Influence of Vegetation Restoration on Topsoil Organic Carbon in a Small Catchment of the Loess Hilly Region, China

Qin, Yunbin; Xin, Zhouping; Yu, Xinxiao; Xiao, Yihong

doi:10.1371/journal.pone.0094489

Cited by 17 publications

(16 citation statements)

References 45 publications

(62 reference statements)

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“…Compared with the Rank-KS method, the MVARC-R-KS method further considers the influence of environmental variables and the spatial heterogeneity of the soil samples. The clustering zones ( Figure 9) indicate correlations between the distribution of SOM and environmental variables; such a result is consistent with the results of previous studies [8,9,25,38,39]. Additionally, the distance to the residential area and soil humidity quantified by the NDMI are the major environmental variables influencing the distribution of SOM content in the study area.…”

Section: Discussionsupporting

confidence: 90%

“…Although relationships between environmental variables and SOM content have been recognized and verified by previous studies [8,9,25,38,39], these relationships are ignored during the selection of the calibration set. In the present study, the MVARC-R-KS method introduces the influence of environmental variables to calibration set selection.…”

Section: Discussionmentioning

confidence: 97%

“…SOM content is considerably influenced by the surrounding environment, and presents spatial heterogeneity [8,9,25]. According to Liu, Guo, Jiang, Zhang and Chen [9], the main environmental variables that affect SOM in the study area include the following three types: (1) soil humidity quantified by the Normalized Difference Moisture Index (NDMI); (2) slope; and (3) distance to residential area.…”

Section: Environmental Variablesmentioning

confidence: 99%

“…In most cases, field samplings are not designed to collect specific samples that are representative of the relationships between VNIR spectra and component concentrations. In addition, the design is difficult to establish because the relationships may vary due to the locations and surrounding environments [8,9]. Therefore, we assume that "component concentration representation", "spectrum representation", and "environment representation" may be equivalent to "relationship representation".…”

Section: Introductionmentioning

confidence: 99%

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Construction of the Calibration Set through Multivariate Analysis in Visible and Near-Infrared Prediction Model for Estimating Soil Organic Matter

et al. 2017

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Abstract:The visible and near-infrared (VNIR) spectroscopy prediction model is an effective tool for the prediction of soil organic matter (SOM) content. The predictive accuracy of the VNIR model is highly dependent on the selection of the calibration set. However, conventional methods for selecting the calibration set for constructing the VNIR prediction model merely consider either the gradients of SOM or the soil VNIR spectra and neglect the influence of environmental variables. However, soil samples generally present a strong spatial variability, and, thus, the relationship between the SOM content and VNIR spectra may vary with respect to locations and surrounding environments. Hence, VNIR prediction models based on conventional calibration set selection methods would be biased, especially for estimating highly spatially variable soil content (e.g., SOM). To equip the calibration set selection method with the ability to consider SOM spatial variation and environmental influence, this paper proposes an improved method for selecting the calibration set. The proposed method combines the improved multi-variable association relationship clustering mining (MVARC) method and the Rank-Kennard-Stone (Rank-KS) method in order to synthetically consider the SOM gradient, spectral information, and environmental variables. In the proposed MVARC-R-KS method, MVARC integrates the Apriori algorithm, a density-based clustering algorithm, and the Delaunay triangulation. The MVARC method is first utilized to adaptively mine clustering distribution zones in which environmental variables exert a similar influence on soil samples. The feasibility of the MVARC method is proven by conducting an experiment on a simulated dataset. The calibration set is evenly selected from the clustering zones and the remaining zone by using the Rank-KS algorithm in order to avoid a single property in the selected calibration set. The proposed MVARC-R-KS approach is applied to select a calibration set in order to construct a VNIR prediction model of SOM content in the riparian areas of the Jianghan Plain in China. Results indicate that the calibration set selected using the MVARC-R-KS method is representative of the component concentration, spectral information, and environmental variables. The MVARC-R-KS method can also select the calibration set for constructing a VNIR model of SOM content with a relatively higher-fitting degree and accuracy by comparing it to classical calibration set selection methods.

