Next Article in Journal
Towards Managing Biodiversity of European Marginal Agricultural Land for Biodiversity-Friendly Biomass Production
Next Article in Special Issue
Effects on Soil Chemical Properties and Carbon Stock Two Years after Compost Application in a Hedgerow Olive Grove
Previous Article in Journal
Management of Eleusine indica (L.) Gaertn Resistance to Glyphosate Herbicide in Indonesia
Previous Article in Special Issue
Digestate Not Only Affects Nutrient Availability but Also Soil Quality Indicators
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Agronomy
Volume 13
Issue 6
10.3390/agronomy13061650
agronomy-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Development of a Quality Index to Evaluate the Impact of Abiotic Stress in Saline Soils in the Geothermal Zone of Los Negritos, Michoacán, Mexico

by
Yanely Bahena-Osorio
1,
Marina Olivia Franco-Hernández
2,
José J. Pueyo
3,* and
María Soledad Vásquez-Murrieta
1,*
1
Escuela Nacional de Ciencias Biológicas, Instituto Politécnico Nacional, Prolongación Carpio y Plan de Ayala s/n, Col. Santo Tomás, Del. Miguel Hidalgo, Ciudad de México C.P. 11340, Mexico
2
Unidad Profesional Interdisciplinaria de Biotecnología, Instituto Politécnico Nacional, Av. Acueducto, Barrio la Laguna Ticomán, Ciudad de México C.P. 07340, Mexico
3
Institute of Agricultural Sciences, ICA-CSIC, Serrano 115-bis, E-28006 Madrid, Spain
*
Authors to whom correspondence should be addressed.
Agronomy 2023, 13(6), 1650; https://doi.org/10.3390/agronomy13061650
Submission received: 25 May 2023 / Revised: 15 June 2023 / Accepted: 15 June 2023 / Published: 20 June 2023
(This article belongs to the Special Issue Soil Conservation Methods for Maintaining Farmlands' Fertility)
Browse Figures
Review Reports Versions Notes

Abstract

:
In recent years, salinity-induced soil quality impairment and the misuse of management practices have led to the reduced productivity of agroecosystems. This has prompted a search for simple and effective agricultural management strategies that improve the sustainability of agricultural production through soil quality assessments. In this context, the objective of this study was to establish an integrated soil quality index (SQI) by assessing the influence of different types of abiotic stress in two different seasons, using physical, chemical and biological indicators at three sites in the geothermal zone of “Los Negritos”, Michoacán, Mexico. Thirty-nine indicators related to soil fertility attributes and C, N, P, and S cycling—identified as the total dataset (TDS)—were evaluated. Principal component analysis (PCA) and the Spearman correlation matrix (r2 ≥ 0.6) were used to calculate the SQI using an integrated quality index (IQI) equation, with the indicators total nitrogen (TN), cation exchange capacity (CEC), lithium (Li), and zinc (Zn) identified as the minimum dataset (MDS). Significantly higher SQI values related to the better performance of soil functions were detected during the rainy season.

1. Introduction

The United Nations has estimated that the world population will reach 8.5 billion people in 2030, 9.7 billion in 2050, and 11.2 billion in 2100, posing a challenge to agricultural production in the face of the global threat of soil degradation from high salt concentrations [1,2,3]. Salinity is defined as the accumulation of water-soluble mineral salts in the soil, with either primary (natural processes) or secondary (human-induced) causes. It is measured based on the electrical conductivity of the soil saturation extract (ECe, dS/m) and—depending on the level—impacts agricultural production, environmental health, and consequently socioeconomic conditions [4,5]. The most recent data issued by the Food and Agriculture Organization of the United Nations (FAO) indicates that 118 countries contain salinity-affected soils, comprising an estimated 424 million hectares in the upper soil layer (0–30 cm) and 833 million hectares of subsoil (30–100 cm) [6]. Therefore, a transition to the proper management of land use is necessary to better understand the role and make decisions that promote sustainable agriculture [7,8].
Soil assessments that allow for monitoring quality for a specific purpose are usually carried out using physical, chemical, and biological indicators that demonstrate an ability to perform a particular function. It is recommended that such assessments should meet—as far as is practicable—universal criteria for different conditions and soil types; represent the precise function for the purpose they were developed; elucidate ecosystem processes; be easily measurable, reliable, integrative, and sensitive to soil alterations; discern between normal situations and stress situations, either by soil management or by climatic conditions on different scales and/or time periods; and be measurable in terms of time and cost [8,9,10,11,12,13].
The reliability of establishing a soil quality index (SQI) depends on using the appropriate analytical methods and integration based on the score of the information of the evaluated indicators; the main evaluation methods are mathematical and statistical in nature. The process seeks to obtain a minimum dataset (MDS) that adequately represents the total dataset (TDS) on quality and that contributes to reducing the cost of evaluation. The factorial analysis usually involves (1) the selection of a TDS of the soil properties in relation to a specific function focused on within the objective of the study, (2) the choice and interpretation of an MDS, and (3) the integration of the scores in an index [8,14,15].
Until now, there has been no consistent methodology for selecting a universal dataset to characterize soil quality across regions and scales, and it has been proposed that the establishment of an SQI be conducted according to specific purposes [14,16]. The main disadvantages are the unequivocal interpretation or the lack of reference values, which affect the subjectivity of the evaluated indicators, which is why it is important to clearly define the objectives of the study [12].
However, the Integrated Quality Index (IQI) is the most widely used index because it has proven to be a flexible, effective, and easy quantification tool for assessing the quality of a given soil or region. Also, it reduces measurement costs by reducing the number of indicators used, and it avoids collinearity [12,14,16]. Some studies have assessed soil quality using MDS to calculate the IQI. For example, Yuan et al. [8] assessed 12 soil properties and established the SQI using parameters such as soil organic carbon (SOC), microbial biomass carbon (MBC), total potassium (TK), oxidation-reduction potential (Eh), and Mn (II) in soils with aquaculture activities. Mamehpour et al. [14] determined 24 variables and, as a result, EC, OC, SAR, CEC, bioavailable Fe, and total Cd and Pb were selected as MDS to evaluate soils in semi-arid calcareous ecosystems. Liu et al. [16], based on 26 parameters, established an MDS with soil organic matter (SOM), total nitrogen (TN), pH, dehydrogenase, and arbuscular mycorrhiza for IQI in agricultural soils.
The objective of this study was to establish a quality index under different soil management practices, integrating the effects of different levels of salinity, as well as temporality, to obtain a minimum set of data that represents greater inference on the performance of the soil, and that serves as a quick tool for quantifying the quality of these soils. Here, we measured different parameters that we used to define an IQI for salinity-affected soils in the Geothermal Zone of Los Negritos, Michoacán, Mexico.

