Global Soil Database Research Articles

Analysis of methane emission characteristics and environmental response in natural wetlands

Wetlands are the most important natural source of methane (CH4) emissions. Apparent wetland CH4 emissions depend largely on the balance between CH4 production and oxidation. Wetland plants play an important role in all processes. However, direct observations and side-by-side comparisons of CH4 emissions from wetlands with different vegetation types are lacking at the global scale. There is still a lack of clarity in characterizing the environmental response to CH4 emissions from wetlands with different vegetation types. Based on the FLUXNET-CH4 dataset and the Global Standard Soil Database, we analyzed the environmental response characteristics of CH4 emissions and clarified the important role of plants in CH4 emissions. We categorized wetlands into vascular plant wetlands (VPWs) and moss plant wetlands (MPWs). VPWs CH4 emissions were typically higher and more heterogeneous, whereas MPWs CH4 emissions were lower and less variable. When the water table is below the surface, VPWs still have high CH4 emission rates, while MPWs have CH4 emission rates close to zero. In addition, the temperature sensitivity of CH4 emissions from VPWs (Q10 = 2.26) is significantly higher than that of MPWs (Q10 = 1.71). Solar radiation was the main factor affecting CH4 emissions from VPWs, and temperature was the main factor affecting CH4 emissions from MPWs. Finally, our results of structural equation model suggested that soil and water environment, temperature, and plants all contribute to wetland CH4 emissions to varying degrees. These findings highlight the important role of wetland vegetation in CH4 emissions, which should be seriously considered to reduce uncertainty in the assessment of wetland CH4 emissions.

Soil's Hidden Power: The Stable Soil Organic Carbon Pool Controls the Burden of Persistent Organic Pollutants in Background Soils.

Persistent organic pollutants (POPs) tend to accumulate in cold regions by cold condensation and global distillation. Soil organic matter is the main storage compartment for POPs in terrestrial ecosystems due to deposition and repeated air-surface exchange processes. Here, physicochemical properties and environmental factors were investigated for their role in influencing POPs accumulation in soils of the Tibetan Plateau and Antarctic and Arctic regions. The results showed that the soil burden of most POPs was closely coupled to stable mineral-associated organic carbon (MAOC). Combining the proportion of MAOC and physicochemical properties can explain much of the soil distribution characteristics of the POPs. The background levels of POPs were estimated in conjunction with the global soil database. It led to the proposition that the stable soil carbon pools are key controlling factors affecting the ultimate global distribution of POPs, so that the dynamic cycling of soil carbon acts to counteract the cold-trapping effects. In the future, soil carbon pool composition should be fully considered in a multimedia environmental model of POPs, and the risk of secondary release of POPs in soils under conditions such as climate change can be further assessed with soil organic carbon models.

Multinational prediction of soil organic carbon and texture via proximal sensors

AbstractNovel technologies help to monitor the environmental impact of human activities, but tests involving datasets from several countries, encompassing a large variability of soil properties, are still scarce. This study utilized proximal sensors to predict soil organic carbon (OC) and soil texture of samples from Brazil, France, India, Mozambique, and United States. A total of 1749 samples were analyzed by portable X‐ray fluorescence (pXRF) spectrometry and visible near‐infrared diffuse reflectance spectroscopy. Sand (R2 = 0.89), silt (0.87), and clay (0.84) predictions were very accurate, despite contrasting climates, soil parent materials, and weathering degrees. Soil OC predictions were similarly successful (0.74) using samples from five countries. pXRF was the optimal sensor for soil texture predictions. The addition of international data may improve local models. Proximal soil sensing can be successfully used with a multinational soil database offering a clean, rapid, and accurate alternative to estimate soil texture and OC with international datasets.

