Explaining the Soil Quality Using Different Assessment Techniques

Hindawi Applied and Environmental Soil Science Volume 2023, Article ID 6699154, 15 pages https://doi.org/10.1155/2023/6699154 Research Article Explaining the Soil Quality Using Different Assessment Techniques Abass Abdu, Fanuel Laekemariam , Gifole Gidago , and Lakew Getaneh Department of Plant Sciences, Wolaita Sodo University, Wolaita Sodo, Ethiopia Correspondence should be addressed to Fanuel Laekemariam; [email protected] Received 5 December 2022; Revised 7 March 2023; Accepted 10 March 2023; Published 24 March 2023 Academic Editor: Fedor Lisetskii Copyright © 2023 Abass Abdu et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Soil quality serves as the basis for both food security and environmental sustainability. To optimize production and implement soil management interventions, understanding the state of the soil quality is fundamental. Tus, this study was conducted to assess the soil quality of arable lands situated in the Nitisols and Luvisols using diferent assessment techniques. A total of 57 georeferenced soil samples were taken at a depth of 20 cm (18 from Nitisols and 39 from Luvisols land). Te soil samples were analyzed for particle size distribution (PSD), texture, pH, organic carbon (OC), total nitrogen (TN), available phosphorus (P), sulfur (S), exchangeable bases (calcium (Ca), magnesium (Mg), and potassium (K)), soil micronutrients (boron (B), copper (Cu), iron (Fe), manganese (Mn), and zinc (Zn)), and cation exchange capacity (CEC). Te techniques used to estimate soil quality includes principal component analysis (PCA), a normalized PCA, and common soil parameters (soil texture, pH, OC, N, P, and K). Te results were expressed in terms of soil quality index (SQI). In addition, the soil fertility/nutrient/index (NI) approach was used. Te result showed that the SQI values using the common parameters approach were 0.17 and 0.30 for the lands belonging to Nitisols and Luvisols and categorized as very poor (<0.2) and poor (0.2–0.4) quality soils, respectively. PCA-SQI and normalized PCA-SQI values for lands in the Nitisols were 0.36 and 0.42, while for Luvisols they were 0.38 and 0.40, respectively. Te soil quality of lands in the Luvisols was rated low (0.38–0.44), while lands in the Nitisols qualifed under very low (<0.38) and low soil quality, respectively. In addition, the value of 1.42 and 1.78 in their order for lands belonging to Nitisols and Luvisols were recorded using the NI method that indicated low and medium soil quality. In conclusion, PCA and common soil parameters techniques regardless of soil types ofered consistently similar information and could be taken as useful techniques for aiding soil management interventions. Furthermore, the result also calls for the need for applying soil management practices. 1. Introduction sustain biological productivity, maintain environmental quality, and promote plant, animal, and human health, is Soils in agriculture are an important part of the ecological now highly related to sustainable and productive agriculture system that produces food and fber for human consump- [2, 7, 8]. Good-quality soils will preserve natural ecosystems tion, but they are a limited and largely non-renewable re- by improving air and water quality for improved food and source [1, 2]. Soils are a key enabling resource and essential fber production while also protecting the environment and to the production of a wide range of goods and services human health [9]. integral to ecosystems and human well-being [3, 4]. Te SQ simultaneously addresses the issues of pro- Nonetheless, soil fertility depletion caused by a variety of ductivity and sustainability and makes it indispensable for factors (soil erosion, acidity, nutrient depletion, lack of soil developing countries such as Ethiopia [2, 4]. A better un- fertility replenishment, nutrient mining, and lack of bal- derstanding of the SQ and the factors that degrade the SQ is anced fertilization) is a signifcant contributor to food in- necessary to fully exploit the potential benefts of soil re- security [5, 6]. sources. For example, poor soil physical and chemical health Soil quality (SQ), which is defned as the capacity of soil is very likely to result in poor aggregate stability, a decline in to function within the ecosystem and land use boundaries to soil OM, nutrient-related plant stresses, crop yield 2 Applied and Environmental Soil Science stagnation, and exacerbate soil degradation [10, 11]. Tis completed prior to sample collection. Prior to sample col- suggests that SQ is linked to chemical properties, biophysical lection, sample points to the study area shape fle were environments, and anthropogenic factors. Meanwhile, SQ assigned in grid patterns using geographical information cannot be measured directly in the feld or laboratory; rather, system (GIS). While conducting the survey, a geographical it is inferred from measured soil physical, chemical, and positioning system (GPS) receiver was used to fnd the biological properties and is thus expressed in terms of soil sample locations. A total of 57 geo-referenced points were quality index (SQI) [2, 8, 12]. used to collect surface soil samples at a depth of 0–20 cm (18 Te SQI could be defned as a minimum set of pa- from Nitisols and 39 from Luvisols). Ten subsamples from rameters that provides numerical data about a soil’s ability to each sample were combined to create one kilogram of perform one or more functions [13]. It aids in assessing composited soil. overall soil condition and management response or resil- ience to natural and anthropogenic forces [1, 7, 14, 15]. 