Interpreting method of regional soil salinity 3D distribution based on inverse distance weighting

为了建立面域三维土壤盐分信息精准解译与表征技术方法,该研究以反距离权重空间数据插值方法结合电磁感应式表观土壤电导率快速测定技术为基础,成功构建出典型干旱区土壤盐分分层精确解译模型,对研究区土壤盐分进行了精度解析与评估,以三维图形方式直观揭示了土壤盐分空间分布特征。研究区不同区位土壤盐分呈现出不同的剖面分布类型特征,研究区主体土壤属于中度以上盐渍化。该研究将为土壤盐分三维空间分布特征解析与评估提供可靠技术方法。     

基于反距离权重插值的土壤盐分三维分布解析方法

为了建立面域三维土壤盐分信息精准解译与表征技术方法,该研究以反距离权重空间数据插值方法结合电磁感应式表观土壤电导率快速测定技术为基础,成功构建出典型干旱区土壤盐分分层精确解译模型,对研究区土壤盐分进行了精度解析与评估,以三维图形方式直观揭示了土壤盐分空间分布特征。研究区不同区位土壤盐分呈现出不同的剖面分布类型特征,研究区主体土壤属于中度以上盐渍化。该研究将为土壤盐分三维空间分布特征解析与评估提供可靠技术方法。     

Scope to map soil management units at the district level from remotely sensed γ‐ray spectrometry and proximal sensed EM induction data

The traditional method of soil mapping involves classifying soil into pre‐existing classes using morphological observations and then air‐photograph interpretation to extrapolate the information. To accelerate the process, less costly ancillary data can be used to assist mapping. However, digital soil mapping (DSM) is still affected by the classifications used to identify soil types. One reason is because the morphological characteristics are not mutually exclusive, which causes misclassification. In this study, we used a DSM approach, where ancillary data were surrogate for morphological data, with soil types identified by numerical clustering of remotely and proximally sensed data collected across a farming district near Gunnedah, Australia. Remotely sensed data were obtained from an air‐borne gamma‐ray (γ‐ray) spectrometer survey, including potassium (K), thorium (Th), uranium (U) and total counts. Proximally sensed data were measured using EM38 (i.e. EM38h and EM38v). Using fuzzy k‐means and a linear mixed model with measured physical (e.g. clay) and chemical (e.g. CEC) properties from the topsoil (0–0.30 m) and subsoil (0.9–1.2 m), we found that k = 5 was also optimal given that mean‐squared prediction error (i.e. ) was minimised. The approach highlighted subtle differences in physical and chemical properties in productive areas. The DSM was unsuccessful in identifying small units; however, inclusion of elevation data might overcome this limitation. This research has implications for providing fast, accurate and meaningful DSM at a district scale, where traditional methods are too expensive.     

A spatial approach to soil‐ecological experimentation at landscape scale

The upscaling of soil‐ecological processes to larger landscape units represents a special challenge to soil ecology. Results from micro‐ or mesocosms cannot easily be transferred to other scales because effects are often scale‐dependent. In this context, field experiments which take into account the heterogeneity of the landscape may be promising. Therefore, we carried out an experiment based on a transect study in the agrolandscape of NE Germany on heterogeneous sandy soil in which the feeding activity of the soil‐organism community was assessed by means of the bait‐lamina test at each of the 101 transect locations. At every 4th position, prior to the measurement the soil biota were stimulated by a treatment consisting of adding easily available C and water to the soil. Our aim was to test whether this kind of spatial approach enables to separate effects induced by treatments from landscape effects. The results showed a highly variable feeding activity along the transect after 4 weeks. Despite this variability, a basic trend could be identified which was related to a landscape factor, i.e., the relief. On upper‐slope positions, the feeding activity tended to be less in comparison to positions down‐slope. At every 4th position of the transect, the stimulating effect of the substrate and water addition could be clearly detected and quantified with spectral and cross‐spectral analysis. It is concluded that effects of treatments in heterogeneous landscapes may be distinguished from site effects when the signal‐to‐noise ratio is high and soil and treatment effects on the variable of interest are sufficiently different from one another. In a heterogeneous landscape with gradients of site properties, a treatment based on the frequency domain and applied in regular intervals can be distinguished with spectral analysis techniques.     

