A Review of Farmland Soil Health Assessment Methods: Current Status and a Novel Approach

Abstract

Healthy soils are vital for food production as 95% of global food production directly or indirectly depends on soils. To ensure the food security of the burgeoning world population, it is necessary to evaluate soil health (SH) with a potential soil health index (SHI). Although there are several reputable methods for SH assessment at present, the connotations of and evaluation methods for SH are still unclear and such indexes are targeted at specific stakeholders or problems. In this study, we reviewed the fundamental steps in current attempts to develop SHIs, SH assessment methods and proposed a unified SHI based on the priorities of stakeholders. The proposed approach was designed as “three sets of dual index systems”, including the soil function (i), nutrition (j), and output (k) index systems, as well as the current (C) and expected (E) values of the respective index systems. The indicators included in index-i, index-j, and index-k reflect the soil regulatory functions, nutrient status, and quality and quantity of the output, respectively. The E values are used as a reference for the C values, and the health status is obtained, by using the ratio (R) of C to E for the respective index systems and their degree of deviation from “1” (R-1). For any evaluated soil, the farther the number of attributes and their ratios deviate from “1”, the unhealthier it is. This approach can provide a unified and comprehensive SH assessment method by diagnosing the most significant, healthy as well as unhealthy indicators. This method can be applied easily, not only by scholars but also by farmers and land managers.

1. Introduction

Soil is a dynamic and diverse natural system that supports many essential ecosystem services (ES), such as carbon sequestration, nutrient cycling, water purification, and habitat provision [1,2,3], as well as maintenance of biological productivity and environmental quality [4]. Land use and management practices can induce changes in soil and alter its functioning [5,6]. To assess these changes, it is necessary to evaluate the key components of soil; i.e., its physical, chemical, and biological components. Since soil has heterogeneous and diverse inherent properties, it is challenging to quantify the synergy and interaction between these components. To cope with these challenges, researchers perform SH assessment [4].

When we assess SH, we refer in particular to agricultural SH as it is associated with many ecosystems [7]. Usually, SH is assessed by formulating a holistic soil health index (SHI) composed of key soil attributes [8,9,10]. A soil health index (SHI) can be defined as, a set of potential soil attributes, that helps to evaluate how well the soil performs its functions in the various fields or ecosystem levels related to different agronomic and management practices. Many attempts have been made to develop holistic SH evaluation indices, through the selection of soil attributes, the assignment of scores and weights, and the integration of these attributes into a final index [5,11]. These indices can be used to assess the current status of SH and identify the most significant and sensitive soil attributes and their interactive relationships. For instance, the Cornell Comprehensive Assessment of Soil Health (CASH) in the USA [6] and the Soil Indicators (SINDI: a web-based soil quality assessment tool) method in New Zealand [12] were developed to assess SH status for the optimization of farmland management practices and the improvement of SH. Furthermore, Rinot et al. [4] suggested a multi-indicator index system for the classification of various soils and the evaluation of the effectiveness of management strategies carried out on degraded agricultural lands. For the selection of appropriate SH attributes and the construction of an SHI related to soil functions or threats, several methods have been developed around the globe, based on mathematical, statistical, and laboratory approaches, such as the meta-analytic hierarchy process [13], the fuzzy comprehensive evaluation method [14], and partial least squares [15]. Likewise, several studies were conducted that considered the concerns of farmers, researchers, and other stakeholders [16,17,18,19,20]. However, there is not yet a unified index system that represents the considerations of these key stakeholders holistically.

For the proper estimation of soil health, a unified index system is required as indices related to a specific soil function or a soil threat do not reflect a panoramic view of the whole soil. In addition, the scoring and weighting approach for the development of SHI also shows some limitations; for instance, Bünemann et al. [11] argued that the scoring method for the selection of indicators based on expert opinion and unsuitable statistical approaches has several limitations in certain cases due to inconsistency, site-specificity, and the heterogeneity of soil. These limitations can only be minimized by taking into account “a full range of indicator values for a specific soil group under specific climatic conditions” [21]. Recently, researchers [4,11,22] have comprehensively reviewed the SH assessment approaches carried out globally, indicating the methods of attribute selection, the conceptual linkages between these attributes and soil threats, and soil ecosystem functioning. However, these conceptual linkages were not evaluated or validated quantitatively.

To sum up, current research still has the following shortcomings: first of all, soil scientists, agronomists, farmers, and ecologists have not reached an agreement on the concept of SH. Consequently, different researchers have different definitions of SH in different periods and fields. In many circumstances, both SH and soil quality (SQ) are considered synonymous with each other. Moreover, the connotations of SH are still not clear; therefore, the SH is still assessed based on index systems and methods defined for SQ evaluation. Secondly, the outputs of the current index systems and evaluation methods are not sufficient to reflect the unhealthy elements in individual soils, as these indices do not readily translate into management recommendations. Consequently, the direction of cultivation and management practices for various crops cannot be determined easily. Last but not least, evaluation index systems and methods are not unified as these indices reflect the needs of specific stakeholders or problems. Hence, further improvement in the current health assessment index systems and methods are vital for SH assessment.

