How to measure, report and verify soil carbon change to realize the potential of soil carbon sequestration for atmospheric greenhouse gas removal

Carbon stock quantification in a floodplain restoration chronosequence along a Mediterranean-montane riparian corridor

Uncertainty in the global carbon (C) budget has been reduced for most stocks, though it remains incomplete by not considering aquatic and transitional zone carbon stocks. A key issue preventing such complete accounting is a lack of available C data within these aquatic and aquatic-terrestrial transitional ecosystems. Concurrently, quantifiable results produced by restoration practices that explicitly target C stock accumulation and sequestration remain inconsistent or undocumented. To support a more complete carbon budget and identify impacts on C stock accumulation from restoration treatment actions, we investigated C stock values in a Mediterranean-montane riparian floodplain system in California, USA. We quantified the C stock in aboveground biomass, large wood, and litter in addition to the C and total nitrogen in the upper soil profile (5 cm) across 23 unique restoration treatments and remnant old-growth forests. Treatments span 40 years of restoration actions along seven river kilometers of the Cosumnes River, and include process-based (limited intervention), assisted (horticultural planting and other intensive restoration activities), hybrid (a combination of process and assisted actions), and remnant (old-growth forests that were not created with restoration actions) sites. Total C values measured up to 1100 Mg ha-1 and averaged 129 Mg ha-1 with biomass contributing the most to individual plot measurements. From 2012 to 2020, biomass C stock measurements showed an average 32 Mg ha-1 increase across all treatments, though treatment specific values varied. While remnant forest plots held the highest average C values across all stocks (336 Mg ha-1), C values of different stocks varied across treatment type. Process-based restoration treatments held more average biomass C (120 Mg ha-1) than hybrid (23 Mg ha-1) or assisted restoration treatments (50 Mg ha-1), while assisted restoration treatments held more average total C in soil and litter (58 Mg ha-1) than hybrid (35 Mg ha-1) and process-based restoration treatments (37 Mg ha-1). Regardless of treatment type, time was a significant factor for all C stock values. These findings support a more inclusive global carbon budget and provide valuable insight into restoration treatment actions that support C stock accumulation.

Topsoil dilution by subsoil admixture had less impact on soil organic carbon stock development than fertilizer form and erosion state

Enhancing the agroecosystems carbon (C) sink function for climate mitigation faced challenges, particularly with traditional measures with limited suitability for increasing soil organic carbon (SOC) stocks. Inducing a SOC undersaturation in the topsoil by abrupt subsoil admixture is a way to create an additional C sink. However, the deep tillage traditionally used for this topsoil dilution was not always successful. It was due to a lack of knowledge and suitable approaches to record the effect of all relevant factors in SOC recovery, including soil conditions and fertilizer forms. We addressed these problems by establishing a three-factorial experiment: I) "moderate topsoil dilution," II) "N fertilization form," and III) "soil erosion state," representing three soil types in the hummocky ground moraine landscape of NE Germany. SOC dynamics were determined over a year of winter rye cropping using a novel robotic chamber system capable of measuring CO2 exchange on 36 experimental plots with a reduced methodological bias than previous measuring systems. The averaged net ecosystem carbon balance, a proxy for SOC stock change, indicated that topsoil dilution only reduced further SOC losses. The N fertilizer form had a significantly stronger and more differentiated effect. While the mineral N fertilization consistently produced only C sources, the organic fertilization, in combination with the diluted topsoil, led to a C sink. This C-sink function was, however, more pronounced in the eroded soil than in the non-eroded soil. Overall, the results have made clear that the impact of topsoil dilution on the further development of the SOC stock is only possible if the effect of other relevant factors, such as N fertilizer form and erosion state, are taken into account.

Comparison of direct and indirect soil organic carbon prediction at farm field scale

To advance sustainable and resilient agricultural management policies, especially during land use changes, it is imperative to monitor, report, and verify soil organic carbon (SOC) content rigorously to inform its stock. However, conventional methods often entail challenging, time-consuming, and costly direct soil measurements. Integrating data from long-term experiments (LTEs) with freely available remote sensing (RS) techniques presents exciting prospects for assessing SOC temporal and spatial change. The objective of this study was to develop a low-cost, field-based statistical model that could be used as a decision-making aid to understand the temporal and spatial variation of SOC content in temperate farmland under different land use and management. A ten-year dataset from the North Wyke Farm Platform, a 20-field, LTE system established in southwestern England in 2010, was used as a case study in conjunction with an RS dataset. Linear, additive and mixed regression models were compared for predicting SOC content based upon combinations of environmental variables that are freely accessible (termed open) and those that require direct measurement or farmer questionnaires (termed closed). These included an RS-derived Ecosystem Services Provision Index (ESPI), topography (slope, aspect), weather (temperature, precipitation), soil (soil units, total nitrogen [TN], pH), and field management practices. Additive models (specifically Generalised Additive Models (GAMs)) were found to be the most effective at predicting space-time SOC variability. When the combined open and closed factors (excluding TN) were considered, significant predictors of SOC were: management related to ploughing being the most important predictor, soil unit (class), aspect, and temperature (GAM fit with a normalised RMSE = 9.1%, equivalent to 0.4% of SOC content). The relative strength of the best-fitting GAM with open data only, which included ESPI, aspect, and slope (normalised RMSE = 13.0%, equivalent to 0.6% of SOC content), suggested that this more practical and cost-effective model enables sufficiently accurate prediction of SOC.

A systematic analysis and review of soil organic carbon stocks in urban greenspaces

Urban greenspaces typically refer to urban wetland, urban forest and urban turfgrass. They play a critical role in carbon sequestration by absorbing carbon from the atmosphere; however, their capacity to retain and store carbon in the form of soil organic carbon (SOC) varies significantly. This study provides a systematic analysis and review on the capacity of different urban greenspace types in retaining and storing SOC in 30 cm soil depth on a global scale. Data came from 78 publications on the subject of SOC stocks, covering different countries and climate zones. Overall, urban greenspace types exerted significant influences on the spatial pattern of SOC stocks, with the highest value of 18.86 ± 11.57 kg m-2 (mean ± standard deviation) in urban wetland, followed by urban forest (6.50 ± 3.65 kg m-2), while the lowest mean value of 4.24 ± 3.28 kg m-2 was recorded in urban turfgrass soil. Soil organic carbon stocks in each urban greenspace type were significantly affected by climate zones, management/environmental settings, and selected soil properties (i.e. soil bulk density, pH and clay content). Furthermore, our analysis showed a significantly negative correlation between SOC stocks and human footprint in urban wetland, but a significantly positive relationship in urban forest and urban turfgrass. A positive correlation between SOC stocks and human footprint indicates that increased human activity and development can enhance SOC stocks through effective management and green infrastructure. Conversely, a negative correlation suggests that improper management of human activities can degrade SOC stocks. This highlights the need for sustainable practices to maintain or enhance SOC accumulation in urban greenspaces.

Potential benefits of cropping pattern change in the climate-sensitive regions of rice production in China

Rice production is a primary contributor to global greenhouse gas emissions, with unclear pathways towards carbon neutrality. Here, through a comprehensive assessment of direct greenhouse gas (GHG) emission using DNDC model and indirect GHG emission using emission factor methods, we estimated the annual crop yield, GHG emission amount and intensity, and economic benefits of different cropping patterns in the climate-sensitive regions of rice production in China. Through the expansion of single-rice and cropping pattern change from the wheat-rice to wheat-rice-rice in the climate-sensitive regions of single and triple-cropping cultivations, the total grain yield increased by 4.4 % and 4.5 % compared with the current national grain production, the GHG emission would increase by 2.4 % and 5.4 % of the current national GHG emissions from rice and wheat production, the net economic benefits could increase 0.9 % and decrease 2.0 % of the national output value of rice and wheat production. The study takes the entire-life cycle of crop growth as the principal line, and could provide a valuable reference for the regulation of the cropping pattern and the formulation of carbon reduction policies in the climate-sensitive region.

Scalable solution for agricultural soil organic carbon measurements using laser-induced breakdown spectroscopy

Effective verification of soil organic carbon (SOC) improvement interventions through soil carbon sequestration (SCS) requires robust methodologies to measure, report, and verify changes in soil carbon (C) levels. Furthermore, soil C must be monitored over time to ensure that sequestered C is not being re-emitted, thus ensuring the permanence of C removals. The traditional methods for soil C measurement are time-consuming, labor-intensive, and energy-intensive, increasing analysis costs. In this article, we verify the use of a commercially available laser-induced breakdown spectroscopy analyzer, the LaserAg-Quantum, coupled with the recursive feature addition, the gradient-boosted decision trees regression model, and the novelty detection model to predict C in soils. The developed method shows promising performance with an average limit of quantification of 0.75% of C and a precision of 4.10%. Accuracy metrics, including R2, mean absolute error, and root mean square error, yielded values of 0.81, 0.27%, and 0.37% for the validation dataset. Additionally, around 10% of validation samples after the novelty detection model exhibited relative error greater than 30%. Finally, our findings demonstrate the potential of the LaserAg-Quantum process to support measuring SOC in agricultural soils on a large scale.