DSOLMap, a novel high-resolution global digital soil property map for the SWAT + model: Development and hydrological evaluation

This research paper addresses the ongoing challenge of developing fine-resolution global digital soil property maps for hydrological modelling applications. Hydrological models are essential for understanding watershed dynamics and the impact of human activities on water resources. Soil data, which plays a crucial role in the hydrological cycle, is a requisite model input. Global digital soil property maps usually have coarse spatial resolutions, adding considerable uncertainty to hydrological models despite calibration efforts. To address this issue, a new global digital soil property map with 250 m spatial resolution, known as Digital Soil Open Land Map (DSOLMap), was developed and evaluated in this study. The DSOLMap has a finer spatial resolution than existing global soil maps and a more detailed soil profile divided into six soil horizons. This new high-resolution global digital soil property map was tailored to the SWAT + model format. SWAT + is the latest released version of the Soil and Water Assessment Tool (SWAT), one of the most comprehensive hydrological models, and is widely used worldwide. A hydrological evaluation was conducted with the DSOLMap and its results were compared to two other global soil databases using the SWAT + model in a basin located in the north of Spain. The findings showed that using more detailed, finer-resolution soil data, such as those that the DSOLMap offers, improved the hydrological performance of the SWAT + model on a daily scale before and after calibration and validation procedures. The DSOLMap represents a global step forward in hydrological modelling, notably for regions with scarce or unavailable soil information. This new digital soil property map can help decision-makers address global challenges related to water resources and environmental issues through hydrological modelling.

Which method to choose for measurement of oranic and inorganic carbon content in carbonate-rich soils? Advantages and disadvantages of dry and wet chemistry

The pedosphere is an essential reservoir of carbon represented by organic (SOC) and inorganic (SIC) forms. Various methods are used worldwide to measure SOC and SIC. The trend towards globalization of data on soils requires having the accumulated information harmonized. Global soil databases are crucial for inventory and mapping, modeling and forecasting, monitoring and rational use of soil resources.This study compared the six most common methods for measuring carbon in soils on calcareous rocks. Standard soil samples and soils of the Polar Urals with a variation in CaCO3 (0–100%) and SOC (0–40%) were selected as research targets.The loss-on-ignition (LOI) method (T = 550 °C) overestimates SOC. It is explained by the fact that the SOC to SOM conversion factor of 1.724 seems inadequate. The soils under study demand a coefficient of 2.326. The second reason is the presence of thermolabile mineral components in soils, as well as adsorbed and chemically bound water. For soils with a carbonate content of 0–100% and SOC < 8.7%, harmonized results of SOC measurements may be obtained on the analyzer and by the dichromatometric method (Walkley-Black) only with conversion factors applied (1.3). At a higher SOC (8.7–40%) content, soils in the area under study require a reduced coefficient of 1.18.The measurement results of SIC within the range of 3.2–12% (CaCO3 27–100%) obtained by LOI methods (T = 800 °C) and with a calcimeter are identical with SOC < 16%. The presence of CaCO3 does not inhibit the use of the dichromatometric method (Walkley-Black, Tyurin) to measure SOC.The reliability of carbon content measurement methods for soils was determined both by the specific composition of the soil and by the accuracy of the method itself. To facilitate future application of the methodology, guidelines were proposed to use methods for quantitative assessment of SOC and SIC.

Validation of SoilGrids 2.0 in an Arid Region of India using In Situ Measurements

As one of the Earth’s most important natural resources, soil plays a prominent role in regulating ecosystem services, human food production systems and in facilitating a region’s sustainable development. Of late, due recognition has been given to soil sciences and soil information systems as they act as a core to achieve the targets of land degradation neutrality and help in fostering soil governance. In this regard, the availability of global soil databases paves the way for implementing successful soil information systems. Currently, harmonized world soil database from the Food and Agriculture Organization and SoilGrids from International Soil References and Information Centre serve various global soil data products in a geospatial-ready format for the scientific fraternity. In this study, SoilGrids 2.0 is validated with in situ measurements in the arid region of the Thar Desert. Soil fractions and pH at the top surface (0–5 cm) and subsurface (5–15 cm) were measured through soil sample analysis collected from the study area and compared with the values retrieved from SoilGrids 2.0 for the same location. This comparison shows that the SoilGrids 2.0 has underestimated the sand fragments up to ~28% and overestimated ~14% for silt and clay fragments. Deviation of pH in SoilGrids 2.0 was also observed with the root mean square error of one unit. However, in the comparison of soil texture classes from the field and the one given by SoilGrids 2.0, a systematic shift was found, indicating the robustness of SoilGrids prediction algorithm that can be fine-tuned by incorporating additional soil profiles (from contributing agencies) as the current snapshot of the soil database lacks dense and well-distributed soil profiles in this arid region.