There is thus a dire need to develop a robust and replicable index system to fill this research gap. It would enable both quantitative and qualitative validation of SH attributes and the interactions among them by addressing the priorities of stakeholders in the index development processes so that, all the concerns could be unified into a single SHI.

Therefore, the current study was devised to address the shortcomings in the current indices by reviewing the current SH assessment methodologies and proposing a conceptual index system to assess SH that would address the concerns related to SHI.

2. Conceptual Framework of Existing SHIs

Several soil health assessment indices and tools have been developed since the 1990s [11]. SHI integrates SH attributes into a simplified layout that can assist in decision-making for sustainable agricultural land management [23]. A robust SHI depends on its ability to detect changes in soil management practices, use of standard soil sampling and analysis techniques, statistical and mathematical models, and a hypothesis to select appropriate soil attributes [15,24]. In addition, it should provide a decision-based output to assist farmers’ management decisions.

The fundamental phases for the SH assessment are identification and selection of minimum data set (MDS) from relevant soil attributes, interpretation and quantification of MDS through standard scoring and weighting approach using advanced statistical or innovative techniques, and integration of MDS into SHI. These steps are briefly reviewed and presented in Figure 1.

2.1. Selection of Attributes

Soil health indicators are the soil’s chemical, physical, biological properties, characteristics, processes, and functions [25,26]. The selection of soil health indicators is a fundamental step toward the development of SHI [20,27,28,29,30]. Such selection depends on the conceptual, sensitive, practical, and interpretable strengths of the indicators [31,32,33,34]. In addition, it is indispensable to select the entry variables from a large number of available attributes to reduce the complexity of the assessment and the cost of measurement [35]. Generally, the selection of soil attributes should follow some principles, such as the selected attributes should portray the chemical, physical and biological processes [36] that are reflected by the complex soil system as well as soil functions related to the ecosystem services, secondly, they should include a sufficient number of attributes to predict the real SH status and the relationship between soil functions and management goals that would serve as the basis for decision making at relevant time scales with all the important information required for sustainable soil management, thirdly, it should be sensitive enough to detect the changes caused by the soil management practices, land use patterns, climate change, and lastly, the selected attributes should be relatively easy to measure, and convenient to use [37,38,39]. Currently, only 20% of the soil attributes used to evaluate soil health met the above standards [40].

As far as the methodology of attribute selection is concerned, there are several approaches including qualitative, semi-quantitative, and quantitative. In the qualitative approach, an expert opinion-based method is used for the selection of soil attributes [20,41,42]. The selection of attributes via this approach is based on the knowledge and experience of a qualified expert as well as a literature acquisition. The major drawback of this approach is that it would be quite difficult to develop a sound relationship between soil processes and soil properties due to subjectivity [43] and can be overcome by incorporating proper statistical methods into the qualitative approach, like principal component analysis (PCA), partial least square (PLS), and discriminant analysis (DA). For instance, in previous studies, expert-based methods combined with statistical approaches were validated for soil quality index (SQI) development in rice and vegetable production systems under different management goals [33,43].

In the case of the semi-quantitative approach, a decision tree is used that utilizes a database encompassing numerical and categorical variables. It is used for the statistical explanation of SH as a response variable based on the threshold values of pre-determined variables [44,45]. For example, a decision tree method was used to evaluate the effect of changes in soil attributes on the population dynamics of soil organisms [46]. Furthermore, it is also used to assess SH based on morphological classification with the help of Visual Soil Assessment (VSA) [47]. Similarly, it was demonstrated that the soil quantitative parameters could be assessed using a decision tree by translating qualitative morphological observations [48]. In addition, it is also helpful in defining the soil mapping units while formulating soil maps [26].

It was previously discussed that the selection of the most appropriate soil attributes depends on precisely defined targeted values that reveal the relationship between soil functioning and ecosystem services. Likewise, the correlation between different attributes should be tested using statistical tools (principal component analysis (PCA), discriminant analysis (DA), redundancy analysis (RDA), and analysis of variance (ANOVA)) to screen the required attribute and validate them quantitatively [4]. Through this approach, the attributes that are resistant to any change concerning land management practices can be excluded easily [26,35,37].

PCA is a widely used statistical tool to narrow down the number of attributes into MDS by reducing the dimensionality and correlation analysis of the data set, so that those attributes can be excluded that are relatively more stable and do not show any significant difference in terms of different land management practices [27,28,43,49]. It is well established that the PCA is one of the flexible means for the identification of the most pertinent attribute under different soil and crop conditions [43], in addition, this tool also highlights the significant attributes of soil functioning and interrelationship among these attributes [29].

