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The nature of the climate–carbon cycle feedback depends critically on the response of soil carbon to climate, including changes in moisture. However, soil moisture–carbon feedback responses have not been investigated thoroughly. Uncertainty in the response of soil carbon to soil moisture changes could arise from uncertainty in the relationship between soil moisture and heterotrophic respiration. We used twelve soil moisture–respiration functions (SMRFs) with a soil carbon model (RothC) and data from a coupled climate–carbon cycle general circulation model to investigate the impact of direct heterotrophic respiration dependence on soil moisture on the climate–carbon cycle feedback. Global changes in soil moisture acted to oppose temperature‐driven decreases in soil carbon and hence tended to increase soil carbon storage. We found considerable uncertainty in soil carbon changes due to the response of soil respiration to soil moisture. The use of different SMRFs resulted in both large losses and small gains in future global soil carbon stocks, whether considering all climate forcings or only moisture changes. Regionally, the greatest range in soil carbon changes across SMRFs was found where the largest soil carbon changes occurred. Further research is needed to constrain the soil moisture–respiration relationship and thus reduce uncertainty in climate–carbon cycle feedbacks. There may also be considerable uncertainty in the regional responses of soil carbon to soil moisture changes since climate model predictions of regional soil moisture changes are less coherent than temperature changes. Key Points Assess the role of soil moisture‐carbon interactions in global carbon feedbacks Assess different soil moisture‐carbon respiration relationships Asess uncertainties in global soil carbon feedbacks

PurposeFlower strips have been shown to increase insect biodiversity and improve agricultural yields through increased pollination and pest predation. Less is known about their potential to increase soil organic carbon (SOC). We aimed to investigate the biomass production and SOC sequestration potential of flower strips as a sustainable management option of temperate agricultural soils.Methods23 flower strips across varying soil types and climatic regions in Germany were sampled for aboveground and belowground peak biomass in order to estimate the annual carbon input to the soil. Those were used as 23 scenarios to model the potential SOC sequestration of the flower strips compared to a business-as-usual scenario for 1533 sites of the German Agricultural Soil Inventory using the RothC model.ResultsOn average, flower strips sequestered 0.48 ± 0.36 Mg C ha−1 year−1 in the initial 20-year period after establishment. Converting 1 % of the total German cropland area into flower strips would thus lead to a mitigation of 0.24 Tg CO2 year−1, which equals 0.4 % of current agricultural greenhouse gas emissions in Germany.We found a negative correlation between C sequestration rate and the number of plant species in the flower strips, mainly related to grasses outcompeting herbaceous species.ConclusionFlower strips are one overlooked option for increasing SOC stocks of croplands that has multiple benefits for agro-ecosystems. However, within a flower strip it might not be possible to maximise both plant biodiversity and SOC sequestration.

The impact of a changing climate on crop and tree growth remains complex and uncertain. Whilst some areas may benefit from longer growing seasons and increased CO 2 levels, others face threats from more frequent extreme weather events. Models can play a pivotal role in predicting future agricultural and forestry scenarios as they can guide decision-making by investigating the interactions of crops, trees, and the environment. This study used the biophysical EcoYield-SAFE agroforestry model to account for the atmospheric CO 2 fertilization and calibrated the model using existing field measurements and weather data from 1989 to 2021 in a case study in Northern Ireland. The study then looked at two future climate scenarios based on the representative concentration pathways (RCP 4.5 and RCP 8.5) for 2020–2060 and 2060–2100. The predicted net impacts of future climate scenarios on grass and arable yields and tree growth were positive with increasing CO 2 fertilization, which more than offset a generally negative effect of increased temperature and drought stress. The predicted land equivalent ratio remained relatively constant for the baseline and future climate scenarios for silvopastoral and silvoarable agroforestry. Greater losses of soil organic carbon were predicted under arable (1.02–1.18 t C ha −1 yr −1 ) than grassland (0.43–0.55 t C ha −1 yr −1 ) systems, with relatively small differences between the baseline and climate scenarios. However, the predicted loss of soil organic carbon was reduced in the long-term by planting trees. The model was also used to examine the effect of different tree densities on the trade-offs between timber volume and understory crop yields. To our best knowledge this is the first study that has calibrated and validated a model that accounts for the effect of CO 2 fertilization and determined the effect of future climate scenarios on arable, grassland, woodland, silvopastoral, and silvoarable systems at the same site in Europe.

Background and aims Carbon inputs to soil are mostly site- and management-nonspecific estimates based on measured yield. However, in grasslands most carbon input is root-derived and plant carbon allocation patterns are known to vary strongly across sites and management regimes. The aim here was to estimate carbon inputs by fitting the RothC model to time series of soil organic carbon (SOC) data from field sites and to explain the observed variability in root: shoot ratio (R:S). Methods Time series of SOC stocks in 15 different temperate grasslands were simulated using eight different literature-derived R:S values, which were compared to the optimised, site-specific R:S. The model-derived root inputs were validated with literature-derived root biomass data. Results A single, static R:S for yield-based carbon input estimation for all grasslands was not appropriate. Nitrogen fertilisation (R² = 0.57) significantly reduced the optimised R:S, which can be explained by the higher investment of plants in roots for nitrogen acquisition under nitrogen deficiency. The average R:S derived was 5.9 ± 1.9 for unfertilised soils and 2.4 ± 1.5 for fertilised soils. Conclusions The results enable distinction of unfertilised and fertilised temperate grasslands regarding carbon input parameterisation for the RothC model and highlight the importance of nutrient regime for the carbon cycle.

