Soil Carbon Model Research Articles

Soil carbon model Yasso07 graphical user interface

In this article, we present a graphical user interface software for the litter decomposition and soil carbon model Yasso07 and an overview of the principles and formulae it is based on. The software can be used to test the model and use it in simple applications. Yasso07 is applicable to upland soils of different ecosystems worldwide, because it has been developed using data covering the global climate conditions and representing various ecosystem types. As input information, Yasso07 requires data on litter input to soil, climate conditions, and land-use change if any. The model predictions are given as probability densities representing the uncertainties in the parameter values of the model and those in the input data – the user interface calculates these densities using a built-in Monte Carlo simulation.

Soil carbon stock increases in the organic layer of boreal middle-aged stands

Abstract. Changes in the soil carbon stock can potentially have a large influence on global carbon balance between terrestrial ecosystems and atmosphere. Since carbon sequestration of forest soils is influenced by human activities, reporting of the soil carbon pool is a compulsory part of the national greenhouse gas (GHG) inventories. Various soil carbon models are applied in GHG inventories, however, the verification of model-based estimates is lacking. In general, the soil carbon models predict accumulation of soil carbon in the middle-aged stands, which is in good agreement with chronosequence studies and flux measurements of eddy sites, but they have not been widely tested with repeated measurements of permanent plots. The objective of this study was to evaluate soil carbon changes in the organic layer of boreal middle-aged forest stands. Soil carbon changes on re-measured sites were analyzed by using soil survey data that was based on composite samples as a first measurement and by taking into account spatial variation on the basis of the second measurement. By utilizing earlier soil surveys, a long sampling interval, which helps detection of slow changes, could be readily available. The range of measured change in the soil organic layer varied from −260 to 1260 g m−2 over the study period of 16–19 years and 23 ± 2 g m−2 per year, on average. The increase was significant in 6 out of the 38 plots from which data were available. Although the soil carbon change was difficult to detect at the plot scale, the overall increase measured across the middle-aged stands agrees with predictions of the commonly applied soil models. Further verification of the soil models is needed with larger datasets that cover wider geographical area and represent all age classes, especially young stands with potentially large soil carbon source.

Old and stable soil organic matter is not necessarily chemically recalcitrant: implications for modeling concepts and temperature sensitivity

AbstractSoil carbon turnover models generally divide soil carbon into pools with varying intrinsic decomposition rates. Although these decomposition rates are modified by factors such as temperature, texture, and moisture, they are rationalized by assuming chemical structure is a primary controller of decomposition. In the current work, we use near edge X‐ray absorption fine structure (NEXAFS) spectroscopy in combination with differential scanning calorimetry (DSC) and alkaline cupric oxide (CuO) oxidation to explore this assumption. Specifically, we examined material from the 2.3–2.6 kg L−1 density fraction of three soils of different type (Oxisol, Alfisol, Inceptisol). The density fraction with the youngest 14C age (Oxisol, 107 years) showed the highest relative abundance of aromatic groups and the lowest O‐alkyl C/aromatic C ratio as determined by NEXAFS. Conversely, the fraction with the oldest C (Inceptisol, 680 years) had the lowest relative abundance of aromatic groups and highest O‐alkyl C/aromatic C ratio. This sample also had the highest proportion of thermally labile materials as measured by DSC, and the highest ratio of substituted fatty acids to lignin phenols as indicated by CuO oxidation. Therefore, the organic matter of the Inceptisol sample, with a 14C age associated with ‘passive’ pools of carbon (680 years), had the largest proportion of easily metabolizable organic molecules with low thermodynamic stability, whereas the organic matter of the much younger Oxisol sample (107 years) had the highest proportion of supposedly stable organic structures considered more difficult to metabolize. Our results demonstrate that C age is not necessarily related to molecular structure or thermodynamic stability, and we suggest that soil carbon models would benefit from viewing turnover rate as codetermined by the interaction between substrates, microbial actors, and abiotic driving variables. Furthermore, assuming that old carbon is composed of complex or ‘recalcitrant’ compounds will erroneously attribute a greater temperature sensitivity to those materials than they may actually possess.

