elocation-id: e3148
One strategy for evaluating soils is through quality indices that depend on specific indicators on the soils sampled, the type of crop and the management carried out. Indicators are measurable physical, chemical or biological variables that affect the capacity of the soil as they perform one or more of its functions. The objective of this research was to examine the methodological use of the different physical, chemical and biological properties of the soil used as quality indicators to determine soil quality indices in Mexico, through a bibliographic review carried out in 2022, for the period 2000-2021 through various search engines of scientific articles and keywords related to the subject, in order to generate a diagnosis and glimpse research opportunities. Greater attention should be paid to the study of soil quality in Mexico, based on technical and scientific information in regions and states where this information remains scarce. As a product of the bibliographic review, the following sets of indicators are proposed: texture, bulk density, aggregate stability, infiltration, penetration resistance, moisture retention curve and soil depth as physical indicators; organic matter, pH, total nitrogen, inorganic nitrogen, phosphorus, potassium, calcium and magnesium as chemical indicators; and microbial biomass carbon, soil respiration, earthworm density, dehydrogenase, β-glucosidase, urease, phosphatase and arbuscular mycorrhizal fungi as biological indicators, opting for a subset of indicators or a minimum data set to form a soil quality index.
physical-chemical-biological soil fertility, principal component analysis, soil properties.
Current pressures on soil are reaching critical limits, in particular, the expected increases in food, fiber and fuel production required to achieve food and energy security imply greater pressure on this non-renewable resource. In Mexico, the presence of 25 of the 32 soil units that appear in the World Reference Base for Soil Resources (WRB) is reported. Two of the most important problems at present, due to their extension, that affect these soils in Mexico are: the loss of surface soil due to water erosion (20 million hectares) and degradation due to decreased fertility (31.6 million hectares) (Álvarez-Arteaga et al., 2020); however, for several sectors of today’s society, knowledge about this problem in many regions of the country is little and uncertain, which explains the low interest placed on this natural resource.
One strategy for assessing soil degradation is through soil quality indices (SQi), which depend on specific indicators related to the soils sampled, the type of crop and its management (Bedolla-Rivera et al., 2020). Indicators can be measurable physical, chemical and biological properties of the soil that affect the ability of the soil to perform one of its functions (Castillo-Valdez et al., 2021). Physical indicators are physical properties associated with the efficient use of water, nutrients and the use of agrochemicals; chemical indicators are related to the chemical conditions that affect soil-plant relationships, water quality, soil buffering capacity and the availability of nutrients for plants and other living beings and biological indicators are those organisms or processes developed by them, which, with their presence or abundance, indicate changes or states of certain properties or processes of the soil.
Indicators constitute a powerful tool for decision-making in land management and use at local, regional and global scales, through their integration, SQi are obtained, which are numerical variables that allow a more accurate and reliable assessment of soil quality using statistical methods such as the minimum data set (MDS) and principal component analysis (PCA) (Bedolla-Rivera et al., 2020). A vast number of physical, chemical and biological parameters have been included in soil quality studies around the world. Nevertheless, their integration into SQi is scarce, uncertain and still remains a pending task.
Based on this, the objective of this research was to examine the methodological use of the different physical, chemical and biological properties of the soil used as quality indicators to determine SQi in Mexico; through a bibliographic review using various search engines of scientific articles and keywords related to the subject, in order to generate a diagnosis and glimpse research opportunities.
The literature search was performed in the following databases: ScienceDirect, Scopus, JSTOR, SciELO, Springer, Redalyc and Google Scholar. The review was conducted in both English and Spanish and included exclusively articles in scientific journals published between 2000 and 2021. The words used focused on the title and keywords related to soil fertility, quality and health in Mexico; they were: quality, soils, indicators, indices and Mexico. The final selection of the articles was based on the reading of the title, the analysis of the information present in the sections of abstract, methodology and conclusions in each of them, and finally, that it led to the theme raised in the objective.
