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Estimation of Soil Quality of under Long Term
Sugar Beet-wheat Cropping System by Factor
Analysis
Hamza Negiş & Cevdet Şeker
To cite this article: Hamza Negiş & Cevdet Şeker (2020) Estimation of Soil Quality of under Long Term Sugar Beet-wheat Cropping System by Factor Analysis, Communications in Soil Science and Plant Analysis, 51:4, 440-455, DOI: 10.1080/00103624.2020.1718689
To link to this article: https://doi.org/10.1080/00103624.2020.1718689
Published online: 21 Jan 2020.
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Estimation of Soil Quality of under Long Term Sugar Beet-wheat
Cropping System by Factor Analysis
Hamza Negiş and Cevdet Şeker
Department of Soil Science and Nutrition, Faculty of Agriculture, Selcuk University, Konya, Turkey
ABSTRACT
Soil quality is a measurement used to calculate how well the soil will perform its functions and ensures the assessment of soils based on their physical, chemical and biological characteristics. The aim of this study was to facilitate the determi-nation of soil quality, with soil properties selected by multi-criteria application, in a shorter time compared to that of conventional soils tests. A soil quality index (SQI) was determined by measuring the soil parameters under laboratory and field conditions in lands where agriculture has been taking place for many years. Based on the results of the factor analysis the 12 soil characteristics were selected for assessing the SQIs of each of the soil samples. The SQI results determined from physical, chemical, and biological soil properties with regards to total weight was calculated as 35.96%, 33.92%, and 30.12%, respectively. Based on the results of this study, it was determined that only 4 physical, 5 chemical and 3 biological properties could be used to evaluate soil quality. The selected indica-tors can be used in subsequent studies for the assessment of soil quality in areas where intensive agriculture has been used for many years.
ARTICLE HISTORY Received 20 November 2019 Accepted 30 December 2019 KEYWORDS
Minimum data set; multi-criteria process; soil quality index; factor analysis
Introduction
Increasing interest in soil quality and assessment in recent years has linked productivity degradation, chemical and heavy metal contamination, and deterioration of air and water quality to overexploita-tion of agricultural lands and its inability to meet the nutrioverexploita-tional needs of the growing world population (Doran and Parkin 1994; Karlen, Andrews, and Doran 2001; Karlen, Gardner, and Rosek 1998; Larson and Pierce1994). Soil functions evaluated in the context of soil quality are as follows: water flow and retention, solute transport and retention, physical stability and support, nutrient retention and plant availability, buffering and filtering of potentially toxic substances, biodiversity, and preservation of habitats (Doran and Parkin 1994; Karlen, Andrews, and Doran
2001; Karlen, Gardner, and Rosek1998). Novel tools and methods for assessing and monitoring soil quality are required, as improper management practices can lead to deterioration in soil functions (Doran and Parkin1994; Harris, Karlen, and Mulla1996).
Soil quality is determined by the characteristics of the soil and dynamic variabilities. The soil quality affected by these dynamic property’s changes depending on land use. This alteration is a function of agroclimatic factors, hydrogeology, and production techniques. Accordingly, there are many factors that affect soil quality such as soil depth, water holding capacity, bulk density, the amount of available nutrients, the amount of organic matter, microbial mass, carbon and nitrogen content, soil structure, and infiltration rate. Due to the correlation between these characteristics, only a few characteristics have been determined as indicators of soil quality. So far, the studies which have been conducted were found insufficient in determining the soil quality and expressing it quantitatively (Arshad and Coen1992; Askari, O’Rourke, and
Holden2015; Vidal Legaz et al.2017; Yang et al.2010).
CONTACTHamza Negiş hnegis@selcuk.edu.tr Department of Soil Science and Nutrition, Faculty of Agriculture, Selcuk University, Konya 42050, Turkey
© 2020 Taylor & Francis Group, LLC
Since there are many characteristics that affect the soil quality at different levels and it is impossible to use all of them, selecting the appropriate indicators for soil quality assessment is of vital importance. Doran and Parkin (1996) suggested that as few parameters as possible had to be used while determining soil quality. Among these parameters, texture, root depth, infiltration rate, bulk density, and water holding capacity can be listed as physical characteristics; pH, total C, electrical conductivity, and amount of nutrients as chemical characteristics; and microbial mass, mineralizable N, and soil respiration as biological characteristics. In addition to the aforementioned, other studies also included aeration, compaction, hydraulic features, aggregation state, consistency features, and surface scaling as physical characteristics (Arshad and Coen1992; Doran and Parkin
1994; Larson and Pierce1991,1994; MacEwan and Carter1996;Şeker et al.2017); salt content, total organic carbon, total nitrogen, organic nitrogen, soluble carbon, mineral nitrogen, total phosphorus, extractable ammonium, nitrate, phosphorus, potassium, calcium, magnesium, micro elements, pollutant elements, cation exchange capacity as chemical characteristics (Doran and Parkin 1994; Harris, Karlen, and Mulla 1996; Larson and Pierce 1994); microbial carbon, microbial nitrogen, biological activity, enzyme activities, root development, germination, and efflux rates as biological characteristics (Blair, Lefroy, and Lisle 1995; Doran and Parkin 1994; Fauci and Dick 1994; Gregorich et al.1994; Harris, Karlen, and Mulla1996; Linden et al.1994); and furthermore included, soil color, structure type, thickness and depth of the genetically composed impermeable layer, thickness of the A horizon, and depth of the lime accumulation horizon as genetic characteristics (Brejda et al.2000; Doran and Parkin1994; Qi et al.2009).
