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Aggregate size distributionAs hypothesized, the improved soil aggregation was observed under ICL, which is attributed to the presence of animals resulting in higher organic matter contents of total C and N fractions that can significantly enhance soil health over time32. Moreover, well-aggregated soils as observed under ICL ( > 4 mm) at site 1 and NE (2–4 mm) at site 2 have a greater potential of retaining their structure and may have higher macropores, which facilitate sustained root growth than soils with low aggregation such as under CNT (corn–soybean without grazing or CC) in this study. It also explains the significance of ICL systems with no-tillage and undisturbed grassland, where the formation of stable macroaggregates may occur as a result of incorporation of plant residues, stimulation of root exudates and increased biological activity. Furthermore, it was noticed that ICL system not only enhanced the macroaggregates but accentuated the presence of microaggregates due to persistent binding agents, which are critical in SOC protection against microbial decomposition. When integrating grazing livestock into crop rotation, soil aggregation is typically improved under moderate and controlled grazing than the high intensity grazing systems33. Compared to the long-term sites ( > 30 years), short-term site 4 did not result in discernible effects of grazing or CC on soil aggregation. However, within this short-term study, grazed pasture mix was able to enhance aggregation of 1–2 and 2–4 mm sized aggregates compared with oats, oats with CC, oats with CC and grazing. To observe the influence of CC and grazing on  > 4 mm or  4 mm) under ICL at site 1 resulted in 1.3–1.5 times significantly higher SOC concentration than NE and CNT. The greater concentration of SOC and N in ICL and NE is attributed to the lack of soil disturbance, crop residue retention, and rhizodeposition, which reduces macroaggregate turnover rate14. At site 3, NE enhanced aggregate-associated C and N concentrations and performed significantly better than both ICL and CNT treatments. The higher C and N accrual in the NE than ICL and CNT, especially at site 3, can be due to massive root systems, long-term establishment and absence of cultivation, which contributes to enhanced soil quality, while reducing nutrient vulnerability to loss by oxidation18,36. For short-term study at site 4, insignificant differences in aggregate-associated SOC suggested that longer study period of at least  > 5 years is required for SOC to respond to grazing and cover crop management. The higher total N under ICL and NE can also be due to the presence of legumes, and brassicas in CC, which are effective at recycling N and may have helped in scavenging N.An overall increase in C and N cycling under ICL and NE systems has been attributed to ingested pasture being converted into urine and manure. Under these systems, livestock catalyze nutrient cycling by breakdown of complex plant molecules, greater soil incorporation and decomposition of plant residues and soil organic matter, which can maintain or even improve soil fertility by production of organic acids such as fulvic and humic acids6,8,19. Moreover, grazing stimulates the carbohydrate exudation from grass roots, which is mostly composed of polysaccharides, a C-O alkyl source37. The enhanced C concentration under ICL and NE can also be associated with higher MWD. Integrated system cool-season pasture and winter CC had significantly higher total C and N than the non-integrated continuous corn in previous study6. The results from another integrated system study7 showed that soybean and oat-Italian ryegrass CC increased total C (1.16 Mg ha−1 yr−1) and N stocks (0.12 Mg ha−1 yr−1) under 7 year study period. It is previously reported that ICL system contains labile organic matter pools10,38, subsequently showing higher C stocks and greater root densities near soil-surface, which promotes aggregate-associated C stabilization18,39,40, higher infiltration rates, thus providing likely benefits to soil function linked to erosion control and soil water relations41.Soil microbial community compositionTotal bacterial biomass, AM fungi, and PLFA were enhanced under NE, which can be result of accumulation of organic residues and higher pasture root mass7,32, pasture being grazed can promote exudation of organic compounds by roots, serving as energy sources for microorganisms. The consistent increase in microbial population under NE can also be result of increased SOC and N, however, the same does not hold true for ICL system, where despite