Combined Land Use Research Articles

Analysis of Nitrate occurrence and distribution in Groundwater in the Gaza Strip using major ion chemistry by Baalousha H.

Nitrate in groundwater in the Gaza Strip, Palestine has become a serious problem in the last decade. As a result of extensive use of fertilizers, discharging of wastewater from treatment plants, and leakage of wastewater form cesspools, increased levels of nitrate up to 400 mg/l have been detected in groundwater. Nitrate concentrations more than 50 mg l-1 are very harmful to infant, foetuses, and people with health problems. The most efficient way to prevent nitrate impacts is to identify the sources of nitrate and to reduce them at the source. In this study, samples of groundwater from 63 wells were collected and chemically analyzed. Analysis of these samples revealed they have nitrate concentrations more than the maximum permissible limit recommended by World Health Organization (WHO). The major ion chemistry analysis was used to identify nitrate sources. Based on the relation between land use and nitrate concentration in groundwater, sources of nitrate were identified. These sources are: (1) leakage from wastewater treatment plants, (2) leakage from cesspits, and (3) intensive agricultural activities. It was found that the leakage of wastewater from cesspits and over-loaded treatment plants has greater influence on groundwater nitrate contamination more than agricultural activities. This study shows the important role of the type of infiltrated water into the sub-soil, combined land use, in determining the source of nitrate in groundwater.

Modeling water flow in a plastic mulched ridge cultivation system on hillslopes affected by South Korean summer monsoon

Intensive agricultural land use in combination with heavy rain storm events during the summer monsoon season plays a key role in groundwater pollution by nutrients and agrochemicals in agricultural catchments in South Korea. A widespread measure for weed control in this region is plastic mulched ridge cultivation. However, it is not well understood, how and to which extent the water flow regime in sloped fields is hereby modified. To evaluate the effect of plastic mulched ridge cultivation (RTpm) on soil water dynamics, we carried out a two-dimensional process-based modeling study using the numerical model Hydrus 2/3D. Subsequently, RTpm was compared to model simulations of ridge cultivation without plastic cover (RT) and flat conventional tillage without ridges and without plastic cover (CT). Datasets of soil water potentials obtained by field measurements at two plastic mulched potato fields (Solanum tuberosum L.) provided the basis for optimizing soil hydraulic parameters inversely by the Levenberg–Marquardt algorithm. We found, that plastic mulching induced horizontal pressure head gradients and forced soil water to move laterally from furrows to ridges under normal weather conditions. During monsoon events, soils were fully saturated and interflow occurred in coarse textured and ploughed topsoil. Further, the water balance of the different model scenarios showed that plastic mulching reduced drainage water up to 16% but concurrently increased the surface runoff up to 65%. The consequences are an increase in runoff peak flow, flood risk and erosion. Therefore, we recommend the application of perforated and biodegradable plastic mulch in regions affected by summer monsoon.

Spatial and Temporal Variation of Water Quality of Typical Rivers in the River-Network Plain to the East of Tai Lake

Nine cross sections of three typical rivers in the river-network plain to the east of Tai Lake were selected to measure water quality and water flow parameters monthly from September 2007 to August 2008. The spatial and temporal features of water quality were analyzed and the water pollution loads at river sections were estimated using statistic analysis. Combining land use in the riparian buffer zone interpreted form Remote Sensing images, the relationship between river water pollution load and land use were analyzed. It was clear that river water in the study area was polluted so seriously that water quality was ranked Grade V or worse. Pollutant input process in most river sections prevailed river self-purifying process. River qualities varied seasonally as that pollutant were denser in spring and winter than that in summer and autumn. Residential land in the 100 m buffer zone and industrial land in the 500 m buffer zone had the greatest influence on water quality. Forested and grassed riparian land appeared important in mitigating water quality degradation.

교통량 배정을 고려한 지구단위설계에 관한 연구

This study tried to analyze traffic assignment volume by application of land use patterns and transport policies in District Unit Design. It is postulated a Toy network and various scenarios which are combined land use patterns and transport policies for analyzing change of assignment volume. As results, this study shows best District Unit Design technique is the policy that develop mid block and introduction of car free zone to inner 2 way streets. Worst design technique is the policy that introduction of joint car parking to mid block. And also worst design technique to residents who are living in inner block is the policy that just develop only one center zone.