Physical and physicochemical characteristics of soil do not change exceedingly unless, the soil suffers a heavy modification (e.g., a natural disaster such as an earth quake, a wild fire, or a mudslide) (Filip2002). That being said however, biological and biochemical parameters are rather sensitive to small changes under any type of deterioration. Therefore, while evaluating soil capacity and taking into consideration the natural characteristics of the soil and its suitability for various uses, it is necessary to assess biological and biochemical indicators, as well as physical and chemical characteristics (Nannipieri, Grego, and Ceccanti1990; Yakovchenko, Sikora, and Kaufman1996).
The study was conducted on common Cumra soil series in Cumra Plain. A total of 108 soil samples were collected from cultivated fields of sugar beet and wheat, from which 37 physical, chemical, and biological characteristics were determined. Factor analysis, correlation analysis, and expert opinion were used in selecting the appropriate soil quality indicators from among the characteristics that were determined.
Materials and methods
Soil characteristics of the study area
This study was conducted on a Cumra soil series with high fertility potential, which is commonly seen within a 7000 hectares (ha) land where irrigated agriculture is carried out. This land is located in the Cumra Plain (i.e. ~ 280000 ha) within the Konya Province (Figure 1). Detailed soil survey reports and maps (1:10000) were used in the determination of the research area. The soil in the research area is deep clay-textured soil formed on alluvial parent material. It represents 20% of the irrigated agriculture carried out in Cumra Plain and it has a mean sea level elevation of 1011 m.
Methods of sampling and analysis
Lands in the Cumra series where wheat and sugar beet cultivation were carried out were determined using coordinated maps of series. Measurements and sampling studies were conducted in these lands between the years of 2013 and 2014 in 108 locations. Furthermore, the coordinates of the sampling points were recorded. Disturbed and undisturbed soil samples were taken from 0–20 cm depth in
each parcel. Some of the soil samples had been kept at +4°C for biological analysis purposes. The remaining was air-dried, sifted through a 2 mm sieve and used in analyses.
Soil compaction was measured using a digital penetrometer (Eijkelkamp) at 0–40 cm depth and repeated 10 times; 0–20 and 20–40 cm penetration resistance (PR) values were calculated by averaging all values in this range.
Texture was designated by adopting the hydrometer method (Gee and Bauder1986), aggregate stability (AS) was calculated using the rain simulator (Gugino et al. 2009), field capacity (FC) was measured at 10 kPa (FC10) and 33 kPa (FC33), plant wilting point was estimated by exerting (PWP)
1500 kPa of pressure, and available water capacity (AWC) was calculated by subtracting wilting point values from field capacity values (i.e., AWC10= FC10–PWP; AWC33= FC33–PWP) (Cassel
and Nielsen 1986). Bulk density (Ρb) was calculated utilizing core samplers with a diameter and
height of 50 × 51 mm (Blake and Hartge 1986a). Porosity (P) values were calculated from bulk density and particle density (Blake and Hartge1986b).
Soil reaction (pH) and electrical conductivity (EC), which are both chemical characteristics of the soil, were measured in a 1:1 soil-distilled water mixture using a digital pH-EC meter (Gugino et al.
2009). Available phosphorus (AP) was detected by adopting the Olsen and Sommers (1982) method. Extractable Na, K, Ca and Mg 1 N were extracted and measured using an ammonium acetate solution (Thomas1982); available Fe, Cu, Mn, and Zn were extracted using the DTPA (diethylenetriaminepen-taacetic acid) extraction method and measurements were made with an Atomic Absorption Spectrophotometer (Lindsay and Norvell 1978). The NO3-N and NH4-N contents in the soil were
identified in extracted solutions using 2N KCI (Page et al.1982). The lime contents were determined using a Scheibler Calcimeter and applying a 1/3 (V/V, acid/water) HCl acid solution (McLean1982).