2.2.2. Soil Sample Preparation and Analysis. Following the Expert opinion (subjective) or mathematical and statistical standard procedure outlined in Sahlemedhin and Taye [21], (objective) methods are used to select a minimum soil data soil samples were processed (air-dried, ground, and passed set (MDS) [13, 16]. Te use of multivariate techniques of through a 2 mm sieve), and some soil physicochemical principal component analysis (PCA) (multiple correlations properties were examined (2000). Tis includes soil pH, and factor analyses) to reduce statistical data has become organic carbon (OC), total nitrogen (TN), available phos- more common [12, 17]. Tus, the SQI, which takes into phorus (P) and sulfur (S), exchangeable bases (calcium (Ca), account the physical, chemical, and biological properties of magnesium (Mg), and potassium (K)), soil micronutrients soils as well as their variability, is critical for long-term (boron (B), copper (Cu), iron (Fe), manganese (Mn), and utilization and site-specifc management of soil resources zinc (Zn)), cation exchange capacity (CEC), and texture [2, 8, 12, 14, 15]. (particle size distribution). Soil pH (1 : 2.5 soil: water sus- Despite the importance of SQ assessment, very few pension) was measured with a glass electrode (ES ISO studies have been conducted on smallholder arable lands in 10390 : 2014). Total N was determined by the wet-oxidation Ethiopia where traditional practices dominate soil man- (wet digestion) procedure of the Kjeldahl method (ES ISO agement [2]. Tis emphasizes the importance of having 11261 : 2015). Organic carbon (OC) was determined fol- adequate soil property information in order to intervene and lowing the wet combustion method of Walkley and Black. prevent soil fertility degradation problems. Against this Available P and S, exchangeable basic cations (Ca, Mg, and backdrop, the present study aimed to explore the soil quality K), and extractable micronutrients (B, Cu, Fe, Mn, and Zn) status of farmlands belonging to diferent soil groups using were determined using the Mehlich-III multinutrient ex- diferent varied approaches. traction method [22]. Te CEC was determined by using the 1 N ammonium acetate (pH 7) method. Particle size analysis 2. Materials and Methods was carried out by the hydrometer method as described by 2.1. Description of the Study Area. Te study sites were [21]. Textural classes were determined by Marshall’s tri- Farawocha farm in Wolaita Zone and Kechi farm in Dawro angular coordinate system. Zone, Southern Ethiopia (Figure 1). Farawocha farm lies between 7°6′34″N to 7°9′0″N latitude and 37°34′54″E to 2.3. Soil Quality Assessment. Since soil quality cannot be 37°37′33″E longitude. Te farm has 3.85 ha (cultivated land) directly measured, it is inferred from other soil properties within an average altitude of 1500 m.a.s.l and a slope of less and expressed as the soil quality index (SQI) [8, 12, 23]. Te than 3%. Ten years (2010–2019) mean annual precipitation is approaches discussed were used in the study to assess the soil 1300 mm, and the monthly temperature fuctuates between quality: 13.8 and 25.3°C with an average of 19.6°C (Figure 2) [18]. Kechi farm lies between 7°1′7″N and 7°5′48″ N latitude and 36°57′5″E and 3700′25″E longitude with an average altitude 2.3.1. SQI Estimate Using an Additive System Based on of 2090 m.a.s.l. It has a total area of 131.26 ha of which the Common Soil Parameters [7, 9, 24]. Te process involved cultivated land shares 32.66 ha, grass land (5.8 ha), and forest three main steps: (i) selecting appropriate indicators; (ii) land (92.8 ha). Kechi farm lies from a gentle to the steep converting indicators into scores; and (iii) combining the slope. Ten years (2010–2019) mean annual precipitation was scores into an index [13, 25]. 1502 mm and the monthly temperature fuctuates between SQI � [(a × RSTC) +(b × RpH) +(c × ROC) +(d × RNPK)], 14.5 and 24.2°C with an average of 19.3°C (Figure 3) [18]. According to WRB [19] and FAO [20], the soil types of the (1) Farawocha farm and Kechi farm are grouped under the where RSTC � assigned ranking values for soil textural class; Nitisols and Luvisols, respectively. RpH � assigned ranking values for soil pH; ROC � assigned ranking values for soil organic carbon; RNPK � assigned 2.2. Soil Sampling Procedure and Analysis ranking values for nitrogen (N); phosphorus (P), and po- tassium (K) (Table 1). Furthermore, a � 0.2, b � 0.1, c � 0.4, 2.2.1. Soil Sampling Procedure. Various tasks, including and d � 0.3 refer to the weighted values corresponding to prefeld work, feldwork, and postfeld work stages, were each of the four parameters. Tat is, out of 1(100%), the Applied and Environmental Soil Science 3 Ethiopia N Dawuro Zone Wolaita Zone 277200 277600 278000 278400 278800 279200 279600 346000 346100 346200 346300 346400 346500 778400 778800 779200 779600 780000 778000 778400 778800 779200 779600 780000 Kechi Study area Farawocha Study area 788700 788800 788900 789000 789100 788700 788800 788900 789000 789100 Kechi Study Area Cultivated Land Soil Sample Points Farawocha Study area Grass Land Soil Sample Points Farawocha Soil Sample Points Forest Land Soil Sample Points 0 300 600 1,200 