2. Materials and Methods

2.1. Description of Site and Soil Collection

The site and soil collection were described by Guevara-Luna et al. [17] as a geothermal zone at the boundary of the Trans-Mexican Volcanic Belt; hydrothermal activity has been reported in this area, and is associated with the presence of mud volcanoes with temperatures between 48 and 94 °C at the surface reported. Soil samples (Figure 1) were collected from nine points per plot by sampling the 15–25 cm top layer after removing the top 0–15 cm layer from two arable sites, S1 (20°03′24.432″ N 102°36′36.632″ W) and S2 (20°03′02.817″ N 102°37′37.013″ W), and one non-cultivable site, S3 (20°03′44.75” N 102°36′46.78″ W), in two seasons (March 2019 and September 2020), coincident with two seasonal variations of an annual cycle (the dry and rainy season, respectively). In the first season, S1 was ready for cultivation, i.e., medium deep furrows were made in the soil using agricultural machinery (tillage) to initiate an agricultural cycle; on the other hand, S2 had a few plant residues of sugar cane (Saccharum officinarum L.) and weed growth on the soil surface, i.e., no tillage had been carried out and no initial agricultural cycle was planned. S3 had no history of cultivation due to its high salinity. In the second season, S1 had a developing maize (Zea mays L.) crop, S2 had abundant plant residues due to maize (Zea mays L.) harvesting, and S3 remained uncultivated. The soil collected from each site was properly transported (labeled in sterile polyethylene bags) to the laboratory and stored until analyzed for its physical, chemical, and biological attributes related to soil fertility, carbon, nitrogen, and phosphorus cycles.

2.2. Soil Quality Indicators

Soil samples were dried at environment temperature and passed through a 2 mm sieve to reduce particle size and remove crop residues. The physical and chemical indicators of relative humidity, water holding capacity (WHC), hydrogen potential (pH), electrical conductivity (EC), cation exchange capacity (CEC), total organic carbon (TOC), total nitrogen (NT) ammonium (NH4+), nitrate (NO3−), nitrite (NO2−) soluble phosphorus (PO43−), carbonate (CO32−), bicarbonate (HCO3−), sulfate (SO42−), chloride (Cl−), and textural classification were determined as described by Guevara-Luna et al. [17]. Trace elements and major cation concentrations were quantified using acid digestion with HNO3/HCl and inductively coupled plasma optical emission spectroscopy analysis (ICP-OES PerkinElmer Avio 500). Calibration was performed with deionized water and appropriate standards at 1 mg L−1 [18]. The biological indicators identified were urease, alkaline phosphatase and acid phosphatase, β-D-glucosidase, and arylsulfatase enzymatic activities, and were identified using modifications of previously established techniques [19,20,21,22,23].

2.3. Development of the Soil Quality Index

To determine the SQI, the methodology proposed by Andrews et al. [24], Mamehpour et al. [14], and Li et al. [25] was followed, with the general approach of choosing the MDS from the TDS of plausible indicators to assess soil quality using multivariate statistical techniques [24,26].
A total of 39 indicators consisting of chemical, physical, and biological properties representing the fertility conditions and the cycle of nutrients C, N, P, and S were evaluated in two seasons at these sites. We performed a two-way variance analysis (ANOVA) of the 39 indicators, with the indicators that showed a significant difference (p ≤ 0.05) between the analyzed sites selected to be part of the TDS. To identify potential soil indicators for the MDS, a principal component analysis (PCA) was performed on the previously standardized TDS matrix. For each principal component (PC), variables with eigenvalue ≥ 1 that explained at least 5% of the TDS variation and up to 85% of the cumulative variation within each PC were considered [26].
Subsequently, for each selected PC, each variable was assigned a weight or factor-loading representing the contribution of that variable to the PC composition. Only highly weighted variables from each PC were considered as candidates for the MDS (those that represented absolute values within 10% of the highest factor loading, or ≥0.40). When more than one variable qualified under the same PC, multivariate correlation coefficients (Spearman (r2 > 0.6)) were used to determine whether variables could be considered redundant, and thus were candidates for removal from the MDS.
The indicators considered were those that were highly weighted and non-redundant; however, if the group of variables was correlated, the absolute values of the correlation coefficients of each were summed and it was assumed that the variable with the highest correlation sum best represented the group and formed the MDS. The choice of correlated variables could also be based on practicality of cost, sampling, interpretation, and importance to the study [26].
After defining the MDS, each variable datum was transformed using three types of nonlinear scoring function. “More is better” and “less is better” score curves were applied to indicators when a soil indicator was considered good for soil quality in increasing order (more is better), such as organic carbon, or in decreasing order (less is better), such as salt content, as well as “optimal” scores considering thresholds and reference values of soil properties [27,28,29]. The first nonlinear scores of the variables were performed using a sigmoidal type of function; Equation (1):
  SNL =                     1                 1 + X X m b
where SNL is the nonlinear score of the soil indicator, a is the maximum score achieved by the function—which is equal to 1 in this study—X is the value of the selected soil indicator, Xm is the average value of each soil indicator, and b is the slope of the equation and is set as −2.5 for a “more is better” and 2.5 for a “less is better” curve.
The third score is the threshold value—those soil indicators where the score is equal to 1 when the value is at an optimal level or is equal to 0 when it is at an unacceptable level; Equation (2):
1       [ 1 + B − L / x − L 2 S B + x − 2 L ]   ,
where B is the reference value of the soil indicator where the score is equal to 0.5, L is the lower threshold, S is the slope of the tangent to the curve at the base line, and x is the value of the soil indicator. Threshold and base line values were based on the literature, reference data, expert opinion, or previously observed measured values in ideal soil conditions for the specific purpose concerned.
After calculating the scores, the SQI described by Doran and Parkin [30] was established with the following equation; Equation (3):
SQI w = ∑ i = 1 n W i × S i  
where SQIw is the soil quality index (weighted additive), Si is the MDS indicator score, n is the number of soil indicators in the MDS, and Wi is the weighting value of the soil indicators, determined by the variation of each respective PC (%) standardized to the unit.

2.4. Soil Quality Grades

Once the quality index had been obtained, to establish different levels the interval of the index obtained (maximum minus the minimum) was divided by the desired number of classifications. The result was used as the base for each level, adding that value to the lowest value of the index to obtain the upper limit of the first interval, and so on, until the upper range was reached [31,32].

2.5. Statistical Analysis

The evaluation of all the physical, chemical, and biological indicators was carried out by replicates (nine repetitions) at separate times. Data distribution was based on the Shapiro–Wilk test, with a significance level of p ≤ 0.05. Significant differences between site indicators in both seasons were determined with a significance level of p ≤ 0.05 using ANOVA. To demonstrate the correlations between the variables, a Spearman correlation matrix was developed (r2 > 0.6). Statistical analyses (ANOVA, PCA, Spearman correlation) were performed with MINITAB 17 and R 4.21 (www.r-project.org, accessed on 10 November 2022).