Evaluating the climatic and socio-economic influences on the agricultural drought vulnerability in Jharkhand.

Environmental hazards like drought lead to degrading food production and adversely impact the agro-economy. This study investigates the contributions of different climatic and socio-economic variables to agricultural drought in Jharkhand. The three primary criteria, i.e., exposure (E), sensitivity (S), and adaptive capacity (AC), responsible for agricultural drought vulnerability, were examined to identify the drought-prone areas. Long-term (1958-2020) gridded climatic datasets obtained from the Terra-climate global dataset, MODIS vegetation index dataset (MOD13Q1) for the years 2001-2020, different soil parameters obtained from the ISRIC global soil database and state agricultural portal of Jharkhand, and different socio-economic datasets obtained from census data (2011) provided by Govt. of India, were utilized for this study. Analytic Hierarchy Process (AHP) was used to estimate the weighted contribution of the indicator variables falling under each criterion (E, S, and AC), and three criteria index maps were generated. These separate maps were further integrated to generate the final vulnerability index map. Finally, the study area was categorized into different zones based on the drought vulnerability index value ranging from 0 to 1, according to the severity of the drought. It was observed that about 4.05%, 28.12%, and 37.07% of the total geographical area is very highly, highly, and moderately vulnerable to agricultural drought, respectively. Amongst the three primary criteria, exposure showed a significant positive correlation (R = 0.61), and sensitivity showed a strong positive correlation (R = 0.55) with vulnerability. The adaptive capacity was negatively correlated (R = -0.75) with the vulnerability. However, putting equal weights to the variables to calculate the vulnerability, the exposure and sensitivity indicators showed a significant positive correlation with the vulnerability, with an R-value of 0.82 and 0.79, respectively. In contrast, the adaptive capacity showed a negative correlation with the vulnerability with R = -0.75.

Evaluation of two new-generation global soil databases for macro-scale hydrological modelling in Norway

Lack of national soil property maps limits the studies of soil moisture (SM) dynamics in Norway. One alternative is to apply the global soil data as input for macro-scale hydrological modelling, but the quality of these data is still unknown. The objectives of this study are 1) to evaluate two recent global soil databases (Wise30sec and SoilGrids) in comparison with data from local soil profiles; 2) to evaluate which database supports better model performance in terms of river discharge and SM for three macro-scale catchments in Norway and 3) to suggest criteria for the selection of soil data for models with different complexity. The global soil databases were evaluated in three steps: 1) the global soil data are compared directly with the Norwegian forest soil profiles; 2) the simulated discharge based on the two global soil databases is compared with observations and 3) the simulated SM is compared with three global SM products. Two hydrological models were applied to simulate discharge and SM: the Soil and Water Integrated Model (SWIM) and the Variable Infiltration Capacity (VIC) model. The comparison with data from local soil profiles shows that SoilGrids has smaller mean errors than Wise30sec, especially for upper soil layers, but both soil databases have large root mean squared errors and poor correlations. SWIM generally performs better in terms of discharge using SoilGrids than using Wise30sec and the simulated SM has higher correlations with the SM products. In contrast, the VIC model is less sensitive to soil input data and the simulated SM using Wise30sec is higher correlated with the SM products than using SoilGrids. Based on the results, we conclude that the global soil databases can provide reasonable soil property information at coarse resolutions and large areas. The selection of soil input data should depend on the characteristics of both models and study areas.