Organic carbon (OC) was calculated adopting the Dumas method and organic matter (OM) was calculated from the OC content (Wright and Bailey2001). Active carbon (AC), potentially miner-alizable nitrogen (PMN), and root health values (RHV) on the other hand, were measured adopting the methods discussed in detail in the study conducted by Gugino et al. (2009). As for enzyme
activities, urease enzyme activity (UA), catalase enzyme activity (CA), soil respiration (R), and dehydrogenase enzyme activity (DA) were identified according to Hoffmann and Teicher (1961), Beck (1971), Isermeyer (1952), and Thalmann (1968) respectively. Mycorrhiza spores in soil were calculated via the Gerdemann and Nicolson (1963) method.
Statistical analyses
The data were statistically analyzed using SPSS 22.0 (SPSS 2010). Initially, the data were tested to meet the normal distribution requirement. The data, which passed the test, were analyzed once again using KMO and Bartlett’s test of sphericity in order to identify if these data were suitable for factor analysis. According to the results of KMO and Bartlett’s test of sphericity, values for physical, chemical, and biological characteristics were 0.597 > 0.5, 0.655 > 0.50 and 0.596 > 0.50 respectively. In addition, Bartlett’s test results were found to be significant for all data sets (P = .000 < 0.05). It is understood that there is a strong correlation between physical, chemical, and biological character-istics and thus they are suitable for factor analysis (Karagöz andİlker2008).
Factor analysis was used initially for determining the degree of correlation between soil char-acteristics, then for classifying the soil characteristics under the main components, and finally, for identifying the high loading factors. Main component factors, whose eigenvalues were >1, were selected since they best define the variabilities (Brejda et al.2000).
Indicator selection
In this study, the soil characteristics that could be utilized for identifying and monitoring quality were selected among many other characteristics for factor analysis. For this purpose, the total data set was initially divided into three groups to create a minimum data set from the total data set of 37 elements. Physical, chemical, and biological characteristics were listed in the first, second, and third groups respectively. As for the second stage, factor analysis was conducted for each data group and correlation matrices of datasets were created in order to form the minimum data set. In this way, minimum data set suggestions were prepared considering expert opinions, correlations between the data and component loads determined by factor analysis in order to identify parameters that can be included in the minimum data set.
Results and discussion
Physical characteristics of the soils
The total data set (TDS) of the physical soil characteristics of the study area is shown inTable 1. Average Sand, Silt, and Clay contents in the soil were found to be 30.21–26.91%, 24.85–22.99%, and 44.94–50.11%, respectively. Moreover, of the 108 soil samples: 86 were categorized as clay, 12 as clay loam, 8 as sandy clay loam, and 2 as loamy clay. The average FC values measured at a pressure of 10 kPa (FC10) and 33 kPa (FC33) were 0.33 and 0.27 g g−1, respectively. On the other hand, the average
PWP value measured at 1500 kPa was 0.17 g g−1. The difference between FC and PWP values are a result of the different textures that these soils have. The average available water capacity at 10 kPa, AWC10,and at 33 kPa, AWC33,were 0.16 and 0.10 g g−1, respectively. Besides, the average AS value,
which is an indicator of a soil’s resistance to disturbance, was found to be 20.65%. Also, average PR values at 0–20 and 20–40 cm depths were 206.60 and 335.60 PSI, respectively. The coefficient of variance (CV) is a measure of how variable the data is. Low variability is observed when CV≤ 15%, whereas medium and high variability occurs when CV≥ 15% but ≤ 35%, and CV ≥ 35%, respectively (Cambardella et al.1994).Ρband P values of the soils in the study area had low variability; whereas,
silt, clay, FC10, FC33, PWP, AWC10, AWC33, PR0-20and PR20-40values had medium variability; and
Chemical characteristics of the soils
TDS of the chemical characteristics of the soils of the study area is shown in Table 1. The average pH value of these soils was found to be 8.09, therefore, all soils fell into the category of alkaline (Richards 1969). The average EC value of the soils was determined to be 595.70μS cm−1 (i.e., moderately saline), however, no salt related issues were observed in the study area. Lime content of samples collected on the surface, at a depth of 0–20 cm, varied between 10.05%-41.50%, and these soils fell into the categories of “Medium” and “Very Limey” (Ülgen and Yurtsever1995). Average TN, NH4-N and NO3-N contents of soils were found 0.11%, 19.37 and
26.53 mg kg−1, respectively. Average AP content of samples was measured to be 23.21 mg kg−1, while available Ca, Mg, Na, and K contents were 4816, 1145, 99.84, and 621.50 mg kg−1, respectively; according to Sillanpää (1990), AP, Ca, Mg, Na, and K contents were sufficient. Average Fe, Cu, Mn, and Zn micronutrient elements were determined to be 13.75, 2.21, 15.47, and 1.54 mg kg−1, respectively. Moreover, all of them were found to be above the critical limits for plant development (Lindsay and Norvell 1978; Sillanpää 1990).
Table 1.Mean soil physical, chemical and biological attributes of soil quality categories at 0–20 cm depth in study area (n = 108).