Importance of Terrain and Climate for Predicting Soil Organic Carbon Is Highly Variable across Local to Continental Scales

Soil organic carbon (SOC) plays a vital role in global carbon cycling and sequestration, underpinning the need for a comprehensive understanding of its distribution and controls. This study explores the importance of various covariates on SOC spatial distribution at both local (up to 1.25 km) and continental (USA) scales using a deep learning approach. Our findings highlight the significant role of terrain attributes in predicting SOC concentration distribution with terrain, contributing approximately one-third of the overall prediction at the local scale. At the continental scale, climate is only 1.2 times more important than terrain in predicting SOC distribution, whereas at the local scale, the structural pattern of terrain is 14 and 2 times more important than climate and vegetation, respectively. We underscore that terrain attributes, while being integral to the SOC distribution at all scales, are stronger predictors at the local scale with explicit spatial arrangement information. While this observational study does not assess causal mechanisms, our analysis nonetheless presents a nuanced perspective about SOC spatial distribution, which suggests disparate predictors of SOC at local and continental scales. The insights gained from this study have implications for improved SOC mapping, decision support tools, and land management strategies, aiding in the development of effective carbon sequestration initiatives and enhancing climate mitigation efforts.

Importance of on-farm research for validating process-based models of climate-smart agriculture

Climate-smart agriculture can be used to build soil carbon stocks, decrease agricultural greenhouse gas (GHG) emissions, and increase agronomic resilience to climate pressures. The US recently declared its commitment to include the agricultural sector as part of an overall climate-mitigation strategy, and with this comes the need for robust, scientifically valid tools for agricultural GHG flux measurements and modeling. If agriculture is to contribute significantly to climate mitigation, practice adoption should be incentivized on as much land area as possible and mitigation benefits should be accurately quantified. Process-based models are parameterized on data from a limited number of long-term agricultural experiments, which may not fully reflect outcomes on working farms. Space-for-time substitution, paired studies, and long-term monitoring of SOC stocks and GHG emissions on commercial farms using a variety of climate-smart management systems can validate findings from long-term agricultural experiments and provide data for process-based model improvements. Here, we describe a project that worked collaboratively with commercial producers in the Midwest to directly measure and model the soil organic carbon (SOC) stocks of their farms at the field scale. We describe this study, and several unexpected challenges encountered, to facilitate further on-farm data collection and the creation of a secure database of on-farm SOC stock measurements.

A synthesis of the evidence regarding the efficacy of alternative field management practices in rice cultivation using life cycle assessment

Field management practices are an important factor in mitigating climate change and increasing agricultural production. However, no study has synthesized the evidence on the efficacy of alternative field management practices and reviewed life cycle assessments that consider all emissions over the entire or part of the life cycle of rice production. Thus, 68 papers were reviewed and grouped into 13 field management categories. The management practices were evaluated in terms of yield, area-scaled greenhouse gas (GHG) emissions, and yield-scaled GHG emissions against base management practices. The yield increase of these field management practices was between -6 % and 12 %, with some exceptions. It was only nonpuddling that simultaneously increased the yield and reduced both the area-scaled GHG and yield-scaled GHG emissions with respect to the base category. Water management, including alternate wetting and drying or single and multiple drainage and no-tillage, performed well in reducing the average area-scaled GHG and yield-scaled GHG emissions, although the average yield was reduced slightly. For the remaining many management practices, the increase in area-scaled GHG emissions was larger than the change in yield, so the yield change had little impact on yield-scaled GHG emissions. The higher increase in area-scaled GHG emissions than the change in yield requires innovative and new approaches, such as implementing alternative management together with water management, of which the effect was observed in some of the papers reviewed in this study. Therefore, this study recommends selecting nonpuddling, water management or no-tillage as climate mitigation management options. The evaluation of field management will be more robust if more impact categories are considered, including missing aspects (i.e., soil fertility).

Using process-oriented model output to enhance machine learning-based soil organic carbon prediction in space and time

Monitoring and modelling soil organic carbon (SOC) in space and time can help us to better understand soil carbon dynamics and is of key importance to support climate change research and policy. Although machine learning (ML) has attracted a lot of attention in the digital soil mapping (DSM) community for its powerful ability to learn from data and predict soil properties, such as SOC, it is better at capturing soil spatial variation than soil temporal dynamics. By contrast, process-oriented (PO) models benefit from mechanistic knowledge to express physiochemical and biological processes that govern SOC temporal changes. Therefore, integrating PO and ML models seems a promising means to represent physically plausible SOC dynamics while retaining the spatial prediction accuracy of ML models. In this study, a hybrid modelling framework was developed and tested for predicting topsoil SOC stock in space and time for a regional cropland area located in eastern China. In essence, the hybrid model uses predictions of the PO model in unsampled years as additional training data of the ML model, with a weighting parameter assigned to balance the importance of SOC values from the PO model and real measurements. The results indicated that temporal trends of SOC stock modelled by PO and ML models were largely different, while they were notably similar between the PO and hybrid models. Cross-validation showed that the hybrid model had the best performance (RMSE = 0.29 kg m-2), with a 19 % improvement compared with the ML model. We conclude that the proposed hybrid framework not only enhances space-time soil carbon mapping in terms of prediction accuracy and physical plausibility, it also provides insights for soil management and policy decisions in the face of future climate change and intensified human activities.

Toward Low-Carbon Rice Production in China: Historical Changes, Driving Factors, and Mitigation Potential

Under the "Double Carbon" target, the development of low-carbon agriculture requires a holistic comprehension of spatially and temporally explicit greenhouse gas (GHG) emissions associated with agricultural products. However, the lack of systematic evaluation at a fine scale presents considerable challenges in guiding localized strategies for mitigating GHG emissions from crop production. Here, we analyzed the county-level carbon footprint (CF) of China's rice production from 2007 to 2018 by coupling life cycle assessment and the DNDC model. Results revealed a significant annual increase of 74.3 kg CO2-eq ha-1 in the average farm-based CF (FCF), while it remained stable for the product-based CF (PCF). The CF exhibited considerable variations among counties, ranging from 2324 to 20,768 kg CO2-eq ha-1 for FCF and from 0.36 to 3.81 kg CO2-eq kg-1 for PCF in 2018. The spatiotemporal heterogeneities of FCF were predominantly influenced by field CH4 emissions, followed by diesel consumption and soil organic carbon sequestration. Scenario analysis elucidates that the national total GHG emissions from rice production could be significantly reduced through optimized irrigation (48.5%) and straw-based biogas production (18.0%). Moreover, integrating additional strategies (e.g., advanced crop management, optimized fertilization, and biodiesel application) could amplify the overall emission reduction to 76.7% while concurrently boosting the rice yield by 11.8%. Our county-level research provides valuable insights for the formulation of targeted GHG mitigation policies in rice production, thereby advancing the pursuit of carbon-neutral agricultural practices.

Global Responses of Soil Carbon Dynamics to Microplastic Exposure: A Data Synthesis of Laboratory Studies

Microplastics (MPs) contamination presents a significant global environmental challenge, with its potential to influence soil carbon (C) dynamics being a crucial aspect for understanding soil C changes and global C cycling. This meta-analysis synthesizes data from 110 peer-reviewed publications to elucidate the directional, magnitude, and driving effects of MPs exposure on soil C dynamics globally. We evaluated the impacts of MPs characteristics (including type, biodegradability, size, and concentration), soil properties (initial pH and soil organic C [SOC]), and experimental conditions (such as duration and plant presence) on various soil C components. Key findings included the significant promotion of SOC, dissolved organic C, microbial biomass C, and root biomass following MPs addition to soils, while the net photosynthetic rate was reduced. No significant effects were observed on soil respiration and shoot biomass. The study highlights that the MPs concentration, along with other MPs properties and soil attributes, critically influences soil C responses. Our results demonstrate that both the nature of MPs and the soil environment interact to shape the effects on soil C cycling, providing comprehensive insights and guiding strategies for mitigating the environmental impact of MPs.

