to soil microbiome and associated functions13, in some cases, such improvement in soil biological attributes does not translate into increased crop productivity14. These variable outcomes highlight the need to optimize CC practices to achieve both food security and environment-friendly services. This optimization requires careful consideration of CC type selection (including mixtures), termination timing, and residue management (Fig. 1a).

Although numerous research efforts aim to maximize the benefits of CCs for agroecosystem services7,8, these studies are predominantly isolated experiments conducted under limited agronomic and biophysical conditions. Most of these efforts focus on individual target services, often lacking a quantitative evaluation of the trade-offs or synergies among them, especially concerning the interactions between CC practices and diverse environmental and management factors15. Such knowledge gap hinders our capacity to propose an ideal CC adoption paradigm and formulate effective measures that can fully harness its multiple benefits7. Additionally, current limitations within statistical procedures—such as the complicated assumptions in process-based modeling and the low predictive accuracy of empirical models—further contribute to a lack of quantitative tools for assessing potential CC benefits and extrapolating these benefits on a global scale, despite some successful regional simulations by crop models16,17.

Here, we conducted a preliminary examination of the critical effects of CC practices on agroecosystem services using meta-analytic techniques, with a primary focus on food supply, C storage, climate mitigation, and soil stabilization (as measured by the mean weight diameter of soil aggregation, MWD). According to the observed performances across these dimensions, we identified the optimal CC portfolio, the key factors influencing its effectiveness, and their interactions. The optimization of CC portfolio was complemented by additional long-term field data on soil health and simulations from the Agricultural Production Systems sIMulator (APSIM). We further introduced an “agroecosystem multiservices” index (see “Methods”) to offer a holistic evaluation of the capability of CCs to simultaneously provide multiple benefits. Finally, we employed a machine-learning approach, which excels at handling variable interactions and non-linearities in the context of meta-analysis, to quantify the potential of optimized CC practices in enhancing synergies among agroecosystem services worldwide. Our hypothesis posits that optimizing CC practices can sustainably deliver spatial and temporal benefits for global agriculture, thereby markedly advancing the SDGs (Fig. 1b).

Results
Partial trade-offs of agroecosystem service promotion by historical CC practices
Drawing insights from 2302 field observations of 219 studies spanning the historical application of CC practices from 1978 to 2020, we determined the effects of CCs on five fundamental agroecosystem services (Supplementary Fig. 1). Overall, CCs significantly increase yields by 2.33% and SOC stocks by 6.46%, alongside an enhancement of 14.3% in MWD (Fig. 2a), illuminating their notable effectiveness in promoting soil C storage, erosion control, and subsequent crop yields7. Nonetheless, N2O and CH4 emissions are also greatly stimulated by 29.5% and 42.3%, respectively. These stimulations of GHGs hinge on cropland type, with N2O emissions only rising in uplands and CH4 emissions in paddy fields (Fig. 3a). Distinct from previous meta-analyses reporting no change or slight reduction10,18, our findings indicate a context-dependent response of N2O emissions to CCs, which can be attributed to the disproportionate contribution of nitrite and ammonium (primary products of CC residue decomposition in upland and paddy fields, respectively)19. Given the assignable role of N2O in global warming and ozone depletion20, there is an urgent need to implement appropriate mitigation measures to counteract such disservice induced by CCs7. Encouragingly, the implementation of long-term CC rotation (≥ 5 years) has shown potential to mitigate additional N2O emissions without sacrificing crop yields (Fig. 2b). By continually providing adequate substrates (residues) and habitats, long-term CC practices are anticipated to increase the diversity of N2O reductors (e.g., nosZ Clades I and II) and their survivability in the soil, thus reducing N2O emissions21,22. The long-term implementation can also effectively strengthen soil stability and C storage, potentially serving as a net sink of GHGs23. In essence, CC practices promote several agroecosystem services but also pose a disservice risk, associated with a spectrum of factors. Identifying the key determinants of CC effects is therefore paramount to competently minimizing their trade-offs and maximizing their synergies.

Optimizing CC practices under the interactions with environmental and management factors
We propose that the characteristics and optimization of CC practices are key to synergistically promoting agroecosystem services (Fig. 1a). Compared to non-legume CCs, legumes and mixtures (a blend of legume and non-legume CCs) have been robustly observed to boost crop yields and N2O emissions (Fig. 2b and Supplementary Fig. 2). For the residues of legumes and mixtures with a low C:N ratio, their sufficient available N not only contributes to crop uptake and growth, but may also exceed the microbial N requirements, thereby increasing soil N2O production and emissions24. This exemplifies a double-edged effect of their biological N fixation and highlights the importance of modulating microbial interactions within CC systems25. Notably, these effects can be modified by CC diversification. The changes in yields and GHGs significantly display concave-down parabolic patterns, while SOC stocks rise linearly with increasing CC diversity (P < 0.05; Fig. 2c), manifesting the superiority of CC mixtures with a species number of 2 or 3. Additionally, mixtures have been found to enhance the root growth of subsequent crops and sequester more C in the surface soil (10‒30 cm) than legume CCs alone (Supplementary Figs. 3, 4). These belowground benefits are supported by a 7-year rotation system, where CC mixtures can facilitate the accrual of mineral-associated organic C fractions through increased root biomass26. Our long-term observations further confirm that CC mixtures particularly promote the accumulation of mineral-associated organic C rather than particulate organic C (Supplementary Fig. 5a, b); the former can limit the accessibility of microbes and enzymes via mineral-organic matter interactions (e.g., adsorption and aggregation), thus exhibiting greater persistence27,28. Given the lower production costs related to breeding and planting7, we cautiously recommend mixtures with a diversity of 2 (i.e., a biculture of legume and non-legume CCs) as the preferred CC type. This coincides with the perspective that a higher functional diversity, rather than species richness, of CCs predominantly supports multiple agroecosystem services15.

We adopted a meta-forest (MF) approach to capture the complex interactions of CC practices with environmental and management factors (Fig. 3‒4 and Supplementary Fig. 6). Among these, soil biodiversity is identified as the most critical determinant of the CC effect on yields (Supplementary Fig. 6a), plausibly attributed to key functions executed by diverse microbial groups, such as nutrient cycling and mutualism13. Partial correlation analysis robustly confirms this biological role even after controlling climate and soil variables (P < 0.001; Fig. 3b). Moreover, the response of crop yields is also dependent on climate, with arid regions experiencing the largest yield increase (Fig. 3a and Supplementary Fig. 2). This is largely due to the capacity of CCs to enhance soil water retention by reducing evaporation and increasing water infiltration8, especially when CC residues are mulched on the soil surface (Fig. 2b and Supplementary Fig. 2). Accordingly, CCs have a great potential to augment crop production in dryland farming systems29. In contrast, the diminished CC biomass input post-termination30 constrains soil C sequestration and yield improvements in cold climates (Fig. 3a and Supplementary Fig. 2), for which the high concentration of particulate organic C fraction may also account (Supplementary Fig. 5c). Regarding SOC stocks, the primary moderators are initial SOC concentration and pH (Supplementary Fig. 6b), with their interactive effects varying across different soil depths (P < 0.001; Supplementary Table 1). This depth-dependent control limits C storage in surface and mid soil layers (Supplementary Fig. 4), probably due to microbial and enzymatic reactions sensitive to C concentration or pH. For instance, glucosaminidase and polyphenol oxidase activities were reported to correlate negatively with subsoil C stocks31. The greater, long-term stimulation of cellulase activity by CCs in surface and mid soil layers than in the topsoil (0‒10 cm) supports this point (Supplementary Fig. 7a, b).

Soil texture, particularly clay content, dictates the CC effects on yields and SOC stocks as well. The negative relationship between SOC change and clay content (P < 0.001) suggests a heightened C sequestration potential and thus a notable yield gain of 7.86% in sandy soils32 (Fig. 3a, b), which is more evident in soils with lower fertility (Supplementary Fig. 8). This relationship also appears in SOC fractions, that is, their higher improvements occur in the soil texture with less clay and SOC, but not in the finest clay soils, especially for mineral-associated organic C (Supplementary Fig. 5c). Similarly, the positive relationship between yield gains and soil bulk density reveal that CCs can favor crop growth in highly compacted soils by fostering root penetration33 (Fig. 3b). In humid tropical climates, CCs can significantly increase MWD by 61.1%, reinforcing their role in mitigating soil hydraulic erosion8, largely through the contribution of high fungal-to-bacterial ratios (P < 0.001; Fig. 3b). The dominance of fungi in these systems leads to the substantial production of glomalin and other extracellular polysaccharides, as well as the formation of extensive hyphal networks, which physically bind soil particles into macroaggregates34. This also highlights the supplementary benefits of CC residues beyond biomass input, as they act as organic binding agents that promote soil aggregation and erosion control13. More importantly, the duration of CC implementation emerges as the primary factor for enhancing MWD (Supplementary Fig. 6e), with long-term application leading to sustained soil stabilization and erosion control (P < 0.001; Fig. 2b and Supplementary Fig. 9). These insights demonstrate the higher benefits of CC practices into harsher environments and spotlight the intrinsic role of manipulating functional microbiota (e.g., N-fixers and mycorrhizal fungi) to strengthen the synergies among agroecosystem services in a changing environment13,25,34.

Aligned with Basche et al.35 and our previous observations, CC type significantly influences N2O emissions (Supplementary Fig. 6c). This effect is particularly pronounced when leguminous species are included and in upland soils where residues are mainly decomposed into nitrite19 (Figs. 2b and 3a). Integrating conservation tillage with CC practices, especially no-tillage combined with CC residue mulching36, is expected to reduce N2O emissions while boosting crop yields (Fig. 4a, c and Supplementary Fig. 2). By moderating soil temperature and aggregate stability29, which in turn enhances the interaction of mycorrhizal fungi with living roots37, mulching CC residues on the soil surface can markedly increase crop yields (Fig. 2b). In comparison, incorporating CC residues into the soil induces microbial turnover and nutrient immobilization38, potentially limiting the yield benefits. The greater enhancement of cellulase activity also indicates the superior microbial functionality under no-tillage (Supplementary Fig. 7c); meanwhile, its active decomposition of organic matter contributes to the formation and stability of soil aggregates13. Furthermore, our analyses indicate that soil C:N ratio serves as a driving force behind CH4 emissions (Fig. 3b and Supplementary Fig. 6d). An elevated C:N ratio exacerbate soil N limitation in general, under which CC biomass input accelerates microbial mineralization of organic matter via N mining, thereby priming CH4 emissions39. This priming effect varies by cropland type and irrigation method, with intermittent irrigation adeptly lowering CC-derived CH4 emissions from paddies (Supplementary Fig. 10). Simultaneously, increasing N supply curbs the significant activation of cellulase and thus the opportunity for organic matter mineralization in paddy soils (P < 0.001; Supplementary Fig. 7d). Just as the synergies in increased yields, enhanced SOC stocks, and reduced CH4 emissions (Fig. 4f), the co-implementation of CCs and intermittent irrigation facilitates both rice production and climate mitigation, chiefly through alleviated soil N limitation and improved aeration by drainage40.

Accordingly, combined with climate-smart management practices6, CC benefits can be further amplified (Fig. 4). Via the potential promotion yield stability41 and soil microbial anabolism42, applying organic fertilizer largely increases the yield improvements based on CCs (Fig. 4a and Supplementary Fig. 2). This combination also effectively controls the production of GHGs (Fig. 4c, d), presenting a strong antagonism to CH4 emissions by −70.3% (Fig. 4f), probably due to the activation of methanotrophs and CH4 oxidation43. It is noteworthy that organic fertilizer and conservation tillage are also inclined to additionally enhance MWD (Fig. 4e), by virtue of their stronger cementation and less physical disruption of soil aggregates44 as well as the amplified role of CC roots in soil stabilization7. N management is deemed as the cornerstone for agricultural and socio-economic sustainability45,46. In this study, excessive N fertilizer application have been affirmed to nonlinearly decrease and even reverse CC-based yield gains once exceeding 100 kg N ha−1, despite the high actual yields (Supplementary Fig. 11). Our APSIM results similarly prove that high N input (>100 kg ha−1) will reduce the yield benefits from CCs (Supplementary Fig. 2). Under these circumstances, the larger N threshold and higher yield benefits accentuate the merits of CC mixtures in N fertilizer reduction and substitution (Supplementary Fig. 11). Frasier et al.47 suggested that the biculture of legume and non-legume CCs can enhance N availability and reduce N losses, thereby increasing subsequent crop yields. A life cycle assessment also revealed that utilizing CC mixtures will reduce N fertilizer rate by 25.0% while bolstering environmental footprint reduction and energy savings by 51.7%48. Furthermore, N fate is largely governed by water management level. Over irrigation inevitably accelerates the leaching of surplus N and gaseous losses of C and N in (semi-)arid regions49, accounting for the less soil C and yield improvements and greater GHG emissions from irrigated uplands (Fig. 4a‒d). On the other hand, through water storage enhancement by increasing SOC and water evaporation reduction by residue mulching8, CCs are more conducive to the yield of rainfed crops in uplands (Fig. 4a and Supplementary Fig. 2).

Given the ascertained trade-offs, especially related to GHG emissions, we further quantified the associations among five services and their strengths of expected influence to narrow the actual gap in accurately evaluating CC applicability. In uplands, yields become the keystone service with the greatest strength, followed by MWD, SOC stocks, and two GHGs (Fig. 5a). Beyond the direct support of SOC stocks for crop yields under CC practices32, the enhanced MWD will indirectly increase yields through improved soil fertility and connectivity (i.e., higher water, oxygen, and nutrient concentrations)13. Owing to the positive effects on food supply, C storage, and soil stabilization, CCs are anticipated to enhance agroecosystem multiservices (AMS) by 1.67% in uplands (Fig. 5c). Conversely, AMS experiences no significant promotion or even a decline in rice paddies, primarily attributed to the high expected influence of CH4 (Fig. 5b, d). While CC biomass input promotes rice yields and soil C balance, it substantially increases CH4 production under the anaerobic conditions of paddy soils12 (Fig. 3a). Effective measures are, thus, necessary to reduce CH4 emissions while maintaining rice productivity under CC practices, such as aerobic pre-digestion of CC residues50 and intermittent irrigation (Fig. 4d, f). Structural equation modelling (SEM) show that climate is the dominant factor influencing AMS indirectly and directly in uplands and paddies, respectively (Fig. 5e‒h), mainly by controlling water availability (precipitation and evapotranspiration; Supplementary Table 2). The direct influence of soil variables is also highly important to AMS. Specifically, soil C:N ratio is positively whereas initial clay and SOC concentrations are negatively associated with AMS, verifying the preferable benefits of CCs for harsh environments (i.e., soils with less organic matter and stronger N limitation or dryland areas; Fig. 1b). Meanwhile, integrating CCs with climate-smart management practices, including reducing N fertilizer rate, organic amendments, and mulching CC residues, has a definitely positive effect on AMS (Supplementary Table 2). Overall, these findings emphasize the importance of bridging the gap between local environment and managerial knowledge to design more sustainable CC systems with fewer trade-offs.

Global implications from optimizing CC practices
CC termination time, hereinafter defined as the time interval between CC sending and the next cash crop planting (Fig. 1a), is acknowledged as the key factor affecting the magnitude and direction of multiple CC benefits7,29. However, its importance is frequently overlooked. According to the maximum positive effect on AMS in moving window analysis (window size = 30 observations; see “Methods” for more details), we estimate an optimal CC termination time of 25 days across the globe (Supplementary Fig. 12). At this time point, there are also positive interactions of climate, soil, and microbial variables on AMS (P < 0.001; Supplementary Fig. 13). Our estimate is supported by Wang et al.51 who reveal that by terminating CCs around three weeks prior to the succeeding cash crop, soil water reserves and thus water use efficiency and yield of crops can be enhanced globally. Both premature and late CC termination are likely to restrict crop growth via insufficient nutrient input and water depletion, respectively29. Though the optimal termination timing is largely site-specific, this macroscopic estimate has assisted the paradigm shift in CC utilization and management, marking an important step in evaluating the global applicability of optimizing CC practices. In the future, the alignment of termination timing with specific site conditions and crop requirements can further refine CC practices and ensure that their benefits are maximized.

Based on observed CC performances and interactions with the environment and management, we provide an optimized portfolio of CC practices here, i.e., the biculture of legume and non-legume CCs combined with surface mulching initiated after a termination period of 25 days. By integrating this portfolio with MF models and serial datasets of predictors (Supplementary Table 3), we scaled up the site-level CC effects to the globe (Fig. 6a‒c). On average, these optimized practices are projected to increase global AMS by 1.25% for the current period (2020). The most significant gains occur in uplands, surpassing those from historical practices (1.86% vs. 1.67%; Fig. 5c), particularly in Africa, Latin America, and Northwest China. Moreover, the implementation of CC practices is expected to significantly improve crop yields and soil health in paddy fields across Southeast Asia (Fig. 6a). These benefits are pivotal to the diet and economy in this region and to global rice production40. However, CC implementation also carries risks caused by reduced MWD and modest yield gains in North America, Europe, and temperate West Asia (Supplementary Figs. 14, 15). At the national scale, Colombia and India, benefiting from the substantially increased crop yields and SOC stocks, are the most applicable countries for CC practices in upland and paddy fields, respectively (Supplementary Figs. 16, 17). In contrast, some European countries like Greece and Portugal may thoroughly experience adverse AMS consequences. Such regional inequality is related to the different climate, soil types, cropping systems, and even socio-economic conditions29,52, but in general, CC practices offer potential to narrow it by delivering greater benefits to those countries with low crop production and high population density53.

Optimizing CC practices is conducive to the synergies among agroecosystem services (Fig. 6d‒f). Regardless of soil stabilization, CC benefits in crop yields and net GHG budget (NGHGB) reduction are synergistically heightened in 64.9% areas, while merely 35.1% have trade-offs or negative responses. Paddy fields show a higher proportion of synergies compared to uplands (69.7% vs. 64.9%), likely due to their larger background C stocks and actual returns in mitigating NGHGB54. These benefits can be converted into crop production by 97.7 Tg yr−1 and CO2 capture by 21.7 Pg yr−1, equivalent to increases of 3.22% in staple crop yields and 0.87% in topsoil C stocks (0‒30 cm) per year of global application (Supplementary Fig. 18a, b). However, there is a slight decrease in MWD by −2.11% during the current period, underscoring the significance of the duration of CC implementation (Supplementary Figs. 9 and 14). In line with our meta-analysis, long-term CC practices (≥ 5 years) will further increase SOC stocks, reduce N2O emissions, and greatly enhance MWD without yield penalty (Fig. 2b and Supplementary Fig. 19). These positive feedbacks constantly intensify with time, offering temporally sustainable benefits for global AMS under a changing climate (Fig. 6g‒o). Processed-based modelling assessments have also confirmed the clear advantages of long-term CC practices in soil C sequestration16,55,56 and erosion control17. We therefore estimate that after 5 years, optimized CC practices can reduce soil erosion displacement by 2.41 Pg annually, with 1.42 Pg in uplands and 0.99 Pg in paddy fields (Supplementary Fig. 18c). In addition, while CC-based yield gains are initially affected by climate change, their resistance and resilience markedly enhance over time, especially in rice paddies (Supplementary Fig. 20), thus aiding in combating the plausible yield losses and maintaining global food security57.

Climate change impacts on AMS vary among cropland types and emission scenarios, but the larger positive changes always exist in warmer regions, even under the mid-high climate emission scenario of Shared Socioeconomic Pathway 370 (Supplementary Fig. 21). Soils in these warmer areas typically have less micronutrients, which often appears in underdeveloped countries58. The use of CCs can replenish the removed soil micronutrients, maintaining their optimum levels that are crucial for crop productivity and soil health59. By linking soil micronutrient concentrations (ref. 58) with their associated AMS, we strikingly discover that AMS benefits from CCs tend to be higher in regions with lower soil micronutrient levels (Supplementary Fig. 22). This reaffirms CC advantages for improving agricultural sustainability against harsh conditions and in dryland areas, because the equilibrium among all macro- and micronutrients can further improve water use efficiency of crops59. More importantly, leveraging the national SDG performance data, we unravel the explicit support of optimizing CC practices for nations with limited progress towards the SDGs (P < 0.001; Supplementary Fig. 23), particularly in upland systems of the least-developed countries (SDG bundle 1 and 2)60. However, despite of the huge potential in these countries (e.g., Nigeria and India; Supplementary Fig. 16), the widespread adoption of effective CC practices is still hindered by limited farmers’ knowledge and subsidies7. To surmount this obstacle and achieve the common SDGs, international assistance and incentive policies (e.g., N Credit System46 and Green Manure Planting Program61) are urgently needed. For developed countries with extensive CC experience but lacking climate action (SDG bundle 5), new high-tech approaches and site-specific CC management are required to counteract adverse outcomes and realize further benefits52,62.

