Airborne radiometric survey data and a DTM as covariates for regional scale mapping of soil organic carbon across Northern Ireland

Soil scientists require cost-effective methods to make accurate regional predictions of soil organic carbon (SOC) content. We assess the suitability of airborne radiometric data and digital elevation data as covariates to improve the precision of predictions of SOC from an intensive survey in Northern Ireland. Radiometric data (K band) and, to a lesser extent, altitude are shown to increase the precision of SOC predictions when they are included in linear mixed models of SOC variation. However the statistical distribution of SOC in Northern Ireland is bimodal and therefore unsuitable for geostatistical analysis unless the two peaks can be accounted for by the fixed effects in the linear mixed models. The upper peak in the distribution is due to areas of peat soils. This problem may be partly countered if soil maps are used to classify areas of Northern Ireland according to their expected SOC content and then different models are fitted to each of these classes. Here we divide the soil in Northern Ireland into three classes, namely mineral, organo-mineral and peat. This leads to a further increase in the precision of SOC predictions and the median square error is 2.2 %². However a substantial number of our observations appear to be mis-classified and therefore the mean squared error in the predictions is larger (30.6 %²) since it is dominated by large errors due to mis-classification. Further improvement in SOC prediction may therefore be possible if better delineation between areas of large SOC (peat) and small SOC (non-peat) could be achieved.     

Neural network ensemble residual kriging application for spatial variability of soil properties

High quality, agricultural nutrient distribution maps are necessary for precision management, but depend on initial soil sample analyses and interpolation techniques. To examine the methodologies for and explore the capability of interpolating soil properties based on neural network ensemble residual kriging, a silage field at Hayes, Northern Ireland, UK, was selected for this study with all samples being split into independent training and validation data sets. The training data set, comprised of five soil properties: soil pH, soil available P, soil available K, soil available Mg and soil available S,was modeled for spatial variability using 1) neural network ensemble residual kriging, 2) neural network ensemble and 3) kriging with their accuracies being estimated by means of the validation data sets. Ordinary kriging of the residuals provided accurate local estimates, while final estimates were produced as a sum of the artificial neural network (ANN) ensemble estimates and the ordinary kriging estimates of the residuals. Compared to kriging and neural network ensemble,the neural network ensemble residual kriging achieved better or similar accuracy for predicting and estimating contour maps. Thus, the results demonstrated that ANN ensemble residual kriging was an efficient alternative to the conventional geo-statistical models that were usually used for interpolation of a data set in the soil science area.     

A coherent geostatistical approach for combining choropleth map and field data in the spatial interpolation of soil properties

Information available for mapping continuous soil attributes often includes point field data and choropleth maps (e.g. soil or geological maps) that model the spatial distribution of soil attributes as the juxtaposition of polygons (areas) with constant values. This paper presents two approaches to incorporate both point and areal data in the spatial interpolation of continuous soil attributes. In the first instance, area-to-point kriging is used to map the variability within soil units while ensuring the coherence of the prediction so that the average of disaggregated estimates is equal to the original areal datum. The resulting estimates are then used as local means in residual kriging. The second approach proceeds in one step and capitalizes on: 1) a general formulation of kriging that allows the combination of both point and areal data through the use of area-to-area, area-to-point, and point-to-point covariances in the kriging system, 2) the availability of GIS to discretize polygons of irregular shape and size, and 3) knowledge of the point-support variogram model that can be inferred directly from point measurements, thereby eliminating the need for deconvolution procedures. The two approaches are illustrated using the geological map and heavy metal concentrations recorded in the topsoil of the Swiss Jura. Sensitivity analysis indicates that the new procedures improve prediction over ordinary kriging and traditional residual kriging based on the assumption that the local mean is constant within each mapping unit.     