Moreover, Dai et al. [50], used PCA to define MDS of soil quality. Through this approach, three principal components (PCs) containing six potential attributes representing the biochemical, essential nutrients, and moisture status of soil were identified [48]. Furthermore, Yu et al. [35] Performed an ANOVA test on 22 soil attributes to assess the effect of four different land use patterns on soil quality indicators, and 13 potential attributes were identified as a total data set (TDS) with a significant difference of (p < 0.05), in north-eastern China. They found four significant principal components and the highest factor loading variables in the same study were Invertase, N:P, Water Extractable organic carbon, and labile carbon representing the C cycle and the intensity of C metabolism, the relative availability of N and P, a fraction of biomass in soil, and soil organic C sequestration respectively. In addition, PCA may be followed by correlation analysis and clustering techniques like Pearson’s correlation analysis [43], best subset regression (BSR), and other soil health modeling techniques like partial least squares (PLS) method [15] and structural equation modeling (SEM) [51] to find the most relevant and potential soil attributes in an index for the assessment of SH status in a simplified way [27].

There is no universal approach to determining the number of attributes for SHI development, as different scientists have adopted different approaches to deciding the number of attributes. It was reported by several scholars that the indices with a large number of attributes could predict the SH status more comprehensively [13,27,38,52], while some studies also support the use of SHI with a small number (<5) of attributes [37,53,54]. However, how to objectively select quantitative and qualitative soil attributes to develop a robust SHI and its application under diverse cropping systems and environmental conditions is still a key challenge in the SH evaluation process.

2.2. Interpretation of Attributes

The second step in index development is an interpretation of attributes, and for that, it is necessary to unify the selected soil attributes through various approaches, such as standard scoring methods, use of raw data, and statistical methods to develop a comprehensive SHI. The use of the most appropriate standard scoring functions is indispensable for the interpretation of measured attribute value to establish SHI having adaptability under diverse and complex soil conditions [11,28]. The attributes included in the SH assessment indices are either quantitative or qualitative. The qualitative (categorical) attributes are interpreted on the basis of visual assessment, while analytical analysis is required for quantitative attributes. Then, the measured values for each attribute are transformed into a unitless score (0–1 or 0–100) using calibration curves [49,55,56]. The shape of these curves determines three ranges that are “more is better,” “optimum range,” and “less is better.” These shapes are obtained by a combination of expert opinion, literature acquisition [41], statistical methods [27], or constructive functions [57]. However, in linear scoring, observed values are divided by the maximum, optimum, or minimum values, so the results are highly dependent on the variance of each attribute, which may cause a biased calculation of scores due to extreme outlier values. In contrast, non-linear scoring is based on the normal distribution of data, as its (scoring) responses depend on non-linear patterns [4,57,58], thus it provides a better reflection of soil system function than the linear scoring approach [35,49] with the exception that the transformation approach for the same attributes may be different based on the area of study [12]. Several researchers considered the non-linear scoring approach as the most proper method for the transformation of measured attributes into unitless scores [13,43,49,59,60]. However, the scoring function developed through these approaches would be site-specific, so the calibration curves and threshold values would also be site-specific because the scoring function developed for a specific area could not be applied at another site.

There is also another way to interpret attributes besides the scoring approaches, as SHI can also be developed using raw values that avoid conversion steps like the partial least squares (PLS) [15] and Haney’s test [53] methods. The PLS is a non-parametric method that evaluates the co-variance between the attributes with predetermined threshold values and interprets the information objectively. Moreover, the constructive functions are used to develop an index with a small number of attributes (e.g., Haney index) based on raw observed values without any conversion step. The transformation of selected attributes using the aforementioned approaches can be used to construct SHI. However, it remains very challenging to define and measure the threshold values due to the site-specificity and heterogeneity of soil properties.

2.3. Integration of Attributes

The integration of measured attributes into the overall SH score is done through several approaches like additive, weighted additive, and statistical methods. The simplest approach is “additive”, in which all the selected attributes are treated equally and assigned equal scores to each attribute, but this approach may exaggerate the results as it may not reflect the impact of different attributes on the soil system functioning [4]. In the case of “weighted additives,” expert judgment and literature acquisition are used to find the relative importance of each attribute based on some specific goal like nutrients management, water use efficiency, productivity, and carbon sequestration [61,62]. For example, in a previous study, both “additive” and “weighted additive” methods were used to develop an SQI under different land use treatments, and it was found that the weighted additive approach showed the best results as compared to the additive method [34]. Furthermore, in another study, Cherubin et al. [63] categorized the measured attributes into “sectors” i.e., physical, biological, and chemical sectors, and an equal weightage (0.33) was assigned to each sector. Moreover, in other studies, attributes were identified based on their specific functions like water regulation, nutrient cycling, sustaining biological activities, etc., and assigned them weightage either equally or differentially [33,62]. Different weighting was based on the number of attributes in a function or the relative importance of that function. In conclusion, both approaches have pros and cons, and comprehensive considerations need to be made in the specific evaluation of SHI, as relying on a specific soil function is not satisfactory to develop a holistic SHI under complex and diverse soil systems.