Though we have presented considerable CC benefits across multiple independent observations, these findings should be taken with caution. First, our understanding of the underlying mechanisms remains limited. Beyond the accelerated microbial turnover and microbial-derived C accumulation under CC practices9, the positive link between MWD and SOC stocks in uplands partly implies the mineral-mediated SOC persistence during the aggregation process28 (Fig. 5a). For more effective climate actions, it is essential to unveil the long-term effects of co-implementing CC practices with novel climate-smart managements (e.g., enhanced silicate rock weathering63) and their mechanisms (e.g., microbial growth and death pathways64) on soil C sequestration. Second, there are some uncertainties in the predictions. Despite the well-assumed variable interactions and non-linearities, as well as the weight of individual studies in MF models, the imbalanced data among different services and some regions will cause large estimation errors, such as CH4 for the Mediterranean and MWD for Malaysia (Supplementary Fig. 24). Additionally, implementation duration is an indispensable factor for predicting CC effects, but most of the studies included are short-term (Supplementary Table 4). Excluding potential moderators of trade-offs among agroecosystem services may also miscalculate the CC benefits, such as elevated CO2 65, management adaptability66, and socio-economic factors67.

Third, the representativeness of selected variables is controversial and subjective to a certain extent. In this study, five services related to food supply, C storage, climate mitigation, and soil stabilization have been applied to assess CC effects (Fig. 1a). While they represent provisioning, regulating, and supporting7, there are still some critical services omitted (e.g., N leaching), likely undermining the reliable estimation of AMS. Cautiously, we assigned weights to these services based on their expected influence and agricultural demand for AMS. This effort greatly avoids the deviation from auto-correlation and gap with reality in the absence of other important services68. Finally, but perhaps most importantly, this study only involves crop rotations without consideration of the intercropping between CCs and perennial crops such as fruit trees. Although this approach targets sustainable agriculture, it neglects the probable returns of CC intercrops in agroforestry systems by increasing N retention and mitigating N leaching globally69. In this framework, crop-specific responses are also not included. Future research should critically address these responses to better quantify the applicability of CC practices across diverse regions, particularly within the context of genotype × environment × management interactions7.

In conclusion, our comprehensive analyses underline the significant potential of optimizing CC practices for improving agricultural sustainability worldwide, particularly in a harmonious balance between food security and environmental protection. Given their notable ameliorative characteristics and sustainable benefits (Fig. 1b), CC practices are emerging as a nature-based solution poised to advance the SDGs. In light of these insights, it is imperative for future research to explore more management combinations to unlock the untapped potential of CC practices. Furthermore, we strongly advocate for the establishment of incentive policies and international collaboration to encourage the widespread adoption of these optimal practices. Achieving this vision will necessitate collaborative endeavors across both global and local sectors.

Methods
Data compilation
Working procedure of this study aims at evaluating CC benefits for global agroecosystems (Supplementary Fig. 25). We conducted a comprehensive search of all relevant peer-reviewed publications on the Web of Science before August 2022 to quantify the effects of CCs on multiple agroecosystem services. For agroecosystem services, we mainly focus on food supply, C storage, climate mitigation, and soil erosion control, which represent provisioning, regulating, and supporting roles. Thus, yield, SOC stock, N2O, CH4, and MWD were identified as target variables. We searched for titles and topics using the formula: (“cover crop” or “catch crop” or “green manure”) and (“yield” or “SOC” or “N2O” or “CH4” or “soil aggregate”) and (“cropland” or “farmland” or “agriculture”) while screening out meta-analyses, reviews, incubation, and pot experiments. Studies included into our dataset must conform to several criteria: (1) they must be field studies reporting mean values and replications of at least one target variable for the control and treatment (CC) groups; (2) both two groups share the same climatic and edaphic conditions before the experiment; (3) geographical coordinates or location are described; (4) both the control and CC treatment are under the same management practices, including fertilization, tillage, and irrigation; (5) detailed management information is stated, even CC and crop calendars; (6) only the CC rotation systems are considered rather than CC intercropping with other perennial crops; (7) inclusion of the same data from other publications is not permitted. Finally, 218 studies from 241 publications were included. These screening procedures follow the PRISMA methodology (Supplementary Fig. 26).

For each study, we extracted the mean values and replications (n) of target variables, and the standard deviation (SD) as much as possible from the tables or figures using GetData software v. 2.25. If the study reported the standard error (SE), we transformed it using the formula: SD = SE × √n; other unreported SDs were approximated by multiplying the mean values by the average coefficient of variation within each group. SOC data provided in concentration units were converted into stocks (Mg ha−1) by soil bulk density and sample depth. Data on environmental conditions (climate, soil, and microbial properties) and management practices (N fertilizer rate, organic fertilizer application, tillage, and irrigation) were also extracted from the corresponding studies, and the missing values were filled by high-resolution global maps (Supplementary Table 3). These maps generally have a good capability to impute missing values, but for some soil variables at different depths, the uncertainties they introduces should be treated with caution (Supplementary Fig. 27). In addition, available information about CC practice characteristics (type, diversity, termination, residue management, and duration) was also recorded. Given the importance of CC termination in rotation systems, we noted the CC growth days and termination time (i.e., the time interval between CCs ending and the next cash crop planting). Based on the above steps, we eventually compiled a big dataset of 2302 field observations for meta-analysis and scaling up (Supplementary Data 1). Overall, our dataset covers five continents and 25 major agricultural countries with broad discrepancies in environment and management, so it can be ideally used to explore the global CC effects and their main drivers. Details are shown in Supplementary Table 4.

Meta-analysis
The natural log response ratio (lnRR) was calculated to quantify the effect sizes of CCs on agroecosystem services: lnRR = ln(Xt / Xc), where Xt and Xc are the mean values of CC treatment and control groups, respectively. On the basis of mean value, replication and SD, we calculated the effect size of each pair data and its variance by the escalc function in the R package metafor70. Then we used the mixed-effects models from the rma.mv function to calculate the overall CC effects and the 95% bootstrapped confidence intervals (CI) for five target variables. These mixed-effects models were weighted by the inverse of variance, and included “study” (independent study sites) as a random effect. Moreover, we examined the presence or absence of publication bias using the funnel plots combined with Egger’s regressions (Supplementary Fig. 28). Desirably, no publication bias was found in all target variables, which supports the robustness of subsequent conclusions. The percentage change ((RR−1)×100) was adopted to denote the weighted effect sizes. If the 95% CI did not overlap with 0, it indicated a substantial positive (>0) or negative (<0) effect of CCs on the agroecosystem service.

To investigate the potential controls of CC effects by environmental and management factors, we divided them into various categories. Cropland types were categorized into upland and paddy. Climate zones were classified into four groups: tropical, arid, temperate, and cold, according to the Köppen-Geiger climate classification71. Soil texture was also grouped into four types that were simplified based on the USDA classification, including clay, clay loam, loam, and sandy. According to the uniform distribution of initial SOC concentration in our dataset, soil fertility was categorized into low (<9.3 g kg−1), moderate (9.3‒17.4 g kg−1), and high levels of SOC concentration (>17.4 g kg−1), which was attained by the R package funModeling72. Soil pH was classified into acid (<6.5), neutral (6.5‒7.5), and alkaline (>7.5). To further investigate CC effects on belowground services, we used mixed-effects models to examine the responses of SOC stock at five depths (0‒10 cm, 10‒30 cm, 30‒60 cm, 60‒100 cm, and >100 cm) and root biomass (n = 152, a subset of 11 studies; Supplementary Data 2) to different types of CCs. The CC types were categorized into three groups: legume, non-legume, and mixture. Residue management included CC residue incorporation into soil (incorporated), residue mulching on soil surface (surface), and residue removal (removed). Implementation duration was divided into short-term (<5 years) and long-term (≥5 years). Besides CC practice characteristics, most studies also involved other agricultural management practices. Within these practices, we classified organic fertilizer application into yes and no, tillage into conservation and conventional tillage, irrigation in upland into rainfed and irrigated, and irrigation in paddy into rainfed, flooding, and intermittent irrigation.

Furthermore, we tested the interaction between CCs and agricultural management using 37 two-factor experiments in our dataset, including CC × organic fertilizer, CC × conservation tillage, and CC × intermittent irrigation. For example, the interactions between CCs and organic fertilizer were calculated as: lnRR = ln(XAB / XB) − ln(XA / XC), where XAB, XB, XA, and XC are the mean values of treatment A plus B (CCs plus organic fertilizer), treatment B (only organic fertilizer), treatment A (only CCs), and control (no CCs and organic fertilizer), respectively. Similarly, if the 95% CI did not overlap with 0, the interaction was considered as a positive (>0) or negative (<0) interaction; otherwise, there was only an additive effect. Meta-regressions were employed to test the dependence of CC effects on continuously predictive variables, including CC diversity, N fertilizer rate, duration, and initial clay and SOC concentrations. Independent associations of CC effects with climate, soil, and microbial properties were validated using partial correlation analyses.

Long-term field data and APSIM model
To complement additional support for the positive effects of CC practices on soil health, we retrieved data on SOC fractions (mineral-associated organic C and particulate organic C; n = 82, 18 studies; Supplementary Data 3) and cellulase activity (n = 305, 13 studies; Supplementary Data 4) from long-term CC experiments, similarly following the PRISMA procedure. We examined CC effects on two SOC fractions and their proportion among various CC types, climate zones, and soil types. For cellulase activity, its responses to long-term CC practices along soil profiles, as well as in combination with tillage and N fertilizer management, were examined. There was also no publication bias found (Supplementary Fig. 29).

We used APSIM v. 7.10-r4218, a flexible crop model that can effectively simulate agricultural systems under a diverse range of complex crop, environmental, and management interactions16,17, to further provide evidence for the long-term CC benefits to global main crop yields and SOC stocks. Specifically, we selected wheat (Wedgetail) as the modelled cash crop and simulated its production in four scenarios: no CCs, legume CCs (field pea), non-legume CCs (oats), and CC mixtures (field pea and oats). Regarding CC mixtures, field pea and oats were treated as intercrops and linked up with the APSIM-canopy module73. All crops in the sequences were sown at 150 plants m−2, with a row spacing of 300 mm74. We collected a suite of observations from sites with a long history of CC practices to optimize the relevant parameters within crop and soil modules, and calibrate the model (Supplementary Data 5). The calibrated model has a relatively good capability to simulate wheat yields and SOC stocks for all scenarios (R2 > 0.60), with relative root mean square errors of 0.35 and 0.31, respectively (Supplementary Fig. 30). Details of the cultivar and soil parameters can be found in Supplementary Table 5. Combined with meteorological, soil, and management data, we finally obtained the outputs of wheat yields and SOC stocks from 1981 to 2020. The model results are shown as the 40-year average CC effects.

Meta-forest analysis
We performed MF analyses using the R package metaforest75 to quantify the relative importance of environmental and management factors for CC effects on five agroecosystem services (lnRRs). MF analysis is a machine-learning method that integrates meta-analysis (the variance and weight of each study) and random forest algorithm, while considering the potential nonlinear relationships and interactions between predictors65,75. Specifically, we first checked the convergence of our original models including all predictors, and then optimized the tuning parameters using the train function in R package caret76 without the predictors of negative importance. In the original models, CC growth days and termination time were included because they jointly determine the input and nutrient release of CC residues and the subsequent benefits to agroecosystem services7. Given the distinct mechanisms of upland and paddy in many processes, we also incorporated cropland type into the models as a predictor. Each final model was obtained based on 10-fold cross-validation with 75% of the dataset used for model fitting and 25% for validation, which had the minimum root mean square error. Moreover, the mean absolute error, out-of-bag R2, and R2 between the predicted and observed values were used to assess the predictive performance of five MF models (Supplementary Table 6 and Supplementary Fig. 31).

Network analysis and SEM
Based on MF predictions, network analysis was conducted to evaluate the associations among five services under CC practices and their relative roles for AMS. First, partial correlation networks were estimated by pairwise Markov random field using the R package bootnet, based on the extended Bayesian information criterion77. Second, the correlation stability of networks was bootstrapped by 1000 iterations, after which the robust centrality metrics (strength) were calculated. Third, each lnRR was weighted by its strength to quantify AMS. It is notable that we adopted the framework proposed by Manning. et al.68 to assign weights to different agroecosystem services based on their relative importance for agricultural demand (supply-benefit relationship). Specifically, a weight of 0.9 was assigned to crop yield due to its priority in intensive agriculture, while a weight of 0.6 was assigned to soil carbon storage, climate mitigation, and soil stabilization, given their secondary yet critical roles in agricultural sustainability68. Last, we calculated the percentage changes of AMS in both upland and paddy fields, as well as their 95% bootstrapped CIs.

Piecewise SEM was performed using the R package piecewiseSEM78 to ascertain the direct and indirect effects of environmental and management factors on AMS. All the predictors were divided into five composite factors (climate, soil, microbe, agricultural management, and CC practice characteristics) based on their standardized coefficients with AMS. Then the piecewise SEM was estimated by computing these composite factors in mixed-effects models from the R package lme479. Marginal and conditional R2 were used to jointly evaluate the interpretability of AMS, with smaller Fisher’s C (P > 0.05) and Akaike information criterion for selecting models with higher goodness. Standardized total effects from SEM referred to various contributions of composite factors to AMS. Two additional multivariate mixed-effects models were used to complementally quantify the individual effects of all predictors on AMS in upland and paddy fields.

Scaling up and future projection
Well-trained MF models were applied to scale up CC effects on five agroecosystem services (%) with a batch of global gridded datasets (Supplementary Table 3). For CC practice characteristics, we selected their optimal portfolio according to our meta-analyses to evaluate the global applicability of optimizing CC practices and its potential benefits. Optimal CC termination time was estimated by moving window analysis, with a window size of 30 consecutive observations sorted by termination time. Briefly, we performed a mixed-effects model to explore how much termination time would exert the maximum positive effect on AMS. The model included CC termination time, climate, soil, microbe, and their multi-order interactions as fixed effects, with a random effect of “study”. Bootstrapped standardized effects fitted nonlinearly with CC termination time and displayed a bimodal pattern, so the optimal CC termination time was determined as the threshold at concave peak. Then, the gridded dataset of CC growth days was accordingly predicted by optimal CC termination time and growth days of main crops from GGCMI Phase 380 (Supplementary Fig. 32). Separating upland from paddy, we generated several global maps of five services and AMS at a resolution of 0.1° × 0.1° for the current period (2020). The associated SD maps were used for evaluating the uncertainties of scaled-up CC effects. Furthermore, we meticulously calculated the actual benefits of CC practices on crop production, net GHG budget (NGHGB), and soil erosion control by multiplying our scaled-up CC effects with a series of baseline datasets, including cropland area, crop yields, SOC stocks (0‒30 cm), GHG emissions, and soil erosion (see more details in Supplementary Table 7). Based on the 100-year global warming potential (GWP) values from the Sixth Assessment Report of the IPCC, the conversion factors (CO2-eq.) for N2O and CH4 to calculate NGHGB were 273 and 2720. The NGHGB estimates the capacity for CO2 sequestration of various agroecosystems under CC practices. It was calculated as the difference between the decrease in GWP due to larger SOC stocks and the increase in GWP derived from direct N2O and CH4 emissions10: NGHGB (Pg CO2 yr−1) = 44/12 × SOCstock − 273 × N2O − 27 × CH4.