基于电磁感应的典型干旱区土壤盐分空间异质性

为研究干旱区土壤盐分空间异质性,指导农业生产实践,运用大地电导率仪（EM38、EM31）对研究区域进行移动式磁感调查,获取表观电导率（ECa）。同时,通过27个校准点的采样和ECa测量,建立土壤盐分的电磁感应解译模型。干旱区土壤盐分质量分数与EM38、EM31水平模式读数（H38、H31）显示出良好的相关性（R=0.935）,可以利用ECa结合GIS和地统计学知识研究土壤盐分的空间分布。采用两种方法进行研究：一种是先利用解译模型获取磁感调查点的土壤盐分质量分数,然后进行地统计分析研究其空间分布;另一种是先利用地统计分析研究H38和H31的空间分布,然后利用解译模型通过栅格运算计算盐分质量分数,精度检验显示前者预测值与实测值之间的相关性更好（R2, 0.888＞0.873）;标准差较低（std. 0.414＜0.426）,具有更高的预测精度。研究结果表明,基于电磁感应研究干旱区土壤盐分空间异质性是切实可行的,这对于土壤盐渍化的快速诊断,指导农业生产和促进精准农业的发展具有重要的意义。     

Mapping soil salinity in 3‐dimensions using an EM38 and EM4Soil inversion modelling at the reconnaissance scale in central Morocco

Large areas of Morocco require irrigation and although good quality water is available in dams, farmers augment river water with poorer quality ground water, resulting in salt build‐up without a sufficient leaching fraction. Implementation of management plans requires baseline reconnaissance maps of salinity. We developed a method to map the distribution of salinity profiles by establishing a linear regression (LR) between calculated true electrical conductivity (σ, mS/m) and electrical conductivity of the saturated soil‐paste extract (ECe, dS/m). Estimates of σ were obtained by inverting the apparent electrical conductivity (ECa, mS/m) collected from a 500‐m grid survey using an EM38. Spherical variograms were developed to interpolate ECa data onto a 100 m grid using residual maximum likelihood. Inversion was carried out on kriged ECa data using a quasi‐3d model (EM4Soil software), selecting the cumulative function (CF) forward modelling and S2 inversion algorithm with a damping factor of 3.0. Using a ‘leave‐one‐out cross‐validation' (LOOCV), of one in 12 of the calibration sites, the use of the q‐3d model yielded a high accuracy (RMSE = 0.42 dS/m), small bias (ME = ?0.02 dS/m) and Lin's concordance (0.91). Slightly worse results were obtained using individual LR established at each depth increment overall (i.e. RMSE = 0.45 dS/m; ME = 0.00 dS/m; Lin's = 0.89) with the raw EM38 ECa. Inversion required a single LR (ECe = 0.679 + 0.041 × σ), enabling efficiencies in estimating ECe at any depth across the irrigation district. Final maps of ECe, along with information on water used for irrigation (ECw) and the characterization of properties of the two main soil types, enabled better understanding of causes of secondary soil salinity. The approach can be applied to problematic saline areas with saline water tables.     

Large‐scale assessment of soil erosion using a neuro‐fuzzy model combined with GIS: A case study of Hubei Province,China

Reliable and cost‐effective soil erosion assessment is an important precondition for soil conservation measures, which remains a major challenge at large scale. Considering that the neuro‐fuzzy model has the special advantage in multi‐index comprehensive assessment and GIS technology is adept at geo‐spatial information processing, through the combination of them, it is possible to provide an effective approach for this difficult problem. Taking Hubei Province as a case study area, five evaluating indicators were selected for the large‐scale assessment, in which the GIS technology was used to construct the classification maps of evaluating indicators and to divide basic assessment units, and the neuro‐fuzzy model was adopted to extract fuzzy rules for individual units assessment from available ground truth data. According to the optimized assessment criteria generated by the neuro‐fuzzy model, the soil erosion state of the entire study area was then assessed. To represent the spatial distribution of soil erosion, a detailed map was produced by statistical mapping, which was represented with six erosion levels (from slight to severe) at a map scale of 1:250 000. The resulting map showed that about 30.1% of the total land area in Hubei was affected by different levels of soil erosion problem. Western high mountains and eastern low mountains suffered from the most serious erosion damage, a strong level of soil erosion was widely observed in these mountains. Large areas of moderate level erosion occurred in the northern hills. In contrast, most of the central plains were characterized as slight level erosion effect. The validation indicated that an overall accuracy of 88% and a κ of 0.89 were achieved, proving that the resulting map was in conformity with actual conditions, which indicates this assessment approach was reasonable and applicable. Copyright © 2009 John Wiley & Sons, Ltd.     