Afforestation enhances glomalin-related soil protein content but decreases its contribution to soil organic carbon in a subtropical karst area

Afforestation on degraded croplands has been proposed as an effective measure to promote ecosystem functions including soil organic carbon (SOC) sequestration. Glomalin-related soil protein (GRSP) plays a crucial role in promoting the accumulation and stability of SOC. Nevertheless, mechanisms underlying the effects of afforestation on GRSP accumulation have not been well elucidated. In the present study, 14 pairs of maize fields and plantation forests were selected using a paired-site approach in a karst region of southwest China. By measuring soil GRSP and a variety of soil biotic and abiotic variables, the pattern of and controls on GRSP accumulation in response to afforestation were explored. The average content of total GRSP (T-GRSP) and its contribution to SOC in the maize field were 5.22 ± 0.29 mg g-1 and 42.33 ± 2.25%, and those in the plantation forest were 6.59 ± 0.32 mg g-1 and 25.77 ± 1.17%, respectively. T-GRSP content was increased by 26.4% on average, but its contribution to SOC was decreased by 39.1% following afforestation. T-GRSP content decreased as soil depth increased regardless of afforestation or not. Afforestation increased T-GRSP indirectly via its positive effects on arbuscular mycorrhizal fungi biomass, which was stimulated by afforestation through elevating fine root biomass or increasing the availability of labile C and N. The suppressed contribution of T-GRSP to SOC following afforestation was due to the relatively higher increase in other SOC components than T-GRSP and the significant increase of soil C:N ratio. Our study reveals the mechanisms underlying the effects of afforestation on T-GRSP accumulation, and is conducive to improving the mechanistic understanding of microbial control on SOC sequestration following afforestation.

Co-benefits for net carbon emissions and rice yields through improved management of organic nitrogen and water

Returning organic nutrient sources (for example, straw and manure) to rice fields is inevitable for coupling crop-livestock production. However, an accurate estimate of net carbon (C) emissions and strategies to mitigate the abundant methane (CH4) emission from rice fields supplied with organic sources remain unclear. Here, using machine learning and a global dataset, we scaled the field findings up to worldwide rice fields to reconcile rice yields and net C emissions. An optimal organic nitrogen (N) management was developed considering total N input, type of organic N source and organic N proportion. A combination of optimal organic N management with intermittent flooding achieved a 21% reduction in net global warming potential and a 9% rise in global rice production compared with the business-as-usual scenario. Our study provides a solution for recycling organic N sources towards a more productive, carbon-neutral and sustainable rice-livestock production system on a global scale.

Modification of the RothC model to evaluate the inconsistent effect of conservation tillage on SOC stock and a suggestion of a national-scale assessment framework

Simulation of conservation tillage effect on soil organic carbon (SOC) stock on the national scale is essential for Tier 3 level greenhouse gas inventory in the agricultural sector. However, the conservation tillage effects varied depending on different soil conditions, potentially leading to inaccurate national assessments. This study aimed to propose a framework for estimating the national scale impact of conservation tillage on SOC. As even in the most commonly used SOC dynamic model, the Rothamsted Carbon Model (RothC), does not reflect the conservation tillage effect in an explicit way, we modified it by developing the tillage rate modifiers (TRMs). First, we investigated the conditions for the inconsistent conservation tillage effects using the decision tree analysis based on 210 field experiment data from the mid-latitude region. The results highlighted that soil sand content and the existing SOC stock were the main factors driving the inconsistencies. After we categorized into four distinctive conditions, the TRMs for each condition were parameterized using a genetic algorithm. The average TRMs were 0.88 in the soils with sand content >37.6 % and 1.58 in the soils with sand content ≤37.6 %, indicating that conservation tillage is more effective in coarse-textured soil, and there is a risk of decreasing SOC stock in the latter condition. Using the modified RothC model, a three-step national-scale simulation framework was suggested: compiling country-specific data, establishing baseline and conservation tillage scenarios, and modeling conservation tillage effects with uncertainty analysis. Our approach also defined the maximum conservation tillage area, factoring in local cropping systems and soil conditions. Our refined RothC model with TRMs provides a nuanced understanding of conservation tillage effects, emphasizing the role of soil characteristics. The proposed national-scale simulation framework offers a reliable tool for evaluating conservation tillage impact on SOC, ensuring more accurate greenhouse gas inventories in agriculture.

Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems

Accurate and cost-effective quantification of the carbon cycle for agroecosystems at decision-relevant scales is critical to mitigating climate change and ensuring sustainable food production. However, conventional process-based or data-driven modeling approaches alone have large prediction uncertainties due to the complex biogeochemical processes to model and the lack of observations to constrain many key state and flux variables. Here we propose a Knowledge-Guided Machine Learning (KGML) framework that addresses the above challenges by integrating knowledge embedded in a process-based model, high-resolution remote sensing observations, and machine learning (ML) techniques. Using the U.S. Corn Belt as a testbed, we demonstrate that KGML can outperform conventional process-based and black-box ML models in quantifying carbon cycle dynamics. Our high-resolution approach quantitatively reveals 86% more spatial detail of soil organic carbon changes than conventional coarse-resolution approaches. Moreover, we outline a protocol for improving KGML via various paths, which can be generalized to develop hybrid models to better predict complex earth system dynamics.

Soil organic carbon stocks in European croplands and grasslands: How much have we lost in the past decade?

The EU Soil Strategy 2030 aims to increase soil organic carbon (SOC) in agricultural land to enhance soil health and support biodiversity as well as to offset greenhouse gas emissions through soil carbon sequestration. Therefore, the quantification of current SOC stocks and the spatial identification of the main drivers of SOC changes is paramount in the preparation of agricultural policies aimed at enhancing the resilience of agricultural systems in the EU. In this context, changes of SOC stocks (Δ SOCs) for the EU + UK between 2009 and 2018 were estimated by fitting a quantile generalized additive model (qGAM) on data obtained from the revisited points of the Land Use/Land Cover Area Frame Survey (LUCAS) performed in 2009, 2015 and 2018. The analysis of the partial effects derived from the fitted qGAM model shows that land use and land use change observed in the 2009, 2015 and 2018 LUCAS campaigns (i.e. continuous grassland [GGG] or cropland [CCC], conversion grassland to cropland (GGC or GCC) and vice versa [CGG or CCG]) was one of the main drivers of SOC changes. The CCC was the factor that contributed to the lowest negative change on Δ SOC with an estimated partial effect of -0.04 ± 0.01 g C kg-1  year-1 , while the GGG the highest positive change with an estimated partial effect of 0.49 ± 0.02 g C kg-1  year-1 . This confirms the C sequestration potential of converting cropland to grassland. However, it is important to consider that local soil and environmental conditions may either diminish or enhance the grassland's positive effect on soil C storage. In the EU + UK, the estimated current (2018) topsoil (0-20 cm) SOC stock in agricultural land below 1000 m a.s.l was 9.3 Gt, with a Δ SOC of -0.75% in the period 2009-2018. The highest estimated SOC losses were concentrated in central-northern countries, while marginal losses were observed in the southeast.

Low risk management intervention: Limited impact of remedial tillage on net ecosystem carbon balance at a commercial Miscanthus plantation

Perennial bioenergy crops are a key tool in decarbonizing global energy systems, but to ensure the efficient use of land resources, it is essential that yields and crop longevity are maximized. Remedial shallow surface tillage is being explored in commercial Miscanthus plantations as an approach to reinvigorate older crops and to rectify poor establishment, improving yields. There are posited links, however, between tillage and losses in soil carbon (C) via increased ecosystem C fluxes to the atmosphere. As Miscanthus is utilized as an energy crop, changes in field C fluxes need to be assessed as part of the C balance of the crop. Here, for the first time, we quantify the C impacts of remedial tillage at a mature commercial Miscanthus plantation in Lincolnshire, United Kingdom. Net ecosystem C production based on eddy covariance flux observations and exported yield totalled 12.16 Mg C ha-1 over the 4.6 year period after tillage, showing the site functioned as a net sink for atmospheric carbon dioxide (CO2). There was no indication of negative tillage induced impacts on soil C stocks, with no difference 3 years post tillage in the surface (0-30 cm) or deep (0-70 cm) soil C stocks between the tilled Miscanthus field and an adjacent paired untilled Miscanthus field. Comparison to historic samples showed surface soil C stocks increased by 11.16 ± 3.91 Mg C ha-1 between pre (October 2011) and post tillage sampling (November 2016). Within the period of the study, however, the tillage did not result in the increased yields necessary to "pay back" the tillage induced yield loss. Rather the crop was effectively re-established, with progressive yield increases over the study period, mirroring expectations of newly planted sites. The overall impacts of remedial tillage will depend therefore, on the longer-term impacts on crop longevity and yields.