A Comparison, Validation, and Evaluation of the S-world Global Soil Property Database

Despite the increased usage of global soil property maps, a proper review of the maps rarely takes place. This study aims to explore the options for such a review with an application for the S-World global soil property database. Global soil organic carbon (SOC) and clay content maps from S-World were studied at two spatial resolutions in three steps. First, a comparative analysis with an ensemble of seven datasets derived from five other global soil databases was done. Second, a validation of S-World was done with independent soil observations from the WoSIS soil profile database. Third, a methodological evaluation of S-world took place by looking at the variation of soil properties per soil type and short distance variability. In the comparative analysis, S-World and the ensemble of other maps show similar spatial patterns. However, the ensemble locally shows large discrepancies (e.g., in boreal regions where typically SOC contents are high and the sampling density is low). Overall, the results show that S-World is not deviating strongly from the model ensemble (91% of the area falls within a 1.5% SOC range in the topsoil). The validation with the WoSIS database showed that S-World was able to capture a large part of the variation (with, e.g., a root mean square difference of 1.7% for SOC in the topsoil and a mean difference of 1.2%). Finally, the methodological evaluation revealed that estimates of the ranges of soil properties for the different soil types can be improved by using the larger WoSIS database. It is concluded that the review through the comparison, validation, and evaluation provides a good overview of the strengths and the weaknesses of S-World. The three approaches to review the database each provide specific insights regarding the quality of the database. Specific evaluation criteria for an application will determine whether S-World is a suitable soil database for use in global environmental studies.

Projecting the urbanization effect on soil organic carbon stocks in polar and steppe areas of European Russia by remote sensing

This paper describes the effect of urbanization on soil organic carbon (SOC) stocks by projecting the main urban land cover classes over the initial pre-urban soil maps. Two cities different in climate and soil conditions as well as in availability of SOC data were chosen as the case studies. Rostov-on-Don is the center of Russian South, where croplands and natural steppes have been conventionally thoroughly studied by soil scientists. In contrast, soils of Murmansk located in Russian Arctics have always been overlooked due to low suitability for agriculture. Global, national and regional soil maps and databases were used to estimate pre-urban SOC stocks in the areas. The outcomes based on Harmonized World Soil Database were highly uncertain, underestimating 0-100 cm SOC stocks in the polar region and overestimating them in the steppe region, whereas the results based on Digital Soil Map of Russia and regional maps were comparable. Land cover structures of Rostov-on-Don and Murmansk were mapped based on the stepwise per-pixel and sub-pixel classification algorithms applied to Sentinel-2 and included the following classes: sealed soils, green lawns, trees and shrubs, bare soils and water. Murmansk was dominated by trees and shrubs (58.1%) with the proportion of area 17.5% covered by sealed soils. In Rostov-on-Don, less than 30% of the total area was covered by trees and shrubs which is also comparable with bare soils (19.6%)_and lawns (23.4%), whereas almost one third of the territory was sealed (27.6%). These land cover structures had a different impact on the topsoil SOC stocks: a 30-50% increase in Murmansk compared to the 18% decrease in Rostov-on-Don. An increase of the 0-100 cm SOC stocks was shown for both regions, however in the polar conditions it was two times higher compared to the steppe. In polar conditions, conversion of natural soils into urban non-sealed soils increased SOC stocks from 30% to more than 4 times in 0-10 cm layer and from 47% to almost 3 times in the 0-100 cm layer. The highest increase was reported for the lawns, whereas SOC under trees and shrublands were considerably lower. In Rostov-on-Don , sealed and bare soils stored less SOC compared to the initial natural soils. The conversion of natural areas into urban green infrastructure increased SOC up to 50-70%. Although the absolute SOC values based on the global and national legacy data are highly uncertain, especially for the polar areas, the research outcomes clearly reveal possible patterns in SOC changes induced by different urbanization pathways in contrast climatic conditions and highlight the complexity of the urbanization effect on soils

Basic Soil Data Requirements for Process-Based Crop Models as a Basis for Crop Diversification

Data from global soil databases are increasingly used for crop modelling, but the impact of such data on simulated crop yield has not been not extensively studied. Accurate yield estimation is particularly useful for yield mapping and crop diversification planning. In this article, available soil profile data across Sri Lanka were harmonised and compared with the data from two global soil databases (Soilgrids and Openlandmap). Their impact on simulated crop (rice) yield was studied using a pre-calibrated Agricultural Production Systems Simulator (APSIM) as an exemplar model. To identify the most sensitive soil parameters, a global sensitivity analysis was performed for all parameters across three datasets. Different soil parameters in both global datasets showed significantly (p &lt; 0.05) lower and higher values than observed values. However, simulated rice yields using global data were significantly (p &lt; 0.05) higher than from observed soil. Due to the relatively lower sensitivity to the yield, all parameters except soil texture and bulk density can still be supplied from global databases when observed data are not available. To facilitate the wider application of digital soil data for yield simulations, particularly for neglected and underutilised crops, nation-wide soil maps for 9 parameters up to 100 cm depth were generated and made available online.

