The role of cover crops for cropland soil carbon, nitrogen leaching, and agricultural yields - A global simulation study with LPJmL (V. 5.0-tillage-cc)

. Land management practices can reduce the environmental impact of agricultural land use and production, improve productivity, and transform cropland into carbon sinks. In our study we assessed the biophysical and biogeochemical impacts and the potential contribution of cover crop practices to sustainable land use. We applied the process-based, global dynamic vegetation model LPJmL5.0-tillage-cc with a modified representation of cover crops, to simulate the growth of grasses on cropland in periods between two consecutive 10 main crops’ growing seasons for near-past climate and land use conditions. We quantified simulated responses of agroecosystem components to cover crop cultivation in comparison to bare soil fallowing practices. on global cropland for a period of 50 years. For cover crops with tillage, we obtained annual global median soil carbon sequestration rates of 0.52 and 0.48 t C ha -1 yr -1 for the first and last decades of the entire simulation period, respectively. We found that cover crops 15 with tillage reduced annual nitrogen leaching rates from cropland soils by median of 39 % and 54 % but also the productivity of the following main crop by average of 1.6 % and 2 % for the two analyzed decades. Largest reduction of productivity were found for rice, modestly lowered for maize and wheat, whereas soybean yield revealed an almost homogenous positive response to cover crop practices replacing bare soil fallow periods. Obtained simulation results of cover crop with tillage practices exhibit a good ability of the model version to 20 reproduce observed effects reported in other studies. Further, the results suggest that no-tillage is a suitable complementary practice to cover crops, enhancing soil carbon sequestration and the reduction of nitrogen leaching while reducing potential trade-offs with the main crop productivity due to their impacts on soil nitrogen and water dynamics. The spatial heterogeneity of simulated impacts of cover crops on the variables assessed here was related to the 25 time period since the introduction of the management practice as well as to environmental and agronomic conditions of the cropland. This study supports findings of other studies, highlighting the substantial potential contribution of cover crop practices to the sustainable development of arable production.

biogeochemical impacts and potential contribution of cover crop cultivation to sustainable arable production at the global scale accounting for differences in environmental and socio-economic conditions. We focus our analysis on effects of herbaceous cover crop species, growing as annual grasses and replacing bare soil fallows on cropland 115 during main crop off-season periods. The objectives of this study were to: i) Assess the temporal and spatial pattern of cover crop cultivation impacts simulated with LPJml5.0-tillage-cc on global cropland soil C stocks, N leaching rates, and agricultural productivity, ii) Quantify responses to cover crop cultivation with regard to tillage practices and the influence of management duration, and iii) Estimate impacts of land management for the historical CA area and the potential contribution of cover crop practices to agricultural production impact and greenhouse gas 120 mitigation efforts .  Schaphoff et al. (2018a);(2018b). The here used model version additionally includes processes associated to global N dynamics in soils and plants (von Bloh et al., 2018) and an explicit representation of tillage and crop residue management (Lutz et al., 2019). 130 In the model three litter layers and five hydrologically active soil layers of differing thickness to a total depth of three meter are distinguished. Each soil layer has its specific temperature and moisture levels, affecting the decomposition rates of soil organic matter, represented in the model by fast and slow decomposing (30 and 1000 years turnover time, respectively) C and N pools (Lutz et al., 2019;Schaphoff et al., 2018a). Carbon and N pools of represented vegetation, litter, and soil layers are updated daily. Biomass formation is represented by a simplified 135 version of photosynthesis according to Farquhar et al. (1980). The phenology of tree and grass plant functional types (PFTs) of the represented natural vegetation are based on Jolly et al. (2005) with modification of the growing season index as described in Forkel et al. (2014). Crop functional types (CFTs, see Table S1.1), representing the vegetation on cropland, are parameterized with specific temperature and phenological heat unit requirements for growth (Müller et al., 2017). 140 Cropland irrigation was mechanistically simulated by either surface flooding, sprinkler, or drip irrigation, here setting one type per country (Jägermeyr et al., 2015;Rohwer et al., 2007). We used the potential irrigation setting to simulate irrigated cropping systems (for cropland within grid cells with areas equipped for irrigation as informed by the input data (see Sect. 2.2) to account for missing representation of ground water sourcing, when this model version only considers surface water withdrawal amounts, in the case of alternatively setting to limited irrigation. 145 The C to N ratio of manure was set 14.5 to 1. Half of the N contained in the manure was assumed as ammonium (NH 4 ) and added to the pool of the upper soil layer, whereas the entire C and the remaining N (assumed as organic share), were transferred to the respective litter pools. Generally, mineral N fertilizer and manure were applied to cropland at the sowing date of an individual main crop (CFT) within a grid cell. If the sum of N from the mineral N fertilizer and from the manure exceeded the threshold value of 50 kg N ha -1 , the remaining mineral N fertilizer 150 amount was applied at a second event during the growing season, when 40 % of the phenological heat sums to reach maturity, were accumulated.
Conventional tillage was assumed as the default historical soil management on all cropland, applied when converting land to cropland, as well as at main crop seeding and harvest events. After harvest of the main crop the tillage routine submerges and transfers 95 % of the aboveground biomass remaining on the field from soil surface 155 to the incorporated soil litter pools. In the model, tillage mostly affects processes in the first soil layer up to 20 cm depth (Lutz et al., 2019). In the case of no-tillage, the remaining aboveground biomass of the main crops' residues left on the field after harvest are added to the surface soil litter pools, representing mulching practices. Herzfeld et al. (2021) examine global soil carbon dynamics affected by historical land use, land-use change, tillage, and crop residue management, based on simulations with a similar model code version, input data, and cropland 160 management representation but different simulation setup than applied here. For the simulated period 2000-2009, the authors found a global cropland soil carbon stock of 170 PgC in response to historical dynamic climate input data, land use change, cropland use, and management practices, which was in good agreement with estimates reported in the literature.