Forty-three scientific articles were found with the above criteria. Subsequently, each of them was taken to a spreadsheet of the Excel® program, where a log was formed with the following sections: year, main author, state of the Mexican Republic, where the research was carried out, physical indicators, chemical indicators and biological soil indicators used, group of soils analyzed and type of SQi used. Based on this log, a diagnosis of the use of indicators and SQi in Mexico was generated.
Forty-three scientific articles related to the methodological use of soil properties used as indicators of soil quality in Mexico were found. The number of articles per year varied from zero to seven on average and although there is no direct trend of increase in publications over time, it is visualized that, in the period from 2015 to 2021, 60.5% of scientific articles were published, compared to the period from 2000 to 2014 with only 39.5% of published articles (Figure 1).
With respect to the number of articles published by state, the State of Mexico concentrates the majority with eight; followed by Oaxaca and Veracruz with six; Puebla with five; Hidalgo, Nayarit and Tabasco with four; Campeche, Chiapas, Guanajuato, Michoacán and Nuevo León with two and Chihuahua, Querétaro, San Luis Potosí, Sinaloa and Zacatecas with one, respectively (Figure 2).
It is necessary to mention that there are regions where more studies on soil quality have been developed, Texcoco in the State of Mexico (Govaerts et al., 2006; Pajares-Moreno et al., 2010; Rodríguez-Serrano et al., 2016; Fonteyne et al., 2021), the Mixteca region in the state of Oaxaca (Estrada-Herrera et al., 2017; Santiago-Mejía et al., 2018; Fonteyne et al., 2021) and the central and mountain regions of Veracruz (Campos-Cascaredo et al., 2007; Fernández-Ojeda et al., 2016; de la Cruz-Elizondo and Fontalvo-Buelvas, 2019; Peña-Morales et al., 2021).
The analysis yielded information on several soil groups (WRB) studied in Mexico, with at least two articles per group (Figure 3). Some other groups studied were: Plinthosol, Solonchak, Leptosol, Planosol, Lixisol, Durisol, Solonetz and Technosol (Alcalá et al., 2009; Huerta et al., 2009; Pajares-Moreno et al., 2010; Alejo-Santiago et al., 2012; Fonteyne et al., 2021; Martínez-Rodríguez et al., 2021). Seven articles do not explicitly refer to any particular soil group (Prieto-Méndez et al., 2011, 2013; Rodríguez-Serrano et al., 2016; de la Cruz-Elizondo and Fontalvo-Buelvas, 2019; Murillo-Cuevas et al., 2019; Acevedo-Gómez et al., 2020; Peña et al., 2021) and Mollisol, Andisol, Ultisol, Inceptisol and Entisol soils under the nomenclature of the USDA Soil Taxonomy are reported in Govaerts et al. (2006), Campos-Cascaredo et al. (2007), Bautista-Cruz et al. (2012) and Chavarin-Pineda et al. (2021).
When analyzing the frequency of study of physical indicators in scientific articles, it was found that the physical properties mostly chosen as physical indicators of soil quality are bulk density (Armida-Alcudia et al., 2005; Campos-Cascaredo et al., 2007; Bugarín et al., 2010; Prieto-Méndez et al., 2013; Murray et al., 2014; Zavala-Cruz et al., 2014; Fernández-Ojeda et al., 2016; Muñoz-Iniestra et al., 2017; Cantú-Silva et al., 2018; Santiago-Mejía et al., 2018; de la Cruz-Elizondo and Fontalvo-Buelvas, 2019; Álvarez-Arteaga et al., 2020; Chavarin-Pineda et al., 2021; Peña-Morales et al., 2021); texture (Prieto-Méndez et al., 2011; Zavala-Cruz et al., 2014; Muñoz-Iniestra et al., 2017; Cantú-Silva et al., 2018; Montaño-Arias et al., 2018; Santiago-Mejía et al., 2018; de la Cruz-Elizondo and Fontalvo Buelvas, 2019; López-Báez et al., 2019; Acevedo-Gómez et al., 2020; Álvarez-Arteaga et al., 2020; Bedolla-Rivera et al., 2020; Cruz-Flores et al., 2020; Peña-Morales et al., 2021).
