Kim, D. - G., Thomas, A. D., Pelster, D., Rosenstock, T. S., & Sanz-Cobena, A. (2016). Greenhouse gas emissions from natural ecosystems and agricultural lands in sub-Saharan Africa: synthesis of available data and suggestions for further research. Biogeosciences, 13(16), 4789–4809.
Abstract: This paper summarizes currently available data on greenhouse gas (GHG) emissions from African natural ecosystems and agricultural lands. The available data are used to synthesize current understanding of the drivers of change in GHG emissions, outline the knowledge gaps, and suggest future directions and strategies for GHG emission research. GHG emission data were collected from 75 studies conducted in 22 countries (n = 244) in sub-Saharan Africa (SSA). Carbon dioxide (CO2) emissions were by far the largest contributor to GHG emissions and global warming potential (GWP) in SSA natural terrestrial systems. CO2 emissions ranged from 3.3 to 57.0 Mg CO2 ha(-1) yr(-1), methane (CH4) emissions ranged from -4.8 to 3.5 kg ha(-1) yr(-1) (-0.16 to 0.12 Mg CO2 equivalent (eq.) ha(-1) yr(-1)), and nitrous oxide (N2O) emissions ranged from -0.1 to 13.7 kg ha(-1) yr(-1) (-0.03 to 4.1 Mg CO2 eq. ha(-1) yr(-1)). Soil physical and chemical properties, rewetting, vegetation type, forest management, and land-use changes were all found to be important factors affecting soil GHG emissions from natural terrestrial systems. In aquatic systems, CO2 was the largest contributor to total GHG emissions, ranging from 5.7 to 232.0 Mg CO2 ha(-1) yr(-1), followed by -26.3 to 2741.9 kgCH(4) ha(-1) yr(-1) (-0.89 to 93.2 Mg CO2 eq. ha(-1) yr(-1)) and 0.2 to 3.5 kg N2O ha(-1) yr(-1) (0.06 to 1.0 Mg CO2 eq. ha(-1) yr(-1)). Rates of all GHG emissions from aquatic systems were affected by type, location, hydrological characteristics, and water quality. In croplands, soil GHG emissions were also dominated by CO2, ranging from 1.7 to 141.2 Mg CO2 ha(-1) yr(-1), with -1.3 to 66.7 kgCH(4) ha(-1) yr(-1) (-0.04 to 2.3 Mg CO2 eq. ha(-1) yr(-1)) and 0.05 to 112.0 kg N2O ha(-1) yr(-1) (0.015 to 33.4 Mg CO2 eq. ha(-1) yr(-1)). N2O emission factors (EFs) ranged from 0.01 to 4.1 %. Incorporation of crop residues or manure with inorganic fertilizers invariably resulted in significant changes in GHG emissions, but results were inconsistent as the magnitude and direction of changes were differed by gas. Soil GHG emissions from vegetable gardens ranged from 73.3 to 132.0 Mg CO2 ha(-1) yr(-1) and 53.4 to 177.6 kg N2O ha(-1) yr(-1) (15.9 to 52.9 Mg CO2 eq. ha(-1) yr(-1)) and N2O EFs ranged from 3 to 4 %. Soil CO2 and N2O emissions from agroforestry were 38.6 Mg CO2 ha(-1) yr(-1) and 0.2 to 26.7 kg N2O ha(-1) yr(-1) (0.06 to 8.0 Mg CO2 eq. ha(-1) yr(-1)), respectively. Improving fallow with nitrogen (N)-fixing trees led to increased CO2 and N2O emissions compared to conventional croplands. The type and quality of plant residue in the fallow is an important control on how CO2 and N2O emissions are affected. Throughout agricultural lands, N2O emissions slowly increased with N inputs below 150 kg N ha(-1) yr(-1) and increased exponentially with N application rates up to 300 kg N ha(-1) yr(-1). The lowest yield-scaled N2O emissions were reported with N application rates ranging between 100 and 150 kg N ha(-1). Overall, total CO2 eq. emissions from SSA natural ecosystems and agricultural lands were 56.9 +/- 12.7 x 10(9) Mg CO2 eq. yr(-1) with natural ecosystems and agricultural lands contributing 76.3 and 23.7 %, respectively. Additional GHG emission measurements are urgently required to reduce uncertainty on annual GHG emissions from the different land uses and identify major control factors and mitigation options for low-emission development. A common strategy for addressing this data gap may include identifying priorities for data acquisition, utilizing appropriate technologies, and involving international networks and collaboration.