Soil classification and salinity mapping for determining restoration potential of cropped riparian areas

Restoration and management of riparian areas have recently become important issues. Soil and salinity surveys are required before planning restoration activities and land‐uses if the riparian area is salt‐affected. In this study, we characterise the soils and salinity conditions of a riparian area that underwent irrigated agriculture with significant soil salinisation, to assess the general site suitability for riparian restoration and potential land‐uses. Throughout the area, 19 profiles were described and classified and 95 soil samples were collected for their chemical and physical characterisation. The salinity of the 35‐ha presumably salt‐affected area was analysed by reading the bulk soil electrical conductivity (ECa) with the hand‐held electromagnetic‐induction sensor Geonics‐EM38 at 558 locations and by measuring the electrical conductivity of the saturation extract (ECe) and sodium adsorption ratio (SAR) of 60 soil samples collected at 30 of those locations. The regression of ECe on EM readings predicted ECe with R² > 0·92 at the 0–100 cm soil depth. The geo‐referenced soil classification (three soil units were established) and salinity maps identified the soil constraints for the area's restoration potential. The major limiting soil factors were soil salinity, sodicity and waterlogging in the southern half of the soil unit #3, and soil compaction in most of the area. The value of those limiting factors, along with differences in soil texture, as a means of assessing restoration potential of riparian vegetation and for identifying suitable land‐uses for the three soil units was discussed. Agro‐forestry, planned grazing, recreational and educational land‐uses are possible for the site. Copyright © 2007 John Wiley & Sons, Ltd.     

基于多源环境变量和随机森林的橡胶园土壤全氮含量预测

土壤全氮与土壤肥力和土壤氮循环紧密相关。掌握土壤全氮详细的空间分布信息对提高土壤肥力管理效率和更好地了解土壤氮循环至关重要。该文以儋州国营橡胶园为研究区域,采集2511个土壤样品,利用随机森林(random forest,RF)、逐步线性回归(stepwise linear regression,SLR)、广义加性混合模型(generalized additive mixed model,GAMM)以及分类回归树(classification and regression tree,CART)结合多源环境变量(成土母质、平均降雨量、平均气温和归一化植被指数)对研究区橡胶园土壤全氮含量进行空间预测,并通过754个独立验证点比较了4种模型的预测精度。结果表明RF对土壤全氮的预测值和实测值的相关系数(0.82)明显高于SLR(0.68)、GAMM(0.70)和CART(0.69),而RF的预测平均绝对误差(0.08836 g/kg)和均方根误差(0.13090 g/kg)均低于SLR、GAMM和CART。此外,RF模型预测结果能反映更为详细的局部土壤全氮含量空间变化信息,与实际情况更为接近。可见,RF模型可作为橡胶园土壤全氮含量空间分布预测的高效方法,为其他土壤属性的空间分布预测提供了一种新的方法。     

Comparison of Geonics EM38 and Dualem 1S electromagnetic induction sensors for the measurement of salinity and other soil properties

The electromagnetic induction (EMI) Geonics EM38 (G‐EM38) and Dualem 1S (D‐1S) sensors are used frequently for assessment of soil salinity and other soil characteristics in irrigated agriculture. We compared these two sensors to determine whether they could be used interchangeably for the measurement of apparent soil electrical conductivity (ECa) in horizontal (ECa‐h) and vertical (ECa‐v) coil receiver modes. Readings were taken at 201 locations identified in three irrigation districts in both modes, and statistical comparisons were made on the raw data and from maps of a 2‐ha irrigated field made using 1680 horizontal mode readings. Both sensors gave the same ECa‐v readings (mean G‐EM38 and D‐1S difference = 0), whereas the ECa‐h readings were slightly greater with the Geonics EM38 than with the Dualem D‐1S (mean difference = 0.075 and 0.05 dS/m for the 201 and 1680 observations, respectively). The degree of coincidence between both sensors for soil profile ECa classification was acceptable: 82% for normal profiles (i.e. ECa‐h/ECa‐v < 0.9) and 90% for inverted profiles (i.e. ECa‐h/ECa‐v > 1.1). In practical terms, Geonics EM38 and Dualem 1S sensors could be used interchangeably with similar or very close results.     