Acute and Chronic Effects of Particles on Hospital Admissions in New-England

BackgroundMany studies have reported significant associations between exposure to PM2.5 and hospital admissions, but all have focused on the effects of short-term exposure. In addition all these studies have relied on a limited number of PM2.5 monitors in their study regions, which introduces exposure error, and excludes rural and suburban populations from locations in which monitors are not available, reducing generalizability and potentially creating selection bias.MethodsUsing our novel prediction models for exposure combining land use regression with physical measurements (satellite aerosol optical depth) we investigated both the long and short term effects of PM2.5 exposures on hospital admissions across New-England for all residents aged 65 and older. We performed separate Poisson regression analysis for each admission type: all respiratory, cardiovascular disease (CVD), stroke and diabetes. Daily admission counts in each zip code were regressed against long and short-term PM2.5 exposure, temperature, socio-economic data and a spline of time to control for seasonal trends in baseline risk.ResultsWe observed associations between both short-term and long-term exposure to PM2.5 and hospitalization for all of the outcomes examined. In example, for respiratory diseases, for every10-µg/m3 increase in short-term PM2.5 exposure there is a 0.70 percent increase in admissions (CI = 0.35 to 0.52) while concurrently for every10-µg/m3 increase in long-term PM2.5 exposure there is a 4.22 percent increase in admissions (CI = 1.06 to 4.75).ConclusionsAs with mortality studies, chronic exposure to particles is associated with substantially larger increases in hospital admissions than acute exposure and both can be detected simultaneously using our exposure models.

Neighborhood Environment and Psychosocial Correlates of Adults’ Physical Activity

There is growing interest in identifying neighborhood environment factors related to physical activity. This study aimed to examine whether objective built (e.g., residential density) and perceived (e.g., aesthetics) environment factors around adults' residence are correlates of their physical activity and reported walking behavior after accounting for known psychosocial (e.g., self-efficacy, barriers to physical activity) and demographic correlates of physical activity. Objective built environment characteristics were created through network buffers around individual participants (n = 2199) selected from neighborhoods differing on walkability characteristics and household income. Participants wore accelerometers to obtain a more objective measure of overall physical activity and self-reported on leisure and transportation-related walking, perceptions of neighborhood environment, psychosocial factors related to physical activity, and demographic factors. Census-level demographic factors were also considered. Retail floor area ratio, a metric combining land use mix and pedestrian design factors, was the environmental factor most related to accelerometry-measured physical activity and self-reported transportation-related walking after accounting for psychosocial and demographic factors. Street connectivity was also related to transportation-related walking, whereas perceived aesthetics was positively related to leisure walking. Environmental factors, particularly the availability of proximal nonresidential destinations designed for pedestrian access, were related to adults' physical activity and walking after accounting for psychosocial and demographic correlates, including reasons for residential selection.

교통부문 CO2저감을 위한 지구단위설계 방법에 관한 연구

본 연구에서는 지구단위 내 교통부문에서 다양한 도시설계 및 교통정책의 적용에 따른 <TEX>$CO_2$</TEX>의 배출량 효과를 분석해보고자 하였다. 이를 위해 가상네트워크(Toy Network)를 설정하고 토지이용패턴과 교통정책을 접목한 19개의 시나리오를 가정한 후 교통수요 예측 프로그램인 EMME3를 이용하여 <TEX>$CO_2$</TEX> 발생량을 산정하고 비교하였다. 그 결과 총 <TEX>$CO_2$</TEX> 배출량이 가장 많이 감소한 정책은 지구의 중간부를 개발하면서 동시에 내부의 2차로 도로는 차량 통행을 금지시키고 보행자 천국을 만드는 것이었다. 총 <TEX>$CO_2$</TEX>량이 가장 많이 발생된 그룹은 교통공학에서 일반적으로 인식되어 왔던 적절한 도로위계에 따른 도로망 구성 및 접근통제 조절 정책이었다. 따라서 지구설계시 도로망 구성 및 접근통제는 세밀한 연구를 통해 실행되어야 할 것임을 알게 되었다. This study tried to analyze <TEX>$CO_2$</TEX> emission volume as green-house gases by application of land use patterns and transport policies in District Unit Design. It is postulated a Toy network and various scenarios which are combined land use patterns and transport policies for analyzing <TEX>$CO_2$</TEX> gas reduction. As results, this study shows best District Unit Design technique is the policy that develop mid block and introduction of car free zone to inner 2 way streets. Worst design technique is the policy that make hierarchical network and introduction of access control to outer roads that have been known as a best road policy till nowadays. Therefore, we need more carefully introduce design technique for reduction of <TEX>$CO_2$</TEX> in District Unit.