Parameters Abbreviation Unit Mean Min. Max. CV %
Physical Properties Sand S % 28.56 ± 12.15 9.60 66.40 42.55 Silt SI % 23.92 ± 4.45 12.50 33.02 18.58 Clay C % 47.52 ± 9.55 21.10 65.40 20.09 Bulk density pb g cm−3 1.34 ± 0.13 1.09 1.75 9.76 Porosity P % 49.49 ± 4.68 34.98 58.87 9.46 Field Capacity10 FC10 g g−1 0.33 ± 0.05 0.21 0.45 16.27 Field Capacity33 FC33 g g−1 0.27 ± 0.05 0.14 0.36 17.41
Permanent wilting point PWP g g−1 0.17 ± 0.04 0.09 0.24 21.09
Available water capacity10 AWC10 g g−1 0.16 ± 0.04 0.06 0.25 23.20
Available water capacity33 AWC33 g g−1 0.10 ± 0.02 0.05 0.14 19.23
Aggregate stability AS % 20.65 ± 9.01 3.15 39.69 43.62
Penetration resistance0-20 PR0-20 PSI 206.60 ± 66.45 60 434 32.16
Penetration resistance20-40 PR20-40 PSI 335.60 ± 106.10 159 606 31.61
Chemical Properties pH - 8.09 ± 0.16 7.66 8.41 1.96 Electrical conductivity EC μS cm−1 595.70 ± 270.60 299 1821 45.43 Lime L % 18.76 ± 6.53 10.05 41.50 34.81 Total nitrogen TN % 0.11 ± 0.03 0.05 0.18 25.32 Ammonium nitrogen NH4-N mg kg−1 19.37 ± 5.81 6.86 40.16 29.99 Nitrate nitrogen NO3-N mg kg−1 26.53 ± 18.00 6.65 97.83 67.84 Available P AP mg kg−1 23.21 ± 16.97 6.97 107.4 73.11 Available Ca Ca mg kg−1 4816.00 ± 1598 2391 8084 33.18 Available Mg Mg mg kg−1 1145.50 ± 442.30 294 2730 38.61 Available Na Na mg kg−1 99.84 ± 61.05 20.50 308 61.14 Available K K mg kg−1 621.50 ± 309.70 157 1970 49.82 DTPA-Fe Fe mg kg−1 13.75 ± 5.06 3.70 24.51 36.82 DTPA-Cu Cu mg kg−1 2.21 ± 0.72 0.93 4.68 32.48 DTPA-Mn Mn mg kg−1 15.47 ± 5.90 6.43 36.19 38.11 DTPA-Zn Zn mg kg−1 1.54 ± 1.01 0.32 5.53 65.52 Biological properties Organic matter OM % 0.82 ± 0.27 0.27 2.20 32.97 Active Carbon AC mg kg−1 599.90 ± 265.20 102.6 1009 44.22
Potential Mineralizable Nitrogen PMN µg g−1w−1 9.78 ± 5.19 0.91 23.82 53.09
Root Health Value RHV - 4.59 ± 1.79 2.00 9.00 39.05
Respiration R mg 100g−124h−1 29.01 ± 6.15 14.66 43.11 21.21
Catalyzing Enzyme Activity CA mg 5g−1 8.33 ± 3.67 1.84 16.92 44.04
Urease Enzyme Activity UA μg g−1 197.00 ± 150.40 55.70 587 76.33
Dehydrogenase Enzyme Activity DA μg g−1 3.27 ± 2.71 0.12 12.50 82.95
Mycorrhizal Fungi Number MFN 10g−1 58.34 ± 56.32 5.95 308 96.54
In the study area, pH values had low variability; L, TN, NH4-N, Ca, and Cu values had medium
variability; and EC, NO3-N, AP, Mg, Na, K, Fe, Mn, and Zn values had high variability
(Cambardella et al. 1994).
Biological characteristics of the soils
TDS of the biological soil characteristics of the study area is shown inTable 1. Average OM content of the soils was calculated as 0.82% and considered low (<3%) in all soil samples. In general, OM content of the soil in arid and semi-arid regions is low owing to the low biomass production and high temperatures; which was the case for the study area. Conventional tillage methods are used in regions where wheat and sugar beet cultivation are often seen; however, this practice is known to reduce the organic matter content, more so than, with any other tillage method (Madejón et al.2007; Melero et al.
2008). The average AC content, which is an indicator of the small portion of the soil organic matter that can serve as a food and energy source and that can easily be provided for the soil microbial community, was calculated to be 599.90 mg kg−1. The PMN value, which is an indicator of the capacity of the soil microbial community to convert nitrogen bonded to complex organic residues into an available nitrogenous form (i.e., ammonium), was measured to be 9.78 µg g−1w−1. In terms of root health, bean plant was grown under controlled conditions. Its average root health value, which refers to the level of developing root diseases, was calculated as 4.59. R efflux measurements varied between 14.66 and 43.11 mg R 100g−124h−1in the study area and were characterized as“very low” and “low”, respectively for soils (Doran, Kettler, and Tsivou1997). In the study area, average CA was calculated to be 8.33 mg 5g−1, UA was 197.00μg g−1, and DA was 3.27μg g−1. The average number of mycorrhiza spores was found to be 58.34 10g−1, and this is considered“low” for mycorrhiza spore density (Sharif and Moawad2006). OM and R values of the soils in the study area had medium variability; while AC, PMN, RHV, CA, DA, and MFN had high variability (Cambardella et al.1994).