Soil carbon change and its affecting factors following afforestation in China

The effects of afforestation on soil carbon (C) stocks are of concern to the plantation carbon budget and the mitigation of global warming. China has the largest tree plantation areas in the world, but the effects of afforestation on soil C are poorly understood. We reviewed Chinese data of soil C, available from 70 publications, to analyze the change of soil C and its affecting factors following afforestation. Our results indicated that afforestation could accumulate soil C in China, with the accumulation rate of 42.05, 30.07 and 73.94 g C m −2 yr −1 in 0–20, 20–40 and 0–40 cm soil depths. Five years after afforestation, plantations began to accumulate soil C rapidly, and plantations with the age of 10–20 years old had the highest soil C accumulation rate. Afforestation on former cropland and wasteland had higher accumulation rates of soil C than on former grassland. In middle-temperature and warmer regions, the accumulation rate of soil C was higher than that in colder regions, and in semi-humid regions, the accumulation rate of soil C was significantly higher than that in semi-arid regions. Soil C accumulated rapidly in broadleaved and mixed plantations, and the accumulation rate in broadleaved was higher than that in coniferous plantations. Although some uncertainties existed with regard to the data and methods, a reasonable analysis of soil C change following afforestation in China was achieved for the first time. Future studies should be fulfilled with improved sampling and analytical techniques, reliable soil carbon models and synchronous investigation of soil nitrogen dynamics.

Modelling soil carbon and nitrogen cycles during land use change. A review

Forested soils are being increasingly transformed to agricultural fields in response to growing demands for food crop. This modification of the land use is known to result in deterioration of soil properties, in particular its fertility. To reduce the impact of the human activities and mitigate their effects on the soil, it is important to understand the factors responsible for the modification of soil properties. In this paper we reviewed the principal processes affecting soil quality during land use changes, focusing in particular on the effect of soil moisture dynamics on soil carbon (C) and nitrogen (N) cycles. Both physical and biological processes, including degradation of litter and humus, and soil moisture evolution at the diurnal and seasonal time scales were considered, highlighting the impact of hydroclimatic variability on nutrient turnover along with the consequences of land use changes from forest to agricultural soil and vice-versa. In order to identify to what extent different models are suitable for long-term predictions of soil turnover, and to understand whether some simulators are more suited to specific environmental conditions or ecosystems, we enumerated the principal features of the most popular existing models dealing with C and N turnover. Among these models, we considered in detail a mechanistic compartment-based model. To show the capabilities of the model and to demonstrate how it can be used as a predictive tool to forecast the effects of land use changes on C and N dynamics, four different scenarios were studied, intertwining two different climate conditions (with and without seasonality) with two contrasting soils having physical properties that are representative of forest and agricultural soils. The model incorporates synthetic time series of stochastic precipitation, and therefore soil moisture evolution through time. Our main findings in simulating these scenarios are that (1) forest soils have higher concentrations of C and N than agricultural soils as a result of higher litter decomposition; (2) high frequency changes in water saturations under seasonal climate scenarios are commensurate with C and N concentrations in agricultural soils; and (3) due to their different physical properties, forest soils attenuate the seasonal climate-induced frequency changes in water saturation, with accompanying changes in C and N concentrations. The model was shown to be capable of simulating the long term effects of modified physical properties of agricultural soils, being thus a promising tool to predict future consequences of practices affecting sustainable agriculture, such as tillage (leading to erosion), ploughing, harvesting, irrigation and fertilization, leading to C and N turnover changes and in consequence, in terms of agriculture production.

Analysis of soil carbon transit times and age distributions using network theories

The long‐term soil carbon dynamics may be approximated by networks of linear compartments, permitting theoretical analysis of transit time (i.e., the total time spent by a molecule in the system) and age (the time elapsed since the molecule entered the system) distributions. We compute and compare these distributions for different network configurations, ranging from the simple individual compartment, to series and parallel linear compartments, feedback systems, and models assuming a continuous distribution of decay constants. We also derive the transit time and age distributions of some complex, widely used soil carbon models (the compartmental models CENTURY and Rothamsted, and the continuous‐quality Q‐Model), and discuss them in the context of long‐term carbon sequestration in soils. We show how complex models including feedback loops and slow compartments have distributions with heavier tails than simpler models. Power law tails emerge when using continuous‐quality models, indicating long retention times for an important fraction of soil carbon. The responsiveness of the soil system to changes in decay constants due to altered climatic conditions or plant species composition is found to be stronger when all compartments respond equally to the environmental change, and when the slower compartments are more sensitive than the faster ones or lose more carbon through microbial respiration.