Simulating cover crop practices with LPJmL5.0-tillage-cc 165
We used LPJmL5.0-tillage-cc with a modified code for the representation of cover crop management. It is built on an earlier version of the model accounting for 'intercrops', as the options to simulate either herbaceous vegetation, or bare soil fallow dynamics on cropland in periods between two consecutive main crops' growing seasons (Bondeau et al., 2007). The functionalities make use of three 'grass' plant functional types (PFTs) already implemented in LPJmL for the natural vegetation, growing on fallow cropland according to their bio-climatic 170 limits as tropical C4, temperate C3, and polar C3 grass (Forkel et al., 2014). In the model, biophysical and biogeochemical dynamics on off-season cropland within a grid cell, are accounted for in routines of the 'setaside stand', preserving the separation of processes in soil columns into rainfed and irrigated shares.
As a first step, we modified the functionalities for the establishment of cover crop (grass), so that it occurs on each crop specific off-season cropland fraction after harvest of the main crop (CFT) within a grid cell. The initial 175 biomass of the cover crop grass sapling (0.05-0.07 g C m -2 ) was changed to be taken from the respective C and N pools of the soil litter layers. We did so, to avoid imposing artificial fertilization effects (Olin et al., 2015), from simply adding contained amounts of the sapling's C and N to the simulated system with the default CFTs establishment model routines, which assume crop seeds as external inputs.

Model input data 190
For the simulations of this study, the model was driven with monthly mean temperature input data from the Climate Research Unit (CRU TS version  The monthly radiation data (shortwave and net longwave downward) was taken from the ERA-Interim dataset 195 (Dee et al., 2011) for the years 1901-2011. Annual atmospheric CO 2 -concentration input data were based on the NOAA/ESRL Mauna Loa station reports (Tans and Keeling, 2015), and natural N deposition data on the ACCMIP database (Lamarque et al., 2013) for the years 1841-2012. Soil texture classes remained static over the simulation period and were based on the Harmonized World Soil Database (Nachtergaele et al., 2009) and soil-pH was taken from the WISE dataset (Batjes, 2006). 200 Model input data on historical land use, distinguishing shares of irrigated and rainfed crop-group specific physical cropland per grid cell, as well as mineral N fertilizer application rates were based on LUH2v2 data by Hurtt et al. (2020). The original data per crop group was (dis-)aggregated and remapped, using the MADRaT tool , to match the crop functional types (CFTs) representing vegetation dynamics on managed land in LPJmL (Table S1.1) and the here targeted model simulation grid cell resolution of 0.5 arc degree (~50 x 50 km at 205 the equator). In the year 2010 there were ~1,500 million ha total global physical cropland ( Fig. S1.2).
Sowing date and phenological heat units were prescribed with a growing season input dataset based  and Sacks et al. (2010), described by Elliott et al. (2015). The historical manure input data was based on the time series of N contained in manure applied on cropland by Zhang et al. (2017). The residue management model input dataset prescribed the fraction of residue biomass remaining on the field after harvest of the main 210 crop. It was generated, by setting residue recycling shares to values per CFT-group (i.e., cereals, fibrous, nonfibrous, and others), which were obtained from Dietrich et al. (2020) and based on national reported cropland data retrieved from FAOSTAT. The data accounts for historical main crop residue removal rates associated to land management practices, such as burning on field, as well as to secondary off-field usages, such as household burning, and livestock fodder. 215