Afforestation has been considered to be the cost-effective way to sequestrate carbon (C) dioxide from the atmosphere in the soils, while concurrent responses of soil inorganic C (SIC) and soil organic C (SOC), and their turnover are still not well-defined. During the C cycle, inorganic C is enriched in heavy isotopes (13C), while organic C, due to photosynthetic fractionation, is enriched in light isotopes (12C). This leads to distinct C isotope fractionation in terrestrial ecosystems. In this study, 72 paired soils (0–20 cm) from poplar shelterbelts and adjacent farmland sites were collected in six regions (Zhaozhou, Fuyu, Dumeng, Zhaodong, Lanling, and Mingshui) of northeastern China. Five soil fractions of dissolved organic C (DOC), particulate organic matter (POM), sand and stable aggregates (S + A), silt and clay (S + C), and resistant SOC (rSOC) and bulk soils were used in C content assay and the natural δ13C determination. The results showed that, compared with SOC, poplar shelterbelts resulted in SIC accrual in the soils across all six sites; however, only half of the six sites showed SOC accrual, indicating an ambiguous effect of afforestation on SOC. The natural δ13C method could identify the SOC turnover owing to the C isotopic discrimination. The δ13C–SOC-derived turnover ratio was 23%. When SIC was included in the δ13C measurement, bulk soils and four soil fractions (S + C, S + A, rSOC, DOC) showed a 2%–10% lower turnover percentage than the δ13C–SOC-derived turnover ratios. The SIC inclusion resulted in the dependency of δ13C–TC (TC = SIC + SOC) values on SOC (negative, R2: 0.21–0.44) and SIC content (positive, R2: 0.39–0.63). By contrast, when SIC was excluded, the δ13C–SOC values were independent of them (R2 < 0.18). Redundancy ordination analysis manifested that more SOC in the soils, together with more POM and farming uses would be accompanied with the lower δ13C values. Moreover, forest characteristics (e.g., age and density) and farmland backgrounds (e.g., crop history and distance between forest and farmland) could explain differences in δ13C-related features. Our results highlighted that SIC in natural δ13C determination underestimated the C turnover ratio in general. However, SIC storage should be included in the soil C sequestration evaluation owing to a general SIC accrual pattern across regions when compared with those of SOC.

Soil organic carbon (SOC) is a major component of the global carbon cycle. This study aimed to: (i) monitor five decades’ land use/land cover (LULC) changes in the northern Nile delta using Landsat imagery; (ii) quantify baseline SOC stocks (SOCs) in 2021; (iii) project SOCs and potential SOC sequestration (PSOCS) to 2100 under four SSP2-4.5 climate scenarios using RothC model; and (iv) evaluate uncertainty in SOCs and PSOCS projections using the Monte Carlo approach. Sixty soil samples were collected during the winter and summer seasons of 2018/2019 (30 per season). Agricultural land expanded from 12% in 1972 to 35% in 2021, while fish farms, established in the 1990s, accounted for 24% of the area by 2021. SOCs varied across LULC types and seasons. Between 13 and 28% of agricultural land exceeding 7 Mg C ha−1 in summer and winter, respectively. Barren land and sabkha were characterized by low SOCs (<3 Mg C ha−1). Model predictions indicate that mean SOCs will increase from 5.83 (2021) to 6.16 (mid-century), followed by a decline to 5.96 Mg C ha−1 by 2100. Estimated PSOCS range from 0.13 to 0.32 Mg C ha−1. Monte Carlo uncertainty analysis yielded median SOCs between 6.01 and 6.27 Mg C ha−1 and median PSOCS between 0.18 and 0.44 Mg C ha−1, reflecting moderate projection uncertainty.

Soil organic carbon (SOC) stocks play an important role in ecosystem functioning and climate regulation. These stocks are declining in many tropical dry forests due to land-use change and degradation. Data on topsoil (0–300 mm) organic C stocks from six experiments conducted in the Dry Chaco region, the world’s largest dry tropical forest, were used to test the predictive performance of the Rothamsted Carbon Model (RothC) after its implementation in an object-oriented graphical programming language. RothC provided promising predictions (i.e., precise and accurate) of the SOC stocks under two representative land covers in the region, native forest and Rhodes grass [relative prediction error (RPE) < 10%, concordance correlation coefficient (CCC) > 0.9, modelling efficiency (MEF) > 0.7]. Comparatively, model predictions of the SOC stocks under degraded Rhodes grass swards were suboptimal. The predictions were sensitive to C inputs; under native forests and Rhodes grass, a high C input improved the predictive performance of the model by reducing the mean bias and increasing the MEF values, compared with mean and low C inputs. Larger datasets and revisiting some of the underlying assumptions in the SOC modelling will be required to improve the model’s performance, particularly under the degraded Rhodes grass land cover.