To explore whether optimizing CC practices can sustain benefits to global agroecosystems under future changing climate conditions, we projected CC effects in two scenarios of Shared Socioeconomic Pathway (SSP) using five bias-corrected climate model outputs provided by the ISIMIP3b simulation (GFDL-ESM-4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, and UKESM1-0-LL)81. These daily model outputs are complementary in terms of model structure and climate sensitivity, derived from CMIP6 (Supplementary Table 8). The two scenarios representing low (SSP126) and mid-high (SSP370) emission scenarios were used to simulate the future changes of CC benefits. In our future projections, we did not consider the adaptability of environment and management caused by climate change, but only its own changes, including temperature, precipitation, and aridity. The future aridity index was calculated by combining temperature and precipitation data from CMIP6 using the R package SPEI82. Specifically, daily mean temperature data were aggregated to a monthly scale for each decade (e.g., 2021‒2030). The aggregated monthly temperature was converted into annual potential evapotranspiration using the Thornthwaite function, which was then combined with annual precipitation data to obtain the 10-year average aridity index. The projected changes in future climate variables and CC benefits were calculated as the difference between 2091‒2100 and current period (2020), and we examined the relationships among these changes in two scenarios to determine the impact of climate change. Climatic resistance of yield change was calculated as the ratio of current yield increase to its reduction caused by climate change: R = Ycurrent / (Ycurrent − Yfuture), where R represents the climatic resistance, Ycurrent and Yfuture are current and future CC-based yield changes, respectively. We also extracted soil micronutrient concentrations from 1,306 sites worldwide (ref. 58) and national SDG data in 2020 (ref. 60) to check their associations with the corresponding values of AMS. All analyses were performed using R statistical software version 4.1.283. The data and code used in this study are publicly available in Zenodo (https://doi.org/10.5281/zenodo.14025624)(ref. 84).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This study presents a compelling exploration of cover crop (CC) practices using meta-analytic, modeling, and machine learning approaches from 2302 field observations to discern optimal CC rotation systems. Notably, the emphasis on long-term implementation and climate-smart management practices such as conservation tillage, organic fertilizer, irrigation management, and their interaction offers a strategic solution to increase crop yield and control GHG emissions. The prioritization of legume and non-legume biculture CCs, along with precise termination and residual mulching, further refines the recommended practices. The projected increase in global agroecosystem multiservices by 1.25% underscores the study's potential impact, providing a comprehensive outlook toward achieving sustainable development goals by balancing food security and environmental protection.\n\nMy major concern is that specific recommendations for optimal CC strategies may oversimplify the diverse global agroecological contexts. The optimal portfolio is mainly based on the performance of cover crops and their interaction with environmental and management factors from the results of the meta-analysis. However, the agricultural system is a complex ecosystem where different management measures, crop varieties, climates, and soils interact. The impact of management measures is not simply adding up. Therefore, judging the best combination solely based on meta-analysis results lacks solid support. Field data or crop model simulations are needed for backing. Refer to Lobell et al., 2014, for more information.\nLobell, D. B., Roberts, M. J., Schlenker, W., Braun, N., Little, B. B., Rejesus, R. M., & Hammer, G. L. (2014). Greater sensitivity to drought accompanies maize yield increase in the US Midwest. Science, 344(6183), 516-519.\n\nAdditionally, the optimistic projections for the year 2100 lack sufficient consideration of evolving climate patterns and uncertainties. A more cautious approach acknowledging the dynamic nature of environmental conditions and potential limitations in generalizing findings could enhance the study's overall credibility.\n\nSome specific comments are below.\nIntroduction\nLine 24-25 Citing relevant references that demonstrate previous research efforts to maximize the benefits of cover crops for agroecosystem services would provide a solid foundation for the findings presented in this paper.\nLine 30-31 Process-based models like crop models are crucial tools for quantifying the impacts of field management practices on crop productivity and other aspects of agricultural systems. It is important also to highlight their value in cover crop research.\nResults\nThis study presents an idealized design of optimal cover crop strategies. I would suggest expanding the analysis to include interactions between cover crops and diverse management factors like tillage, fertilization, and other climate-smart practices, testing a broader range of factor combinations. Incorporating some on-ground yield measurements, soil carbon changes, etc., from long-term cover cropping studies could complement the meta-analysis and modeling results. Sensitivity analyses could also help test the robustness of the estimations.\nLine 72-73: Please briefly explain the analysis of root growth in the Materials and Methods section.\nLine 78-79: Cite references to support this result.\nLine 113: It should be \"It is noteworthy that organic fertilizer and conservation tillage also tended to further enhance MWD\" instead of \"It is noteworthy that organic and conservation agriculture also tended to further enhance MWD\". Organic agriculture and conservation agriculture are broad terms that encompass more than just organic fertilizer use or reduced tillage.\nLine 141-142: Nothing has been mentioned about pH in Supplementary Fig. 3.\nDiscussion\nMore detailed discussion is needed regarding the specific recommendations for optimal cover crop strategies derived from the meta-analysis. More details on model specifications, accuracy metrics, and cross-validation approaches are also needed. Validating the projections using real-world data is required, or at least a discussion of data limitations. Further discussion on tradeoffs between yield, soil health, and greenhouse gas emissions under different conditions is warranted.\nData\nCompiling data from 219 studies helps overcome the limitations of individual location-specific experiments. However, inclusion of more long-term on-farm cover cropping trials could be valuable. The number of observations obtained for different ecosystem services varies substantially (e.g., only 162 for CH4 vs 1149 for yield). This imbalance needs to be discussed when interpreting the relative effect sizes.\nAssimilating high-resolution global datasets to fill in missing predictor values enables robust upscaling, but also introduces uncertainties that should be discussed.\nLines 51 and 290: Data periods are inconsistent (1978-2020 vs. Aug 2022).\nMethods\nMeta-analysis is used effectively to analyze cover crop impacts on yield, GHG, SOC, etc. However, the meta-analysis mostly shows positive effects, lacking explanation in the discussion.\nMany ML methods exist like XGBoost, neural networks, SVM. Explain why random forest was chosen.\nML requires large training datasets, but research sites are mainly in North America/Europe/Asia. Some responses like N2O have <400 trials. Explain how models can be reliably trained on such limited global data.\nLine 379: Explain why out-of-bag R2 values from the 5 MF models are relatively low (<0.64) while R2 between observed and predicted is >0.5.\nLines 422-423: Explain conversion factors and sources.\nLines 431-433: Specify time scale for SPEI drought index (1 month, 6 months, etc.).\nLines 436-437: Provide detailed explanation and references for calculating Climatic Resistance of Yield change.", "reviewer_2": "General comments:\nThe manuscript reports the results of a meta-analysis designed to assess ecosystem services and trade-offs of cover cropping (CC) – a farming practice that has gained wide acceptance as an approach to maximize agroecosystems productivity and minimize the environmental externalities associated with intensive agriculture. To quantitatively evaluate the overall effects of CC and estimate the contribution of management and climatic factors on these effects, the authors propose a comprehensive index called AMS (agroecosystem multi-services) index. The AMS index was applied to a database of 219 studies from around the globe. Among other results, quantitative estimates of the impact of CC on crop yield, soil organic storage, and soil structure stability (as determined by mean-weighted diameter of soil aggregates) are reported. The results also highlighted the significance of field location (upland vs paddy), botanical composition of cover crops (legumes vs mixture of several species), and termination date on synergistic effects and various tradeoffs of CC.\nOverall, there is a strong rationale for this study, and authors should be commended for their analysis and for producing noteworthy results. The AMS conceptual framework is very well designed and presented; however, in the Methods section, authors have not adequately description the index and how it is computed. Likewise, the graphs (especially the 6 key graphs) associated with the manuscript are well presented, easy to understand and contain the right amount of information. Some of the reported results could be impactful in terms of future research they could trigger and inform.\nThe manuscript, unfortunately, suffers from lack of clarity and other language deficiencies that made it difficult to fully evaluate the soundness of the research and the conclusions reported in the manuscript.\nRecommendation: major editing followed by a second review.\nThe significance of termination date is highlighted in the Abstract and elsewhere in the manuscript. Specifically, authors noted that CC benefits are maximized when there is a time gap of 25 days between CC termination and cash-crop planting. This could be an important finding. However, it is unclear where this information is reported in the manuscript. In the manuscript (L 188-189), reference is made to Extended Fig 4, but inspection of that figure showed to mention to termination date.\nEditorial Comments:\nAbstract:\n- suggestion for the first sentences – “The practice of cover cropping (CC) in agriculture has gained significant popularity as a means to improve agricultural sustainability. Yet, its full potential is constrained by possible environmental trade-offs that arise from …..”.\n- The phrase “underemphasized utilization” is unclear. Likewise, a few sentences later, the phrase “as the priority” is difficult to understand.\n- In the sentence that begins with “This is equivalent to” – the increase in crop production lacks precision. Does that refer to grains production or all crops? How were these numbers computed? These details are not provided in the Methods section.\n- Authors use the word “residual” several times in the Abstract and elsewhere. Proper terminology is “crop residue”.\nL 1-2. Suggestion “To meet the ever-growing food demand, the expansion of global cropland and the intensification of global agricultural production systems have caused a myriad of negative environmental impacts, such as climate change, biodiversity loss, and nitrogen (N) pollution”.\nL 19-21. Suggestion “Although CC practices have generally been found to be beneficial to soil microbiome and associated functions, in some cases, these improvements in soil biological attributes did not translate into improved crop productivity”.\nL 21-23. Suggestion “These variable results suggest that efforts should be made to optimize CC practices and achieve both food security and environment-friendly services, and this optimization would require careful consideration of CC selection (including mixtures), termination, and residue management.”\nL 26-28. Please edit this sentence for clarity. This sentence is crucial so readers can understand the rationale behind this meta-analysis.\nL 29-33 Suggestion. “Such knowledge gap hinders our capacity to propose an ideal CC adoption paradigm and develop effective management approaches that maximize the many benefits of CC”.\nL 30-32. Suggestion “Moreover, the limitations of statistical procedures (e.g., complicated assumptions in process-based modeling and low predictive accuracy of empirical models), has also led to a lack of quantitative tools to assess CC potential benefits and upscale these benefits to agroecosystems across the globe”.\nL 148- 149. The information presented is probably (technically) correct, but the connection to CH4 emission would not be readily apparent to most ready and might be context-dependent (true in paddy soil but not in upland soil). For example, looking at the information presented in Fig 3 (panel a), one may ask the question “why would CC positively impact N2O flux in upland soils and not in paddy soils?” Unlike in upland soils, it is possible that, due to wet conditions in paddy soils, the decomposition of CC residue primarily produces NH4 (not NO3 – the main substrate for N2O formation). Therefore, lesser impact of CC residue on N2O flux in wet paddy soils. In contrast, CC residue provides methanogens with fresh organic substrate for CH4 production in paddy soils.\nAlthough the paper’s primary focus is on presentation of meta-analysis results, authors should provide brief mechanistic explanation of some important results.\nFig 2, panel b. It is quite puzzling to see significant effect of CC on SOC stock even in less than 5 years of adoption (similar result about MWD). Measurable change in SOC stock is generally a slow process; typically, one may detect change in the surface soil layers (0-5 cm) in the first 5-10 years of implementation of CC (mostly in sand/silt soils, not in clay soils). This result seems inconsistent with most published empirical data.\nL 154. You can start a paragraph with the word “However” – an adverb that’s used to connect contrasting ideas. Please edit.\nL 233-250. In my opinion, this section should be removed. It is too speculative to stretch the results of this meta-analysis by inclusion of climate scenarios and socio-economic data. Consequently, Fig 6 should be modified – keep panels a to f (delete the rest – starting with panel g).\nNumerous sentences read like a collection of words and lack meaning. Below are just a few examples (this is not a complete list) include:\nL 14 “”resort soil penetration”.\nL 3-4 “Breakthrough of the planetary boundaries… critical services”.\nL 24-26. “Despite many …biophysical attributes”.\nL 34 “and their global …approaches”.\nL 38-40 “Based on the observed….their interactions”.\nL 58-59 “prevented N2O emission on the basis of stable yield”.\nL 67 “alerted concern”.\nL 143 “microbial and… to them”.\nL 146 “an impetus of soil C:N to CH4 emission”. Do you been “connection”?\nL 252 “mechanistic understating remains finite”."}, "author_response": "RESPONSE TO THE REVIEWERS’ COMMENTS\nReviewer #1 (Remarks to the Author):\nThis study presents a compelling exploration of cover crop (CC) practices using meta-\nanalytic, modeling, and machine learning approaches from 2302 field observations to\ndiscern optimal CC rotation systems. Notably, the emphasis on long-term\nimplementation and climate-smart management practices such as conservation tillage,\norganic fertilizer, irrigation management, and their interaction offers a strategic solution\nto increase crop yield and control GHG emissions. The prioritization of legume and\nnon-legume biculture CCs, along with precise termination and residual mulching,\nfurther refines the recommended practices. The projected increase in global\nagroecosystem multiservices by 1.25% underscores the study's potential impact,\nproviding a comprehensive outlook toward achieving sustainable development goals\nby balancing food security and environmental protection.\nResponse: We are grateful for your positive and insightful comments, which have\ngreatly strengthened our study. Following your suggestions, we have substantially\nrevised the manuscript (visible in red color). Our point-by-point responses are listed\nbelow. To avoid confusion, all line numbers are based on the Revised Manuscript\nCLEAN version.\nMy major concern is that specific recommendations for optimal CC strategies may\noversimplify the diverse global agroecological contexts. The optimal portfolio is\nmainly based on the performance of cover crops and their interaction with\nenvironmental and management factors from the results of the meta-analysis. However,\nthe agricultural system is a complex ecosystem where different management measures,\ncrop varieties, climates, and soils interact. The impact of management measures is not\nsimply adding up. Therefore, judging the best combination solely based on meta-\nanalysis results lacks solid support. Field data or crop model simulations are needed for\nbacking. Refer to Lobell et al., 2014, for more information.\nLobell, D. B., Roberts, M. J., Schlenker, W., Braun, N., Little, B. B., Rejesus, R. M., &\nHammer, G. L. (2014). Greater sensitivity to drought accompanies maize yield increase\nin the US Midwest. Science, 344(6183), 516-519.\nResponse: Thank you very much for your insightful comment. We strongly agree with\nyou about the realistic complexity of the optimal CC portfolio and appreciate your\nsuggestions for improving this. To address your concern, we have run the Agricultural\nProduction Systems sIMulator (APSIM, a crop model that enables the simulation of\nsystems for a diverse range of plant, soil, climate, and management interactions) to\nprovide useful model simulations. These simulations are aimed at exploring the\ninteractions of CC practices with various agroecological contexts and largely consistent\nwith our meta-analysis results. Additionally, we have also collected additional long-\nterm field data to provide empirical support and validate our findings. The long-term\nfield data consummates the actual benefits of CCs for soil health (Karlen et al., 2019).\nFurthermore, thank you for directing us to the relevant literature by Lobell et al. (2014),\nwhich we have reviewed and integrated into our discussion to better depict the concept\nof agricultural system complexity. More importantly, Lobell et al. (2014) also used\nAPSIM model to the response and sensitivity of crop yields to climate change, which\nlargely supports the credibility of our methodology. We believe that these efforts will\nsignificantly enhance the robustness and applicability of our recommendations. The\nadded results and discussions are listed below for your reference:\n“The optimization of CC portfolio was complemented by additional long-term field\ndata on soil health and simulations from the Agricultural Production Systems\nsIMulator (APSIM).” (lines 62-63)\n“Compared to non-legume CCs, legumes and mixtures (the blend of legume and non-\nlegume CCs) have been robustly observed to boost crop yields and N O emissions (Fig.\n2\n2b and Supplementary Fig. 2).” (lines 94-96)\n“Moreover, the response of crop yields is also dependent on climate, with arid regions\nexperiencing the largest yield increase (Fig. 3a and Supplementary Fig. 2). This is\nlargely due to the capacity of CCs to enhance soil water retention by reducing\nevaporation and increasing water infiltration8, especially when CC residues are\nmulched on the soil surface (Fig. 2b and Supplementary Fig. 2).” (lines 117-121)\n“In contrast, the diminished CC biomass input post-termination25 constrains soil C\nsequestration and yield improvements in cold climates (Fig. 3a and Supplementary Fig.\n2), for which the high concentration of particulate organic C fraction may also account\n(Supplementary Fig. 5c).” (lines 122-125)\n“Integrating conservation tillage with CC practices, especially no-tillage combined\nwith CC residue mulching31, is expected to reduce N O emissions while boosting crop\n2\nyields (Fig. 4a,c and Supplementary Fig. 2).” (lines 154-156)\n“Via the potential promotion yield stability31 and soil microbial anabolism32, applying\norganic fertilizer largely increases the yield improvements based on CCs (Fig. 4a and\nSupplementary Fig. 2).” (lines 174-176)\n“Our APSIM results similarly prove that high N input (> 100 kg ha-1) will reduce the\nyield benefits from CCs (Supplementary Fig. 2).” (lines 184-185)\n“On the other hand, through water storage enhancement by increasing SOC and water\nevaporation reduction by residue mulching8, CCs are more conducive to the yield of\nrainfed crops in uplands (Fig. 4a and Supplementary Fig. 2).” (lines 194-196)\nSupplementary Fig. 2 APSIM results verifying the benefits (%, 40-year average) of\nCC practices for global major crop (wheat) yields (a, d, g) and SOC stocks (b, e, h)\nfrom 1981 to 2020. Boxplots show the median, 25th and 75th percentiles, and outliers\nof yields (red color) and SOC stocks (yellow color) in each climate zone (c, f, i), or\nunder each N fertilizer rate (j), use of manure (k), and tillage (l). The classification of\nclimate zones is according to the Köppen-Geiger climate classification. Low N and high\nN denote ≤ 100 kg N ha-1 and > 100 kg N ha-1, respectively.\n“Our long-term observations further confirm that CC mixtures especially promote the\naccumulation of mineral-associated organic C rather than particulate organic C,\ndespite the larger proportional increase of the latter in other CC scenarios\n(Supplementary Fig. 5a,b).” (lines 105-107)\n“This depth-dependent control limits C storage in surface and mid soil layers\n(Supplementary Fig. 4), probably due to the microbial and enzymatic reactions\nsensitive to C concentration or pH. For instance, glucosaminidase and polyphenol\noxidase activities were reported to correlate negatively with subsoil C stocks26. The\ngreater, long-term stimulation of cellulase activity by CCs in surface and mid soil\nlayers than in the topsoil (0‒10 cm) supports this point (Supplementary Fig. 7a,b).”\n(lines 127-132)\nSupplementary Fig. 5 Long-term effects of CCs on SOC fractions (a) and their\nvariations under different CC types (b), climates and soil types (c). Dots represent the\nweighted CC effect sizes and their 95% confidence intervals. Solid and empty dots\nindicate significant and non-significant changes, respectively. Numbers above error\nbars denote the number of observations. Bars in (c) indicate the initial concentrations\nor ratios of SOC fractions (means ± standard error), with different lowercase letters\nindicating significant differences (P < 0.05) among climate zones or soil types. POC,\nparticulate organic C; MAOC, mineral-associated organic C; POC:MAOC, the ratio of\nparticulate organic C to mineral-associated organic C.\n“The greater enhancement of cellulase activity also indicates the superior microbial\nfunctionality under no-tillage (Supplementary Fig. 7c); meanwhile, its active\ndecomposition of organic matter contributes to the formation and stability of soil\naggregates13.” (lines 160-162)\n“Simultaneously, increasing N supply curbs the significant activation of cellulase and\nthus the opportunity for organic matter mineralization in paddy soils (P < 0.001;\nSupplementary Fig. 7d). Just as the synergies in increased yields, enhanced SOC stocks,\nand reduced CH emissions (Fig. 4f), the co-implementation of CCs and intermittent\n4\nirrigation facilitates both rice production and climate mitigation, chiefly through\nalleviated soil N limitation and improved aeration by drainage35.” (lines 168-172)\nSupplementary Fig. 7 Responses of cellulase activity along soil profiles to long-term\nCC practices (a, b) with tillage and fertilizer management (c, d). Dots indicate the\nweighted CC effect sizes and their 95% confidence intervals. Numbers above error bars\ndenote the number of observations. Colors in (d) represent cellulase responses in\ndifferent cropland types.