Meters 0 50 100 200 Meters 277200 277600 278000 278400 278800 279200 279600 346000 346100 346200 346300 346400 346500 Figure 1: Location maps and soil sample collection points of both Farawocha and Kechi study areas. weighting value for soil textural class (a) � 0.2 (20%), soil 2.3.2. Soil Fertility/Nutrient/Index. Te calculation is based pH (b) � 0.1(10%), soil organic carbon (c) � 0.4 (40%), and on the number of samples classifed as low, medium, or high soil macronutrient contents (N, P, and K) (d) � 0.3 (30%). and the rating classes of the measured soil parameters, which 4 Applied and Environmental Soil Science 250 35 30 200 Temperature (°C) 25 Rain Fall (mm) 150 20 100 15 10 50 5 0 0 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Months Rain Fall 2010-2019 Minimum Temperature 2010-2019 Maximum Temperature 2010-2019 Figure 2: Ten years (2010–2019) monthly average rainfall and temperature data of Farawocha farm [18]. 300 30 250 25 Temperature (°C) Rain Fall (mm) 200 20 150 15 100 10 50 5 0 0 JAN FEB MAR APR MAY JUN JUL AUG SEP OCTNOV DEC Months Rain Fall 2010-2019 Minimum Temperature 2010-2019 Maximum Temperature 2010-2019 Figure 3: Ten years (2010–2019) monthly average rainfall and temperature data of Kechi farm [18]. Table 1: SQI evaluation based on assigned range values of soil parameters. Ranking values Parameters 0.2 0.4 0.6 0.8 1 Soil textural class C, S CL, SC, SiC Si, LS L, SiL, SL SiCL Soil pH <4 4.1–4.9 5–5.9 6–6.4 6.5–7.5 N, P, and K Very low Low Moderate High Very high SOC Very low Low Moderate High Very high Weighted values a � 0.2 b � 0.1 c � 0.4 d � 0.3 SQI Very poor Poor Fair Good Best Source: Bajracharya et al. [24], where C-clay;S-sand;CL-clay loam; SC-sandy clay; SiC-silty clay; Si-silt;LS-loamy sand; L-loam;SiL-silty loam; SL-sandy loam; LS-loamy sand; SiL-silty loam; SL-sandy loam; SiCL-silty clay loam; SCL-sandy clay loam; SQI-soil quality index. Note. Te ranges for which each of the parameter values are assigned are based upon corresponding ratings from low to high levels following the appropriate standard rating. are multiplied by 1, 2, and 3, respectively. If the index value is NH � number of samples in high category, and NT � total less than 1.67, the fertility status is low; if the index value is number of samples. between 1.67–2.33, the fertility status is medium; and if the index value is greater than 2.33, then the fertility status is high [25]. 2.3.3. Principal Component Analysis (PCA) Based SQI (Statistical Model-Based SQI). A statistics-based model is used Fertility/Nutrient NL x 1􏼁 + NM x 2􏼁 + NH x 3􏼁􏼁 to estimate SQI using PCA [17, 26]. Te PCA method is more Soil �􏼢 􏼣, Inde x NT objective because it makes use of a variety of statistical tools (2) (multiple correlation, factor, and analyses), which could prevent bias and data redundancy by selecting a minimal dataset (MDS) where NL � number of samples in low category; using formulas [12]. Te PCA model included all the original NM � number of samples in the medium category; observations of each soil parameter. Applied and Environmental Soil Science 5 Te PCs with high eigenvalues represented the maxi- parameters. If the parameters were signifcantly correlated mum variation in the dataset, while most studies have as- (r > 0.70), then the one with the highest loading factor was sumed to examine PCs only the variables having high factor retained in the MDS and all others were eliminated from the loadings with eigenvalues >1.0 that explained at least 5% of MDS to avoid redundancy. the data variations were retained for indexing [12, 17]. Still, the normalized PCA of SQI would be calculated if Under a given PC, each variable had a corresponding more than one highest eigenvectors were retained in the eigenvector weight value or factor loading. Only the “highly MDS [12, 23]. Te noncorrelated and highly weighted pa- weighted” variables were retained in the MDS. Te “highly rameters under a particular PC were considered important weighted” variables were defned as the highest weighted and retained in the data. Each PC explained a certain variable under a certain PC and absolute factor loading value amount of variation in the dataset, which was divided by the within 10% of the highest values under the same PC [12, 23]. maximum total variation of all the PCs selected for the MDS However, when more than one variable was retained under to get a certain weightage value under a particular PC a particular PC, a multivariate correlation matrix is used to [12, 26]. Tereafter, the SQI-3 (PCA) was computed using determine the correlation coefcients between the the following equation: SQI − 3 (PCA) � 􏽘 PC Weight ∗ Individual soil parameter score, (3) where PC Weight is the weightage factor determined from and in the Luvisols lands, 95% and 5% of the samples were the ratio of the total percentage of variance from each factor under low (<20 mg·kg−1) and optimum level to the maximum cumulative variance coefcients of the PC (20–22.66 mg·kg−1), respectively [27]. According to Landon considered; individual soil parameter score is the score of [28], exchangeable Ca levels of arable lands in the Nitisols each parameter in the MDS. were found low (2–5 Cmol (+) kg−1), whereas in the Luvi- sols, 13% and 87% of the samples showed medium (5.1–10 Cmol (+) kg−1) and high (10–20 Cmol (+) kg−1) 2.4. Data Analysis. Description of data analysis was per- levels, respectively. Exchangeable Mg was entirely