Incorporating bioaccessibility into human health risk assessments of heavy metals in urban park soils

Contaminants in urban soils can directly pose significant human health risks through oral ingestion, particle inhalation, and dermal contact, especially for children in public parks. Both non-carcinogenic and carcinogenic risks of heavy metals (Cd, Co, Cr, Cu, Mn, Ni, Pb, and Zn) were characterized in 40 surface soils (exposed lawns) from 14 urban parks in Xiamen, China. Results based on total metal concentrations may overestimate the actual risks in comparison with oral bioaccessibility assessment that were estimated by a simplified physiologically based extraction test (SBET). After considering the soil-specific bioaccessibility (Cd>Cu>Pb>Mn>Zn>Co~Ni>Cr), the non-cancer hazard of Pb to children via oral ingestion should be a consideration though its Hazard Index (HI) was below one. The overall cancer risks to adults still exceeded the target value 10−6, mainly contributed by Cr (93.8%) and Pb (6.19%) via dermal contact (68.3%) and oral ingestion (30.4%). To produce a more realistic estimation for human health risks of metal contamination in urban soils, a framework combining land use type and bioaccessibility is recommended and thereby should be applied for the derivation of risk-based, site-specific soil guidelines.

Combined land-use and water allocation planning

A general framework for a combined land use and water management is described. An optimization problem is formulated that combines combinatorial and spatial characteristics. The aim of the planning is to maximize economic benefit, while minimizing water extraction and transportation cost under ecological constraints. A genetic algorithm is employed endowed with a new neighborhood operator. This operator acts on a local level, but it produces global results. Although the computational scheme does not include compactness as a separate objective, compact patterns are produced as emergent results. The algorithm is tested on a fictive area represented as a grid with 15×15 land blocks and, also, on a real-world case study.

Comparison of indirect nitrous oxide emission through lysimeter drainage between an Andosol upland field and a Fluvisol paddy field

Indirect emission of nitrous oxide (N2O) due to nitrogen (N) leaching and runoff from agricultural soils is a major source of atmospheric N2O. To evaluate the effect of agricultural land use in combination with soil type on indirect N2O emission through groundwater, we compared the indirect N2O emission between an upland field of Andosol and a paddy field of Fluvisol in a 1-year lysimeter experiment. We established a shallow groundwater table during the non-flooded fallow period in the Fluvisol paddy field to simulate moisture conditions in a lowland soil. Drainage was 795 mm yr−1 (median, n = 6) in the Andosol upland field, versus 1583 mm yr−1 in the Fluvisol paddy field due to flooding during part of the year. The total leached nitrate in the Andosol upland field (4.24 g N m−2 yr−1) was comparable to that in the Fluvisol paddy field (5.57 g N m−2 yr−1). The total indirect N2O emission in the Fluvisol paddy field (88.6 mg N m−2 yr−1) was 55 times that in the Andosol upland field (1.62 mg N m−2 yr−1). The daily indirect N2O emission during the flooded period (0.239 mg N m−2 d−1) was comparable to that in the non-flooded period (0.156 mg N m−2 d−1) in the Fluvisol paddy field. The decrease in dissolved N2O concentration due to the flooding was offset by the increase in the drainage volume. The groundwater emission factors (EF5g) were 0.0003 in the Andosol upland field and 0.0160 in the Fluvisol paddy field versus the IPCC default value of 0.0025. Although the lysimeters had some limitations to simulate actual field conditions, the results indicate that agricultural land use in combination with soil type can strongly affect indirect N2O emission through groundwater.