Simulation setup of land management scenarios
As a first step, we conducted a 7000 years spin-up simulation with LPJmL5.0-tillage-cc, in order to get natural vegetation pattern and soil pools into a dynamic equilibrium state, recycling the first 30 years of climate input data following the procedures described in von Bloh et al. (2018). Subsequently, we ran a second spin-up simulation, with fixed cropland distribution pattern and most of the land management as provided by the model input data for 220 the year 2010 (Sect. 2.3). We assumed bare soil fallow on cropland during the main crops off-season periods as well as tillage to be the default historical management practices. By keeping land use and management constant during this simulation step, we aimed to establish an equilibrium state between the C and N pools and the fluxes.
We assumed that cropland had been already cultivated for a longer time at the beginning of the actual management simulation period so that our results can be more easily compared to literature values e.g., obtained from 225 experiments conducted on already established cropland plots. Starting with cropland soil pools from this spin-up procedure, we simulated the control as reference scenario (REF) for 50 years of the historical period to present day, maintaining land use pattern and all land management model settings as during the land use spin-up period. By using dynamic climate and CO 2 model forcing data during the actual management simulation period (years 1962-2011), we aimed to mimic near-past environmental production conditions. Three alternative cropland 230 management scenario simulations were generated with: cover crops replacing bare soil fallow periods (CC), notillage (NT) applied as single, as well as combined cover crop and no-tillage practices (CCNT) on global cropland for the same 50 year simulation period and all other model settings as used in the reference scenario (REF) (see Supplement Table S1.3 for more details on simulation setup). On the one hand, this 50 year time frame has been chosen for analysis because it is stated as minimum duration required to re-establish a new steady state in soil C 235 pools after the introduction of a new soil management practice involving altered biomass input levels to soils (Kaye and Quemada, 2017;Poeplau and Don, 2015). On the other hand, the 50 years were chosen for analysis because of spanning the maximum duration found for values reported in the literature and used here for evaluating simulated responses to cover crop practices ( Table 1, Table S2.6).
Soil C stock change was quantified up to a 30 cm soil depth by adding C pool model outputs for the litter, the first soil layer (0-20 cm soil depth), and one third of the second soil layer (20-50 cm soil depth). Responses of cropland 245 soil C stock to altered management scenario (CC, CCNT, and NT) in comparison to the control (REF) were generated, assuming a 'paired plot' (West et al., 2004) where ∆ , , is the annual soil C sequestration rate in t C ha -1 yr -1 per alternative scenarios s, in grid cell i, and time step t, as the absolute difference between the annual absolute soil C stock p s,i,t in t C ha -1 yr -1 in each of the where ∆ , , is the relative difference in percent (%) between the assessed variable ( , , ) per alternative management scenario s compared to the baseline value ( , , ), per hectare of cropland area in grid cell i, and time step t.
We report global aggregates of simulated values and differences as area-weighted median (Q2 as q = 0.5 as ∆̃, , ), the first (Q1 as q = 0.25) and third quartiles (Q3 as q = 0.75) per management scenario s, and per time step t. Time 270 step t is annual (yr -1 ) either reported for the first (years 1 to 10), or last (years 41 to 50) decade of the 50 simulation years, in order to contrast short from long term management effects, or for an else indicated time period. For areaweighting of global aggregated changes in soil carbon and N leaching rate, we applied the sum of the physical cropland per grid cell using the land use data of the year 2010 (see Sect. 2.3, S1.2).
For evaluating LPJmL5.0-tillage-cc model results, we compare modeled responses to cover crop cultivation on 275 soil carbon, nitrogen, and water dynamics to values reported in the literature, which use bare soil fallowing practices and conventional tillage in the control treatment (Table 1). Meta-analyses and reviews on cropland management effects summarize experimental studies' findings, covering a variety of temporal scales and crop production conditions regarding climate, soil, and management intensities. Although many studies present averages across experiment sites and years (Nyawira et al., 2016), we computed spatial and temporal aggregated 280 median (and quartiles) changes to exclude outliers stronger influence on global spatial aggregated mean values.

Soil carbon responses to altered land management and duration
We found increased cropland soil carbon stocks in the three simulated alternative land management scenarios 300 compared to the control (REF), indicated by positive annual area-weighted spatial aggregated median soil carbon sequestration rates (Fig. 1, for respective spatial patterns see Fig. S2.1.1). During the first decade of the 50 year simulation period the median soil C sequestration rates in the three alternative management scenario simulations CC, CCNT and NT were higher (0.52, 0.72, and 0.08 t C ha -1 yr -1 ) than during the last decade (0.48, 0.54, and 0.01 t C ha -1 yr -1 ) ( Table 1, Table S2.2). The maximum annual median soil C sequestration rates within both cover crop 305 scenarios CC and CCNT (0.79, 1.03 t C ha -1 yr -1 ) were reached in the sixth year of the analyzed 50 year simulation period, whereas in NT (0.11 t C ha -1 yr -1 ) already in the third year since introduction of altered management. After these peaks within each of the scenarios, the annual soil C accumulation effect persist over the course of the remaining simulation period, but with lower rates.

Simulated impacts of land management on soil N and water dynamics 315
All three alternative management scenarios exhibited higher transpiration but lower evaporation rates than found in the baseline (Fig. 2 a and b). In both cover crop simulations (CC and CCNT) the transpiration rates were higher because of the extended vegetative growth per cropland area unit compared to scenarios with the bare soil fallow during primary crop off-season periods (REF and NT). With CC, transpiration increased more strongly than evaporation was reduced, so that total evapotanspiration water fluxes were higher than in REF. In CCNT and NT, 320 we found lowerded evaporation rates outweighing elevated transpiration rates compared to in REF with tillage.
Cover crops in CC and CCNT led to lower but still positive median N net-mineralization rates (as the difference of soil N gross mineralization and immobilization rates) compared to bare soil fallowing practices in REF and NT ( Fig. 2 c). This decline was driven by larger increases of the soil N immobilization than of gross mineralization rates, especially within the first 10 years after introduction of cover crop practices (Fig S2.3). In both cover crop 325 scenarios (CC and CCNT) N leaching rate shares of applied mineral N fertilizer were decreased faster and more strongly than in NT compared to in REF over the course of the simulation period ( Fig. 2 d). After the first three initial years the N leaching rate responses were stabilizing for all three alternative scenarios. Median reductions in N leaching rates in simulations including cover crops (CC and CCNT) were about two to 340 three times higher than in NT.