\n“For developed countries with extensive CC experience but lacking in climate action\n(SDG bundle 5), new high-tech approaches and site-specific CC management are\nrequired to counteract adverse outcomes and realize further benefits47,57.” (lines 293-\n297)\nKarlen, D. L., Veum, K. S., Sudduth, K. A., Obrycki, J. F., & Nunes, M. R. Soil health\nassessment: Past accomplishments, current activities, and future opportunities. Soil Till.\nRes. 195, 104365 (2019).\n57. Lobell, D. B. et al. Greater sensitivity to drought accompanies maize yield increase\nin the US Midwest. Science 344, 516–519 (2014).\nFor the long-term field data and APSIM model, we have also explicitly clarified their\nprocedures and applicability. The added section “Long-term field data and APSIM\nmodel” is listed below for your reference:\n“Long-term field data and APSIM model\nTo complement additional support for the positive effects of CC practices on soil health,\nwe retrieved data on SOC fractions (mineral-associated organic C and particulate\norganic C; n = 82, 18 studies) and cellulase activity (n = 305, 13 studies) from long-\nterm CC experiments, similarly following the PRISMA procedure. We examined CC\neffects on two SOC fractions and their proportion among various CC types, climate\nzones, and soil types. For cellulase activity, its responses to long-term CC practices\nalong soil profiles, as well as in combination with tillage and N fertilizer management,\nwere examined. There was also no publication bias found (Supplementary Fig. 29).\nWe used APSIM v. 7.10-r4218, a flexible crop model that can effectively simulate\nagricultural systems under a diverse range of complex crop, environmental, and\nmanagement interactions16,17, to further provide evidence for the long-term CC benefits\nto global main crop yields and SOC stocks. Specifically, we selected wheat as the\nmodelled cash crop and simulated its production in four scenarios, including no CCs,\nlegume CCs (field pea), non-legume CCs (oats), and CC mixtures (field pea and oats).\nRegarding CC mixtures, field pea and oats were treated as intercrops and linked up\nwith the APSIM-canopy module69. We collected a suit of observations from sites with a\nlong history of CC practices to optimize the relevant parameters within crop and soil\nmodules, and calibrate the model. The calibrated model has a relatively good capability\nto simulate wheat yields and SOC stocks for all scenarios (R2 > 0.60), with their relative\nroot mean square errors of 0.35 and 0.31, respectively (Supplementary Fig. 30).\nCombined with meteorological, soil, and management data, we finally obtained the\noutputs of wheat yields and SOC stocks from 1981 to 2020. The model results are shown\nas the 40-year average CC effects.” (lines 420-440)\nSupplementary Fig. 29 Funnel plots illustrating the absence of publication bias within\nthe long-term field data. P > 0.05 denotes no publication bias according to Egger’s\nregression.\nSupplementary Fig. 30 Predictive performance of simulations from APSIM model.\nDifferent shapes of points represent four CC scenarios. Black solid lines denote the\nsignificant correlations. RRMSE, relative root mean square error.\n16. He, Q. et al. Modelling interactions between cowpea cover crops and residue\nretention in Australian dryland cropping systems under climate change. Agr. Ecosyst.\nEnviron. 353, 108536 (2023).\n17. Basche, A. D. et al. Simulating long-term impacts of cover crops and climate\nchange on crop production and environmental outcomes in the Midwestern United\nStates. Agr. Ecosyst. Environ. 218, 95–106 (2016).\n69. Garba, I. I. et al. Modelling the impacts of diverse cover crops on soil water and\nnitrogen and cash crop yields in a sub-tropical dryland. Field Crop. Res. 301, 109019\n(2023).\nAdditionally, the optimistic projections for the year 2100 lack sufficient consideration\nof evolving climate patterns and uncertainties. A more cautious approach\nacknowledging the dynamic nature of environmental conditions and potential\nlimitations in generalizing findings could enhance the study's overall credibility.\nResponse: Thank you for your thoughtful comment and concern regarding our future\nprojections. We fully acknowledge the inherent uncertainties related to future climate\nscenarios and the potential limitations of extending our predictions to such a long-term\nperiod. We have incorporated more detailed discussions in the revised manuscript\nregarding the uncertainties in our model predictions. These uncertainties particularly\npertain to potential moderators such as elevated CO , management adaptability, and\n2\nsocio-economic factors, which are not fully captured by using only CMIP6\nmeteorological data. However, we emphasize that despite these uncertainties, long-term\nprojections remain essential for illustrating the sustainable benefits of optimizing CC\npractices on global agriculture. They provide a framework for policymakers to assess\nand plan for long-term agricultural strategies.\nWe also highlight that these projections reveal an important finding, i.e., optimizing CC\npractices has great potential to enhance the resistance and resilience of agroecosystems\nto future climate change (Supplementary Figs. 19‒20), thereby contributing to the\nachievement of the of the sustainable development goals (SDGs). To confirm this, we\nfurther use the open national-scale SDG data, supporting the key finding of our study:\ncontinued implementation of long-term CC practices will provide consistent resistance\nto climate-related yield losses and contribute to progress towards climate neutrality and\nsoil stabilization, especially in harsh and underdeveloped areas.\nWe believe that these measures effectively convey our viewpoint on the potential\nsustainable benefits of optimizing CC practices in the future, while maintaining the\nrigor and transparency of our study. The related results and discussions are listed below\nfor your reference:\n“More importantly, leveraging the national SDG performance data, we unravel the\nexplicit support of optimizing CC practices for nations with limited progress towards\nthe SDGs (P < 0.001; Supplementary Fig. 23), particularly in upland systems of the\nleast-developed countries (SDG bundle 1 and 2)55.” (lines 288-291)\n“For developed countries with extensive CC experience but lacking in climate action\n(SDG bundle 5), new high-tech approaches and site-specific CC management are\nrequired to counteract adverse outcomes and realize further benefits47,57.” (lines 295-\n297)\nSupplementary Fig. 23 Relationship of CC benefits with national sustainable\ndevelopment levels. (a) Black and color solid lines denote the significant correlations\nin general and different cropland types, respectively. (b) Boxplots showing national CC\nbenefits within bundle 1 (nations with lowest development and good environment),\nbundle 2 (nations with low development and degraded environment), bundle 3 (nations\nwith middle development while needing environment and climate action),\nbundle 4 (nations with middle development, inequality, and degraded environment), and\nbundle 5 (nations with high development while needing climate action). Significance of CC\nbenefits for agroecosystem multiservices (P < 0.001) among different bundles has been\ntested using Kruskal-Wallis test.\nSome specific comments are below.\nIntroduction\nLine 24-25 Citing relevant references that demonstrate previous research efforts to\nmaximize the benefits of cover crops for agroecosystem services would provide a solid\nfoundation for the findings presented in this paper.\nResponse: Thank you for your comment. We have added two systematic reviews as\nsupplementary references (Daryanto et al., 2018; Lamichhane and Alletto, 2022), which\nthorougly reviewed previous research efforts and knowledge gaps in implementing\nCC practices to benefit agroecosystem services. In addition, we have further included\na relevant reference illustrating the neglected trade-offs or synergies among multiple\nservices by these research efforts (Finney and Kaye, 2017). The revised sentences and\nadded references are listed below for your reference:\n“Although numerous research efforts aim to maximize the benefits of CCs for\nagroecosystem services7,8, these studies are predominantly isolated experiments\nconducted under limited agronomic and biophysical conditions.” (lines 44-46)\n“Most of these efforts focus on individual target services, often lacking a quantitative\nevaluation of the trade-offs or synergies among them, especially concerning the\ninteractions between CC practices and diverse environmental and management\nfactors15.” (lines 46-48)\n7. Lamichhane, J. R., & Alletto, L. Ecosystem services of cover crops: a research\nroadmap. Trends Plant Sci. 27, 758–768 (2022).\n8. Daryanto, S., Fu, B., Wang, L., Jacinthe, P. A., & Zhao, W. Quantitative synthesis\non the ecosystem services of cover crops. Earth-Sci. Rev. 185, 357–373 (2018).\n15. Finney, D. M., & Kaye, J. P. Functional diversity in cover crop polycultures\nincreases multifunctionality of an agricultural system. J. Appl. Ecol. 54, 509–517\n(2017).\nLine 30-31 Process-based models like crop models are crucial tools for quantifying the\nimpacts of field management practices on crop productivity and other aspects of\nagricultural systems. It is important also to highlight their value in cover crop research.\nResponse: Thank you very much. We quite agree with you that crop models play a\npivotal role in predicting and quantifying CC effects on agroecosystem services (for\nexample, refs.16,17). These models offer insights into the consequences of agricultural\npractices by simulating the interactions between environmental factors, crop\nestablishment and growth, as well as soil processes. However, it is also important to\nacknowledge their limitations in the complexity of parameterization, the accuracy of\npredictions, and the applicability to specific regions or crop types. Integrating machine\nlearning (ML) approaches can help overcome these limitations. ML can handle large\ndatasets, identify complex nonlinear relationships, and enhance the predictive power of\nmodels. ML is particularly suited for dealing with high-dimensional data and\nuncovering complex interactions within the data, which is crucial for understanding and\noptimizing CC practices. Despite these, we have now integrated your comment to\nhighlight the value of process-based modeling. The revised sentence reads as follows:\n“Additionally, current limitations within statistical procedures—such as the\ncomplicated assumptions in process-based modeling and the low predictive accuracy\nof empirical models—further contribute to a lack of quantitative tools for assessing\npotential CC benefits and extrapolating these benefits on a global scale, despite some\nsuccessful regional simulations by crop models16,17. Given the critical need for\nsynergistic agroecosystem services, it becomes imperative to globally explore and\nimplement the most-effective CC practices, leveraging ubiquitous data and emerging\nmethodologies.” (lines 50-56)\n16. He, Q. et al. Modelling interactions between cowpea cover crops and residue\nretention in Australian dryland cropping systems under climate change. Agr. Ecosyst.\nEnviron. 353, 108536 (2023).\n17. Basche, A. D. et al. Simulating long-term impacts of cover crops and climate\nchange on crop production and environmental outcomes in the Midwestern United\nStates. Agr. Ecosyst. Environ. 218, 95–106 (2016).\nResults\nThis study presents an idealized design of optimal cover crop strategies. I would suggest\nexpanding the analysis to include interactions between cover crops and diverse\nmanagement factors like tillage, fertilization, and other climate-smart practices, testing\na broader range of factor combinations. Incorporating some on-ground yield\nmeasurements, soil carbon changes, etc., from long-term cover cropping studies could\ncomplement the meta-analysis and modeling results. Sensitivity analyses could also\nhelp test the robustness of the estimations.\nResponse: Thank you for your insightful comment and suggestions. We fully recognize\nthe need for more long-term field data to complement our meta-analysis and predictions.\nIn this regard, we have incorporated additional variables related to soil health (i.e., soil\ncarbon fractions and cellulase activity) from long-term studies (≥ 5 years) to support\nour selection of optimal CC portfolio and provide a more robust foundation for our\nrecommendations under the interactions of CC practices with agricultural management\n(e.g., tillage and fertilization). More importantly, we have substantially rewritten the\nsection “Optimizing CC practices under the interactions with environmental and\nmanagement factors”, so as to better clarify the key moderators and their complex\ninteractions with CC practices, and enlighten the future implementation of CC practices\naccording to local conditions. The improved results and discussions are listed below for\nyour reference:\n“Additionally, mixtures have been found to enhance the root growth of subsequent\ncrops and sequester more C in the surface soil (10‒30 cm) than legume CCs alone\n(Supplementary Figs. 3‒4). These belowground benefits are supported by a 7-year\nrotation system, where CC mixtures can facilitate the accrual of mineral-associated\norganic C fractions through increased root biomass23. Our long-term observations\nfurther confirm that CC mixtures especially promote the accumulation of mineral-\nassociated organic C rather than particulate organic C, despite the larger proportional\nincrease of the latter in other CC scenarios (Supplementary Fig. 5a,b).” (lines 101-107)\n“Regarding SOC stocks, the primary moderators are initial SOC concentration and pH\n(Supplementary Fig. 6b), with their interactive effects varying across different soil\ndepths (P < 0.001; Supplementary Table 1). This depth-dependent control limits C\nstorage in surface and mid soil layers (Supplementary Fig. 4), probably due to\nmicrobial and enzymatic reactions sensitive to C concentration or pH. For instance,\nglucosaminidase and polyphenol oxidase activities were reported to correlate\nnegatively with subsoil C stocks26. The greater, long-term stimulation of cellulase\nactivity by CCs in surface and mid soil layers than in the topsoil (0‒10 cm) supports\nthis point (Supplementary Fig. 7a,b).” (lines 125-132)\n“Integrating conservation tillage with CC practices, especially no-tillage combined\nwith CC residue mulching31, is expected to reduce N O emissions while boosting crop\n2\nyields (Fig. 4a,c and Supplementary Fig. 2). By moderating soil temperature and\naggregate stability24, which in turn enhances the interaction of mycorrhizal fungi with\nliving roots32, mulching CC residues on the soil surface can markedly increase crop\nyields (Fig. 2b). In comparison, incorporating CC residues into the soil induces\nmicrobial turnover and nutrient immobilization33, potentially limiting the yield benefits.\nThe greater enhancement of cellulase activity also indicates the superior microbial\nfunctionality under no-tillage (Supplementary Fig. 7c); meanwhile, its active\ndecomposition of organic matter contributes to the formation and stability of soil\naggregates13.” (lines 154-162)\n“Furthermore, our analyses indicate that soil C:N ratio serves as a driving force\nbehind CH emissions (Fig. 3b and Supplementary Fig. 6d). An elevated C:N ratio\n4\nexacerbates soil N limitation in general, under which CC biomass input accelerates\nmicrobial mineralization of organic matter via N mining, thereby priming CH\n4\nemissions34. This priming effect varies by cropland type and irrigation method, with\nintermittent irrigation adeptly lowering CC-derived CH emissions from paddies\n4\n(Supplementary Fig. 10). Simultaneously, increasing N supply curbs the significant\nactivation of cellulase and thus the opportunity for organic matter mineralization in\npaddy soils (P < 0.001; Supplementary Fig. 7d). Just as the synergies in increased\nyields, enhanced SOC stocks, and reduced CH emissions (Fig. 4f), the co-\n4\nimplementation of CCs and intermittent irrigation facilitates both rice production and\nclimate mitigation, chiefly through alleviated soil N limitation and improved aeration\nby drainage35.” (lines 162-172)\nWe hope that these efforts can address your concerns and enhance the overall credibility\nof our study. For more revised discussions, please see the Revised Manuscript\nCLEAN version. Thanks!\nLine 72-73: Please briefly explain the analysis of root growth in the Materials and\nMethods section.\nResponse: Thank you for your comment. Specifically, we did not conduct targeted\nsearch for the literature of CC and root biomass, but just compiled a dataset on root\nbiomass during the process of yield data extraction (i.e., each root biomass observation\nmatches one yield observation). Therefore, this dataset is a subset of the full dataset,\ncontaining 11 studies and 152 observations (Supplementary Data 2).\nRegarding statistical analysis, we also used a mixed-effects model to quantify the\nresponse of root biomass to different types of CCs, thereafter disclosing the superiority\nof CC mixtures in belowground services (Supplementary Fig. 3). We believe that this\nmethod is valid and reliable, since no publication bias has been detected (Fig. R1).\nFig. R1 Funnel plot indicating no publication bias for root biomass based on Egger’s\nregression test (P > 0.05).\nWe have further added the information about data source and statistical model of root\nbiomass in the Methods section. The related sentence is listed below for your reference:\n“To further investigate CC effects on belowground services, we used mixed-effects\nmodels to examine the responses of SOC stock at five depths (0‒10 cm, 10‒30 cm, 30‒\n60 cm, 60‒100 cm, and > 100 cm) and root biomass (n = 152, a subset of 11 studies)\nto different types of CCs.” (lines 397-400)\nLine 78-79: Cite references to support this result.\nResponse: Thank you for your comment. We have added two relevant references about\nmulching CC residues on soil surface to increase crop yield (refs.24,32). According to\nthese references and our results (Fig. 2b), this is mainly due to the greater benefit on\nmoderating soil temperature and aggregate stability, which can in turn enhance the\ninteraction between arbuscular mycorrhizal fungi and living CC roots. The revised\nsentence and added references are listed below for your reference:\n“Integrating conservation tillage with CC practices, especially no-tillage combined\nwith CC residue mulching31, is expected to reduce N O emissions while boosting crop\n2\nyields (Fig. 4a,c and Supplementary Fig. 2). By moderating soil temperature and\naggregate stability24, which in turn enhances the interaction of mycorrhizal fungi with\nliving roots32, mulching CC residues on the soil surface can markedly increase crop\nyields (Fig. 2b).” (lines 154-158)\n24. Blanco‐Canqui, H. et al. Cover crops and ecosystem services: Insights from studies\nin temperate soils. Agron. J. 107, 2449–2474 (2015).\n32. Lehman, R. M. et al. Soil microbial community response to corn stover harvesting\nunder rain-fed, no-till conditions at multiple US locations. Bioenerg. Res. 7, 540–550\n(2014).\nLine 113: It should be \"It is noteworthy that organic fertilizer and conservation tillage\nalso tended to further enhance MWD\" instead of \"It is noteworthy that organic and\nconservation agriculture also tended to further enhance MWD\". Organic agriculture and\nconservation agriculture are broad terms that encompass more than just organic\nfertilizer use or reduced tillage.\nResponse: Yes, we quite agree with you. We have rephrased this sentence based on\nyour suggestion. The revised sentence is listed below for your reference:\n“It is noteworthy that organic fertilizer and conservation tillage are also inclined to\nadditionally enhance MWD (Fig. 4e), by virtue of their stronger cementation and less\nphysical disruption of soil aggregates39 as well as the amplified role of CC roots in soil\nstabilization7.” (lines 178-181)\nLine 141-142: Nothing has been mentioned about pH in Supplementary Fig. 3.\nResponse: Thank you for pointing it out. Our original intention was that the interactive\ncontrol of initial SOC concentration and pH on SOC stock relied on soil depth.\nAccording to Supplementary Fig. 4 (previous Supplementary Fig. 3), this depth-\nspecific control hindered the enhancement of C storage in surface and mid soil layers,\nlikely due to the sensitivity of microbial and enzymatic activities to SOC concentration\nand pH. For example, in a 29-year cropping experiment, Chen et al. (2022) found that\nSOC stock was positively correlated with the content of amino sugars solely in the\ntopsoil, but it was negatively correlated with N-acetyl-β-D-glucosaminidase and\npolyphenol oxidase activities in the subsoil. These microbial necromass and enzymes\nhave been widely confirmed to be sensitive to SOC concentration and pH.\nTo avoid ambiguity, the related sentences have been rephrased as:\n“Regarding SOC stocks, the primary moderators are initial SOC concentration and pH\n(Supplementary Fig. 6b), with their interactive effects varying across different soil\ndepths (P < 0.001; Supplementary Table 1). This depth-dependent control limits C\nstorage in surface and mid soil layers (Supplementary Fig. 4), probably due to\nmicrobial and enzymatic reactions sensitive to C concentration or pH. For instance,\nglucosaminidase and polyphenol oxidase activities were reported to correlate\nnegatively with subsoil C stocks26.” (lines 126-131)\n26. Chen, J., Luo, Y., Kätterer, T., & Olesen, J. E. Depth-dependent responses of soil\norganic carbon stock under annual and perennial cropping systems. Proc. Natl Acad.\nSci. USA 119, e2203486119 (2022).\nDiscussion\nMore detailed discussion is needed regarding the specific recommendations for optimal\ncover crop strategies derived from the meta-analysis. More details on model\nspecifications, accuracy metrics, and cross-validation approaches are also\nneeded. Validating the projections using real-world data is required, or at least a\ndiscussion of data limitations. Further discussion on tradeoffs between yield, soil\nhealth, and greenhouse gas emissions under different conditions is warranted.\nResponse: Thank you for your valuable feedback. We appreciate the opportunity to\nexpand our discussion and provide more detailed insights into our study. Below are our\ndetailed revisions in the discussion according to your suggestions:\n(1) Specific recommendations for optimal cover crop strategies:\n“Notably, these effects can be modified by CC diversification. The changes in yields\nand GHGs significantly display concave-down parabolic patterns, while SOC\nstocks rise linearly with increasing CC diversity (P < 0.05; Fig. 2c), manifesting\nthe superiority of CC mixtures with a species number of 2 or 3. Additionally,\nmixtures have been found to enhance the root growth of subsequent crops and\nsequester more C in the surface soil (10‒30 cm) than legume CCs alone\n(Supplementary Figs. 3‒4). These belowground benefits are supported by a 7-year\nrotation system, where CC mixtures can facilitate the accrual of mineral-associated\norganic C fractions through increased root biomass23. Our long-term observations\nfurther confirm that CC mixtures especially promote the accumulation of mineral-\nassociated organic C rather than particulate organic C, despite the larger proportional\nincrease of the latter in other CC scenarios (Supplementary Fig. 5a,b).