Bayesian calibration as a tool for initialising the carbon pools of dynamic soil models

The most widely applied soil carbon models partition the soil organic carbon into two or more kinetically defined conceptual pools. The initial distribution of soil organic matter between these pools influences the simulations. Like many other soil organic carbon models, the DAYCENT model is initialised by assuming equilibrium at the beginning of the simulation. However, as we show here, the initial distribution of soil organic matter between the different pools has an appreciable influence on simulations, and the appropriate distribution is dependent on the climate and management at the site before the onset of a simulated experiment. If the soil is not in equilibrium, the only way to initialise the model is to simulate the pre-experimental period of the site. Most often, the site history, in terms of land use and land management is often poorly defined at site level, and entirely unknown at regional level. Our objective was to identify a method that can be applied to initialise a model when the soil is not in equilibrium and historic data are not available, and which quantifies the uncertainty associated with initial soil carbon distribution. We demonstrate a method that uses Bayesian calibration by means of the Accept–Reject algorithm, and use this method to calibrate the initial distribution of soil organic carbon pools against observed soil respiration measurements. It was shown that, even in short-term simulations, model initialisation can have a major influence on the simulated results. The Bayesian calibration method quantified and reduced the uncertainties in initial carbon distribution.

Short- and long-term responses of total soil organic carbon to harvesting in a northern hardwood forest

The long-term response of total soil organic carbon pools (‘total SOC’, i.e. soil and dead wood) to different harvesting scenarios in even-aged northern hardwood forest stands was evaluated using two soil carbon models, CENTURY and YASSO, that were calibrated with forest plot empirical data in the Green Mountains of Vermont. Overall, 13 different harvesting scenarios that included four levels of aboveground biomass removal (20%, 40%, 60% and 90%) and four different rotation lengths (60 year, 90 year, 120 year, and No Rotation (NR)) were simulated for a 360 year period. Simulations indicate that following an initial post-harvest increase, total SOC decreases for several decades until carbon inputs into the soil pool from the re-growth are greater than losses due to decomposition. At this point total SOC begins to gradually increase until the next harvest. One consequence of this recovery pattern is that between harvests, the size of the SOC pool in a stand may change from −7 to 18% of the pre-harvest pool, depending on the soil pool considered. Over 360 years, the average annual decrease in total SOC depends on the amount of biomass removed, the rotation length, and the soil pool considered. After 360 years a stand undergoing the 90yr-40% scenario will have 15% less total SOC than a non-harvested stand. Long-term declines in total SOC greater than 10% were observed in the 60yr-60%, 60yr-90%, and 90yr-90% scenarios. Long-term declines less than 5% were observed in scenarios with 120 year rotations that remove 60% or less of the aboveground biomass. The long-term decreases simulated here for common management scenarios in this region would require intensive sampling procedures to be detectable.

Modeling watershed-scale sequestration of soil organic carbon for carbon credit programs

Impending risks associated with climate change have forced the global community to devise tradable pollution permit or “cap and trade” approaches to control the release of greenhouse gases. In the U.S, soils have the potential to offset about 10 percent of annual CO 2 emissions; however, if carbon credits are to be included in greenhouse gas control programs, soil organic carbon (SOC) sequestration rates associated with agricultural land uses must be computed at a watershed scale. The Soil Water Assessment Tool (SWAT) water quality model, the Water Erosion Prediction Project (WEPP) erosion model, and the CENTURY 4.0 a soil carbon model were used to simulate carbon sequestration rates for 160 crop-tillage rotations in 272 sub-basins of the Big Creek watershed (∼12,300 hectares). Under annual crops, only no-till in a corn-soybean rotation, on low to moderate slopes results in net gains in SOC. Substantial annual rates of SOC sequestration occur only under perennial crops such as Conservation Reserve Program (CRP; 0.14 t/ha without erosion; 0.08 with erosion), pasture (0.67 t/ha without erosion; 0.58 with erosion), hay (0.88 t/ha without erosion; 0.52 with erosion), and forest (2.66 t/ha without erosion; 2.49 with erosion). Erosion thus has a large effect on the spatial distribution of field-measured SOC by moving it down slope and increasing its spatial variability. Because of this, carbon credit programs should be based on field practices, thus targeting the locations where the sequestration of atmospheric carbon actually occurs and minimizing monitoring costs. Developing model-based estimates of SOC sequestration rates of field practices at many locations would thus greatly serve the needs of carbon crediting programs.

