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Carbon Farming: Nature-Based Solutions in Brazil
Abstract
Agriculture can breach the emission gap between countries' and companies' declared goals and actual achievements related to carbon neutrality. But to do so, techniques must change from the monocultural to more integrated systems that provide many eco-services, among which carbon sequestration. The Research Centre for Greenhouse Gas Innovation, which was created in 2016, in its last renewal in 2021, established the first nationwide nature-based solutions empirical data collection from the seven Brazilian biomes, on forestry, pasture, and agriculture, more specifically researching the role of agriculture for carbon sequestration and the possibilities to implement low emissions pastures. Some of the experts that take part in this centre were the source of the information this paper brings, and that is the result of action research techniques, combined with content analyses assisted by Atlas TI. The main conclusions of this paper are: a) that soil health increases the capacity to sequester carbon inside the soil at the same time that it also promotes socio-economic development because of more productivity in the long term and also by bringing extra economic value derived from the better quality sustainability can provide; b) the transition needed away from low-productivity pastures and in direction to carbon farming regenerative projects can contribute to meeting the emission goals; c) there is the risk of carbon pricing increase the value of land, cause social exclusion or influence production decisions away from food; therefore regulation will need to play an important role, d) Brazil has an opportunity to promote circular sustainable bioeconomy and doing so to assume its position as an agri-environmental power.
... The highest gain in soil C stock was 59.63 Mg ha −1 , in an Oxisol under agroforestry [11]. The diversification of plant species is one of the aspects that significantly contribute to increase soil C stocks [39]. This is not only due to the quantity, but, mainly, to the quality of these In the 0-30 cm layer, eight data items of soil C stock indicated losses above 25 Mg ha −1 [15,16,21,26]. ...
... The highest gain in soil C stock was 59.63 Mg ha −1 , in an Oxisol under agroforestry [11]. The diversification of plant species is one of the aspects that significantly contribute to increase soil C stocks [39]. This is not only due to the quantity, but, mainly, to the quality of these plant residues added to the soil with different C/N and lignin/N ratios [40], as is also the case in agroforestry systems [41]. ...
New agricultural practices and land-use intensification in the Cerrado biome have affected the soil carbon stocks. A major part of the native vegetation of the Brazilian Cerrado, a tropical savanna-like ecoregion, has been replaced by crops, which has caused changes in the soil carbon (C) stocks. To ensure the sustainability of this intensified agricultural production, actions have been taken to increase soil C stocks and mitigate greenhouse gas emissions. In the last two decades, new agricultural practices have been adopted in the Cerrado region, and their impact on C stocks needs to be better understood. This subject has been addressed in a systematic review of the existing data in the literature, consisting of 63 articles from the Scopus database. Our review showed that the replacement of Cerrado vegetation by crop species decreased the original soil C stocks (depth 0–30 cm) by 73%, with a peak loss of 61.14 Mg ha⁻¹. However, when analyzing the 0–100 cm layer, 52.4% of the C stock data were higher under cultivated areas than in native Cerrado soils, with a peak gain of 93.6 Mg ha⁻¹. The agricultural practices implemented in the Brazilian Cerrado make low-carbon agriculture in this biome possible.
... Forests are natural carbon and water reserves that regulate air temperature, prevent landslides, and mitigate the effects of climate change [2][3][4]. They also contribute to socioeconomic considerations, where sustainable forestry is now a priority for governments around the world [5,6], especially in emerging economies [7,8]. In turn, forests face important challenges, including natural and anthropogenic disturbances such as pest outbreaks, deforestation, fires, carbon dioxide, warming, drought, and nitrogen deposition [9,10]. ...