observing greater SOC and N, a significant decrease in the microbial population at site 2 was noticed. The enhanced total PLFA under NE system at site 2 is due to concomitant increase in AM fungi, gram (−), fungal/bacterial ratio, and total bacterial biomass compared to ICL. The fungal to bacterial ratio was reduced under ICL compared to NE at sites 1 and 2, pertaining to relatively low abundance of the fungal fatty acid 18:2ɷ6 in grazed system as compared to unmanaged grassland. This finding corroborates the notion that livestock-grazing systems contain bacterial-based decomposition channels and are mostly dominated by gram (+) bacteria and that the fungal population is comparatively more important in decomposer food-webs of native grasslands. These results coincide with previous studies42,43. Moreover, the increase in fungal to bacterial ratios under NE system in contrast to ICL at sites 1 and 2 can relate to modifications in soil health with C sequestration, as fungal populations incline towards higher C assimilation proficiencies and greater storage of metabolized C than bacterial populations9,44. The grazing intensity also plays a significant role in bacterial and fungal presence. It is previously reported that high grazing intensity had greater bacterial PLFA concentration than the low grazing counterparts in grassland systems45. It is considered that under heavily grazed sites in grasslands, bacteria-based energy channels of decomposition dominate other microbial communities, while fungi can successfully enable decomposition in both slightly grazed and non-grazed systems43. Grazed pasture mix at short-term study site 4 showed 12–21% higher total PLFA than the oats, oats with CC, oats with CC + grazing systems. It is also possible that this increased total PLFA at site 4 under grazed pasture mix contributed to enhancing the 1–2 and 2–4 mm sized aggregates compared to other treatments. It indicated that though physicochemical properties can take longer ( > 8–15 years) in significantly responding to changes in management systems, soil microbial community and structure may show a rapid response (~ 3 years), thus it can be used as an early indicator while assessing the variations in soil health18,46.Overall, NE exemplified the undisturbed grazed mixture with a greater microbial population at sites 1, 2, and 3, when compared to other agricultural systems. Our findings coincide with previous studies where pasture systems performed better than the agricultural soil, in terms of, showing greater microbial biomass and fatty acid signatures related to bacterial and fungal populations, which is mostly attributed to greater surface coverage and absence of tillage practices in pasture systems9,47,48. Lower soil microbial communities under ICL system than native Cerrado pasture have been found previously because of reduced soil porosity and macropore continuity resulting in restricted gas diffusion and water movement18.Although the AM fungi abundance was not significant for sites 3 and 4, and significantly lower for ICL system than NE at sites 1 and 2, it should be taken into consideration that FAME analysis cannot reflect species-level changes for fungi and/or bacteria and the variations in microbial community structure for ICL system can be due to changes in abundance and distribution among microbial groups. For example, in a previous study9, while increased bacterial population was observed for continuous cotton compared to the ICL system, however, pyrosequencing for bacterial diversity assessment demonstrated disparities between both systems, where greater Proteobacteria was seen under ICL system than continuous cotton. Numerous factors such as degree of disturbance, pH level, bulk density, porosity, soil water content, C and N distribution, and residue positioning regulate the amount of bacterial and fungal biomass in agroecosystems18,49. Arbuscular mycorrhizal fungi are responsible for formation of macroaggregates ( > 0.25 mm) by producing a glycoprotein called glomalin, which is present abundantly in natural and agricultural systems. However, increased grazing intensity, use of excess fertilizers and fungicides can directly or indirectly reduce mycorrhizal population by influencing soil organisms accountable for converting soil organic matter into plant nutrients38. Animals may also cause moderate soil compaction affecting the fungal biodiversity and soil pore space6,38.Relationship among measured soil propertiesBased on PCA, it is derived that integrated crop–livestock and natural ecosystem of native grassland can provide substrate for the microbial composition and enhance aggregate-associated C and N fractions. A positive correlation between SOC and microbial communities suggested the inclination of microbes to affect SOC and N turnover and vice-versa through interaction with crop–livestock grazing, vegetation, and soil properties. Fungi exhibited insignificant responses to changes in soil pH and bulk density than bacteria because chitinous cell walls make fungi more resistant and resilient to variations in soil conditions50,51. A reduction in gram (−) bacteria may indicate the presence of stressed soil conditions due to pH and increased bulk density, which has previously been observed in other studies52,53. 