The charcoal trap: Miombo forests and the energy needs of people

<p>Abstract</p> <p>Background</p> <p>This study evaluates the carbon dioxide and other greenhouse gas fluxes to the atmosphere resulting from charcoal production in Zambia. It combines new biomass and flux data from a study, that was conducted in a <it>miombo </it>woodland within the Kataba Forest Reserve in the Western Province of Zambia, with data from other studies.</p> <p>Results</p> <p>The measurements at Kataba compared protected area (3 plots) with a highly disturbed plot outside the forest reserve and showed considerably reduced biomass after logging for charcoal production. The average aboveground biomass content of the reserve (Plots 2-4) was around 150 t ha^-1, while the disturbed plot only contained 24 t ha^-1. Soil carbon was not reduced significantly in the disturbed plot. Two years of eddy covariance measurements resulted in net ecosystem exchange values of -17 ± 31 g C m^-2y^-1, in the first and 90 ± 16 g C m^-2in the second year. Thus, on the basis of these two years of measurement, there is no evidence that the <it>miombo </it>woodland at Kataba represents a present-day carbon sink. At the country level, it is likely that deforestation for charcoal production currently leads to a per capita emission rate of 2 - 3 t CO₂y^-1. This is due to poor forest regeneration, although the resilience of <it>miombo </it>woodlands is high. Better post-harvest management could change this situation.</p> <p>Conclusions</p> <p>We argue that protection of <it>miombo </it>woodlands has to account for the energy demands of the population. The production at national scale that we estimated converts into 10,000 - 15,000 GWh y^-1of energy in the charcoal. The term "Charcoal Trap" we introduce, describes the fact that this energy supply has to be substituted when woodlands are protected. One possible solution, a shift in energy supply from charcoal to electricity, would reduce the pressure of forests but requires high investments into grid and power generation. Since Zambia currently cannot generate this money by itself, the country will remain locked in the charcoal trap such as many other of its African neighbours. The question arises whether and how money and technology transfer to increase regenerative electrical power generation should become part of a post-Kyoto process. Furthermore, better inventory data are urgently required to improve knowledge about the current state of the woodland usage and recovery. Net greenhouse gas emissions could be reduced substantially by improving the post-harvest management, charcoal production technology and/or providing alternative energy supply.</p

Opportunities for an African greenhouse gas observation system

Global population projections foresee the biggest increase to occur in Africa with most of the available uncultivated land to ensure food security remaining on the continent. Simultaneously, greenhouse gas emissions are expected to rise due to ongoing land use change, industrialisation, and transport amongst other reasons with Africa becoming a major emitter of greenhouse gases globally. However, distinct knowledge on greenhouse gas emissions sources and sinks as well as their variability remains largely unknown caused by its vast size and diversity and an according lack of observations across the continent. Thus, an environmental research infrastructure—as being setup in other regions—is more needed than ever. Here, we present the results of a design study that developed a blueprint for establishing such an environmental research infrastructure in Africa. The blueprint comprises an inventory of already existing observations, the spatial disaggregation of locations that will enable to reduce the uncertainty in climate forcing’s in Africa and globally as well as an overall estimated cost for such an endeavour of about 550 M€ over the next 30 years. We further highlight the importance of the development of an e-infrastructure, the necessity for capacity development and the inclusion of all stakeholders to ensure African ownership

Key traits for ruminant livestock across diverse production systems in the context of climate change: perspectives from a global platform of research farms

Ruminant livestock are raised under diverse cultural and environmental production systems around the globe. Ruminant livestock can play a critical role in food security by supplying high-quality, nutrient-dense food with little or no competition for arable land while simultaneously improving soil health through vital returns of organic matter. However, in the context of climate change and limited land resources, the role of ruminant-based systems is uncertain because of their reputed low efficiency of feed conversion (kilogram of feed required per kilogram of product) and the production of methane as a by-product of enteric fermentation. A growing human population will demand more animal protein, which will put greater pressure on the Earth’s planetary boundaries and contribute further to climate change. Therefore, livestock production globally faces the dual challenges of mitigating emissions and adapting to a changing climate. This requires research-led animal and plant breeding and feeding strategies to optimise ruminant systems. This study collated information from a global network of research farms reflecting a variety of ruminant production systems in diverse regions of the globe. Using this information, key changes in the genetic and nutritional approaches relevant to each system were drawn that, if implemented, would help shape more sustainable future ruminant livestock systems

The ABCflux database : Arctic-boreal CO2 flux observations and ancillary information aggregated to monthly time steps across terrestrial ecosystems

Past efforts to synthesize and quantify the magnitude and change in carbon dioxide (CO2) fluxes in terrestrial ecosystems across the rapidly warming Arctic-boreal zone (ABZ) have provided valuable information but were limited in their geographical and temporal coverage. Furthermore, these efforts have been based on data aggregated over varying time periods, often with only minimal site ancillary data, thus limiting their potential to be used in large-scale carbon budget assessments. To bridge these gaps, we developed a standardized monthly database of Arctic-boreal CO2 fluxes (ABCflux) that aggregates in situ measurements of terrestrial net ecosystem CO2 exchange and its derived partitioned component fluxes: gross primary productivity and ecosystem respiration. The data span from 1989 to 2020 with over 70 supporting variables that describe key site conditions (e.g., vegetation and disturbance type), micrometeorological and environmental measurements (e.g., air and soil temperatures), and flux measurement techniques. Here, we describe these variables, the spatial and temporal distribution of observations, the main strengths and limitations of the database, and the potential research opportunities it enables. In total, ABCflux includes 244 sites and 6309 monthly observations; 136 sites and 2217 monthly observations represent tundra, and 108 sites and 4092 observations represent the boreal biome. The database includes fluxes estimated with chamber (19 % of the monthly observations), snow diffusion (3 %) and eddy covariance (78 %) techniques. The largest number of observations were collected during the climatological summer (June-August; 32 %), and fewer observations were available for autumn (September-October; 25 %), winter (December-February; 18 %), and spring (March-May; 25 %). ABCflux can be used in a wide array of empirical, remote sensing and modeling studies to improve understanding of the regional and temporal variability in CO2 fluxes and to better estimate the terrestrial ABZ CO2 budget. ABCflux is openly and freely available online (Virkkala et al., 2021b, https://doi.org/10.3334/ORNLDAAC/1934).Peer reviewe

Gap-filling eddy covariance methane fluxes : Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting halfhourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET).Peer reviewe

The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.Peer reviewe

Climate control of terrestrial carbon exchange across biomes and continents

Peer reviewe