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Section: Discussionsupporting

confidence: 90%

Section: Discussionmentioning

confidence: 97%

Section: Environmental Variablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Construction of the Calibration Set through Multivariate Analysis in Visible and Near-Infrared Prediction Model for Estimating Soil Organic Matter

et al. 2017

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“…However, the influence of the flooding was relatively small in the seldom flooded zone and adjacent upland, and thus, the frequency occurrence and weight of the plant litter in these areas were higher than those in the frequently flooded zone. In addition, management measures in the farmland of the adjacent upland may also contribute to the loss of plant litter (Qin et al 2014). Local farmers usually remove trimmed branches and litter from farmlands and decrease weed competition for easier management, which reduces the amount of organic compounds that are incorporated into the soil.…”

Section: Differences In Plant Littermentioning

confidence: 99%

Comparison of topsoil organic carbon and total nitrogen in different flood-risk riparian zones in a Chinese Karst area

2016

Self Cite

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Riparian zones play a significant role in the soil organic carbon (SOC) sequestration to mitigate the increasing atmospheric CO 2 . SOC storages vary spatially within and among riparian soils by the influence of multiple unique biotic and biophysical landscape characteristics. Understanding the distribution characteristics of SOC from the water's edge to the upland is essential to effectively assess SOC storage and the function of carbon sink of riparian zones. In this study, the goals were to quantify the SOC density (SOCD) and total nitrogen content (TN) (0-20 cm of topsoil) in different flood-risk riparian zones of the Lijiang River [frequently flooded zones (FFz) (flood frequency C3 times/year), seldom flooded zones (SFz) (flood \3 times/year) and adjacent uplands (AU)], which are located in a typical karst geomorphology region, and to assess the contribution of soil texture, plant litter and soil pH to SOCD variation. The results of a soil survey of typical riparian areas (N = 15) showed that the average SOCD and TN content were 31.52 ± 13.25 Mg/ha and 0.094 ± 0.043 %, respectively, in the entire riparian zone (0-20 cm of topsoil). Compared with the SOCD in AU (19.98 ± 3.06 Mg/ha), the SOCD of FFz and SFz was significantly higher by 70.92 and 49.70 % (p \ 0.05), respectively. Stepwise regression analyses indicated that soil texture was the most important factor that influenced the SOCD variation (0-20 cm of topsoil) in FFz, which could account for 67 % of the SOCD variation, while plant litter was the most important factor in SFz, which could account for 80 % of the SOCD variation. Lastly, our findings indicate that riparian zones have more SOC storages than adjacent uplands in the Lijiang River. Therefore, the exploitation of riparian zones for agricultural purposes may weaken the function of riparian on the SOC sequestration.

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Introducing Chicken Farming into Traditional Ruminant‐Grazing Dominated Production Systems for Promoting Ecological Restoration of Degraded Rangeland in Northern China

Liu

et al. 2017

Land Degrad Dev

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Free‐range chicken farming (CF) has been demonstrated to be effective in controlling locust plagues and restoring degraded vegetation in rangelands. However, the effects of CF on vegetation growth and soil conditions of rangeland ecosystems are not yet well known. We studied the effects of CF (treated by three stocking rates from low‐intensity to high‐intensity), livestock grazing (LG) and zero grazing (ZG) on vegetation characteristics and soil nutrient conditions in a degraded rangeland of northern China. CF significantly improved vegetation growth and soil quality compared with LG. The moderate CF2 with 333 birds per hectare enhanced the aboveground net primary production to 204·0 g m−2 yr−1 and vegetation cover to 88·8%, which is significantly higher than LG by 80·7% and 62·9%, respectively. Moreover, CF2 neutralized the pH value and raised soil organic matter, total N content, available N content and available P content to approximately 7·59 g kg−1, 0·34 g kg−1, 83·52 mg kg−1 and 3·4 mg kg−1, respectively. Compared with ZG, the positive impacts of CF on rangelands depended on the stocking rate, and better performance was achieved when lower stocking rates were used. We also analysed the economic benefits of CF through a cost–benefit analysis. A local family could obtain a net present value of at least $252·19 per hectare by engaging in CF, which was two‐fold higher than LG. Finally, we proposed a novel land‐use model that introduces CF into traditional LG dominated system and can be widely applied to promote socio‐ecological sustainable development in degraded rangelands. Copyright © 2017 John Wiley & Sons, Ltd.

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Influence of Vegetation Restoration on Topsoil Organic Carbon in a Small Catchment of the Loess Hilly Region, China

Cited by 17 publications

References 45 publications

Construction of the Calibration Set through Multivariate Analysis in Visible and Near-Infrared Prediction Model for Estimating Soil Organic Matter

Construction of the Calibration Set through Multivariate Analysis in Visible and Near-Infrared Prediction Model for Estimating Soil Organic Matter

Comparison of topsoil organic carbon and total nitrogen in different flood-risk riparian zones in a Chinese Karst area

Introducing Chicken Farming into Traditional Ruminant‐Grazing Dominated Production Systems for Promoting Ecological Restoration of Degraded Rangeland in Northern China

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