Yield change of following main crop due to altered management and duration
The simulated impacts of cover crop cultivation (CC) on the following main crop yields exhibited large spatial 350 variability and differences of effects between the analyzed crop types. The productivity for maize and rice in northern cold and tropical humid climates was lowered with cover crops (CC), whereas drier temperate regions e.g., in the Western USA and Mediterranean reveal prominently enhanced average yield effects for the four assessed crop types (Fig. 4). Comparing the changes across the alternative management scenarios, following main crop average productivity 360 decreased most strongly in CC and increased most in NT relative to the baseline with tillage and bare soil fallow practices (REF) (Fig. 5 a-d). In CC, rice yield declines were largest, whereas reduction for this crop type was halved on the majority of global cropland in the CCNT simulation. In contrast to mostly lowered maize yield in CC, we found positive median responses for this crop type in CCNT but with higher spatial variability of impact magnitude and direction (Fig. 6, Table S2.2). Wheat yield responses to any of the three alternative managements 365 were very low in overall magnitude, being slightly reduced in both cover crop scenarios, but improved in NT (Fig.   5, Table S2.2). Soybean yield, responded positively to all simulated alternative management practices, although by median less than 1 % in CC, we calculated around 9 % higher medians in CCNT, and NT compared to in REF.
Exploring simulated land management impacts on the following main crop productivity separated by water regimes revealed larger spatial variability of responses for rainfed than for irrigated crop yields (Fig. S2.4). 370 Soybeans in irrigated systems show no response to altered management practices. For the other assessed cereal crop types', median yield responses to cover crop practices (CC, CCNT) were found to be either more negative or changing from a positive to a negative response in irrigated systems compared to rainfed systems. for the year 2010 for wheat (~333 million ha), maize (~369 million ha), rice (~132 million ha), and soybean (~94 million ha). Irrigated shares of total global crop-specific physical cropland were 16 % for wheat, 12 % for maize, 35 % for rice, and 11 % for soybean cropping system area (Sect. 2.3). 385

Cover crop and no-tillage impacts on Conservation Agriculture cropland
Applying the obtained responses to altered land management practices to the temporal and spatial pattern of the mapped CA cropland time series dataset (Sect. S1.4), we found lowest soil C sequestration rates and reductions of N leaching rates assuming no-tillage practices and highest for combined cover crop and no-tillage practices (Table   S2.5). We calculated aggregated median soil C sequestration rates of 0.27 t C ha -1 yr -1 for no-tillage and bare soil 390 fallowing, 0.47 t C ha -1 yr -1 for cover crops with tillage, and 0.85 t C ha -1 yr -1 for cover crops with no-tillage. We estimated the total historical soil C net-accumulation by CA practices on the mapped cropland ranging between 0.4 -1.4 PgC in the period 1974-2010, depending on the management practice. For the annual N leaching rates, we find the reduction by the single or by the combined adoption of no-tillage and cover crop practices ranging between 18.4 -56.9 % across global CA area compared to cropping systems with conventional tillage and bare 395 soil fallowing practices.
We found average yields of the four main crops mostly enhanced with no-tillage, whereas for cover crop with tillage practices the productivity response was neutral or revealed decreases. In response to cover crops applied with no-tillage practices (CCNT), which scenario we used as proxy for the full set of CA practices, positive yield changes (Fig. 6) dominate in areas mapped with Conservation Agriculture practices (Fig. S1.4). 400 Calculating median (quartiles) for yield changes on CA areas only, we found for cover crop with no-tillage that the average productivity (median (quartiles)) of wheat, maize, and soybean was almost exclusively enhanced (6.4 (0.2, 29.4); 23.7 (3.3, 84.1); 27.8 (3.1, 79.0) %, respectively). Although rice yield largely increased with the combined practices but can be lowered as well (5.6 (-3.1, 34.8) %). 410

Overview of simulated responses to cover crop practices compared to other studies' findings
Simulated cover crop impacts exhibit positive soil carbon sequestration rates and reduced N leaching rates on the majority of global cropland, but at the cost of largely lowered average yields of the following main crops in both analyzed decades (Table 1 see Table S2.2 for respective aggregated results per decade for CCNT and NT) The 415 here estimated changes of agroecosystem components due to cover crops (CC) compared to bare soil fallow (REF) on cropland between two consecutives main crop growing seasons, are consistent with the magnitude and direction of effects reported in other studies (Table 1, see Supplement Table S2.6 for an extended comparison to literature values,.