3. Results and Discussion

3.1. Soil Quality Indicators

Based on the evaluation of 39 soil parameters and a two-way ANOVA, a significant difference was observed in most of the estimated parameters between soils from the same season and between seasons; for example, the nutrient content TOC, TN, NH4+, NO3−, and PO43− (Table 1). The soils were classified as sandy clay loam and sandy loam; however, although they showed the same textural class, their sand, silt, and clay contents differed significantly. It has been shown that the balance of particles forming the structure of a soil influences water movement, aeration, and the ease of root growth [16]. The area is characterized by soils classified as having light to extreme salinity, with EC values ranging from 1.18 to 34.38 dS m−1 [33].
The pH data ranged between 6.63 and 9.31, indicating that these soils were neutral and alkaline, with some within the optimal pH range—between 5.8 and 7.5—for agricultural soils [34]. The salinity levels and pH were associated with the presence and availability of salts at the sites, determining concentrations in the order of SO42− > Cl− > HCO3− > CO32− in both seasons. The presence of 16 elements was also observed in the soils—among them, high levels of Ca, Fe, Mg, Mn, and Li, characteristic of the parent rocks of a brackish area, which is suggestive of the area’s geological origin [5,14].
Enzyme activities related to nutrient cycling showed a significant trend in S2 and S1 for the dry season and S2 and S3 for the rainy season in the five enzyme activities. The evaluation of biological indicators in soils is usually highly sensitive because they demonstrate the availability of nutrients and reflect the activity of microbial populations [29,35].

3.2. Development of the Soil Quality Index

Soil variables that showed significant differences between sites or seasons were included in the PCA and considered as members of the TDS. The first four principal components (PC) had eigenvalues >1.0 and variance >5% and together explained 80.39% of the variance of the original data (Figure 2). More than one highly weighted variable was considered for each PC and considered as a candidate for the MDS, distributed as follows: PC1 had four variables (TN, CO32−, glucosidase, and V) explaining 36.96% of the variance; PC2 presented 19.08% of the variance constituted by three variables (CEC, TOC, and urease); and PC3 and PC4 were represented by one variable each—Li and Zn, explaining 12.72% and 11.61% of the variance, respectively.
The PCA technique has been widely used by soil-quality researchers for its ability to introduce less subjectivity in data selection, to help reduce bias and data redundancy, and to select the most representative indicators from a dataset [24,25,36,37]. However, this method requires an initial large dataset, more time for sampling and laboratory analyses, and more complex data interpretation [29]. On the other hand, the Spearman matrix (r2 > 0.6) identified that all variables presented at least one significant correlation (Figure 3); therefore, we proceeded to the sum of the absolute values of the correlation coefficients of each variable. The NT, CEC, Li, and Zn variables (Table 2) were selected as best representing each component, and were designated as members of the MDS to constitute the SQI in the soils of the geothermal zone of “Los Negritos”.
Nitrogen—a critical macro-element in soil due to the plant biochemical processes in which it is involved, from root growth to maturation, including photosynthesis and nitrogen fixation [38]—was the most weighted indicator in this study. Nitrogen facilitates ecosystem balance and productivity due to N transformations driven by different microbial groups with different metabolic versatility and environmental tolerance [39]. Together with soil organic matter (SOM), nitrogen is considered a key component contributing to soil fertility [16]. The next indicator selected was the CEC, which represents the sum of exchangeable cations in the soil (K, Ca, Mg, and Na), and several available micronutrients [40]. CEC has been considered important for evaluating soil productivity in semi-arid ecosystems and for problems of high calcium content [14] because it shows the reserve of nutrients; a high level is associated with important levels of organic C in soils, essential for biological activity [7]. Lithium, another indicator chosen, is an element related to geothermal zones, brines, and magmatic and sedimentary rocks [41]. Natural Li concentrations depend on characteristics such as lithology, temperature, salinity, and water–rock interaction [42]; furthermore, Li has economic value in numerous industries, such as ceramics, glass, polymer production, and energy devices (batteries), which has increased its presence in agricultural soils [43]. It is an element whose role in the development of plants and animals—including humans—is unclear. Its toxicity has been reported at different concentrations [44]. In plants, Li influences physiology and biochemistry, reduces growth, and causes oxidative damage to the photosynthetic apparatus, metabolite composition, and nucleic acid and protein synthesis [45]. Zinc was the last designated indicator; it is an essential plant micronutrient involved in the synthesis of proteins, nucleic acids, and carbohydrates, as well as in the activation of enzymes and cell differentiation. Zinc accumulation in the soil depends on particles such as iron oxides and calcites that cause low availability for plant uptake [25]. When contamination is suggested, it is related to irrigated crops or soils adjacent to industrial areas [45].
The variables that comprised the MDS were transformed using (non-linear) scoring equations in terms of the property and its function in the soil [27,28]. Using Equations (1) and (2), a “more is better” curve was applied to the NT and CEC indicator, and “optimal” was given for Li and Zn. The transformed indicator scores and the values of the contribution of the individual indicators to the variance of their respective PC were then integrated into additive and weighted SQI using Equation (3), as follows:
SQIw = ∑ (NT score × Si score) + (CEC score × Si score) + (Li score × Si score) + (Zn score × Si score).
The sum of these values gave the soil quality indices for the three sites in both seasons. In the dry season, S3 had the highest soil quality—and this result was significant (p ≤ 0.05)—whereas S1 and S2 had the highest soil quality in the rainy season (Figure 4). Furthermore, globally (during the annual cycle), no significant differences were observed between the three sites (Figure 5). The opposite occurred between seasons, with the statistically highest-quality soils occurring in the rainy season (p ≤ 0.05) (Figure 5). A better knowledge of soils is crucial to maintaining or increasing soil sustainability, identifying the most relevant soil attributes, and monitoring the changes generated by an event in each area [16,30]. Within soil quality observation and assessment studies, there are several methodologies for achieving this, ranging from multivariate geostatistical methods, factorial statistics, assessments based on crop growth and/or yields, visual assessment methods, expert opinion, and scoring and weighting methods for a set of indicators. The SQI formulated from obtaining MDS and non-linear scores—evidenced as a low-cost quantitative method due to the reduced number of indicators evaluated—provides the necessary information for decision making through the variability and sensitivity of indicators that represent the effects of changes in soil management and seasonality [14,25,27,29,46,47].