Sustainable forestry for the management of forest resources is more important today than ever before because keeping forests healthy has an impact on human health. Recent advances in Unmanned Aerial Vehicles (UAVs), computer vision, and Deep Learning (DL) models make remote sensing for Forest Insect Pest and Disease (FIPD) possible. In this work, a UAV-based remote sensing process, computer vision, and a Deep Learning framework are used to automatically and efficiently detect and map areas damaged by bark beetles in a Mexican forest located in the Hidalgo State. First, the image dataset of the region of interest (ROI) is acquired by a UAV open hardware platform. To determine healthy trees, we use the tree crown detection prebuilt Deepforest model, and the trees diseased by pests are recognized using YOLOv5. To map the area of the damaged region, we propose a method based on morphological image operations. The system generates a comprehensive report detailing the location of affected zones, the total area of the damaged regions, GPS co-ordinates, and both healthy and damaged tree locations. The overall accuracy rates were 88% and 90%, respectively. The results obtained from a total area of 8.2743 ha revealed that 16.8% of the surface was affected and, of the 455 trees evaluated, 34.95% were damaged. These findings provide evidence of a fast and reliable tool for the early evaluation of bark beetle impact, which could be expanded to other tree and insect species.
... It can also be regarded as a promising practice for achieving food security and reducing carbon emissions. Denny et al. [81] showed that soil health improves the soil carbon sequestration capacity and that shifting from low-productivity pastures to carbonbased agricultural regeneration projects can help meet emissions targets. In addition, the large-scale use of biomass in energy-related applications is essential to reduce CO 2 emissions from fossil fuels. ...
Global climate change caused by carbon dioxide emissions has become a hot topic globally. It is of great significance to study how low-carbon landscapes can reduce carbon emissions and improve the ecological environment. In this study, CiteSpace software was used to conduct a bibliometric analysis of the research field. The analysis data were based on 2910 studies published in the research field from 2002 to 2023. By analyzing the number of publications in the research field, cooperation networks, keywords, etc., the research status, processes, and hotspots of low-carbon landscapes were systematically reviewed. The results show the following: (1) Between 2002 and 2023, low-carbon landscape research developed rapidly, gradually becoming a multidisciplinary field. A large number of studies were conducted by relevant institutions and scholars from 106 countries. (2) The research focuses on carbon emission reduction, renewable energy, life cycle assessment, etc. The research mainly goes through the following stages: theoretical research on low-carbon technology, the application of low-carbon technology, and the development of the low-carbon economy. (3) Research frontiers focus on low-carbon landscape emission-reduction technologies, low-carbon landscape research methods, and the development and application of low-carbon materials. This study deeply analyzes the research process of low-carbon landscapes and puts forward a research direction for low-carbon landscapes in future urban development, such as economic benefit assessments, ecosystem restoration and protection, social participation, and policy support, in order to provide a reference for low-carbon landscape research.
... Agriculture, forestry, and other land use (AFOLU) is unique for GHG mitigation, as it can act as a carbon sink and help other sectors [7][8][9]. The Intergovernmental Panel on Climate Change (IPCC) reported that agriculture alone can contribute to net emissions reductions of around 3.5 Gt CO 2-eq yr −1 [9]. ...
Agriculture has historically relied on fossil fuels as the primary source of energy, leading to significant greenhouse gas (GHG) emissions and exacerbating climate change. Brazil, as the third-largest producer and exporter of agricultural goods globally, plays a pivotal role in the transformation towards more sustainable practices. To this end, we propose a methodology to estimate CO2 equivalent (CO2-eq) emissions in agriculture, leveraging previous research on energy use in 23 crops in Brazil. The methodology aims to facilitate the comparison of emissions across different crops and production systems. Indirect emissions account for 36% of the total, while direct emissions account for 64%. Most direct emissions are due to the consumption of fertilizers and pesticides. The average emission per mass of product was 749.53 kg CO2-eq Mg⁻¹, with cotton having the highest emissions and eucalyptus having the lowest emissions per product. The results highlight the importance of assessing GHG emissions from crops to identify emission reduction opportunities and promoting more sustainable agricultural practices. The study’s findings can inform policy recommendations and contribute to the development of sustainable agriculture practices globally, ultimately leading to a more environmentally friendly and economically viable agricultural sector.
... In Equation (1), Cnn e is the assignment made using the e-th expert's suggestion, and Cnn is the geometric average of all assignments, which is the final position value (19,20). Then, the weights of each factor can be determined by calculating the eigenvectors and maximum eigenvalues of the matrix, as shown in Equation (2). ...