Soil carbon sequestration
The generated median soil C sequestration rates for the simulation with cover crops replacing bare soil fallow periods were within the upper end of range of values reported in the literature (Table 1, Table S2.6). Few regions in temperate and dry climatic conditions, e.g. in Western USA, Turkey, Iraq, and Iran, reveal a neutral or declining trend in response to cover crop cultivation (Fig. S2.1.1). In line with findings of West and Six (2007) both regions profiting from cover crop practices, because there, water is a less limiting factor to biomass production and additional inputs to the soil pools provided by cover crops, enhance soil C accumulation.
Assuming the median soil C sequestration rate of 0.55 t C ha -1 yr -1 (or mean of 0.61 t C ha -1 yr -1 ) during a period 440 of 50 years for the estimated 400 million hectare cropland potentially available annually for cover crop practices (Kaye and Quemada, 2017;Poeplau and Don, 2015), we estimated the global potential soil C sequestration of 0.22 or 0.24 PgC yr -1 in the top 30 cm. This equivalents to about 7-12 % of the 2-3 PgC yr -1 annual sequestration on global agricultural soils until the year 2030 targeted by the '4per1000' initiative (Minasny et al., 2017). However, our estimate is higher than the 0.12 ± 0.03 PgC yr -1 (mean and standard deviation) found by Poeplau and Don 445 (2015) simulating cover crops effects with the RothC model for a similar time frame but for 0 -22 cm soil depth.
Lower annual median soil C sequestration rates with cover crops (CC) in the first three simulation years, reveal a time lag of response to altered management (Fig. 1). A similar effect is also apparent for N and water fluxes (Fig.   2). On the one hand, this may be because cover crops are first established at the end of the first main crop growing season, so that the full effect becomes visible in the second year only. On the other hand, a temporal delay of 450 detectable cover crop impacts on soil organic C concentration within the first years of practice was also found in the review of ecosystem services of cover crop practices by Blanco-Canqui et al. (2015), due to the complexity of biophysical processes affected by changes in biomass inputs to soils due to altered management practices. This suggests that cover crops need to be cultivated for at least three years to take effect. Duration, as the number of years a system has been under a management practice, was also identified as one of the most important factors to 455 reap the benefits of altered soil physical properties from soil C storage enhancing management, such as cover crop s practices (Laborde et al., 2020;Nouri et al., 2020;West and Six, 2007).
The higher soil C sequestration rates calculated for the first than for the last decade of the 50 year simulation period (Table 1 The median soil C sequestration rate for both cover crop scenarios (CC and CCNT) were higher than for no-tillage 480 (NT) (Fig. 1, Table 1, Table S2.6), which is in line with findings of the review by Kaye and Quemada (2017). The effect of combined cover crop and no-tillage practices (CCNT) exhibited the largest soil C sequestration rate with median 0.72 t C ha -1 yr -1 in the first decade. Our result were higher than Franzluebbers (2010) finding a soil C sequestration rate of 0.45 ± 0.04 t C ha -1 yr -1 for experiments comparing cover crops combined with tillage and no-tillage in Southeast USA for about 11 years and were within the range stated in the meta-analysis of experiments 485 conducted in Brazil (0.4-1.9 t C ha -1 yr -1 ) and France (0.1-0.4 t C ha -1 yr -1 ) with a duration of 5-28 years by Scopel et al. (2013). The simulated higher effect of cover crops combined with no-tillage (CCNT) on soil C stocks is also supported by Corbeels et al. (2018) finding higher soil C stocks in case of CA compared to conventionally tilled systems, whereas Abdalla et al. (2019) and Poeplau and Don (2015), find no significant differences due to changed tillage practices with cover crops in their meta-analyses. 490

Nitrogen leaching
The derived N leaching rate reduction in CC were at the upper end of the -70 to -50 % range of effects reported in literature (Table 1, Table S2.6), except during the initial simulation years after introduction of the alternative management practice. The majority of values found for changes due to cover crops reported in other studies and used here for comparison to our obtained simulation results, are for rainfed cropping systems or detailed 495 management information on irrigation practices was missing (Table S2.6). However, Quemada et al. (2013), explicitly focusing in their meta-analysis on cover crop effects replacing bare fallows in irrigated cropping systems, also state a reduction of N leaching rate by 50 % with non-leguminous cover crop species but no effects in experiments with leguminous cover crop types.
For the spatial effects of cover crops (CC), it can be depicted, that most cropland can profit from about halved N 500 leaching rates (Fig. 3, Fig. S2.1.2). One important driver of the size of the effect of cover crops is the length of the fallow season. In northern regions, main crop growing seasons are rather short and aligned across crop types, so that a lot of off-season cropland area is available for cover crops for relatively longer time. Largest N leaching rate reduction with simulated cover crop practices can be found in cold temperate regions (such as in Russia) and in humid tropics (e.g., large parts in Africa), where external N inputs (i.e. mineral N fertilizer rates, also see Sect. 505 S1.2 for rates used here) are rather low. On the one hand, the variance of simulated cover crop effects across global cropland can be attributed to management intensity (e.g., fertilizer application rates), in this study prominently seen as differences at some national borders (USA and Canada). According to Wittwer et al. (2017) efficiency of cover crops to reduce N leaching is decreasing with management intensity (including fertilizer application rates and tillage practices). On the other hand, the spatial variance of cover crop effects within countries suggest 510 differences due to soil and climatic conditions. Only few drier regions reveal either a neutral response or slight increase of N leaching rates due to cover crops ( Fig. S2.1.2). This can be attributed to reduced growth of cover crops, limiting their capacity for N uptake of excess N remaining in the soil column after harvest of the main crop.
Cover crops were also found to increase transpiration while reducing drainage (Meyer et al., 2018), which leads to lower soil water percolation (Abdalla et al., 2019) and restrict the advective export of reactive N from the soil. 515 However, in dry regions, depending on the area share and the type of irrigation system, the additional water applied to fields can result in enhanced drainage. The effect is pronounced for surface irrigation and weaker for drip irrigation. As a result, for irrigated cropping systems in dry areas, N leaching may increase with cover crop practices due to increased biomass inputs to soil which lead to increased N in the soil water solution as a result of decomposition processes of the added plant material. 520 Because the plant material from cover crops that drives the C sequestration with the practices (Sect. 3.1, 4.2) has a wider C to N ratio than the soils, it leads to stronger immobilization of mineral N in the soil column (Fig. S2.3).
Increased evapotranspiration and immobilization but also uptake of N by cover crop plants were found to reduce the soil N (Quemada et al., 2013;Thapa et al., 2018;Zhu et al., 2012), which would be susceptible to leaching from cropland soils during primary crop off-season periods (Abdalla et al., 2019;Alonso-Ayuso et al., 2014;525 Delgado et al., 2007;Tonitto et al., 2006). For their efficiency in N uptake, grass cover crops are also described as 'scavengers' (Blanco-Canqui et al., 2015). Therefore, grass cover crops can be regarded especially suitable for high-input farming systems, where surplus N left in the soil after harvest of the main crop can be retained in the biomass of the cover crop. After termination, the C and N contained in the cover crops biomass, can serve as 'green manure' temporally fixed in compounds of the soil organic matter (Zomer et al., 2017). 530

