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Inclination and spatial variability in soil and litter properties influence soil greenhouse gas (GHG) fluxes, and thus on-going climate change, but their relationship in forest ecosystems is poorly understood. To elucidate this, we explored the effect of inclination, distance to a stream, soil moisture, soil temperature, and other soil and litter properties on soil-atmosphere fluxes of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) with automated static chambers in a temperate upland forest in Eastern Austria. We hypothesised that soil CO2 emissions and CH4 uptake are higher in sloped locations with lower soil moisture content, whereas soil N2O emissions are higher in flat, wetter locations. During the measurement period, soil CO2 emissions were significantly higher on flat locations (p < 0.05), and increased with increasing soil temperature (p < 0.001) and decreasing soil moisture (p < 0.001). The soil acted as a CH4 sink, and CH4 uptake was not significantly related to inclination. However, CH4 uptake was significantly higher at locations furthest away from the stream compared to at the stream (p < 0.001), and positively related to litter weight and soil C content (p < 0.01). N2O fluxes were significantly higher on flat locations and further away from the stream (p < 0.05), and increased with increasing soil moisture (p < 0.001), soil temperature (p < 0.001) and litter depth (p < 0.05). Overall, this study underlines the importance of inclination and the resulting soil and litter properties in predicting GHG fluxes from forest soils and therefore their potential source-sink balance.

To better understand interglacial paleosol development by quantifying the paleosol development processes on the Chinese Loess Plateau (CLP), we need a soil genesis model calibrated for long timescales. Here, we calibrate a process-based soil genesis model, SoilGen2, by confronting simulated and measured soil properties for the Holocene and MIS-13 paleosols formed in the CLP for various parameter settings. The calibration was made sequentially on three major soil process formulations, including decalcification, clay migration and soil organic carbon, which are represented by various process parameters. The order of the tuned parameters was based on sensitivity analyses performed previously on the loess in West European and the CLP. After the calibration of the intrinsic soil process parameters, the effect of uncertainty of dust deposition rate on calibration results was assessed. Our results show that the simulated soil properties are very sensitive to ten reconstructed dust deposition scenarios, reflecting the propagation of uncertainty of dust deposition in model simulations. Our results also show the equal importance of calibrating soil process parameters and defining correct external forcings in the future use of soil models. Our calibrated model allows interglacial soil simulation in the CLP over long timescales.

We quantified some mental and qualitative concepts about the soil-landscape relationships by numerical analysis of landforms in soil identification using diversity indices and conditional probability with a given sample size in Darab and Khosuyeh plains (a rural district) in the south of Iran in Fars province. The geomorphology map was prepared based on the Zinck method and used as a basic design for soil sampling. Finally, 200 soil profiles (0-150 cm) were excavated and described. Diversity indices and conditional probability were calculated based on soil taxonomic and geomorphological hierarchies. The results showed that diversity indices increase from landscape to landform level. The lowest and highest diversity indices were obtained at each geomorphic level for the soil order and soil family. The geomorphic diversity based on the soil taxonomy hierarchy showed that soil orders, including Entisols and Inceptisols, are observed in various landscapes and landforms. In contrast, some soil classes, such as Mollisols and its lower levels (suborder, great group, etc.), did not have geomorphic diversity. The conditional probability based on the geomorphological hierarchy indicated that the presence possibility of specific soil at the higher level (landscape) is less than, the lower level (landform), which indicates the more homogeneity of soils at lower geomorphic levels. However, the probability of observing a certain geoform increased according to the soil classification hierarchy, consistent with the results of diversity indices. The efficiency of diversity indices and conditional probability in showing the distribution and possibility of soil separation depends on the alignment of soil and geomorphological processes and the diagnosis of these processes.

Loess deposits often contain paleosols that are documenting phases of soil formation associated with interglacialsor interstadials. Linkage of the paleosols to paleoclimates is not straightforward when paleoclimates arerepresented by dynamic (simulated) variables and paleosols by static (measured) soil parameters. We thereforepropose to combine a dynamic soil model with a climate model. We define the required processes in such soilmodel and the output variables that would allow usage of the soil-climate model combination to be used for(past and future) climate change studies. Issues to be considered are the time- and spatial scale of the soil andthe climate model. For predictive (global change) studies, the usage of (soil) model outputs to quantify the evolutionof the soil natural capital and of ecosystem services must be considered. We give examples for the ChineseLoess Plateau of the evaluation of paleoclimate-paleosol linkages and of simulated soil natural capital and soilecosystem services with the LOVECLIM-earth system model linked to the SoilGen soil evolution model andconclude that such model combination is an important step forward.