Introduction
With the acceleration of urbanization, public health issues have become increasingly prominent in smart city construction, especially in the face of sudden public health crises. A deep research method for public health management based on a 4M perspective (human, machine, materials, methods) is proposed to effectively address these challenges. Methods: The method involves studying the impact of human factors such as population age, gender, and occupation on public health from a human perspective. It incorporates a machine perspective by constructing a public health prediction model using deep neural networks. Additionally, it analyzes resource allocation and process optimization in public health management from the materials and methods perspectives.
Results
The experiments demonstrate that the public health prediction model based on deep neural networks achieved a prediction accuracy of 98.6% and a recall rate of 97.5% on the test dataset. In terms of resource allocation and process optimization, reasonable adjustments and optimizations increased the coverage of public health services by 20% and decreased the response time to public health events by 30%.
Discussion
This research method has significant benefits for addressing the challenges of public health in smart cities. It can improve the efficiency and effectiveness of public health services, helping smart cities respond more quickly and accurately to potential large-scale public health events in the future. This approach holds important theoretical and practical significance.
Introduction. The issues of sustainable development, environmental protection and transition to a low-carbon economy remain relevant for countries worldwide, including the Russian Federation. In the context of global climate change and depletion of traditional energy sources, the need to develop "green" energy has become increasingly important. However, despite the presence of a certain potential, this sector in Russia is still underdeveloped, which is due to various economic, technological and legislative factors. There are numerous theoretical studies on renewable energy sources in the scientific literature, but many aspects of their functioning and development remain insufficiently explored. This scientific gap makes it difficult to fully understand the mechanisms and strategies for its further development.
In connection with the above, this study aims to analyze the current features and trends in the development of green energy in the Russian Federation. The study will also identify potential obstacles and opportunities for the expansion of green energy, as well as ways to overcome negative aspects of its functioning. The objectives of the authors of the study are focused on analyzing the priorities of public policy in Russia as a signatory to the UN Framework Convention on Climate Change, the Paris Climate Agreement, and the Kyoto Protocol on Reducing Greenhouse Gas Emissions, on determining the impact of public policy, technology and investment on the development of renewable energy sources, as well as on studying factors that can accelerate the country's transition to more sustainable energy models. The results of the study aim not only to fill a current gap in scientific knowledge, but also to provide the basis for developing recommendations that can help optimize energy policy in the country.
Materials and Methods. The authors analyzed legislative materials related to the topic of study. Statistical data on the types of energy capacities in the country over the past decade were used. The study was conducted based on regulatory and legal acts of the Russian Federation. Results from monitoring the implementation of government programs and strategies on the issue were also studied. Content analysis, structural and functional analysis were used as main research methods. The current state of affairs in the energy industry was presented based on the analysis of hierarchies, which was a set of elements, each of which reflected a specific step in achieving the goal.
Results. The clear growth of renewable energy sources (RES) use in the total global energy capacity has been explicated. It has been established that Russia is paying significant attention to the development of renewable energy sources, with a focus on introducing public-private partnerships in this area. Methods and principles of government support aimed at developing this sector, as well as competitive technologies (RES.1 and RES.2) for the commissioning of generating facilities using various forms of renewable energy, have been analyzed. It has been confirmed that the energy development roadmap in Russia corresponds to a proposed hierarchy of strategies based on a hybrid approach. Priorities in reducing greenhouse gas emissions and combating pollution have also been established.
Discussion and Conclusion. The data obtained from the research conducted by the authors indicates that Russia has started to move towards the active implementation of renewable energy sources while not disregarding the use of traditional energy from non-renewable resources, taking steps to minimize associated costs. The development of green energy in the country is still proceeding at a slow rate. Its development is hampered, on the one hand, by the already powerful potential of energy capacities, and on the other hand, by negative factors affecting both the production and use of renewable energy sources. While advocating for a faster transition to green energy, it is important to acknowledge that this process is fraught with challenges that must be predicted and addressed. To do so, a thorough analysis of the impact of different types of renewable energy sources on the environment and human health is necessary. Therefore, the introduction of renewable energy sources should be phased and carefully thought out. In this regard, this topic requires further study.