under low formed using Microsoft Excel. All these values presented as level (<1.5 Cmol (+) kg−1) [28] in Nitisols; while in Luvisols, mean, minimum, maximum, SD, CV, PCA, and MDS se- 2%, 44%, and 54% of the samples were under low (<1.5 Cmol lections were performed using statistics-8 and Microsoft (+) kg−1), medium (1.51–3.3 Cmol (+) kg−1), and high level Excel software. In addition, Pearson correlation analysis on (3.31–8.0 Cmol (+) kg−1) [28], respectively. Furthermore, selected parameters was performed. exchangeable K (Cmol (+) kg−1) in 6% and 94% of the soil samples were under low (0.2–0.5 Cmol (+) kg−1) and op- 3. Results and Discussion timum level (0.5–1.5 Cmol (+) kg−1) [27] in Nitisols; while in Luvisols, 10%, 64%, and 26% of the samples were under low 3.1. Characteristics of Surface Soil Properties. Te particle size (0.2–0.5 Cmol (+) kg−1), optimum (0.5–1.5 Cmol (+) kg−1), distribution (PSD) in both soils was the order of and high level (1.5–2.3 Cmol (+) kg−1), respectively [27]. clay > silt > sand. Soil samples belonging to Nitisols had About half (50%) of samples taken from Nitisols lands were a clay texture with a strongly acidic reaction (pH < 5.5) [27]. under low (5–15 Cmol (+) kg−1) CEC, and the remaining Te samples taken from Luvisols revealed a textural class of were under medium level (15–25 Cmol (+) kg−1) [28] in loam (5% samples), clay loam (54%), clay (31%), and silty CEC; while the entire sample from Luvisols land recorded clay loam (10%). Regarding soil reaction, about 56%, 39%, high CEC (25–40 Cmol (+) kg−1) [28]. and 5% of samples in the Luvisols were strongly acidic, Data regarding B indicated that about 89% and 11% of moderately acidic (pH 5.6–6.5), and neutral (pH 6.6–6.67) soil samples from Nitisol lands were under low reactions, respectively [27]. In contrast to Luvisols, which (<0.8 mg·kg−1) and very high levels (>4 mg·kg−1) [27]; while had 56% low (2–4%) and 44% medium (4–10%) soil OC, the in the Luvisols, 98% and 2% of the samples were under low entire samples from Nitisols’ lands contained low soil OC, and optimum (0.8–2.0 mg·kg−1) categories [27]. Extractable according to Landon [28]. Cu (mg·kg−1) for about 72% and 28% of Nitisols samples According to Landon [28], Luvisols’ TN content was were under low (<0.9) and optimum levels 95% low (0.1-0.2%) and 5% high (0.51–1%), compared to (1.0–20.0 mg·kg−1) [27], as well as 21% and 79% of Luvisols Nitisols’ wholly low TN content (0.1–0.20%) [28]. Regarding samples were under low and optimum level [27]. About 12% available P (mg·kg−1), approximately 77%, 18%, and 5% of and 88% of samples from Nitisols lands were under low the samples in the Luvisols were under low (15–30), opti- (<80 mg·kg−1) and optimum level (80–300 mg·kg−1) levels in mum (30–80), and high (80–150) categories, respectively, extractable Fe [27], respectively; while in Luvisols, 72% and whereas available P (mg·kg−1) of samples taken from the 28% of the samples were under optimum and high Fe level Nitisols was entirely low [27]. (300–400 mg·kg−1) [27], respectively. Te Mn contents for In the Nitisols, 89% and 11% of the samples were under 6% and 94% of samples taken from Nitisols land were under low (10–20 mg·kg−1) and optimum S levels (20–80 mg·kg−1), low (<25 mg·kg−1) and optimum levels (>25 mg·kg−1) [27];  6 Table 2: Soil quality index of soil samples from Nitisols land based on common soil parameters. Textural Ranking Weighted Ranking Weighted OC Ranking Weighted Ranking Ranking Ranking Weighted ID Rxa pH value Rxb Rxc TN P K Rxd class value (R) value (a) value (R) value (b) (%) value (R) value (c) value (R) value (R) value (R) value (d) 1 Clay 0.2 0.2 0.04 5.08 0.6 0.1 0.06 2.8 0.2 0.4 0.08 0.2 0.2 4.37 0.2 0.41 0.2 0.3 0.0024 2 Clay 0.2 0.2 0.04 5.04 0.6 0.1 0.06 2.33 0.2 0.4 0.08 0.17 0.2 3.5 0.2 0.53 0.4 0.3 0.0048 3 Clay 0.2 0.2 0.04 4.76 0.4 0.1 0.04 2.21 0.2 0.4 0.08 0.16 0.2 3.71 0.2 0.85 0.4 0.3 0.0048 4 Clay 0.2 0.2 0.04 4.42 0.4 0.1 0.04 2.21 0.2 0.4 0.08 0.17 0.2 4.74 0.2 0.57 0.4 0.3 0.0048 5 Clay 0.2 0.2 0.04 4.47 0.4 0.1 0.04 2.09 0.2 0.4 0.08 0.15 0.2 4.37 0.2 0.77 0.4 0.3 0.0048 6 Clay 0.2 0.2 0.04 4.69 0.4 0.1 0.04 2.15 0.2 0.4 0.08 0.16 0.2 4.58 0.2 0.75 0.4 0.3 0.0048 7 Clay 0.2 0.2 0.04 4.29 0.4 0.1 0.04 2.25 0.2 0.4 0.08 0.17 0.2 4.16 0.2 0.69 0.4 0.3 0.0048 8 Clay 0.2 0.2 0.04 4.47 0.4 0.1 0.04 2.17 0.2 0.4 0.08 0.16 0.2 4.12 0.2 0.66 0.4 0.3 0.0048 9 Clay 0.2 0.2 0.04 4.36 0.4 0.1 0.04 2.65 0.2 0.4 0.08 0.21 0.4 4.94 0.2 0.59 0.4 0.3 0.0096 10 Clay 0.2 0.2 0.04 4.52 0.4 0.1 0.04 2.03 0.2 0.4 0.08 0.14 0.2 6.03 0.2 0.83 0.4 0.3 0.0048 11 Clay 0.2 0.2 0.04 4.26 0.4 0.1 0.04 2.44 0.2 0.4 0.08 0.17 0.2 3.2 0.2 0.64 0.4 0.3 0.0048 12 Clay 0.2 0.2 0.04 4.68 0.4 0.1 0.04 2.11 0.2 0.4 0.08 0.16 0.2 3.91 0.2 0.61 0.4 0.3 0.0048 13 Clay 0.2 0.2 0.04 4.65 0.4 0.1 0.04 2.41 0.2 0.4 0.08 0.17 0.2 3.71 0.2 0.7 0.4 0.3 0.0048 14 Clay 0.2 0.2 0.04 4.59 0.4 0.1 0.04 2.23 0.2 0.4 0.08 0.16 0.2 5.15 0.2 0.54 0.4 0.3 0.0048 15 Clay 0.2 0.2 0.04 4.50 0.4 0.1 0.04 2.23 0.2 0.4 0.08 0.16 0.2 4.53 0.2 0.78 0.4 0.3 0.0048 16 Clay 0.2 0.2 0.04 4.54 0.4 0.1 0.04 2.21 0.2 0.4 0.08 0.16 0.2 3.91 0.2 0.86 0.4 0.3 0.0048 17 Clay 0.2 0.2 0.04 4.45 0.4 0.1 0.04 2.19 0.2 0.4 0.08 0.16 0.2 3.71 0.2 0.62 0.4 0.3 0.0048 18 Clay 0.2 0.2 0.04 5.09 0.6 0.1 0.06 2.17 0.2 0.4 0.08 0.17 0.2 3.71 0.2 1.16 0.4 0.3 0.0048 0.72/ 0.78/ 1.44/ 0.0888/ Textural class SQI Soil pH SQI OC SQI NPK SQI 18 � 0.04 18 � 0.04 18 � 0.08 18 � 