Estimating the Soil Carbon Stocks for a Pinus densiflora Forest Using the Soil Carbon Model, Yasso

The soil carbon stock for a Pinus densiflora forest at Gwangneung, central Korea was estimated using the soil carbon model, Yasso. The soil carbon stock measured in the forest was 43.73 t C ha-1, and the simulated initial (steady state) soil carbon stock and the simulated current soil carbon stock in 2007 were 39.19 t C ha-1 and 38.90 t C ha-1, respectively. Under the assumption of a 0.1℃ increase in mean annual temperature per year, the decomposition and litter fractionation rates increased from 0.28 to 0.56 % year-1 and the soil carbon stock decreased from 0.03 to 0.12 % year-1. Yasso is a simple and general model that can be applied in cases where there is insufficient input information. However, in order to obtain more accurate estimates in Korea, parameters need to be recalibrated under Korean climatic and vegetation conditions. In addition, the Yasso model needs to be linked to other models to generate better litter input data.

Simulating soil organic matter with CQESTR (v. 2.0): Model description and validation against long-term experiments across North America

Soil carbon (C) models are important tools for examining complex interactions between climate, crop and soil management practices, and to evaluate the long-term effects of management practices on C-storage potential in soils. CQESTR is a process-based carbon balance model that relates crop residue additions and crop and soil management to soil organic matter (SOM) accretion or loss. This model was developed for national use in U.S and calibrated initially in the Pacific Northwest. Our objectives were: (i) to revise the model, making it more applicable for wider geographic areas including potential international application, by modifying the thermal effect and incorporating soil texture and drainage effects, and (ii) to recalibrate and validate it for an extended range of soil properties and climate conditions. The current version of CQESTR (v. 2.0) is presented with the algorithms necessary to simulate SOM at field scale. Input data for SOM calculation include crop rotation, aboveground and belowground biomass additions, tillage, weather, and the nitrogen content of crop residues and any organic amendments. The model was validated with long-term data from across North America. Regression analysis of 306 pairs of predicted and measured SOM data under diverse climate, soil texture and drainage classes, and agronomic practices at 13 agricultural sites having a range of SOM (7.3–57.9 g SOM kg −1), resulted in a linear relationship with an r 2 of 0.95 ( P < 0.0001) and a 95% confidence interval of 4.3 g SOM kg −1. Using the same data the version 1.0 of CQESTR had an r 2 of 0.71 with a 95% confidence interval of 5.5 g SOM kg −1. The model can be used as a tool to predict and evaluate SOM changes from various management practices and offers the potential to estimate C accretion required for C credits.

Modelling soil carbon sequestration of intensively monitored forest plots in Europe by three different approaches

Information on soil carbon sequestration and its interaction with nitrogen availability is rather limited, since soil processes account for the most significant unknowns in the C and N cycles. In this paper we compare three completely different approaches to calculate carbon sequestration in forest soils. The first approach is the limit-value concept, in which the soil carbon accumulation is estimated by multiplying the annual litter fall with the recalcitrant fraction of the decomposing plant litter, which depends on the nitrogen and calcium content in the litter. The second approach is the N-balance method, where carbon sequestration is calculated from the nitrogen retention in the soil multiplied with the present soil C/N ratio in organic layer and mineral topsoil. The third approach is the dynamic SMART2 model in combination with an empirical approach to assess litter fall inputs. The comparison is done by first validating the methods at three chronosequences with measured C pools, two in Denmark and one in Sweden, and then application on 192 intensive monitoring plots located in the Northern and Western part of Europe. Considering all three chronosequences, the N-balance method was generally most in accordance with the C pool measurements, although the SMART2 model was also quite consistent with the measurements at two chronosequences. The limit-value approach generally overestimated the soil carbon sequestration. At the intensive monitoring plots, the limit-value concept calculated the highest carbon sequestration, ranging from 160 to 978 kg ha −1 year −1, followed by the N-balance method which ranged from 0 to 535 kg ha −1 year −1. With SMART2 we calculated the lowest carbon sequestration from −30 to 254 kg ha −1 year −1. All the three approaches found lower carbon sequestration at a latitude from 60 to 70° compared to latitudes from 40 to 50 and from 50 to 60. Considering the validation of the three approaches, the range in results from both the N-balance method and SMART2 model seems most appropriate.

