Predictive mapping of soil total nitrogen at a regional scale: A comparison between geographically weighted regression and cokriging

Ku, Wang; Zhang, Chuanrong; Li, Weidong

doi:10.1016/j.apgeog.2013.04.002

Cited by 147 publications

(76 citation statements)

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“…The co-kriging method (Dirks, 1998;Goovaerts, 1999;Wang et al, 2013) was applied with 0.1 precision intervals (quartiles) and two variables: botanic marks and digital elevation model with a resolution of 1 × 1 m.…”

Section: Statistical and Spatial Analysismentioning

confidence: 99%

Rainfall and human activity impacts on soil losses and rill erosion in vineyards (Ruwer Valley, Germany)

et al. 2015

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Abstract. Vineyards are one of the eco-geomorphological systems most conditioned by human activity in Germany. The vineyards of the Ruwer Valley (Germany) are characterized by high soil erosion rates and rill problems on steep slopes (between 23 and 26 • ) caused by the increasingly frequent heavy rainfall events as well as deterioration due to incorrect land use managements.The objective of this paper is to determine and to quantify the hydrological and erosive phenomena in one vineyard in Germany during different seasons and under different management conditions (before, during and after vintage).For this purpose, a combined methodology was applied. Climatic (rainfall depth distributions and return periods), pedological (soil analysis and classification), geomorphological (sediment movements and rills evolution) and biological (botanic marks on the vines) variables were used on the two experimental plots in the village of Waldrach (Trier, region of Rhineland-Palatinate).The results showed high infiltration rates (near 100 %) and subsurface flow which were detected by rainfall simulations performed at different times of the year (between September and December). The highest variations of the monitored rills (lateral and frontal movements) were noted before and during vintage, when footsteps occurred concentrated during a short period of time (between September and October). Finally, two maps of soil loss were generated, indicated by botanic marks on the graft union of the vines. 62.5 t ha −1 yr −1 soil loss was registered in the experimental plots of the new vineyards (2 years), while 3.4 t ha −1 yr −1 was recorded in the old one (35 years).

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Section: Statistical and Spatial Analysismentioning

confidence: 99%

Rainfall and human activity impacts on soil losses and rill erosion in vineyards (Ruwer Valley, Germany)

et al. 2015

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“…The spatial dependence of soil is accounted for by using the distance decay function. Many applications of GWR have shown good results for spatial non-stationarity modeling of soil variation (Mishra and Riley, 2012;Song et al, 2016;Wang et al, 2013;Zhang et al, 2011). However, the relationships between soil and some environmental covariates may be constant, not always varying as modeled in GWR in a given study area.…”

Section: Introductionmentioning

confidence: 99%

Mapping soil organic matter concentration at different scales using a mixed geographically weighted regression method

et al. 2016

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The present regression models in digital soil mapping usually assume that relationships between soil properties and environmental variables are always fixed (as in MLR) or varying (as in GWR) in geographical space. In reality, some of the environmental variables may be fixed in affecting soil property variation and some are local varying. In this study, a mixed geographically weighted regression (MGWR) method which can deal with fixed and varying spatial relationships between a target variable and its environmental variables were proposed and used to predict topsoil soil organic matter (SOM) concentration in two study areas (Heshan, Heilongjiang province and Xuancheng, Anhui province, China) at two scales. Three groups of sample sets were created based on the total samples in the study areas to evaluate the robustness and stability of the model. Multiple linear regression (MLR), geographically weighted regression (GWR), GWR-kriging (GWRK), local regression-kriging (LRK), kriging with an external drift (KED), and ordinary kriging (OK) were used for comparison with MGWR. The validation results showed that the use of MGWR reduced the RMSE of GWR by 10.5% and 7.6% on average, reduced the RMSE of MLR by 12.8% and 9.9% on average for Heshan and Xuancheng study areas respectively. MGWR also showed a good competitiveness when compared with GWRK, LRK, KED and OK. In Heshan study area, the influence of flow length, relative position index, foot slope and distance to the nearest drainage were constant, whereas the elevation, topographic wetness index and valley index showed different influence in different regions. In Xuancheng study area, the fixed environmental variables were profile curvature, topographic wetness index and slope, whereas the varying environmental variables were precipitation, temperature, elevation, and limestone. The results indicate that the accuracy of predictions can be improved by adaptive coefficient according to the variation of environmental variables as implemented in MGWR compared with others considering only the local or global relationships. It was concluded that mixed geographically weighted regression model could be a potential method for digital soil mapping.

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“…Interpolation results are best when the data are normally distributed for kriging and co-kriging. Wang et al (2013) found that N values interpolated by ordinary kriging perform well. The drawback to ordinary kriging is that it causes smoothing effects and has some difficulty dealing with co-variables.…”

Section: Soil Datamentioning

confidence: 92%

Spatial Analysis and Modeling the Nitrogen Flush after Rainfall Events at the Field Scale in SWAT

Haney¹,

Haney²,

Arnold³

et al. 2016

American Journal of Environmental Sciences

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Current nitrogen (N) models tend to neglect the contribution of the microbial population to the plant available N pool, resulting in an underestimation of yield and possible over or underestimation of N runoff from natural and agricultural landscapes. We used the measurement of microbial activity coupled with the measurement of their food source, water extractable nitrogen (N) and carbon (C), to add a flush of N after rainfall events in the Soil Water Assessment Tool (SWAT). Soil test data and spatial analysis of N mineralization values were used to: (1) Quantify spatial variation of water extractable organic and inorganic N, soil inorganic N and microbial activity; (2) develop a field scale model to determine N mineralization using updated soil testing methods for integration into the SWAT model; and (3) predict wheat yield. Simulation results indicate that yearly yield values and the variability of these yield values were consistently greater from the modified N model than from the SWAT model, as would be expected with the addition of N mineralization resulting from microbial activity. The spatial variability in yield results increased with the modified N model as compared to the SWAT model. The yield data resulting from the modified N model simulation were sensitive to soil nutrient values as well as variations in elevation. Temporal and climatic variability is accounted for by including a precipitation trigger for N mineralization. The equations used to model the complex biogeochemical N cycling relationships are elegant in their simplicity, yet capture the spatial complexity associated with their processes. The modified N model may be useful to regulators to help with the simulation of new conservation practices that include the effect of lower fertilizer inputs on nutrient runoff and pollution.

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Predictive mapping of soil total nitrogen at a regional scale: A comparison between geographically weighted regression and cokriging

Cited by 147 publications

References 50 publications

Rainfall and human activity impacts on soil losses and rill erosion in vineyards (Ruwer Valley, Germany)

Rainfall and human activity impacts on soil losses and rill erosion in vineyards (Ruwer Valley, Germany)

Mapping soil organic matter concentration at different scales using a mixed geographically weighted regression method

Spatial Analysis and Modeling the Nitrogen Flush after Rainfall Events at the Field Scale in SWAT

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