0.005 3.03/ Final SQI (very poor) 18 � 0.17 Applied and Environmental Soil Science  Table 3: Soil quality index of soil samples from Luvisols land based on common soil parameters. Textural Ranking Weighted Ranking Weighted OC Ranking Weighted Ranking Ranking Ranking Weighted ID Rxa pH value Rxb Rxc TN P K Rxd class value (R) value (a) value (R) value (b) (%) value (R) value (c) value (R) value (R) value (R) value (d) 1 Loam 0.8 0.2 0.16 5.84 0.6 0.1 Rxb 4.39 0.4 0.4 0.16 0.44 0.6 66.4 0.6 1.39 0.6 0.3 0.06 2 Clay 0.2 0.2 0.04 4.81 0.4 0.1 0.06 3.46 0.2 0.4 0.08 0.34 0.6 4.0 0.2 0.34 0.2 0.3 0.01 3 Loam 0.8 0.2 0.16 5.88 0.6 0.1 0.04 3.76 0.2 0.4 0.08 0.31 0.6 20.1 0.2 2.61 1 0.3 0.04 Clay 4 0.4 0.2 0.08 5.60 0.6 0.1 0.06 5.09 0.6 0.4 0.24 0.45 0.6 52.6 0.6 1.64 0.8 0.3 0.09 loam Clay 5 0.4 0.2 0.08 5.65 0.6 0.1 0.06 4.17 0.4 0.4 0.16 0.39 0.6 4.9 0.2 1.93 0.6 0.3 0.02 loam 6 Clay 0.2 0.2 0.04 5.27 0.6 0.1 0.06 3.83 0.2 0.4 0.08 0.42 0.6 6.3 0.2 1.08 0.6 0.3 0.02 7 Clay 0.2 0.2 0.04 5.83 0.6 0.1 0.06 4.78 0.4 0.4 0.16 0.46 0.6 18.3 0.2 1.82 0.6 0.3 0.02 8 Clay 0.2 0.2 0.04 4.01 0.4 0.1 0.06 3.99 0.2 0.4 0.08 0.53 0.6 4.9 0.2 0.47 0.2 0.3 0.01 9 Clay 0.2 0.2 0.04 4.96 0.6 0.1 0.04 3.72 0.2 0.4 0.08 0.43 0.6 3.6 0.2 1.48 0.6 0.3 0.02 Clay 10 0.4 0.2 0.08 5.47 0.6 0.1 0.06 4.95 0.4 0.4 0.16 0.48 0.6 4.6 0.2 1.22 0.6 0.3 0.02 loam Applied and Environmental Soil Science 11 Clay 0.2 0.2 0.04 5.30 0.6 0.1 0.06 3.87 0.2 0.4 0.08 0.35 0.6 52.3 0.6 1.35 0.6 0.3 0.06 Clay 12 0.4 0.2 0.08 4.82 0.4 0.1 0.06 3.78 0.2 0.4 0.08 0.39 0.6 10.3 0.2 0.42 0.6 0.3 0.02 loam Clay 13 0.4 0.2 0.08 5.61 0.6 0.1 0.04 4.83 0.4 0.4 0.16 0.46 0.6 94.3 0.8 2.5 0.6 0.3 0.09 loam 14 Clay 0.2 0.2 0.04 5.29 0.6 0.1 0.06 4.37 0.4 0.4 0.16 0.37 0.6 4.3 0.2 1.14 0.6 0.3 0.02 Clay 15 0.4 0.2 0.08 5.43 0.6 0.1 0.06 3.74 0.2 0.4 0.08 0.39 0.6 75.6 0.6 2.09 0.6 0.3 0.06 loam 16 Clay 0.2 0.2 0.04 5.06 0.6 0.1 0.06 2.88 0.2 0.4 0.08 0.39 0.6 4.8 0.2 0.74 0.6 0.3 0.02 Clay 17 0.4 0.2 0.08 5.92 0.6 0.1 0.06 4.99 0.4 0.4 0.16 0.44 0.6 7.0 0.2 0.24 0.6 0.3 0.02 loam Clay 18 0.4 0.2 0.08 5.46 0.6 0.1 0.06 3.07 0.2 0.4 0.08 0.33 0.6 6.4 0.2 1.22 0.6 0.3 0.02 loam 19 SCL 1 0.2 0.2 5.56 0.6 0.1 0.06 3.83 0.2 0.4 0.08 0.37 0.6 21.0 0.2 1.5 0.6 0.3 0.02 20 C.L 0.4 0.2 0.08 5.65 0.6 0.1 0.06 4.31 0.4 0.4 0.16 0.40 0.6 48.6 0.6 1.3 0.6 0.3 0.06 Clay 21 0.4 0.2 0.08 5.54 0.6 0.1 0.06 3.72 0.2 0.4 0.08 0.39 0.6 19.1 0.2 1.44 0.6 0.3 0.02 loam Clay 22 0.4 0.2 0.08 4.89 0.4 0.1 0.06 5.43 0.6 0.4 0.24 0.52 0.8 4.1 0.2 0.85 0.6 0.3 0.03 loam 23 Clay 0.2 0.2 0.04 4.30 0.4 0.1 0.04 4.48 0.4 0.4 0.16 0.44 0.6 2.7 0.2 1.46 0.6 0.3 0.02 Clay 24 0.4 0.2 0.08 5.10 0.6 0.1 0.06 2.49 0.2 0.4 0.08 0.24 0.6 5.7 0.2 0.69 0.6 0.3 0.02 loam Clay 25 0.4 0.2 0.08 6.64 0.8 0.1 0.08 4.77 0.4 0.4 0.16 0.44 0.6 95.2 0.8 7.1 0.6 0.3 0.09 loam Clay 26 0.4 0.2 0.08 5.78 0.6 0.1 0.06 3.50 0.2 0.4 0.08 0.39 0.6 25.6 0.2 1.66 0.6 0.3 0.02 loam Clay 27 0.4 0.2 0.08 6.67 0.6 0.1 0.06 5.26 0.6 0.4 0.24 0.55 0.8 25.2 0.2 7.11 0.6 0.3 0.03 loam Clay 28 0.4 0.2 0.08 4.93 0.6 0.1 0.06 3.92 0.2 0.4 0.08 0.40 0.6 4.6 0.2 0.5 0.6 0.3 0.02 loam 29 SCL 1 0.2 0.2 5.25 0.6 0.1 0.06 3.90 0.2 0.4 0.08 0.41 0.6 11.7 0.2 0.75 0.6 0.3 0.02 Clay 30 0.4 0.2 0.08 5.73 0.6 0.1 0.06 3.15 0.2 0.4 0.08 0.33 0.6 46.0 0.6 1.58 0.6 0.3 0.06 loam 31 Clay 0.2 0.2 0.04 4.60 0.4 0.1 0.04 4.31 0.4 0.4 0.16 0.39 0.6 3.8 0.2 0.59 0.6 0.3 0.02 Clay 32 0.4 0.2 0.08 5.54 0.6 0.1 0.06 3.18 0.2 0.4 0.08 0.35 0.6 31.6 0.6 1.22 0.6 0.3 0.06 loam 33 Clay 0.2 0.2 0.04 5.35 0.6 0.1 0.06 3.30 0.2 0.4 0.08 0.32 0.6 3.1 0.2 0.74 0.6 0.3 0.02 7  8 Table 3: Continued. Textural Ranking Weighted Ranking Weighted OC Ranking Weighted Ranking Ranking Ranking Weighted ID Rxa pH value Rxb Rxc TN P K Rxd class value (R) value (a) value (R) value (b) (%) value (R) value (c) value (R) value (R) value (R) value (d) Clay 34 0.4 0.2 0.08 4.76 0.4 0.1 0.04 4.00 0.4 0.4 0.16 0.43 0.6 3.8 0.2 0.55 0.6 0.3 0.02 loam 35 SCL 1 0.2 0.2 5.06 0.6 0.1 0.06 3.95 0.2 0.4 0.08 0.42 0.6 11.9 0.2 1.02 0.6 0.3 0.02 36 Clay 0.2 0.2 0.04 5.22 0.6 0.1 0.06 4.01 0.4 0.4 0.16 0.38 0.6 6.4 0.2 0.84 0.6 0.3 0.02 37 SCL 1 0.2 0.2 5.56 0.6 0.1 0.06 2.79 0.2 0.4 0.08 0.28 0.6 19.1 0.2 0.57 0.6 0.3 0.02 Clay 38 0.4 0.2 0.08 5.44 0.6 0.1 0.06 3.94 0.2 0.4 0.08 0.39 0.6 11.6 0.2 1.09 0.6 0.3 0.02 loam Clay 39 0.4 0.2 0.08 5.22 0.6 0.1 0.06 4.36 0.4 0.4 0.16 0.42 0.6 2.9 0.2 0.94 0.6 0.3 0.02 loam 3.28/ 2.22/ 4.72/ 1.3/ Textural class SQI Soil pH SQI OC SQI NPK SQI 39 � 0.08 39 � 0.06 39 � 0.12 39 � 0.03 11.52/ Final SQI (poor) 39 � 0.30 Applied and Environmental Soil Science  Applied and Environmental Soil Science Table 4: Classifcation criteria of the fnal soil quality index for the MDS. Soil quality grade Indicator Grade 1 Grade 2 Grade 3 Grade 4 Grade 5 Source Very high/best/ High/good/ Moderate/fair/ Low/poor/ Very low/very poor/ SQI >0.60 0.55–0.60 0.45–0.54 0.38–0.44 <0.38 Li et al. [17]; Agnieszka et al. [13] SQI 0.8–1.0 0.6–0.8 0.4–0.6 0.2–0.4 ≤0.2 Bajracharya et al. [24]; Nepal and Mandal [9]; Manisha et al. [7] 9 10 Applied and Environmental Soil Science Table 5: Te soil quality of lands in the Nitisols and Luvisols based on the soil fertility/nutrient/index approach. Nitisols sample status Luvisols sample status Parameters ∗ 1 ∗ 2 ∗ 3 Index ∗ 1 ∗ 2 ∗ 3 Index Very low-low Medium High-very high Very low-low Medium High-very high pH 18 0 0 1.0 22 15 2 1.5 OC 18 0 0 1.0 22 17 0 1.4 TN 17 1 0 1.1 0 37 2 2.1 P 18 0 0 1.0 39 0 