Aggregate stability (Sustaita-Rivera et al., 2000; Govaerts et al., 2006; Medina-Méndez et al., 2006; Prieto-Méndez et al., 2013; Hernández-González et al., 2018; Castillo-Valdez et al., 2021; Fonteyne et al., 2021) (Figure 4). These indicators are related to very important soil ecosystem services and ecological functions, as suggested by Bünemann et al. (2018).
To measure the bulk density, the cylinder method is reported, for the texture the Bouyoucos hydrometer, aggregate stability through wet and dry sieving, infiltration by double cylinder, field capacity with a pressure pot, permanent wilting point with a pressure membrane, moisture retention curve at 33, 50, 150, 500, 1 000 and 1 500 kPa, total porosity using the bulk density and actual density value of 2.65 Mg m-3, penetration resistance with the help of a dynamic penetrometer, usable moisture as the difference between field capacity and permanent wilting point and gravimetric moisture by weighing a sample of wet soil, drying it at 105 °C for 24 h to obtain the weight of dry soil.
With regard to the methods used for determining the physical indicators of the soil in order to assess its quality, it should be noted that indicators that limit root growth, seedling emergence, infiltration or movement of water within the soil profile should be considered and should be measured rather than derived. To integrate an MDS, texture (% sands, % silt and % clays), bulk density, aggregate stability, infiltration, penetration resistance, moisture retention curve and soil depth could be considered (Bünemann et al., 2018).
Soil organic carbon or organic matter is the soil property most commonly used as a chemical indicator (Sustaita-Rivera et al., 2000; Armida-Alcudia et al., 2005; Medina-Méndez et al., 2006; Bugarín et al., 2010; Uribe-Hernández et al., 2010; Prieto-Méndez et al., 2011; Alejo-Santiago et al., 2012; Murray et al., 2014; Zavala-Cruz et al., 2014; Cruz-Ruiz et al., 2015; Fernández-Ojeda et al., 2016; Estrada-Herrera et al., 2017; Hernández-Ordoñez et al., 2017; Muñoz-Iniestra et al., 2017; Rangel-Peraza et al., 2017; Cantú-Silva et al., 2018; Hernández-González et al., 2018; Santiago-Mejía et al., 2018; de la Cruz-Elizondo and Fontalvo Buelvas, 2019; López-Báez et al., 2019; Acevedo-Gómez et al., 2020; Castillo-Valdez et al., 2021; Duché-García et al., 2021; Fonteyne et al., 2021; Martínez-Rodríguez et al., 2021; Peña-Morales et al., 2021).
It is followed by pH (Bugarín et al., 2010; Uribe-Hernández et al., 2010; Prieto-Méndez et al., 2011; Bautista-Cruz et al., 2012; Alejo-Santiago et al., 2012; Fernández-Ojeda et al., 2016; Estrada-Herrera et al., 2017; Muñoz-Iniestra et al., 2017; Rangel-Peraza et al., 2017; Yáñez-Díaz et al., 2018; de la Cruz-Elizondo and Fontalvo Buelvas, 2019; López-Báez et al., 2019; Acevedo-Gómez et al., 2020; Álvarez-Arteaga et al., 2020; Cruz-Flores et al., 2020; Castillo-Valdez et al., 2021; Fonteyne et al., 2021; Martínez-Rodríguez et al., 2021; Peña-Morales et al., 2021).