Selecting physical quality indicators of the soils
Varimax factor analysis was used to separate the 13 physical soil characteristics into their main components and to ensure maximum factor variances. Thus, the eigenvalue of the first four main components were found to be >1 and these components represented 81.19% of the total variance. These four main components were used while selecting physical quality indicators (Table 2). According to the four main components, FC10, FC33, C, Pb, and P contributed≥90% to the variance;
while AWC33, PWP, PR0-20,and PR20-40contributed≥80%; and SI contributed ≥75% (Table 2). The
first main component explained 27.59% of the variance (Table 2). Factors that affected this group positively were FC10(0.889), AWC10(0.795), AWC33(0.783), FC33(0.736), and C (0.640). In spite of
this positive effect, the combination of these variables is rather complex and highly correlated thus no single conclusion could be determined. Since FC10, AWC10, AWC33, FC33,and C, which are listed
under the available water group, provided high correlation, FC10,was selected as the main
compo-nent for PC-1 in accordance with, expert opinion. The second main compocompo-nent explained 21.70% of the variance (Table 2). Pb (−0.926) affects the second main component group adversely, whereas P (0.992) and PWP (0.658) affect it positively. When the second main component group was assessed, it was observed that Pb and P have high explanation rates. Since it is not possible to calculate P without knowing Pb, on top of Pb having a higher explanation rate, Pb was selected as the second main component. The third main component explained 18.45% of the variance (Table 2). SI (0.859), S (−0.770), and AS (0.612) were determined as the main physical soil characteristics that affected the third main component (Table 2).Table 3shows that SI is the soil characteristic with the highest correlation. However, in accordance with expert opinion, it was observed that AS had low values in these soils and affected the physical quality immensely. Thus, it was decided to select this soil characteristic (i.e. AS) for determining and monitoring soil quality as the main component of PC-3. The fourth main component explained 13.59% of the variance (Table 2). Finally, factors that
affected each one of the four main components were designated as the soil compaction group. the penetration resistance values measured at 0–20 and 20–40 cm depth. PR0-20 and PR20-40 were
selected for the main component of PC-4, given there was not a marginal difference between their values (i.e.0.867–0.856) and they had high correlation. As a result, 5 out of 12 soil characteristics were selected for evaluating and monitoring the physical soil quality and they are as follows: FC10,
Pb, AS, PR0-20and PR20-40.
Selecting chemical quality indicators of the soils
Varimax factor analysis was used to separate 15 chemical soil characteristics into their main components and to ensure maximum factor variances. Thus, the eigenvalue of the first five main components were found to be >1 and these components represented 76.57% of the total variance. These five main components were used while selecting chemical quality indicators (Table 4). According to the five main components, Ca and NH4-N contributed ≥85% to the variance; EC,
pH, and Na contributed≥80%; and AP, TN, K, L, and Mg contributed ≥75 (Table 4). The first main component explained 23.53% of the variance (Table 4). The high-load variables under PC-1 con-sisted of EC (0.845), AP (0.838), NO3-N (0.793), and Zn (0.775). When the correlation matrix was
created in an attempt to reveal the relationship between them, it was determined that EC was highly related to other components (Table 5). However, in accordance with expert opinion, it was observed that EC did not constitute as a problem in the study area, whereas AP was very inconsistent in these soils (Table 1). Other studies conducted also proved that AP is a prominent chemical soil quality indicator (Gugino et al. 2009; Tesfahunegn 2016). Given the aforementioned reasons, AP was selected as an indicator for PC-1. Similarly, the high-load variables under PC-2 were determined to be Fe (0.833), Mn (0.811), TN (0.701), and Ca (0.442). FromTable 5 it is evident that Mn had a high correlation with 5 characteristics in total, however, TN had a high correlation with 6 chemical characteristics. Characteristics with high explanation rates assert that a large part of the variance is explained by this factor, therefore it is the preferential factor (Johnson and Wichern2002). For this reason, TN content was considered suitable for PC-2. The high-load variables under PC-3 consisted of Cu (0.714), NH4-N (0.714), pH (0.651), and Na (0.577); and shown in Table 5 have a close
Table 2.Factor loadings of soil physical attributes from soil quality categories in Cumra series. Principal component, PCa,b,c
Soil quality attribute 1 2 3 4 Communalities
Eigenvalue 3.57 2.81 3.40 1.76 -Variance (%) 27.49 21.70 18.45 13.59 -Cumulative variance (%) 27.49 49.19 67.64 81.19 -Eigenvector FC10 0.889 0.932 AWC10 0.795 0.783 AWC33 0.783 0.834 FC33 0.736 0.516 0.927 C 0.640 0.579 0.914 pb −0.926 0.906 P 0.922 0.921 PWP 0.526 0.658 0.833 SI 0.859 0.780 S −0.522 −0.770 0.660 AS 0.612 0.438 PR0-20 0.867 0.813 PR20-40 0.856 0.813
SeeTable 1for abbreviations.