3.3. Soil Quality Grades

Five different levels of soil quality were established for this study (Table 3), for which the sites were assessed in the two seasons (Table 4) and globally (Table 5 and Table 6). The global assessment of the sites was carried out considering the data from both stations without differentiating between them; similarly, the stations were assessed without differentiating between the sites. The soils were classified as “high”. Between seasons, the dry season were classified as “low” and for the rainy season as “high”.
The results showed a better performance of soil functions in the rainy season and in the sites with vegetation cover, because of the higher salinity concentration in the dry season and lower salinity concentration in the rainy season, closely related to the seasonal patterns of temperature, precipitation, pH, organic matter, the difference in agricultural practices, and the phenological cycle of plants.
In this study, water was an important factor in the soil, as it is a means of transport for the substrates in the hydrolysis processes, and in the control of microbial activity that determines mineralization rates, nutrient cycling, the maintenance of plant diversity, soil fertility, and ecosystem sustainability [4,13,48,49]. Similarly, crop residues on topsoil have been noted to promote microbial growth, decrease temperature, prevent erosion, and be a great source of material for mineralization [50]. On the other hand, low water content, high temperature, and little or no vegetation lead to high soil evapotranspiration, which causes the transport of large amounts of salt to the soil surface, affecting plant and bacterial communities and nutrient distribution, as well as altering the physical and chemical properties of the site and leading to a deterioration in soil functions [51].
The classification of the soil quality of the sites in this study was similar to those reported by several authors who generated their own classification in the search to quantify the soil quality of a particular area, such as the classification system of Cantú et al. [52], for evaluating soils with different agricultural uses and management; Karaca et al. [53], for grassland soils in semi-arid ecosystems; Mamehpour et al. [14], for evaluating urban cultivated soils in a semi-arid calcareous ecosystem; Santos-Francés et al. [47], for agricultural soils in a semi-arid ecosystem; and Sanchez-Navarro et al. [31], for soils in semi-arid Mediterranean regions.

4. Conclusions

Soil salinity is a latent threat to ecosystems and agricultural production by reducing plant growth and microbial functioning, so it is crucial to assess its interaction with other factors to highlight the critical needs of a soil. Currently, there is no universal SQI that can be used in multiple natural and anthropogenic ecosystems, so targeted indexing strategies have been developed and implemented for specific environmental conditions around the world.
The applied methodology reduced the number of physical, chemical, and biological indicators analyzed from 39 to only 4, which allowed the establishment of a minimum set of data focused on indicating the quality of the soil in the study area. The soil quality classification proposed in this study during the annual monitoring evidenced the quality class of the sites: high, including two classifications for seasons: low and high. The analysis between seasons allowed us to propose a strategy to improve saline soils: the addition of organic materials such as plant residues to improve the nutrient content and the activity of microbial tolerance to saline stress. Likewise, the integrated quality index represented an effective tool to evaluate the impact of saline soil management practices and seasonality in an adequate and quantitative manner on soil functions with the use of selected MDS (NT, CIC, Li, and Zn). The selected indicators indicated a higher sensitivity in the chemical properties. However, the assessment of other biological properties that better reflect microbial activity and that can be used to relate abiotic soil properties in terms of biochemical transformations, as well as vegetation potential and performance under the same ecological conditions in this region, could be considered for practical, economical, and reliable results.

Author Contributions

Conceptualization, Y.B.-O., M.S.V.-M. and J.J.P.; Data curation, Y.B.-O. and M.S.V.-M.; Formal analysis, Y.B.-O.; Funding acquisition, M.S.V.-M. and J.J.P.; Investigation, Y.B.-O.; Methodology, Y.B.-O., M.S.V.-M. and M.O.F.-H.; Project administration, M.S.V.-M.; Resources, Y.B.-O.; Validation, Y.B.-O., M.S.V.-M. and J.J.P.; Visualization, Y.B.-O.; Writing—original draft, Y.B.-O., M.S.V.-M. and J.J.P.; Writing—review and editing, M.S.V.-M. and J.J.P. All authors have read and agreed to the published version of the manuscript.

Funding

This study was financially supported by the projects of Instituto Politécnico Nacional (IPN) (Nos. SIP20200229 and SIP 20210819) Prolongación Carpio y Plan de Ayala s/n, Col. Santo Tomás, Del. Miguel Hidalgo, C.P. 11340. Ciudad de México, Mexico, by grants from the Agencia Estatal de Investigación, AEI, Spain, (No. PID2021-125371OB-I00), and the Agencia Estatal Consejo Superior de Investigaciones Científicas, CSIC, Spain (No. COOPA20458).

Data Availability Statement

Data are available from corresponding authors.