Crop yields
The average main crop yield change computed for the cover crop scenario (CC) were mostly within the range of values found in literature, but effects vary largely per crop type and location considered ( Table 1, Table S2.6).
Reduced productivity levels of the following main crop are reported mostly in the context of competition with the cover crops for water and nutrients (Abdalla et al., 2019;Tonitto et al., 2006;Valkama et al., 2015). The increased 535 immobilization of soil N after the introduction of cover crops is thought to actually exacerbate N stress (Abdalla et al., 2019;Erenstein, 2003;Kuo and Sainju, 1998;Ranaivoson et al., 2019). Marcillo and Miguez (2017) assume that lower maize yields found with cover crops may also be caused by a temporal asynchrony between periods of soil N mineralization and high N demand of the main crop. Several authors (Marcillo and Miguez, 2017;Thapa et al., 2018;Tonitto et al., 2006) report no significant effects of non-leguminous cover crop species on yields of the 540 subsequent main crop, which may be caused by the mainly intensively fertilized experiments considered, e.g. in Tonitto et al. (2006). This is in line with our findings for soybean, which is an N fixer (not subject to N limitations in LPJmL) and sees hardly any yield penalty from cover crops. Also, the mostly negative responses to cover crops (CC) for the three cereal crop types in irrigated systems (Fig. S2.4.2), where water is not a growth-limiting factor for the main crop, can only be explained by a decrease in N availability for the main crop. In the meta-analysis by 545 Quemada et al. (2013) a reduction of irrigated main crop yields by 3 % was found due to cover crops, which effect is slightly higher than the decadal median reductions of the following main crop yields by 2.5 % and 2.9 % (average across changes of irrigated wheat, rice, maize, and soybean yields for the first and last decade of the 50 year simulation period). The generated results for irrigated soybean productivity reveal no sensitivity to cover crop and changes in tillage practices (Fig. S2.4.2). In our simulations the majority of cropland of wheat maize, rice, and 550 soybean was rainfed (see caption of Fig. 5). Therefore, the found neutral or positive responses of following main crop average yield to cover crop practices for temperate dry areas in the US and the Mediterranean region may result from the relatively higher mineral N fertilizer application rates there (Fig. S1.2), and larger shares of irrigated cropping system area on cropland per grid cell, wherein the effects of cover crops' competition for water and nutrients with the following main crop are diminished. Cover crops affect soil water in different ways: cover crops 555 tend to increase transpiration (see Fig. 2 b), but at the same time reduce soil evaporation (Fig. 2 a) and increase infiltration (Dabney et al., 2001). Depending on the relative magnitude of these processes, soil water availability for the main crop can increase or decrease at different locations. This is clearly shown in Fig. S2.4, where yield responses to cover crops in rainfed systems reveal a much larger variability than in irrigated systems. The spatial variability of yield responses to cover crops for different crops (Fig. 4 and 5) is the result of differences in how 560 cover crops impact water availability of the main crop, how water limited the main crop is, and how strongly the cover crop the reduces N availability for the main crop. However, sensitivity to changes in water availability is highest in rainfed systems in water limited environments, on soil types of low soil water holding capacity, or insufficient recharge, which limits their applicability under such conditions (Marcillo and Miguez, 2017).
In contrast to CC, a mostly enhancing effect on productivity was found with the NT scenario for all four analyzed 565 main crop types. Also Su et al. (2021) find for wheat, maize, and soybean, that although no-tillage could lead to yield declines in cooler and wetter regions, this loss to be more than compensated at the global scale by increased productivity in arid rainfed cropping areas. In our model, the yield increase can mainly be attributed to the watersaving effects simulated with no-tillage compared to both REF and CC scenarios with conventional tillage (Fig.   2, Fig. 5). This is caused by the built up of a litter layer due to simulated no-tillage practices covering the soil as 570 mulch, which increases infiltration rates as well as reduces evaporation and surface runoff rates, mainly benefitting soil water dynamics and crop productivity in arid regions (Jägermeyr et al., 2016;Lutz et al., 2019). Lutz et al. (2019) estimated the difference between tillage to no-tillage for rainfed yields of wheat of median 2.5 % in the simulation with all main crop residues retained, and -5.9 % with 90 % of the residues extracted from the field. For rainfed maize yields they found 1.8 % median increases in their simulation with all main crop residues retained, 575 and -5 % when 90 % residues extracted, after 10 years since the introduction of no-tillage practices. Our calculated changes in yields due to no-tillage are within these ranges (rainfed wheat: 1.7 % for CCNT to CC, and 1.3 % NT to REF; rainfed maize: 4.8 % for CCNT to CC, and 6.3 % for NT to REF as aggregated relative differences for the simulated years 9-11).
In CCNT, the simulated effects of cover cops and no-tillage are combined. Cover crops provide vegetative soil 580 cover on cropland during main crop off-season, and when terminated serve as additional mulching material during the following main crop growing periods. This additional mulch layer in combination with no-tillage counteract the higher transpiration from cover crops by improving infiltration and reducing evaporation (Abdalla et al., 2019; Scopel et al., 2013). Enhanced median maize and soybean yields, as well as less rice yield reductions found with CCNT than with CC compared to REF (Table 1, Table S2.4), reveal co-benefits of both practices (Fig. 5). The 585 assumption of synergetic effects of both practices in CCNT were supported by the even higher median yield responses derived here for cropland with Conservation Agriculture practices (Sect 3.4), which area was mapped with a higher likelihood to arid regions (Porwollik et al., 2019). Laborde et al. (2020) find higher likelihoods of beneficial main crop yield effects of CA for rainfed cropping systems in areas with higher temperature (above 20 degree Celsius) and lower precipitation rates (< 350 mm), due to water-preservation, when the mulching practices 590 reduce evaporation losses compared to experiments with conventional land management practices.
The here presented yield responses to different management settings (NT, CCNT) are only partly in line with findings of Pittelkow et al. (2015), analyzing experiments lasting 1-31 years, who find largest declines (-9.9 %) when no-tillage was adopted alone and decreased negative effects (-6.2 %) when no-tillage was applied with crop rotation. However, cover crops as modelled in our CCNT scenario are only one aspect of crop rotation 595 enhancement considered in the analyses by Pittelkow et al. (2015), which limits the comparability between our and their findings.