a
Rotation method: Varimax with Kaiser normalization.
bBoldface eigenvalues correspond to the PCs examined for the index.
Boldface factor loadings are considered highly weighed; Bold-underlined factors correspond to the indicators included in the index.
c
Table 3. Correlations matrix for measured soil physical attributes across the study depths (0 –20 cm layer) and sites (n = 108). Soil attributes S SI C pb P FC 10 FC 33 PWP AWC 10 AWC 33 AS PR 0-20 SI − 0.705*** C − 0.944*** 0.431*** pb 0.382*** − 0.278** − 0.356*** P − 0.388*** 0.270* 0.369*** − 0.991*** FC 10 − 0.583*** 0.195 0.651*** − 0.280** 0.306** FC 33 − 0.729*** 0.312** 0.782*** − 0.518*** 0.524*** 0.799*** PWP − 0.664*** 0.288** 0.711*** − 0.614*** 0.615*** 0.715*** 0.919*** AWC 10 − 0.177 − 0.022 0.236* 0.174 − 0.135 0.742*** 0.295** 0.099 AWC 33 − 0.524*** 0.224* 0.562*** − 0.118 0.127 0.576*** 0.695*** 0.373*** 0.490*** AS − 0.391*** 0.376*** 0.322** − 0.332*** 0.306** 0.189* 0.308** 0.327** − 0.069 0.122 PR 0-20 0.338*** − 0.287** − 0.296** 0.363*** − 0.370*** − 0.347*** − 0.364*** − 0.399*** − 0.102 − 0.123 − 0.191* PR 20-40 0.260** − 0.232* − 0.222* 0.348*** − 0.328** − 0.343*** − 0.382*** − 0.419*** − 0.085 − 0.144 − 0.146 0.652*** See Table 1 for abbreviations. *** P < .001. ** P < .01. * P < .05.
relationship with each other. pH, which affects the uptake and availability of nutrients, is regarded as an important soil characteristic in terms of plant growth. pH has been considered as a soil quality indicator in many studies conducted (Andrews, Karlen, and Cambardella 2004; De La Rosa and Sobral2008; Gugino et al.2009;Şeker et al.2017). Hence, pH was selected for PC-3. The high-load variables in PC-4 were K (0.776) and L (0.725). K is a macronutrient that plays an important role in plant growth (Reitemeier1951) and has been considered as an indicator in many studies conducted. Thus, in adopting a similar approach, K was selected for PC-4 in order to determine the chemical soil quality. Finally, Mg was selected for PC-5 as a soil characteristic that affects the chemical soil quality. As a result, from the 15 chemical soil characteristics the following were selected for evaluating and monitoring the chemical soil quality: AP, TN, pH, K, and Mg.
Selecting biological quality indicators of the soils
Varimax factor analysis was used to separate 9 biological soil characteristics into their main nents and to ensure maximum factor variances. Thus, the eigenvalue of the first three main compo-nents were found to be >1 and these main compocompo-nents represented 67.99% of the total variance. These three main components were used while selecting biological quality indicators (Table 6). According to three basic components, DA contributed≥85% to the variance; PMN contributed ≥80%; R contributed ≥75%; and RHV, MFN, UA, and CA contributed ≥60% (Table 6). The first main component explained 25.91% of the variance (Table 6). The biological characteristics with high explanation rates in PC-1 consisted of AC (0.919), DA (−0.861), and RHV (0.629) (Table 6). When the correlation matrices of these variables were examined, AC was selected for PC-1 since it had the highest correlation. In previous studies, the AC content was used as the biological soil quality parameter (Karlen, Gardner, and Rosek1998; Schindelbeck et al.2008; Weil et al.2003). The second main component explained 21.06% of the variance (Table 6). The biological characteristics with high explanation rate in PC-2 were MFN (0.784), UA (0.775), and OM (0.636) (Table 6). However, after further inquiry the indicators show close correlations with each other (Table 7). Thus, the selection should be made in accordance
Table 4.Factor loadings of soil chemical attributes from soil quality categories in Cumra series. Principal component, PCa,b,c
Soil quality attribute 1 2 3 4 5 Communalities
Eigenvalue 3.53 2.65 2.26 1.56 1.49 -Variance (%) 23.53 17.64 15.04 10.41 9.95 -Cumulative variance (%) 23.53 41.17 56.21 66.63 76.57 -Eigenvector EC 0.845 0.842 AP 0.838 0.789 NO3-N 0.793 0.673 Zn 0.775 0.708 Fe 0.833 0.658 Mn 0.811 0.669 TN 0.701 0.759 Ca 0.442 0.817 0.875 Cu 0.714 0.733 NH4-N 0.714 0.862 pH 0.407 0.651 0.811 Na 0.577 −0.539 0.815 K 0.776 0.777 L 0.725 0.758 Mg −0.529 −0.404 0.756
SeeTable 1for abbreviations.
