Assessing Variation of Soil Quality in Agroecosystem in an Arid Environment Using Digital Soil Mapping

Sedigheh Maleki,Mojtaba Zeraatpisheh,Alireza Karimi,Gholamhossein Sareban,Lin Wang

doi:10.3390/agronomy12030578

Sedigheh Maleki, Mojtaba Zeraatpisheh + Show 3 more

Open Access

https://doi.org/10.3390/agronomy12030578

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Journal: Agronomy	Publication Date: Feb 25, 2022
Citations: 7	License type: CC BY 4.0

Affiliation: Henan University, Ferdowsi University of Mashhad

Abstract

Monitoring the soil quality (SQ) in agricultural ecosystems is necessary for using sustainable soil and land resources. Therefore, to evaluate the SQ variation in an arid environment in the Bajestan region, northeastern Iran, two soil quality indices (weighted additive soil quality index-SQIw and nemoro soil quality index-SQIn) were applied. SQIs were assessed in two datasets (total data set-TDS and minimum data set-MDS) by linear (L) and nonlinear (NL) scoring methods. Physicochemical properties of 223 surface soil samples (0–30 cm depth) were determined. The random forest (RF) model was used to predict the spatial variation of SQIs. The results showed the maximum values of the SQIs in areas with saffron land covers, while the minimum values were acquired in the north of the study area where pistachio orchards are located due to higher EC and SAR. The environmental variables such as topographic attributes and groundwater quality parameters were the main driving factors that control SQIs distribution. These findings are beneficial for identifying suitable locations sites to plan agricultural management and sustainable usage of groundwater resources strategy to avoid further increase of soil salinity.

Highlights

Due to the limitation of arable land in the arid and semiarid area and the lack of water, increasing production by adding the area under cultivation is impossible [1]
The results demonstrated that the range of soil properties were: 0.14 to 46.60 for electrical conductivity (EC); 6.10 to 9.62 for pH; 18.0 to 54.23 (%) for saturation percentage (SP); 2.0 to 45.0 (%) for calcium carbonate equivalent (CCE); 0.02 to 3.06 (%) for SOC; 0 to 0.91 (%) for Total nitrogen (TN); 0.20 to 80.0 for Pav; 17.0 to 695.0 for Kav; 1.50 to 73.60 for Caaq; 0.60 to 56.0 for Mgaq; 0.46 to 272.0 for Naaq; and 0.46 to 41.0 for Sodium adsorption ratio (SAR) (Table 1)
The results showed that the covariates, including wind effect, multiresolution valley bottom flatness index (MRVBF), valley depth, and groundwater quality parameters, were considered as the most important factors to predict the spatial soil quality indicators (SQI) variability based on total data set (TDS) and minimum data set (MDS) (Figure 7)

Summary

Introduction

Due to the limitation of arable land in the arid and semiarid area and the lack of water, increasing production by adding the area under cultivation is impossible [1]. Meeting the nutritional needs of this growing population requires optimal use of agricultural land and water, where inappropriate land use and soil management cause land degradation [2]. Proper identification of different soil physicochemical properties has a significant role in determining the erodibility, soil degradation, and management of agricultural lands and protection of soils, especially in arid and semiarid regions [4–7]. Soil quality (SQ) assessment is a practical approach to identifying the primary effects of management practices [8]. Understanding SQ is important for identifying problem areas and evaluating sustainable agricultural management [1,4]

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