A new multi-objective self-organizing optimization algorithm (MOSOA) for spatial optimization problems

Spatial planning is an important and complex activity. It includes land use planning and resource allocation as basic components. An abundance of papers can be found in the literature related to each one of these two aspects separately. On the contrary, a much smaller number of research reports deal with both aspects simultaneously. This paper presents an innovative evolutionary algorithm for treating combined land use planning and resource allocation problems. The new algorithm performs optimization on a cellular automaton domain, applying suitable transition rules on the individual neighbourhoods. The optimization process is multi-objective, based on non-domination criteria and self-organizing. It produces a Pareto front thus offering an advantage to the decision maker, in comparison to methods based on weighted-sum objective functions. Moreover, the present multi-objective self-organizing algorithm (MOSOA) can handle both local and global spatial constraints. A combined land use and water allocation problem is treated, in order to illustrate the cellular automaton optimization approach. Water is allocated after pumping from an aquifer, thus contributing a nonlinearity to the objective function. The problem is bi-objective aiming at (a) the minimization of soil and groundwater pollution and (b) the maximization of economic profit. An ecological and a socioeconomic constraint are imposed: (a) Groundwater levels at selected places are kept above prescribed thresholds. (b) Land use quota is predefined. MOSOA is compared to a standard multi-objective genetic algorithm and is shown to yield better results both with respect to the Pareto front and to the degree of compactness. The latter is a highly desirable feature of a land use pattern. In the land use literature, compactness is part of the objective function or of the constraints. In contrast, the present approach renders compactness as an emergent result.

Simulating the impacts of future land use and climate changes on surface water quality in the Des Plaines River watershed, Chicago Metropolitan Statistical Area, Illinois

Modeling the effects of past and current land use composition and climatic patterns on surface water quality provides valuable information for environmental and land planning. This study predicts the future impacts of urban land use and climate changes on surface water quality within Des Plaines River watershed, Illinois, between 2010 and 2030. Land Change Modeler (LCM) was used to characterize three future land use/planning scenarios. Each scenario encourages low density residential growth, normal urban growth, and commercial growth, respectively. Future climate patterns examined include the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emission Scenario (SRES) B1 and A1B groups. The Soil and Water Assessment Tool (SWAT) was employed to estimate total suspended solids and phosphorus concentration generated at a 10 year interval. The predicted results indicate that for a large portion of the watershed, the concentration of total suspended solids (TSS) would be higher under B1 and A1B climate scenarios during late winter and early spring compared to the same period in 2010; while the summer period largely demonstrates a reverse trend. Model results further suggest that by 2020, phosphorus concentration would be higher during the summer under B1 climate scenario compared to 2010, and is expected to wane by 2030. The projected phosphorus concentrations during the late winter and early spring periods vary across climate and land use scenarios. The analysis also denotes that middle and high density residential development can reduce excess TSS concentration, while the establishment of dense commercial and industrial development might help ameliorate high phosphorus levels. The combined land use and climate change analysis revealed land use development schemes that can be adopted to mitigate potential future water quality impairment. This research provides important insights into possible adverse consequences on surface water quality and resources under certain climate change and land use scenarios.

Combining Land Use Information and Small Stream Sampling with PCR-Based Methods for Better Characterization of Diffuse Sources of Human Fecal Pollution

Diffuse sources of human fecal pollution allow for the direct discharge of waste into receiving waters with minimal or no treatment. Traditional culture-based methods are commonly used to characterize fecal pollution in ambient waters, however these methods do not discern between human and other animal sources of fecal pollution making it difficult to identify diffuse pollution sources. Human-associated quantitative real-time PCR (qPCR) methods in combination with low-order headwatershed sampling, precipitation information, and high-resolution geographic information system land use data can be useful for identifying diffuse source of human fecal pollution in receiving waters. To test this assertion, this study monitored nine headwatersheds over a two-year period potentially impacted by faulty septic systems and leaky sanitary sewer lines. Human fecal pollution was measured using three different human-associated qPCR methods and a positive significant correlation was seen between abundance of human-associated genetic markers and septic systems following wet weather events. In contrast, a negative correlation was observed with sanitary sewer line densities suggesting septic systems are the predominant diffuse source of human fecal pollution in the study area. These results demonstrate the advantages of combining water sampling, climate information, land-use computer-based modeling, and molecular biology disciplines to better characterize diffuse sources of human fecal pollution in environmental waters.

Bioenergy in Australia: An improved approach for estimating spatial availability of biomass resources in the agricultural production zones

Bioenergy production from crops and agricultural residues has a greenhouse gas mitigation potential. However, there is considerable debate about the size of this potential. This is partly due to difficulties in estimating the feedstock resource base accurately and with good spatial resolution. Here we provide two techniques for spatially estimating crop-based bioenergy feedstocks in Australia using regional agricultural statistics and national land use maps. The approach accommodates temporal variability by estimating ranges of feedstock availability and the shifting nature of zones of the highest spatial concentration of feedstocks. The techniques are applicable to biomass production from forestry, agricultural residues or oilseeds, all of which have been proposed as biofuel feedstocks. ► Dasymetric mapping appoach for producing spatial and temporal variation maps in feedstock production.► Combines land use and crop statistics to produce regionally precise feedstock maps. ► Feedstock concentrations and feedstock density maps enable identification of feedstock concentration spatially and comparison of yearly variation in production.