Methodological limitations and implications
A detailed evaluation of the implemented model code functionalities for the representation of cover crop practices remains challenging, due to the limited available statistical data on the practice at the global scale. We mainly 600 focused the comparison of modeled effects to findings of meta-analyses and reviews. The results indicate in general reliability of the here used model version LPJml5.0-tillag-cc to reproduce ranges of reported temporal and spatial pattern, magnitude, as well as the sign of direction of simulated cover crop impacts at the global scale (Table 1).
However, aggregated changes of agroecosystem variables due to cover crop cultivation (CC) compared to bare fallowing practices presented here, were not always matching other studies' findings (Table S2.6). On the one 605 hand, these deviations may result from different soil depth considered or meta-analyses reporting averages across different years, crop types, and experiments (Nyawira et al., 2016). Further uncertainties are related to literature values, which may include experiment results from measurements during the main crop growing season only instead of covering the entire year (Quemada et al., 2013). Values derived from field experiments or reported at the national scale (Table S2.6) may rather reflect changes due to local specific and highly controlled crop 610 production conditions rather than covering the variance of environmental and socio-economic conditions captured with the global gridded model setup applied in our analysis. On the other hand, important processes that determine the effect of cover crops in field trials, such as erosion, weeds, pests, or diseases, are not accounted for in this model version.
For the 50 year simulation period we used dynamic historical climate model input data for the years 1962-2011 615 and all stylized management scenarios were introduced from the year 1962 onwards. Therefore, the first decade after the introduction of the practice is not directly comparable to the last decade in terms of environmental cropping system conditions but we assume this error to be small, given that we average over larger areas and report differences to values obtained in the baseline scenario that always refers to the same simulated period when reporting our results. 620 Further, our simulations include changes in atmospheric CO 2 concentration levels during the spin up and historical simulation period, which affect soil C dynamics as well., through biomass growth feedbacks but also temperature and soil moisture effects driving decomposition of the soil organic carbon in cropland soils (Herzfeld et al., 2021).
However, some of the field experiments to which we compare our simulated management results have been conducted over comparable time periods (up to 54 years) and are therefore affected by increasing atmospheric CO 2 625 concentration levels during the near-past period, as well.
The high C sequestration rates calculated for CC, e.g. in the humid tropics ( Fig. S2.1.1) may be due to an overestimation of the simulated fallow period length for cropland in this climatic region. In the model version used here, only the main representative growing season of a crop is simulated per year, so that multiple cropping practices for areas where several crop harvests per year are common Waha et al., 2020) are 630 not well covered, resulting in distorted cover crop productivity levels and biomass input to the soil pools. The here applied model setting for the representation of irrigated cropland in the simulations, assuming unlimited water availability for irrigation practices, may cause an overestimation of main crop productivity as well as resulting main crop residue input amounts to the soil pools.
The computed initial soil C pools do not represent the conditions on current croplands because our simulations 635 excluded historical land use dynamics, to which responses in soil usually are slow and of long-term (Nyawira et al., 2016). Pugh et al. (2015) find, that the soil legacy flux from land use and land cover change may dominate ecosystem carbon losses for a timescale up to a century. By starting the simulations from soil C pools in equilibrium, we aimed to make sure that the acquired response is due to altered management. The deviations in initial soil C and N pools were accounted for in this study by presenting responses to alternative management The simulated crop-specific yield reductions and gains in soil carbon storage obtained at the grid scale for the cover crop management scenarios (CC and CCNT) as a trade-off cannot always be linked directly, due to the missing accounting of emissions associated to the changes in management in our study. Further, modeled responses to cover crop cultivation are determined by the spatial pattern of the crop type, the area share within a 645 grid cell, the crop specific growing season length, fertilizer application rates (Fig S1.2b), the water regime (S2.4), and other crop management modeled at the grid scale.
The potential trade-off between environmental benefits (reduced N leaching, soil C sequestration) and main crop productivity changes found here for cover crops with conventional tillage practices, suggest the requirement for the complementary modification of fertilizer management and of main crop irrigation or the parallel adoption soil 650 water preserving practices, such as no-tillage, and mulching practices to maintain current main crop yield levels.
Our findings on nearly neutral effects on the four analyzed following main crop yields in irrigated cropping systems with cover crops (CC) may result of the here used potential irrigation setting in the management scenariosproviding unlimited water amounts to satisfy completely main crop plant growth requirements during the entire growing season. The obtained results may underestimate yield declining effects because local specific limitations 655 to irrigation water withdrawal amounts are not accounted for in our analysis. However, it can be assumed that increased irrigation water requirements, because of the increased evapotranspiration losses from cover crops, may constrain main crop yield gains obtained with irrigation when adopting the practice with conventional tillage in dry areas.
This study is the first to consider combined cover crop and no-tillage effects as practices recommended under CA 660 employing modeled results employing the generated annual gridded CA area time series dataset.. However, we assume that the here employed method for mapping CA area led to several uncertainties of estimated effects on agroecosystem components. The downscaling approach of national reported CA area to the grid scale targeted a coarser resolution than used in Porwollik et al. (2019) as well as included the entire cropland within a grid cell assumed under this practice, independent of the crop type and water regime of the cropland, whereas in the 665 previous approach only rainfed cropping system area was considered as suitable. The national CA area statistics reported in the FAO also includes area of perennial crop types, which in LPJmL are represented as the CFT 'others' with annual growth dynamics only, and of grassland, which dynamics are not included in our analysis.
Further, our management simulation scenarios do not include a change in main crop residue removal rates, which historical rates may deviate from minimum levels of 30 % soil surface cover by biomass remaining on the field 670 after harvest required by CA. Also, secondary usages of cropland products are likely, so that our estimates of cover crop biomass input rates on the mapped historical CA area maybe overestimating, when assuming all cover crop biomass remaining on the field in the CC and CCNT scenario. Nevertheless, the dataset was used as model input data for simulating historical tillage and no-tillage practices in Herzfeld et al. (2021) to assess global soil C dynamics. The 'partial adoption' of CA practices, which mostly refers to the adoption reduced tillage but not 675 necessarily to the diversification of the crop rotation and the soil cover management as suggested under CA, was discussed as uncertainty related to reported CA area included in the national FAO statistics (Porwollik et al., 2019;Prestele et al., 2018). Therefore, the uncertainty of the historical ecosystem services provided by historical CA cropping systems due to reporting schemes in the literature and statistics, as well as the here used model, mapping, and calculation approaches may be better reflected by the range of values obtained for the three alternative management scenarios, so that upscaling efforts of the practice need to account for differences in environmental and socio-economic conditions of cropping systems.
Further global scale modeling assessments of sustainable land management practices may include leguminous (N fixing) cover crop species, or mixes of them with the here presented grass type. Production costs associated to 685 additional irrigation water requirement and seed purchase for cover cropping (Alonso-Ayuso et al., 2020).
Opportunity costs for field activities of the farmer in otherwise off-season periods (Lee and Thierfelder, 2017) need to be evaluated in integrated assessments against the environmental benefits from cover crop practices (Blanco-Canqui et al., 2015). Further studies are needed for the quantification of cover crop impacts with climate change and to explore options for adaptation of the practice to regionally specific environmental and economic 690 conditions, influencing farming decisions and land management practices.