In the case of statistical methods, for the integration of soil attributes into SHI, the most commonly used statistical methods include the least square models and principal component analysis. In the least square models, a certain coefficient is provided to each attribute based on its relative contribution to the index and the coefficient values. The attributes with relatively higher values are retained for indexing and those attributes with relatively smaller values are eliminated. For instance, it was proposed to use the least square models on different ecosystem services (ES) to select a more appropriate ES for the development of a new model [4]. Meanwhile, in the PCA method, measured attributes can be weighted based on PCA results. Each PC explains a certain part of the variation in the total data set. Moreover, PCs with high eigenvalues (>1.0) and attributes with high loadings represent the most sensitive components, so these attributes are retained for SHI development [13]. However, when more than one attribute retains in a PC, only the highest loading attribute will be considered if there is no significant difference exist otherwise both attributes will be considered for indexing. Moreover, for the determination of the redundancy among the highly weighted attributes, multivariate correlation analysis can also be used. Although the PCA is the most common method to select soil attributes under different cropping systems or soil conditions, however, some significant attributes related to soil function may be excluded when using the PCA method. Therefore, while using the PCA method it is necessary to consider soil functions and evaluation objectives in combination to get the desired results that can accurately reflect the health status of the soil.

3. Common Soil Health Assessment Methods

SH assessment is of great significance for clarifying the current status of soil and guiding management practices. A lot of research work has been carried out in the field of SH to develop different conceptual frameworks and models. However, these frameworks and models are also developing and changing constantly. Common SH assessment methods with their pros and cons are presented in Table 1. This section mainly describes common SH assessment approaches, including analytical and visual soil assessment approaches.

3.1. Comprehensive Assessment of Soil Health (CASH)

The Comprehensive Assessment of Soil Health index (CASH) was initially established by a research team at Cornell University in 2006, but over time, this index system was also modified due to advanced research technologies, and it was made more simple and cost-effective by reducing the number of attributes to 12 that include chemical (pH, organic matter content, P, K), physical (texture, aggregate stability, available water capacity, penetration resistance), and biological attributes (soil respiration, soil proteins, soil pathogens, active Carbon) for the assessment of SH. Furthermore, there is room to replace the attributes based on different factors such as key soil processes, correlations among attributes, sensitivity to management practices, and the required cost and time. It also encompasses some available attributes as add-ons that are mineralizable nitrogen, root pathogen pressure, salinity and sodicity, boron, and heavy metals. In addition, this approach considers the soil texture as a vital element affecting the SH assessment results. Therefore, the whole SH assessment work is performed according to the textural class of the corresponding soil. In this assessment process, the measured attributes need to be converted into scores via cumulative normal distribution function. This method uses three scoring functions (increasing, optimal, and decreasing). The SHI is calculated according to the obtained scores (0–100) and expressed in percentage with a five-color system: red, orange, yellow, light green, and dark green for very low (0~20), low (20~40), medium (40~60), high (60~80), and very high (80~100) respectively [6].

CASH approach reflects the health status of soil comprehensively. However, the weight of indicators does not consider for the integration of attributes into the final SHI. Meanwhile, this approach has comparatively high accuracy on the field scale as compared to the regional scale, so further improvement is needed [72].

3.2. Soil Management Assessment Framework (SMAF)

The Soil Management Assessment Framework (SMAF) was developed jointly by USDA-NRCS and USDA-ARS that is relatively flexible in terms of selection and measurement of soil attributes related to the soil functions under consideration. In SMAF, using robust decision rules, out of 81 potential attributes, a minimum dataset can be selected based on the targeted objective and management goals with the flexibility to modify the proposed dataset by the user. This approach uses chemical, physical, and biological attributes to predict the SH condition. The whole assessment process can be carried out in three steps, including attributes selection, attributes interpretation, and attributes integration into SHI [55]. These steps were already discussed previously.

Currently, many scholars adopt SMAF for their studies, as this approach reflects the SH status more comprehensively, but the selection of attributes is not fixed in this approach, so the minimum dataset for different cropping systems and management practices are different even in the same cropping system with different soil layers. Additionally, it was also found to be less accurate on the regional scale as compared to the field scale [72].