Acknowledgments

Yanely Bahena-Osorio received grant-aided support from Consejo Nacional de Ciencia y Tecnología (CONACyT) and Beca de Estímulo Institucional de Formación de Investigadores-IPN (BEIFI). Marina Olivia Franco-Hernández, and María Soledad Vásquez-Murrieta received grant-aided support from Comisión de Operación y Fomento de Actividades Académicas-IPN (COFAA), Estímulos al Desempeño de los Investigadores-IPN (EDI), and Sistema Nacional de Investigadores-CONACyT (SNI). We thank Dioselina Álvarez-Bernal, Salvador Ochoa-Estrada, and Leonardo Yoguez-Alcantar (Centro Interdisciplinario de Investigación para el Desarrollo Integral Regional, Unidad Michoacán) for access and guidance in soil sampling, and Ciro Eliseo Márquez-Herrera (Facultad de Química UNAM, Mexico) for support in ICP-OES analysis.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Daliakopoulos, I.N.; Tsanis, I.K.; Koutroulis, A.; Kourgialas, N.N.; Varouchakis, A.E.; Karatzas, G.P.; Ritsema, C.J. The threat of soil salinity: A European scale review. Sci. Total Environ. 2016, 573, 727–739. [Google Scholar] [CrossRef]
  2. Ivushkin, K.; Bartholomeus, H.; Bregt, A.K.; Pulatov, A.; Kempen, B.; De Sousa, L. Global mapping of soil salinity change. Remote Sens. Environ. 2019, 231, 111260. [Google Scholar] [CrossRef]
  3. Corwin, D.L. Climate change impacts on soil salinity in agricultural areas. Eur. J. Soil Sci. 2020, 72, 842–862. [Google Scholar] [CrossRef]
  4. Yan, N.; Marschner, P.; Cao, W.; Zuo, C.; Qin, W. Influence of salinity and water content on soil microorganisms. Int. Soil Water Conserv. Res. 2015, 3, 316–323. [Google Scholar] [CrossRef] [Green Version]
  5. Negacz, K.; Malek, Ž; de Vos, A.; Vellinga, P. Saline soils worldwide: Identifying the most promising areas for saline agriculture. J. Arid Environ. 2022, 203, 104775. [Google Scholar] [CrossRef]
  6. Organización de las Naciones Unidas para la Alimentación y la Agricultura. Global Map of Salt Affected Soils Version 1.0. Available online: https://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/global-map-of-salt-affected-soils (accessed on 3 December 2021).
  7. Nguemezi, C.; Tematio, P.; Yemefack, M.; Tsozue, D.; Silatsa, T.B.F. Soil quality and soil fertility status in major soil groups at the Tombel area, South-West Cameroon. Heliyon 2020, 6, e03432. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  8. Yuan, P.; Wang, J.; Li, C.; Xiao, Q.; Liu, Q.; Sun, Z.; Wang, J.; Cao, C. Soil quality indicators of integrated rice-crayfish farming in the Jianghan Plain, China using a minimum data set. Soil Tillage Res. 2020, 204, 104732. [Google Scholar] [CrossRef]
  9. Doran, J.W.; Parkin, T.B. Quantitative indicators of soil quality: A minimum data set. In Methods for Assessing Soil Quality; Doran, J.W., Jones, A.J., Eds.; Soil Science Society of America: Madison, WI, USA, 1996; pp. 25–37. [Google Scholar] [CrossRef]
  10. Burns, R.G.; Nannipieri, P.; Benedetti, A.; Hopkins, D.W. Defining soil quality. In Microbiological Methods for Assessing Soil Quality; CABI Publishing: Wallingford, UK, 2006; pp. 15–22. [Google Scholar]
  11. García, Y.; Ramírez, W.; Sánchez, S. Soil quality indicators: A new way to evaluate this resource. Inf. Express. Pastos Forrajes 2012, 35, 125–138. [Google Scholar]
  12. Bünemann, E.K.; Bongiorno, G.; Bai, Z.; Creamer, R.E.; De Deyn, G.; de Goede, R.; Fleskensd, L.; Geissend, V.; Kuyperb, T.W.; Mädera, P.; et al. Soil quality–A critical review. Soil Biol. Biochem. 2018, 120, 105–125. [Google Scholar] [CrossRef]
  13. Schloter, M.; Nannipieri, P.; Sørensen, S.J.; Van-Elsas, J.D. Microbial indicators for soil quality. Biol. Fertil. Soils 2018, 54, 1–10. [Google Scholar] [CrossRef] [Green Version]
  14. Mamehpour, N.; Rezapour, S.; Ghaemian, N. Quantitative assessment of soil quality indices for urban croplands in a calcareous semi-arid ecosystem. Geoderma 2021, 382, 114781. [Google Scholar] [CrossRef]
  15. Tang, D.; Yang, J.; Cheng, P. Comprehensive Evaluation of Soil Substrate Improvement Based on the Minimum Data Set Method. Sustainability 2022, 14, 3939. [Google Scholar] [CrossRef]
  16. Liu, Z.; Zhou, W.; Shen, J.; Li, S.; He, P.; Liang, G. Soil quality assessment of Albic soils with different productivities for eastern China. Soil Tillage Res. 2014, 140, 74–81. [Google Scholar] [CrossRef]
  17. Guevara-Luna, J.; Hernández-Guzmán, M.; Montoya-Ciriaco, N.; Dendooven, L.; Franco-Hernández, M.O.; Estrada-de los Santos, P.; Vásquez-Murrieta, M.S. The bacterial and archaeal community in saline soils from “Los Negritos” (Mexico) a geothermal area. Pedosphere 2021, 33, 312–320. [Google Scholar] [CrossRef]
  18. Franco-Hernández, M.O.; Vásquez-Murrieta, M.S.; Patiño-Siciliano, A.; Dendooven, L. Heavy metals concentration in plants growing on mine tailings in Central Mexico. Bioresour. Technol. 2010, 101, 3864–3869. [Google Scholar] [CrossRef]
  19. Tabatabai, M.A. Soil enzymes. In Methods of Soil Analysis. Part 2. Microbiological and Biochemical Properties; Weaver, R.W., Angle, J.R., Bottomley, P.S., Eds.; Soil Science Society of America: Madison, WI, USA, 1994; pp. 775–833. [Google Scholar]
  20. Klose, S.; Tabatabai, M.A. Urease activity of microbial biomass in soils. Biol Fertil Soils. 1999, 31, 205–211. [Google Scholar] [CrossRef]
  21. Tabatabai, M.A.; Bremner, J.M. Use of p-nitrophenyl phosphate for assay of soil phosphatase activity. Soil Biol. Biochem. 1969, 1, 301–307. [Google Scholar] [CrossRef]
  22. Eivazi, F.; Tabatabai, M.A. Glucosidases and galactosidases in soils. Soil Biol. Biochem. 1988, 20, 601–606. [Google Scholar] [CrossRef]