Global estimates of soil carbon sequestration via livestock waste: a STELLA simulation

It has become increasingly well documented that human activities are enhancing the greenhouse effect and altering the global climate. Identifying strategies to mitigate atmospheric carbon dioxide emissions on the national level are therefore critical. Fossil fuel combustion is primarily responsible for the perturbation of the global carbon cycle, although the influence of humans extends far beyond the combustion of fossil fuels. Changes in land use arising from human activities contribute substantially to atmospheric carbon dioxide; however, land use changes can act as a carbon dioxide sink as well. A soil carbon model was built using STELLA to explore how soil organic carbon sequestration (SOC) varies over a range of values for key parameters and to estimate the amount of global soil carbon sequestration from livestock waste. To obtain soil carbon sequestration estimates, model simulations occurred for 11 different livestock types and with data for eight regions around the world. The model predicted that between 1980 and 1995, United States soils were responsible for the sequestration of 444–602 Tg C from livestock waste. Model simulations further predicted that during the same period, global soil carbon sequestration from livestock waste was 2,810–4,218 Tg C. Our estimates for global SOC sequestration are modest in proportion to other terrestrial carbon sinks (i.e. forest regrowth); however, livestock waste does represent a potential for long-term soil carbon gain. SOC generated from livestock waste is another example of how human activities and land use changes are altering soil processes around the world.

Comparison of multivariate methods for inferential modeling of soil carbon using visible/near-infrared spectra

In order to reduce costs and time in the analysis of soil properties, visible/near-infrared diffuse reflectance spectroscopy (VNIRS) has been proposed. Since various pre-processing transformations and calibration techniques are in use to analyze soil spectral data, much uncertainty still exists about predictive soil modeling. We investigated the feasibility of VNIRS to determine the concentration of carbon in soils collected in the Santa Fe River Watershed, Florida. A total of 554 soil samples (400 for calibration, and 154 for validation) were collected to a depth from 0 to 180 cm. Total carbon was measured by dry combustion, after sieving (2 mm), air-drying and ball-milling, and is reported in mg kg − 1 . Reflectance measurements from 350 nm to 2500 nm were collected in a controlled laboratory environment. Five multivariate techniques (stepwise multiple linear regression, principal components regression, partial least-squares regression, regression tree and committee trees) and thirty pre-processing transformations (including derivatives, normalization and non-linear transformations) of spectral data were compared with the aim of identifying the best combination to predict soil carbon. The coefficient of determination ( R 2), the root mean square error (RMSE), and the residual prediction deviation (RPD) were used to evaluate the models. The combination of multivariate technique and pre-processing transformation that provided the highest coefficient of determination for the validation set ( R v 2) and RPD, and lowest root mean square error for the validation set (RMSE v), was committee trees associated with Norris gap derivative with a search window of 7 measurements ( R v 2 = 0.86, RMSE v = 0.170, RPD = 2.68). When considering the overall results of the multivariate techniques across all tested pre-processing transformations, partial least-squares regression performed best (lowest average RMSE v across all pre-processing transformations), followed by stepwise multiple linear regression, and committee trees. In terms of pre-processing transformations, Savitzky–Golay derivatives consistently improved the models of soil carbon, being among the five best pre-processing transformations for all of the multivariate techniques tested. Norris gap derivative was the preferred data preparation for the tree-based techniques. Except for standard variate transformation, normalization techniques performed worse than expected. The RPD of the best VNIRS models were higher than 2.50, which suggest that the VNIRS models produced in this study are robust and stable enough to be applied for similar soils.