\nGiven the lower production costs related to breeding and planting7, we cautiously\nrecommend mixtures with a diversity of 2 (i.e., a biculture of legume and non-\nlegume CCs) as the preferred CC type. This coincides with the perspective that a\nhigher functional diversity, rather than species richness, of CCs predominantly\nsupports multiple agroecosystem services15.” (lines 98-111)\n(2) Validation using real-world data and discussion of data limitations:\n“Second, there are some uncertainties in the predictions. Despite the well-assumed\nvariable interactions and nonlinearities, as well as the weight of individual studies\nin MF models, the imbalanced data among different services and some regions will\ncause large estimation errors, such as CH for the Mediterranean and MWD for\nindispensable factor for predicting CC effects, but most of the studies included are\nshort-term (Supplementary Table 4). Excluding potential moderators of trade-offs\namong agroecosystem services may also miscalculate the CC benefits, such as\nelevated CO 61, management adaptability62, and socio-economic factors63.” (lines\n2\n305-313)\n(3) Trade-offs between yield, soil health, and greenhouse gas emissions:\n“Given the ascertained trade-offs, especially related to GHG emissions, we further\nquantified the associations among five services and their strengths of expected\ninfluence to narrow the actual gap in accurately evaluating CC applicability.\nIn uplands, yields become the keystone service with the greatest strength, followed by\nMWD, SOC stocks, and two GHGs (Fig. 5a). Beyond the direct support of SOC\nstocks for crop yields under CC practices27, the enhanced MWD will indirectly\nincrease yields through improved soil fertility and connectivity (i.e., higher water,\noxygen, and nutrient concentrations)13. Owing to the positive effects on food supply,\nC storage, and soil stabilization, CCs are anticipated to enhance agroecosystem\nmultiservices (AMS) by 1.67% in uplands (Fig. 5c). Conversely, AMS experiences\nno significant promotion or even a decline in rice paddies, primarily attributed to\nthe high expected influence of CH (Fig. 5b,d). While CC biomass input promotes\n4\nrice yields and soil C balance, it substantially increases CH production under the\n4\nanaerobic conditions of paddy soils12 (Fig. 3a). Effective measures are, thus,\nnecessary to reduce CH emissions while maintaining rice productivity under CC\n4\npractices, such as aerobic pre-digestion of CC residues45 and intermittent irrigation\n(Fig. 4d,f). Structural equation modelling (SEM) show that climate is the dominant\nfactor influencing AMS indirectly and directly in uplands and paddies, respectively\n(Fig. 5e‒h), mainly by controlling water availability (precipitation and\nevapotranspiration; Supplementary Table 2). The direct influence of soil variables\nis also highly important to AMS. Specifically, soil C:N ratio is positively whereas\ninitial clay and SOC concentrations are negatively associated with AMS, verifying\nthe preferable benefits of CCs for harsh environments (i.e., soils with less organic\nmatter and stronger N limitation or dryland areas; Fig. 1b). Meanwhile, integrating\nCCs with climate-smart management practices, including reducing N fertilizer rate,\norganic amendments, and mulching CC residues, has a definitely positive effect on\nAMS (Supplementary Table 2). Overall, these findings emphasize the importance of\nbridging the gap between local environment and managerial knowledge to design\nmore sustainable CC systems with fewer trade-offs.” (lines 197-221)\nHope that these modifications can address your concerns and enhance the overall\ncredibility of our study. Thank you again for your insightful comment.\nData\nCompiling data from 219 studies helps overcome the limitations of individual location-\nspecific experiments. However, inclusion of more long-term on-farm cover cropping\ntrials could be valuable. The number of observations obtained for different ecosystem\nservices varies substantially (e.g., only 162 for CH4 vs 1149 for yield). This imbalance\nneeds to be discussed when interpreting the relative effect sizes.\nResponse: Thank you for highlighting these important considerations. While our meta-\nanalysis already includes long-term data where available, we have complemented our\nstudy with additional indicators related to soil health, such as soil carbon fractions and\ncellulase activity. The positive effects observed in these long-term studies further\nvalidate CC benefits over extended periods. These results also support our insights into\noptimizing CC practices and their interactions with agricultural management (e.g.,\ntillage and fertilization). The supplementary discussions are listed below for your\nreference:\n“Our long-term observations further confirm that CC mixtures especially promote the\naccumulation of mineral-associated organic C rather than particulate organic C,\ndespite the larger proportional increase of the latter in other CC scenarios\n(Supplementary Fig. 5a,b).” (lines 105-107)\n“In contrast, the diminished CC biomass input post-termination25 constrains soil C\nsequestration and yield improvements in cold climates (Fig. 3a and Supplementary Fig.\n2), for which the high concentration of particulate organic C fraction may also account\n(Supplementary Fig. 5c).” (lines 122-125)\n“The greater, long-term stimulation of cellulase activity by CCs in surface and mid soil\nlayers than in the topsoil (0‒10 cm) supports this point (Supplementary Fig. 7a,b).”\n(lines 131-132)\n“This relationship also appears in SOC fractions, that is, their higher improvements\noccur in the soil texture with less clay and SOC, but not in the finest clay soils,\nespecially for mineral-associated organic C (Supplementary Fig. 5c).” (lines 136-138)\n“The greater enhancement of cellulase activity also indicates the superior microbial\nfunctionality under no-tillage (Supplementary Fig. 7c); meanwhile, its active\ndecomposition of organic matter contributes to the formation and stability of soil\naggregates13.” (lines 160-162)\n“Simultaneously, increasing N supply curbs the significant activation of cellulase and\nthus the opportunity for organic matter mineralization in paddy soils (P < 0.001;\nSupplementary Fig. 7d).” (lines 168-169)\nAdditionally, we recognize the imbalance in the number of observations for different\nagroecosystem services, which indeed needs careful discussion when interpreting the\nrelative effect sizes. However, due to the lack of existing data, we have to accept the\nsecond best option to discuss more limitations:\n“Despite the well-assumed variable interactions and nonlinearities, as well as the\nweight of individual studies in MF models, the imbalanced data among different\nservices and some regions will cause large estimation errors, such as CH for the\nAssimilating high-resolution global datasets to fill in missing predictor values enables\nrobust upscaling, but also introduces uncertainties that should be discussed.\nResponse: We strongly agree with you and appreciate this crucial comment. In our\nanalysis, assimilating high-resolution global datasets to fill missing predictor values\nwas crucial for robust global upscaling, while introducing of certain uncertainties.\nThese uncertainties primarily stem from potential mismatches in spatial resolutions\nbetween global datasets and field observations, and the representativeness of global\ndatasets in specific regions. To largely reduce these uncertainties, we employed cross-\nvalidation with multiple data sources (i.e., processed-based model simulations\nfollowing your suggestions), selected highly accurate and reliable global datasets\n(Supplementary Table 3), and conducted sensitivity analysis (random missing value\nimputation) to assess the robustness of data imputation (Supplementary Fig. 27).\nHowever, as Meyer and Pebesma (2022) mentioned, when the coverage of training data\nin the prediction space is not high, it may produce inapplicable results; therefore, we\nhope to improve the generation approaches for global maps and conduct more local\nstudies to enhance data accuracy and representativeness in the future.\nThe newly added sensitivity analysis and sentences about the uncertainties from the\nassimilation of high-resolution global datasets are listed below for your reference:\n“These maps generally have a good capability to impute missing values, but for some\nsoil variables at different depths, the uncertainties they introduce should be treated with\ncaution (Supplementary Fig. 27).” (lines 363-365)\nSupplementary Fig. 27 Sensitivity analysis (random missing value imputation) of\nfilling missing values of environmental factors with high-resolution global maps.\nBlack solid lines denote the significant correlations.\nMeyer, H., & Pebesma, E. Machine learning-based global maps of ecological variables\nand the challenge of assessing them. Nat. Commun. 13, 2208 (2022).\nLines 51 and 290: Data periods are inconsistent (1978-2020 vs. Aug 2022).\nResponse: Thank you for your comment. Indeed, this inconsistency is the distinction\nbetween two different concepts: (i) the actual time frame of the studies conducted\n(1978–2020), and (ii) the time point when the literature search and data collection\nwere completed (Aug 2022). This clear differentiation is crucial for understanding the\nscope and limitations of the dataset, as well as for clarifying the temporal sensitivity\nand practical applicability of the analyses, including the challenge of upscaling.\nSpecifically, the actual time frame of the studies conducted (1978–2020) refers to the\nperiod covered by the studies included in the analysis; these years indicate the historical\noption of CC practices and reflect the temporal span of the collected data. In contrast,\nthe time point when the literature search and data collection were completed (Aug 2022)\nmarks the completion of data compilation efforts, which means that any CC studies\npublished after this point would not be included in our meta-analysis and upscaling.\nMethods\nMeta-analysis is used effectively to analyze cover crop impacts on yield, GHG, SOC,\netc. However, the meta-analysis mostly shows positive effects, lacking explanation in\nthe discussion.\nResponse: Thank you for your valuable comment. We recognize the importance of\nunderstanding not just the meta-analysis results but also their underlying mechanisms.\nThis insight is very crucial for the scientific significance of our findings and their\nguiding role in agricultural practices. In response to your comment, and in line with our\nrevisions addressing a similar concern raised by Reviewer #2, we have undertaken\nseveral efforts to improve the discussions about mechanistic explanations in the revised\nmanuscript, including but not limited to:\n(1) The support of key functional microbes to yield gains: We have strengthened\ndiscussions about the pivotal role of synergies among key functional microbes (e.g.,\nN-fixers and arbuscular mycorrhizal fungi) and CC roots in promoting crop\nproductivity (e.g., lines 113-116, lines 147-150, lines 156-158, and lines 174-176).\n(2) Context-dependent GHG emissions: Following Reviewer #2's suggestions, we\nelaborated on the underlying mechanisms of the primed N O and CH emissions in\n2 4\ndifferent cropland types. We delved into how CC residues, soil properties, and\nmicrobial communities interacted on these GHGs (e.g., lines 83-85, lines 152-154,\nand lines 162-172).\n(3) The role of root and microbial activities in enhanced MWD and SOC stocks:\nWe underlined that the incorporation of CCs not only improve soil structure and\nmicrobial biodiversity, but also produce binding substances that can promote the\nformation of soil aggregates. This enhancement in MWD is conducive to SOC\nstabilization, but contingent upon distinct microbial and enzymatic responses across\nsoil profiles (e.g., lines 101-105, lines 127-132, lines 140-144, and lines 160-162).\nWe are grateful for your suggestions, which have significantly improved the depth and\nclarity of our study. We welcome any further feedback you may have.\nMany ML methods exist like XGBoost, neural networks, SVM. Explain why random\nforest was chosen.\nResponse: Thank you for your comment. As a decision tree integration approach,\nrandom forest has many remarkable advantages, including robustness and accuracy,\ncapability of handling high-dimensional data, evaluation of feature importance, and\nflexibility (it can handle both classification and regression), especially when dealing\nwith complex ecological and agricultural data (Breiman, 2001). Although other\nmachine learning methods such as XGBoost, neural network, and support vector\nmachine also have their own advantages and applicable scenarios, random forest is\nconsidered as a powerful and practical choice in many cases because of its above\nadvantages.\nMeta-forest (MF) is an advanced method based on the random forest approach,\ndesigned to tackle more complex data analysis and prediction within the context of\nmeta-analysis. It inherits the core strengths of random forest while introducing new\nfeatures and improvements to enhance model performance and applicability. Below are\nsome new advantages of MF compared to traditional random forest (Van Lissa, 2017,\n2020):\n(1) Improved feature selection and interaction detection: MF employs more\nsophisticated algorithms to more effectively identify and evaluate complex\ninteractions between variables, which is particularly important for understanding\nhow environmental and managemental factors jointly control CC effects.\n(2) Enhanced generalization capability: By integrating a larger number of decision\ntrees and employing more complex combination rules, MF can improve prediction\naccuracy on unseen data. This enables MF models to better extrapolate CC effects\nto new datasets (especially on the global scale), thus improving its reliability for\npractical applications.\n(3) Efficient handling of large datasets: Compared to traditional random forest, MF\nis designed with a focus on efficiently processing and analyzing big data. This\ncapability is especially critical within the context of meta-analysis.\n(4) More flexible model tuning: MF offers a wider range of parameters and tuning\noptions, allowing researchers to finely adjust the model to fit specific data\ncharacteristics and research objectives. This flexibility makes MF a more powerful\nand customizable tool.\nOverall, MF presents a more efficient, accurate, and user-friendly approach for dealing\nwith complex data analyses. These features make MF highly practical in this study\ninvolving large datasets and complex interactions among variables. This approach has\nalso been successfully applied to latest high-level studies in the field of ecology (e.g.,\nTerrer et al., 2021; Wang et al., 2022).\nBreiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).\nTerrer, C. et al. A trade-off between plant and soil carbon storage under elevated CO .\nVan Lissa, C. J. MetaForest: Exploring heterogeneity in meta-analysis using random\nforests (2017).\nVan Lissa, C. J. Small sample meta-analyses: Exploring heterogeneity using\nMetaForest. (Routledge, 2020).\nWang, M. et al. Global soil profiles indicate depth-dependent soil carbon losses under\na warmer climate. Nat. Commun. 13, 5514 (2022).\nML requires large training datasets, but research sites are mainly in North\nAmerica/Europe/Asia. Some responses like N2O have <400 trials. Explain how models\ncan be reliably trained on such limited global data.\nResponse: Thank you for your comment. Regarding how to train a reliable model with\nlimited data, we believe that it can be explained from the following aspects:\nFirst, although research sites are primarily located in North America, Europe, and Asia,\nwe have compiled a representative global dataset encompassing 25 major agricultural\ncountries, with a relatively equal distribution across different climatic zones\n(Supplementary Table 4).\nSecond, MF is particularly capable for handling small datasets. It employs an ensemble\nlearning approach by constructing multiple decision trees to enhance model stability\nand accuracy. Each tree is trained on a randomly selected subset of data and features.\nThis technique can effectively reduce overfitting and improve model generalization\nability on limited data.\nThird, the improved feature selection and model interpretability of MF can efficiently\nidentify and utilize the most important variables within the data, even if the data volume\nis small. This allows the model to focus on factors that contribute most significantly to\nthe results, thus improving the reliability and interpretability of models trained on\nlimited data.\nFurthermore, by employing 10-fold cross-validation, we ensure that the model performs\nwell across different data subsets, aiding in the assessment of model stability and\ngeneralization ability. We have also applied various metrics (such as root mean squared\nerror, mean absolute error, and out-of-bag R2) to comprehensively assess model\nperformance (Supplementary Table 5).\nMore importantly, Terrer et al. (2021) have used the same approach and steps to train a\nreliable model on a very limited dataset (n = 108), thereafter accurately quantifying the\nimpact of elevated CO on soil carbon storage and successfully extrapolating it on a\n2\nglobal scale. This demonstrates that, despite the current challenge of handling limited\nglobal data using Machine Learning, these methods and strategies can enhance model\nstability, accuracy, and generalization ability, enabling the training of reliable models\non limited datasets.\nTerrer, C. et al. A trade-off between plant and soil carbon storage under elevated CO .\nLine 379: Explain why out-of-bag R2 values from the 5 MF models are relatively low\n(<0.64) while R2 between observed and predicted is >0.5.\nResponse: Thank you for your comment. In our view, this discrepancy between the\nout-of-bag (OOB) R2 and the R2 between observed and predicted values can be ascribed\nto following reasons (Breiman, 2001):\n(4) Difference in model generalization vs. internal assessment: OOB score is a\nunique feature of Random Forest models (including MF), which uses the samples\nnot selected for training in each tree (OOB samples) to test the model. This score\nprovides an unbiased estimate of model generalization ability. In contrast, the R2\nbetween observed and predicted values is usually calculated on the existing dataset,\nwhich may better reflect the fitting degree of model to a specific dataset.\n(5) Conservative nature of OOB score: OOB score is generally considered more\nconservative as a performance evaluation method. Since each OOB sample is\npredicted only by a portion of the trees (those not used for training), this could lead\nto more stringent or underestimated performance metrics compared to evaluations\ndone on the entire dataset (like the R2 between observed and predicted values).\n(6) Data characteristics and model complexity: If the data inherently has high non-\nlinearity, complex interactions among features, even advanced ensemble methods\nlike MF may struggle to capture all data patterns perfectly. Under such\ncircumstances, the model performance on unknown data (reflected by OOB scores)\nmight not be as good as its performance on the training set (reflected by the R2\nbetween observed and predicted values).\n(7) Impact of sample size and distribution: If the dataset is relatively small or\nunevenly distributed (especially GHGs with small sample sizes; Supplementary\nTable 4), OOB samples may not adequately represent the distribution of the entire\ndataset, potentially leading to lower OOB R2 values.\nBreiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).\nLines 422-423: Explain conversion factors and sources.\nResponse: Thank you for your comment. The conversion factors were derived from\nIPCC AR6 100-year Global Warming Potential (GWP ) values, and they were\n100\nexpressed as CO -eq (273 for N O and 27 for CH ) to calculate the net greenhouse gas\n2 2 4\nbudget (NGHGB). The revised sentence and added reference are listed below for your\nreference:\n“Based on 100-year global warming potential values from the Sixth Assessment Report\nof IPCC, the conversion factors (CO -eq.) of N O and CH to calculate NGHGB were\n2 2 4\n273 and 2776.” (lines 500-502)\n76. IPCC. Climate Change 2022：Mitigation of Climate Change. Contribution of\nWorking Group III to the Sixth Assessment Report of the Intergovernmental Panel on\nClimate Change. https://www.ipcc.ch/report/ar6/wg3 (2022).\nLines 431-433: Specify time scale for SPEI drought index (1 month, 6 months, etc.).\nResponse: Thank you for your comment. Specifically, we first aggregated daily\ntemperature data from CMIP6 into monthly temperature data. And the aggregated\nmonthly temperature was then converted into annual potential evapotranspiration by\nThornthwaite equation using the R package SPEI (Beguería et al., 2014). Further, we\ncombined annual potential evapotranspiration with annual precipitation to obtain the\n10-year average aridity index (e.g., 2021‒2030).\nWe have revised the relevant sentences in this paragraph and attached them below for\nyour reference:\n“To explore whether optimizing CC practices can sustain the benefits to global\nagroecosystems under future changing climate, we projected CC effects in two\nscenarios of Shared Socioeconomic Pathway (SSP) using five bias-corrected climate\nmodel outputs provided by ISIMIP3b simulation77. These daily model outputs are\ncomplementary in terms of model structure and climate sensitivity, derived from CMIP6\n(Supplementary Table 7).” (lines 503-507)\n“Future aridity index was calculated by combining temperature and precipitation from\nCMIP6 using the R package SPEI78. Specifically, the aggregated monthly temperature\nwas converted into annual potential evapotranspiration by the Thornthwaite equation,\nand then combined with annual precipitation to obtain the average aridity index for a\ndecade (e.g., 2021‒2030).” (lines 510-514)\nBeguería, S., Vicente‐Serrano, S. M., Reig, F., & Latorre, B. Standardized\nprecipitation evapotranspiration index (SPEI) revisited: parameter fitting,\nevapotranspiration models, tools, datasets and drought monitoring. Int. J. Climatol. 34,\n3001–3023 (2014).\nLines 436-437: Provide detailed explanation and references for calculating Climatic\nResistance of Yield change.\nResponse: Thank you for your comment. Climatic resistance of yield change was\ncalculated using this formula: R = 𝑌 /(𝑌 −𝑌 ), where R represents\n𝑐𝑢𝑟𝑟𝑒𝑛𝑡 𝑐𝑢𝑟𝑟𝑒𝑛𝑡 𝑓𝑢𝑡𝑢𝑟𝑒\nthe climatic resistance, 𝑌 and 𝑌 are current and future CC-based yield\n𝑐𝑢𝑟𝑟𝑒𝑛𝑡 𝑓𝑢𝑡𝑢𝑟𝑒\nchanges, respectively. Although the yield benefit brought by CC (𝑌 ) can be\n𝑐𝑢𝑟𝑟𝑒𝑛𝑡\nundermined by climate change (𝑌 −𝑌 ; Supplementary Fig. 19a,b), this\n𝑐𝑢𝑟𝑟𝑒𝑛𝑡 𝑓𝑢𝑡𝑢𝑟𝑒\nnegative impact will reduce with CC duration, indicating an enhanced climatic\nresistance of yield change (Supplementary Fig. 20).\nWe have added this formula to the sentence. The formula is based on the calculation of\nforest resistance to extreme drought by Liu et al. (2022). However, given the weak\nthematic relevance to our study, we did not add this reference to the revised manuscript.\nThe revised sentence and related reference are listed below for your reference:\n“Climatic resistance of yield change was calculated as the ratio of current yield\nincrease to its reduction caused by climate change: R = 𝑌 /(𝑌 −𝑌 ),\n𝑐𝑢𝑟𝑟𝑒𝑛𝑡 𝑐𝑢𝑟𝑟𝑒𝑛𝑡 𝑓𝑢𝑡𝑢𝑟𝑒\nwhere R represents the climatic resistance, 𝑌 and 𝑌 are current and\n𝑐𝑢𝑟𝑟𝑒𝑛𝑡 𝑓𝑢𝑡𝑢𝑟𝑒\nfuture CC-based yield changes, respectively.” (lines 517-520)\nLiu, D., Wang, T., Peñuelas, J., & Piao, S. Drought resistance enhanced by tree species\ndiversity in global forests. Nat. Geosci. 15, 800–804 (2022).\nReviewer #2 (Remarks to the Author):\nGeneral comments:\nThe manuscript reports the results of a meta-analysis designed to assess ecosystem\nservices and trade-offs of cover cropping (CC) – a farming practice that has gained wide\nacceptance as an approach to maximize agroecosystems productivity and minimize the\nenvironmental externalities associated with intensive agriculture. To quantitatively\nevaluate the overall effects of CC and estimate the contribution of management and\nclimatic factors on these effects, the authors propose a comprehensive index called\nAMS (agroecosystem multi-services) index. The AMS index was applied to a database\nof 219 studies from around the globe. Among other results, quantitative estimates of\nthe impact of CC on crop yield, soil organic storage, and soil structure stability (as\ndetermined by mean-weighted diameter of soil aggregates) are reported. The results\nalso highlighted the significance of field location (upland vs paddy), botanical\ncomposition of cover crops (legumes vs mixture of several species), and termination\ndate on synergistic effects and various tradeoffs of CC.