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Tropical forest restoration areaTo determine the geographic distribution of land available for tropical forest restoration, we used a widely applied global forest restoration map2. This dataset limits potential restoration area to regions that are biogeophysically suitable for forest, and excludes croplands. To define the tropics, we masked the potential restoration map with the following three ecoregions from the Ecoregions2017 vegetation map34: ‘Tropical and Subtropical Moist Broadleaf Forests’, ‘Tropical and Subtropical Dry Broadleaf Forests’, and ‘Tropical and Subtropical Coniferous Forests’. The resulting restoration mask includes all tropical and subtropical forest ecoregions with some that are outside the tropical latitudes, but excludes wetlands and high mountain areas (Extended Data Fig. 4). The restoration mask was converted from a presence–absence raster at its native ~350 m resolution to a 0.5° geographical grid by aggregating to the fraction of each 0.5° grid cell available for restoration. Any uncertainties in the allocation of restorable area, distinguishing crop and pasture, and forest to non-forest classification from the original forest restoration map were also implicitly included in our restoration extent. While the resulting restoration area is relatively small, its spatial distribution is representative for most of the humid tropics.To prioritize for carbon uptake capacity, we selected all grid cells with restoration area greater than 1 ha and ranked these by carbon storage density (above ground and below ground; g m−2) at 2100 under the default scenario. We then selected the top n grid cells with greatest carbon density until cumulatively 64 Mha of restored area was reached. Similarly, for cost we calculated the restoration cost for each grid cell following ref. 27 and sorted the grid cells by their cost, beginning with the lowest value, until 64 Mha were reached. To consider the combined impact of carbon uptake and restoration costs, we divided our restoration cost layer by the total carbon uptake per grid cell from restoration and ranked the cost per carbon uptake from cheapest to most expensive, selecting the n grid cells with the lowest values until 64 Mha were reached. We then used the selected grid cells to mask carbon uptake under the various climate change and CO2 fertilization scenarios. To factor in climate change in the prioritization process, we used the same restoration cost layer but used the carbon density and total carbon uptake layers with climate change impacts in CO22014 for the year 2100.Vegetation modelWe used the LPJ-LMfire DGVM19, a version of the Lund-Potsdam-Jena DGVM (LPJ)35. LPJ-LMfire is driven by gridded fields of climate, soil texture and topography at 0.5° resolution, and with a time series of atmospheric CO2 concentrations (see Supplementary Information). To simulate land use, LPJ-LMfire separates grid cells into fractional tiles of ‘unmanaged’ land that has never been under land use, ‘managed’ land, and areas ‘recovering’ from land use36. Restoration removes land from the ‘managed’ tile and transfers it to the ‘recovering’ tile; land is never reallocated to the ‘unmanaged’ tile. The tiles are treated differently with respect to wildfire: on the ‘unmanaged’ and ‘recovering’ tiles, lightning-ignited wildfires are not suppressed, while fire is excluded from ‘managed’ tiles. For our analysis of total carbon (above and below ground), we only used the ‘recovering’ tile.Climate dataClimate forcing used to drive LPJ-LMfire comes from the output of 13 GCMs in simulations produced for the CMIP6 Supplementary Table 2 (refs. 37,38). For each GCM, we obtained simulations for the historical period (1850–2014) and four future SSPs (SSP1-26, SSP2-45, SSP3-70 and SSP5-85 covering 2015–2100). We used only GCMs that archived all seven climate variables needed to run LPJ-LMfire: 2 m temperature (tas, K), precipitation (pr, kg m−2 s−1), convective