Spatial prediction of soil properties using EBLUP with the Matérn covariance function

Spatial prediction with the presence of spatially dense ancillary variables has attracted research in pedometrics. While soil survey and analysis of soil properties are still expensive and time consuming, the secondary data can be made available on a dense grid for the whole area of interest. The main aim of using the ancillary data is to enhance prediction of soil properties by making use of the ancillary variables as covariates. Methods that can be used for this purpose are kriging with external drift, cokriging, regression kriging, and REML-EBLUP (Residual Maximum Likelihood-Empirical Best Linear Unbiased Predictor). Regression kriging is a sub-optimal method that has been utilised extensively because it is easy to use and has been shown empirically to perform as well as other methods. A statically sound method is REML-EBLUP. This paper examines the use of REML-EBLUP in combination with the Matérn covariance function for spatial prediction of soil properties. Methods for estimating parameters of the Matérn variogram using REML, and prediction with EBLUP are described. The prediction capability of REML-EBLUP, regression kriging, and ordinary kriging is compared for four datasets. Results show that although REML-EBLUP generally improves the prediction, the improvement is small compared with regression kriging. Thus, for practical applications regression kriging appears to be a robust method. REML-EBLUP is useful when the trend is strong, and the number of observations is small (< 200). We concluded that improvement in the prediction of soil properties does not rely on more sophisticated statistical methods, but rather on gathering more useful and higher quality data.     

对比景观土壤性质普通克里格法和回归克里格法的比较研究

The accuracy between ordinary kriging and regression kriging was compared based on the combined consideration of sample size, spatial structure, and auxiliary variables (terrain indices and electromagnetic induction surveys) for a variety of soil properties in two contrasting landscapes (agricultural vs. forested). When spatial structure could not be well captured by point-based observations (e. g., when the ratio of sample spacing over correlation range was > 0.5), or when a strong relationship existed between target soil properties and auxiliary variables (e. g., their R² was > 0.6), regression kriging (RK) was more accurate for interpolating soil properties in both landscapes studied. Otherwise, ordinary kriging (OK) was better. Soil depth and wetness condition did not appear to affect the selection of kriging for soil moisture interpolation, because they did not significantly change the ratio of sample spacing over correlation range and the relationship with the auxiliary variables. Because of a smaller ratio of elevation change over total study area (E/A = 1.2) and multiple parent materials in the agricultural land, OK was generally more accurate in that landscape. In contrast, a larger E/A ratio of 6.8 and a single parent material led to RK being preferable in the steep-sloped forested catchment. The results from this study can be useful for selecting kriging for various soil properties and landscapes.     

不同方法预测河北省土壤有机碳密度空间分布特征的研究

运用多元线性回归、泛克里格和回归克里格三种方法,结合由DEM获取的地形属性因子预测了河北省土壤有机碳密度的空间分布.多元线性回归预测的残差较大,模型对总方差的解释仅18.6%,采用泛克里格方法后,预测残差降低,预测结果的极差范围变宽,低碳密度区的局部变异得以体现,模型对总方差的解释程度提高到53%.而回归克里格方法应用后预测残差和均方根预测误差进一步降低,模型对总方差的解释程度提高到65%,回归克里格方法也能更好地反映碳密度与地形的关系以及局部变异.三种方法中回归克里格预测效果最好,泛克里格次之,而多元线性回归方法最差.     

Spatial prediction of soil nutrient in a hilly area using artificial neural network model combined with kriging

It is widely recognized that using correlated environmental factors as auxiliary variables can improve the prediction accuracy of soil properties. In this study, a radial basis function neural network (RBFNN) model combined with ordinary kriging (OK) was proposed to predict spatial distribution of four soil nutrients based on the same framework used by regression kriging (RK). In RBFNN_OK, RBFNN model was used to explain the spatial variability caused by the selected auxiliary factors, while OK was used to express the spatial autocorrelation in RBFNN prediction residuals. The results showed that both RBFNN_OK and RK presented prediction maps with more details. However, RK does not always obtain mean errors (MEs) which were closer to 0 and lower root mean square errors (RMSEs) and mean relative errors (MREs) than OK. Conversely, MREs of RBFNN_OK were much closer to 0 and its RMSEs and MREs were relatively lower than OK and RK. The results suggest that RBFNN_OK is a more unbiased method with more stable prediction performance as well as improvement of prediction accuracy, which also indicates that artificial neural network model is more appropriate than regression model to capture relationships between soil variables and environmental factors. Therefore, RBFNN_OK may provide a useful framework for predicting soil properties.     