Assessing HC27 Soil Database for Modeling Plant Production

Soil information is a vital input for crop models applications in various large area studies including climate change impact and food security. One of the global soil databases that provide full information for crop models is HC27 of IFPRI. The quality of the database has not been assessed for crop modeling so far. A tested crop simulation model (SSM-iCrop2) was used for this purpose that needs soil water related properties (i.e., depth, albedo, curve number for runoff, drainage coefficient, and soil water limits at saturation, drained upper limit and lower limit) for the simulation of crop properties. Actual data of two soil profiles from three different climate zones (locations) were used as model inputs to simulate potential yield, evapotranspiration (under rainfed conditions) or net irrigation water requirement (under irrigated conditions) of some important plant species (alfalfa, sugar beet, sugar cane, wheat, olive, soybean, apricot and chickpea) under rainfed and irrigated conditions of Iran. Results showed that the application of HC27 soil information in the SSM-iCrop2 model resulted in model output that was not different from the model output with actual soil information with respect to mean, variance, and distribution. No statistically significant difference was found in the simulation of various combinations of soil profiles-plant species-locations. It was concluded that HC27 information can be used in simulation studies with SSM-iCrop2 or other similar simple models for the simulation of potential yield, net irrigation water, or evapotranspiration that are commonly required for food security and climate change studies.

Global convergence in correlations among soil properties.

The correlations among major soil properties at a global scale are essential for explaining the global convergence of ecological processes in terrestrial ecosystems but have never been assessed. In this study, a global soil database was analyzed to determine whether correlations among soil properties were consistent and how such correlations varied among continents and land use types. Across the entire dataset, the electrical conductivity of soils increased significantly with increasing pH; additionally, the total nitrogen and cation exchange capacity increased significantly with increasing organic carbon content, while the organic carbon content and cation exchange capacity decreased significantly with increasing sand content in soil. The correlations between paired soil properties were consistent among continents and land use types. The slopes of the relationships, however, varied significantly by continent and land use type. The results indicated a global convergence of correlations among soil properties and variation in the slopes of specific relationships between paired soil properties among continents and land use types. Such consistent global correlations and different slopes of specific correlations can have important implications in explaining the global patterns of biogeochemical processes and can provide some basis for linking soil resources with ecological processes on a global scale. Keywords: continent, land use, organic carbon, electricity, pH, CEC DOI: 10.25165/j.ijabe.20201303.4547 Citation: Xie B C, Zhang C X, Wang G D, Xie Y G. Global convergence in correlations among soil properties. Int J Agric & Biol Eng, 2020; 13(3): 108–116.

Plant ecological indicator values as predictors of fine‐root trait variations

Abstract Fine roots play key roles in the capacity of plants to face environmental constraints and their traits reflect adaptations to the environment, including soil structure, resource availability and climate. However, the inaccuracy of global soil and climate databases to account for the large environmental variation occurring at small spatial scale prevents accurate estimations of the linkages between environmental variables and fine‐root strategies. Here, using two global databases on fine‐root traits (Rhizopolis‐db) and species phylogenetic relatedness, and a regional database of species ecological indicator values (Baseflor), we quantified the predictive value of ecological indicator values, as an alternative to classical coarse soil and climate indicators, on the variation in four major fine‐root traits. A strong phylogenetic signal was found among species for fine‐root mean diameter, specific root length (SRL) and root tissue density (RTD), but less so for root nitrogen concentration (RNC). After accounting for this relatedness, ecological indicators still explained a large part of trait variation in our dataset for SRL, RTD and RNC. Multi‐indicators best model R2 reached .40 for SRL and RTD, and .44 for RNC, whereas it was only 0.10 for diameter. Ecological indicators of nutrient availability and soil texture were those that most strongly related to SRL, RTD and RNC. Specifically, plant fast resources use strategies characterized by high SRL, RNC and low RTD occurred more frequently in nutrient‐rich soils and in soils with light sandy textures. Additionally, light availability and atmospheric temperature were negatively related with SRL and continentality negatively influenced RNC. With respect to both nutrient and water availability ecological indicator values, opposite adaptations were observed between growth forms, particularly between woody and herbaceous species, limiting our ability to define simple, widely applicable patterns of trait–environment relationships. Synthesis. Our analysis demonstrates that species ecological indicator values are valuable predictors of plant below‐ground strategies. It provides original evidence that herbaceous species with fine‐root traits representative of fast resource use strategies typically occur in more favourable soil habitats (high nutrient and water availability); meanwhile, woody species may show the opposite trend. Other important environmental parameters concomitantly influence fine‐root trait variation in contrasting ways.

