Explaining the Soil Quality Using Different Assessment Techniques

Abdu, Abass; Laekemariam, Fanuel; Gidago, Gifole; Getaneh, Lakew

doi:https://doi.org/10.1155/2023/6699154

Applied and Environmental Soil Science

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Abstract Introduction Materials and Methods Results and Discussion Conclusion Data Availability Conflicts of Interest Authors’ Contributions Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2023 | Article ID 6699154 | https://doi.org/10.1155/2023/6699154

Explaining the Soil Quality Using Different Assessment Techniques

Abass Abdu,¹Fanuel Laekemariam,¹Gifole Gidago,¹and Lakew Getaneh¹

Academic Editor: Fedor Lisetskii

Received05 Dec 2022

Revised07 Mar 2023

Accepted10 Mar 2023

Published24 Mar 2023

Abstract

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. Thus, this study was conducted to assess the soil quality of arable lands situated in the Nitisols and Luvisols using different 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). The 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). The 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). The results were expressed in terms of soil quality index (SQI). In addition, the soil fertility/nutrient/index (NI) approach was used. The 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. The soil quality of lands in the Luvisols was rated low (0.38–0.44), while lands in the Nitisols qualified 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 offered 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

Soils in agriculture are an important part of the ecological system that produces food and fiber for human consumption, but they are a limited and largely non-renewable resource [1, 2]. Soils are a key enabling resource and essential to the production of a wide range of goods and services integral to ecosystems and human well-being [3, 4]. Nonetheless, soil fertility depletion caused by a variety of factors (soil erosion, acidity, nutrient depletion, lack of soil fertility replenishment, nutrient mining, and lack of balanced fertilization) is a significant contributor to food insecurity [5, 6].

Soil quality (SQ), which is defined as the capacity of soil to function within the ecosystem and land use boundaries to sustain biological productivity, maintain environmental quality, and promote plant, animal, and human health, is now highly related to sustainable and productive agriculture [2, 7, 8]. Good-quality soils will preserve natural ecosystems by improving air and water quality for improved food and fiber production while also protecting the environment and human health [9].

The SQ simultaneously addresses the issues of productivity and sustainability and makes it indispensable for developing countries such as Ethiopia [2, 4]. A better understanding of the SQ and the factors that degrade the SQ is necessary to fully exploit the potential benefits of soil resources. For example, poor soil physical and chemical health is very likely to result in poor aggregate stability, a decline in soil OM, nutrient-related plant stresses, crop yield stagnation, and exacerbate soil degradation [10, 11]. This suggests that SQ is linked to chemical properties, biophysical environments, and anthropogenic factors. Meanwhile, SQ cannot be measured directly in the field or laboratory; rather, it is inferred from measured soil physical, chemical, and biological properties and is thus expressed in terms of soil quality index (SQI) [2, 8, 12].

The SQI could be defined as a minimum set of parameters that provides numerical data about a soil’s ability to perform one or more functions [13]. It aids in assessing overall soil condition and management response or resilience to natural and anthropogenic forces [1, 7, 14, 15]. Expert opinion (subjective) or mathematical and statistical (objective) methods are used to select a minimum soil data set (MDS) [13, 16]. The use of multivariate techniques of principal component analysis (PCA) (multiple correlations and factor analyses) to reduce statistical data has become more common [12, 17]. Thus, the SQI, which takes into account the physical, chemical, and biological properties of soils as well as their variability, is critical for long-term utilization and site-specific management of soil resources [2, 8, 12, 14, 15].

Despite the importance of SQ assessment, very few studies have been conducted on smallholder arable lands in Ethiopia where traditional practices dominate soil management [2]. This emphasizes the importance of having adequate soil property information in order to intervene and prevent soil fertility degradation problems. Against this backdrop, the present study aimed to explore the soil quality status of farmlands belonging to different soil groups using different varied approaches.