  23. Tabatabai, M.A.; Bremner, J.M. Arylsulfatase activity of soils 1. Soil Sci. Soc. Am. J. 1970, 34, 427–429. [Google Scholar] [CrossRef]
  24. Andrews, S.S.; Karlen, D.L.; Mitchell, J.P. A comparison of soil quality indexing methods for vegetable production systems in Northern California. Agric. Ecosyst. Environ. 2002, 90, 25–45. [Google Scholar] [CrossRef]
  25. Li, K.; Wang, C.; Zhang, H.; Zhang, J.; Jiang, R.; Feng, G.; Yu, B. Evaluating the effects of agricultural inputs on the soil quality of smallholdings using improved indices. Catena 2022, 209, 105838. [Google Scholar] [CrossRef]
  26. Andrews, S.S.; Carroll, C.R. Designing a soil quality assessment tool for sustainable agroecosystem management. Ecol. Appl. 2001, 11, 1573–1585. [Google Scholar] [CrossRef]
  27. Lima, A.C.R.; Brussaard, L.; Totola, M.R.; Hoogmoed, W.B.; De Goede, R.G.M. A functional evaluation of three indicator sets for assessing soil quality. Appl. Soil Ecol. 2013, 64, 194–200. [Google Scholar] [CrossRef]
  28. Cherubin, M.R.; Karlen, D.L.; Cerri, C.E.; Franco, A.L.; Tormena, C.A.; Davies, C.A.; Cerri, C.C. Soil quality indexing strategies for evaluating sugarcane expansion in Brazil. PLoS ONE 2016, 11, e0150860. [Google Scholar] [CrossRef] [PubMed]
  29. Yu, P.; Liu, S.; Zhang, L.; Li, Q.; Zhou, D. Selecting the minimum data set and quantitative soil quality indexing of alkaline soils under different land uses in northeastern China. Sci. Total Environ. 2018, 616, 564–571. [Google Scholar] [CrossRef] [PubMed]
  30. Doran, J.W.; Parkin, T.B. Defining and assessing soil quality. In Defining Soil Quality for a Sustainable Environment; Doran, J.W., Coleman, D.C., Bezdicek, D.F., Stewart, B.A., Eds.; SSSA: Madison, WI, USA, 1994; pp. 3–21. [Google Scholar] [CrossRef] [Green Version]
  31. Sánchez-Navarro, A.; Gil-Vázquez, J.M.; Delgado-Iniesta, M.J.; Marín-Sanleandro, P.; Blanco-Bernardeau, A.; Ortiz-Silla, R. Establishing an index and identification of limiting parameters for characterizing soil quality in Mediterranean ecosystems. Catena 2015, 131, 35–45. [Google Scholar] [CrossRef]
  32. Xian, X.; Pang, M.; Zhang, J.; Zhu, M.; Kong, F.; Xi, M. Assessing the effect of potential water and salt intrusion on coastal wetland soil quality: Simulation study. J. Soils Sediments 2015, 19, 2251–2264. [Google Scholar] [CrossRef]
  33. Omuto, C.T.; Vargas, R.; Viatkin, K.; Yigini, Y. Mapeo de Suelos Afectados por Salinidad: Lección 4- Modelado Espacial de Suelos Afectados por Salinidad; FAO: Roma, Italy, 2021; pp. 2–15. [Google Scholar]
  34. Yáñez-Díaz, M.I.; Cantú-Silva, I.; González-Rodríguez, H. Efecto del cambio de uso de suelo en las propiedades químicas de un vertisol. Terra Latinoam. 2018, 36, 369–379. [Google Scholar] [CrossRef]
  35. Nannipieri, P.; Ascher-Jenull, J.; Ceccherini, M.T.; Pietramellara, G.; Renella, G.; Schloter, M. Beyond microbial diversity for predicting soil functions: A mini review. Pedosphere 2020, 30, 5–17. [Google Scholar] [CrossRef]
  36. Bi, C.; Chen, Z.; Wang, J.; Zhou, D. Quantitative assessment of soil health under different planting patterns and soil types. Pedosphere 2013, 23, 194–204. [Google Scholar] [CrossRef]
  37. Marion, L.F.; Schneider, R.; Cherubin, M.R.; Colares, G.S.; Wiesel, P.G.; da Costa, A.B.; Lobo, E.A. Development of a soil quality index to evaluate agricultural cropping systems in southern Brazil. Soil Tillage Res. 2022, 218, 105293. [Google Scholar] [CrossRef]
  38. Wawire, A.W.; Csorba, Á.; Kovács, E.; Mairura, F.S.; Tóth, J.A.; Michéli, E. Comparing farmers’ soil fertility knowledge systems and scientific assessment in Upper Eastern Kenya. Geoderma 2021, 396, 115090. [Google Scholar] [CrossRef]
  39. Zhang, X.; Liu, W.; Schloter, M.; Zhang, G.; Chen, Q.; Huang, J.; Han, X. Response of the abundance of key soil microbial nitrogen-cycling genes to multi-factorial global changes. PLoS ONE 2013, 8, e76500. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  40. Kopittke, P.M.; Dalal, R.C.; Menzies, N.W. Changes in exchangeable cations and micronutrients in soils and grains of long-term, low input cropping systems of subtropical Australia. Geoderma 2017, 285, 293–300. [Google Scholar] [CrossRef]
  41. Sanjuan, B.; Gourcerol, B.; Millot, R.; Rettenmaier, D.; Jeandel, E.; Rombaut, A. Lithium-rich geothermal brines in Europe: An up-date about geochemical characteristics and implications for potential Li resources. Geothermics 2022, 101, 102385. [Google Scholar] [CrossRef]
  42. Armienta, M.A.; Rodríguez, R.; Ceniceros, N.; Cruz, O.; Aguayo, A.; Morales, P.; Cienfuegos, E. Groundwater quality and geothermal energy. The case of Cerro Prieto geothermal field, México. Renew. Energ. 2014, 63, 236–254. [Google Scholar] [CrossRef]
  43. Tanveer, M.; Hasanuzzaman, M.; Wang, L. Lithium in environment and potential targets to reduce lithium toxicity in plants. J. Plant Growth Regul. 2019, 38, 1574–1586. [Google Scholar] [CrossRef]
  44. Shahzad, B.; Tanveer, M.; Hassan, W.; Shah, A.N.; Anjum, S.A.; Cheema, S.A.; Ali, I. Lithium toxicity in plants: Reasons, mechanisms and remediation possibilities—A review. Plant Physiol. Biochem. 2016, 107, 104–115. [Google Scholar] [CrossRef]
  45. Nehrani, S.H.; Askari, M.S.; Saadat, S.; Delavar, M.A.; Taheri, M.; Holden, N.M. Quantification of soil quality under semi-arid agriculture in the northwest of Iran. Ecol. Indic. 2020, 108, 105770. [Google Scholar] [CrossRef]
  46. Qi, Y.; Darilek, J.L.; Huang, B.; Zhao, Y.; Sun, W.; Gu, Z. Evaluating soil quality indices in an agricultural region of Jiangsu Province, China. Geoderma 2009, 149, 325–334. [Google Scholar] [CrossRef]