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Minet, J., Laloy, E., Tychon, B., & François, L. (2015). Bayesian inversions of a dynamic vegetation model at four European grassland sites. Biogeosciences, 12(9), 2809–2829.
Abstract: Eddy covariance data from four European grassland sites are used to probabilistically invert the CARAIB (CARbon Assimilation In the Biosphere) dynamic vegetation model (DVM) with 10 unknown parameters, using the DREAM((ZS)) (DiffeRential Evolution Adaptive Metropolis) Markov chain Monte Carlo (MCMC) sampler. We focus on comparing model inversions, considering both homoscedastic and heteroscedastic eddy covariance residual errors, with variances either fixed a priori or jointly inferred together with the model parameters. Agreements between measured and simulated data during calibration are comparable with previous studies, with root mean square errors (RMSEs) of simulated daily gross primary productivity (GPP), ecosystem respiration (RECO) and evapotranspiration (ET) ranging from 1.73 to 2.19, 1.04 to 1.56 g C m(-2) day(-1) and 0.50 to 1.28 mm day(-1), respectively. For the calibration period, using a homoscedastic eddy covariance residual error model resulted in a better agreement between measured and modelled data than using a heteroscedastic residual error model. However, a model validation experiment showed that CARAIB models calibrated considering heteroscedastic residual errors perform better. Posterior parameter distributions derived from using a heteroscedastic model of the residuals thus appear to be more robust. This is the case even though the classical linear heteroscedastic error model assumed herein did not fully remove heteroscedasticity of the GPP residuals. Despite the fact that the calibrated model is generally capable of fitting the data within measurement errors, systematic bias in the model simulations are observed. These are likely due to model inadequacies such as shortcomings in the photosynthesis modelling. Besides the residual error treatment, differences between model parameter posterior distributions among the four grassland sites are also investigated. It is shown that the marginal distributions of the specific leaf area and characteristic mortality time parameters can be explained by site-specific ecophysiological characteristics.
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Lehtonen, H., Irz, X., Kahiluoto, H., Jansik, C., Kuisma, M., Kuosmanen, N., et al. (2013). Adaptation of the food sector and socio-economic impacts, of climate change in North-East Europe : Dairy sector adaptations and socio-economic, implications of climate change in Finland and Leningrad Oblast in, Russia (ADIOSO)..
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Rusu, T., Moraru, P., Coste, C., Cacovean, H., Chetan, F., & Chetan, C. (2014). Impact of climate change on climatic indicators in Transylvanian Plain, Romania. Journal of Food, Agriculture and Environment, 12(1), 469–473.
Abstract: The condition of land degradation in Transylvanian Plain and its effects, being the result of local extreme physical-geographical conditions, is susceptible to degradation (evidenced by the erodibility index), which overlaps the extreme climatic conditions. Thermal and hydric regime monitoring is necessary in order to identify and implement measures of adaptation to the impacts of climate change. Soil moisture and temperature regimes were evaluated using a set of 20 data logging stations positioned throughout the plain. Each station stores electronic data of ground temperature at 3 depths (10, 30, 50 cm), the humidity at the depth of 10 cm, the air temperature (at 1 m) and precipitations. Climate change in the past few years has significantly altered the climatic indicators of the Transylvanian Plain. Precipitations, although deficient in terms of annual amounts, through their regime, have a negative influence on the plant carpet. Pluvial aggressiveness index reveals, for the research period, a first peak of pluvial aggressiveness during the months of February-April, then in July and in autumn, the months of October-November. This requires special measures for soil conservation, both in autumn and early spring, soil tillage measures being recommended, which ensure the presence of plant debris and vegetation in early spring but especially in summer and autumn. Climatic indicators determined for the period 2008 – 2012 point out, in Transylvanian Plain, a semi-arid Mediterranean climate through the rain factor Lang, respectively semi-arid (in the South) – semi-wet (in the North) according to the De Martonne index. This climatic characterization requires special technological measures for soil conservation.
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Twardy, S., & Kopacz, M. (2014). Comparison of Concentrations and Loads of Macronutrients Brought with Precipitation and Leaching from the Soil Profile..
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