2. Materials and Methods

2.1. Description of the Study Area

The study sites were Farawocha farm in Wolaita Zone and Kechi farm in Dawro 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 37°37′33″E longitude. The farm has 3.85 ha (cultivated land) within an average altitude of 1500 m.a.s.l and a slope of less than 3%. Ten years (2010–2019) mean annual precipitation is 1300 mm, and the monthly temperature fluctuates between 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 of 2090 m.a.s.l. It has a total area of 131.26 ha of which the cultivated land shares 32.66 ha, grass land (5.8 ha), and forest land (92.8 ha). Kechi farm lies from a gentle to the steep slope. Ten years (2010–2019) mean annual precipitation was 1502 mm and the monthly temperature fluctuates between 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 Farawocha farm and Kechi farm are grouped under the Nitisols and Luvisols, respectively.

2.2. Soil Sampling Procedure and Analysis

2.2.1. Soil Sampling Procedure

Various tasks, including prefield work, fieldwork, and postfield work stages, were completed prior to sample collection. Prior to sample collection, sample points to the study area shape file were assigned in grid patterns using geographical information system (GIS). While conducting the survey, a geographical positioning system (GPS) receiver was used to find the sample locations. A total of 57 geo-referenced points were used to collect surface soil samples at a depth of 0–20 cm (18 from Nitisols and 39 from Luvisols). Ten subsamples from each sample were combined to create one kilogram of composited soil.

2.2.2. Soil Sample Preparation and Analysis

Following the standard procedure outlined in Sahlemedhin and Taye [21], soil samples were processed (air-dried, ground, and passed through a 2 mm sieve), and some soil physicochemical properties were examined (2000). This includes soil pH, organic carbon (OC), total nitrogen (TN), available phosphorus (P) and sulfur (S), exchangeable bases (calcium (Ca), magnesium (Mg), and potassium (K)), soil micronutrients (boron (B), copper (Cu), iron (Fe), manganese (Mn), and zinc (Zn)), cation exchange capacity (CEC), and texture (particle size distribution). Soil pH (1 : 2.5 soil: water suspension) was measured with a glass electrode (ES ISO 10390 : 2014). Total N was determined by the wet-oxidation (wet digestion) procedure of the Kjeldahl method (ES ISO 11261 : 2015). Organic carbon (OC) was determined following the wet combustion method of Walkley and Black. Available P and S, exchangeable basic cations (Ca, Mg, and K), and extractable micronutrients (B, Cu, Fe, Mn, and Zn) were determined using the Mehlich-III multinutrient extraction method [22]. The CEC was determined by using the 1 N ammonium acetate (pH 7) method. Particle size analysis was carried out by the hydrometer method as described by [21]. Textural classes were determined by Marshall’s triangular coordinate system.

2.3. Soil Quality Assessment

Since soil quality cannot be directly measured, it is inferred from other soil properties and expressed as the soil quality index (SQI) [8, 12, 23]. The approaches discussed were used in the study to assess the soil quality:

2.3.1. SQI Estimate Using an Additive System Based on Common Soil Parameters [7, 9, 24]

The process involved three main steps: (i) selecting appropriate indicators; (ii) converting indicators into scores; and (iii) combining the scores into an index [13, 25].where RSTC = assigned ranking values for soil textural class; RpH = assigned ranking values for soil pH; ROC = assigned ranking values for soil organic carbon; RNPK = assigned ranking values for nitrogen (N); phosphorus (P), and potassium (K) (Table 1). Furthermore, a = 0.2, b = 0.1, c = 0.4, and d = 0.3 refer to the weighted values corresponding to each of the four parameters. That is, out of 1(100%), the weighting value for soil textural class (a) = 0.2 (20%), soil pH (b) = 0.1(10%), soil organic carbon (c) = 0.4 (40%), and soil macronutrient contents (N, P, and K) (d) = 0.3 (30%).