  47. Santos-Francés, F.; Martínez-Graña, A.; Ávila-Zarza, C.; Criado, M.; Sánchez, Y. Comparison of methods for evaluating soil quality of semiarid ecosystem and evaluation of the effects of physico-chemical properties and factor soil erodibility (Northern Plateau, Spain). Geoderma 2019, 354, 113872. [Google Scholar] [CrossRef]
  48. Goswami, M.; Suresh, D.E.K.A. Plant growth-promoting rhizobacteria—Alleviators of abiotic stresses in soil: A review. Pedosphere 2020, 30, 40–61. [Google Scholar] [CrossRef]
  49. Jia, Z.; Myrold, D.D.; Conrad, R. Soil biodiversity in a rapidly changing world. Pedosphere 2020, 30, 1–4. [Google Scholar] [CrossRef]
  50. Parihar, C.M.; Singh, A.K.; Jat, S.L.; Dey, A.; Nayak, H.S.; Mandal, B.N.; Yadav, O.P. Soil quality and carbon sequestration under conservation agriculture with balanced nutrition in intensive cereal-based system. Soil Tillage Res. 2020, 202, 104653. [Google Scholar] [CrossRef]
  51. He, B.; Cai, Y.; Ran, W.; Jiang, H. Spatial and seasonal variations of soil salinity following vegetation restoration in coastal saline land in eastern China. Catena 2014, 118, 147–153. [Google Scholar] [CrossRef]
  52. Cantú, M.P.; Becker, A.R.; Bedano, J.C.; Schiviano, H.F.; Parra, B.J. Evaluation of the impact of land use and management change by means of soil quality indicators, Cordoba, Argentina. Cadernos Lab. Xeoloxico Laxe Coruna 2009, 34, 203–214. [Google Scholar]
  53. Karaca, S.; Dengiz, O.; Turan, İ.D.; Özkan, B.; Dedeoğlu, M.; Gülser, F.; Ay, A. An assessment of pasture soils quality based on multi-indicator weighting approaches in semi-arid ecosystem. Ecol. Indic. 2021, 121, 107001. [Google Scholar] [CrossRef]
Agronomy 13 01650 g001 550
Figure 1. Sites from which saline soils were sampled in the geothermal zone of “Los Negritos”, Villamar, Michoacán, Mexico.
Figure 1. Sites from which saline soils were sampled in the geothermal zone of “Los Negritos”, Villamar, Michoacán, Mexico.
Agronomy 13 01650 g001
Agronomy 13 01650 g002 550
Figure 2. Percentage variance of TDS.
Figure 2. Percentage variance of TDS.
Agronomy 13 01650 g002
Agronomy 13 01650 g003 550
Figure 3. Spearman’s correlation matrix. Indicators with positive correlation in purple, indicators with negative correlation in blue.
Figure 3. Spearman’s correlation matrix. Indicators with positive correlation in purple, indicators with negative correlation in blue.
Agronomy 13 01650 g003
Agronomy 13 01650 g004 550
Figure 4. Box plots showing median soil quality index (SQI) levels between seasons: (a) dry season SQI and (b) rainy season SQI. Black dots indicate outliers.
Figure 4. Box plots showing median soil quality index (SQI) levels between seasons: (a) dry season SQI and (b) rainy season SQI. Black dots indicate outliers.
Agronomy 13 01650 g004
Agronomy 13 01650 g005 550
Figure 5. Box plots showing median global soil quality index (SQI) levels between season and sites: (a) global SQI season and (b) global SQI site. Black dots indicate outliers.
Figure 5. Box plots showing median global soil quality index (SQI) levels between season and sites: (a) global SQI season and (b) global SQI site. Black dots indicate outliers.
Agronomy 13 01650 g005
Table
Table 1. Values of physical, chemical, and biological indicators of analyzed soils from “Los Negritos” geothermal zone in Villamar, Michoacán, Mexico.
Table 1. Values of physical, chemical, and biological indicators of analyzed soils from “Los Negritos” geothermal zone in Villamar, Michoacán, Mexico.
Site S1S2S3S1S2S3
IndicatorsUnitDry SeasonRainy Season
Moisture
content		%7.30 ± 1.11 Bb11.21 ± 1.80 aB10.11 ± 2.45 aB22.72 ± 1.29 bA24.65 ± 1.43 aA16.05 ± 0.97 cA
WHCmg kg−1925.7 ± 116.2 bA1204.9 ± 63.7 aA991.1 ± 84.4 bA919.17 ± 15.48 bA1010.1 ± 56.8 aB864.50 ± 21.07 cB
pH 6.63 ± 0.29 cB6.96 ± 0.12 bB9.12 ± 0.14 aB7.74 ± 0.09 bB7.61 ± 0.13 cA9.31 ± 0.02 aA
ECdS m−1 at 25 °C2.23 ± 0.41 cA12.41 ± 1.86 bA34.38 ± 2.77 aA1.18 ± 0.53 cB10.2 ± 2.26 bB26.97 ± 4.32 aB
CECcmolc kg−17.01 ± 4.30 aB5.06 ± 2.34 aB1.31 ± 0.65 bB43.33 ± 2.72 bA54.58 ± 6.09 aA31.81 ± 3.25 cA
TOCmg kg−1149.33 ± 4.45 aB126.93 ± 4.56 bB28.80 ± 5.37 cB527.84 ± 15.29 aA586.4 ± 184.9 aA496.48 ± 17.48 aA
TN1.63 ± 0.17 bA2.07 ± 0.29 aA0.049 ± 0.38 cB1.74 ± 0.18 aA1.76 ± 0.12 aB0.213 ± 0.06 bA
NH4+32.21 ± 13.90 bA85.01 ± 38.7 aA3.33 ± 1.04 cB15.99 ± 0.76 bB18.97 ± 1.98 aB5.72 ± 1.15 cA
NO2−95.98 ± 6.91 aA91.31 ± 1.98 aA92.59 ± 1.16aA66.91 ± 5.11 bB78.46 ± 2.44 aB68.02 ± 3.93 bB
NO3−1908 ± 947 aA55.23 ± 7.94 bB98.42 ± 25.62bB94.8 ± 33.0 bB287.6 ± 46.6 aA349.5 ± 132.3 aA
PO43−103.8 ± 42.5 bA218.2 ± 40.3 aA76.28 ± 14.56bA34.40 ± 5.58 bB33.18 ± 2.27 bB61.95 ± 6.32 aB
CO32−NDND186.70 ± 17.71BNDND319.4 ± 11.7 A
HCO3−111.85 ± 13.21 bB130.49 ± 8.04 aB32.20 ± 17.79 cB230.5 ± 40.7 bA325.4 ± 30.5 aA122.0 ± 68.2 cA
SO42−723.9 ± 61.0 cA1372.7 ± 236.7 bA1917.4 ± 116.4 aA303.3 ± 142.4 cB429.81 ± 15.61 bB1832 ± 74.6 aA
Cl−110.93 ± 22.90 cA205.95 ± 8.02 bA450.19 ± 3.65 aA20.25 ± 0.0 cB225.02 ± 26.2 bA475.9 ± 88.9 aA
Sand516.31 ± 25.0 bB536.31 ± 10.0 aB542.97 ± 10.0 aB593.48 ± 10.0 aA596.8 ± 596.8 aA583.48 ± 25.0 aA
Clay326.95 ± 5.0 aA43.62 ± 5.00 cB260.3 ± 30.0 bA106.59 ± 10.0 bB63.19 ± 10.0 cA243.19 ± 10.0 aA
Silt156.74 ± 27.84 cB420.07 ± 8.66 aA196.74 ± 20.0 bA299.93 ± 17.32 bA340.0 ± 39.7 aB173.33 ± 21.79 cA
Asmg kg−1118.39 ± 67.0 aA61.9 ± 44.1 abA20.7 ± 32.4 bA157.1 ± 45.3 aA19.2 ± 57.5 bA17.7 ± 35.5 bA
Ca10198 ± 6438 bA14381 ± 3989 bA47741 ± 24224 aA18638 ± 15578 bA11605 ± 1461 bA49495 ± 17203 aA
Cd7.96 ± 2.70 abA7.52 ± 1.65 bA13.79 ± 8.36 aA10.12 ± 7.29 aA6.21 ± 0.62 abB2.84 ± 3.37 bB