Utilizing a DUALEM‐421 and inversion modelling to map baseline soil salinity along toposequences in the Hunter Valley Wine district

In the oldest commercial wine district of Australia, the Hunter Valley, there is the threat of soil salinization because marine sediments underlie the area. To understand the risk requires information about the spatial distribution of soil properties. Electromagnetic (EM) induction instruments have been used to identify and map the spatial variation of average soil salinity to a certain depth. However, soils vary with depth dependent on soil forming factors. We collected data from a single‐frequency and multiple‐coil DUALEM‐421 along a toposequence. We inverted this data using EM4Soil software and evaluated the resultant 2‐dimensional model of true electrical conductivity (σ – mS/m) with depth against electrical conductivity of saturated soil pastes (EC_p – dS/m). Using a fitted linear regression (LR) model calibration approach and by varying the forward model (cumulative function‐CF and full solution‐FS), inversion algorithm (S1 and S2), damping factor (λ) and number of arrays, we determined a suitable electromagnetic conductivity image (EMCI), which was optimal (R² = 0.82) when using the full solution, S2, λ = 3.6 and all six coil arrays. We conducted an uncertainty analysis of the LR model used to estimate the electrical conductivity of the saturated soil‐paste extract (EC_e – dS/m). Our interpretation based on estimates of EC_e suggests the approach can identify differences in salinity, how these vary with parent material and how topography influences salt distribution. The results provide information leading to insights into how soil forming factors and agricultural practices influence salinity down a toposequence and how this can guide soil management practices.     

Studying the effects of Mahonia aquifolium populations on small‐scale mountain agro‐ecosystems in Hungary with the view to minimise land degradation

After the political changeover in Hungary in 1989, many farm owners faced problems typical of a small sized farm (<1 ha) as well as a lack of various resources. This led to many farms with great horticultural backgrounds being abandoned, especially in mountainous areas where erosion control is essential for soil conservation. Severe changes can occur on soil through property abandonment. The local socio‐economic aspects must be taken into consideration as well as the problem of soil degradation as this will be a motivating factor in preservation initiatives. Under horticultural management, Mahonia aquifolium was tested to determine its efficiency in water erosion control in mountainous conditions in Hungary. Soil loss and water runoff were measured after every rainfall event at six different plots: four cultivated M. aquifolium populations with different ages (4, 12, 20 and 25 years old) and two control plots. A bare soil field and a grass field represented the control plots. The results show that as the age of the M. aquifolium populations increase, the cumulative runoff and sediment values decrease. It also showed that 2 years after setting up a new M. aquifolium plantation, the cumulative runoff decreased by 69 per cent and the cumulative soil loss decreased by 74 per cent. This indicates that a M. aquifolium population could play a significant role in erosion control within a short period of time. Copyright © 2009 John Wiley & Sons, Ltd.     

Evaluating a low‐cost portable NIR spectrometer for the prediction of soil organic and total carbon using different calibration models

This study aims to assess the performance of a low‐cost, micro‐electromechanical system‐based, near infrared spectrometer for soil organic carbon (OC) and total carbon (TC) estimation. TC was measured on 151 soil profiles up to the depth of 1 m in NSW, Australia, and from which a subset of 24 soil profiles were measured for OC. Two commercial spectrometers including the AgriSpec^TM (ASD) and NeoSpectra^TM (Neospectra) with spectral wavelength ranges of 350–2,500 and 1,300–2,500 nm, respectively, were used to scan the soil samples, according to the standard contact probe protocol. Savitzky–Golay smoothing filter and standard normal variate (SNV) transformation were performed on the spectral data for noise reduction and baseline correction. Three calibration models, including Cubist tree model, partial least squares regression (PLSR) and support vector machine (SVM), were assessed for the prediction of soil OC and TC using spectral data. A 10‐fold cross‐validation analysis was performed for evaluation of the models and devices accuracies. Results showed that Cubist model predicts OC and TC more accurately than PLSR and SVM. For OC prediction, Cubist showed R² = 0.89 (RMSE = 0.12%) and R² = 0.78 (RMSE = 0.16%) using ASD and NeoSpectra, respectively. For TC prediction, Cubist produced R² = 0.75 (RMSE = 0.45%) and R² = 0.70 (RMSE = 0.50%) using ASD and NeoSpectra, respectively. ASD performed better than NeoSpectra. However, the low‐cost NeoSpectra predictions were comparable to the ASD. These finding can be helpful for more efficient future spectroscopic prediction of soil OC and TC with less costly devices.     