普通克里格法在土壤有机碳制图中的应用

The quantification of the pattern and spatial distribution of soil organic carbon (SOC) is fundamental to understand many ecosystem processes.This study aimed to apply ordinary kriging (OK) to model the spatial distribution of SOC in a selected part of Zambia.A total of 100 soil samples were collected from the study area and analyzed for SOC by determining soil oxidizable carbon using the Walkley-Black method.An automated fitting procedure was followed when modeling the spatial structure of the SOC data with the exponential semivariogram.The results indicated that the short range spatial dependence of SOC was strong with a nugget close to zero.The spatial autocorrelation was high to medium with a nugget to sill ratio of 0.25.The root mean square error of the predictions was 0.64,which represented 58.18％ of the mean observed data for SOC.It can be concluded that the generated map could serve as a proxy for SOC in the region where evidence of spatial structure and quantitative estimates of uncertainty are reported.Therefore,the maps produced can be used as guides for various uses including optimization of soil sampling.     

Estimation of soil organic carbon stock and its spatial distribution in the Republic of Ireland

Data scarcity often prevents the estimate of regional (or national) scale soil organic carbon (SOC) stock and its spatial distribution. This study attempts to overcome the data limitations by combining two existing Irish soil databases [SoilC and national soil database (NSD)] at the national scale, to create an improved estimate of the national SOC stock. Representative regression models between the near‐surface SOC concentration and those of deeper depths, and between SOC concentration and bulk density (BD) were developed based on the SoilC database. These regression models were then applied to the NSD derived SOC concentration map, resulting in an improved SOC stock and spatial distribution map for the top 10 cm, 30 cm and 50 cm depths. Western Ireland, particularly coastal areas, was found to have higher SOC densities than eastern Ireland, corresponding to the spatial distribution of peatland. We estimated the national SOC stock at 383 ± 38 Tg for the near‐surface of 0–10 cm depth; 1016 ± 118 Tg for 0–30 cm depth; and 1474 ± 181 Tg for 0–50 cm depth.     

利用数字高程模型改进高山灰岩坑土壤pH值预测

Among spatial interpolation techniques,geostatistics is generally preferred because it takes into account the spatial correlation between neighbouring observations in order to predict attribute values at unsampled locations.A doline of approximately 15 000 m 2 at 1 900 m above sea level (North Italy) was selected as the study area to estimate a digital elevation model (DEM) using geostatistics,to provide a realistic distribution of the errors and to demonstrate whether using widely available secondary data provided more accurate estimates of soil pH than those obtained by univariate kriging.Elevation was measured at 467 randomly distributed points that were converted into a regular DEM using ordinary kriging.Further,110 pits were located using spatial simulated annealing (SSA) method.The interpolation techniques were multi-linear regression analysis (MLR),ordinary kriging (OK),regression kriging (RK),kriging with external drift (KED) and multi-collocated ordinary cokriging (CKmc).A cross-validation test was used to assess the prediction performances of the different algorithms and then evaluate which methods performed best.RK and KED yielded better results than the more complex CKmc and OK.The choice of the most appropriate interpolation method accounting for redundant auxiliary information was strongly conditioned by site specific situations.     

田间尺度下测定土壤有效K、P的采样方法

Field nutrient distribution maps obtained from the sutdy on soil variations within fields are the basis of precision agriculture,The quality of these maps for maagement depends on the accuracy of the predicted values,which depends on the initial sampling.to Produce reliable predictions efficently the minimal sampling size and combination should be decided firstly,which could avoid the misspent funds for field sampling work.A 7.9 hectare silage field close to the Agricultural Research Institute at Hillaborough,Northern Irealnd,was selected for the study,Soil samples were collected from the field at 25m intervals in a rectangular grid to provide a database of selected soil propoerties.Different data combinations were subsequently abstracted from this database for comparison purposes,and ordinary krging used to produce interpolated soil maps.These prediced data groups were compared using least significant difference(LSD) test method.The results showed that the 62 sampling sizes of triangle arrangement for soil available K were sufficient to reach the required accuracy.The triangular sample combination proved to be superior to a rectangular one of similar sample size.     

Selection of a Geostatistical Method to Interpolate Soil Properties of the State Crop Testing Fields using Attributes of a Digital Terrain Model

The three most common techniques to interpolate soil properties at a field scale—ordinary kriging (OK), regression kriging with multiple linear regression drift model (RK + MLR), and regression kriging with principal component regression drift model (RK + PCR)—were examined. The results of the performed study were compiled into an algorithm of choosing the most appropriate soil mapping technique. Relief attributes were used as the auxiliary variables. When spatial dependence of a target variable was strong, the OK method showed more accurate interpolation results, and the inclusion of the auxiliary data resulted in an insignificant improvement in prediction accuracy. According to the algorithm, the RK + PCR method effectively eliminates multicollinearity of explanatory variables. However, if the number of predictors is less than ten, the probability of multicollinearity is reduced, and application of the PCR becomes irrational. In that case, the multiple linear regression should be used instead.     

Spatial variability of soil organic carbon in a catchment of the Loess Plateau

Abstract

The issue of soil organic carbon (SOC) is of increasing concern. Because SOC, as an important soil component in farming systems, is essential for improving soil quality, sustaining food production and quality, and maintaining water quality and as a major part of the terrestrial carbon reservoir, it plays an important role in the global carbon cycle. In this paper, a total of 665 soil samples from different depths were collected randomly in the autumn of 2007, and the spatial variability of SOC content at a small catchment of the Loess Plateau was analysed using classical statistics and geo-statistical analysis. In nonsampled areas classical kriging was utilized for interpolation of SOC estimation. The classic statistical analysis revealed moderate spatial variability with all five layers of SOC-content. In addition, the average SOC content decreased with soil depth and the relationship can be modelled by an exponential equation (y=3.1795x ^?1.2015, R ²=0.9866) and all of the SOC-content data in the different depth were normally distributed. The geo-statistical analysis indicated a moderate spatial dependence in 0–60 cm, while in the 60–80 cm depth spatial dependence was strong. The semi-variogram could be fitted by an exponential model for 0–10 cm depth; by a spherical model for 10–20 cm depth and 60–80 cm depth; and by a Gaussian model for 20–60 cm depth. The range increases with increasing depth. In addition, classical kriging could successfully interpolate SOC content in the catchment. In general, the geo-statistics method on a watershed scale could be accurately used to evaluate spatial variability of the SOC content in the Loess Plateau, China.     

Modelling soil organic carbon concentration of mineral soils in arable land using legacy soil data

Soil organic carbon (SOC) concentration is an essential factor in biomass production and soil functioning. SOC concentration values are often obtained by prediction but the prediction accuracy depends much on the method used. Currently, there is a lack of evidence in the soil science literature as to the advantages and shortcomings of the different commonly used prediction methods. Therefore, we compared and evaluated the merits of the median approach, analysis of covariance, mixed models and random forests in the context of prediction of SOC concentrations of mineral soils under arable management in the A‐horizon. Three soil properties were used in all of the developed models: soil type, physical clay content (particle size <0.01 mm) and A‐horizon thickness. We found that the mixed model predicted SOC concentrations with the smallest mean squared error (0.05%²), suggesting that a mixed‐model approach is appropriate if the study design has a hierarchical structure as in our scenario. We used the Estonian National Soil Monitoring data on arable lands to predict SOC concentrations of mineral soils. Subsequently, the model with the best prediction accuracy was applied to the Estonian digital soil map for the case study area of Tartu County where the SOC predictions ranged from 0.6 to 4.8%. Our study indicates that predictions using legacy soil maps can be used in national inventories and for up‐scaling estimates of carbon concentrations from county to country scales.     