Assessment of MIROC3.2 HiRes Climate and CLUE-s Land Use Change Impacts on Watershed Hydrology Using SWAT

The aim of this study was to evaluate hydrologic impacts of potential climate and land use changes in a mountainous watershed in South Korea. The climatic data predicted by MIROC3.2 HiRes GCM A1B for three time periods (2010-2039, 2040-2069, and 2070-2099) were prepared using a change factor statistical downscaling method. The future land uses were predicted using the Conservation of Land Use and its Effects at Small regional extent (CLUE-s) model by establishing logistic regression model for five land use types with 11 driving forces represented by spatial information. By applying the climate and land use predictions to the Soil and Water Assessment Tool (SWAT), the watershed hydrologic components (including evapotranspiration, surface runoff, groundwater recharge, and streamflow) were evaluated. For the predicted 2070-2099 temperature and precipitation changes (+4.8C and +34.4%), and 6.2% decrease in forest areas and 1.7% increase in urban areas, the combined land use with climate change scenario resulted in more streamflow change (+55.4%) than the single climate and single land use change scenario (+39.8% and +10.8%), respectively. The predicted large increase in future precipitation and the corresponding decrease in forest land are predicted to have substantial impacts on watershed hydrology, especially on surface runoff and streamflow. Therefore, to mitigate negative hydrologic impacts and utilize positive impacts, both land use and climate changes should be considered in water resource planning for the Chungju dam watershed.

Creating Liveable Neighborhoods through Context-Sensitive Multimodal Road Planning

Improving mobility is seen as key to facilitating the economic uplift of the urban poor. In South Africa, the majority of the urban poor live on the periphery of cities. They travel long distances at great cost to go to work and school and are dependent on public transport and nonmotorized transport (NMT) (walking or cycling) for their travel needs. Despite legislation and policies that emphasize the role of public transport and NMT, road planning practice in South Africa continues to be automobile-centric. The needs of other road users are often overlooked, even in areas where they are in the majority. This paper describes the use of spatial multicriteria evaluation to rank modes according to their suitability at points along a defined route by using land use, socioeconomic, environmental, and transportation factors in combination to describe the contextual setting of the route. A case study conducted along an existing arterial route in Cape Town, South Africa, is used to demonstrate the method and the results of the analysis. The research finds that contextual regimes can be identified along the route and shows that each of these regimes has differing implications for the various modes that pass through these corridors.

Biological Diversity in Agriculture and Global Change

Biological diversity of agriculture consists of several analytic levels and spatial management scales that are subject to complex interactions with global change. The complexity of interactions is related to the bidirectional impacts and influences of global land use and climate change in combination with social-environmental shifts (globalization of agricultural development; market integration; technological change; and regulation through global treaties, policies, and institutions). This article develops a conceptual framework of the complexity of interactions using four thematic nodes—biological diversity in agriculture; global change; management and scale; and social-environmental adaptation, vulnerability, and resilience. It argues for the increased relevance of this framework. Linking expanded scientific research and policy to this group of conceptual nodes yields insight into the impacts of global change on biological diversity in agriculture and into the design of conservation strategies, monitoring approaches, and sustainability policies. Future policy must anticipate interactions of biological diversity, agroecosystem complexity, and global change stemming from the acceleration and integration of region-scale land-use intensification and disintensification.

Microsimulating parcel-level land use and activity-based travel: Development of a prototype application in San Francisco

This paper develops a prototype of an integrated microsimulation model system combining land use at a parcel level with activity-based travel in San Francisco, California. The paper describes the motivation for the model system, its design, data development, and preliminary application and testing. The land use model is implemented using UrbanSim and the Open Platform for Urban Simulation (OPUS), using parcels and buildings rather than zones or grid cells as spatial units of analysis. Measures of accessibility are derived from the San Francisco SF-CHAMP activity-based travel model, and the predicted locations of households and business establishments are used to update the micro-level inputs needed for the activity-based travel model. Data used in the model include business establishments linked to parcels over several years, and a panel of parcels that allow modeling of parcel development over time. This paper describes several advances that have not been previously integrated in an operational model system, including the use of parcels and buildings as units of location for consumers and developers of real estate, the use of business establishments to represent economic activity, and the interfacing of this microsimulation land use model with a microsimulation activity-based travel model. Computational performance and development effort were found to be modest, with land use model run times averaging one minute per year on a current desktop computer, and two to three minutes on a current laptop. By contrast, long run times of the travel model suggest that there may be a need to reconsider the level of complexity in the travel model for an integrated land use and transportation model system application to be broadly usable. The land use model is currently in refinement, being used to identify input data and model specification adjustments needed in order to bring it into operational use, which is planned over the next several months.

Modelling air pollution for epidemiologic research — Part I: A novel approach combining land use regression and air dispersion

A common limitation of epidemiological studies on health effects of air pollution is the quality of exposure data available for study participants. Exposure data derived from urban monitoring networks is usually not adequately representative of the spatial variation of pollutants, while personal monitoring campaigns are often not feasible, due to time and cost restrictions. Therefore, many studies now rely on empirical modelling techniques, such as land use regression (LUR), to estimate pollution exposure. However, LUR still requires a quantity of specifically measured data to develop a model, which is usually derived from a dedicated monitoring campaign. A dedicated air dispersion modelling exercise is also possible but is similarly resource and data intensive. This study adopted a novel approach to LUR, which utilised existing data from an air dispersion model rather than monitored data. There are several advantages to such an approach such as a larger number of sites to develop the LUR model compared to monitored data. Furthermore, through this approach the LUR model can be adapted to predict temporal variation as well as spatial variation. The aim of this study was to develop two LUR models for an epidemiologic study based in Greater Manchester by using modelled NO 2 and PM 10 concentrations as dependent variables, and traffic intensity, emissions, land use and physical geography as potential predictor variables. The LUR models were validated through a set aside “validation” dataset and data from monitoring stations. The final models for PM 10 and NO 2 comprised nine and eight predictor variables respectively and had determination coefficients (R²) of 0.71 (PM 10: Adj. R² = 0.70, F = 54.89, p < 0.001, NO 2: Adj. R² = 0.70, F = 62.04, p < 0.001). Validation of the models using the validation data and measured data showed that the R² decreases compared to the final models, except for NO 2 validation in the measured data (validation data: PM 10: R² = 0.33, NO 2: R² = 0.62; measured data: PM 10: R² = 0.56, NO 2: R² = 0.86). The validation further showed low mean prediction errors and root mean squared errors for both models.

Improved modeling of soil organic carbon in a semiarid region of Central East Kazakhstan using EPIC

Inappropriate land use and soil mismanagement produced wide-scale soil and environmental degradation to the short-grass steppe ecosystem in the semiarid region of central east Kazakhstan. A limitation for determining the impacts of land use changes on soil organic carbon (SOC) is the dearth of information on SOC stocks under the predominant land uses in the region. Here we used the Environmental Policy Integrated Climate (EPIC) model to study long-term impacts of land use changes and soil management on SOC to a depth of 50 cm during 1955–2030, in degraded agricultural lands of central east Kazakhstan. Simulated land uses were: native rangeland vegetation, wheat (Triticum aestivum L.), wheatgrass (Agropyron cristatum L.), and abandoned croplands. The EPIC model was initialized with soil properties obtained from a soil map of the study area. Data on crop management, fertilizer application and tillage practices were gathered from local expert knowledge. Simulations were performed for each polygon on a land use classification map, resulting in 4661 simulations. Our results showed that simulated SOC explained 50% of the variation in measured SOC. Of the 1.38 million hectares in the study area, 78% were under native vegetation, 3% cultivated to wheat, 8% on wheatgrass, and 11% were abandoned croplands in 2005. If land use remained constant, total stock of SOC would decrease at an annual rate of 723 kg C ha−1. However, if best management practices are implemented, resulting in reallocation of land use according to the land capability with abandoned croplands being converted to reduced-tillage wheat or wheatgrass, total stock of SOC would increase to an equivalent of 4700 kg C ha−1 yr−1. Combining land use classification and soil maps with EPIC, proved valid for studying impacts of land use changes and management practices on SOC; an important aspect of this approach is the ability to scale up site-specific SOC to the region. With the available data, EPIC produced relatively accurate results but more data on spatial and temporal variation in SOC are needed to improve model calibration and validation.