To combat climate change, carbon dioxide must be prevented from entering the atmosphere or even removed from it. Biochar is one potential practice to sequester carbon, but its climate change mitigation potential depends on a multitude of parameters. Differentiating areas of low and high climate change mitigation through biochar addition is key to maximize its potential and effectively use the available feedstock for its production. This study models the realistic application of 1 metric tonne (t) per hectare (ha) of forest harvest residue derived biochar over the climatically and pedologically diverse agricultural area of British Columbia, Canada, and provides a framework and assumptions for reproducibility in other parts of the world. The model accounts for the direct (input of organic carbon) and indirect (enhanced plant biomass) effects of biochar on soil organic carbon stock, its impact on nitrous oxide emissions from soils, and the avoided emissions from the reduced lime requirement due to biochar's alkalinization potential. Impacts are modelled over 20‐year time horizon to account for the duration and magnitude variation over time of biochar effect on plant biomass and nitrous oxide emissions from soil and conform to the IPCC GWP 20‐year time horizon reporting. The results show that a single application of 1 t of biochar per ha−1 can mitigate between 3 and 5 t CO2e ha−1 over a 20‐year time frame. Applied to the 746,000 ha of agricultural land of British Columbia this translate to the mitigation of a total of 2.5 million metric tonnes (Mt) CO2e over a 20‐year time frame. Further, the results identify agricultural areas in the Lower Mainland region (the southwestern corner of British Columbia) as the area maximizing climate change mitigation potential through biochar addition due to a combination of relative high temperature, high precipitation, and crops with high nitrogen requirement. This study models the application of 1 t ha−1 of forest harvest residue derived biochar over the agricultural area of British Columbia, Canada, and offers a framework for reproducibility. It accounts for the direct (input of organic carbon) and indirect (enhanced plant biomass) effects of biochar on soil organic carbon stock, its impact on nitrous oxides emissions from soils, and the reduced lime requirement due to biochar's alkalinization potential. The results show that a single application of 1 t of biochar can mitigate between 3 and 5 t CO2e per hectare.

Adoption of the farming systems that aim to sequester carbon in agricultural soils is one of the ways to mitigate global climate change. This study focuses on the estimation of organic carbon sequestration potential of the Russian croplands in the upper (0–30 cm) soil layer by creating a set of maps using the data from global and national databases as the input data. The maps are generated within the FAO Global Soil Organic Carbon Sequestration Potential Map (GSOCseq) project according to the unified methodology using the RothC model to predict the rate of carbon sequestration in 2020–2040 under a business as usual scenario (BAU), as well as under sustainable soil management scenarios with additional different C input (+5, +10, and +20%) resulting from the use of sustainable soil management (SSM) scenarios. The total potential sequestration rate by the croplands of the Russian Federation in the 0–30-cm layer under a BAU scenario is assessed at 8.5 Mt/year and the estimate under SSM scenarios, up to 25.5 Mt/year. The carbon sequestration by the cropland of each soil ecological zone (except for the light chestnut (Eutric Cambisols (Protocalcic)) and brown semidesert (Luvic Calcisols) soils, where it is around zero) and on a national scale is positive. The Altai krai, Omsk oblast, Novosibirsk oblast, and Krasnoyarsk krai have the greatest potential for sequestration. Several subjects of the Russian Federation—Krasnodar krai, Republic of Crimea, Rostov oblast, Primorskii krai, Republic of Adygea, and Kaliningrad oblast—demand the adoption of sustainable management of soil resources.

Salinity is a key abiotic stress limiting crop growth. Accurate quantification of carbon budgets and their environmental responses is critical for sustainable cotton production, yet regional-scale assessments remain scarce. To clarify the evolutionary patterns and driving mechanisms of soil organic carbon (SOC) in saline cotton fields of arid Central Asia, this study focused on Xinjiang and modified the RothC model by integrating salinity adjustment factors and vegetation carbon decomposition indices, simulating SOC dynamics (1980–2022) with multi-source data. Results showed the improved model achieved high accuracy in capturing SOC dynamics in salinized cotton fields. Spatially, SOC exhibited high levels south of the Tianshan Mountains and low levels in southwestern Xinjiang; temporally, it showed an overall fluctuating upward trend, though both high- and low-value zones displayed localized declines. Geodetector analysis revealed fertilizer application as the primary driver of SOC spatial variation, followed by straw return, precipitation, and temperature, with most factors showing synergistic enhancement effects. Human management (fertilization and straw return) is the core regulator of SOC, and its synergy with natural factors shapes SOC spatiotemporal patterns. The salinization-adapted RothC model provides a novel framework for arid cotton field SOC simulation, offering scientific support for carbon pool optimization and sustainable agriculture in arid regions.