Prediction of spatial soil property information from ancillary sensor data using ordinary linear regression: Model derivations, residual assumptions and model validation tests

Geospatial measurements of ancillary sensor data, such as bulk soil electrical conductivity or remotely sensed imagery data, are commonly used to characterize spatial variation in soil or crop properties. Geostatistical techniques like kriging with external drift or regression kriging are often used to calibrate geospatial sensor data to specific soil or crop properties. More traditional statistical methods such as ordinary linear regression models are also commonly used. Unfortunately, some soil scientists see these as competing and unrelated modeling approaches and are unaware of their relationship. In this article we review the connection between the ordinary linear regression model and the more comprehensive geostatistical mixed linear model and describe when and under what conditions ordinary linear regression models represent valid spatial prediction models. The formulas for the ordinary linear regression model parameter estimates and best linear unbiased predictions are derived from the geostatistical mixed linear model under two different residual error assumptions; i.e., strictly uncorrelated (SU) residuals and effectively uncorrelated (EU) residuals. The theoretically optimal (best linear unbiased) and computable (linear unbiased) predictions and variance estimates derived under the EU error assumption are examined in detail. Statistical tests for detecting spatial correlation in LR model residuals are also reviewed, in addition to three LR model validation tests derived from classical linear modeling theory. Two case studies are presented that highlight and demonstrate the various parameter estimation, response variable prediction and model validation techniques discussed in this article.     

Spatial prediction of soil organic matter in the presence of different external trends with REML-EBLUP

The objective of our study was to compare the performance of the empirical best linear unbiased predictor (E-BLUP) with residual maximum likelihood (REML) with that of regression kriging (RK) for predicting soil organic matter (SOM) with the presence of different external drifts. The study was conducted on a 933 km² area in Pinggu district of Beijing. Terrain attributes (elevation, slope and topographic wetness index) calculated from DEM were used as external drift variable. The root mean squared errors (RMSE) and the mean squared deviation ratio (MSDR) were used to assess the accuracy of prediction and the goodness of fit of the theoretical estimate of error respectively. RK resulted in both the most and least accurate predictions. REML-EBLUP provided more correct residual variogram models than RK for each trend model. Our results have shown that when the value of adjusted R² is greater than 0.45, there is litter difference in the ability to increase the accuracy between REML-EBLUP and RK; and when the value is less than 0.45, the performance of REML-EBLUP is significantly better than RK. It also suggested that topographical data can further improve the accuracy of the spatial predictions of SOM by using RK and REML-EBLUP.     

Applying statistical methods to map soil organic carbon of agricultural lands in northeastern coastal areas of China

ABSTRACT

Soil organic carbon (SOC) is an important indicator to evaluate agricultural soil quality. Precise mapping SOC can help to facilitate soil and environmental management decisions. This study applied multiple stepwise regression (MSR), boosted regression trees (BRT) model, and boosted regression trees hybrid residuals kriging (BRTRK) to map SOC of agricultural lands in Wafangdian City, northeastern China. A 10-fold cross-validation procedure was used to evaluate the performance of the three models. The BRTRK method exhibited the best predictive performance and explained 78% of the total SOC variability. The distribution of SOC was mainly explained by elevation, followed by soil-adjusted vegetation index (SAVI), and topographic wetness index (TWI). We conclude that the BRTRK was the most accurate method in predicting spatial distribution of SOC. In addition, our study indicated that topographic variables as key factors to affect SOC should be considered in future SOC mapping.     