Co8.64 ± 4.35 abA11.47 ± 1.02 aA7.49 ± 2.46 bA10.79 ± 1.30 aA11.60 ± 1.31 aA3.97 ± 4.93 bA
Cr51.32 ± 28.41 aA70.52 ± 10.40 aA55.03 ± 5.76 aA70.82 ± 6.81 aA77.93 ± 11.67 aA27.6 ± 33.3 bB
Cu31.94 ± 17.34 bA45.26 ± 3.50 aA37.45 ± 8.06 abA42.91 ± 1.48 aA43.71 ± 9.32 aA17.27 ± 20.70 bB
Fe14213 ± 8124 bA21353 ± 1951 aA9431 ± 4811 bA18173 ± 3697 aA22844 ± 3210 aA11027 ± 5014 bA
Li45.21 ± 15.85 bB57.11 ± 3.85 abA73.09 ± 26.18 aA61.50 ± 7.57 aA60.53 ± 6.18 aA70.70 ± 40.1 aA
Mg6879 ± 3910 bA10290 ± 980 bA19113 ± 8582 aA11497 ± 5583 bA11316 ± 1607 bA23635 ± 5384 aA
Mn353.0 ± 200.9 aA388.5 ± 91.8 aA309.7 ± 117.2 aA457.9 ± 44.5 aA333.3 ± 72.5 bA267.6 ± 51.8 bA
Mo15.34 ± 6.82 aB12.5 ± 5.32 aA4.84 ± 5.88 bA44.02 ± 36.6 aA38.0 ± 48.1 abA1.59 ± 4.77 bA
Ni36.6 ± 33.8 aA44.86 ± 22.77 aA22.18 ± 5.27 aA28.99 ± 3.61 aA30.96 ± 7.05 aA12.32 ± 14.88 bA
Sr84.2 ± 48.8 bA147.99 ± 24.62 bA521 ± 271 aA206.7 ± 177.8 bA159.83 ± 25.16 bA549.7 ± 171.9 aA
Ti465.0 ± 264.8 bA974.6 ± 191.9 aA360.2 ± 211.3 bA548.5 ± 98.2 bA1050.0 ± 230.8 aA420.4 ± 290.3 bA
V46.96 ± 22.66 aA59.58 ± 3.42 aA26.88 ± 12.18 bA54.51 ± 12.20 aA58.90 ± 6.42 aA14.82 ± 21.20 bA
Zn73.7 ± 39.9 aA88.53 ± 12.05 aA100.4 ± 32.7 aA96.45 ± 10.74 aA75.47 ± 10.68 aB29.7 ± 35.4 bB
β-glucosidasemg p-nitrophenol g−1 h−164.51 ± 1.43 bA68.08 ± 2.03 aA55.38 ± 1.31 cA61.45 ± 1.10 bB66.60 ± 4.12 aA52.68 ± 0.93 cB
Alkaline phosphatase52.92 ± 1.10 bA63.04 ± 1.45 aA53.52 ± 0.87 bA51.67 ± 0.56 cB63.00 ± 0.70 aA52.76 ± 0.52 aB
Acid
phosphatase		55.35 ± 1.03 bA61.48 ± 1.65 aA52.60 ± 1.06 cA51.65 ± 0.83 bB62.19 ± 1.05 aA52.20 ± 0.24 aA
Arylsulfatase52.49 ± 1.22 bA62.65 ± 1.33 aA52.87 ± 0.45 bA49.70 ± 0.43 cB59.36 ± 0.12 aB52.11 ± 0.14 aB
Ureasemg NH4+-N kg−1 h−1112.16 ± 4.28 bB130.22 ± 3.03 aB102.47 ± 1.21 cB194.12 ± 0.61 bA232.93 ± 0.98 aA194.28 ± 0.57 bA
WHC: water holding capacity, EC: electrolytic conductivity, CEC: cation exchange capacity, TOC: total organic carbon. Groups not sharing a letter are significantly different from each other (p > 0.05). Lower case letters indicate significant differences between soils of the same season, and upper-case letters between soils of different seasons. ND: not detected. Values are the mean of the results per indicator ± standard deviation (9 replicates).
Table
Table 2. Principal component analysis (PCA) output of the studied soil properties.
Table 2. Principal component analysis (PCA) output of the studied soil properties.
Principal ComponentPC1PC2PC3PC4
Eigenvalue14.417.444.964.52
Variance %36.9619.0812.7211.61
Cumulative %36.9656.0568.7880.39
CEC0.2600.9360.023−0.074
TOC0.1530.9100.068−0.134
NT0.897−0.0330.264−0.151
CO32−−0.8820.138−0.280−0.081
Glucosidase0.866−0.2120.129−0.220
Urease0.2260.921−0.077−0.235
Li−0.1700.165−0.6410.515
V0.906−0.029−0.0910.289
Zn0.440−0.212−0.3390.717
CEC: cation exchange capacity, TOC: total organic carbon, and NT: total nitrogen. Bold numbers are the correlated parameters that contribute most to each CP, and indicators are considered as MDS.
Table
Table 3. Soil quality classes and saline soil index values.
Table 3. Soil quality classes and saline soil index values.
Soil QualityVery LowLowModerateHighVery High
Scale<0.160.17–0.320.33–0.480.49–0.64>0.8
ClassIIIIIIIVV
Table
Table 4. Soil quality classes of saline sites between seasons.
Table 4. Soil quality classes of saline sites between seasons.
Season
Site		Dry Rainy
123123
SQIw0.26 bB0.24 bB0.43 aA0.99 aA1.04 aA0.56 bA
Soil Quality ClassLowLowModerateVery HighVery HighHigh
Means that do not share a lower case letter are significantly different between sites in the same season, and the upper case letters denote significant difference between sites in different seasons. (p ≤ 0.05).
Table
Table 5. Global soil quality classes of saline sites.
Table 5. Global soil quality classes of saline sites.
Site123
Global SQIw0.62 a0.64 a0.49 a
Soil Quality ClassHighHighHigh
Means that do not share a letter are significantly different (p ≤ 0.05).
Table
Table 6. Global soil quality classes between seasons.
Table 6. Global soil quality classes between seasons.
SeasonDryRainy
Global SQIw0.31 b0.86 a
Soil Quality ClassLowHigh
Means that do not share a letter are significantly different (p ≤ 0.05).
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Bahena-Osorio, Y.; Franco-Hernández, M.O.; Pueyo, J.J.; Vásquez-Murrieta, M.S. Development of a Quality Index to Evaluate the Impact of Abiotic Stress in Saline Soils in the Geothermal Zone of Los Negritos, Michoacán, Mexico. Agronomy 2023, 13, 1650. https://doi.org/10.3390/agronomy13061650

AMA Style

Bahena-Osorio Y, Franco-Hernández MO, Pueyo JJ, Vásquez-Murrieta MS. Development of a Quality Index to Evaluate the Impact of Abiotic Stress in Saline Soils in the Geothermal Zone of Los Negritos, Michoacán, Mexico. Agronomy. 2023; 13(6):1650. https://doi.org/10.3390/agronomy13061650

Chicago/Turabian Style

Bahena-Osorio, Yanely, Marina Olivia Franco-Hernández, José J. Pueyo, and María Soledad Vásquez-Murrieta. 2023. "Development of a Quality Index to Evaluate the Impact of Abiotic Stress in Saline Soils in the Geothermal Zone of Los Negritos, Michoacán, Mexico" Agronomy 13, no. 6: 1650. https://doi.org/10.3390/agronomy13061650

APA Style

Bahena-Osorio, Y., Franco-Hernández, M. O., Pueyo, J. J., & Vásquez-Murrieta, M. S. (2023). Development of a Quality Index to Evaluate the Impact of Abiotic Stress in Saline Soils in the Geothermal Zone of Los Negritos, Michoacán, Mexico. Agronomy, 13(6), 1650. https://doi.org/10.3390/agronomy13061650

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop