Soil quality assessment of coastal wetlands in the Yellow River Delta of China based on the minimum data set

Abstract

Little information is available to assess the dynamic changes in wetland soil quality in coastal regions, though it is essential for wetland conservation and management. Soil samples were collected in Suaeda salsa wetlands (SWs), Tamarix chinensis wetlands (TWs), Suaeda salsa-Tamarix chinensis wetlands (STWs), freshwater Phragmites australis wetlands (FPWs) and saltwater Phragmites australis wetlands (SPWs) in three sampling periods (i.e., summer and autumn of 2007 and spring of 2008). According to the flooding characteristics of these wetlands, the study area could be grouped into three sub-regions: short-term flooding region (STFR), seasonal flooding region (SFR) and tidal flooding region (TFR). Soil quality was evaluated using the soil quality index (SQI), which was calculated using the selected minimum data set (MDS) based on principal components analysis (PCA). Our results showed that soil salt content (SSC), total carbon (TC), magnesium (Mg), nitrate nitrogen (NO 3-N) and total sulfur (TS) consisted of a MDS among 13 soil properties. The SQI values varied from 0.18 to 0.66 for all soil samples, of which the highest and lowest SQI values were observed in TFR. The average SQI values were significantly higher in summer (0.50±0.13) than in spring (0.37±0.13) and autumn (0.36±0.11) in the whole study area (p<0.05). The average SQI values followed the order STFR (0.44±0.12) >

Developing a Soil Physical Quality Index (SPQi) for lowlands under different deployment times of no-tillage

“…According to Armenise et al (2013), the FA general rules is to receive high eigenvalues (> 1.00) and to select variables with high factor loadings. These components allow to obtain the best parameter representative and retain it for screening of MDS (Chen et al, 2013;Yao et al, 2013;Zornoza et al, 2015;Zhang et al, 2016;Rojas et al, 2016).…”

“…; "midpoint optimum" has positive derivative until a maximum value and is used in indicators that positively affect soil quality until certain values that, if passed, cause negative influence such as bulk density, penetration resistance, etc. The curve for the condition "less is better" has negative derivative and is used in indicators that negatively affect the soil quality index, such as compactness degree (Chen et al, 2013;Nakajima et al, 2015;Zhang et al, 2016).…”

“…Impacts of management systems on soil physical quality (SPQ) have been studied by the S index (Dexter, 2004), but its inconsistency has also been highlighted (van Lier, 2014;Moncada et al, 2015). In this sense, soil quality indices (SQI) were developed based on the appropriate selection of soil quality indicators to compose a minimum data set (MDS) (Karlen and Stott, 1994;Karlen et al, 2001;Lima et al, 2008;Maia, 2013;Chen et al, 2013;Yao et al, 2013;Zornoza et al, 2015;Zhang et al, 2016;Rojas et al, 2016).…”

“…In MDS, attributes can be chosen by statistical methods (Paz-Kagan et al, 2014;Raiesi and Kabiri, 2016;Obade and Lal, 2016), such as the factor analysis (FA), which reduces redundant information from the original data set and groups soil attributes highly interrelated in a smaller group of representative and independent attributes (Zhang et al, 2016;Raiesi and Kabiri, 2016), helping to understand the effects of changing from CT to the NT system on soil physical quality.…”

Soil Quality Improvement through Conversion to Sprinkler Irrigation

“…Their results suggested greater soil degradation under rice cultivation. Zhang et al (2016) evaluated short-term flooded, seasonal flooded, and tidal flooded regions of China's Yellow River Delta using a soil quality index approach. The authors found that soil salt content, total C, Mg, NO 3 -N, and total S comprised the minimum component dataset needed to successfully quantify soil quality.…”