Conclusion
This study presents the first global temporal and spatially explicit quantification of impacts of cover crop cultivation in combination with tillage practices. The routines of cover crops implemented into LPJmL5.0-tillagecc, allow for consistent, global-scale assessments of biophysical, biogeochemical, and agronomic effects, such as 695 on mapped CA cropland during the period 1974 to 2010 and for exploring potentials of sustainable cropland management practices.
We found, that cover crops enable soil C sequestration and reduce N losses through leaching on the majority of global cropland, except in few and mostly unproductive arid regions. Cover crop with conventional tillage practices increase evapotranspiration fluxes and decrease soil N net-mineralization rates compared to bare soil 700 fallowing practices by lowering plant available soil water and nitrogen, leading to reduced growth and yield of the following main crop. Declining average yield effects due to cover crops were found for rice, but also for maize, and wheat, most pronounced for cropping areas in northern cold climatic regions. Enhanced productivities with cover crops and tillage for these three staple crops were depicted for temperate regions with high mineral N fertilizer application rates and for almost all soybean production. 705 The yield responses to altered management generated for all four crop types were rather constant over time, whereas for changes in soil N leaching rate and C sequestration pronounced temporal dynamics were found. For soil C sequestration and N leaching the sign of changes was mostly homogeneous across global cropland, whereas for productivity, the direction and magnitude of changes vary considerably among crop types and for different world regions. 710 For cover crops applied with no-tillage (CCNT), both the soil C sequestration rate and the reduction of N leaching were largest. The combined practices take advantage of the additional biomass production by cover crops and of the soil water saving effects associated to no-tillage, which results in increasing inputs to the soil, improved nutrient cycling, and substantially reduced rainfed crop yield penalties.
We conclude from the findings, that the heterogeneity of cover crop impacts on C, N, and water processes are 715 determined by the primary crop type cultivated, water regime (rainfed or irrigated), tillage and mulching practices, location, as well as management duration. This study's results demonstrate the potential role of cover crop practices as a nature based solution (Keestra et al., 2018) to transform croplands to C sinks for climate change mitigation and the reduction of environmental impacts of arable production without compromising food security targets.
Code and data availability. The LPJml5.0-tillage-cc model code version, model output data, and R-scripts used for post-processing data accompanying this study are available online at the Zenodo data repository: https://doi.org/10.5281/zenodo.5178070 (Porwollik et al., 2021).

Supplement link.
The supplement related to this article is available online at: Author contributions. VP, CM, SR, and JH designed the research. VP and CM implemented the cover crops code 725 functionalities with the support of all other authors. VP generated the CA cropland dataset and conducted the simulations. VP and CM analyzed results. VP prepared the manuscript and all co-authors contributed by commenting and editing.
Competing interests. The authors declare that they have no conflict of interest.