a
Rotation method: Varimax with Kaiser normalization.
bBoldface eigenvalues correspond to the PCs examined for the index.
Boldface factor loadings are considered highly weighed; Bold-underlined factors correspond to the indicators included in the index.
c
Table 5. Correlations matrix for measured soil chemical attributes across the study depths (0 –20 cm layer) and sites (n = 108). Soil attributes pH EC L TN NH 4 -N NO 3 -N AP Ca Mg Na K Fe Cu Mn EC − 0.333*** L 0.153 0.122 TN 0.075 0.223* 0.382*** NH 4 -N 0.016 0.480*** − 0.021 0.138 NO 3 -N − 0.261** 0.801*** 0.205* 0.247** 0.444*** AP − 0.106 0.522*** 0.365*** 0.325** 0.261** 0.467*** Ca 0.580*** 0.101 0.159 0.486*** 0.400*** 0.150 0.046 Mg − 0.202* 0.047 − 0.247** 0.134 0.059 − 0.117 − 0.109 0.024 Na 0.190* − 0.168 − 0.333*** 0.002 − 0.160 − 0.254** − 0.238* 0.109 0.171 K 0.241* 0.276** − 0.196* 0.028 0.344*** 0.113 0.321** 0.231* 0.116 0.244* Fe 0.285** − 0.293** 0.063 0.359*** − 0.163 − 0.257** − 0.155 0.343*** 0.102 0.497*** − 0.223* Cu 0.221* 0.309** 0.169 0.410*** 0.436*** 0.309** 0.303** 0.650*** 0.287** − 0.041 0.226* 0.302** Mn 0.333*** − 0.056 0.323*** 0.571*** 0.035 0.039 0.107 0.561*** − 0.190* 0.188 − 0.087 0.595*** 0.232** Zn − 0.028 0.501*** 0.457*** 0.455*** 0.285** 0.499*** 0.788*** 0.255** − 0.060 − 0.354*** 0.114 − 0.106 0.426*** 0.229* See Table 1 for abbreviations. *** P < .001. ** P < .01. * P < .05.
with expert opinion. Though OM content of the soils in this region is low (Table 1), it is a parameter that most positively affects the soil characteristics (Gregorich et al.1994; Six et al.2002). Therefore, selecting OM for PC-2 was deemed appropriate. Finally, 3 biological soil characteristics for PC-3 were ranked according to their explanation rates: R (0.777), PMN (0.685), and KA (−0.604). When the PC-3 correlation matrix was examined, the R indicator was found to be the most appropriate, considering its high correlation with other biological indicators, and was hence, selected for PC-3. In many studies, it was observed that R had been selected as soil quality indicator (Andrews, Karlen, and Cambardella
2004; Doran and Parkin1994; Gugino et al.2009;Şeker et al.2017). Thus, AC, OM, and R were the selected soil characteristics for evaluating and monitoring the biological soil quality.
Evaluation of soil quality
A combined PCA-SQI consisting of FC10, Pb, AS, PR0-20, PR20-40as physical characteristics; AP, TN, pH,
K, and Mg as chemical characteristics; and AC, OM, and R as biological characteristics were used for assessing and monitoring the overall soil quality. The PCAs developed based on the weight factors and the explainable percentage change were calculated separately for physical, chemical, and biological characteristics and are shown in equations 1, 2, and 3, respectively. Critical values of soils were determined in accordance with expert opinion. Based on the critical values, the soil parameter values
Table 6.Factor loadings of soil biological attributes from soil quality categories in Cumra series. Principal component PCa, b, c
Soil quality attribute 1 2 3 Communalities
Eigenvalue 2.33 1.90 1.89 -Variance (%) 25.91 21.06 21.02 -Cumulative variance (%) 25.91 46.97 67.99 -Eigenvector AC 0.919 0.409 DA −0.861 0.887 RHV 0.629 −0.570 0.606 MFN 0.784 0.736 UA 0.775 0.662 OM 0.636 0.581 R 0.777 0.754 PMN 0.685 0.803 CA −0.429 −0.604 0.681
SeeTable 1for abbreviations.
a
Rotation method: Varimax with Kaiser normalization.
bBoldface eigenvalues correspond to the PCs examined for the index.