precipitation (prc, kg m−2 s−1), cloud cover (clt, %), minimum and maximum daily temperature (tmin, tmax, K), and 10 m surface wind speed (sfcWind, m s−1) (Supplementary Fig. 2). For each model, we concatenated the historical simulation with a future scenario, calculated anomalies with respect to 1971–1990 and added those to observed 30 year climatologies to create bias-corrected monthly climate time series covering 1850–2100 (see Supplementary Information). Where multiple ensemble members were available from a GCM, we chose the first simulation.Simulation protocolWe drove LPJ-LMfire with the GCM simulations described in the previous section, and the same atmospheric CO2 concentrations and land use boundary conditions as those used in the CMIP6 simulations. All forcings cover the historical period (1850–2014) and the individual future SSPs (2015–2100). Each LPJ-LMfire simulation was initialized for 1,020 years with 1850 atmospheric CO2 and land use, and the 1850s climatology of each CMIP6 GCM. This was followed by simulations with transient climate from 1850 to 2100 for each CMIP6 GCM under each of the four SSPs. For each the 13 CMIP6 GCMs running each of the SSP scenarios, we conducted two CO2 experiments (CO22014 and CO2free) and two fire experiments. In total, we ran 221 vegetation model simulations covering the range of future climate, CO2 and fire scenarios.Atmospheric CO2 in these simulations either followed the CMIP6 historical and SSP trajectory for the entire 1850–2100 run (CO2free), or followed the historical CMIP6 trajectory until 2014, and was then fixed at 2014 concentrations for the remainder of the simulation (CO22014). This allowed us to test the vegetation response to future climate change in the absence of additional CO2 fertilization of photosynthesis. Our simulations ended with the standard SSP projections in 2100, 80 years after restoration begins. We therefore could not assess the fate of restored carbon beyond that point. On the basis of the trends in the multi-model mean carbon uptake rates, we estimated that only under severe climate change will carbon storage be reduced shortly after 2100 in CO22014.In control simulations, land use followed the historical CMIP6 trajectory until 2014, after which it was fixed under 2014 conditions until 2100. Land use after 2014 was fixed at 2014 levels because it is the last year with common land use between all scenarios, which allowed us to identify future climate change impacts on restoration permanence and avoid influences from land abandonment and expansion prescribed in the different SSP scenarios.In the restoration experiments, land use also followed the historical CMIP6 trajectory until 2014, but then diverged: cropland extent remained at 2014 levels until 2100, while pasture (or non-cropland land use) remained constant from 2014 to 2020 and was then linearly reduced by the restoration area from 2020 to 2030. From 2030, land use remained constant at that lower level until 2100. The amount of restoration in a grid cell was limited by the pasture area, that is, once all of the available pasture area had been restored, no additional restoration took place. Because it is highly unlikely to be practical to restore the entire target area of tropical forest at once, we linearly increased the restoration area from 2020 to 2030, which caused an expansion-driven increase in carbon uptake over the 11 year period (Extended Data Fig. 1). This means that two factors controlled carbon uptake over time in our experimental design: first the expansion of the restoration area, accounting for approximately 19.7 Pg C, and second the long-term effect of carbon accumulation (Extended Data Fig. 5).Primary climate change impacts, such as drought and heat stress that reduce carbon uptake, were implicitly included in the climate forcing data, while secondary climate change impacts from wildfire were simulated by LPJ-LMfire on the basis of climate. To quantify the contribution of wildfire on the carbon storage from restoration, we repeated the simulations described above with fires turned off in LPJ-LMfire.Restoration opportunity indexWe created a restoration opportunity index to evaluate the suitability of locations for restoration on the basis of the ability for restoration to result in net carbon uptake over 2020–2100 and to store this carbon without episodes of major loss. For each of the 13 realizations of the four SSPs in the CO22014 experiment, we identified all restoration grid cells (1) that had a net carbon uptake by 2100 relative to 2030, and (2) where temporal reductions in total carbon storage over 2030–2100 were More