Interchangeable potassium (Govaerts et al., 2006; Prieto-Méndez et al., 2011; Alejo-Santiago et al., 2012; Zavala-Cruz et al., 2014; Palma-López et al., 2015; Fernández-Ojeda et al., 2016; Estrada-Herrera et al., 2017; Santiago-Mejía et al., 2018; López-Báez et al., 2019; Acevedo-Gómez et al., 2020; Álvarez-Arteaga et al., 2020; Cruz-Flores et al., 2020; Chavarin-Pineda et al., 2021; Duché-García et al., 2021; Fonteyne et al., 2021; Martínez-Rodríguez et al., 2021; Trejo et al., 2021), Kjeldahl nitrogen (Govaerts et al., 2006; Campos-Cascaredo et al., 2007; Pajares-Moreno et al., 2010, 2011; Prieto-Méndez et al., 2011; Cruz-Ruiz et al., 2015; Palma-López et al., 2015; Fernández-Ojeda et al., 2016; Hernández-Ordoñez et al., 2017; Muñoz-Iniestra et al., 2017; Hernández-González et al., 2018; Santiago-Mejía et al., 2018; Acevedo-Gómez et al., 2020; Álvarez-Arteaga et al., 2020; Chavarin-Pineda et al., 2021; Duché-García et al., 2021) and phosphorus (Alejo-Santiago et al., 2012; Bautista-Cruz et al., 2012; Palma-López et al., 2015; Fernández-Ojeda et al., 2016; Estrada-Herrera et al., 2017; Hernández-Ordoñez et al., 2017; López-Báez et al., 2019; Acevedo-Gómez et al., 2020; Álvarez-Arteaga et al., 2020; Castillo-Valdez et al., 2021; Martínez-Rodríguez et al., 2021; Trejo et al., 2021) (Figure 5).
To determine the organic carbon content, the articles report the Walkley-Black method and the organic matter content by multiplying the organic carbon by some factor: 1.724 or 2. The pH was determined in a suspension of soil:water: 1:1, 1:2 or 1:2.5, also using calcium chloride (CaCl2) or potassium chloride (KCl) with the help of a potentiometer. K, Ca and Mg were determined in most articles using an extractor solution of ammonium acetate (1 N pH = 7). The total nitrogen was determined by the Kjeldahl or micro-Kjeldahl digestion procedure. For usable phosphorus, the procedures by Bray & Kurtz, Olsen and citric acid are reported.
Chemical indicators should describe soil-plant interactions, availability and mobility of nutrients, water for plants and other organisms. An MDS would be considering organic matter, pH, nitrogen, phosphorus and exchangeable bases (Bünemann et al., 2018).
The biological properties that are most frequently used as indicators of soil quality were microbial biomass carbon (Armida-Alcudia et al., 2005; Campos-Cascaredo et al., 2007; Pajares-Moreno et al., 2010; Estrada-Herrera et al., 2017; Cruz-Flores et al., 2020); respiration (Pajares-Moreno et al., 2010; Rodríguez-Serrano et al., 2016; de la Cruz-Elizondo and Fontalvo-Buelvas, 2019; Bedolla-Rivera et al., 2020); earthworms (Huerta et al., 2009; de la Cruz-Elizondo and Fontalvo-Buelvas, 2019; Castillo-Valdez et al., 2021; Peña-Morales et al., 2021); enzymes involved in intracellular metabolism, nitrogen, carbon and phosphorus (Pajares-Moreno et al., 2010, 2011; Cruz-Ruiz et al., 2015; Cruz-Flores et al., 2020); soil mesofauna and macrofauna (Uribe-Hernández et al., 2010; Rodríguez-Serrano et al., 2016; Murillo-Cuevas et al., 2019) and arbuscular mycorrhizal fungi, bacteria and actinomycetes (Murillo-Cuevas et al., 2019; Cruz-Flores et al., 2020; Duché-García et al., 2021; Trejo et al., 2021).
To determine the microbial biomass carbon, fumigation-incubation methods are reported in the reviewed articles; for soil respiration, the closed chamber method is reported; for earthworms, the method of extraction of soil monoliths of dimensions of 25 cm x 25 cm x 30 cm depth is mentioned; enzymes through various methodologies, as described by Pajares-Moreno et al. (2010); mesofauna and macrofauna by the Berlese funnel, this methodology is described in detail in Rodríguez-Serrano et al. (2016) and the quantification of fungi, bacteria and actinomycetes in different culture media, as described in Murillo-Cuevas et al. (2019); Duché-García et al. (2021). An MDS would be considering microbial biomass carbon, soil respiration, earthworm density, enzymes: dehydrogenase, β-glucosidase, urease and phosphatase, and arbuscular mycorrhizal fungi (Bünemann et al., 2018).