2.3.2. Soil Fertility/Nutrient/Index

The calculation is based on the number of samples classified as low, medium, or high and the rating classes of the measured soil parameters, which are multiplied by 1, 2, and 3, respectively. If the index value is less than 1.67, the fertility status is low; if the index value is 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].where N_L = number of samples in low category; N_M = number of samples in the medium category; N_H = number of samples in high category, and N_T = total number of samples.

2.3.3. Principal Component Analysis (PCA) Based SQI (Statistical Model-Based SQI)

A statistics-based model is used to estimate SQI using PCA [17, 26]. The PCA method is more objective because it makes use of a variety of statistical tools (multiple correlation, factor, and analyses), which could prevent bias and data redundancy by selecting a minimal dataset (MDS) using formulas [12]. The PCA model included all the original observations of each soil parameter.

The PCs with high eigenvalues represented the maximum variation in the dataset, while most studies have assumed to examine PCs only the variables having high factor loadings with eigenvalues >1.0 that explained at least 5% of the data variations were retained for indexing [12, 17].

Under a given PC, each variable had a corresponding eigenvector weight value or factor loading. Only the “highly weighted” variables were retained in the MDS. The “highly weighted” variables were defined as the highest weighted variable under a certain PC and absolute factor loading value within 10% of the highest values under the same PC [12, 23]. However, when more than one variable was retained under a particular PC, a multivariate correlation matrix is used to determine the correlation coefficients between the parameters. If the parameters were significantly correlated (r > 0.70), then the one with the highest loading factor was retained in the MDS and all others were eliminated from the MDS to avoid redundancy.

Still, the normalized PCA of SQI would be calculated if more than one highest eigenvectors were retained in the MDS [12, 23]. The noncorrelated and highly weighted parameters under a particular PC were considered important and retained in the data. Each PC explained a certain amount of variation in the dataset, which was divided by the maximum total variation of all the PCs selected for the MDS to get a certain weightage value under a particular PC [12, 26]. Thereafter, the SQI-3 (PCA) was computed using the following equation:where PC Weight is the weightage factor determined from the ratio of the total percentage of variance from each factor to the maximum cumulative variance coefficients of the PC considered; individual soil parameter score is the score of each parameter in the MDS.

2.4. Data Analysis

Description of data analysis was performed using Microsoft Excel. All these values presented as mean, minimum, maximum, SD, CV, PCA, and MDS selections were performed using statistics-8 and Microsoft Excel software. In addition, Pearson correlation analysis on selected parameters was performed.

3. Results and Discussion

3.1. Characteristics of Surface Soil Properties

The particle size distribution (PSD) in both soils was the order of clay > silt > sand. Soil samples belonging to Nitisols had a clay texture with a strongly acidic reaction (pH < 5.5) [27]. The samples taken from Luvisols revealed a textural class of loam (5% samples), clay loam (54%), clay (31%), and silty clay loam (10%). Regarding soil reaction, about 56%, 39%, and 5% of samples in the Luvisols were strongly acidic, moderately acidic (pH 5.6–6.5), and neutral (pH 6.6–6.67) reactions, respectively [27]. In contrast to Luvisols, which had 56% low (2–4%) and 44% medium (4–10%) soil OC, the entire samples from Nitisols’ lands contained low soil OC, according to Landon [28].

According to Landon [28], Luvisols’ TN content was 95% low (0.1-0.2%) and 5% high (0.51–1%), compared to Nitisols’ wholly low TN content (0.1–0.20%) [28]. Regarding available P (mg·kg⁻¹), approximately 77%, 18%, and 5% of the samples in the Luvisols were under low (15–30), optimum (30–80), and high (80–150) categories, respectively, whereas available P (mg·kg⁻¹) of samples taken from the Nitisols was entirely low [27].