3.3. Meta-Analytic Hierarchy Process (Meta-AHP)

The Meta-Analytic Hierarchy Process index (Meta-AHP) was proposed by Xue et al. [13], which is a more consistent and sensitive SH assessment approach that provides information about SH status and functions of different land-use treatments. This approach was devised through Analytical Hierarchy Process (AHP), expert scoring, and meta-analysis. Meta-analysis was employed for the selection of attributes based on their sensitivity to management practices, functions, and correlations among them. For example, Trivedi et al. [73] evaluated the correlation between soil nutritional health and soil properties using meta-analysis. In Meta-AHP, the global function weight (GFW) of attributes is calculated using AHP and an expert scoring method in three steps, including the construction of the hierarchical model, assigning weight for attributes, and calculation of GFW with the integration of attributes into SHI, whereas the global sensitivity weight (GSW) is calculated via meta-analysis. Furthermore, the scoring function mainly includes three types: increasing type (More is better), decreasing type (Less is better), and intermediate optimal type (Optimum). Finally, the SHI is calculated according to the weight and scores of the attributes.

The use of the Meta-AHP approach in selecting, weighting, and scoring the attributes for the SH assessment not only reduces the requirement of labor and cost of analysis but also increases the discrimination ability and sensitivity of the index. However, such expert-based indices may face a lack of simplicity and methodological transparency. In addition, to ensure the credibility of scores and weights, it is necessary to consult a competent expert [11].

3.4. Soil Indicator Assessment (SINDI)

The SINDI (soil indicator) was established by Lilburne et al. [64] in New Zealand. This is an online assessment tool that compares the properties of measured soil samples with the database information to predict the SH status and recommends corresponding management measures related to management objectives and land-use methods. This index integrates soil attributes reflecting major soil properties such as soil fertility, soil structure, soil organisms, nutrient storage, and organic resources mainly including 7 attributes i.e., soil pH (pH), available phosphorus (AP), total carbon (TC), total nitrogen (TN), mineralizable nitrogen (MN), bulk density (BD), and macro-porosity (MP). Combining the basic soil properties with other secondary data such as climatic conditions, soil types, and land-use patterns, experts established soil response curves and color-coded soil attributes with green, orange, or red color. Where “green” represents optimal growth conditions, “orange” represents potential impact and “red” represents unfavourable crop growth [74].

The SINDI method is easy to manipulate and offers adequate guidelines for local crop production in New Zealand. However, it has regional limitations. Besides, the assessment process primarily uses a single attribute as the assessment unit and does not fully consider other attributes to develop a comprehensive index.

3.5. Other Soil Health Assessment Methods

In the modern era of the green revolution, stakeholders focus on soil functions not only for food production but also for its services related to the ecosystem. The leading functions of agricultural soil in the ecosystem include primary productivity, water and nutrient regulation, maintenance of soil biodiversity, carbon sequestration, and climate regulation [7]. SH assessment based on soil functions is different from the assessment of several attributes, as various soil functions act as assessment units, and a single function may contain several chemical, physical and biological attributes [11]. Recently several studies have been carried out representing this perspective, such as multivariate SHI [4], “Interactive Soil Quality Assessment in Europe and China for Agricultural Productivity and Environmental Resilience” (iSQAPER) [75], and the EU LANDMARK project [65].

Rinot et al. [4] comprehensively reviewed the previous studies and proposed a multivariate conceptual framework for the assessment of soil health-based ecosystem services (ES). This index quantifies the relationship between soil attributes and ES. Furthermore, it establishes a minimum data set for the most sensitive attributes using autocorrelation, PCA screening, and proper clustering techniques. The potential soil attributes will be transformed into 0–100% standardized score functions. Then, certain coefficients will be assigned through the least-squares model to each attribute. It expresses the weight of each attribute based on its contribution to various soil ecosystem services and the entire model. For each ecosystem service, different Least Square Models can be applied. Finally, a holistic index can be constructed by combining all significant ES. At present, the proposed framework is still at the theoretical stage and has not been used widely.

Similarly, in Europe under the pilot project EU LANDMARK soil health assessment model, the “Soil Navigator decision support model” was developed using the analytical hierarchy process and the decision expert method (AHP-DEX). The evaluation process is based on data mining and machine learning techniques as well as evaluation of the existing database of the project. Furthermore, the soil’s physical, chemical, biological, management, and metrological parameters are used as input in the model. These characteristics are evaluated and graded separately presenting the final result as high, medium, and low levels of different soil functions. In addition, this system also offers an evaluation of alternative parameters based on the user’s demand and soil management practices and offers targeted SH improvement programs at the field scale.

Moreover, many other soil assessment programs were developed focusing on soil quality, biodiversity, and other functions. For instance, Huber et al. [76] proposed a common European soil monitoring framework to monitor the existing activities. In support of the common European soil monitoring framework, other projects like the ENVASSO (Environmental Assessment of Soil for monitoring) [77] and the RECARE (Preventing and Remediating Degradation of Soils in Europe through Land Care) [78] were launched to assess the soil threats as well as to monitor the implementation of directives of European soil monitoring framework [79].