0 1.0 S 18 0 0 1.0 30 7 2 1.3 Ca 16 2 0 1.1 37 2 0 1.1 Mg 18 0 0 1.0 0 5 34 2.9 K 18 0 0 1.0 1 17 21 2.5 K : Mg 1 17 0 1.9 4 25 10 2.2 CEC 1 0 17 2.9 36 0 3 1.2 PBS 9 9 0 1.5 0 0 39 3.0 ESP 16 2 0 1.1 9 18 12 2.1 B 1 11 6 2.3 39 0 0 1.0 Cu 16 2 0 1.1 38 1 0 1.0 Fe 13 5 0 1.3 8 31 0 1.8 Mn 2 16 0 1.9 0 28 11 2.3 Zn 1 17 0 1.9 0 39 0 2.0 SQI (low) 24.11/17 � 1.42 SQI (medium) 30.18/17 � 1.78 while in Luvisols, it was entirely under optimum level [27]. total variation (36.2% and 16.4%) in Luvisol lands and 59.6% Regarding extractable Zn content, the entire samples from of the total variation (39.1% and 20.5%) in Nitisols lands. Nitisol lands were under optimum level (1.5–10 mg·kg−1) Eight soil parameters, including S, Mg, Na, B, Cu, Fe, Mn, and [27]; while 33% and 67% of the samples from Luvisols were Zn for the land in the Nitisols and fve soil parameters, in- under optimum (1.5–10 mg·kg−1) and high level cluding pH, Ca, PBS, B, and Fe in Luvisols land from PC 1, (>10 mg·kg−1), respectively [27]. Overall, both soil types were correlated (bolded parameters) to observe their close investigated mainly revealed acidic soil reaction, low soil interrelationship and to choose for the minimum data set OC, and limitation of N, P, S, B, and Cu nutrients and needs (MDS) (Table 6). Tus, the highest factor loadings from each soil interventions. PC analysis were found for six parameters, including silt, pH, OC, Ca, B, and Zn for samples taken from lands located in Nitisols and fve parameters, including TN, S, Ca, Mg, and Mn 3.2. Soil Quality Index. Based on the common soil parameter in the Luvisols lands (bold underlined) (Table 7). Tese pa- approach, the SQI values for the soils taken from Nitisols rameters were then retained in the MDS (Table 7). In ad- and Luvisols were 0.17 (Table 2) and 0.30 (Table 3). Te dition, for normalized PCA-based SQI estimation, the MDS shares of each indicator in the Nitisols lands were 0.04 (soil was retained following the approach indicated by Tesfahu- textural class), 0.04 (soil pH), 0.08 (soil OC), and 0.005 (N-P- negn [23]; Podwika et al., [12] (Table 8). Subsequently, the K) (Table 2); and 0.08 (soil textural class), 0.06 (soil pH), 0.12 estimated SQI values following the PCA and normalized PCA (soil OC), and 0.03 (N-P-K) in Luvisols lands (Table 3). techniques (Tables 7 and 8) for the soils belonging to the According to Bajracharya et al. [24], the SQI was classifed as Nitisols revealed 0.42 and 0.36, whereas the values were 0.40 very poor quality if the ranking value was less than 0.2, poor and 0.38 for the Luvisols, respectively (Table 8). if it was between 0.2 and 0.4, fair if it was between 0.4 and 0.6, According to Li et al. [17], grading values for PCA and good if it was between 0.6 and 0.8, and best if it was between normalized PCA-SQI values were very low (<0.38), low 0.8 and 1 (Table 4). Te soil quality of lands in the Nitisols (0.38–0.44), moderate (0.45–0.54), high (0.55–0.60), and was rated very poor (<0.2), whereas the soil quality of very high (>0.60) soil quality. In view of this, the soil quality Luvisols lands was poor (0.2–0.4) [7, 9]. belonging to Nitisols using the PCA and normalized PCA Te evaluation using the soil fertility/nutrient/index approaches were rated very low and low, respectively, method also revealed values of 1.42 and 1.78 for Nitisols and whereas the soil quality of lands belonging to Luvisols was Luvisols situated lands, respectively (Table 5). Te index qualifed as low level (0.38–0.44). value is rated low fertility status, if less than 1.67, medium Overall, the estimated SQI values of both soil types using [1.67 and 2.33], and high [>2.33]. Terefore, the soil fertility various techniques demonstrated poor-quality soils (Ta- status of the lands found in the Nitisols was low in soil ble 9). About 50% of the essential nutrients that come from fertility while it was medium in the Luvisols lands [25]. the soil, N, P, S, Ca, Mg, and B, were low from soil samples Five principal components (PCs) with eigenvalues >1 taken in the Nitisols, and 36% of the nutrients, N, P, K, S, and were identifed by the PCA SQI in both Nitisols and Luvisols B, from lands in the Luvisols were found to be inadequate. In land, accounting for 89.3% and 81% of the total variation, both soil types, the soil pH was strongly acidic, which is respectively (Table 6). PC 1 and 2 accounted for 52.6% of the problematic for nutrient availability and microbial activity.  Table 6: Principal component analysis (PCA) of factor loadings for lands in the Nitisols and Luvisols. Nitisols Luvisols Parameters PC_1 PC_2 PC_3 PC_4 PC_5 PC_1 PC_2 PC_3 PC_4 PC_5 Eigenvalues 7.432 3.892 2.848 1.668 1.114 6.869 3.125 2.381 1.756 1.274 % Of variance 39.1 20.5 15 8.8 5.9 36.2 16.4 12.5 9.2 6.7 Applied and Environmental Soil Science Cumulative % of variance 39.1 59.6 74.6 83.4 89.3 36.2 52.6 65.1 74.3 81 PC weight 0.43785 0.22956 0.16797 0.09854 0.06607 0.447 0.202 0.154 0.114 0.083 Variables Factor loadings/eigenvectors/ Factor loadings/eigenvectors/ Silt 0.1291 0.1314 0.3148 0.4341 −0.2157 0.111 −0.196 −0.45 0.195 0.249 Clay −0.0595 −0. 065 −0.1903 −0.2157 −0.0055 −0.255 0.005 0.254 −0.234 −0.108 pH 0.0445 0.2686 0.2703 −0.4376 0.0463 0.312 −0.004 −0.09 −0.295 −0.123 OC 0.0146 0.4423 −0.121 0.1392 0.2473 0.193 −0.227 0.274 0.318 −0.102 TN −0.0149 0. 