Assessment of Soil Quality of Croplands in the Corn Belt of Northeast China

Abstract

Abstract:The increasing global demands for land resource with increasing population have resulted in occurrence of soil degradation in many regions of the world. Assessment of soil quality has become the basic work for agricultural sustainable development and selecting regional indicators effectively has become very important since there are no standard evaluation methods and universal indicators. In this study, taking the Corn Belt of Northeast China as the study area, seven indicators-obstacle horizon thickness, cation exchange capacity, pH, soil organic matter, total nitrogen, total potassium, and available Fe-were selected to constitute the minimum data set from sixteen indictors of the total data set to assess the soil quality. The soil quality of the study area was dominated by moderate grade, increasing from west to east. The soil quality of Yushu, Changchun and Shuangyang had higher values, and that of Nongan was the lowest. We found that the distribution of cation exchange capacity has a good consistency with the assessment result of the soil quality. Black soils were distributed in the middle part of the study region from north to south and accounted for a higher quality, exactly where the areas of rapid urbanization are located. An ANOVA analysis showed that soil quality in the Corn Belt of Northeast China was greatly affected by topographic factors and agricultural management and climate was not the principal factor affecting soil quality. Though the minimum data set slightly reduced the evaluation accuracy, a large sampling density in our study was able to improve the precision loss that resulted from reducing the number of indicators to a certain extent.

Soil quality index for agricultural areas under different levels of anthropopressure

“…Different methods and approaches are reported in the literature as a tool for the selection of indicators for the minimum data set and calculation of soil quality indices. These methods are based either on expert opinion (Asensio et al, 2013;Masto et al, 2015), statistical and mathematical analysis (Armenise et al, 2013;Bera et al, 2016;Li et al, 2018), or a combination of both approaches (Mukherjee and Lal, 2014;Vasu et al, 2016;Bünemann et al, 2018). In our study the identification of MDS and the calculation of SQI was based on the method described earlier by Rahmanipour et al (2014) and Vasu et al (2016).…”

“…In the HAP area of high anthroporessure, the highest factor loadings were for organic matter composition (mainly HU -the most stable fraction of organic matter), soil acidity, texture and the level of soil pollution. As stated by Masto et al 2015 Li et al (2018) the most common indicator used in SQI evaluation is the total organic carbon content, while studies which include the fractional composition of soil organic matter into MDS are rather rare (Asensio et al, 2013). Soil pH and texture (mainly clay content) are also frequently considered in the quality assessment of agricultural soils (Bastida et al, 2008;Armenise et al, 2013;Rahmanipour et al, 2014;Bünemann et al, 2018), however literature data concerning the use of the concentration of contaminants for SQI assessment are rather insufficient (Volchko et al, 2014).…”

“…However, the observed differences were rather small, which indicates that despite the high level of pollutants (Table 1) in the HAP area, soil quality in this region is also influenced by biological and physicochemical properties. Volchko et al (2014) and Li et al (2018) have both proposed soil classifications based on SQIs (Table 4), they distinguished five classes of soil quality: very poor, poor, medium, good and very good depending on the values of SQIs. In general, the quality of soils from the two regions varied from low to high, but the percentage share of soils in the individual quality grades differed distinctly (Fig.…”

“…2). Most soils from the area of high anthropopressure (HAP) had very low SQI values with an average value of 0.28 and were of very poor (72%) and poor (9%) quality according to the proposed system of Li et al (2018). The soils classified in the good quality category consisted of only 14% of the total area (Fig.…”

“…Classification of the SQI values for the area under study, percentage share of soils in specific soil quality grades according to the criteria proposed byLi et al (2018), LAP -area of low anthropopressure, HAP -area of high anthropopressure.…”