Furthermore, the “Interactive Soil Quality Assessment in Europe and China for Agricultural Productivity and Environmental Resilience” (iSQAPER) was co-founded by the European Commission, the Government of Switzerland, and the Government of China, to develop an interactive soil quality assessment application (SQAPP) to evaluate the effect of agricultural land management measures on the properties and functions of soil [80].

Besides, Li et al. [5] used the Moderate-resolution Imaging Spectroradiometer (MODIS) and normalized difference vegetation index (NDVI) time-series data in the establishment of SHI, which reflects the level of regional SH. Researchers combine the soil attributes, climatic conditions, crop productivity, and other environmental factors to indicate the SH status. In this method, crop productivity is assessed based on the difference between the near-infrared spectrum and the red spectrum [81]. Although, several methods have been used to study the soil primary productivity using the NDVI profiles. However, these methods were based on assumptions, phonological information, and trends of the NDVI profiles, and only a qualified person can operate such a complex assessment index [5].

3.6. Visual Soil Assessment Approaches

Contrary to analytical assessment approaches that require laboratory facilities, visual assessment approaches focus on qualitative indicators that can be easily carried out in the field and provide results immediately. Commonly, this method is used as a rudimentary monitoring system in the case of the non-availability of technological means.

This approach is useful in educating the farmers and enhancing their communications with scientists. For instance, the Grow Observatory was developed in the UK to design a simple field base and educational tool to support farmers and other stakeholders in soil management strategies [82]. The visual assessment approaches are commonly categorized into two methods; the whole profile method and the spade method [83]. The whole profile method provides the results more comprehensively, while the spade method is easier to perform and thus more appropriate for farmers [84].

The whole Profile method was recommended by Batey [85], which offers a general technique to assess the impact of anthropogenic activities on soil structure and quality through a soil profile study. The aggregate stability, clay content, the existence of pans, the presence of compaction, and the shape and size of aggregates are evaluated in this approach. The spade method requires soil sample blocks up to 50 cm in depth via spade extraction for analysis. The spade method can be performed either by aggregate exposure method or drop test method. Both procedures assess the impact of anthropogenic activities on the soil structure. However, the latter was more comprehensive with the option of providing a single score result at the end [86].

Many soil visual assessment Approaches were developed to assess the SH conditions including Visual Soil Assessment (VSA) in New Zealand [66], Muencheberg Soil Quality Rating (M-SQR) in Germany [67], SOILpak, and VS-Fast in Australia [71,87], Profile cultural in France [68], Peerlkamp in the UK [69], Visual evaluation of soil structure (VESS) in Brazil and UK [70] and SH score cards [88]. Most of these approaches mainly deal with soil structure, texture, and consistency, while rarely with land degradation and productivity [89]. The inexpensive equipment, straight forward interpretation, and immediate results are undoubtedly strengths of visual soil assessment however, the result of the visual assessment is different from the laboratory, as it could not predict the biochemical characteristics of soils.

In conclusion, the review of SHI development approaches and the current soil assessment methods led to the conclusion that these indices are usually based on specific soil attributes that are generally nominated by researchers merely, and the subjectivity component is a common deficiency in the previous indices, and the involvement of stakeholders such as farmers, land managers, and consumer in the evaluation process is negligible, as none of them performed any leading role in the whole process. Therefore, an imperative shift was proposed in the assessment process of SH and developed a new SH assessment approach by incorporating the priorities of these stakeholders in the entire process, classifying the attributes based on their consideration, and unifying the attributes into a single SHI to boost the sense of ownership and reciprocity toward the research results.

4. A New Soil Health Assessment Approach

It was revealed by the review that most of the previous approaches were based on specific soil functions, threats, or ecosystem services, and they were unable to unify the considerations of key stakeholders in a single index. Consequently, these approaches have rarely been adopted and implemented by farmers, land managers, policymakers, and end-users. Therefore, a new approach has been established based on the principal soil properties and the considerations of potential stakeholders.

In this study, the potential stakeholders were classified into three categories; soil experts or scholars, land managers or farmers, and end-users or owners based on the study carried out by Bünemann et al. [11].

4.1. Design and Description

This evaluation approach is designed as “three sets of dual index systems”, and these three sets include the soil function index system (i), the nutrition index system (j), and the output index system (k). The dual indexing includes the current value of function index (Ci) and its expected value (Ei), the Current value of nutrition index (Cj) and its expected value (Ej), and the Current value of output index (Ck) and its expected value (Ek). In addition, the three-set index system is further divided into three levels, including the major soil functions and their respective principal soil properties. Moreover, the expected value represents the farmlands with the best performance and the current value represents the targeted soils. The indicators included in the soil function index system (i) the nutrition index system (j), and the output index system (k) are mainly considered by soil experts, land managers, and owner/end-user, respectively. The detailed scheme of the novel index system and the rationale for the selection of attributes based on soil functions are given in Table S1 (supplementary material).