The Chinese loess-paleosol sequences provide valuable records of the Quaternary climate changes. However, the relative contributions of the Quaternary paleoclimate (e.g. precipitation, temperature, evapotranspiration, vegetation and dust addition) on interglacial paleosol development, and which factor plays a key role are inadequately studied in the Chinese Loess Plateau (CLP). Here, we examine the relative contributions of these factors on paleosol development on the CLP by a sensitivity analysis. For this, we simulate paleosol development using the climate-soil model LOVECLIM-SoilGen2.The sensitivity analysis was done during two interglacials, MIS11 and MIS13, where MIS11 shows quite contrasting climate forcing with MIS-13. Several simulations were run; in each simulation, one parameter is allowed to change at a time (e.g. precipitation), and others are kept constant (as in reference). A reference simulation is included 100-year average values of the end of Holocene simulation mimicking the Pre-Industrial climate. Additionally, combined effects of climatic parameters (e.g. precipitation and evapotranspiration) were investigated, and mean absolute error was calculated between each of the sensitivity simulation and the actual simulation (combination of all soil forming factors). Our results show that precipitation has the largest influence on the depth redistribution of soil properties (calcite and clay) in MIS11 and MIS13 and dust addition is the dominant factor affecting the amount of simulated calcite and clay. Our results indicate that potential evapotranspiration has a pronounced impact during MIS13. Unlike the simulated MIS11 paleosol, which is explained by precipitation plus dust addition, the MIS13 paleosol development is better explained when potential evapotranspiration is also taken into account during this extreme interglacial.

Land use through its control on vegetation and fertilization can impact on soil geochemistry which in turn alsoinfluences the stabilization of soil organic carbon (SOC). Here, we assess soil organic carbon pools following afractionation method by Zimmermann et al. (2007), and analyse the fate of SOC with a process-based soil genesismodel, SoilGen2. We hypothesized that geochemical properties influenced the distribution of SOC and theseproperties can be applied in a model context to modify the decay rate of soil carbon pool. A set of volcanic soilsdata from Mt.Tangkuban Perahu and Mt. Burangrang in Indonesia covering different land uses (primary forest,pine forest, and agriculture) from Holocene age was used in this study. In the model, calibration was donesequentially including (i) weathering of amorphous and primary minerals, and (ii) decay of soil organic carbon.These processes are represented by various process parameters, and each simulation was run on a 8-10k yeartime scale. Our SOC fractionation study showed that the dominant SOC pool was located in sand-aggregatefractions and was higher with agricultural land use. This pool was positively correlated to pH, exchangeable Ca,aluminum-oxalate extraction (Alo), and amorphous materials. This result is also in line with a better performancein the SOC model by applying geochemically-modified rates. Our calibrated model shows the advantage ofincluding geochemical rate modifier in the volcanic soils. Further, the SOC levels will also be investigated underdifferent climate projection using SoilGen model.

Land use is recognized to impact soil geochemistry on the centennial to millennial time scale, with implications for the distribution and stability of soil organic carbon (SOC). Juvenile volcanic soils in tropical areas are subject to much faster pedogenesis, with then also possibly a significant mediation by land use on much shorter centennial or even decadal scale. Very scarce observational evidence exists and so such indirect implications of land use on SOC cycling are largely unknown. We here investigated SOC fractions, substrate specific mineralization (SOC or added plant residue), and net priming of SOC in function of forest or agricultural land use on Indonesian volcanic soils. The content of oxalate-extracted Al and exchangeable Ca correlated well with OC associated with sand–sized aggregates. The concomitant near doubling of the proportion of SOC in sand-sized aggregates compared to forest and likewise contrasts in Al and Ca suggest an enhanced formation of Al- (hydr)oxides and liming promoted aggregation and physical occlusion of OC. This was importantly also consistent with a relatively lesser degradability of SOC in the agricultural sites, though the net priming of SOC and degradability of added 13C-labelled ryegrass was not found to depend on land use. We expected that the formation of amorphous Al after conversion of native forest to agriculture would mainly have promoted mineral-association of SOC compared to under pine forest but found no indications for this. Enhanced weathering but improved small scale aggregation of tropical Andosols caused by conversion to agriculture may thus partially counter the otherwise expectable decline of SOC stocks following cultivation. Such indirect land use effects on the SOC balance appeared relevant for correct interpretation and prediction of the long-term C-balance of (agro)ecosystems with soil subject to intense development, like the here studied tropical Andosols.

As part of an article symposium on Partha Dasgupta and Sanjeev Goyal’s “Narrow Identities” (2019, Journal of Institutional and Theoretical Economics), Peter Finke offers a critical anthropological perspective on the concept of social identity and its modeling in economics.

We investigated changes in geochemical soil properties in response to deposition age and land use management over 30 - 50 years on tropical volcanic soils. Our purpose was to find out how weathering stage and land use interactively affect soil properties and organic carbon, and to check if phenoforms (management-related soil subtypes) exist within the genoforms (genetic soil types). Soil samples were taken at land uses that have been converted (pine forest and agricultural land) and a natural forest as the original land use. The results showed that pine forest soil displayed more intense weathering as indicated by higher values of three weathering indices. Intensive agricultural practices also improved soil chemical properties such as pH, exchangeable base cations, base saturation, and organic carbon stock leading to WRB-qualifier of "eutric" in cultivated soils, whereas the average of bulk density was relatively similar between forests and cultivated soils. Positive correlations were found between amorphous materials and Al-o, specific surface area, and micropore volume. Correlations between the content of short-range order Al (hydr-) oxides (indicated by Al-o) and organic carbon were found in pine forest and agricultural soils, particularly in subsoils. Our results clearly indicate the increase of base cations retention due to less acidification and an increase of organic carbon stock under agricultural land use, likely due to stabilization with non-crystalline materials.