Boldface factor loadings are considered highly weighed; Bold-underlined factors correspond to theindicators included in the index.
c
Explanatory values greater than≥0.40 was shown.
Table 7.Correlations matrix for measured soil biological attributes across the study depths (0–20 cm layer) and sites (n = 108).
Soil attributes OC AC PMN RHV R CA UA DA AC 0.049 PMN 0.121* 0.417*** RHV 0.083 0.381*** −0.078 R 0.222* 0.262** 0.430*** −0.260** CA −0.002 0.052 −0.233* 0.285** −0.349*** UA 0.391*** 0.302** 0.155 0.245** 0.172* −0.322*** DA −0.042 −0.808*** −0.210* −0.379*** −0.286** 0.086 −0.480*** MFN 0.293** 0.015 0.066 −0.078 0.278** −0.433*** 0.464*** −0.169
SeeTable 1for abbreviations. ***P < .001.
**P < .01. *P < .05.
were converted to 0 and1 using the non-linear scoring function. PCA-SQI values were divided into physical, chemical, and biological components to better demonstrate the effects of soil quality compo-nents on disturbance and sustainability of soil (Figure 2). As seen in the figure, physical and biological soil characteristics had lower scores, while chemical soil characteristics had a high score regarding soil quality. It has been emphasized that the selected physical and biological characteristics play an important role in the sustainability of soil quality and these parameters should be taken into consideration.
PCA SQI ¼ 0:275FC10 þ 0:217Pbþ0:185 AS þ 0:136PR020 þ 0:136PR2040
Normalized PCA SQI ¼ð0:275FC10 þ 0:217Pbþ0:185 AS þ 0:136PR020 þ 0:136PR2040Þ 0:949
¼ 0:290FC10 þ 0:229Pbþ0:195 AS þ 0:143PR020þ 0:143PR2040
(1)
PCA SQI ¼ 0:235 AP þ 0:176 TN þ 0:150 pH þ 0:104K þ 0:100 Mg Normalized PCA SQI ¼ð0:235 AP þ 0:176 TN þ 0:150 pH þ 0:104 K þ 0:100 MgÞ
0:765
¼ 0:307 AP þ 0:230 TN þ 0:196 pH þ 0:136 K þ 0:131 Mg (2)
PCA SQI ¼ 0:259 AC þ 0:211 OM þ 0:210 R Normalized PCA SQI ¼ð0:259 AC þ 0:211 OM þ 0:210 RÞ
0:680 ¼ 0:381 AC þ 0:310 OM þ 0:309 R (3) 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 FC10 Pb AS PR 0-20 PR 20-40 AC OM R AP TN Ph K Mg
Physical Biological Chemical
Soil Quality Index
An overall SQI was calculated by adding up the scores and dividing by the number of indicators. The overall SQI was also subdivided into physical (FC10, Pb, AS, PR0-20and PR20-40), chemical (AP, TN,
pH, K and Mg), and biological (AC, OM, and R) components, as well as their relative contribution to the overall SQI. This approach helps identify the management areas of greatest concern (i.e., lowest index scores) so that land managers can be given better guidance on how to most effectively restore or improve SQ at that specific location (Karlen et al.2014).
Examining the effects of the main component results of the physical, chemical, and biological soil characteristics on total quality, it is evident that physical, chemical, and biological characteristics affect the total quality score by 35.96%, 33.92% and 30.12% (Figure 3). Though, physical soil characteristics have a greater overall impact on quality, the three biological characteristics had a contribution of 30.12%, which can be considered higher, when compared with the five physical characteristics.
Conclusions
Soil quality is determined by taking all physical, chemical, and biological soil characteristics into consideration. The indicators arising from 38 soil characteristics analyzed were FC10, Pb, AS, PR0-20,
PR20-40, AP, TN, pH, K, Mg, AC, OM, and R. The selected soil characteristics were intended to assess
the soil quality including, soil fertility and environmental conservation, by creating a minimum data set under integrated tillage-water-nutrient management, SQI, and their results. This study also revealed that the soils in the study area were physically and biologically weak. Considering this data, it has been determined that the selected parameters can be used for determining and monitoring soil quality.
Acknowledgments
This research was taken from my doctoral thesis titled“Determination of Çumra Series Soil Quality Indices in Çumra Plain”
and supported by TUBITAK (Scientific and Technological Research Council of Turkey, project no. TOVAG 112O314) and Selcuk University (S.U.) BAP Office (Coordinating Office of Scientific Research Projects, project no. 09201086).
Funding
This work was supported by the Türkiye Bilimsel ve Teknolojik Araştirma Kurumu [112O314].
ORCID
Hamza Negiş http://orcid.org/0000-0002-1880-9188
35.96 33.92 30.12 0 10 20 30 40 50 60 70 80 90 100
Physical Chemical Biological
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