235 −0.1723 0.1188 0.2128 0.162 −0.239 0.291 0.466 −0.009 P −0.0987 −0.0327 0.1288 0.1388 −0.7388 0.261 −0.023 −0.033 −0.216 0.38 S 0.3508 −0.0067 −0.0956 −0.0678 −0.0229 0.154 −0.073 0.478 0.17 −0.112 Ca −0.2586 0.0424 0.3586 −0.2331 0.0898 0.305 0.299 −0.114 0.015 −0.089 Mg 0.328 −0.0197 0.076 −0.2664 0.0355 0.192 0.431 0.065 0.062 −0.24 K 0.0031 −0. 07 −0.0598 −0.2039 −0.0125 0.278 −0.125 0.257 −0.288 0.01 Na 0.3 05 0.0836 0.011 −0.1313 −0.1562 −0.208 −0.008 −0.013 −0.127 0.048 K : Mg −0.2288 −0.1279 −0.2951 0.3508 0.0199 0.036 −0.318 0.245 −0.391 0.246 CEC −0.1546 0.3204 −0.1314 −0.3515 −0.2895 0.254 0.253 0.041 0.218 0.206 PBS 0.0598 −0.2663 0.3784 0.1176 0.4099 0.28 0.236 −0.096 −0.087 −0.307 B 0.3561 −0.0016 −0.0667 −0.0736 −0.0365 0.30 −0.076 0.129 −0.307 −0.195 Cu 0.3322 0.0014 −0.0089 −0.006 −0.1026 0.185 −0.343 −0.234 0.113 −0.181 Fe −0.335 0.0179 0.1908 −0.0938 −0.0168 0.307 0.061 −0.098 −0.036 0.307 Mn −0.3 37 0.062 0.0513 −0.1854 −0.0078 −0.056 −0.269 −0.247 −0.089 −0.559 Zn 0.0806 −0.0034 0.5408 0.1514 −0.013 0.227 −0.37 −0.199 0.01 −0.022 NB: bold values under each principal component are highly weighted and italic values are selected in the MDS. 11 12 Applied and Environmental Soil Science Table 7: Soil quality index (SQI) values of lands in the Nitisols and Luvisols based on PCA. Lands in the Nitisols Lands in the Luvisols Parameters PC1 PC2 PC3 PC4 PC5 PC1 PC2 PC3 PC4 PC5 Silt 0.03894 Clay pH −0.0393 OC 0.09243 TN 0.053 P S 0.074 Ca 0.136 Mg 0.087 K Na K : Mg CEC PBS 0.0247 B 0.14193 Cu Fe Mn −0.046 Zn 0.08269 PCA-SQI � 0.42 (low) PCA-SQI � 0.40 (low) NB: Nitisol lands PCA-SQI � (0.391 B + 0.205 OC + 0.15 Zn + 0.088 Silt + 0.088 pH + 0.059 PBS)/(0.981) � 0.42 � low Luvisol lands PCA-SQI � 0.362 Ca + 0.164 Mg + 0.125 S + 0.092 TN + 0.0.067 Mn/(0.81) � 0.40 � low. Table 8: Soil quality index (SQI) values of lands in the Nitisols and Luvisols based on a statistical model of normalized PCA. Lands in the Nitisols Lands in the Luvisols Parameters PC1 PC2 PC3 PC4 PC5 PC1 PC2 PC3 PC4 PC5 Silt 0.011 −0.043 Clay pH −0.012 0.087 OC 0.027 TN 0.033 P S 0.041 0.046 Ca 0.085 Mg 0.039 0.054 K Na 0.04 K : Mg CEC PBS 0.007 B 0.042 Cu 0.039 Fe −0.039 Mn −0.04 −0.029 Zn 0.0244 Nitisol normalized PCA-SQI � 0.36 Luvisol normalized PCA-SQI � 0.38 NB: Nitisol normalized PCA-SQI � (0.391 S + 0.391 Mg + 0.391 Na + 0.391 B + 0.391 Cu + 0.391 Fe + 0.391 Mn + 0.205 OC + 0.15 Zn + 0.088 Silt + 0.088 pH + 0.059 PBS)/(3.327) � 0.1175 S + 0.1175 Mg + 0.1175 Na + 0.1175 B + 0.1175 Cu + 0.1175 Fe + 0.1175 Mn + 0.0616 OC + 0.0451 Zn + 0.0265 Silt + 0.0265 pH + 0.0177 PBS � 0.36 � very low, Luvisol normalized PCA-SQI � (0.362 pH + 0.362 Ca + 0.164 Mg + 0.125 Silt + 0.125 S + 0.092 TN + 0.067 Mn)/(1.297) � 0.279 pH + 0.279 Ca + 0.126 Mg + 0.096 Silt + 0.096 S + 0.071 TN + 0.052 Mn � 0.38 � low. A further restriction in both soil types was their low organic which in turn accelerated nutrient defciencies [6, 29]. matter levels. A lack of organic matter input and the removal According to the fndings, problem-focused soil manage- of basic nutrients caused the soil to become more acidic, ment interventions are urgently required [12, 13].  Applied and Environmental Soil Science Table 9: Summary of soil quality index values of lands in the Nitisols and Luvisols using diferent SQI evaluation techniques. Te land belongs to SQI evaluation methods SQI value Quality of the soil Reference Common indicators 0.17 Very poor Bajracharya et al. [24]; Nepal and Mandal [9]; Manisha et al. [7] Soil fertility/nutrient/index 1.42 Low Khadka et al. [25] Nitisols PCA (i) Normalized PCA-SQI 0.36 Very low Tesfahunegn [23]; Li et al. [17]; Podwika et al. [12] (ii) PCA-SQI 0.42 Low Mukherjee and Lal [26]; Li et al. [17] Common indicators 0.30 Poor Nepal and Mandal [9]; Manisha et al. [7] Soil fertility/nutrient/index 1.78 Medium Khadka et al. [25] Luvisols PCA (i) Normalized PCA-SQI 0.38 Low Tesfahunegn [23]; Li et al. [17]; Podwika et al. [12] (ii) PCA 0.4 Low Mukherjee and Lal [26]; Li et al. [17] 13 14 Applied and Environmental Soil Science 4. Conclusion Open Access Journal of Environmental and Soil Sciences, vol. 5, no. 2, pp. 614–621, 2020. Diferent techniques, including principal component anal- [8] H. Haryuni, H. Wirawati, S. Minardi, and S. Supriyadi, ysis (PCA), common soil parameters, and the soil fertility/ “Assessment of soil quality in organic and non-organic paddy nutrient/index approach, were employed to estimate the soil felds with technical irrigation system in Susukan,” Polish quality. All evaluation techniques for the lands belonging to Journal of Soil Science, vol. 53, no. 1, p. 81, 2020. Nitisols consistently demonstrated comparable soil quality [9] S. Nepal and R. A. Mandal, “Soil quality index and nutrient in badekhola and brindaban catchments, Nepal,” MOJ Ecology status, whereas PCA and common soil parameter techniques and Environmental Science, vol. 3, Article ID 00066, 2018. generated similar results for the Luvisols lands. Based upon [10] A. N. Kwabena, Using Soil Fertility index to Evaluate Two the consistency of the outcomes generated in both soil types, Diferent Sampling Schemes in Soil Fertility Mapping: A Case the use of PCA and the common soil parameters approach Study of HvanneyriUnited Nations University Land Resto- could be taken as useful tools to assess soil quality. Fur- ration Training Programme, Hvanneyri, Iceland, 2011. thermore, it was noted that low soil quality necessitates the [11] F. Laekemariam, K. Kibret, T. Mamo, E. Karltun, and use of management