The degradation of pastures in tropical regions, particularly in sandy soils, poses significant challenges to sustainable agricultural practices. Crop–livestock integration (CLI) systems have emerged as a promising strategy to restore these degraded soils. This study evaluated the impact of land-use transitions on soil health in Western São Paulo, Brazil, focusing on the conversion from pasture (Urochloa brizantha) to CLI systems with U. brizantha (CLI-u) and M. maximum (CLI-m). A comprehensive set of chemicals (pH, phosphorus, potassium), physical (aggregate stability, bulk density), and biological (β-glucosidase activity, soil organic carbon) indicators were assessed across four land-use types: native vegetation (NV), pasture (PA), CLI-u, and CLI-m. The Soil Management Assessment Framework (SMAF) was applied to calculate the Soil Health Index (SHI) across three soil depths (0–0.1 m, 0.1–0.2 m, 0.2–0.3 m). At the surface layer (0–0.1 m), PA and NV exhibited the highest SHI values (0.65 and 0.63, respectively), while CLI-m showed a lower SHI (0.56). In the subsurface layer (0.1–0.2 m), CLI-m and NV presented the highest SHI values (0.66 and 0.67, respectively), whereas PA and CLI-u had lower values (0.52 and 0.58). At the deepest layer (0.2–0.3 m), SHI values in CLI systems were comparable to NV (0.56), while PA recorded the lowest SHI (0.48). These results demonstrate that land-use transitions and management practices significantly affect soil health in sandy soils. The findings underscore the potential of CLI systems, particularly those incorporating M. maximum, to enhance biological and chemical soil health indicators in tropical agroecosystems. Further refinement of CLI management strategies is essential to optimize soil health recovery in sandy soil ecosystems.
Addressing the climate crisis is one of the most pressing issues of our time. Confronting climate change and meeting the 1.5 °C target set by the Conference of Parties (COP 28) requires the implementation of long-term carbon-sink measures. Carbon farming (CF) is a scalable, cost-effective, and efficient approach to achieving negative emissions that aligns with the larger goals of sustainability and climate resilience. CF is a carbon management system that facilitates the accumulation and storage of greenhouse gases within the Earth's systems. Notably, one-third of the Earth's land is used for crops and grazing, creating a significant opportunity to capture atmospheric CO2 and convert it into soil organic carbon (SOC). CF enables to establish a mechanism for sequestering carbon in long-term storage forms by improving soil health and agricultural output in the framework of nature-based solutions (NBS). In the midst of growing global efforts to combat climate change, the implementation of sustainable agriculture and soil conservation services (SCS) via ‘carbon farming’ is emerging as a critical approach to addressing environmental issues and promoting a resilient future. Voluntary participation in future carbon offset markets may provide incentives for this approach.
Purpose
Integrating artificial intelligence and quantitative investment has given birth to various agricultural futures price prediction models suitable for nonlinear and non-stationary data. However, traditional models have limitations in testing the spatial transmission relationship in time series, and the actual prediction effect is restricted by the inability to obtain the prices of other variable factors in the future.
Design/methodology/approach
To explore the impact of spatiotemporal factors on agricultural prices and achieve the best prediction effect, the authors innovatively propose a price prediction method for China's soybean and palm oil futures prices. First, an improved Granger Causality Test was adopted to explore the spatial transmission relationship in the data; second, the Seasonal and Trend decomposition using Loess model (STL) was employed to decompose the price; then, the Apriori algorithm was applied to test the time spillover effect between data, and CRITIC was used to extract essential features; finally, the N-Beats model was selected as the prediction model for futures prices.
Findings
Using the Apriori and STL algorithms, the authors found a spillover effect in agricultural prices, and past trends and seasonal data will impact future prices. Using the improved Granger causality test method to analyze the unidirectional causality relationship between the prices, the authors obtained a spatial effect among the agricultural product prices. By comparison, the N-Beats model based on the spatiotemporal factors shows excellent prediction effects on different prices.
Originality/value
This paper addressed the problem that traditional models can only predict the current prices of different agricultural products on the same date, and traditional spatial models cannot test the characteristics of time series. This result is beneficial to the sustainable development of agriculture and provides necessary numerical and technical support to ensure national agricultural security.
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