\nOverall, there is a strong rationale for this study, and authors should be commended for\ntheir analysis and for producing noteworthy results. The AMS conceptual framework\nis very well designed and presented; however, in the Methods section, authors have not\nadequately description the index and how it is computed. Likewise, the graphs\n(especially the 6 key graphs) associated with the manuscript are well presented, easy to\nunderstand and contain the right amount of information. Some of the reported results\ncould be impactful in terms of future research they could trigger and inform.\nThe manuscript, unfortunately, suffers from lack of clarity and other language\ndeficiencies that made it difficult to fully evaluate the soundness of the research and the\nconclusions reported in the manuscript.\nRecommendation: major editing followed by a second review.\nResponse: We appreciate your positive evaluation of our study, encouraging comments,\nand constructive suggestions. According to your suggestions, we have revised the\nmanuscript (visible in red color). In particulate, we believe that the clarity of language,\nthe integrity of method description, and mechanistic interpretation of results have been\nsignificantly improved.\nOur point-by-point responses are listed below. To avoid confusion, all line numbers are\nbased on the Revised Manuscript CLEAN version.\nThe significance of termination date is highlighted in the Abstract and elsewhere in the\nmanuscript. Specifically, authors noted that CC benefits are maximized when there is a\ntime gap of 25 days between CC termination and cash-crop planting. This could be an\nimportant finding. However, it is unclear where this information is reported in the\nmanuscript. In the manuscript (L 188-189), reference is made to Extended Fig 4, but\ninspection of that figure showed to mention to termination date.\nResponse: Thank you for pointing out this important detail. We appreciate the\nopportunity to clarify the significance of CC termination time in our study. To\ndetermine the optimal CC termination time (herein defined as the time interval between\nCCs ending and the next cash crop planting), we employed a moving windows analysis\nto evaluate the effect of termination time on agroecosystem multiservices (AMS). This\nanalysis sorted the termination time into windows of 30 consecutive observations. We\nthen applied a mixed-effects model that included CC termination time, climate,\nsoil, and microbial properties to identify the time point that maximizes the positive effect on\nAMS.\nOur analysis revealed a peak effect at a standardized termination time (Z-score = 0.46),\nwhich corresponds to approximately 25 days before planting the next cash crop\n(Supplementary Fig. 12). This peak indicates the optimal CC termination time where\nthe interactions of climate, soil, and microbial variables with termination time on AMS\nare also significantly positive (Supplementary Fig. 13). Additionally, we referenced\nWang et al. (2021)46 to support our findings. Their study demonstrated that terminating\nCCs around three weeks prior to planting the succeeding cash crop enhances soil water\nstorage and crop water use efficiency globally. However, we are well aware that this\nmacroscopic estimate is not the optimal strategy for scattered locations, so we also\nexplicitly clarify the importance and limitations of this estimate, and point out that\nfuture research should strive to overcome these limitations.\nTo address your concern and enhance clarity in the manuscript, we have revised the\nrelevant sections to clearly state these findings. Here are the revised sentences:\n“CC termination time, hereinafter defined as the time interval between CCs ending and\nthe next cash crop planting (Fig. 1a), is acknowledged as the key factor affecting the\nmagnitude and direction of multiple CC benefits7,24. However, its importance is\nfrequently overlooked. According to the maximum positive effect on AMS in moving\nwindow analysis (window size = 30 observations; see Methods for more details), we\nfirst estimate an optimal CC termination time of 25 days across the globe\n(Supplementary Fig. 12). At this time point, there are also positive interactions of\nclimate, soil, and microbial variables on AMS (P < 0.001; Supplementary Fig. 13). Our\nestimate is supported by Wang et al.46, who reveal that by terminating CCs around\nthree weeks prior to the succeeding cash crop, soil water reserves and thus water use\nefficiency and yield of crops can be enhanced globally. Both premature and late CC\ntermination are likely to restrict crop growth via insufficient nutrient input and water\ndepletion, respectively24. Though the optimal termination timing is largely site-specific,\nthis macroscopic estimate has assisted the paradigm shift in CC utilization and\nmanagement, marking an important step in evaluating the global applicability of\noptimizing CC practices. In the future, the alignment of termination timing with specific\nsite conditions and crop requirements can further refine CC practices and ensure that\ntheir benefits are maximized.” (lines 224-238)\n“Optimal CC termination time was estimated by moving window analysis, with a\nwindow size of 30 consecutive observations sorted by termination time. Briefly, we\nperformed a mixed-effects model to explore how much termination time would exert the\nmaximum positive effect on AMS. The model included CC termination time, climate,\nsoil, microbe, and their multi-order interactions as fixed effects, with a random effect\nof “study”. Bootstrapped standardized effects fitted nonlinearly with CC termination\ntime and displayed a bimodal pattern, so the optimal CC termination time was\ndetermined as the threshold at concave peak.” (lines 485-492)\nSupplementary Fig. 12 Moving windows analysis demonstrating the nonlinear\nrelationship of standardized effects of CC termination time and environmental factors\non agroecosystem multiservices. The window size is 30 consecutive observations. Blue\nand red dots indicate positive and negative standardized effects, respectively. Black\nsolid line denotes the correlation trend fitted by a generalized additive model\nincorporating CC termination time, climate, soil, microbe, and their multi-order\ninteractions. Z-score represents the CC termination time (d) after z-score normalization.\nSupplementary Fig. 13 Distribution of standardized interactive effects of CC\ntermination time and environmental factors on agroecosystem multiservices (n = 500).\nBoxplots show the median, 25th and 75th percentiles of each distribution. Z-score\nrepresents the CC termination time (d) after z-score normalization.\n46. Wang, J., Zhang, S., Sainju, U. M., Ghimire, R., & Zhao, F. A meta-analysis on\ncover crop impact on soil water storage, succeeding crop yield, and water-use efficiency.\nAgr. Water Manage. 256, 107085 (2021).\nEditorial Comments:\nAbstract:\n- suggestion for the first sentences – “The practice of cover cropping (CC) in agriculture\nhas gained significant popularity as a means to improve agricultural sustainability. Yet,\nits full potential is constrained by possible environmental trade-offs that arise from …..”.\nResponse: Corrected. This sentence has been rephrased as:\n“The practice of cover crops (CCs) in agriculture has gained significant popularity as\na strategy to improve agricultural sustainability. Yet, its full potential is often\nconstrained by environmental trade-offs stemming from improper use and\nmanagement.” (lines 1-3)\n- The phrase “underemphasized utilization” is unclear. Likewise, a few sentences later,\nthe phrase “as the priority” is difficult to understand.\nResponse: Thank you for your comment. These phrases have been changed to “its\nneglected use” and “as the preferred selection”, respectively. The revised sentences are\nlisted below for your reference:\n“Yet, its full potential is often constrained by environmental trade-offs stemming from\nimproper use and management.” (lines 2-3)\n“Among various CC types, a biculture of legume and non-legume CCs is recommended,\nwith an optimal termination of approximately 25 days before planting the next crop,\nfollowed by CC residue mulching.” (lines 9-11)\n- In the sentence that begins with “This is equivalent to” – the increase in crop\nproduction lacks precision. Does that refer to grains production or all crops? How were\nthese numbers computed? These details are not provided in the Methods section.\nResponse: Thank you for your comment and suggestion. “The increase in crop\nproduction” mentioned in our analyses refers to all types of crops, including grains,\npulses, etc. Specifically, this increase was estimated by integrating baseline data on\nglobal cropland area (Portmann et al., 2010) and agricultural production (Yu et al.,\n2020), with our gridded CC effects on crop yield. Likewise, we also leveraged baseline\ndata on SOC stocks (0‒30 cm; Hengl et al., 2017), GHG emissions (Crippa et al., 2021),\nand soil erosion (Borrelli et al., 2022) to estimate the actual benefits of CC practices on\nnet GHG budget and soil erosion control. The specific details of these estimations have\nbeen supplemented in the Methods section to ensure the precision and transparency of\nour results.\nThe revised sentences and relevant references are listed below for your reference:\n“Furthermore, we meticulously calculated the actual benefits of CC practices on crop\nproduction, net GHG budget (NGHGB), and soil erosion control by multiplying our\nscaled-up CC effects with a series of baseline datasets, including cropland area,\ncrop yields, SOC stocks (0‒30 cm), GHG emissions, and soil erosion (see more details in\nSupplementary Table 6).” (lines 496-500)\nBorrelli, P., Ballabio, C., Yang, J. E., Robinson, D. A., & Panagos, P. GloSEM: High-\nresolution global estimates of present and future soil displacement in croplands by\nwater erosion. Sci. Data 9, 406 (2022).\nCrippa, M. et al. GHG emissions of all world countries. Publications Office of the\nEuropean Union. https://publications.jrc.ec.europa.eu/repository/handle/JRC126363\n(2021).\nHengl, T. et al. SoilGrids250m: Global gridded soil information based on machine\nlearning. PLoS ONE 12, e0169748 (2017).\nPortmann, F. T., Siebert, S., & Döll, P. MIRCA2000—Global monthly irrigated and\nrainfed crop areas around the year 2000: A new high-resolution data set for agricultural\nand hydrological modeling. Global Biogeochem. Cy. 24, GB1011 (2010).\nYu, Q. et al. A cultivated planet in 2010–Part 2: The global gridded agricultural-\nproduction maps. Earth Syst. Sci. Data 12, 3545–3572 (2020).\n- Authors use the word “residual” several times in the Abstract and elsewhere.\nProper terminology is “crop residue”.\nResponse: Thank you for your comment. We have corrected “CC residual” to “CC\nresidue” throughout the manuscript.\nL 1-2. Suggestion “To meet the ever-growing food demand, the expansion of global\ncropland and the intensification of global agricultural production systems have caused\na myriad of negative environmental impacts, such as climate change, biodiversity loss,\nand nitrogen (N) pollution”.\nResponse: Thank you. This sentence has been rephrased as:\n“To meet the ever-growing food demand, the globally unprecedented cropland\nexpansion and agricultural intensification have caused a myriad of negative\nenvironmental impacts, such as climate change, biodiversity loss, and nitrogen (N)\npollution1,2.” (lines 20-22)\nL 19-21. Suggestion “Although CC practices have generally been found to be beneficial\nto soil microbiome and associated functions, in some cases, these improvements in soil\nbiological attributes did not translate into improved crop productivity”.\nResponse: Thanks. This sentence has been rephrased as:\n“Although CC practices are generally beneficial to soil microbiome and associated\nfunctions13, in some cases, such improvement in soil biological attributes does not\ntranslate into increased crop productivity14.” (lines 38-40)\nL 21-23. Suggestion “These variable results suggest that efforts should be made to\noptimize CC practices and achieve both food security and environment-friendly\nservices, and this optimization would require careful consideration of CC selection\n(including mixtures), termination, and residue management.”\nResponse: Thanks. This sentence has been rephrased as:\n“These variable outcomes highlight the need to optimize CC practices to achieve both\nfood security and environment-friendly services. This optimization requires careful\nconsideration of CC type selection (including mixtures), termination timing, and\nresidue management (Fig. 1a).” (lines 40-43)\nL 26-28. Please edit this sentence for clarity. This sentence is crucial so readers can\nunderstand the rationale behind this meta-analysis.\nResponse: Thank you for your comment and suggestion. We have re-edited this\nsentence to provide a clearer context for this paragraph and the whole study. The revised\nsentence is listed below for your reference:\n“Most of these efforts focus on individual target services, often lacking a quantitative\nevaluation of the trade-offs or synergies among them, especially concerning the\ninteractions between CC practices and diverse environmental and management\nfactors15.” (lines 46-48)\nL 29-33 Suggestion. “Such knowledge gap hinders our capacity to propose an ideal CC\nadoption paradigm and develop effective management approaches that maximize the\nmany benefits of CC”.\nResponse: Thanks for the suggestion. This sentence has been rephrased as:\n“Such knowledge gap hinders our capacity to propose an ideal CC adoption paradigm\nand formulate effective measures that can fully harness its multiple benefits7.” (lines\n48-50)\nL 30-32. Suggestion “Moreover, the limitations of statistical procedures (e.g.,\ncomplicated assumptions in process-based modeling and low predictive accuracy of\nempirical models), has also led to a lack of quantitative tools to assess CC potential\nbenefits and upscale these benefits to agroecosystems across the globe”.\nResponse: Thanks. This sentence has been rephrased as:\n“Additionally, current limitations within statistical procedures—such as the\ncomplicated assumptions in process-based modeling and the low predictive accuracy\nof empirical models—further contribute to a lack of quantitative tools for assessing\npotential CC benefits and extrapolating these benefits on a global scale, despite some\nsuccessful regional simulations by crop models16,17” (lines 50-54)\nL 148-149. The information presented is probably (technically) correct, but the\nconnection to CH4 emission would not be readily apparent to most ready and might be\ncontext-dependent (true in paddy soil but not in upland soil). For example, looking at\nthe information presented in Fig 3 (panel a), one may ask the question “why would CC\npositively impact N2O flux in upland soils and not in paddy soils?” Unlike in upland\nsoils, it is possible that, due to wet conditions in paddy soils, the decomposition of CC\nresidue primarily produces NH4 (not NO3 – the main substrate for N2O formation).\nTherefore, lesser impact of CC residue on N2O flux in wet paddy soils. In contrast, CC\nresidue provides methanogens with fresh organic substrate for CH4 production in\npaddy soils.\nResponse: Thank you for your comment and suggestion. According to your suggestion,\nwe have significantly improved the clarity of the interpretation of CC residue\ndecomposition, GHG emissions, and their context-dependence, with citing useful\nreferences. The revised sentences and relevant references are listed below for your\nreference:\n“Distinct from two previous meta-analyses reporting no change or slight reduction10,18,\nour findings indicate a context-dependent response of N O emissions to CCs. This\n2\nvariability can be attributed to the disproportionate contribution of nitrite and\nammonium (primary products of CC residue decomposition in upland and paddy fields,\nrespectively) to microbial-driven N O production19.” (lines 81-85)\n2\n“Aligned with Basche et al.30 and our previous observations, CC type significantly\ninfluences N O emissions (Supplementary Fig. 6c). This effect is particularly\n2\npronounced when leguminous species are included and in upland soils where residues\nare mainly decomposed into nitrite19 (Figs. 2b and 3a).” (lines 151-154)\n“Furthermore, our analyses indicate that soil C:N ratio serves as a driving force\nbehind CH emissions (Fig. 3b and Supplementary Fig. 6d). An elevated C:N ratio\n4\nexacerbates soil N limitation in general, under which CC biomass input accelerates\nmicrobial mineralization of organic matter via N mining, thereby priming CH\n4\nemissions34. This priming effect varies by cropland type and irrigation method, with\nintermittent irrigation adeptly lowering CC-derived CH emissions from paddies\n4\n(Supplementary Fig. 10). Simultaneously, increasing N supply curbs the significant\nactivation of cellulase and thus the opportunity for organic matter mineralization in\npaddy soils (P < 0.001; Supplementary Fig. 7d). Just as the synergies in increased\nyields, enhanced SOC stocks, and reduced CH emissions (Fig. 4f), the co-\n4\nimplementation of CCs and intermittent irrigation facilitates both rice production and\nclimate mitigation, chiefly through alleviated soil N limitation and improved aeration\nby drainage35.” (lines 162-172)\n10. Abdalla, M. et al. A critical review of the impacts of cover crops on nitrogen\nleaching, net greenhouse gas balance and crop productivity. Glob. Chang. Biol. 25,\n2530–2543 (2019).\n18. Muhammad, I. et al. Regulation of soil CO and N O emissions by cover crops: A\n2 2\nmeta-analysis. Soil Till. Res. 192, 103–112 (2019).\n19. Li, Y., Wang, Z., Ju, X., & Wu, D. Disproportional oxidation rates of ammonia and\nnitrite deciphers the heterogeneity of fertilizer-induced N O emissions in agricultural\n2\nsoils. Soil Biol. Biochem. 191, 109325 (2024).\n30. Basche, A. D., Miguez, F. E., Kaspar, T. C., & Castellano, M. J. Do cover crops\nincrease or decrease nitrous oxide emissions? A meta-analysis. J. Soil Water Conserv.\n69, 471–482 (2014).\n34. Zhu, Z. et al. Rice rhizodeposits affect organic matter priming in paddy soil: the\nrole of N fertilization and plant growth for enzyme activities, CO and CH emissions.\n2 4\nSoil Biol. Biochem. 116, 369–377 (2018).\n35. Bo, Y. et al. Global benefits of non-continuous flooding to reduce greenhouse gases\nand irrigation water use without rice yield penalty. Glob. Chang. Biol. 28, 3636–3650\n(2022).\nAlthough the paper’s primary focus is on presentation of meta-analysis results, authors\nshould provide brief mechanistic explanation of some important results.\nResponse: Thanks a lot for your constructive suggestion to provide mechanistic\nexplanations for some key results presented in meta-analysis. We agree with your\nperspective that such explanations will significantly enhance the scientific significance\nof our findings. In response, we have added several new discussions in the revised\nmanuscript, especially regarding the primed N O and CH emissions in the context of\n2 4\ndifferent cropland types, to provide more valuable information about the impact of CC\nresidues on GHG production and its mitigation (e.g., lines 81-85 and lines 162-172).\nBesides, we also provided more mechanistic interpretations in the support of key\nfunctional microbes to yield gains (e.g., lines 113-116, lines 147-150, lines 156-158,\nand lines 174-176) and the role of root and microbial activities in enhanced MWD and\nSOC stock (e.g., lines 101-105, lines 127-132, lines 140-144, and lines 160-162). These\ndiscussions were closely related to recent references, offering a plausible scientific\nrationale for the observed results. We believe that these revisions will enrich the depth\nand breadth of our paper, and welcome any further feedback to ensure the clarity and\naccuracy of our explanations.\nFig 2, panel b. It is quite puzzling to see significant effect of CC on SOC stock even in\nless than 5 years of adoption (similar result about MWD). Measurable change in SOC\nstock is generally a slow process; typically, one may detect change in the surface soil\nlayers (0-5 cm) in the first 5-10 years of implementation of CC (mostly in sand/silt soils,\nnot in clay soils). This result seems inconsistent with most published empirical data.\nResponse: Thank you for raising this important point. We understand that detecting\nsignificant changes in SOC stocks within a short time (less than 5 years) is generally\nchallenging and seems inconsistent with the slow process of SOC accumulation\nreported in many studies. However, we believe that our results from historical CC\npractices are realistic, and more importantly, we propose several factors to help explain\nthem. First, compared to natural ecosystems, agroecosystems undergoing CC practices\noften experience higher rates of SOC sequestration due to regular soil disturbance and\norganic matter inputs from CC residues (Poeplau and Don, 2015). Second, our dataset\nincludes a substantial number of studies from colder climates and temperate regions\n(Supplementary Table 4), where slower decomposition rates allow for more rapid\naccumulation of organic matter. Last, the management practices involved in these\nstudies, such as no-till or reduced-till practices, further enhance SOC sequestration by\nminimizing soil disturbance and increasing organic matter retention (Franzluebbers,\n2010).\nSupplementary Table 4 Counting summary of experimental attributes and response\nvariables across all CC studies included in the dataset.\nCategory N Category N\nCountry Asia 7 Organic fertilizer Yes 190\nEurope 11 No 2112\nAfrica 2 Tillage Conventional 1090\nAustralia 1 Conservation 1212\nNorth America 2 Irrigation (upland) Rainfed 1483\nSouth America 2 Irrigated 566\nCropland type Upland 2049 Irrigation (paddy) Rainfed 43\nPaddy 253 Flooding 135\nClimate zone Cold 911 Intermittent 75\nTemperate 1042 CC type Legume 730\nArid 290 Non-legume 1134\nTropical 59 Mixture 438\nSoil texture Sandy 349 Residue management Incorporated 1158\nLoam 1162 Surface 1012\nClay loam 411 Removed 132\nClay 380 Duration <5 years 1518\nSoil fertility Low 769 ≥5 years 784\nModerate 769 Response variable Yield 1149\nHigh 764 SOC 1105\nstock\nSoil pH Acid 911 N O 363\nFranzluebbers, A. J. Achieving soil organic carbon sequestration with conservation\nagricultural systems in the Southeastern United States. Soil Sci. Soc. Am. J. 74, 347–\n357 (2010).\nPoeplau, C., & Don, A. Carbon sequestration in agricultural soils via cultivation of\ncover crops–A meta-analysis. Agr. Ecosyst. Environ. 200, 33–41 (2015).\nL 154. You can start a paragraph with the word “However” – an adverb that’s used to\nconnect contrasting ideas. Please edit.\nResponse: Thank you very much for your comment. Referring to your suggestion,\nwe have rewritten the beginning sentence in order to make a direct transition to the\nparagraph. We believe that this revision can enhance the overall clarity and\npersuasiveness of our argument in this paragraph by spotlighting the critical\nrecommendations regarding CC practices and multiple agroecosystem services. The\nrewritten sentence is listed below for your reference:\n“Given the ascertained trade-offs, especially related to GHG emissions, we further\nquantified the associations among five services and their strengths of expected\ninfluence to narrow the actual gap in accurately evaluating CC applicability.” (lines\n197-199)\nL 233-250. In my opinion, this section should be removed. It is too speculative to stretch\nthe results of this meta-analysis by inclusion of climate scenarios and socio-economic\ndata. Consequently, Fig 6 should be modified – keep panels a to f (delete the rest –\nstarting with panel g).