4.2. Determination of Current Value (C)and Health Value or Expected Value (E)

This approach deals with a two-way analysis system, and it examine the results and predicts the health status of problematic soil (targeted soils) compared with the best performing soil. The criteria for best-performing soils are soil productivity, yield, grain quality- weight, the growth rate of the previous crop, and resistance to disease incidence. The reference soil should be from the same soil type with the best performance as a health criterion so that the unhealthy attributes that are deteriorating the soil health would be identified. The determination of SH status by developing an SHI is given in Figure 2.

In the first step identification of best-performing soils and farmlands (targeted soils) need to be done in order to measure E and C values respectively. Next to identification is the sampling of soil from targeted farmlands, and the samples should be collected from the tillage layer (0–20 cm) before the application of fertilizer or after harvest so that the actual soil health status can be predicted. Three potential soil sampling methods are identified for this approach (a) Composite sampling: collect 5–8 representative samples from each field, then a composite sample should be obtained by mixing all the representative samples, (b) Ring knife sampling: collect 3–5 samples from undisturbed soil for each field, using the ring cutter-100 cm³, and (c) Microbial sampling: a composite sample should be collected, and it should be stored at 4 °C to avoid any biochemical change that may disturb the microbial populations.

The second phase of the process involves the analysis of the collected soil samples. The values obtained thus serve to evaluate the status of each attribute of respective index systems to calculate the C and E values:

• Soil function index-i: The attributes like field water capacity and the wilting coefficient will be measured by pressure membrane instruments from ring knife soil samples, while soil bulk density is measured directly using the same soil samples, whereas soil organic matter, cation exchange capacity (CEC), pH, and enzyme activity will be determined by conventional analytical methods using composite soil samples, and soil microbial biomass, functional genes, and groups will be determined by high-throughput sequencing using composite refrigerated soil samples.

• Soil nutrient index-j: The total and available amount of soil macro and micro elements will be determined through conventional analytical methods as described by Liu et al. [90].

• Soil output index-k: The yield and income should be calculated through conventional tools and sale registration calculations, respectively, whereas the product quality should be measured according to the corresponding product quality measurement method. The analytical results of both high-performing soils and targeted soils will represent the E and C values.

4.3. Evaluation of Soil Health

Based on analytical results, the current SH status would be obtained using the ratio of three sets of the dual index systems. The new index system uses the current index value divided by the health or expected value index, and it evaluates the health degree according to the number of indicators of the ratio and their deviation degree from 1 (R-1). The deviation from 1 indicates the difference with the healthier status. Thus, the farther the number of attributes and their ratios deviate from “1”, the unhealthier it is. The ratio of three sets of a dual index system would be obtained by the following formulae.

• a. 

  Current value of function index (Ci)/Expected value of function index (Ei) = Ri

• b. 

  Current value of nutrition index (Cj)/Expected value of nutrition index (Ej) = Rj

• c. 

  Current value of output index (Ck)/Expected value of output index (Ek) = Rk

The ratio Ri, Rj, and Rk should be used to evaluate the health status of soil function, nutrition, and output indexes, respectively. For the determination of qualitative indicators, such as the number of major functional genes, microbial groups, and product quality, the ratio should be calculated after assigning values (0–1.0) for each parameter hypothetically. For example, more, medium, less, little or good, general, low, and extremely poor, the values can be assigned as 1, 0.8, 0.6, and 0.4, respectively. The evaluation methods for health degrees are presented in Table 2.

4.4. The Advantages of This System and Method

This novel SHI is designed while taking into account the priorities of stakeholders that comprehensively address the requirement of soil experts, land managers, and end-users in a pertinent and systematic way. The soil health indicators are classified based on key soil functions and the needs of the key stakeholders. The SH is obtained by comparing soil with the best performance and targeted soil using their ratio and degree of deviation from 1. For example, if the ratio of soil pH is far deviated from 1, meaning the evaluated soil needs the adjustment of alkalinity or acidity. This approach will facilitate the soil experts, land managers, owners, and end-users to understand the soil health status, the management practice, and required management strategies.

