Diminishing Spatial Heterogeneity in Soil Organic Matter across a Prairie Restoration Chronosequence

2005 ◽  
Vol 13 (2) ◽  
pp. 403-412 ◽  
Author(s):  
Diana R. Lane ◽  
Hormoz BassiriRad
2018 ◽  
Vol 15 (3) ◽  
Author(s):  
Krista Marshall ◽  
Nick Balster ◽  
Alex Bajcz

The evaluation of prairie restorations tends to focus on aboveground properties such as changes in plant diversity and the encroachment of non-native species. As a result, knowledge gaps persist concerning belowground controls of restoration success. To address these gaps at a 13-year-old prairie restoration site in Madison, Wisconsin, we spatially compared soil chemical, physical, and hydrological properties in two adjacent parcels that differed markedly in response to a tallgrass prairie restoration. We hypothesized that soil properties and their heterogeneity would differ significantly between the two parcels and that these differences would help explain the divergent response. In support of this hypothesis, soil organic matter, pH, and total nitrogen were significantly lower (p = 0.007, p < 0.001, and p = 0.006, respectively) in the restored parcel compared to the parcel that has yet to respond to any restoration efforts. Moreover, despite no significant difference in soil average bulk density between the two parcels, the restored parcel had significantly lower sand and silt fractions overall (p = 0.039 and p = 0.040, respectively). In contrast, except for total nitrogen, there were no apparent differences in the spatial heterogeneity of the measured soil properties between the restored and unrestored parcels, which did not support the second hypothesis of this study. These results demonstrate the utility of measuring belowground properties when assessing unexpected outcomes of prairie restorations as well as inform future hypothesis-driven experiments to determine which soil properties impede restoration and under what circumstances. KEYWORDS: Prairie Restoration; Bulk Density; Soil Organic Matter; Soil Properties; Soil Texture; Spatial Heterogeneity


2021 ◽  
Vol 11 (2) ◽  
pp. 566
Author(s):  
Xiaomi Wang ◽  
Can Yang ◽  
Mengjie Zhou

Under the influence of complex environmental conditions, the spatial heterogeneity of soil organic matter (SOM) is inevitable, and the relationship between SOM and visible and near-infrared (VNIR) spectra has the potential to be nonlinear. However, conventional VNIR-based methods for soil organic matter estimation cannot simultaneously consider the potential nonlinear relationship between the explanatory variables and predictors and the spatial heterogeneity of the relationship. Thus, the regional application of existing VNIR spectra-based SOM estimation methods is limited. This study combines the proposed partial least squares–based multivariate adaptive regression spline (PLS–MARS) method and a regional multi-variable associate rule mining and Rank–Kennard-Stone method (MVARC-R-KS) to construct a nonlinear prediction model to realize local optimality considering spatial heterogeneity. First, the MVARC-R-KS method is utilized to select representative samples and alleviate the sample global underrepresentation caused by spatial heterogeneity. Second, the PLS–MARS method is proposed to construct a nonlinear VNIR spectra-based estimation model with local optimization based on selected representative samples. PLS–MARS combined with the MVARC-R-KS method is illustrated and validated through a case study of Jianghan Plain in Hubei Province, China. Results showed that the proposed method far outweighs some available methods in terms of accuracy and robustness, suggesting the reliability of the proposed prediction model.


2016 ◽  
Vol 36 (20) ◽  
Author(s):  
景莎 JING Sha ◽  
田静 TIAN Jing ◽  
M.Luke McCormack M.Luke McCormack ◽  
王晶苑 WANG Jingyuan ◽  
王秋凤 WANG Qiufeng ◽  
...  

2014 ◽  
Vol 72 (1) ◽  
pp. 275-288 ◽  
Author(s):  
Shaoliang Zhang ◽  
Xingyi Zhang ◽  
Zhihua Liu ◽  
Yankun Sun ◽  
Wei Liu ◽  
...  

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