\nResponse: We really appreciate your opinion and suggestion. We understand the\nconcern about stretching the results of our meta-analysis by including climate scenarios\nand socio-economic data. Just as our reply to Reviewer #1, we have undertaken careful\nrevisions to address the speculative nature of these projections while underscoring their\nsignificance:\n(1) We have revised this section to more clarify that these projections are partly\nspeculative, but based on the best available models and data. This clarification is\nintended to frame the projections as an exploratory analysis that aims to shed light\non the potential benefits of long-term CC practices under climate change.\n(2) We provide the reason for retaining this section, i.e., its value in illustrating the\nbroader implications of our findings for global agricultural sustainability and\nnature-based climate solutions. This perspective is crucial for revealing the\ncontribution of most-effective CC practices to the SDGs.\n(3) We have enhanced the transparency of our methodology in this section, detailing\nthe models used, the assumptions made, and the limitations of extending our meta-\nanalysis findings to future scenarios. At the same time, we focus on the most robust\nand directly related findings, ensuring the speculative nature of these projections\ndoes not overshadow the main findings of our study.\nWe hope that these revisions and clarifications can address your concerns and justify\nour inclusion of future projections, highlighting their importance for policy formulation\nin the context of global agricultural sustainability. If you have any further suggestions,\nwe are very willing to listen and adopt them.\nNumerous sentences read like a collection of words and lack meaning. Below are just\na few examples (this is not a complete list) include:\nL 14 “resort soil penetration”.\nL 3-4 “Breakthrough of the planetary boundaries… critical services”.\nL 24-26. “Despite many …biophysical attributes”.\nL 34 “and their global …approaches”.\nL 38-40 “Based on the observed….their interactions”.\nL 58-59 “prevented N2O emission on the basis of stable yield”.\nL 67 “alerted concern”.\nL 143 “microbial and… to them”.\nL 146 “an impetus of soil C:N to CH4 emission”. Do you been “connection”?\nL 252 “mechanistic understating remains finite”.\nResponse: Many thanks for your careful review and comments. We have checked the\nwhole manuscript and attempted to improve the clarity of numerous sentences so that\nreaders can easily understand their specific meanings.\nThe following are the revised sentences you listed here. Other revised sentences can be\nfound in the revised manuscript.\n“For example, legume CCs substantially improve soil nutrition through biological N\nfixation, while some non-legume CCs enhance soil structure and penetration to support\nwell-developed root systems of primary crops8.” (lines 31-34)\n“As these impacts escalate and break through the corresponding planetary boundaries,\nthe sustainability of agroecosystems and their several critical services will be\nirreversibly undermined3,4.” (lines 22-24)\n“Although numerous research efforts aim to maximize the benefits of CCs for\nagroecosystem services7,8, these studies are predominantly isolated experiments\nconducted under limited agronomic and biophysical conditions.” (lines 44-46)\n“Given the critical need for synergistic agroecosystem services, it becomes imperative\nto globally explore and implement the most-effective CC practices, leveraging\nubiquitous data and emerging methodologies.” (lines 54-56)\n“According to the observed performances across these dimensions, we identified the\noptimal CC portfolio, the key factors influencing its effectiveness, and their\ninteractions.” (lines 60-62)\n“Encouragingly, the implementation of long-term CC rotation (≥ 5 years) has shown\npotential to mitigate additional N O emissions without sacrificing crop yields (Fig. 2b).\nnet sink of GHGs20.” (lines 85-88)\n“Compared to non-legume CCs, legumes and mixtures (the blend of legume and non-\nlegume CCs) have been robustly observed to boost crop yields and N O emissions (Fig.\n2\n2b and Supplementary Fig. 2). This exemplifies a double-edged effect of their biological\nN fixation21 and highlights the importance of modulating microbial interactions within\nCC systems22.” (lines 94-98)\n“This depth-dependent control limits C storage in surface and mid soil layers\n(Supplementary Fig. 4), probably due to microbial and enzymatic reactions sensitive to\nC concentration or pH.” (lines 127-129)\n“Furthermore, our analyses indicate that soil C:N ratio serves as a driving force\nbehind CH emissions (Fig. 3b and Supplementary Fig. 6d).” (lines 162-164)\n4\n“First, our understanding of the underlying mechanisms remains limited.” (lines 299-\n300)"}, {"round": 2, "reviewer_comments": {"reviewer_1": "General Comments:\nThe authors have put considerable effort into enhancing the study. Here are some additional questions and suggestions.\nThe APSIM model simulation selects wheat as the cash crop. Typically, wheat can also serve as a cover crop; it is used here as a cash crop. Please explain the reasoning behind this choice. The authors have not provided wheat's variety and parameter configuration, and it is unclear whether spring wheat or winter wheat is used. To ensure more reliable and generalizable results, it is recommended to include additional crops, such as corn and rice, as cash crops for the simulations.\nThe article categorizes farmland types into upland and paddy fields without considering the effects of cover crops (CC) on different crop types. The responses of different crop types to CC should be evaluated, and the addition of relevant charts would help illustrate this.\nIn Fig. 6, the global simulation results show numerous missing data points, particularly in key agricultural regions like the North China Plain and the central plains of India. The reasons for these missing points should be clarified.\nSpecific comments:\nLine 107-109: Consider including a brief discussion on potential mitigation strategies or best practices to address the increased GHG emissions. This would provide a more balanced view of both the benefits and drawbacks of CC practices.\nLine 113- 115: Clarify how long-term CC rotations affect soil microbial communities and their role in reducing N2O emissions. Adding a brief discussion on the mechanisms involved will critically strengthen this section.\nLine 122-124: Provide a citation that supports your results, specifically how legumes and mixtures boost crop yields and N2O emissions.\nLine 133-136: Include a brief explanation of why the accumulation of mineral-associated organic C is beneficial compared to particulate organic C. This will help readers understand the significance of this finding.\nLine 168-170: Provide more detail on the role of fungal-to-bacterial ratios in soil stabilization and erosion control. Additional insights into how these ratios influence MWD would be helpful.\nLine 288-289: What are the factors contributing to negative responses?\nLine 414: There is an error in the special symbols within the formula.\nLine 493-497: The network analysis and piecewise SEM are complex and well-thought-out. However, please clarify how the weights assigned to different services (e.g., 0.9 for crop yield) were determined and whether these values were based on empirical data.\nLines 456-468: Please provide detailed site data and sources. Which specific parameters of APSIM were adjusted? Please provide the corresponding parameter table.\nLine 525: The definition and formula for NGHGB should be included.\nLines 532-533: The names of the chosen General Circulation Models (GCMs) should be specified.\nLines 540-542: The authors calculate the aridity index using the R package \"SPEI\" (https://cran.r-project.org/web/packages/SPEI/SPEI.pdf). According to the documentation, the input is monthly meteorological data, and relevant time scales can be defined for SPEI calculation. However, the authors aggregate temperature and precipitation data to the annual scale for calculation, which differs from the description in the R package documentation. The authors should explain the rationale behind this approach to ensure the calculation's validity.\n(Remarks on code availability)\nThe README file provides only a summary of the manuscript and lacks detailed instructions on how to install and run the code.\nThe code does not include the program for calculating the aridity index using the R package SPEI as mentioned in the manuscript.", "reviewer_2": "The authors have done an excellent job addressing the comments made on the previous version of the manuscript. This reviewer is satisfied with the authors' responses to the questions raised during the initial review and the revisions made. The manuscript will be of great interest to readers interested in sustainable agriculture and holds promise to be a foundational paper guiding future research in that area.\nRecommendation: accept for publication.\n(Remarks on code availability)"}, "author_response": "RESPONSE TO THE REVIEWERS’ COMMENTS\nReviewer #1 (Remarks to the Author):\nGeneral Comments:\nThe authors have put considerable effort into enhancing the study. Here are some\nadditional questions and suggestions.\nResponse: We sincerely appreciate your positive and insightful comments, which have\ngreatly contributed to the improvement of our study. We appreciate very much your\nforward-thinking perspective and comprehensive understanding of cover crop research,\nparticularly in the fields of crop modeling and global predictions. We are grateful for\nyour constructive feedbacks and hope for more opportunities to collaborate with you in\nthe future. In response to your suggestions, we have carefully revised the manuscript\n(visible in red color). Our detailed, point-by-point responses are provided below. To\navoid confusion, all line numbers refer to the Revised Manuscript CLEAN version.\nThe APSIM model simulation selects wheat as the cash crop. Typically, wheat can also\nserve as a cover crop; it is used here as a cash crop. Please explain the reasoning behind\nthis choice. The authors have not provided wheat's variety and parameter configuration,\nand it is unclear whether spring wheat or winter wheat is used. To ensure more reliable\nand generalizable results, it is recommended to include additional crops, such as corn\nand rice, as cash crops for the simulations.\nResponse: Thank you for your valuable feedback regarding the APSIM model. Below,\nwe provide a detailed explanation of our rationale for selecting wheat as the cash crop\nand address your suggestion to include additional crops such as maize and rice.\nFirst, we chose wheat as the modelled crop due to its globally widespread cultivation,\nmaking it the most important food crop (Martre et al. 2024), and its representativeness\nfor the dataset we complied. In this context, wheat serves as an appropriate system to\nexplore the effects of cover crops (CCs) on various agroecosystem services. Moreover,\nthis study primarily focuses on optimizing CC practices under complex interactions\nwith environmental and management factors, with wheat providing a consistent basis\nfor ensuring comparability across scenarios.\nSecond, the APSIM model was configured using the winter wheat variety “Wedgetail”,\nwhich aligns with the predominant cultivar grown in the study area, particularly well-\nsuited for drylands where the APSIM-wheat model is most applicable (ref. 73).\nThe\ncultivar parameters, including growth rates, nitrogen demand, etc., were calibrated\nbased on local data, ensuring that the model accurately reflects regional agricultural\npractices. These details are now provided in Supplementary Table 5 for clarity.\nThird, while incorporating additional cash crops like maize and rice could potentially\nenhance the generalizability of our findings, doing so presents several challenges: a)\nIncreased complexity: Including multiple crops could shift the focus away from the\nprimary objective of this study, which is to examine the effects of CCs on specific\nagroecosystem services rather than comparing the responses of different cash crops. b)\nRisk of overreliance on crop models: Crop models, including APSIM, often lack local\nvalidation and rely on gridded datasets and generalized assumptions about cropping\nsystems (Aramburu-Merlos et al. 2024). Expanding the model to include diverse crops\ncould lead to overfitting or exaggeration of conclusions based on modeled outputs,\nwhich may not be fully supported by observational data. c) Computational and time\nconstraints: Simulating multiple crops would significantly increase the computational\nburden and time required, potentially compromising data quality. It would also\nnecessitate extensive data collection and model calibration, which is particularly\nchallenging in regions with limited data availability (Grassini et al. 2015).\nGiven these considerations, we believe that focusing on wheat as the sole cash crop\nallows for a clear and robust analysis while minimizing unnecessary complexity and\npotential biases. Nevertheless, we fully acknowledge your suggestion and plan to\nexplore the effects of CCs across a broader range of cash crops in future research to\nbetter validate and generalize our findings. We hope these revisions address your\nconcerns and justify our selection of the modelled crop. If you have any further\nsuggestions, we are more than willing to consider them. The revised sentences and\nsupplementary table are listed below for your reference:\n“Specifically, we selected wheat (Wedgetail) as the modelled cash crop and simulated\nits production in four scenarios: no CCs, legume CCs (field pea), non-legume CCs\n(oats), and CC mixtures (field pea and oats).” (lines 474-476)\n“All crops in the sequences were sown at 150 plants m−2, with a row spacing of 300\nmm74. We collected a suite of observations from sites with a long history of CC practices\nto optimize the relevant parameters within crop and soil modules, and calibrate the\nmodel. The calibrated model has a relatively good capability to simulate wheat yields\nand SOC stocks for all scenarios (R2 > 0.60), with relative root mean square errors of\n0.35 and 0.31, respectively (Supplementary Fig. 30). Details of the cultivar and soil\nparameters can be found in Supplementary Table 5.” (lines 477-483)\n73. Flohr, B. M., Meier, E. A., Hunt, J. R., McBeath, T. M., & Llewellyn, R. S. A\nmodelled quantification of reduced nitrogen fertiliser requirement and associated trade-\noffs from inclusion of legumes and fallows in wheat-based crop sequences. Field Crop.\nRes. 307, 109236 (2024).\nAramburu-Merlos, F., van Loon, M. P., van Ittersum, M. K., & Grassini, P. High-\nresolution global maps of yield potential with local relevance for targeted crop\nproduction improvement. Nat. Food 5, 667–672 (2024).\nGrassini, P. et al How good is good enough? Data requirements for reliable crop yield\nsimulations and yield-gap analysis. Field Crops Res. 177, 49–63 (2015).\nMartre, P. et al. Global needs for nitrogen fertilizer to improve wheat yield under\nclimate change. Nat. Plants 10, 1081–1090 (2024).\nSupplementary Table 5 The optimization of crop and soil parameters in the APSIM-Wheat model.\nUnit Default value(s) Adjusted value(s) Relevance\nWheat parameters\nknh4 / 0.0 0.1 Ammonium nitrogen supply\nminimum_grain_n_filling_rate g/grain/d 0.000015 0.000014 Grain nitrogen demand\nN_fact_photo / 1.5 1.65 Nitrogen deficit effect on phenology\nN_fact_expansion / 1.0 21.5 Nitrogen deficit effect on expansion\nN_fact_grain / 1.5 1.6 Nitrogen deficit effect on grain\ny_n_conc_crit_leaf / 0.063 0.063 0.042 0.015 0.008 0.0035 0.047 0.05876 0.040 0.011 0.0462 0.0035 Critical leaf nitrogen concentrations\ny_n_conc_max_leaf / 0.070 0.070 0.050 0.020 0.010 0.005 0.070 0.070 0.050 0.020 0.050 0.005 Maximum leaf nitrogen concentrations\ny_n_conc_crit_stem / 0.050 0.050 0.020 0.010 0.005 0.0035 0.0485 0.0699 0.030 0.0035 0.00402 0.0035 Critical stem nitrogen concentrations\ny_n_conc_max_stem / 0.070 0.070 0.040 0.01 0.015 0.015 0.070 0.080 0.040 0.015 0.015 0.015 Maximum stem nitrogen concentrations\ny_n_conc_min_pod / 0.025 0.025 0.004 0.003 0.003 0.0025 0.025 0.025 0.004 0.009 0.00493 0.0025 Minimum pod nitrogen concentrations\nn_conc_crit_root / 0.020 0.0148 Critical root nitrogen concentrations\nleaf_n_init_conc / 0.050 0.0575 Initial leaf nitrogen concentration\nleaf_n_sen_conc / 0.005 0.0001 Senescent leaf nitrogen concentration\nSoil parameters\nsw_top_crit / 0.9 1.2 Critical water ratio in top soil layer\ngravity_gradient / 0.00002 0.000015 Vertical movement of soil moisture\nnitrification_pot ppm 40.0 6.0 Nitrification potential\nnh4_at_half_pot ppm 90 80 Half the maximum ammonium\nThe article categorizes farmland types into upland and paddy fields without considering\nthe effects of cover crops (CC) on different crop types. The responses of different crop\ntypes to CC should be evaluated, and the addition of relevant charts would help\nillustrate this.\nResponse: Thank you for highlighting this important point. We greatly appreciate your\ninsight into the crop-specific effects of CCs. Based on your feedback, we re-examined\nthe crop information and preliminarily explored the responses of difference crop types\nto CCs. Interestingly, we found that the adoption of CCs significantly improves the\nyield of C4 crops (e.g., maize and sorghum) but not C3 crops (Fig. R1). Such\ndiscrepancy may be related to the higher water use efficiency of the former crops,\nespecially when water availability is limited (Rezaei et al. 2023). Yet, while these\nfindings are indeed significant, they currently fall outside the scope of this study.\nRegarding the suggestion to evaluate crop-specific responses, we considered that our\ncurrent categorization of cropland types into upland and paddy fields aligns more\nclosely with the overall objectives and findings of this study. These two distinct\nagroecosystems differ fundamentally in water management, soil characteristics, and\nenvironmental drivers, which collectively influence the effectiveness of CC practices\nin delivering multiple agroecosystem services, such as crop production, soil\nstabilization, and greenhouse gas mitigation. Evaluating crop-specific responses could\nadd complexity that might obscure the broader effects of CCs across the two major\ncropland types. For this reason, we believe that focusing on cropland type provides a\nmore coherent framework for understanding the role of CC practices in agricultural\nsustainability.\nHowever, we are fully aware of the value of your suggestion and have clarified the\npotential implications of crop-specific responses in the limitations section. We are very\nopen to any further suggestions you may have and sincerely thank you for your\nthoughtful comments. The added sentences are listed below for your reference:\n“In this framework, crop-specific responses are also not included. Future research\nshould critically address these responses to better quantify the applicability of CC\npractices across diverse regions, particularly within the context of genotype ×\nenvironment × management interactions7.” (lines 364-367)\n7. Lamichhane, J. R., & Alletto, L. Ecosystem services of cover crops: a research\nroadmap. Trends Plant Sci. 27, 758–768 (2022).\nRezaei, E. E. et al. Climate change impacts on crop yields. Nat. Rev. Earth Env. 4, 831–\n846 (2023).\nFig. R1 Crop-specific effects of CCs on five agroecosystem services (%). Solid and\nempty dots indicate significant and non-significant changes, respectively. Figures on\nthe side of error bars refer to the number of observations.\nIn Fig. 6, the global simulation results show numerous missing data points, particularly\nin key agricultural regions like the North China Plain and the central plains of India.\nThe reasons for these missing points should be clarified.\nResponse: Thank you for your keen observation regarding the missing data points in\nglobal predictions. We appreciate the opportunity to clarify this aspect of our study. We\nhave carefully re-examined the upscaling results to ensure their completeness and\nconsistency across the dataset. The upscaling approach we employed involved\naggregating data from multiple sources and extrapolating the results to broader\ngeographic regions. This method allowed us to estimate the benefits of CC practices for\nglobal agroecosystems. However, during this process, some data points were inevitably\nmissed. The primary reasons for these missing data points are:\n1. Aggregation of multiple crops within upland fields: In regions with diverse\ncropping systems, the aggregation of multiple crops can result in data loss or an\ninsufficient representation of specific crops, leading to gaps in the dataset.\n2. Coarse resolution of original data sources: The use of relatively coarse-resolution\ndata (e.g., MIRCA2000 for crop fractions), combined with a high crop fraction\nthreshold required for calculating pixels, has impacted the accuracy of our spatial\nupscaling. This limitation becomes particularly evident in heterogeneous and\nintensively cultivated areas where finer-scale data would be more appropriate.\nThese challenges reflect the current limitations in global predictions and mapping, as\nhighlighted in previous research (Meyer and Pebesma, 2022). Addressing these issues\nwill require more comprehensive field data and higher-resolution datasets, as well as\nimprovements in data integration methods.\nWe strongly encourage collaboration with other researchers and modelers to share data\nand insights, which would greatly enhance the accuracy and robustness of future\nassessments. Once again, we sincerely appreciate your valuable feedback and hope this\nexplanation clarifies the reasons for the missing data points. We remain committed to\nimproving the quality and completeness of our study and welcome any further\nsuggestions you may have.\nMeyer, H., & Pebesma, E. Machine learning-based global maps of ecological variables\nand the challenge of assessing them. Nat. Commun. 13, 2208 (2022).\nSpecific comments:\nLine 107-109: Consider including a brief discussion on potential mitigation strategies\nor best practices to address the increased GHG emissions. This would provide a more\nbalanced view of both the benefits and drawbacks of CC practices.\nResponse: Thank you for your insightful comment. We agree that discussing potential\nmitigation strategies provides a more balanced view of the dual roles of CC practices.\nAs Lamichhane and Alletto (2022) highlighted, although CCs offer numerous benefits,\nthey may also contribute to disservices, especially in increasing GHG emissions.\nRegarding CH emissions, we have thoroughly identified the main drivers under CC\n4\npractices and suggested effective mitigation measures, such as combining CCs with\nintermittent irrigation in paddy fields (see Optimizing CC practices under the\ninteractions with environmental and management factors). Similarly, we have\ndiscussed feasible approaches for controlling N O emissions, including the selection of\nGiven your next comment, we have focused on extending our discussion of N O\n2\nmitigation strategies in this section. We particularly elaborate on mechanisms and\nmeasures for enhancing N O reduction under long-term CC practices by providing\n2\nadequate substrates and habitats for N O reductors (e.g., nosZ-harboring bacteria; refs.\n2\n21,22). More importantly, we explicitly clarify the potential disservice risk posed by CCs,\nproviding a balanced perspective on their effects on agroecosystem services and\nemphasizing the need to minimize the trade-offs. The revised sentences are listed below\nfor your reference:\n“Nonetheless, N O and CH emissions are also greatly stimulated by 29.5% and 42.3%,\n2 4\nrespectively. These stimulations of GHGs hinge on cropland type, with N O emissions\n2\nonly rising in uplands and CH emissions in paddy fields (Fig. 3a). Distinct from\n4\nprevious meta-analyses reporting no change or slight reduction10,18, our findings\nindicate a context-dependent response of N O emissions to CCs, which can be\n2\nattributed to the disproportionate contribution of nitrite and ammonium (primary\nproducts of CC residue decomposition in upland and paddy fields, respectively)19.