The impact of organic fertilizer replacement on greenhouse gas emissions and its influencing factors

Although organic fertilizers played an important role in enhancing crop yield and soil quality, the effects of organic fertilizers replacing chemical fertilizers on greenhouse gas (GHG) emissions remained inconsistent, and further impeding the widespread adoption of organic fertilizers. Therefore, a global meta-analysis used 568 comparisons from 137 publications was conducted to evaluate the responses of GHG emissions to organic fertilizers replacing chemical fertilizers. The results indicated that organic fertilizers replacing chemical fertilizers significantly decreased N2O emissions, but increasing global warming potential (GWP) by enhancing CH4 and CO2 emissions. When replacing chemical fertilizers with organic fertilizers, a variety of factors such as climate conditions, soil conditions, crop types and agricultural practices influenced the GHG emissions and GWP. Among these factors, fertilizer organic C and available N level were the main factors affecting GHG and GWP. However, considering the feasibility and ease of optimizing these factors, fertilizer organic C, C/N and N substitution rate showed a more favorable choice for GWP reduction, and their interactions significantly affecting GWP. Moreover, considering the distinct GHG emissions patterns in dryland and paddy field, the analysis of optimizing GWP based on fertilizer organic C, C/N and N substitution rate was separately conducted. According to the simulation optimization, the optimal combination of fertilizer organic C (137.2-228.8 g·kg-1), C/N (6.9-52.0) and N substitution rate (20.0-22.5 %) effectively suppressed the extent of increase in GWP in paddy field compared with chemical fertilizers. In dryland, optimizing fertilizer organic C (100-278 g·kg-1), C/N (70.7-76.6) and N substitution rate (10.2-16.0 %) led to a reduction in GWP compared with chemical fertilizers, indicating that dryland are more suitable for promoting organic fertilizer application. In conclusion, this meta-analysis study quantitatively assessed the GHG emissions when organic fertilizers replacing chemical fertilizers, and also provided a scientific basis for the mitigation of GHG emissions by organic fertilizers management.

Building soil to reduce climate change impacts on global crop yield

Improving soil health and resilience is fundamental for sustainable food production, however the role of soil in maintaining or improving global crop productivity under climate warming is not well identified and quantified. Here, we examined the impact of soil on yield response to climate warming for four major crops (i.e., maize, wheat, rice and soybean), using global-scale datasets and random forest method. We found that each °C of warming reduced global yields of maize by 3.4%, wheat by 2.4%, rice by 0.3% and soybean by 5.0%, which were spatially heterogeneous with possible positive impacts. The random forest modeling analyses further showed that soil organic carbon (SOC), as an indicator of soil quality, dominantly explained the spatial heterogeneity of yield responses to warming and would regulate the negative warming responses. Improving SOC under the medium SOC sequestration scenario would reduce the warming-induced yield loss of maize, wheat, rice and soybean to 0.1% °C-1, 2.7% °C-1, 3.4% °C-1 and - 0.6% °C-1, respectively, avoiding an average of 3%-5% °C-1 of global yield loss. These yield benefits would occur on 53.2%, 67.8%, 51.8% and 71.6% of maize, wheat, rice and soybean planting areas, respectively, with particularly pronounced benefits in the regions with negative warming responses. With improved soil carbon, food systems are predicted to provide additional 20 to over 130 million tonnes of food that would otherwise lose due to future warming. Our findings highlight the critical role of soil in alleviating negative warming impacts on food security, especially for developing regions, given that sustainable actions on soil improvement could be taken broadly.

Risk to rely on soil carbon sequestration to offset global ruminant emissions

Carbon sequestration in grasslands has been proposed as an important means to offset greenhouse gas emissions from ruminant systems. To understand the potential and limitations of this strategy, we need to acknowledge that soil carbon sequestration is a time-limited benefit, and there are intrinsic differences between short- and long-lived greenhouse gases. Here, our analysis shows that one tonne of carbon sequestrated can offset radiative forcing of a continuous emission of 0.99 kg methane or 0.1 kg nitrous oxide per year over 100 years. About 135 gigatonnes of carbon is required to offset the continuous methane and nitrous oxide emissions from ruminant sector worldwide, nearly twice the current global carbon stock in managed grasslands. For various regions, grassland carbon stocks would need to increase by approximately 25% - 2,000%, indicating that solely relying on carbon sequestration in grasslands to offset warming effect of emissions from current ruminant systems is not feasible.

Reducing spatial resolution increased net primary productivity prediction of terrestrial ecosystems: A Random Forest approach

Net primary production (NPP) is a pivotal component of the terrestrial carbon dynamic, as it directly contributes to the sequestration of atmospheric carbon by vegetation. However, significant variations and uncertainties persist in both the total amount and spatiotemporal patterns of terrestrial NPP, primarily stemming from discrepancies among datasets, modeling approaches, and spatial resolutions. In order to assess the influence of different spatial resolutions on global NPP, we employed a random forest (RF) model using a global observational dataset to predict NPP at 0.05°, 0.25°, and 0.5° resolutions. Our results showed that (1) the RF model performed satisfactorily with modeling efficiencies of 0.53-0.55 for the three respective resolutions; (2) NPP exhibited similar spatial patterns and interannual variation trends at different resolutions; (3) intriguingly, total global NPP varied greatly across different spatial resolutions, amounting 57.3 ± 3.07 for 0.05°, 61.46 ± 3.27 for 0.25°, and 66.5 ± 3.42 Pg C yr-1 for 0.5°. Such differences may be associated with the resolution transformation of the input variables when resampling from finer to coarser resolution, which significantly increased the spatial and temporal variation characteristics, particularly in regions within the southern hemisphere such as Africa, South America, and Australia. Therefore, our study introduces a new concept emphasizing the importance of selecting an appropriate spatial resolution when modeling carbon fluxes, with potential applications in establishing benchmarks for global biogeochemical models.

Integrated LiDAR-supported valuation of biomass and litter in forest ecosystems. A showcase in Spain

Belowground components (biomass and soils) can stock as much carbon as the aboveground component of forest ecosystems. In this study, we present a fully-integrated assessment of the biomass budget and the three pools evaluated: aboveground (AGBD) and belowground biomass in root systems (BGBD) and litter (LD). We turned National Forest Inventory data, airborne Light Detection and Ranging (LiDAR) data actionable to map three biomass compartments at 25-m resolution over more than 2.7 million ha of Mediterranean forests in the South-West of Spain. We assessed distributions and balanced among the three modelled components for the entire region of Extremadura and specifically for five representative forest types. Our results showed belowground biomass and litter represent an important 61 % of the AGBD stock. Among forest types, AGBD stocks were the dominant pool in pine-dominated areas while its lowers contribution was found over sparse oak forests. The three biomass pools estimated at the same resolution were used to produce ratio-based indicators to highlight areas where the contribution of belowground biomass and litter can exceed AGBD and where carbon-sequestration and conservation practices should acknowledge belowground-oriented carbon management. The recognition and valuation of biomass and carbon stocks beyond the AGBD is a must step forward that the scientific community must support in order to properly assess living components of the ecosystem such as root systems sustaining AGBD stocks and to value carbon-oriented ecosystem services related to soil-water dynamics and soil biodiversity. This study aims at enforcing a change of paradigm in forest carbon accounting, advocating for a better recognition and broader integration of living biomass in land carbon mapping.

The AMG model coupled with Rock-Eval® analysis accurately predicts cropland soil organic carbon dynamics in the Tuojiang River Basin, Southwest China

Accurate soil organic carbon models are key to understand the mechanisms governing carbon sequestration in soil and to help develop targeted management strategies to carbon budget. The accuracy and reliability of soil organic carbon (SOC) models remains strongly limited by incorrect initialization of the conceptual kinetic pools and lack of stringent model evaluation using time-series datasets. Notably, due to legacy effects of management and land use change, the traditional spin-up approach for initial allocation of SOC among kinetic pools can bring substantial uncertainties in predicting the evolution of SOC stocks. The AMG model can fulfill these conditions as it is a parsimonious yet accurate SOC model using widely-available input data. In this study, we first evaluated the performance of AMGv2 before and after optimizing the potential mineralization rate (k0) of SOC stock following a leave-one-site-out cross-validation based on 24 long-term field experiments (LTEs) in the Southwest of China. Then, we used Rock-Eval® thermal analysis results as input variables in the PARTYSOC machine learning model to estimate the initial stable SOC fraction (CS/C0) for the 14 LTEs where soil samples were available. The results showed that initializing the CS/C0 ratio using PARTYSOC combined with the optimized k0 further improved the accuracy of model simulations (R2 = 0.87, RMSE = 0.25, d = 0.90). Combining average measured CS/C0 and k0 optimization across all 24 LTEs also improved the model predictive capability by 25% compared to using default parameterization, thus suggesting promising avenue for upscaling model applications at the regional level where only a few measurement data on SOC stability can be available. In conclusion, the new version of the AMG model developed in the Tuojiang River Basin context exhibits excellent performance. This result paves the way for further calibration and validation of the AMG model in a wider set of contexts, with the potential to significantly improve confidence in SOC predictions in croplands over regional scales.