Global soil, landuse, evapotranspiration, historical and future weather databases for SWAT Applications

Large-scale distributed watershed models are data-intensive, and preparing them consumes most of the research resources. We prepared high-resolution global databases of soil, landuse, actual evapotranspiration (AET), and historical and future weather databases that could serve as standard inputs in Soil and Water Assessment Tool (SWAT) models. The data include two global soil maps and their associated databases calculated with a large number of pedotransfer functions, two landuse maps and their correspondence with SWAT’s database, historical and future daily temperature and precipitation data from five IPCC models with four scenarios; and finally, global monthly AET data. Weather data are 0.5° global grids text-formatted for direct use in SWAT models. The AET data is formatted for use in SWAT-CUP (SWAT Calibration Uncertainty Procedures) for calibration of SWAT models. The use of these global databases for SWAT models can speed up the model building by 75–80% and are extremely valuable in areas with limited or no physical data. Furthermore, they can facilitate the comparison of model results in different parts of the world.

Global soil–climate–biome diagram: linking surface soil properties to climate and biota

Abstract. Surface soils interact strongly with both climate and biota and provide fundamental ecosystem services that maintain food, climate and human security. However, the quantitative linkages between soil properties, climate and biota remain unclear at the global scale. By compiling a comprehensive global soil database, we mapped eight major soil properties (bulk density; clay, silt, and sand fractions; soil pH; soil organic carbon, SOC, density; soil total nitrogen, STN, density; and soil C:N mass ratios) in the surface soil layer (0–30 cm), based on machine learning algorithms, and demonstrated the quantitative linkages between surface soil properties, climate and biota at the global scale, which we call the global soil–climate–biome diagram. In the diagram, bulk density increased significantly with higher mean annual temperature (MAT) and lower mean annual precipitation (MAP); soil clay fraction increased significantly with higher MAT and MAP; soil pH decreased with higher MAP and lower MAT and the “critical MAP”, which means the corresponding MAP at a soil pH of =7.0 (a shift from alkaline to acidic soil), decreased with lower MAT. SOC density and STN density were both jointly affected by MAT and MAP, showing an increase at lower MAT and a saturation towards higher MAP. Surface soil physical and chemical properties also showed remarkable variation across biomes. The soil–climate–biome diagram suggests shifts in soil properties under global climate and land cover change.

Impact of spatial variability in hydraulic parameters on plume migration within unsaturated surficial formations

Heterogeneities in textural properties of surficial formations at field scale result in spatial variations in hydraulic parameters governing unsaturated zone flow. This study aims at quantifying the influence of such variations on solute transport resulting from a localized source of radioactive contaminant at ground level.The study focuses on three hydraulic parameters related to the Mualem-van Genuchten formalism, namely the saturated hydraulic conductivity Ks, the parameter α inversely proportional to the air-entry value, and the parameter n related to the pore-size distribution. Sets of random fields accounting for spatial variability of these parameters are generated using lognormal distributions with different variances and correlation lengths. These random fields are used as inputs to an unsaturated flow and transport model to simulate radionuclide plume migration.Each simulated plume is characterized by its size (plume surface area), position (location of center of mass) and shape (elongation ratio) within the unsaturated zone. By comparison with the homogeneous medium, Ks-, α- and n-random fields generated with the mean variances computed through the analysis of a global soil database respectively result in average in (i) 25 (variable Ks), 20 (variable α) and 65% (variable n) increase in plume size; (ii) 0.8, 1 and 1.8 m horizontal offsets of the plume center; and (iii) 20, 30 and 50% decrease in plume circularity. In addition, changes in the variance values within one order of magnitude appear to have critical consequences only for the n parameter.The issue of spatial variability of hydraulic parameters is thus crucial for characterizing the evolution of pollutant plumes within an unsaturated zone and for developing better remediation strategies for industrial sites.