Pastoralists in Western Mongolia face a range of challenges during their annual grazing cycle due to the simultaneous but conflicting needs for secure allocation rules and a high degree of flexibility. In this paper, I analyze the seasonal arrangements and corresponding strategies that households adopt to deal with unpredictable environmental conditions and the demands of supplying livestock with sufficient forage throughout the year. In contrast to game theory simulations, I argue that dilemmas are: a) often not a question of cooperating or not, but form continua of more or less; and b) socially embedded as people operate in a multitude of relationships that influence their decision-making. Therefore, sharing information in a manner that prioritizes the requirements of individual households while not jeopardizing social reputation is crucially important. Rhetorical skills play an important role in this regard, but there are also situations when herders blatantly ignore state law or local institutional arrangements and risk confrontation to ensure the survival of livestock leading to a situation where free-riding may at times become the dominant strategy.

The Quaternary loess-paleosol deposits in the Chinese Loess Plateau (CLP) provide complete records of paleoclimates for better understanding differences in paleosol development. However, soil properties are overwritten due to climatic conditions in later periods; therefore the true paleosol development cannot easily be quantified. Only a calibrated soil model allows interglacial soil simulation in the CLP over long timescales. Therefore, our objective is to quantify the degree of paleosol development in three different paleosols in the CLP: S2, S4 and S5-1 using a calibrated process-based soil development model, SoilGen2. The variations of paleosol development were compared by clay% and calcium carbonate content% and anorthite content (kg/m2) in the simulated paleosol. Our results show that paleosol development is stronger in S5-1, S4, S2 in the studied paleosols in decreasing order of intensity. Each paleosol showed distinct variations in clay migration and decalcification processes under varying climate and dust deposition at the time of development. The model correctly simulated the two-sub paleosol units in paleosol S2. In conclusion, the results indicate that the (calibrated) SoilGen2 model combined with modeled climate can quantify and describe the paleosol development in the CLP fairly.

While laboratory methods of elemental analysis of soil nutrients are used frequently to support soil studies, the implementation of more portable and cost-efficient methods lingers behind. The portable (handheld) X-ray fluorescence spectrometer (XRF) is one such tool enabling onsite elemental analysis in a straightforward manner. However, in soil studies the use of XRF often remains cumbersome, following the poor performance of the method for low-Z elemental analysis and the complex nature of the soil matrix, introducing background noise. Here, we therefore evaluate how the potential use of a portable XRF for predicting potassium (K), phosphorus (P), magnesium (Mg) and calcium (Ca) can be improved through the analysis of XRF spectral data with the Random Forest (RF) machine learning method. A total of 105 soil samples from a wide range of soils collected from 10 different countries (D.R. Congo, Belgium, Ivory Coast, Italy, The Netherlands, Saudi-Arabia, South Africa, Spain, Switzerland, and Zimbabwe) were scanned using an Oxford XMET8000 XRF spectrometer (Oxford Instruments, UK). Spectral data of the calibration set (n = 74) were pulled in one matrix alongside measured elemental concentrations and subjected to RF analysis. Resulting models were validated using an independent validation set (n = 31). The best RF prediction result was obtained for K followed successively by Ca, Mg and P with coefficient of determination (R2) values of 0.83, 0.76, 0.69, and 0.47, and root mean squared error of prediction (RMSEP) of 2283.8, 6818.7, 1511.8, and 538.08 mg kg−1, respectively. The RF modelling procedure provided improved prediction performance compared to the calibration models provided by the manufacturer (R2 = 0.65, 0.75, 0.65, and 0.22, for K, Ca, Mg and P, respectively). Our results suggest that portable XRF instruments coupled with spectral data analysis by RF allows for rapid and low-cost analysis of soil K and Ca with remarkable accuracy. Still, the lower measurement accuracy for P and Mg suggests further work is needed to test whether the prediction can be improved by better calibration models, and how such approaches can help overcoming instrumental limitations.

Over the last two centuries, ‘tradition’1 has been invoked in Central Asia by Russian and Soviet regimes, as well as by local Central Asian elites, largely in two ways: either as a deficiency to be...

Increasing demand for food production, global change, and growing population are the enormous challenges in recent decades. Accurate soil maps and adequate models are indispensable tools to assist managers, scientists, and decision-makers in addressing these challenges. Legacy soil polygon maps at national and regional scales are available widely, but lack detail, and therefore effective methods such as digital soil mapping (DSM) are needed to disaggregate these maps. The objective of this study was to disaggregate a legacy 1:1,000,000 soil map by three methods of disaggregation: a supervised classification method (DSMART algorithm) and two unsupervised classification methods including fuzzy c-means (FCM) and k-means (KM) clustering in Borujen region, Chaharmahal-Va-Bakhtiari Province, Central Iran for both great group and subgroup Taxonomic levels. Although field validation indicated that the accuracy of the disaggregated soil maps was lower than that of the conventional soil map at both levels of Soil Taxonomy, disaggregated approaches produced more detailed soil maps when compared with the first, second, and third most probable soil classes of the conventional soil map. The higher overall accuracy of the conventional soil map was due to soil association units which consist of more than one soil taxonomic class. FCM and DSMART methods produced more accurate and detailed disaggregated soil maps than KM clustering algorithm at the great group and subgroup levels, respectively. We concluded that the decision on what method to use depends on the map, the level of available information (map detail), available expert knowledge, and the availability of the soil unit composition percentages in the soil map legend.