Soil carbon modelling as a tool for carbon balance studies in forestry

Soils represent a remarkable stock of carbon, and forest soils are estimated to hold half of the global stock of soil carbon. Topical concern about the effects of climate change and forest management on soil carbon as well as practical reporting requirements set by climate conventions have created a need to assess soil carbon stock changes reliably and transparently. The large spatial variability of soil carbon commensurate with relatively slow changes in stocks hinders the assessment of soil carbon stocks and their changes by direct measurements. Due to these difficulties in measuring soil carbon, models widely serve to estimate carbon stocks and stock changes in soils. This dissertation aimed to develop the soil carbon model YASSO for upland forest soils. The model was aimed to take into account the most important processes controlling the decomposition in soils, yet remain simple enough to ensure its practical applicability in different applications. The model structure and assumptions were presented and the model parameters were defined with empirical measurements. The model was evaluated by studying the sensitivities of the model results to parameter values, by estimating the precision of the results with an uncertainty analysis, and by assessing the accuracy of the model by comparing the predictions against measured data and by comparing the model results to the results of an alternative model. The model was applied at the stand level to study the effects of intensified biomass extraction on the forest carbon balance. In another application, the effects of energy use of forest residues on soil carbon were quantified with the model. The model calculated soil carbon deficit was presented as an indirect CO2 emission. This emission was then compared to other emissions from the forest residue production chain and burning. Finally, the model was applied in an inventory based method to assess the national scale forest carbon balance for Finland’s forests from 1922 to 2004. According to the results of the uncertainty and sensitivity analyses, the soil carbon stock estimates of the model are uncertain, because those parameters that most strongly affect these estimates are poorly known. Carbon stock change estimates, on the other hand, are rather reliable, because the parameters determining these estimates are known better. According to a test conducted with a Canadian litterbag experiment, YASSO managed to describe sufficiently the effects of both the variable litter and climatic conditions on decomposition. When combined with the stand models or other systems providing litter information, the dynamic approach of the model proved to be powerful for estimating changes in soil carbon stocks on different scales. The climate dependency of the model, the effects of nitrogen on decomposition and forest growth as well as the effects of soil texture on soil carbon stock dynamics are areas for development when considering the applicability of the model to different research questions, different land use types and wider geographic regions. Intensified biomass extraction affects soil carbon stocks, and these changes in stocks should be taken into account when considering the net effects of forest residue utilisation as energy. On a national scale, soil carbon stocks play an important role in forest carbon balances.

Factors controlling spatio‐temporal variation in carbon dioxide efflux from surface litter, roots, and soil organic matter at four rain forest sites in the eastern Amazon

This study explored biotic and abiotic causes for spatio‐temporal variation in soil respiration from surface litter, roots, and soil organic matter over one year at four rain forest sites with different vegetation structures and soil types in the eastern Amazon, Brazil. Estimated mean annual soil respiration varied between 13–17 t C ha−1 yr−1, which was partitioned into 0–2 t C ha−1 yr−1 from litter, 6–9 t C ha−1 yr−1 from roots, and 5–6 t C ha−1 yr−1 from soil organic matter. Litter contribution showed no clear seasonal change, though experimental precipitation exclusion over a one‐hectare area was associated with a ten‐fold reduction in litter respiration relative to unmodified sites. The estimated mean contribution of soil organic matter respiration fell from 49% during the wet season to 32% in the dry season, while root respiration contribution increased from 42% in the wet season to 61% during the dry season. Spatial variation in respiration from soil, litter, roots, and soil organic matter was not explained by volumetric soil moisture or temperature. Instead, spatial heterogeneity in litter and root mass accounted for 44% of observed spatial variation in soil respiration (p &lt; 0.001). In particular, variation in litter respiration per unit mass and root mass accounted for much of the observed variation in respiration from litter and roots, respectively, and hence total soil respiration. This information about patterns of, and underlying controls on, respiration from different soil components should assist attempts to accurately model soil carbon dioxide fluxes over space and time.

Changes in mineral soil organic carbon stocks in the croplands of European Russia and the Ukraine, 1990–2070; comparison of three models and implications for climate mitigation

Three soil carbon models (RothC, CANDY and the Model of Humus Balance) were used to estimate the impacts of climate change on agricultural mineral soil carbon stocks in European Russia and the Ukraine using detailed spatial data on land-use, future land-use, cropping patterns, agricultural management, climate and soil type. Scenarios of climate were derived from the Hadley Centre climate Version 3 (HadCM3) model; future yields were determined using the Soil–Climate–Yield model, and land use was determined from regional agricultural and economic data and a model of agricultural economics. The models suggest that optimal management, which entails the replacement of row crops with other crops, and the use of extra years of grass in the rotation could reduce Soil organic carbon (SOC) loss in the croplands of European Russia and the Ukraine by 30–44% compared to the business-as-usual management. The environmentally sustainable management scenario (SUS), though applied for a limited area within the total region, suggests that much of this optimisation could be realised without damaging profitability for farmers.