interventions. H. Gebrekidan, “Physiographic characteristics of agricultural lands and farmers’ soil fertility management practices in Data Availability Wolaita zone, Southern Ethiopia,” Environmental Systems Research, vol. 5, no. 1, p. 24, 2016. Te data used to support the fndings of this study are [12] M. Podwika, K. Solek-Podwika, D. Kaleta, and K. Ciarkowska, available from the corresponding author upon request. “Te efect of land-use change on urban grassland soil quality (Southern Poland),” Journal of Soil Science and Plant Nu- trition, vol. 20, no. 2, pp. 473–483, 2020. Conflicts of Interest [13] K. Agnieszka, U. J. Aleksandra, and S. Bożena, “Soil quality index for agricultural areas under diferent levels of anthro- Te authors declare that there are no conficts of interest. popressure,” International Agrophysics, vol. 33, pp. 455–462, 2019. Authors’ Contributions [14] O’geen and T. Anthony, A Revised Storie index for Use with Digital Soils Information, UCANR Publications, Irvine, CA, Te authors collected, analyzed, interpreted, and prepared USA, 2008. the manuscript. [15] F. Lisetskii, V. F. Stolba, and O. Marinina, “Indicators of agricultural soil genesis under varying conditions of land use, Acknowledgments Steppe Crimea,” Geoderma, vol. 239-240, pp. 304–316, 2015. [16] E. K. Bünemann, G. Bongiorno, Z. Bai et al., “Soil quality – Te authors would like to acknowledge Wolaita Sodo a critical review,” Soil Biology and Biochemistry, vol. 120, University for fnancing the research. pp. 105–125, 2018. [17] X. Li, H. Li, L. Yang, and Y. Ren, “Assessment of soil quality of croplands in the corn belt of northeast China,” Sustainability, References vol. 10, no. 1, p. 248, 2018. [1] T. Vassilios, K. Achilleas, K. Chariklia, and P. Angelos, “An [18] Pdav (Power Data Access Viewer), “Nasa,” 2020, https:// assessment of the soil quality index in a mediterranean agro power.larc.nasa.gov/data-access-viewer/. ecosystem,” Emirates Journal of Food and Agriculture, vol. 30, [19] Wrb (World Reference Base for Soil Resources), “In- no. 12, pp. 1042–1050, 2018. ternational soil classifcation system for naming soils and [2] Y. Mulat, K. Kibret, B. Bedadi, and M. Mohammed, “Soil creating legends for soil maps,” World Soil Resources Reports, quality evaluation under diferent land use types in Kersa sub- vol. 106, 2014. watershed, eastern Ethiopia,” Environmental Systems Re- [20] Fao (Food and Agriculture Organization), Guidelines for Soil search, vol. 10, no. 1, p. 19, 2021. Description, Food and Agriculture Organization of the United [3] T. G. Pham, H. T. Nguyen, and M. Kappas, “Assessment of Nations, Rome, Italy, 4 edition, 2006. soil quality indicators under diferent agricultural land uses [21] S. Sahlemedhin and B. Taye, Procedures for Soil and Plant and topographic aspects in Central Vietnam,” International Analysis National Soil Research Center, EARO, Addis Ababa, Soil and Water Conservation Research, vol. 6, no. 4, Ethiopia, National Soil Research Center, Addis Ababa, pp. 280–288, 2018. Ethiopia, 2000. [4] S. A. H. Selmy, S. H. A. Abd Al-Aziz, R. Jiménez-Ballesta, [22] A. Mehlich, “Mehlich 3 soil test extractant: a modifcation of F. Jesús Garcı́a-Navarro, and M. E. Fadl, “Soil quality as- Mehlich 2 extractant,” Communications in Soil Science and sessment using multivariate approaches: a case study of the Plant Analysis, vol. 15, no. 12, pp. 1409–1416, 1984. dakhla oasis arid lands,” Land, vol. 10, p. 1074, 2021. [23] G. B. Tesfahunegn, “Soil quality assessment strategies for [5] F. Laekemariam, K. Kibret, T. Mamo, and H. Shiferaw, evaluating soil degradation in northern Ethiopia,” Applied “Accounting spatial variability of soil properties and mapping and Environmental Soil Science, vol. 2014, Article ID 646502, fertilizer types using geostatistics in southern Ethiopia,” 14 pages, 2014. Communications in Soil Science and Plant Analysis, vol. 49, [24] R. M. Bajracharya, B. K. Sitaula, S. Sharma, and A. Jeng, “Soil no. 1, pp. 124–137, 2018. quality in the Nepalese context – an analytical review,” In- [6] B. Iticha and C. Takele, “Digital soil mapping for site-specifc ternational Journal of Ecology & Environmental Sciences, management of soils,” Geoderma, vol. 351, pp. 85–91, 2019. vol. 33, no. 2-3, pp. 143–158, 2007. [7] G. Manisha, A. M. Ram, and B. M. Ajay, “Evaluating the soil [25] D. Khadka, S. Lamichhane, P. Bhantana, A. R. Ansari, S. Joshi, quality of three sub-watersheds in Udayapur District, Nepal,” and P. Barwal, “Soil fertility assessment and mapping of Applied and Environmental Soil Science 15 chungbang farm, pakhribas, dhankuta, Nepal,” Advances in Plants & Agriculture Research, vol. 8, no. 3, pp. 219–227, 2018. [26] A. Mukherjee and R. Lal, “Comparison of soil quality index using three methods,” PLoS One, vol. 9, no. 8, Article ID e105981, 2014. [27] EthioSIS (Ethiopia Soil Information System), Soil Fertility Status and Fertilizer Recommendation Atlas for Tigray Re- gional State, Ethiopia, Agricultural Transformation Agency, Addis Ababa, Ethiopia, 2014. [28] J. R. Landon, Booker Tropical Soil Manual: A Handbook for Soil Survey and Agricultural Land Evaluation in the Tropics and Subtropics, Longman Scientifc and Technical, Essex, England, 2014. [29] M. Razan, S. Shankar, K. Dinesh, and R. B. Chet, “Soil fertility mapping and assessment of the spatial distribution of sarlahi district, Nepal,” American Journal of Agricultural Science, vol. 7, pp. 8–16, 2020.