In the Nitisols, 89% and 11% of the samples were under low (10–20 mg·kg⁻¹) and optimum S levels (20–80 mg·kg⁻¹), and in the Luvisols lands, 95% and 5% of the samples were under low (<20 mg·kg⁻¹) and optimum level (20–22.66 mg·kg⁻¹), respectively [27]. According to Landon [28], exchangeable Ca levels of arable lands in the Nitisols were found low (2–5 Cmol (+) kg⁻¹), whereas in the Luvisols, 13% and 87% of the samples showed medium (5.1–10 Cmol (+) kg⁻¹) and high (10–20 Cmol (+) kg⁻¹) levels, respectively. Exchangeable Mg was entirely under low level (<1.5 Cmol (+) kg⁻¹) [28] in Nitisols; while in Luvisols, 2%, 44%, and 54% of the samples were under low (<1.5 Cmol (+) kg⁻¹), medium (1.51–3.3 Cmol (+) kg⁻¹), and high level (3.31–8.0 Cmol (+) kg⁻¹) [28], respectively. Furthermore, exchangeable K (Cmol (+) kg⁻¹) in 6% and 94% of the soil samples were under low (0.2–0.5 Cmol (+) kg⁻¹) and optimum level (0.5–1.5 Cmol (+) kg⁻¹) [27] in Nitisols; while in Luvisols, 10%, 64%, and 26% of the samples were under low (0.2–0.5 Cmol (+) kg⁻¹), optimum (0.5–1.5 Cmol (+) kg⁻¹), and high level (1.5–2.3 Cmol (+) kg⁻¹), respectively [27]. About half (50%) of samples taken from Nitisols lands were under low (5–15 Cmol (+) kg⁻¹) CEC, and the remaining were under medium level (15–25 Cmol (+) kg⁻¹) [28] in CEC; while the entire sample from Luvisols land recorded high CEC (25–40 Cmol (+) kg⁻¹) [28].

Data regarding B indicated that about 89% and 11% of soil samples from Nitisol lands were under low (<0.8 mg·kg⁻¹) and very high levels (>4 mg·kg⁻¹) [27]; while in the Luvisols, 98% and 2% of the samples were under low and optimum (0.8–2.0 mg·kg⁻¹) categories [27]. Extractable Cu (mg·kg⁻¹) for about 72% and 28% of Nitisols samples were under low (<0.9) and optimum levels (1.0–20.0 mg·kg⁻¹) [27], as well as 21% and 79% of Luvisols samples were under low and optimum level [27]. About 12% and 88% of samples from Nitisols lands were under low (<80 mg·kg⁻¹) and optimum level (80–300 mg·kg⁻¹) levels in extractable Fe [27], respectively; while in Luvisols, 72% and 28% of the samples were under optimum and high Fe level (300–400 mg·kg⁻¹) [27], respectively. The Mn contents for 6% and 94% of samples taken from Nitisols land were under low (<25 mg·kg⁻¹) and optimum levels (>25 mg·kg⁻¹) [27]; while in Luvisols, it was entirely under optimum level [27]. Regarding extractable Zn content, the entire samples from Nitisol lands were under optimum level (1.5–10 mg·kg⁻¹) [27]; while 33% and 67% of the samples from Luvisols were under optimum (1.5–10 mg·kg⁻¹) and high level (>10 mg·kg⁻¹), respectively [27]. Overall, both soil types investigated mainly revealed acidic soil reaction, low soil OC, and limitation of N, P, S, B, and Cu nutrients and needs soil interventions.

3.2. Soil Quality Index

Based on the common soil parameter approach, the SQI values for the soils taken from Nitisols and Luvisols were 0.17 (Table 2) and 0.30 (Table 3). The shares of each indicator in the Nitisols lands were 0.04 (soil textural class), 0.04 (soil pH), 0.08 (soil OC), and 0.005 (N-P-K) (Table 2); and 0.08 (soil textural class), 0.06 (soil pH), 0.12 (soil OC), and 0.03 (N-P-K) in Luvisols lands (Table 3). According to Bajracharya et al. [24], the SQI was classified as very poor quality if the ranking value was less than 0.2, poor if it was between 0.2 and 0.4, fair if it was between 0.4 and 0.6, good if it was between 0.6 and 0.8, and best if it was between 0.8 and 1 (Table 4). The soil quality of lands in the Nitisols was rated very poor (<0.2), whereas the soil quality of Luvisols lands was poor (0.2–0.4) [7, 9].