Knowledge about distribution of soil properties over the landscape is required for a variety of land management applications and resources, modeling, and monitoring practices. The main aim of this research was to conduct a spatially prediction of the top soil properties such as soil organic carbon (SOC), calcium carbonate equivalent (CCE), and clay content using digital soil mapping (DSM) approaches in Borujen region, Chaharmahal-Va-Bakhtiari province, central Iran. To achieve this goal, a total of 334 soil samples were collected from 0 to 30 cm depth. Three non-linear models including Cubist (Cu), Random Forest (RF), Regression Tree (RT) and a Multiple Linear Regression (MLR) were used to link environmental covariates and the studied soil properties. The environmental covariates were obtained from a digital elevation model (DEM) and satellite imagery (Landsat Enhanced Thematic Mapper; ETM). The model was calibrated and validated by the 10-fold cross-validation approach. Root mean square error (RMSE) and coefficient of determination (R2) were used to determine the performance of the models, and relative RMSE (RMSE%) was used to define prediction accuracy. According to the RMSE and R2, Cu and RF resulted in the most accurate predictions for CCE (R2 = 0.30 and RMSE = 9.52) and clay contents (R2 = 0.15 and RMSE = 7.86), respectively, while both of RF and Cu models showed the highest performance to predict SOC content (R2 = 0.55). Results showed that remote sensing covariates (Ratio Vegetation Index and band 4) were the most important variables to explain the variability of SOC and CCE content, but only topographic attributes were responsible for clay content variation. According to RMSE% results, it could be concluded that the best model is not necessarily able to make the most accurate estimation. This study recommended that more observations and denser sampling should be carried out in the entire study area. Alternatively, stratified sampling by elevation in homogeneous sub-areas was recommended. The stratified sampling probably will increase the performance of models.

To better understand interglacial paleosol formation by quantifying the paleosol formation processes on the Chinese Loess Plateau (CLP), we need a soil genesis model calibrated for long timescales. Here, we calibrate a process-based soil genesis model, SoilGen2, by confronting simulated and measured soil properties for interglacial soils formed in the CLP for various parameter settings. After the calibration of the intrinsic soil process parameters, the effect of uncertainty of external forcings (e.g. dust deposition) on calibration results was assessed.This calibration comprises three major soil process formulations, represented by various process parameters. Sequentially : [1]. decalcification by tuning (i) the dissolution constant of calcite (ii) the interception evaporation fraction [2]. clay migration by tuning (iii) the volume of clay in-contact with macropores (iv) the filter coefficient (v) physical weathering (vi) the ectorganic layer thickness [3]. soil organic carbon by tuning the decay rates of (vii) humus and (viii) resistant plant material, and (ix) the ratio of ectorganic/endorganic litter (natural vegetations) (x) the ratio of carbon mineralized (CO2) over that still in the food web (biomass and humus) during decomposition. The order of the tuned parameters was based on sensitivity analyses on parameters for modelling (de-)calcification and clay migration done for West European leaching climates, and on C-cycling parameters done for both West European and Chinese circumstances. These parameters, [1 and 3] and [2] were successfully calibrated to the Holocene and the Marine Isotope Stage (MIS) 13 climate evolution of the CLP, respectively. After calibration, soil properties show a strong response to 10 reconstructed dust deposition scenarios reflecting the propagation of uncertainty in dust deposition.Our results emphasize the equal importance of calibrating soil process parameters and defining correct external forcings in the future use of soil models. Nevertheless, this calibrated model permits interglacial soil simulation in the CLP over long timescales.

The impact of soil age on geochemical properties and carbon cycling has been studied via chronosequences. However, only few studies have addressed how land-use and soil age might interactively shape properties of Andosols and in turn their capability to retain organic carbon (OC). Geochemical soil analyses and laboratory incubation experiments were carried out to assess soil characteristics and mineralization of soil organic carbon (SOC) in Indonesian soils with two contrasting land uses, viz. pine forest and horticulture. Both of these land uses are the results of conversion of primary forest which had similar parent materials, soil age, as well as weathering intensity. Results showed that intensive agricultural practices (+ 40-50 years) did not result in a significant loss of SOC or the increase of bulk density compared to forest. On the other hand, they were found to increase pH, exchangeable cations, base saturation, and most strikingly non-crystalline materials (i.e. Alo + ½ Feo) leading to phenotype formation in agricultural soils. Positive correlations were found between non-crystalline materials with properties such as soil specific surface area and micropores volume, and it was also positively correlated with SOC, particularly in the subsoil. This study highlighted the resilience of Andosols to soil degradation under agricultural practices and its ability to stabilize SOC.

The Quaternary loess-paleosol deposits in the Chinese Loess Plateau (CLP) provide complete records of paleoclimates for better understanding differences in paleosol development. However, soil properties are overwritten due to climatic conditions in later periods; therefore the true paleosol development cannot easily be quantified. Only a calibrated soil model allows interglacial soil simulation in the CLP over long timescales. Therefore, our objective is to quantify the degree of paleosol development in three different paleosols in the CLP: S2, S4 and S5-1 using a calibrated process-based soil development model, SoilGen2. The variations of paleosol development were compared by clay% and calcium carbonate content% and anorthite content (kg/m2) in the simulated paleosol. Our results show that paleosol development is stronger in S5-1, S4, S2 in the studied paleosols in decreasing order of intensity. Each paleosol showed distinct variations in clay migration and decalcification processes under varying climate and dust deposition at the time of development. The model correctly simulated the two-sub paleosol units in paleosol S2. In conclusion, the results indicate that the (calibrated) SoilGen2 model combined with modeled climate can quantify and describe the paleosol development in the CLP fairly.

To better understand interglacial paleosol formation by quantifying the paleosol formation processes on the Chinese Loess Plateau (CLP), we need a soil genesis model calibrated for long timescales. Here, we calibrate a process-based soil genesis model, SoilGen2, by confronting simulated and measured soil properties for interglacial soils formed in the CLP for various parameter settings. After the calibration of the intrinsic soil process parameters, the effect of uncertainty of external forcings (e.g. dust deposition) on calibration results was assessed. This calibration comprises three major soil process formulations, represented by various process parameters. Sequentially : [1]. decalcification by tuning (i) the dissolution constant of calcite (ii) the interception evaporation fraction [2]. clay migration by tuning (iii) the volume of clay in-contact with macropores (iv) the filter coefficient (v) physical weathering (vi) the ectorganic layer thickness [3]. soil organic carbon by tuning the decay rates of (vii) humus and (viii) resistant plant material, and (ix) the ratio of ectorganic/endorganic litter (natural vegetations) (x) the ratio of carbon mineralized (CO2) over that still in the food web (biomass and humus) during decomposition. The order of the tuned parameters was based on sensitivity analyses on parameters for modelling (de-)calcification and clay migration done for West European leaching climates, and on C-cycling parameters done for both West European and Chinese circumstances. These parameters, [1 and 3] and [2] were successfully calibrated to the Holocene and the Marine Isotope Stage (MIS) 13 climate evolution of the CLP, respectively. After calibration, soil properties show a strong response to 10 reconstructed dust deposition scenarios reflecting the propagation of uncertainty in dust deposition. Our results emphasize the equal importance of calibrating soil process parameters and defining correct external forcings in the future use of soil models. Nevertheless, this calibrated model permits interglacial soil simulation in the CLP over long timescales.

Loess deposits often contain paleosols that are documenting phases of soil formation associated with interglacials or interstadials. Linkage of the paleosols to paleoclimates is not straightforward when paleoclimates are represented by dynamic (simulated) variables and paleosols by static (measured) soil parameters. We therefore propose to combine a dynamic soil model with a climate model. We define the required processes in such soil model and the output variables that would allow usage of the soil-climate model combination to be used for (past and future) climate change studies. Issues to be considered are the time- and spatial scale of the soil and the climate model. For predictive (global change) studies, the usage of (soil) model outputs to quantify the evolution of the soil natural capital and of ecosystem services must be considered. We give examples for the Chinese Loess Plateau of the evaluation of paleoclimate-paleosol linkages and of simulated soil natural capital and soil ecosystem services with the LOVECLIM-earth system model linked to the SoilGen soil evolution model and conclude that such model combination is an important step forward.

Land use change can significantly influence both mineralogy and chemical soil properties. This conversion, particularly from forest to agricultural system occurs often in volcanic soils due to their favorable properties for food production. Under agriculture, minerals can weather faster than in natural vegetation and this also impacts soil functioning. We aim to assess the impact of land use on geochemical soil properties and soil organic carbon across soils of different age. This study was conducted in Mt. Tangkuban Perahu and Mt. Burangrang where the soils were derived from similar andesitic parent material and have different ages based on their lithology. Five sites were selected representing land uses that have been converted (pine forest and agricultural land) and one site of natural forest as the origin of land use. The results showed that land use management enhances the mineral transformation. Pine forest and agricultural sites displayed higher weathering degree than natural forests as indicated by higher clay content, iron crystallinity index and the presence of gibbsite. The weathering degree of soils in agricultural sites might result from the length of cultivation period and soil age. Land use conversion also altered chemical properties such as pH, CEC, basic cations, and the proportion of amorphous materials. Non-crystalline Al and Fe minerals as indicated by Alo+1/2Feo were highly correlated with organic carbon and specific surface area (SSA) in the subsoils of all land uses. However, we did not see the accumulation of organic carbon in subsoils compared to topsoils as the amount of non-crystalline Al and Fe minerals increases with depth, especially in agricultural lands where the organic fertilizer input is very high. In addition, a significant proportion of carbon was stored in sand aggregate fractions in agricultural land which have longer cultivation period, while it was more readily found in silt and clay fractions in the site with shorter period.

Identity is a key term in anthropology but it is also a contested one, dealing with the question of who we are in relation to others. It relates, on the one hand, to categories of the individual or sameness with oneself and, on the other, to collective distinctions of otherness. It is both a practice and a process of cognitive classification. It is fluid and transcends boundaries but, to a certain degree, has to be stable in order for others to identify one as theirs. Furthermore, it is at the same time a public discourse and an obvious phenomenon in the world and is thus an object of inquiry if not an analytical category. Most prominent among the many identity aspects we employ is probably the ethnic one, which hinges on a bundle of markers used to distinguish each other in presumed cultural terms, always embedded in power games that try to secure political support and loyalty.

Quantifying soil carbon dynamics is of utmost relevance in the context of global change because soils play an important role in land-atmosphere gas exchange. Our current understanding of both present and future carbon dynamics is limited because we fail to accurately represent soil processes across temporal and spatial scales, partly because of the paucity of data on the relative importance and hierarchical relationships between microbial, geochemical and climatic controls. Here, using observations from a 3,000-kyr-old soil chronosequence preserved in alluvial terrace deposits of the Merced River, California, we show how soil carbon dynamics are driven by the relationship between short-term biotic responses and long-term mineral weathering. We link temperature sensitivity of heterotrophic respiration to biogeochemical soil properties through their relationship with microbial activity and community composition. We found that soil mineralogy, and in particular changes in mineral reactivity and resulting nutrient availability, impacts the response of heterotrophic soil respiration to warming by altering carbon inputs, carbon stabilization, microbial community composition and extracellular enzyme activity. We demonstrate that biogeochemical alteration of the soil matrix (and not short-term warming) controls the composition of microbial communities and strategies to metabolize nutrients. More specifically, weathering first increases and then reduces nutrient availability and retention, as well as the potential of soils to stabilize carbon.

Models can serve as temporal interpolators for landscape reconstruction, and geostatistical interpolation can assist in obtaining full-cover maps. Both activities are associated with uncertainty. We applied a hydrological model (MODFLOW), a soil development model (SoilGen) and regression kriging to reconstruct a Middle Bronze Age landscape. This reconstruction comprises maps of a set of land characteristics and is input for a land evaluation meant to explain low occupancy during this period. The land evaluation showed marginal suitability in 81% of the area for Bronze Age land utilization types, which explains the low occupancy. We evaluated the effects of model accuracy and interpolation accuracy on the uncertainty of the land evaluation, assuming no uncertainty about the land evaluation protocol. The effect of model (in)accuracy was assessed by generating 100 perturbations using a variance-covariance error matrix filled using independent measurements from 1953 CE. The effect of spatial interpolation errors was assessed by generating 100 realizations of the land characteristics by Sequential Gaussian Simulation. Results show that uncertainty contributions of the model errors and interpolation errors are similar in terms of entropy H′ between land evaluation maps (fairly low H′ of 0.47 and 0.38 for model and interpolation error respectively). Model-reconstructed land characteristics led to fairly reliable land evaluations. Priority improvements for the model were identified and ranked. Most improvements come at high computational cost.

Over the last decades, numerous studies have used the loess-paleosol sequences in China to reconstruct the East Asian climate and to investigate their linkage with global climate change. The paleosols embedded in the loess developed during warm periods and contain valuable information on climate and vegetation under warm conditions. However, because soil formation is controlled by multiple factors, it is not straightforward to obtain a pure climate signal based on soil property analyses. This leads often to debates and questions. Here, for the first time, we use a soil formation model together with a climate model to identify the main factors that control the paleosol formation. A case study has been performed on paleosols in the Chinese Loess Plateau of the last interglacial (Marine Isotope Stage [MIS] 5e) and the interglacial at ca. 500 kyr B.P. (MIS 13). Our results show that although the peak warmth and peak summer monsoon precipitation are stronger during MIS 5e, the soil formation is stronger during MIS 13, which is supported by field evidence. This is mainly due to larger accumulative precipitation surplus, weaker dust deposition, and longer interglacial duration during MIS 13. Our results provide a new interpretation of the climate signal recorded by the paleosols, and an explanation for the seeming paradox that strongly developed soils formed during relatively weak interglacials. They also highlight the necessity to include proxy modeling in paleoclimate studies.

Quantification of soil stocks and ecosystem services (ES) under changing climate need time series of climate and soil data. Here, we propose a new approach combining process-based soil and climate simulations to obtain such time series, and apply it onto a recent interglacial, Marine Isotope Stage (MIS) 5e. We combined the LOVECLIM climate model and the SoilGen2 soil evolution model to simulate soil development under two scenarios: dust addition plus erosion and erosion-only (both with five erosion rates corresponding to low, natural erosion rates: 0, 0.5, 1.0, 1.5 and 2.0 Mg ha(-1) y(-1)). We quantified five target variables: two soil stocks, Exchangeable Bases (EB) and Soil Organic Carbon (SOC), and three ES: Carbon Sequestration Capacity (CSC), Water Yield (WY) and the ratio of actual over potential evapotranspiration (omega) at four sites on an aridity gradient on the Chinese Loess Plateau (CLP). We used the obtained time series of these target variables to estimate Soil Loss Tolerance (SLT) threshold values over the full extent of MIS 5e (22 ka). Under increased erosion, EB increases while SOC always declines in both scenarios. In both scenarios, the simulated CSC increases with erosion; contrastingly, the WY decreases with increasing erosion rates. Both CSC, WY and omega gradually decrease towards the northwest CLP (semi-arid region). For EB and SOC, the determined SLT thresholds are relatively higher in the dust addition than in the erosion-only scenarios, and strictly follow the climate gradient in the CLP. Combined performance index for (1-CSC), WY and omega, showed that soil ES performance is worse above 1.0 Mg ha(-1) y(-1). This implies that benchmark levels must be chosen carefully. Our research highlights the potential of using SoilGen2 with LOVECLIM for quantifying soil-based ES and SLT.

The efficiency of different digital and conventional soil mapping approaches to produce categorical maps of soil types is determined by cost, sample size, accuracy and the selected taxonomic level. The efficiency of digital and conventional soil mapping approaches was examined in the semi-arid region of Borujen, central Iran. This research aimed to (i) compare two digital soil mapping approaches including Multinomial logistic regression and random forest, with the conventional soil mapping approach at four soil taxonomic levels (order, suborder, great group and subgroup levels), (ii) validate the predicted soil maps by the same validation data set to determine the best method for producing the soil maps, and (iii) select the best soil taxonomic level by different approaches at three sample sizes (100, 80, and 60 point observations), in two scenarios with and without a geomorphology map as a spatial covariate. In most predicted maps, using both digital soil mapping approaches, the best results were obtained using the combination of terrain attributes and the geomorphology map, although differences between the scenarios with and without the geomorphology map were not significant. Employing the geomorphology map increased map purity and the Kappa index, and led to a decrease in the ‘noisiness’ of soil maps. Multinomial logistic regression had better performance at higher taxonomic levels (order and suborder levels); however, random forest showed better performance at lower taxonomic levels (great group and subgroup levels). Multinomial logistic regression was less sensitive than random forest to a decrease in the number of training observations. The conventional soil mapping method produced a map with larger minimum polygon size because of traditional cartographic criteria used to make the geological map 1:100,000 (on which the conventional soil mapping map was largely based). Likewise, conventional soil mapping map had also a larger average polygon size that resulted in a lower level of detail. Multinomial logistic regression at the order level (map purity of 0.80), random forest at the suborder (map purity of 0.72) and great group level (map purity of 0.60), and conventional soil mapping at the subgroup level (map purity of 0.48) produced the most accurate maps in the study area. The multinomial logistic regression method was identified as the most effective approach based on a combined index of map purity, map information content, and map production cost. The combined index also showed that smaller sample size led to a preference for the order level, while a larger sample size led to a preference for the great group level.

This paper presents the results of multi-disciplinary research carried out on the deposits of Moervaart depression, NW Belgium, one of the largest palaeolakes (∼25 km2) that existed during the Lateglacial interstadial in NW Europe. The multi-proxy study, including physical (organic matter and calcium carbonate, magnetic susceptibility, micromorphological), botanical (pollen, macrofossils, diatoms), zoological (ostracods, molluscs, chironomids) and chemical analyses (stable carbon and oxygen isotopes) has resulted in a detailed reconstruction of the Lateglacial landscape as well of the local conditions that prevailed in the lake itself. A chronology of the record was provided by radiocarbon dating and comparison with radiocarbon dates of the nearby Rieme site. These yielded a good match with the regional biostratigraphy. During the Lateglacial, vegetation and geomorphology of the landscape in general changed from a tundra landscape to a boreal forest. The vegetation development, however, was interrupted by a number of cold reversals. Three centennial-scale cold oscillations are present in the record: 1) the so-called Older Dryas corresponding to GI-1d in the Greenland ice-cores, 2) a short and pronounced cold event during the early Allerød, which could be correlated to GI-1c2 and 3) a cooling event during the late Allerød probably corresponding to the Intra Allerød Cold Period (IACP) or GI-1b. The latter most likely was responsible for the disappearance of the Moervaart palaeolake.

International audience; To protect soils against threats, it is necessary to predict the consequences of human activities and global change on their evolution on a ten to hundred year time scale. Mechanistic modelling of soil evolution is then a useful tool. We analysed the ability of the SoilGen model to be used for projections of soil characteristics associated to various soil threats: vertical distributions of

Introduction and Rationale of Research The loess-soil sequences in Chinese Loess Plateau (CLP) are considered as an inevitable and continuous terrestrial record for evaluating paleoclimate. Paleosols and intercalated loess layers have long been studied and correlated with modelled paleoclimates. Interglacials are the major periods of soil formation and greatly differ from other interglacials by duration and strength. It has been identified that paleosol formation does not strongly concur with globally established paleoclimate signals especially for interglacial Marine Isotope Stages (MIS) 5e, MIS9 and MIS13. Analysis of just soil data would not be straightforward to identify relations between modelled past climate intensity and soil development. Not only the past climate (precipitation, temperature) has an effect on soil formation but also vegetation and interglacial duration plays a vital role. Additionally, pedogenetic overwriting during later periods of soil formation may affect the observed soils. Therefore, there is still a knowledge gap: which of these variations would contribute most to soil formation during interglacials. The intention of this research study is to identify the relative importance of soil forming factors on soil formation during six interglacials in the past 500 000 years in the CLP in China under paleoclimate changes. In the meantime, this research study facilitated to resolve a doubtful question, why and how soil development became weaker in strongest interglacial during MIS 5e than MIS13, which was a less warm interglacial, but prevailed in a longer period. MIS 5e, the last interglacial about 125 kyr B.P resulted in a poorly developed paleosol unit in a shorter period than MIS 13 paleosol. However, the best-developed paleosol S5-1 is attributed as a result of strong East Asian Summer Monsoon (EASM) during the MIS 13, a relatively weaker interglacial about 500 kyr B.P. Research methodology Combination of the soil formation model SoilGen and the earth system model LOVECLIM is a promising tool to compare simulated paleosol properties to paleoclimate. Results The combination of LOVECLIM-SoilGen model was used to unravel differences in paleosols corresponding to MIS 5e and MIS 13 and paleoclimate. Simulated soil properties showed that, significantly higher clay content and mineral Anorthite content in MIS5e paleosol in studied eight sites in the CLP. Conclusion There were much stronger weathering of the mineral Anorthite and clay migration in MIS13 than MIS5e paleosol. The clay differences between MIS 5e and MIS 13 soils become less with increasing aridity. Precipitation surplus is the leading soil forming factor for soil horizonation during MIS13 than MIS5e. References Finke, P. A. (2012). Modeling the genesis of luvisols as a function of topographic position in loess parent material. Quaternary International, 265, 3-17 Finke, P. A., Yin, Q., Bernardini, N. J., & Yu, Y. (2017). Climate-soil model reveals causes of differences between Marine Isotope Stage 5e and 13 paleosols. Geology. Acknowledgement This work was supported by Professor Peter Finke for the SoilGen modelling and Professor Qiuzhen Yin for the construction of climate data.

Agricultural land use has been established as the dominant prehistoric human activity in early cultural centers for thousands of years. However, because of lack of data, there is still considerable debate about the amount and spatial distribution of prehistoric land use across the world. Quantitative reconstruction of it on the basis of human activity records, for example, archaeological data, is the key to resolving the issue. Here, we focus on one of the most representative regions for prehistoric human activity in northern China, the Wei River valley. Based on archaeological and environmental data, a recently developed quantitative prehistoric land use model (PLUM) is applied to reconstruct spatial and temporal changes of land use between 8 and 4 ka BP. The results reveal that in line with increases in the total number of archaeological sites (from 24 to 3222) and population (from 4000 to 1,550,000), the land area of the valley used by humans increased from 0.2% to 12% during the study interval, expanding from the gentle slopes along the lower reaches of the river to the middle and upper reaches. Meanwhile, the average population for an individual site increased from 160 to 481, but the average land use area per site decreased from 12.84 to 4.68 km2. Since 6 ka BP, the significant land use increase occurred synchronously in the Wei River valley and other key regions of agricultural origin across the world, which highlights the important role of agriculture activity in transforming the nature of global land cover during the prehistoric period.

Soil is central in the terrestrial ecosystem, linking and providing feedback responses to the other components, i.e., water, atmosphere, and vegetation. However, the role of soil in landscape evolution is usually not well acknowledged. In modeling landscape evolution, soil is only treated as a residue of weathering that is transported and redistributed along the hillslope. Weathering is considered as a process that produces clays and generates unconsolidated materials available for erosion. While pedology has been debating the form of qualitative factorial models for 75 years; models for soil water, heat, solute, gas and chemical reactions in a profile have matured. As soils are distributed continuously in three dimensions across landscapes, the profile models need to consider lateral fluxes. This review outlines the role of soil in landscape modeling. First, we review the role of soil in the current landscape evolution models. We then review data and models on soil weathering rates and transport processes. We discuss soil profile models that simulate soil formation processes, and combined soil–landscape evolution models. Finally we discuss how the models can be tested and validated in the real world and suggest how both soil scientists and landscape modelers can work together to address the grand challenges in modeling earth surface processes.

To accurately estimate past terrestrial carbon pools is the key to understanding the global carbon cycle and its relationship with the climate system. SoilGen2 is a useful tool to obtain aspects of soil properties (including carbon content) by simulating soil formation processes; thus it offers an opportunity for both past soil carbon pool reconstruction and future carbon pool prediction. In order to apply it to various environmental conditions, parameters related to carbon cycle process in SoilGen2 are calibrated based on six soil pedons from two typical loess deposition regions (Belgium and China). Sensitivity analysis using the Morris method shows that decomposition rate of humus (kHUM), fraction of incoming plant material as leaf litter (frecto) and decomposition rate of resistant plant material (kRPM) are the three most sensitive parameters that would cause the greatest uncertainty in simulated change of soil organic carbon in both regions. According to the principle of minimizing the difference between simulated and measured organic carbon by comparing quality indices, the suited values of kHUM, (frecto and kRPM in the model are deduced step by step and validated for independent soil pedons. The difference of calibrated parameters between Belgium and China may be attributed to their different vegetation types and climate conditions. This calibrated model allows more accurate simulation of carbon change in the whole pedon and has potential for future modeling of carbon cycle over long timescales.