The combined impacts of land use change and climate change on soil organic carbon stocks in the Ethiopian highlands

Land Use Change (LUC), especially deforestation in tropical regions, significantly contributes to global anthro-pogenic greenhouse gas (GHG) emissions. Here, we address potential combined impacts of LUC and Climate Change (CC) on Soil Organic Carbon (SOC) stocks in the Ethiopian highlands. The soil model Q was employed to predict SOC stocks for various combinations of LUC and CC scenarios until the year 2100. Four reference sce-narios (cropland, bushland, natural forest, and Eucalyptus plantations under contemporary climatic conditions) were evaluated against reported measurements of SOC stocks. We studied impacts of six common LUC scenarios, including deforestation and planting Eucalyptus, on SOC stocks under contemporary and future climates. To assess the impact of CC, effects of elevated temperature (mean annual temperature + 2.6 degrees C) together with three litterfall scenarios (no change in litterfall, a 5% reduction and 22% increase, designated CC0, CCd, and CCi, respectively) were considered to test potential vegetation responses to increases in temperature and atmospheric CO2 concentrations. Most of the tested combinations of LUC and CC led to losses of SOC stocks. Losses were most severe, both relatively and absolutely, in the deforestation scenarios: up to 30% was lost if natural forest was converted to cropland and temperature increased (under the CC0 scenario). Gains in SOC stocks of 4-19% were modelled when sparse vegetation was converted to more dense vegetation like Eucalyptus plantation with sub-stantially increased litterfall (the CCi scenario). Elevated temperature accelerated decomposition rates, leading to circa 8% losses of SOC stocks.We conclude that effects of LUC and CC on SOC stocks are additive and changes in litterfall caused by LUC determine which has the largest impact. Hence, deforestation is the biggest threat to SOC stocks in the Ethiopian highlands, and stocks in sparse vegetation systems like cropland and bushland are more sensitive to CC0 than LUC. We recommend conservation of natural forests and longer rotation periods for Eucalyptus plantations to preserve SOC stocks.Finally, we suggest that use of the Q model is a viable option for national reporting changes in SOC stocks at Tier 3 within the LULUCF sector to the United Nations Framework Convention on Climate Change (UNFCCC) as it is widely applicable and robust, although it only requires input data on a few generally available variables

Knowledge gaps in soil carbon and nitrogen interactions - From molecular to global scale

16 pages, 2 figures, 233 references. Gärdenäs, Annemieke I., et al.-- This paper is the outcome of the workshop entitled ‘Knowledge gaps in soil C and N interactions’ 2–5 June 2008 (Gärdenäs and Stendahl, 2008 A. Gärdenäs and J. Stendahl, Focus on Soils’ Workshop ‘Knowledge Gaps in Soil C and N Interactions’, Dept. of Soil and Environment, Swedish University of Agricultural Sciences (2008) 978-91-85911-52-3 , pp. 40. Gärdenäs and Stendahl, 2008). It was organised and funded by the graduate school ‘Focus on Soils’ at the Swedish University of Agricultural Sciences.The objective of this review was to identify, address and rank knowledge gaps in our understanding of five major soil C and N interactions across a range of scales – from molecular to global. The studied five soil C and N interactions are: i) N controls on the soil emissions of greenhouse gases, ii) plant utilisation of organic N, iii) impact of rhizosphere priming on C and N cycling, iv) impact of black N on the stabilisation of soil organic matter (SOM) and v) representation of fractions of SOM in simulation models. We ranked the identified knowledge gaps according to the importance we attached to them for functional descriptions of soil–climate interactions at the global scale, for instance in general circulation models (GCMs). Both the direct and indirect influences on soil–climate interactions were included. We found that the level of understanding declined as the scale increased from molecular to global for four of the five topics. By contrast, the knowledge level for SOM simulation models appeared to be highest when considered at the ecosystem scale. The largest discrepancy between knowledge level and importance was found at the global modelling scale. We concluded that a reliable quantification of greenhouse gas emissions at the ecosystem scale is of utmost importance for improving soil–climate representation in GCMs. We see as key questions the identification of the role of different N species for the temperature sensitivity of SOM decomposition rates and its consequences for plant available N.Peer reviewe

Impacts of organic amendments on carbon stocks of an agricultural soil - Comparison of model-simulations to measurements

Kristiina Karhu, et al, 'Impacts of organic amendments on carbon stocks of an agricultural soil - Comparison of model-simulations to measurements', Geoderma Vols. 189-190, pp. 606-616, first published online 24 August 2012. The version of record is available online at doi: http://dx.doi.org/10.1016/j.geoderma.2012.06.007 © 2012 Elsevier B.V. All rights reserved.Organic amendments such as straw, green manure or farmyard manure are used to mitigate the soil carbon (C) losses from cultivated soils. We investigated the role of various organic amendments with different C quality for development of soil C stocks, by simulating the Ultuna long-term soil organic matter experiment in Sweden with the Yasso07 model. The aim was to evaluate the performance of the Yasso07 soil carbon model in predicting changes in soil C stocks by comparing modeled C stocks to measurements between years 1956-1991. Uncertainty bounds were calculated from the estimated uncertainty in the C inputs and model parameters. The model performance was assessed in terms of regression coefficient (R 2), root mean square error (RMSE) and model efficiency (ME). The model could very accurately predict the decrease in soil C stock in bare fallow, and in treatments receiving crop litter inputs and N fertilization. Yasso07 could also predict the increase in C stocks due to different organic matter applications, based on the varying quantity and quality of these C inputs. These results support the use of the model for testing the long-term effects of different agricultural measures aiming to mitigate soil C losses.Peer reviewe

Linear regression model of monthly rainfall based on observed data and model (n = number of months, a₁ = trend, a₀ = intercept (mm), and R² = determination coefficient).

<p>Linear regression model of monthly rainfall based on observed data and model (n = number of months, a₁ = trend, a₀ = intercept (mm), and R² = determination coefficient).</p

Location of (a) meteorological stations within the upper Blue Nile Basin (b) the region within Ethiopia.

<p>The altitudinal ranges are divided in accordance with the national agro-climate zonation.</p

Observed rainfall data (1952–2004) and predicted rainfall data (2052–2100).

<p>Observed rainfall data (1952–2004) and predicted rainfall data (2052–2100).</p

Presence of <i>Kiremt</i> and duration of <i>Kiremt</i> and <i>Bega</i> correlated to the index of coordinates, <i>I</i>.

<p>The correlation represents observed data (1952–2004) in circles and predicted data a century later (2052–2100) in squares over the upper Blue Nile Basin.</p

Linear correlation of <i>Kiremt</i> (circles) and annual rainfall (squares) to the index of coordinates, <i>I (Longitude/Latitude)</i>.

<p>Open symbols represent observed rainfall 1952–2004 and filled symbols represent predicted rainfall for 2052–2100.</p

10 Estimating soil carbon stock changes by process-based models and soil inventories - uncertainties and complementarities

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