The evaluation using the soil fertility/nutrient/index method also revealed values of 1.42 and 1.78 for Nitisols and Luvisols situated lands, respectively (Table 5). The index value is rated low fertility status, if less than 1.67, medium [1.67 and 2.33], and high [>2.33]. Therefore, the soil fertility status of the lands found in the Nitisols was low in soil fertility while it was medium in the Luvisols lands [25].

Five principal components (PCs) with eigenvalues >1 were identified by the PCA SQI in both Nitisols and Luvisols land, accounting for 89.3% and 81% of the total variation, respectively (Table 6). PC 1 and 2 accounted for 52.6% of the total variation (36.2% and 16.4%) in Luvisol lands and 59.6% of the total variation (39.1% and 20.5%) in Nitisols lands. Eight soil parameters, including S, Mg, Na, B, Cu, Fe, Mn, and Zn for the land in the Nitisols and five soil parameters, including pH, Ca, PBS, B, and Fe in Luvisols land from PC 1, were correlated (bolded parameters) to observe their close interrelationship and to choose for the minimum data set (MDS) (Table 6). Thus, the highest factor loadings from each PC analysis were found for six parameters, including silt, pH, OC, Ca, B, and Zn for samples taken from lands located in Nitisols and five parameters, including TN, S, Ca, Mg, and Mn in the Luvisols lands (bold underlined) (Table 7). These parameters were then retained in the MDS (Table 7). In addition, for normalized PCA-based SQI estimation, the MDS was retained following the approach indicated by Tesfahunegn [23]; Podwika et al., [12] (Table 8). Subsequently, the estimated SQI values following the PCA and normalized PCA techniques (Tables 7 and 8) for the soils belonging to the Nitisols revealed 0.42 and 0.36, whereas the values were 0.40 and 0.38 for the Luvisols, respectively (Table 8).

According to Li et al. [17], grading values for PCA and normalized PCA-SQI values were very low (<0.38), low (0.38–0.44), moderate (0.45–0.54), high (0.55–0.60), and very high (>0.60) soil quality. In view of this, the soil quality belonging to Nitisols using the PCA and normalized PCA approaches were rated very low and low, respectively, whereas the soil quality of lands belonging to Luvisols was qualified as low level (0.38–0.44).

Overall, the estimated SQI values of both soil types using various techniques demonstrated poor-quality soils (Table 9). About 50% of the essential nutrients that come from the soil, N, P, S, Ca, Mg, and B, were low from soil samples taken in the Nitisols, and 36% of the nutrients, N, P, K, S, and B, from lands in the Luvisols were found to be inadequate. In both soil types, the soil pH was strongly acidic, which is problematic for nutrient availability and microbial activity. A further restriction in both soil types was their low organic matter levels. A lack of organic matter input and the removal of basic nutrients caused the soil to become more acidic, which in turn accelerated nutrient deficiencies [6, 29]. According to the findings, problem-focused soil management interventions are urgently required [12, 13].

4. Conclusion

Different techniques, including principal component analysis (PCA), common soil parameters, and the soil fertility/nutrient/index approach, were employed to estimate the soil quality. All evaluation techniques for the lands belonging to Nitisols consistently demonstrated comparable soil quality status, whereas PCA and common soil parameter techniques generated similar results for the Luvisols lands. Based upon the consistency of the outcomes generated in both soil types, the use of PCA and the common soil parameters approach could be taken as useful tools to assess soil quality. Furthermore, it was noted that low soil quality necessitates the use of management interventions.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that there are no conflicts of interest.

Authors’ Contributions

The authors collected, analyzed, interpreted, and prepared the manuscript.

Acknowledgments

The authors would like to acknowledge Wolaita Sodo University for financing the research.

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Copyright © 2023 Abass Abdu et al. This 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.

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