A Modelling Framework for Addressing the Synergies Between Global Conventions Through Land Use Changes: Carbon Sequestration, Biodiversity Conservation, Prevention of Land Degradation and Food Security in Agricultural and Forested Lands in Developing Countries

This paper proposed a methodological framework for the assessment of carbon stocks and the development and identification of land use, land use change and land management scenarios, whereby enhancing carbon sequestration synergistically increases biodiversity, the prevention of land degradation and food security through the increases in crop yields. The framework integrates satellite image interpretation, computer modelling tools (i.e. software customization of off-the-shelf soil organic matter turnover simulation models) and Geographical Information Systems (GIS). The framework addresses directly and indirectly the cross-cutting ecological concerns foci of major global conventions: climate change, biodiversity, the combat of desertification and food security. Their synergies are targeted by providing procedures for assessing and identifying simultaneously carbon sinks, potential increases in plant diversity, measures to prevent land degradation and enhancements in food security through crop yields, implicit in each land use change and land management scenario. The scenarios aim at providing “win-win” options to decision makers through the framework’s decision support tools. Issues concerning complex model parameterization and spatial representation were tackled through tight coupling soil carbon models to GIS via software customization. Results of applying the framework in the field in two developing countries indicate that reasonably accurate estimates of carbon sequestration can be obtained through modeling; and that alternative best soil organic matter management practices that arrest shifting “slash-and-burn” cultivation and prevent burning and emissions, can be identified. Such options also result in increased crop yields and food security for an average family size in the area, while enhancing biodiversity and preventing land degradation. These options demonstrate that the judicious management of organic matter is central to greenhouse gas mitigation and the attainment of synergistic ecological benefits, which is the concern of global conventions. The framework is to be further developed through successive approximations and refinement in future, extending its applicability to other landscapes.

Modeling Soil Carbon and Nitrogen Dynamics in No‐till and Conventional Tillage Using PASTIS Model

The performance of the PASTIS model was evaluated to simulate soil C and N fluxes under real field conditions with conventional moldboard plowing (CT) and no‐tillage (NT) systems differentiated for 33 yr for a loamy soil in northern France. Afterward, the influences on the C and N fluxes by soil temperature, soil water content, and quantity and localization of soil organic matter (SOM) and crop residues in the soil profile were determined. The model PASTIS was able to provide good simulations for the dynamics of soil water content and temperature, CO2 emissions, residue decomposition, and N mineralization. Simulation showed that the presence of the mulch layer in NT reduced cumulative total water evaporation and increased water drainage at the bottom of the 25‐cm depth. Furthermore, simulation showed that the larger cumulative total CO2 flux in NT resulted from larger CO2 emissions as a product of crop residue decomposition and not as product of SOM decomposition. The larger amount of accumulated residues of previous crops in NT more than compensated for the slower residue decomposition rate of surface compared with incorporated residues. It was the water content of the surface crop residues that largely controlled the magnitude of this difference in decomposition rate of the crop residues between CT and NT. This means that, besides the amount of crop residues in both tillage systems, the distribution of rainfall and potential evaporation have a large influence on the differences in C and N fluxes between the two tillage systems

Evaluation of two soil carbon models using two Kenyan long term experimental datasets

RothC and Century are two of the most widely used soil organic matter (SOM) models. However there are few examples of specific parameterisation of these models for environmental conditions in East Africa. The aim of this study was therefore, to evaluate the ability of RothC and the Century to estimate changes in soil organic carbon (SOC) resulting from varying land use/management practices for the climate and soil conditions found in Kenya. The study used climate, soils and crop data from a long term experiment (1976–2001) carried out at The Kabete site at The Kenya National Agricultural Research Laboratories (NARL, located in a semi-humid region) and data from a 13 year experiment carried out in Machang’a (Embu District, located in a semi-arid region). The NARL experiment included various fertiliser (0, 60 and 120 kg of N and P 2O 5 ha −1), farmyard manure (FYM—5 and 10 t ha −1) and plant residue treatments, in a variety of combinations. The Machang’a experiment involved a fertiliser (51 kg N ha −1) and a FYM (0, 5 and 10 t ha −1) treatment with both monocropping and intercropping. At Kabete both models showed a fair to good fit to measured data, although Century simulations for treatments with high levels of FYM were better than those without. At the Machang’a site with monocrops, both models showed a fair to good fit to measured data for all treatments. However, the fit of both models (especially RothC) to measured data for intercropping treatments at Machang’a was much poorer. Further model development for intercrop systems is recommended. Both models can be useful tools in soil C predictions, provided time series of measured soil C and crop production data are available for validating model performance against local or regional agricultural crops.