\nGiven the assignable role of N O in global warming and ozone depletion20, there is an\n2\nurgent need to implement appropriate mitigation measures to counteract such\ndisservice induced by CCs7. Encouragingly, the implementation of long-term CC\nrotation (≥ 5 years) has shown potential to mitigate additional N O emissions without\n2\nsacrificing crop yields (Fig. 2b). By continually providing adequate substrates\n(residues) and habitats, long-term CC practices are anticipated to increase the\ndiversity of N O reductors (e.g., nosZ Clades I and II) and their survivability in the soil,\n2\nthus reducing N O emissions21,22. The long-term implementation can also effectively\n2\nstrengthen soil stability and C storage, potentially serving as a net sink of GHGs23. In\nessence, CC practices promote several agroecosystem services but also pose a\ndisservice risk, associated with a spectrum of factors. Identifying the key determinants\nof CC effects is therefore paramount to competently minimizing their trade-offs and\nmaximizing their synergies.” (lines 107-123)\n7. Lamichhane, J. R., & Alletto, L. Ecosystem services of cover crops: a research\nroadmap. Trends Plant Sci. 27, 758–768 (2022).\n20. IPCC. Climate Change 2022：Mitigation of Climate Change. Contribution of\nWorking Group III to the Sixth Assessment Report of the Intergovernmental Panel on\nClimate Change. https://www.ipcc.ch/report/ar6/wg3 (2022).\n21. Shan, J. et al. Beyond denitrification: the role of microbial diversity in controlling\nnitrous oxide reduction and soil nitrous oxide emissions. Glob. Change Biol. 27, 2669–\n2683 (2021).\n22. Hiis, E. G. et al. Unlocking bacterial potential to reduce farmland N O emissions.\nLine 113-115: Clarify how long-term CC rotations affect soil microbial communities\nand their role in reducing N2O emissions. Adding a brief discussion on the mechanisms\ninvolved will critically strengthen this section.\nResponse: Thank you very much. Based on your suggestion, we have added a brief but\nsignificant discussion on the primary mechanism by which long-term CC practices\nreduce soil N O emissions through the regulation of key microbial taxa. Specifically,\n2\nlong-term CC implementation can enhance the diversity and abundance of N O\n2\nreductors (e.g., nosZ Clades I and II) by providing ample substrates and suitable\nvectors21,22. This improvement in microbial diversity and survivability plays a crucial\nrole in transforming N O into N and thereby reducing N O emissions. The related\n2 2 2\nsentences are as follows:\n“Encouragingly, the implementation of long-term CC rotation (≥ 5 years) has shown\npotential to mitigate additional N O emissions without sacrificing crop yields (Fig. 2b).\npractices are anticipated to increase the diversity of N O reductors (e.g., nosZ Clades\n2\n114-119)\n21. Shan, J. et al. Beyond denitrification: the role of microbial diversity in controlling\nnitrous oxide reduction and soil nitrous oxide emissions. Glob. Change Biol. 27, 2669–\n2683 (2021).\n22. Hiis, E. G. et al. Unlocking bacterial potential to reduce farmland N O emissions.\nLine 122-124: Provide a citation that supports your results, specifically how legumes\nand mixtures boost crop yields and N2O emissions.\nResponse: Thanks for your valuable suggestion. From the perspective of residue input,\nthe residues of CC legumes and mixtures at the immature stage generally have low C:N\nratios and sufficient available N, which facilitates the subsequent N uptake and yield of\ncash crops. However, this may also result in excessive N for microbial demand during\nCC residue decomposition. Such N excess will inevitably accelerate the production of\nN O and inhibit its formation to N , leading to increased N O emissions from the soil.\n2 2 2\nThe related discussions and citations have been incorporated as follows:\n“Compared to non-legume CCs, legumes and mixtures (a blend of legume and non-\nlegume CCs) have been robustly observed to boost crop yields and N O emissions (Fig.\n2\n2b and Supplementary Fig. 2). For the residues of legumes and mixtures with a low\nC:N ratio, their sufficient available N not only contributes to crop uptake and growth,\nbut may also exceed microbial N requirements, thereby increasing soil N O production\n2\nand emissions24. This exemplifies a double-edged effect of their biological N fixation\nand highlights the importance of modulating microbial interactions within CC\nsystems25.” (lines 127-133)\n24. Olesen, J. E. et al. Challenges of accounting nitrous oxide emissions from\nagricultural crop residues. Glob. Change Biol. 29, 6846–6855 (2023).\n25. Zhou, G. et al. Synergistic effects of diazotrophs and arbuscular mycorrhizal fungi\non soil biological nitrogen fixation after three decades of fertilization. iMeta 1, e81\n(2023).\nLine 133-136: Include a brief explanation of why the accumulation of mineral-\nassociated organic C is beneficial compared to particulate organic C. This will help\nreaders understand the significance of this finding.\nResponse: Thank you for your insightful comment. Mineral associations play a critical\nrole in transforming labile organic C into more stable forms through adsorption,\nocclusion, and aggregation, particularly in CC systems with substantial input of plant\nand microbial residues. These processes largely limit the access of microbial groups\nand their enzymes to mineral-associated organic C, thereby enhancing its persistence.\nBased on this understanding, we have incorporated the relevant explanations, which are\nprovided below for your reference:\n“These belowground benefits are supported by a 7-year rotation system, where CC\nmixtures can facilitate the accrual of mineral-associated organic C fractions through\nincreased root biomass26. Our long-term observations further confirm that CC mixtures\nparticularly promote the accumulation of mineral-associated organic C rather than\nparticulate organic C (Supplementary Fig. 5a,b); the former can limit the accessibility\nof microbes and enzymes via mineral-organic matter interactions (e.g., adsorption and\naggregation), thus exhibiting greater persistence27,28.” (lines 138-144)\n26. Zhang, Z., Kaye, J. P., Bradley, B. A., Amsili, J. P., & Suseela, V. Cover crop\nfunctional types differentially alter the content and composition of soil organic carbon\nin particulate and mineral-associated fractions. Glob. Change Biol. 28, 5831–5848\n(2022).\n27. Kleber, M. et al. Dynamic interactions at the mineral–organic matter interface. Nat.\nRev. Earth Env. 2, 402–421 (2021).\n28. Xiao, K. Q. et al. Introducing the soil mineral carbon pump. Nat. Rev. Earth Env.\n4, 135–136 (2023).\nLine 168-170: Provide more detail on the role of fungal-to-bacterial ratios in soil\nstabilization and erosion control. Additional insights into how these ratios influence\nMWD would be helpful.\nResponse: Thank you for your comment. We have expanded the discussion to include\nmore details on how fungal-to-bacterial ratios influence soil stabilization and erosion\ncontrol. Specifically, fungal-dominated microbiomes significantly contribute to soil\naggregation through the production of glomalin and other extracellular polysaccharides.\nThese substances enhance soil structure by improving MWD, particularly when\ninteracting with CC residues that also act as organic binding agents. In contrast, bacteria\nprimarily contribute to the formation of microaggregates, which are generally less\nstable than fungal-driven macroaggregates. Moreover, the extensive hyphal networks\nformed by fungi, especially in soil systems with high fungal-to-bacterial ratios, play a\ncritical role in physically binding soil particles, thereby resisting hydraulic erosion. We\nhave incorporated these details into the revised manuscript as follows:\n“In humid tropical climates, CCs can significantly increase MWD by 61.1%,\nreinforcing their role in mitigating soil hydraulic erosion8, largely through the\ncontribution of high fungal-to-bacterial ratios (P < 0.001; Fig. 3b). The dominance of\nfungi in these systems leads to the substantial production of glomalin and other\nextracellular polysaccharides, as well as the formation of extensive hyphal networks,\nwhich physically bind soil particles into macroaggregates34. This also highlights the\nsupplementary benefits of CC residues beyond biomass input, as they act as organic\nbinding agents that promote soil aggregation and erosion control13.” (lines 177-184)\nLine 288-289: What are the factors contributing to negative responses?\nResponse: Thank you for raising this important point. We believe that the negative\nresponses of agroecosystem multiservices are primarily attributed to the absence of\ncrop-soil synchrony in CC practices (Fontaine et al. 2023). The first factor is resource\ncompetition between CCs and subsequent main crops, which can lead to reduced crop\nyields if CCs excessively deplete soil moisture and nutrients. To mitigate potential yield\npenalties, it is crucial to ensure the appropriate termination time of CCs before planting\nthe next cash crop (Qin et al., 2021).\nSecond, in developed agricultural regions, although there is often a long history of CC\npractices, the lack of updated supporting technologies and site-specific management\ncan result in suboptimal outcomes (Fig. 6). Denies et al. (2023) evaluated the recent\nyield impact of CCs across the large-scale Corn Belt in the United States, and found\nthat despite the widespread adoption and financial support for CCs in developed\ncountries, ignoring site-specific management can still lead to yield losses. Our results\nlargely demonstrate that combining climate-smart management practices enhances the\nsynergies among crop production, soil carbon storage, and climate mitigation (Fig. 4).\nWithout the integration of advanced technologies and tailored strategies, CC benefits\ncannot be fully realized, leading to trade-offs or even adverse effects. To maximize the\ncost-benefit relationship, adopting more targeted, knowledge-based CC management\nshould be a common goal of both developed and underdeveloped countries.\nIn conclusion, while CC practices generally enhance agroecosystem multiservices,\nthese factors may sometimes result in trade-offs or negative responses. This emphasizes\nthe need to design and adopt sustainable CC practices to address these challenges. We\nmust acknowledge that the optimal CC portfolio may not be applicable in all regions,\npotentially contributing to the trade-offs. Nevertheless, our study provides a key\nparadigm shift for leveraging CC towards the achievement of sustainable development\ngoals (ref. 7).\n7. Lamichhane, J. R., & Alletto, L. Ecosystem services of cover crops: a research\nroadmap. Trends Plant Sci. 27, 758–768 (2022).\nDeines, J. M. et al. Recent cover crop adoption is associated with small maize and\nsoybean yield losses in the United States. Glob. Change Biol. 29, 794–807 (2023).\nFontaine, S. et al. Plant–soil synchrony in nutrient cycles: Learning from ecosystems to\ndesign sustainable agrosystems. Glob. Change Biol. 30, e17034 (2024).\nQin, Z. et al. Assessing the impacts of cover crops on maize and soybean yield in the\nU.S. Midwestern agroecosystems. Field Crops Res. 273, 108264 (2021).\nLine 414: There is an error in the special symbols within the formula.\nResponse: Thank you for pointing out the error. The special symbol has been corrected,\nand we have thoroughly checked in the Methods section. The revised sentence is listed\nbelow for your reference:\n“The percentage change ((RR − 1) × 100) was adopted to denote the weighted effect\nsizes.” (lines 425-426)\nLine 493-497: The network analysis and piecewise SEM are complex and well-thought-\nout. However, please clarify how the weights assigned to different services (e.g., 0.9\nfor crop yield) were determined and whether these values were based on empirical data.\nResponse: Thank you for your valuable feedback. Like all agricultural management\nstrategies, the primary goal of CC practices is to produce more food while protecting\nthe environment. Before implementing relevant agricultural policies, it is necessary to\nassess the effectiveness of CC practices in terms of their individual and holistic\nsustainable benefits, to maximize beneficial services and reduce disservices. The\nconcept of “ecosystem-service multifunctionality” or “ecosystem multiservices”,\nproposed by Manning et al. (2018), provides a framework for addressing this issue. It\ndefines multiservices as the co-supply of multiple ecosystem services relative to human\ndemand (supply-benefit relationship), making it particularly relevant for stakeholder\nobjectives.\nThis framework has been empirically validated at the landscape level and successfully\nextended to larger spatial and temporal scales (e.g., Neyret et al., 2023; Richter et al.,\n2024; Scherzinger et al., 2024). In our network and subsequent analyses, the weights\nassigned to different agroecosystem services were based on this framework.\nSpecifically, these weights (e.g., 0.9 for crop yield and 0.6 for soil carbon storage,\nclimate mitigation, and soil stabilization) were determined by considering the relative\nbenefits of each service for agricultural demand, as outlined by Manning et al. We then\nmultiplied the CC effects on individual services by the weights, as well as their\nstrengths in network analysis, to quantify the overall agroecosystem multiservices\nprovided by CCs. This approach offers a balanced view for evaluating the capability of\nCCs to provide multiple benefits simultaneously. We have clarified this rationale in the\nrevised manuscript, as follows:\n“Specifically, a weight of 0.9 was assigned to crop yield due to its priority in intensive\nagriculture, while a weight of 0.6 was assigned to soil carbon storage, climate\nmitigation, and soil stabilization, given their secondary yet critical roles in agricultural\nsustainability68.” (lines 512-515)\n68. Manning, P. et al. Redefining ecosystem multifunctionality. Nat. Ecol. Evol. 2,\n427–436 (2018).\nNeyret, M. et al. Landscape management strategies for multifunctionality and social\nequity. Nat. Sustain. 6, 391–403 (2023).\nRichter, F. J. et al. Effects of management practices on the ecosystem-service\nmultifunctionality of temperate grasslands. Nat. Commun. 15, 3829 (2024).\nScherzinger, F. et al. Sustainable land management enhances ecological and economic\nmultifunctionality under ambient and future climate. Nat. Commun. 15, 4930 (2024).\nLines 456-468: Please provide detailed site data and sources. Which specific parameters\nof APSIM were adjusted? Please provide the corresponding parameter table.\nResponse: Thank you for highlighting these important considerations. We have\nuploaded a file named Supplementary Data 5.xls, which is provided with the paper.\nThis file contains comprehensive records of the data on site biophysical attributes,\ncropping systems, and output variables used for APSIM model calibration. Additionally,\nas mentioned in our previous response to your comment, we have included\nSupplementary Table 5 to outline the specific parameters that were adjusted in the\nmodel along with their respective ranges of values. We sincerely appreciate your careful\nreview and hope these additions address your concerns fully.\nLine 525: The definition and formula for NGHGB should be included.\nResponse: Thank you for your comment. We have now included the definition and\nformula for NGHGB. In this study, NGHGB was used to estimates the capacity for CO\n2\nsequestration of various agroecosystems under CC practices. It was calculated based on\nthe balance between direct soil GHG emissions and changes in SOC storage10. The\nformula is as follows:\nNGHGB (Pg CO yr−1) = 44/12 × SOC − 273 × N O − 27 × CH\n2 stock 2 4\nwhere SOC , N O, and CH are the actual changes in SOC stocks, N O, and CH\nstock 2 4 2 4\nemissions due to CC practices, respectively. Their conversion factors are according to\n100-year global warming potential (GWP) values from the Sixth Assessment Report of\nIPCC20.\nAlso, the added sentences are listed below for your reference:\n“Based on the 100-year global warming potential (GWP) values from the Sixth\nAssessment Report of the IPCC, the conversion factors (CO -eq.) for N O and CH to\n2 2 4\ncalculate NGHGB were 273 and 2720. The NGHGB estimates the capacity for CO\n2\nsequestration of various agroecosystems under CC practices. It was calculated as the\ndifference between the decrease in GWP due to larger SOC stocks and the increase in\nGWP derived from direct N O and CH emissions10: NGHGB (Pg CO yr−1) = 44/12 ×\n2 4 2\nSOC − 273 × N O − 27 × CH .” (lines 547-552)\nstock 2 4\n10. Abdalla, M. et al. A critical review of the impacts of cover crops on nitrogen\nleaching, net greenhouse gas balance and crop productivity. Glob. Chang. Biol. 25,\n2530–2543 (2019).\n20. IPCC. Climate Change 2022：Mitigation of Climate Change. Contribution of\nWorking Group III to the Sixth Assessment Report of the Intergovernmental Panel on\nClimate Change. https://www.ipcc.ch/report/ar6/wg3 (2022).\nLines 532-533: The names of the chosen General Circulation Models (GCMs) should\nbe specified.\nResponse: Thank you for your valuable comment. We have specified the names of five\nclimate models following your suggestion and provided their detailed information in\nSupplementary Table 8. The revised sentence is listed below:\n“To explore whether optimizing CC practices can sustain benefits to global\nagroecosystems under future changing climate conditions, we projected CC effects in\ntwo scenarios of Shared Socioeconomic Pathway (SSP) using five bias-corrected\nclimate model outputs provided by the ISIMIP3b simulation (GFDL-ESM-4, IPSL-\nCM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, and UKESM1-0-LL)81.” (lines 553-557)\nSupplementary Table 8 Five bias-corrected CMIP6 model outputs provided by\nISIMIP3b for projecting the CC benefits under the SSP126 and SSP370 scenarios\n(2021‒2030, 2041‒2050, 2061‒2070, and 2091‒2100).\nClimate\nAffiliation Resolution\nsensitivity\nGeophysical Fluid Dynamics Laboratory,\nGFDL-ESM-4 National Oceanic and Atmosphere 0.5°, daily High\nAdministration (USA)\nIPSL-CM6A-LR Institute Pierre Simon Laplace (France) 0.5°, daily Low\nMax Planck Institute for Meteorology\nMPI-ESM1-2-HR 0.5°, daily High\n(Germany)\nMeteorological Research Institute, Japan\nMRI-ESM2-0 0.5°, daily High\nMeteorological Agency (Japan)\nNational Centre for Atmospheric Science and\nUKESM1-0-LL 0.5°, daily Low\nMet Office Hadley Centre (UK)\n81. Lange, S., & Büchner, M. Secondary ISIMIP3b bias-adjusted atmospheric climate\ninput data. https://esg.pik-potsdam.de/search/isimip (2022).\nLines 540-542: The authors calculate the aridity index using the R package \"SPEI\"\n(https://cran.r-project.org/web/packages/SPEI/SPEI.pdf). According to the\ndocumentation, the input is monthly meteorological data, and relevant time scales can\nbe defined for SPEI calculation. However, the authors aggregate temperature and\nprecipitation data to the annual scale for calculation, which differs from the description\nin the R package documentation. The authors should explain the rationale behind this\napproach to ensure the calculation's validity.\nResponse: Thank you for your insightful observation. We appreciate the opportunity\nto clarify our approach to calculating the future aridity index (AI). As noted, the R\npackage SPEI (version 1.8.1; Vicente-Serrano et al. 2010) is typically used with\nmonthly meteorological data. However, given the need for AI calculation over a longer\ntemporal scale, we made some modifications to align with our study’s focus.\nSpecifically, we followed these steps:\n1. Data aggregation: We first aggregated the daily mean temperature data from\nCMIP6 (e.g., 2021–2030) to a monthly scale, resulting in 120 monthly temperature\nvalues.\n2. Potential evapotranspiration (PET) calculation: Using the thornthwaite function\nfrom the SPEI package, we calculated the annual PET for each 12-month period\n(yielding 10 values) and averaged them to obtain a 10-year mean PET. The\nthornthwaite function is defined as follows:\nthornthwaite (Tave, lat, na.rm = FALSE, verbose=TRUE)\nwhere Tave is a numeric vector of monthly mean temperatures (°C), and lat represents\nthe latitude of the sites (degrees).\n3. AI calculation: Finally, we used the formula: AI = MAP/PET to derive the 10-year\naverage AI. Similarly, MAP denotes the 10-year mean annual precipitation data.\nThis approach allowed us to maintain consistency and accuracy while accommodating\nthe decadal scale required by our study. We have clarified this methodology in the\nrevised manuscript as follows:\n“The future aridity index was calculated by combining temperature and precipitation\ndata from CMIP6 using the R package SPEI82. Specifically, daily mean temperature\ndata were aggregated to a monthly scale for each decade (e.g., 2021‒2030). The\naggregated monthly temperature was converted into annual potential\nevapotranspiration using the Thornthwaite function, which was then combined with\nannual precipitation data to obtain the 10-year average aridity index.” (lines 562-566)\n82. Beguería, S., Vicente-Serrano, S. M., Reig, F., & Latorre, B. Standardized\nprecipitation evapotranspiration index (SPEI) revisited: parameter fitting,\nevapotranspiration models, tools, datasets and drought monitoring. Int. J. Climatol. 34,\n3001–3023 (2014).\nVicente-Serrano, S. M., Beguería, S., & López-Moreno, J. I. A multiscalar drought\nindex sensitive to global warming: the standardized precipitation evapotranspiration\nindex. J. Climate 23, 1696–1718 (2010).\nReviewer #1 (Remarks on code availability):\nThe README file provides only a summary of the manuscript and lacks detailed\ninstructions on how to install and run the code.\nResponse: Thank you very much for taking the time to review the code and provide\nthis important feedback. We have revised the README file to fully align with the\neditorial policy. The updated README now includes detailed instructions on the\npurpose and operation of individual scripts, as well as the relevant data they generate.\nWe believe that these improvements will make it easier for readers to understand and\neffectively use the provided code.\nThe code does not include the program for calculating the aridity index using the R\npackage SPEI as mentioned in the manuscript.\nResponse: Thank you for your careful examination and bringing this to our attention.\nWe sincerely apologize for any oversight and have thoroughly reviewed the codes we\nsubmitted. We ensure that the code for calculating the aridity index using the R package\nSPEI has been deposited in GitHub (https://github.com/TianyiQiu13/Cover-crop-\nmultiservices). In the meantime, we have also checked all other code files to ensure\ntheir completeness. Really appreciate your diligence in helping us improve the\ntransparency and reproducibility of our research.\nReviewer #2 (Remarks to the Author):\nThe authors have done an excellent job addressing the comments made on the previous\nversion of the manuscript. This reviewer is satisfied with the authors' responses to the\nquestions raised during the initial review and the revisions made. The manuscript will\nbe of great interest to readers interested in sustainable agriculture and holds promise to\nbe a foundational paper guiding future research in that area.\nRecommendation: accept for publication.\nResponse: Thank you very much for your positive evaluation and kind words. We are\ntruly grateful for your thorough review and the constructive feedback that helped us\nimprove our manuscript. We greatly appreciate your recommendation for publication\nand the opportunity to share our findings with the broader research community."}, {"round": 3, "reviewer_comments": {"reviewer_1": "I appreciate the authors' tremendous effort to improve the study. They have thoroughly addressed my comments from the second revision of the manuscript. Their responses are clear and comprehensive. I recommend the manuscript for publication."}, "author_response": ""}]

Decision letter