Predicting spatiotemporal soil organic carbon responses to management using EPIC-IIASA meta-models

The management of Soil Organic Carbon (SOC) is a critical component of both nature-based solutions for climate change mitigation and global food security. Agriculture has contributed substantially to a reduction in global SOC through cultivation, thus there has been renewed focus on management practices which minimize SOC losses and increase SOC gain as pathways towards maintaining healthy soils and reducing net greenhouse gas emissions. Mechanistic models are frequently used to aid in identifying these pathways due to their scalability and cost-effectiveness. Yet, they are often computationally costly and rely on input data that are often only available at coarse spatial resolutions. Herein, we build statistical meta-models of a multifactorial crop model in order to both (a) obtain a simplified model response and (b) explore the biophysical determinants of SOC responses to management and the geospatial heterogeneity of SOC dynamics across Europe. Using 5600 unique simulations of crop growth from the gridded Environmental Policy Integrated Climate-based Gridded Agricultural Model (EPIC-IIASA GAM) covering 86,000 simulation units across Europe, we build multiple polynomial regression ensemble meta-models for unique combinations of climate and soil across Europe in order to predict SOC responses to varying management intensities. We find that our biophysically-explicit meta models are highly accurate (R2 = 0.97) representations of the full mechanistic model and can be used in lieu of the full EPIC-IIASA GAM model for the estimation of SOC responses to cropland management. Model stratification by means of climate and soil clustering improved the performance of the meta-models compared to the full EU-scale model. In regional and local validations of the meta-model predictions, we find that the meta-models largely capture broad SOC dynamics such as the linear nature of SOC responses to residue application, yet they often underestimate the magnitude of SOC responses to management. Furthermore, we find notable differences between the results from the biophysically-specific models throughout Europe, which point to spatially-distinct SOC responses to management choices such as nitrogen fertilizer application rates and residue retention that illustrate the potential for these models to be used for future management applications. While more accurate input data, calibration, and validation will be needed to accurately predict SOC change, we demonstrate the use of our meta-models for biophysical cluster and field study scale analyses of broad SOC dynamics with basically zero fine-tuning of the models needed. This work provides a framework for simplifying large-scale agricultural models and identifies the opportunities for using these meta-models for assessing SOC responses to management at a variety of scales.

Carbon farming: Climate change mitigation via non-permanent carbon sinks

The role of carbon farming in agriculture or forestry to mitigate climate change is currently under intensive scientific discussion along with the gradual but progressing evolution of the voluntary carbon market and its certification. An overarching issue is the question of the permanence of terrestrial carbon sinks. In this comment, I discuss the climate benefit of non-permanent carbon sinks in light of a recent publication stating that carbon certificates fall short of expectations for climate change mitigation because of their non-permanence. The beneficial effect of short-lived sinks is real and quantifiable, and this understanding is applicable within ex ante biophysical discounting, which has the potential to improve the trustworthiness of climate change mitigation via carbon farming.

The legacy of one hundred years of climate change for organic carbon stocks in global agricultural topsoils

Soil organic carbon (SOC) of agricultural soils is observed to decline in many parts of the world. Understanding the reasons behind such losses is important for SOC accounting and formulating climate mitigation strategies. Disentangling the impact of last century's climate change from effects of preceding land use, management changes and erosion is difficult and most likely impossible to address in observations outside of warming experiments. However, the record of last century's climate change is available for every part of the globe, so the potential effect of climate change on SOC stocks can be modelled. In this study, an established and validated FAO framework was used to model global agricultural topsoil (0-30 cm) SOC stock dynamics from 1919 to 2018 as attributable to climate change. On average, global agricultural topsoils could have lost 2.5 ± 2.3 Mg C ha^-1 (3.9 ± 5.4%) with constant net primary production (NPP) or 1.6 ± 3.4 Mg C ha^-1 (2.5 ± 5.5%) when NPP was considered to be modified by temperature and precipitation. Regional variability could be explained by the complex patterns of changes in temperature and moisture, as well as initial SOC stocks. However, small average SOC losses have been an intrinsic and persistent feature of climate change in all climatic zones. This needs to be taken into consideration in reporting or accounting frameworks and halted in order to mitigate climate change and secure soil health.

The assay of soil carbon with naturally occurring cosmic ray neutrons

The detection of gamma rays induced in soil by naturally occurring cosmic ray neutrons is explored with the Geant4 Simulation Toolkit to monitor carbon sequestration in soil. The simulated soil is a uniform mixture of minerals, air, water, and soil organic carbon. As the soil organic carbon increases from 0% to 15% by volume, the mineral matter decreases, and gamma ray counts from mineral-related isotopes decrease. Characteristic gamma ray energies from a variety of elements are collected near the surface with a germanium detector. Of these, the 2.224 MeV gamma ray from hydrogen is sensitive to changes in soil organic carbon as low as 0.12% after counting for the equivalent of 3.45 days. Counting longer is recommended to reduce the sensitivity of the primary 4.438 MeV gamma ray from carbon below its current value of 2.81% in the simulation.

Assessing long-term impacts of cover crops on soil organic carbon in the central US Midwestern agroecosystems

Cover crops have been reported as one of the most effective practices to increase soil organic carbon (SOC) for agroecosystems. Impacts of cover crops on SOC change vary depending on soil properties, climate, and management practices, but it remains unclear how these control factors affect SOC benefits from cover crops, as well as which management practices can maximize SOC benefits. To address these questions, we used an advanced process-based agroecosystem model, ecosys, to assess the impacts of winter cover cropping on SOC accumulation under different environmental and management conditions. We aimed to answer the following questions: (1) To what extent do cover crops benefit SOC accumulation, and how do SOC benefits from cover crops vary with different factors (i.e., initial soil properties, cover crop types, climate during the cover crop growth period, and cover crop planting and terminating time)? (2) How can we enhance SOC benefits from cover crops under different cover crop management options? Specifically, we first calibrated and validated the ecosys model at two long-term field experiment sites with SOC measurements in Illinois. We then applied the ecosys model to six cover crop field experiment sites spanning across Illinois to assess the impacts of different factors on SOC accumulation. Our modeling results revealed the following findings: (1) Growing cover crops can bring SOC benefits by 0.33 ± 0.06 MgC ha^-1  year^-1 in six cover crop field experiment sites across Illinois, and the SOC benefits are species specific to legume and non-legume cover crops. (2) Initial SOC stocks and clay contents had overall small influences on SOC benefits from cover crops. During the cover crop growth period (i.e., winter and spring in the US Midwest), high temperature increased SOC benefits from cover crops, while the impacts from larger precipitation on SOC benefits varied field by field. (3) The SOC benefits from cover crops can be maximized by optimizing cover crop management practices (e.g., selecting cover crop types and controlling cover crop growth period) for the US Midwestern maize-soybean rotation system. Finally, we discussed the economic and policy implications of adopting cover crops in the US Midwest, including that current economic incentives to grow cover crops may not be sufficient to cover costs. This study systematically assessed cover crop impacts for SOC change in the US Midwest context, while also demonstrating that the ecosys model, with rigorous validation using field experiment data, can be an effective tool to guide the adaptive management of cover crops and quantify SOC benefits from cover crops. The study thus provides practical tools and insights for practitioners and policy-makers to design cover crop related government agricultural policies and incentive programs for farmers and agri-food related industries.

Across-model spread and shrinking in predicting peatland carbon dynamics under global change

Large across-model spread in simulating land carbon (C) dynamics has been ubiquitously demonstrated in model intercomparison projects (MIPs), and became a major impediment in advancing climate change prediction. Thus, it is imperative to identify underlying sources of the spread. Here, we used a novel matrix approach to analytically pin down the sources of across-model spread in transient peatland C dynamics in response to a factorial combination of two atmospheric CO₂ levels and five temperature levels. We developed a matrix-based MIP by converting the C cycle module of eight land models (i.e., TEM, CENTURY4, DALEC2, TECO, FBDC, CASA, CLM4.5 and ORCHIDEE) into eight matrix models. While the model average of ecosystem C storage was comparable to the measurement, the simulation differed largely among models, mainly due to inter-model difference in baseline C residence time. Models generally overestimated net ecosystem production (NEP), with a large spread that was mainly attributed to inter-model difference in environmental scalar. Based on the sources of spreads identified, we sequentially standardized model parameters to shrink simulated ecosystem C storage and NEP to almost none. Models generally captured the observed negative response of NEP to warming, but differed largely in the magnitude of response, due to differences in baseline C residence time and temperature sensitivity of decomposition. While there was a lack of response of NEP to elevated CO₂ (eCO₂ ) concentrations in the measurements, simulated NEP responded positively to eCO₂ concentrations in most models, due to the positive responses of simulated net primary production. Our study used one case study in Minnesota peatland to demonstrate that the sources of across-model spreads in simulating transient C dynamics can be precisely traced to model structures and parameters, regardless of their complexity, given the protocol that all the matrix models were driven by the same gross primary production and environmental variables.

Research needs for a food system transition

The global food system, and animal agriculture in particular, is a major and growing contributor to climate change, land system change, biodiversity loss, water consumption and contamination, and environmental pollution. The copious production and consumption of animal products are also contributing to increasingly negative public health outcomes, particularly in wealthy and rapidly industrializing countries, and result in the slaughter of trillions of animals each year. These impacts are motivating calls for reduced reliance on animal-based products and increased use of replacement plant-based products. However, our understanding of how the production and consumption of animal products, as well as plant-based alternatives, interact with important dimensions of human and environment systems is incomplete across space and time. This inhibits comprehensively envisioning global and regional food system transitions and planning to manage the costs and synergies thereof. We therefore propose a cross-disciplinary research agenda on future target-based scenarios for food system transformation that has at its core three main activities: (1) data collection and analysis at the intersection of animal agriculture, the environment, and societal well-being, (2) the construction of target-based scenarios for animal products informed by these new data and empirical understandings, and (3) the evaluation of impacts, unintended consequences, co-benefits, and trade-offs of these target-based scenarios to help inform decision-making.

Avoiding emissions versus creating sinks-Effectiveness and attractiveness to climate finance

The perception of greater impact via new sinks, as opposed to through avoided emissions, has already led some large investors to focus on sink-related projects. This is a flawed perception when applied universally and carries a risk that effective routes to mitigation through avoiding emissions are side-lined. In reality, both emissions avoidance and emissions removal are needed, and both can be a cost-effective means of delivering mitigation.

A simple soil mass correction for a more accurate determination of soil carbon stock changes

Agricultural soils can act as a sink for large quantities of soil organic carbon (SOC) but can also be sources of carbon to the atmosphere. The international standard for assessing SOC stock and measuring stock change stipulates fixed depth sampling to at least 30 cm. The tendency of bulk density (BD) to decrease with decreasing disturbance and increasing SOC concentration and the assumption of constant SOC and BD within this depth profile promotes error in the estimates of SOC stock. A hypothetical but realistic change in BD from 1.5 to 1.1 g cm^-3 from successive fixed depth sampling to 30 cm underestimates SOC stock change by 17%. Significant effort has been made to evaluate and reduce this fixed depth error by using the equivalent soil mass (ESM) approach, but with limited adoption. We evaluate the error in SOC stock assessment and change generated from fixed depth measurements over time relative to the ESM approach and propose a correction that can be readily adopted under current sampling and analytical methods. Our approach provides a more accurate estimate of SOC stock accumulation or loss that will help incentivize management practice changes that reduce the environmental impacts of agriculture and further legitimize the accounting practices used by the emerging carbon market and organizations that have pledged to reduce their supply chain greenhouse gas (GHG) footprints.

Fungal necromass increases soil aggregation and organic matter chemical stability under improved cropland management and natural restoration

The formation and stability of soil organic matter (SOM) is crucial for food security, soil health, and climate change mitigation. Although various SOM stabilization mechanisms have been proposed and investigated, the contribution of plant- and microbial-derived carbon into physical and chemical stabilization processes remain unclear. Therefore, this study investigates lignin phenols, microbial necromass, soil aggregation and SOM chemical composition under three cropland management and two natural restoration strategies: NPK, NPK + manure (NPK + M) and NPK + peat vermiculite (NPK + PV) after 5 years, and natural restoration for 10 and 40 years (NR10 and NR40, respectively). Addition of manure or peat vermiculite and NR40 increased soil organic carbon (SOC) by 86-122 % and 16 %, respectively, compared to the NPK fertilization. Lignin phenols and bacterial necromass-C were the highest under NPK + M, and lignin phenols increased by 0.07 g and microbial necromass-C by 0.44 g with each additional 1 g of SOC. Fungal necromass-C in NPK + PV was 0.14-1.1 times higher than in other treatments. The mean weight diameter of aggregates was the highest, while macroaggregate turnover was the slowest under NPK + PV, indicating increased soil aggregation and physical stability. Natural restoration reduced lignin phenols by 33-40 % and labile O-alkyl C by 4-9 %, but increased resistant alkyl C by 9-15 % compared with other treatments, reflecting the highest chemical stability. High fungal necromass was beneficial to the accumulation of particulate and mineral-associated C and aggregate stability, and decelerated macroaggregate turnover. Aromatic C increased but aliphatic-C/aromatic-C decreased with increasing fungal necromass-C. Consequently, fungal necromass C increases SOM physical stability by slowing aggregate turnover and enhances the chemical stability through the accumulation of recalcitrant C under improved cropland management and natural restoration.

A widely-used eddy covariance gap-filling method creates systematic bias in carbon balance estimates

Climate change mitigation requires, besides reductions in greenhouse gas emissions, actions to increase carbon sinks in terrestrial ecosystems. A key measurement method for quantifying such sinks and calibrating models is the eddy covariance technique, but it requires imputation, or gap-filling, of missing data for determination of annual carbon balances of ecosystems. Previous comparisons of gap-filling methods have concluded that commonly used methods, such as marginal distribution sampling (MDS), do not have a significant impact on the carbon balance estimate. By analyzing an extensive, global data set, we show that MDS causes significant carbon balance errors for northern (latitude [Formula: see text]) sites. MDS systematically overestimates the carbon dioxide (CO[Formula: see text]) emissions of carbon sources and underestimates the CO[Formula: see text] sequestration of carbon sinks. We also reveal reasons for these biases and show how a machine learning method called extreme gradient boosting or a modified implementation of MDS can be used to substantially reduce the northern site bias.

Achieving the nutrient reduction objective of the Farm to Fork Strategy. An assessment of CAP subsidies for precision fertilization and sustainable agricultural practices in Germany

The Farm to Fork Strategy of the EU aims at sustainable food systems. One objective of the Strategy is to reduce nutrient losses by at least 50% resulting in at least 20% less fertilizer use by 2030. To this end, Member States are expected to extend digital precision fertilization and sustainable agricultural practices through the Common Agricultural Policy. In this context, this article applies a qualitative governance analysis which aims to assess the extent to which the measures proposed by the Farm to Fork Strategy, i.e., digital precision fertilization and sustainable agricultural practices, contribute to the nutrient objective of the Farm to Fork Strategy. The article analyses how these measures are implemented through the Common Agricultural Policy in Germany and Saxony. Results show that the nutrient objective of the Farm to Fork Strategy itself offers shortcomings. Germany offers some, yet overall limited, support for sustainable agricultural practices and digital precision fertilization. Hence, the Common Agricultural Policy will to a limited extend only contribute to the objective of the Strategy. The results furthermore highlight some general shortcomings of digitalization as sustainability strategy in the agricultural sector including typical governance issues (rebound and enforcement problems), and point to the advantages of quantity-based policy instruments.

From prairie to crop: Spatiotemporal dynamics of surface soil organic carbon stocks over 167 years in Illinois, U.S.A

Quantifying spatiotemporal dynamics of soil organic carbon (SOC) stocks is needed to understand the impact of land use change and can help target carbon sequestration efforts. In the recently and radically transformed landscapes of the state of Illinois, U.S.A., we evaluated surface SOC stocks under land use change using a space-for-time substitution method over 167 years. Additionally, we determined SOC stocks for the A horizon vs 0-30 cm depth to evaluate pedogenically-informed vs more commonly used fixed depth approaches. Legacy soil datasets from 1980 to 2012 were combined with environmental covariates using a random forest algorithm. To more accurately estimate pre-agricultural land use SOC stocks (i.e., pre-1845), SOC observations collected from soils under native prairie and forest were extracted from peer-reviewed publications. The model was validated on 25 % of the total 627 test data (R_A-hor²: 0.59 and R_0-30²: 0.56; RMSE_A-hor: 20.5 and RMSE_0-30:19.3 Mg/ha) independent of the 75 % of data for calibration (R²: 0.91; RMSE_A-hor:10.1 and RMSE_0-30:9.6 Mg/ha). SOC stocks were largest under prairie (A horizon: 156.1 Mg/ha; 0-30 cm: 152.4 Mg/ha) and lowest under pasture (A horizon: 33.2, 0-30 cm: 44.6 Mg/ha). SOC stocks varied less by soil order than by land use. Between 1845 and 2012, surface SOC stocks decreased for most of Illinois, with greatest losses in central (-16.3 Mg/ha) and east-central Illinois (-47.0 Mg/ha) where approximately 80 % of prairie was converted to cropland. A slight increase in surface SOC stocks occurred in the unglaciated northwest region and the less recently glaciated south region, as well as in alluvial corridors. This study (i) highlights how estimating spatiotemporal dynamics of surface SOC stocks over centennial timescales can benefit from including measures of SOC under native land use not usually contained in legacy pedon datasets, and (ii) illustrates the potential of identifying localized hotspots of historical SOC loss and thus deficits that can be prioritized for carbon sequestration efforts.

Recycling paper to recarbonise soil

Soil organic carbon can be increased through sympathetic land management and/or directly by incorporating carbon rich amendments. Herein, a field experiment amended paper crumble (PC) to soil at a normal deployment rate of 50 t ha^-1, and at higher rates up to 200 t ha^-1. The nominal 50 t ha^-1 PC amendment resulted a mean increase in soil carbon of 12.5 g kg^-1. Using a modified Roth-C carbon fate model, the long-term (50 years) carbon storage potential of a 50 t ha^-1 PC amendment was determined to be 0.36 t_OC ha^-1. Modelling a rotational (4 yearly) 50 t ha^-1 PC amendment indicated 6.65 t_OC ha^-1 uplift would accrue after 50 years. Contextualised for the average farm in the East of England (~120 ha, with 79 % as arable), PC derived increases in SOC would be equivalent to 2310 t CO₂e. These results support the use of PC to deliver significant levels of soil recarbonisation. Beyond carbon, PC was observed to influence other soil properties. Benefits observed included, decreased bulk density, increased water holding capacity, and increased cation exchange capacity. While PC amendment did not significantly increase wheat (Triticum aestivum) crop yield, manifold benefits in terms of increased SOC, long-term carbon storage potential, and improved soil quality sustain PC as a beneficial soil conditioner.

Environmental factors contributing to variations in CO2 flux over a barley-rice double-cropping paddy field in the Korean Peninsula

Understanding the CO₂ flux over agricultural crop fields is critical because the temporal cycle is driven by both ecological environment and anthropogenic change. We analyzed the net ecosystem exchange of CO₂ measured over a barley-rice double-cropping field using the eddy covariance method for 5 years. We conducted gap-filling based on u_*-threshold criteria and partitioned the net ecosystem exchange into gross primary production and respiration. The relative importance analysis of solar radiation, temperature, soil heat flux, soil water content, and vapor deficit revealed that solar radiation and temperature were the dominant contributors to net ecosystem exchange. The annual variation in the net ecosystem exchange followed a bimodal pattern driven by CO₂ uptake by both barley and rice, displaying two negative peaks in late April and mid-August. The elongation stages of the crops exhibited the highest flux. Gross primary production and respiration were closely related to solar radiation and nighttime temperature, respectively. The relative importance of the other environmental variables was affected by the cultivation season and irrigation water. In the period of rice cultivation, respiration was approximately 3 µmol m^-2 s^-1 higher during rice drainage than during the flooded period. The accumulated net ecosystem production was estimated to be 315 gC m^-2 and 349 gC m^-2 for the barley and rice growing periods, respectively, and 649 gC m^-2 for the annual total. These values are comparable with the results of other studies on barley-rice double-cropping fields.

Roadmap for achieving net-zero emissions in global food systems by 2050

Food systems (FSs) emit ~ 20 GtCO₂e/y (~ 35% of global greenhouse gas emissions). This level tends to raise given the expected increases in food demands, which may threaten global climate targets. Through a rapid assessment, evaluating 60+ scenarios based on existing low-emission and carbon sequestration practices, we estimate that intensifying FSs could reduce its emissions from 21.4 to − 2.0 GtCO₂e/y and address increasing food demands without relying on carbon offsets (e.g., related to afforestation and reforestation programs). However, given historical trends and regional contexts, a more diverse portfolio of practices, including diet shifts and new-horizon technologies, will be needed to increase the feasibility of achieving net-zero FSs. One likely pathway consists of implementing practices that shift food production to the 30th-percentile of least emission-intensive FSs (~ 45% emissions reduction), sequester carbon at 50% of its potential (~ 5 GtCO₂e/y) and adopt diet shifts and new-horizon technologies (~ 6 GtCO₂e/y). For a successful transition to happen, the global FSs would, in the next decade (2020s), need to implement cost-effective mitigation practices and technologies, supported by improvements in countries’ governance and technical assistance, innovative financial mechanisms and research focused on making affordable technologies in the following two decades (2030–2050). This work provides options and a vision to guide global FSs to achieving net-zero by 2050.

Spatiotemporal changes in greenhouse gas emissions and soil organic carbon sequestration for major cropping systems across China and their drivers over the past two decades

Chinese agricultural systems have experienced dramatic changes in crop planting area, cropping system, irrigation and fertilization managements, and crop yields in recent decades. These changes can substantially affect greenhouse gases (GHG) emissions and soil organic carbon (SOC) sequestration in croplands. However, the spatiotemporal patterns, as well as their driving factors and mechanisms, have not been well understood. Here, the Denitrification-Decomposition model is calibrated and validated to estimate nitrous oxide (N₂O) and methane (CH₄) emissions and SOC sequestration for seven major cropping systems in China during 2001-2020. The Logarithmic Mean Divisia Index method is further applied to attribute the net GHG emissions (NGEs) trend to various drivers. The results show that the total N₂O emissions, CH₄ emissions, and SOC sequestration were approximately 23.7, 182.0, and 177.6 Tg CO₂-eq/year in the croplands across China. The national average NGEs per unit area ranged from -8705 to 8431 kg CO₂-eq ha^-1 year^-1 across the major cropping systems. During 2001-2020, the trend in national annual NGEs was 0.66 kg CO₂-eq ha^-1 year^-2, ranging from -78.9 to 82.2 kg CO₂-eq ha^-1 year^-2 across the major cropping systems. The paddy lands were mainly a carbon source due to the large amount of CH₄ emissions while the uplands could be a carbon sink owing to SOC sequestration. As a whole, the cropland in China was a carbon source with the NGEs equal to 28.4 Tg CO₂-eq/year, and the NGEs increased by 0.047 Tg CO₂-eq/year² in the past 20 years. Nationally, changes in crop planting area and yields reduced the NGEs whereas changes in nitrogen use efficiency and cropping systems increased them, although the major factors and their impacts varied greatly among regions. Optimizing cropping systems and nitrogen fertilization based on the local genotype, environment and management should be the most effective method to reduce the NGEs in croplands.

Satellite Imagery to Map Topsoil Organic Carbon Content over Cultivated Areas: An Overview

There is a need to update soil maps and monitor soil organic carbon (SOC) in the upper horizons or plough layer for enabling decision support and land management, while complying with several policies, especially those favoring soil carbon storage. This review paper is dedicated to the satellite-based spectral approaches for SOC assessment that have been achieved from several satellite sensors, study scales and geographical contexts in the past decade. Most approaches relying on pure spectral models have been carried out since 2019 and have dealt with temperate croplands in Europe, China and North America at the scale of small regions, of some hundreds of km2: dry combustion and wet oxidation were the analytical determination methods used for 50% and 35% of the satellite-derived SOC studies, for which measured topsoil SOC contents mainly referred to mineral soils, typically cambisols and luvisols and to a lesser extent, regosols, leptosols, stagnosols and chernozems, with annual cropping systems with a SOC value of ~15 g·kg−1 and a range of 30 g·kg−1 in median. Most satellite-derived SOC spectral prediction models used limited preprocessing and were based on bare soil pixel retrieval after Normalized Difference Vegetation Index (NDVI) thresholding. About one third of these models used partial least squares regression (PLSR), while another third used random forest (RF), and the remaining included machine learning methods such as support vector machine (SVM). We did not find any studies either on deep learning methods or on all-performance evaluations and uncertainty analysis of spatial model predictions. Nevertheless, the literature examined here identifies satellite-based spectral information, especially derived under bare soil conditions, as an interesting approach that deserves further investigations. Future research includes considering the simultaneous analysis of imagery acquired at several dates i.e., temporal mosaicking, testing the influence of possible disturbing factors and mitigating their effects fusing mixed models incorporating non-spectral ancillary information.