Potential of multi‐objective models for risk‐based mapping of the resilience characteristics of soils: demonstration at a national level

Policy makers rely on risk‐based maps to make informed decisions on soil protection. Producing the maps, however, can often be confounded by a lack of data or appropriate methods to extrapolate using pedotransfer functions. In this paper, we applied multi‐objective regression tree analysis to map the resistance and resilience characteristics of soils onto stress. The analysis used a machine learning technique of multiple regression tree induction that was applied to a data set on the resistance and resilience characteristics of a range of soils across Scotland. Data included both biological and physical perturbations. The response to biological stress was measured as changes in substrate mineralization over time following a transient (heat) or persistent (copper) stress. The response to physical stress was measured from the resistance and recovery of pore structure following either compaction or waterlogging. We first determined underlying relationships between soil properties and its resistance and resilience capacity. This showed that the explanatory power of such models with multiple dependent variables (multi‐objective models) for the simultaneous prediction of interdependent resilience and resistance variables was much better than a piecewise approach using multiple regression analysis. We then used GIS techniques coupled with an existing, extensive soil data set to up‐scale the results of the models with multiple dependent variables to a national level (Scotland). The resulting maps indicate areas with low, moderate and high resistance and resilience to a range of biological and physical perturbations applied to soil. More data would be required to validate the maps, but the modelling approach is shown to be extremely valuable for up‐scaling soil processes for national‐level mapping.     

terraGIS – a web GIS for delivery of digital soil maps in cotton‐growing areas of Australia

Effective management of soil requires the spatial distribution of its various physical, chemical and hydrological properties. This is because properties, for example clay content, determine the ability of soil to hold cations and retain water. However, data acquisition is labour intensive and time‐consuming. To add value to the limited soil data, remote sensing (e.g. airborne gamma‐ray spectrometry) and proximal sensing, such as electromagnetic (EM) induction, are being used as ancillary data. Here, we provide examples of developing Digital Soil Maps (DSM) of soil physical, chemical and hydrological properties, for seven cotton‐growing areas of southeastern Australia, by coupling soil data with remote and proximal sensed ancillary data. A greater challenge is how to get these DSM to a stakeholder in a way that is useful for practical soil use and management. This study describes how we facilitate access to the DSMs, using a simple‐to‐use web GIS platform, called terraGIS. The platform is underpinned by Google Maps API, which is an open‐source development environment for building spatially enabled Internet applications. In conclusion, we consider that terraGIS and the supporting information, available on the sister web page ( http://www.terragis.bees.unsw.edu.au/ ), allow easy access to explanation of DSM of soil properties, which are relevant to cotton growers, farm managers, consultants, extension staff, researchers, state and federal government agency personnel and policy analysts. Future work should be aimed at developing error budget maps to identify where additional soil and/or ancillary data is required to improve the accuracy of the DSMs.     

Equations to compensate for the temperature effect on readings from dielectric Decagon MPS‐2 and MPS‐6 water potential sensors in soils

Dielectric sensors use electrical permittivity as a proxy for water content. They determine permittivity by using sensor‐type‐specific techniques and calibration functions, and relate it to a volumetric water content. Water potential sensors then translate the water content into a potential based on the sensor‐specific moisture characteristic curve. Dielectric readings are affected by temperature, which may distort hydraulically‐induced changes in soil water content. Methods for the removal of spurious temperature effects are lacking for dielectric water potential sensors. With this study, we aimed to fill this knowledge gap for the dielectric Decagon MPS‐2 and MPS‐6 water potential sensors. We first determined the temperature effect on MPS readings with laboratory experiments in which temperature was cycled between 4 and 26°C in different soil types. We then fitted single empirical equations that compensated for the temperature effect on MPS readings. Finally, we validated temperature‐compensated MPS soil water potentials, and therefore the equations, in a multi‐year field study in two forest soils where hourly data from three sensor models were available, i.e., from MPS‐2, MPS‐6, and a heat capacity sensor (ecoTech pF‐Meter) that is not sensitive to temperature effects. The temperature fluctuation experiments showed that the strongest temperature effects on MPS readings occur under dry conditions and that the MPS sensors themselves are largely responsible for these effects. Likewise, the field‐based validation showed that the MPS readings were highly affected by temperature under dry conditions. Applying a temperature compensation to these readings, using the equations from the temperature fluctuation experiments, resulted in strong correlations near the 1:1 line between data from the MPS and pF‐Meter sensors. Therefore, we recommend using the equations to remove spurious temperature effects from MPS‐2 and MPS‐6 readings in non‐saline soils with water potentials between –100 and –2000 kPa (at 22°C) and temperatures between 4 and 26°C.     

Soil‐quality‐index model for assessing the impact of groundwater on soil in an intensively farmed coastal area of E China

Soil quality is important in measuring sustainable land‐use and soil‐management practices. It is usually assessed by evaluating important physical, chemical, and biological soil properties. For this study, a site‐specific 22 variables representing pertinent soil (0–10 cm) and groundwater properties were selected as potential soil‐quality indicators in a coastal salt‐affected farmland of E China. To investigate the role of groundwater in soil‐quality assessment, we designed two sets of minimum data sets (MDSs). Minimum data set 1 (MDS1) had inputs of the 19 soil chemical and physical properties whereas MDS2 was based on the 22 soil and groundwater properties. Using principal‐component analysis, discriminant analysis, and soil‐quality‐index (SQI) model, we demonstrated the procedures of MDS selection, indicator normalization, and integration of MDS into SQI value for soils used for the two cropping systems. Results indicated selection of SOCD, AK, and ρ_b as MDS1 indicators but MDS2 indicators included SOM, SOCD, Cl, Na, WT_g, and EC_g. These were found to be the most effective discriminators between the two cropping systems. Available K (AK) made greatest contribution to SQI using MDS1 indicators, however, WT_g, EC_g, and Cl were the greatest contributors to the SQI for MDS2. Contribution of SOCD to SQI was severely inhibited in cotton–barley rotation system while EC_g and WT_g contributions to SQI were inhibited in rice–rape rotation system. In general, cotton–barley rotation system had a better soil quality over rice–rape rotation system as the former had higher SQI values than the latter for both MDSs. Crop parameters did also exhibit significant relationship with the SQI values using MDS2 but it was not significant for MDS1. Our results suggest that in addition to soil chemical, physical, and biological indicators, groundwater properties particularly the WT_g and EC_g are also important for assessing soil quality in an intensively farmed coastal area.     

Soil organic carbon stock variability in the Northern Gangetic Plains of India: interaction between agro‐ecological characteristics and cropping systems

Soil organic carbon (SOC) content and its spatial distribution in the Northern Gangetic Plain (NGP) Zone of India were determined to establish the cause–effect relationship between agro‐ecological characteristics, prevailing crop management practices and SOC stock. Area Spread Index (ASI) approach was used to collect soil samples from the NGP areas supporting predominant cropping systems. Exponential ordinary kriging was found most suitable geo‐statistical model for developing SOC surface maps of the NGP. Predicted surface maps indicated that 43.7% area of NGP had 0.5–0.6% SOC, while the rest of the area was equally distributed with high (0.61–0.75%) and low (< 0.5%) SOC content levels. Averaged across cropping systems, maximum SOC content was recorded in Bhabar and Tarai Zone (BTZ), followed by Central Plain Zone (CPZ), Mid‐Western Plain Zone (MWPZ), Western Plain Zone (WPZ) and South‐Western Plain Zone (SWPZ) of the NGP. The SOC stock was above the optimum threshold (> 12.5 Mg/ha) in 97.8, 57.6 and 46.4% areas of BTZ, CPZ and MWPZ, respectively. Only 9.8 and 0.4% area of WPZ and SWPZ, respectively, had SOC stock above the threshold value. The variation in SOC stock was attributed largely to carbon addition through recycling of organic sources, cropping systems, tillage intensity, crop or residue cover and land‐use efficiency, nutrient‐use pattern, soil texture and prevailing ecosystem. Adoption of conservation agriculture, balanced use of nutrients, inclusion of legumes in cropping systems and agro‐forestry were suggested for enhancing SOC stock in the region.