5. Conclusions

The expansion in the world population exerts considerable pressure on the earth’s soil resources resulting in the deterioration of soil health. Given the population explosion, it has become a global challenge to provide food, fiber, and fuel to such a burgeoning population, and without the maintenance and protection of soils, it is not possible to provide these necessities to the future generations. Therefore, the world is in dire need of devising SH evaluation strategies so that this precious resource could be managed sustainably. In this study, the previous SH evaluation models were reviewed with all their strengths and drawbacks, and a new model was proposed to access soil health that not only takes into account the concerns of all the stakeholders but also provides a unified index system. This approach suggests the “Three Sets of Dual Evaluation Index System” to evaluate the SH of agricultural lands. The key stakeholders and their priorities have been classified and all their priorities have been unified into a single index to enhance the implementation and adaptability of this index. This approach is going to facilitate the soil experts, land managers, owners, and end-users to understand the soil health status, the management practices, and required management strategies for maximum productivity without compromising the soil health.

Author Contributions

Conceptualization: G.L. and Z.H.; supervision, G.L.; G.L. and Z.H. collected and compiled the literature; Z.H., L.D., X.W., R.C. and G.L. designed the structure of the literature review paper, analyzed, synthesized and wrote the paper. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures and Tables

Figure 1. Conceptual framework of existing Soil Health Indices development.

Figure 2. Graphical description of proposed soil health assessment index.

Table 1

Commons soil health assessment methods with pros and cons.

Analytical Soil Health Assessment Methods
Methods	Country	Pros/Principle	Cons
Comprehensive Assessment of Soil Health (CASH)	USAMoebius-Clune et al. [6]	Evaluate soil health, address soil health degradation	Does not consider the weight of attributes, high accuracy on the field, and low at a regional scale.
Soil Management Assessment Framework (SMAF)	USAAndrews et al. [55]	Evaluate quality and vulnerability to change and management practices	The selection of attributes is not flexible, Development of MDS confronts subjectivity
Meta-Analytic Hierarchy Process (Meta-AHP)	ChinaXue et al. [13]	Consistent and sensitive SH assessment approach for different land use treatments	May confront lack of simplicity and methodological transparency,required competent expert
Soil Indicator Assessment (SINDI)	New ZealandLilburne etal. [64]	The online tool, easy to access, compares the measured properties with the database information. Use for environmental reporting	Has regional limitations,uses a single attribute as the assessment unit
Quantifying Soil Attributes and ES	IsraelRinot et al. [4]	Use of soil ecosystem services (ES) as a target value, quantify the relative contribution of indicators to each ES	This framework is still at the theoretical stage and has not been used widely.
Soil Navigator decision support model	EUEU LANDMARK project [65]	Data mining and machine learning techniques assess basic soil properties, metrological and management parameters	May confront lack of simplicity,Required competent expertRegional limitations
Visual soil health assessment methods
Visual Soil Assessment (VSA)	New Zealand Shepherd et al. [66]	Assess soil qualitySpade method	The result of the visual assessment is different from the laboratory as well as it couldn’t predict the biochemical characteristics of soil solely.
Muencheberg Soil Quality Rating (M-SQR)	GermanyMueller et al. [67]	Assess yield potential concerning soil propertiesPit method
Profil cultural	FranceRoger Estrade et al. [68]	Assess soil structure,trench method
Peerlkamp	UKBall et al. [69]	Assess soil structure,Spade method
VESS	BrazilGuimaraeset al. [70]	Assess soil structure,Spade method
SOILpak	AustraliaMcKenzie [71]	Assess soil structure, root growthSpade method

Table 2

Evaluation method of health degree.

The Ratio (Ri, Rj, Rk) Deviated From “1,” and Their Health Grading		Classification of Soil General Health Status
Ratio ( Ri, Rj, Rk )	Level and Their Health Grading	Number of Indicators q = m + n + p *	Health Level
$0\le \left|\mathrm{R}i \mathrm{or} \mathrm{R}j \mathrm{o}\mathrm{r} \mathrm{R}k-1 \right|<0.2$	0, Health	≥80% of indicators meet level “0”	Healthy
$0.2\le \left|\mathrm{R}i \mathrm{or} \mathrm{R}j \mathrm{o}\mathrm{r} \mathrm{R}k-1\right|<0.4$	1, Sub health	≥20% of indicators meet level “1”	Sub healthy
$0.4\le \left|\mathrm{R}i \mathrm{or} \mathrm{R}j \mathrm{o}\mathrm{r} \mathrm{R}k-1\right|<0.6$	2, Weak	≥20% of indicators meet level “2”	Weak
$0.6\le \left|\mathrm{R}i \mathrm{or} \mathrm{R}j \mathrm{o}\mathrm{r} \mathrm{R}k-1\right|$	3, Degraded	≥20% of indicators meet level “3”	Degraded

* q is the sum of all three indices, whereas, m is the total number of indicators from “index-i”, n is the total number of indicators from “index-j”, and p is the total number of indicators from “index-k”. * If the number of indicators becomes <80% but falls into two health categories then the first level will consider.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su14159300/s1, Table S1: Three sets of dual index systems and rationale for the selection of soil attributes.

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