Most of the articles reviewed use a set of physical, chemical or biological properties as quality indicators, using the value or content of said property, comparing it with previously established or reported intervals, as described in Yáñez-Díaz et al. (2018) and Martínez-Rodríguez et al. (2021). Other authors (Prieto-Méndez et al., 2013; Estrada-Herrera et al., 2017; Muñoz-Iniestra et al., 2017; Hernández-González et al., 2018; Duché-García et al., 2021) employ equations to normalize the values of soil properties used as indicators.
Where: Vl: normalized value of the indicator (linear); Im= experimental value of the soil property considered as an indicator; Imin= minimum value of the soil property considered as an indicator; Imax= maximum value of the soil property considered as an indicator.
Equation (1) is applied to those quality indicators where high values are desirable (e.g., organic carbon) and equation (2) to those where low values are suitable (e.g., bulk density). PCA is reported by several authors as useful in defining SQi (Govaerts et al., 2006; Campos-Cascaredo et al., 2007; Bautista-Cruz et al., 2012; Rangel-Peraza et al., 2017; Cruz-Flores et al., 2020; Bedolla-Rivera et al., 2020; Castillo-Valdez et al., 2021; Chavarin-Pineda et al., 2021; Duché-García et al., 2021; Fonteyne et al., 2021; Martínez-Rodríguez et al., 2021).
For the formation of an MDS through quality indicators, the principal components that explain at least a significant part of the variability (80%) with an eigenvalue >1 are chosen. The indicators with the highest weights have the greatest influence on soil quality within each principal component, for this case only indicators within the range of 10% are considered: SQIc-0.1*SQIc. Where: SQIc= weight of the soil quality indicator with the highest weight within the principal component.
When more than one quality indicator is retained in the different principal components with the above rule, a redundancy analysis is performed using a Pearson correlation. If there is a significant correlation, the indicator with more weight is retained, otherwise both indicators are retained. After normalizing the retained quality indicators through any of equations 1-9, the SQi are generated through equations 10 and 11 (Bedolla-Rivera et al., 2020; Martínez-Rodríguez et al., 2021):
Where: Vnl, f(x)l= normalized value of the indicator (nonlinear and linear, respectively); equations 3-5= a higher value of the indicator is better; equations 6-8= a lower value of the indicator is better; equation 9= optimal value; Ī: average value of the indicator in the study area; L= lower threshold; U= upper threshold; L1 and U1= value of the indicator for a lower and upper baseline.
Where: SQiA; SQiAW= soil quality index (additive and additive weighted, respectively); Si= normalized soil quality indicator; n= number of soil quality indicators in the MDS; Wi= weighting obtained by dividing the percentage of variance explained by the principal component by the percentage of variance accumulated by the retained principal components.
From the situation described above, it follows the need for greater efforts to generate knowledge about soil quality since information is scarce and the existing one is concentrated only in some regions of the country for some groups of soils. To address this situation in Mexico, it is necessary to develop indicators and indices of soil quality and health based on physical, chemical, biological properties based on technical and scientific information in the short (2022-2024), medium (2025-2030) and long term (beyond 2030).
Greater attention should be paid to the study of soil quality indicators and indices in Mexico. This attention should be focused at the state or regional level or on soil groups where information is not available. The following is proposed: texture, bulk density, aggregate stability, infiltration, penetration resistance, moisture retention curve and soil depth as physical indicators; organic matter, pH, total nitrogen, inorganic nitrogen, phosphorus, potassium, calcium and magnesium as chemical indicators; and microbial biomass carbon, soil respiration, earthworm density, dehydrogenase, β-glucosidase, urease, phosphatase and arbuscular mycorrhizal fungi as biological indicators.
From the set of physical, chemical and biological indicators, a subset of these should be chosen or reduced to a minimum data set to form a soil quality index.
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