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  • Chapter Three - Soil carbon accumulation in crop-livestock systems in acid soil savannas of South America: A review

    Acid soil savannas of tropical America are a vast resource to expand agricultural production, alleviate the pressure on tropical rainforest and reduce greenhouse gas (GHG) emissions. During the past three decades there have been major changes in land use in the Cerrados of Brazil and to a lesser extent in the Llanos of Colombia. Monocropping and improved pasture grasses were adopted widely to boost crop and animal production. Various types of integrated crop-livestock systems and no-till cropping systems were introduced to not only recuperate degraded pastures but also to sustain crop and livestock productivity. Several studies showed that well-managed pastures based on deep rooted tropical forage grass and legume species could accumulate significant amounts of soil organic carbon (SOC) in deeper soil layers. Among the number of factors that influence SOC accumulation, deep rooting ability of grasses and high root turnover seem to play a major role in accumulation of SOC in deeper soil layers in the form of particulate organic carbon (POC) and mineral associated organic carbon (MAOC). This review provides insights toward some key approaches and management options to increase both POC and MAOC accumulation and particularly MAOC accumulation in deeper soil layers in crop-livestock systems. There are some important gaps in our knowledge, particularly regarding the influence of length of pasture phase on MAOC accumulation in deeper soil layers from crop-livestock systems. Finally, we highlight the importance of land use policies and suggest some future research priorities for consideration to increase benefits from the use of integrated crop-livestock systems in acid soil savannas.
  • ACCELERATING CARBON FARMING Joint 4 per 1000 Initiative / European Parliament webinar 8 Sept 2021

    ACCELERATING CARBON FARMING How to make Carbon Farming a Success for Climate, Environment and Farmers ? The Green Deal has been marked as the new economic strategy of the Union. It is changing our overall legislative framework to allow the deployment of technologies and unlock the investment that will make possible carbon neutrality, and a new model of prosperity. Agriculture and farmers have an essential role to play since this is the one of the few sectors that has the ability to shift from a net emitter of CO2 to a net sequestered of CO2. As part of the European Green deal, a series of initiatives can become true game changer: the reform of LULUCF regulation, the Carbon Farming initiative and the regulatory framework for certifying carbon removal. If we design the framework right, carbon farming can create a new profitable business model and support the transition. The European Green deal offers an opportunity to build a transition profitable for all: farmers would be able to get money for their contribution to climate mitigation, and also additional support to enhance their capacity to stock carbon in farmland, which is ultimately needed if we are serious about being carbon neutral by 2050. This is how we will bring everybody on board with the transition. During this panel, each speaker is asked to discuss the following questions: - How to ensure than carbon farming actually delivers the expected outcome in terms of climate mitigation and biodiversity protection on one side,and on farmers’ additional revenues on the other side? - How the carbon market, and at which price, can incentivize properly farmers? - How to ensure permanence of action in order to secure lasting climate action? - Who would be liable for the proper management and maintenance of agricultural practices benefiting the climate? (And more) Speakers: Mr. Pascal Canfin - Chair of the Committee on the Environment, Public Health and Food Safety (ENVI Committee) Mr. Norbert Lins - Chair of the Committee on Agriculture and Rural Development (AGRI Committee) Mr. Christian Holzleitner - Head of the Unit Land Use & Finance for Innovation (DG CLIMA.C.3) at DG CLIMA (Directorate General Climate Action) Ms. Claire Chenu - Director of Research Coordinator of EJP Soil "European Joint Programming Co-fund on Agricultural Soil Management" Member of the Scientific and Technical Committee of the "4 per 1000" Initiative Ms. Anne Trombini - Director "Pour une Agriculture du Vivant - Pour une Agriculture du Vivant" Ms. Léa Lugassy - Scientific Coordinator "Pour une Agriculture du Vivant - Pour une Agriculture du Vivant" Ms. Margaret Kim - CEO "The Gold Standard" Mr. Georg Goeres - Head of Europe "Indigo" Mr. Quentin Sannié Founder & CEO "Greenback"
  • Understanding farmers' commitments to carbon projects

    This study focuses on identifying the factors that influence farmers' commitment to carbon projects. Based on studying the components of organizational commitment, project commitment and environmental commitment, we developed a regression model that consists of five independent variables, i.e., project-related incomes, persistence in the project, perception of government support, perception of the project and knowledge about carbon sequestration. The model was tested using survey data from 127 smallholder farmers taking part in a carbon project in Suichang, China. The results indicate that farmers' commitment to carbon projects depends on project-related incomes, persistence in the project, and perception of the project. Governmental support and environmental belief do not necessarily affect farmers' commitment.
  • Soil and vegetation carbon stocks after land-use changes in a seasonally dry tropical forest

    The lack of robust scientific data still hinders estimates of soil and plant carbon (C) losses due to land-use changes in most dry tropical ecosystems. The present study investigated the effects of land-use and cover changes on total ecosystem C stocks in NE Brazil, aiming to quantify C losses after the removal of the native forest, known as Caatinga. The sampling design included the four main land-use/cover types (Dense Caatinga, Open Caatinga, Pastures and Crop fields) and the seven main soil classes (Arenosols, Acrisols, Regosols, Ferrasols, Luvisols, Planosols, and Leptosols), a combination that represents over 90% of the region. This design resulted in 192 sampling points (48 in each land-use), distributed proportionally to the area of occurrence of each soil class. In each sampling point, we determined C stocks in soil organic matter (SOM) and roots (to a depth of 1 m or rock layer), aboveground vegetation biomass (trees and herbs, separately), deadwood, and surface litter. Areas covered by Dense Caatinga store, on average, nearly 125 Mg ha−1 of C. Most of this C is stored in the soil organic matter (72.1%), followed by aboveground biomass (15.9%), belowground biomass (7.3%), deadwood (2.9%), litter (1.3%), and herbaceous biomass (0.5%). The substitution of Dense Caatinga to plant pastures and crop fields caused losses of >50% of ecosystem C stocks, reaching almost 65 Mg ha−1 of C, with nearly equal losses from the SOM and vegetation biomass compartments. Open Caatinga store nearly 30% less C than Dense Caatinga. Contrary to what was expected, the overall differences in C stocks between soil classes were not significant, with a few exceptions. We expect that the findings of this study will contribute to a more robust inventory of GHG emissions/removals due to land-use changes in NE Brazil and other dry tropical regions of the globe.
  • Stock and stability of organic carbon in soils under major agro-ecological zones and cropping systems of sub-tropical India

    We evaluated long-term impact of agro-ecological zones and cropping systems on stock and stability of organic C in soils along depth. Sixty geo-referenced soil samples were collected from Terai (TZ) and New Alluvial (NAZ) agro-ecological zones of Wet Bengal. In each zone, soil samples were drawn at 0−0.3, 0.3−0.6 and 0.6−0.9 m depth from rice-potato-jute (R-P-J), rice-vegetables-vegetables (R-V-V), vegetables-vegetables-vegetables (V-V-V) and rice-rice (R-R) cropping systems. Total organic C (TOC) in soils and its pools viz., easily oxidizable Walkley and Black C (CWB), very labile (CVL), labile (CL), less labile (CLL), non-labile (CNL), active (CAP), and passive (CPP) were measured separating on the basis of ease of oxidation with chromic acid for computing different indices of soil organic C (SOC). Among the cropping systems, on average, rice-based systems had higher TOC and CNL than non-rice (V-V-V) one, particularly in NAZ; while non-rice system had a higher per cent allocation of SOC in CVL. Again, a greater per cent of SOC occurred in CPP and CAP under rice-based and non-rice systems, respectively. Anaerobiosis thus facilitated formation of a higher proportion of SOC in recalcitrant pools. Stratification ratios of organic C were higher for soils under NAZ than those under TZ, and also for soils under rice-based systems than those under non-rice one indicating a better soil quality in the former than the latter zone and systems. This was again corroborated with higher values of carbon management index for NAZ than TZ and R-R than V-V-V systems. Further, the recalcitrant indices (RIs) of SOC were higher for soils of TZ than those of NAZ, contrary to the values of lability index (LI). Among the cropping systems, V-V-V had the highest LI values followed by R-V-V > R-P-J = R-R. Along depth, the values of RIs increased, but LI decreased. There was thus an overriding influence of rice and its ecology on the stock and stability of SOC masking the influence of its companion crops in rice-based systems. Therefore, rice-based systems, grown by default in many regions, had a better C economy in soils of this sub-tropical part of the world.
  • Barriers and strategies to boost soil carbon sequestration in agriculture

    The Paris Agreement calls for limiting global warming below 2°C. The “4 per 1,000 Initiative: Soils for food security and climate” was launched in 2015 to increase soil organic carbon sequestration with three objectives: mitigation of climate change, adaptation to climate change and improved food security. One of the challenges of the Initiative relates to its feasibility in contrasted biophysical, social and economic environments, questioning the adoption rate of required new practices. We conducted participatory multi-stakeholder workshops in France and Senegal to collect knowledge and perception of farmers, NGOs, agro-industries, administrations, donors and researchers on barriers and coping strategies for 4 per 1,000 innovations. Results in both countries reveal the predominance of social and economic barriers such as lack of knowledge or training, increased difficulties of fieldwork, workload, risk handling, funding and social pressure. Biophysical constraints such as limited potential of soil organic matter storage or rainfall scarcity and variability appear more important in Senegal. Identified actions to foster the sequestration of soil carbon call for an improved policy context leading to innovations in land planning, stakeholder communication, demonstration facilities, capacity building or financial support. Fewer constraints and coping strategies mention technical issues, showing that fostering agricultural soil carbon sequestration is more a question of enabling environment than technical innovations or farmers' willingness for change. We conclude that actions to support the 4 per 1,000 Initiative need to include a variety of stakeholders such as extension services, private sector, civil society, local institutions, policy makers, consumers, and not only farmers.
  • Mapping topsoil organic carbon concentrations and stocks for Tanzania

    Tanzania is one of the countries that has embarked on a national programme under the United Nations collaborative initiative on Reducing Emissions from Deforestation and forest Degradation (REDD). Tanzania is currently developing the capacity to enter into a carbon monitoring REDD+ regime. In this context spatially representative soil carbon datasets and accurate predictive maps are important for determining the soil organic carbon pool. The main objective of this study was to model and map the SOC stock for the 0–30-cm soil layer to provide baseline information for REDD+ purposes. Topsoil data of over 1400 locations spread throughout Tanzania from the National Forest Monitoring and Assessment (NAFORMA), were used, supplemented by two legacy datasets, to calibrate simple kriging with varying local means models. Maps of SOC concentrations (g kg−1) were generated for the 0–10-cm, 10–20-cm, 20–30-cm, 0–30-cm layers, and maps of bulk density and SOC stock (kg m−2) for the 0–30-cm layer. Two approaches for modelling SOC stocks were considered here: the calculate-then-model (CTM) approach and the model-then-calculate approach (MTC). The spatial predictions were validated by means of 10-fold cross-validation. Uncertainty associated to the estimated SOC stocks was quantified through conditional Gaussian simulation. Estimates of SOC stocks for the main land cover classes are provided. Environmental covariates related to soil and terrain proved to be the strongest predictors for all properties modelled. The mean predicted SOC stock for the 0–30-cm layer was 4.1 kg m−2 (CTM approach) translating to a total national stock of 3.6 Pg. The MTC approach gave similar results. The largest stocks are found in forest and grassland ecosystems, while woodlands and bushlands contain two thirds of the total SOC stock. The root mean squared error for the 0–30-cm layer was 1.8 kg m−2, and the R2-value was 0.51. The R2-value of SOC concentration for the 0–30-cm layer was 0.60 and that of bulk density 0.56. The R2-values of the predicted SOC concentrations for the 10-cm layers vary between 0.46 and 0.54. The 95% confidence interval of the predicted average SOC stock is 4.01–4.15 kg m−2, and that of the national total SOC stock 3.54–3.65 Pg. Uncertainty associated with SOC concentration had the largest contribution to SOC stock uncertainty. These findings have relevance for the ongoing REDD+ readiness process in Tanzania by supplementing the previous knowledge of significant carbon pools. The soil organic carbon pool makes up a relatively large proportion of carbon in Tanzania and is therefore an important carbon pool to consider alongside the ones related to the woody biomass. Going forward, the soil organic carbon data can potentially be used in the determination of reference emission levels and the future monitoring, reporting and verification of organic carbon pools.
  • Soil nutrient maps of Sub-Saharan Africa: assessment of soil nutrient content at 250 m spatial resolution using machine learning

    Spatial predictions of soil macro and micro-nutrient content across Sub-Saharan Africa at 250 m spatial resolution and for 0–30 cm depth interval are presented. Predictions were produced for 15 target nutrients: organic carbon (C) and total (organic) nitrogen (N), total phosphorus (P), and extractable—phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), sodium (Na), iron (Fe), manganese (Mn), zinc (Zn), copper (Cu), aluminum (Al) and boron (B). Model training was performed using soil samples from ca. 59,000 locations (a compilation of soil samples from the AfSIS, EthioSIS, One Acre Fund, VitalSigns and legacy soil data) and an extensive stack of remote sensing covariates in addition to landform, lithologic and land cover maps. An ensemble model was then created for each nutrient from two machine learning algorithms—random forest and gradient boosting, as implemented in R packages ranger and xgboost—and then used to generate predictions in a fully-optimized computing system. Cross-validation revealed that apart from S, P and B, significant models can be produced for most targeted nutrients (R-square between 40–85%). Further comparison with OFRA field trial database shows that soil nutrients are indeed critical for agricultural development, with Mn, Zn, Al, B and Na, appearing as the most important nutrients for predicting crop yield. A limiting factor for mapping nutrients using the existing point data in Africa appears to be (1) the high spatial clustering of sampling locations, and (2) missing more detailed parent material/geological maps. Logical steps towards improving prediction accuracies include: further collection of input (training) point samples, further harmonization of measurement methods, addition of more detailed covariates specific to Africa, and implementation of a full spatio-temporal statistical modeling framework.