Cool Farm Tool Water: A global on-line tool to assess water use in crop production

The agricultural sector accounts for 70% of all water consumption and poses great pressure on ground water resources. Therefore, evaluating agricultural water consumption is highly important as it allows supply chain actors to identify practices which are associated with unsustainable water use, which risk depleting current water resources and impacting future production. However, these assessments are often not feasible for crop producers as data, models and experiments are required in order to conduct them. This work introduces a new on-line agricultural water use assessment tool that provides the water footprint and irrigation requirements at field scale based on an enhanced FAO56 approach combined with a global climate, crop and soil databases. This has been included in the Cool Farm Tool – an online tool which already provides metrics for greenhouse gas emissions and biodiversity impacts and therefore allows for a more holistic assessment of environmental sustainability in farming and agricultural supply chains. The model is tested against field scale and state level water footprint data providing good results. The tool provides a practical, reliable way to assess agricultural water use, and offers a means to engage growers and stakeholders in identifying efficient water management practices.

Predicting Potential Distribution and Evaluating Suitable Soil Condition of Oil Tea Camellia in China

Oil tea Camellia, as a major cash and oil crop, has a high status in the forestry cultivation systems in China. To meet the current market demand for oil tea Camellia, its potential distribution and suitable soil condition was researched, to instruct its cultivation and popularization. The potential distribution of oil tea Camellia in China was predicted by the maximum entropy model, using global environmental and soil databases. Then, we collected 10-year literature data about oil tea Camellia soil and applied multiple imputation and factor modeling for an in-depth analysis of soil suitability for growing of oil tea Camellia. The prediction indicated that oil tea Camellia was mainly distributed in Hunan, Jiangxi, Zhejiang, Hainan, East Hubei, Southwest Anhui and most of Guangdong. Climatic factors were more influential than soil factors. The minimum temperature of the coldest month, mean temperature of the coldest quarter and annual precipitation were the most significant contributors to the habitat suitability distribution. In the cultivated area of oil tea Camellia, soil fertility was poor, organic matter was the most significant factor for the soil conditions. Based on climatic and soil factor analyses, our data suggest there is a great potential to spread the oil tea Camellia cultivation industry.

Developing a soil erodibility map across Mongolia

Wind erosion and dust emissions are land surface processes that characterize drylands globally. Wind erosion depends on the relationship between erosivity and erodibility factors. Soil erodibility measures the relative potential for soils to be eroded by wind. In this study, soil erodibility was estimated from soil and vegetation data. Global soil texture data, a global soil database and a Normalized Difference Vegetation Index (NDVI) dataset were used to evaluate soil erodibility over Mongolia in terms of two parameters: wind erodible fraction of soil (a measure of erodibility related to soil properties) and threshold friction velocity. Threshold friction velocity was estimated using the frontal area of the roughness element that was calculated using NDVI-derived vegetation cover. A novel integrated soil erodibility map over Mongolia was developed using the two erodibility parameters and a soil classification map. The estimated wind erodible fraction of soil showed that erodibility is higher in the desert and the desert steppe regions. The estimated threshold friction velocity exhibited also a regional pattern. The threshold friction velocity was lower in the desert and desert steppe regions and higher in the forest area. The integrated soil erodibility map revealed widespread areas of high erodibility from the desert and desert steppe regions in the south to the sandy areas in the west. Low-erodibility areas extended from western to northern Mongolia, while medium-erodibility areas mostly covered the dry steppe regions and the desert steppe in the west. The integrated map will provide a useful climatological basis for studying dust emission processes and for developing practical dust risk assessment and early warning system applications.