基于地形属性使用回归, 克里格和人工神经网络方法估算饱和导水率空间分布

Several methods,including stepwise regression,ordinary kriging,cokriging,kriging with external drift,kriging with varying local means,regression-kriging,ordinary artificial neural networks,and kriging combined with artificial neural networks,were compared to predict spatial variation of saturated hydraulic conductivity from environmental covariates.All methods except ordinary kriging allow for inclusion of secondary variables.The secondary spatial information used was terrain attributes including elevation,slope gradient,slope aspect,profile curvature and contour curvature.A multiple jackknifing procedure was used as a validation method.Root mean square error (RMSE) and mean absolute error (MAE) were used as the validation indices,with the mean RMSE and mean MAE used to judge the prediction quality.Prediction performance by ordinary kriging was poor,indicating that prediction of saturated hydraulic conductivity can be improved by incorporating ancillary data such as terrain variables.Kriging combined with artificial neural networks performed best.These prediction models made better use of ancillary information in predicting saturated hydraulic conductivity compared with the competing models.The combination of geostatistical predictors with neural computing techniques offers more capability for incorporating ancillary information in predictive soil mapping.There is great potential for further research and development of hybrid methods for digital soil mapping.     

Impact of disaggregation of composite foods on estimates of intakes of meat and meat products in Irish adults

OBJECTIVE: To evaluate the impact of the disaggregation of composite foods on intake estimates of meat and individual meat categories and on the contribution of meat to nutrient intakes in Irish adults. DESIGN: Data were analysed from the North/South Ireland Food Consumption Survey, which used a 7-day food diary to estimate food intake. Of 742 food codes that contained meat, 320 were codes for meat consumed as an individual portion and 422 were composite foods and were disaggregated to estimate the meat content. SUBJECTS: A nationally representative sample of 475 men and 483 women (not pregnant or lactating) from the Republic of Ireland aged 18-64 years. RESULTS: The mean intake of meat was 134 g day(-1) in consumers (98.5%) and men (168 g day(-1)) consumed significantly more (P<0.001) than women (102 g day(-1)). Mean intakes of meat were higher in subjects with manual skilled occupations (P<0.01) and lower in those with third-level educational qualifications (P<0.05). Without disaggregating meat from composite foods, meat intake was overestimated by 43% (57 g day(-1)) and varied widely by meat category. Meat disaggregated from composite foods contributed 25% of meat intake. The contribution meat made to nutrient intakes ranged from 29% for protein, vitamin B12, zinc and niacin to 20% for vitamin D, 16% for vitamin B6, 15% for thiamine and 14% for iron. CONCLUSIONS: Failure to disaggregate meat from composite foods substantially overestimates meat intake, with a large variation between meat categories. This has important implications for estimates of meat intakes in nutritional epidemiological studies and for food safety purposes.     

Land use changes induced soil organic carbon variations in agricultural soils of Fuyang County, China

Purpose

The purpose of this study is to understand spatial and temporal variations of soil organic carbon (SOC) under rapid urbanization and support soil and environmental management.

Materials and methods

SOC data in 1979 and 2006, of 228 and 1,104 soil samples respectively, were collected from surface agricultural lands in Fuyang County, East of China. Land use data were also gathered at the same time.

Results and discussion

The mean SOC was 17.3 (±4.6) g/kg for the 1979 data and 18.5(±5.8) g/kg for 2006. There was a significant difference in SOC between the 2 years according to the t test result. Geostatistical analysis indicated that SOC had a moderate spatial correlation controlled by extrinsic anthropogenic activities. The spatial distribution of SOC, derived from ordinary kriging, matched the distribution of industry and urbanization. Using a six-level SOC classification scheme (<3.5, 3.5–5.8, 5.8–11.6, 11.6–17.4, 17.4–23.2, and >23.2 g/kg) created by Zhejiang Province, approximately 15 % of soil had SOC increase from low to high levels from 1979 to 2006.

Conclusions

The main cause of SOC variation in the study area was land use change from agriculture to industrial or urbanized uses. The increasing SOC trend near most towns may be attributed to use of organic manure, urban wastes, sewage sludge, and chemical fertilizers on agricultural land.