Informing Nature-based Climate Solutions for the United States with the best-available science

Nature-based Climate Solutions (NbCS) are managed alterations to ecosystems designed to increase carbon sequestration or reduce greenhouse gas emissions. While they have growing public and private support, the realizable benefits and unintended consequences of NbCS are not well understood. At regional scales where policy decisions are often made, NbCS benefits are estimated from soil and tree survey data that can miss important carbon sources and sinks within an ecosystem, and do not reveal the biophysical impacts of NbCS for local water and energy cycles. The only direct observations of ecosystem-scale carbon fluxes, for example, by eddy covariance flux towers, have not yet been systematically assessed for what they can tell us about NbCS potentials, and state-of-the-art remote sensing products and land-surface models are not yet being widely used to inform NbCS policymaking or implementation. As a result, there is a critical mismatch between the point- and tree-scale data most often used to assess NbCS benefits and impacts, the ecosystem and landscape scales where NbCS projects are implemented, and the regional to continental scales most relevant to policymaking. Here, we propose a research agenda to confront these gaps using data and tools that have long been used to understand the mechanisms driving ecosystem carbon and energy cycling, but have not yet been widely applied to NbCS. We outline steps for creating robust NbCS assessments at both local to regional scales that are informed by ecosystem-scale observations, and which consider concurrent biophysical impacts, future climate feedbacks, and the need for equitable and inclusive NbCS implementation strategies. We contend that these research goals can largely be accomplished by shifting the scales at which pre-existing tools are applied and blended together, although we also highlight some opportunities for more radical shifts in approach.

Climate change induces carbon loss of arable mineral soils in boreal conditions

One-fourth of the global soil organic carbon (SOC) is stored in the boreal region, where climate change is predicted to be faster than the global average. Planetary warming is accelerated if climate change promotes SOC release into the atmosphere as carbon dioxide. However, the soil carbon-climate feedbacks have been poorly confirmed by SOC measurements despite their importance on global climate. In this study, we used data collected as part of the Finnish arable soil monitoring program to study the influence of climate change, management practices, and historical land use on changes in SOC content using a Bayesian approach. Topsoil samples (n = 385) collected nationwide in 2009 and 2018 showed that SOC content has decreased at the rate of 0.35% year^-1 on average. Based on the Bayesian modeling of our data, we can say with a certainty of 79%-91% that increase in summertime (May-Sep) temperature has resulted in SOC loss while increased precipitation has resulted in SOC loss with a certainty of 90%-97%. The exact percentages depend on the climate dataset used. Historical land use was found to influence the SOC content for decades after conversion to cropland. Former organic soils with a high SOC-to-fine-fraction ratio were prone to high SOC loss. In fields with long cultivation history (>100 years), however, the SOC-to-fine-fraction ratio had stabilized to approximately 0.03-0.04 and the changes in SOC content leveled off. Our results showed that, although arable SOC sequestration can be promoted by diversifying crop rotations and by cultivating perennial grasses, it is unlikely that improved management practices are sufficient to counterbalance the climate change-induced SOC losses in boreal conditions. This underlines the importance of the reduction of greenhouse gas emissions to avoid the acceleration of planetary warming.

The Role of Soil Carbon Sequestration as a Climate Change Mitigation Strategy: An Australian Case Study

Soil carbon sequestration (SCS) is a key priority in the Australian government’s Long-Term Emissions Reduction Plan. Under the government’s Emission Reduction Fund (ERF), farmers are encouraged to change to a management practice that will increase their soil carbon (C) stock and earn Australian Carbon Credit Units (ACCUs). The projections of net C abatement nationally range from 17 to 103 Mt carbon dioxide equivalent annually up to 2050. This huge range reflects the uncertainties in achieving net SCS due to biophysical constraints, such as those imposed by the paucity and variability of Australian rainfall and the difficulty of measuring small changes in soil C stock. The uptake by farmers is also uncertain because of compliance costs, opportunity costs of a practice change and the loss of business flexibility when a farmer must commit to a 25-year permanence period. Since the program’s inception in 2014, only one soil C project has been awarded ACCUs. Nevertheless, an increase in soil C is generally beneficial for farm productivity. As a voluntary C market evolves, the government is expecting that farmers will sell their ACCUs to businesses seeking to offset their greenhouse gas emissions. The risk is that, in buying cheap offsets, businesses will not then invest in new energy-efficient technologies to reduce their emissions at source.

Interactive impacts of climate change and agricultural management on soil organic carbon sequestration potential of cropland in China over the coming decades

Cropland plays an important role in Soil Organic Carbon (SOC) sequestration. Although the SOC stock and its dynamic in the past decades have been extensively investigated, the information as to where, how much, and how SOC could be potentially sequestered in the coming decades has rarely been available. Here, the Rothamsted Carbon model was applied to investigate the spatiotemporal pattern of SOC sequestration potential for China's cropland in 2021-2040 at 1 km resolution, as well as the interactive impacts of climate change and agricultural management on SOC sequestration. Under the combined impacts of climate change and C input, the SOC sequestration of China's cropland in 2021-2040 would be about 0.56 Mg C ha^-1 (0.06% yr^-1), 1.33 Mg C ha^-1 (0.15% yr^-1), 2.10 Mg C ha^-1 (0.24% yr^-1), and 3.65 Mg C ha^-1 (0.41% yr^-1), with no increase, 5%, 10%, and 20% increase of C input, respectively. Therefore, a >20% increase in C input would be necessary to realize the promise of the '4 per 1000' initiative. Climate change would decrease SOC sequestration by 26.6-27.6 Tg yr^-1 (or 60.4-62.7%). An increase of C input by 0%, 5%, 10%, and 20% relative to business as usual (BAU) would increase SOC sequestration by 4.8 (or 10.8%), 6.6 (or 14.9%), 13.1 (or 29.8%), and 26.2 (or 59.6%) Tg yr^-1, respectively. The contributions of temperature, precipitation, and C input to SOC sequestration will be averagely 18.6%, 22.4%, and 59.0%, respectively. Our findings quantify the SOC sequestration in 2021-2040 at a high spatial resolution under the interactive impacts of climate change and agricultural management, which help to identify potential foci and develop region-specific measures to increase SOC sequestration efficiently.

Exploring Operational Procedures to Assess Ecosystem Services at Farm Level, including the Role of Soil Health

Reaching the land-related UN Sustainable Development Goals (SDGs) and similar goals articulated by the EU Green Deal (GD) by 2030 presents a major challenge and requires a pragmatic approach focused on joint learning by land users (mostly farmers), researchers and other stakeholders in “Living Labs” and system experiments at experimental farms of research organizations. Defining specific indicators and thresholds for ecosystem services in line with land-related SDGs is crucial to establish “Lighthouses” that can act as inspiring examples if they meet the various thresholds. This exploratory paper discusses indicators and thresholds for an arable farm operating on marine, calcareous light clay soils in the Netherlands. Studies of a system experiment are used to discuss and test operational methodology to be widely applied when characterizing many “Living Labs” in future, as planned by the European Union. The important role of soils in contributing to ecosystem services is discussed in terms of soil health. Recommendations are made for innovative methodology to be associated with all land-related SDGs. Satisfying the thresholds of ecosystem services, which will vary by soil type, region and farm type, can be the basis for farm subsidies, such as the Common Agricultural Policy (CAP). Research on Living Labs and in system experiments has to be judged by different criteria than those associated with traditional linear research. The important contributions of soils to achieve ecosystem services are framed in terms of soil health and are the most effective way to promote soil science in a by now widely desired inter- and transdisciplinary context.

Changes in organic carbon to clay ratios in different soils and land uses in England and Wales over time

Realistic targets for soil organic carbon (SOC) concentrations are needed, accounting for differences between soils and land uses. We assess the use of SOC/clay ratio for this purpose by comparing changes over time in (a) the National Soil Inventory of England and Wales, first sampled in 1978–1983 and resampled in 1994–2003, and (b) two long-term experiments under ley-arable rotations on contrasting soils in the East of England. The results showed that normalising for clay concentration provides a more meaningful separation between land uses than changes in SOC alone. Almost half of arable soils in the NSI had degraded SOC/clay ratios (< 1/13), compared with just 5% of permanent grass and woodland soils. Soils with initially large SOC/clay ratios (≥ 1/8) were prone to greater losses of SOC between the two NSI samplings than those with smaller ratios. The results suggest realistic long-term targets for SOC/clay in arable, ley grass, permanent grass and woodland soils are 1/13, 1/10, and > 1/8, respectively. Given the wide range of soils and land uses across England and Wales in the datasets used to test these targets, they should apply across similar temperate regions globally, and at national to sub-regional scales.