Diagnostic Limitations in Soil Health Frameworks for Tropical Perennial Systems: A Critical Review and Implications for Regenerative Agriculture in Southeast Asia

Abstract

Tropical perennial systems in Southeast Asia, including oil palm, rubber, and cocoa, are established on highly weathered soils under monsoonal climates that differ fundamentally from temperate systems for which most soil health frameworks were developed. Growing certification, environmental, social, and governance (ESG) reporting, and regenerative agriculture requirements demand demonstrable soil health outcomes, yet the diagnostic infrastructure needed to generate and verify such outcomes remains limited. Existing approaches rely on episodic, indicator-based assessment that captures static conditions rather than functional state, applying benchmarks from dissimilar agroecological contexts with limited basis for resolving system readiness, biological constraint, or functional trajectory. This creates a persistent diagnostic gap between measurement and decision-making, disproportionately affecting smallholders required to meet compliance frameworks calibrated for fundamentally different systems. This critical review synthesizes literature on conventional soil testing, indicator-based frameworks, monitoring programs, and biological diagnostics to identify the structural origins of this gap. Soil health surveillance is identified as a review-derived diagnostic logic, i.e., longitudinal, function-oriented, and crop-calibrated, integrative across chemical, physical, and biological dimensions, and tiered from field-level screening to longitudinal datasets supporting monitoring, reporting, and verification (MRV) reporting. Empirical calibration, context-specific threshold development, and longitudinal validation at the individual system scale are identified as research priorities for operationalizing governance-ready soil health assessment in tropical perennial agriculture.

1. Introduction

Southeast Asia supports some of the world’s most extensive perennial cropping systems, dominated by oil palm (Elaeis guineensis), rubber (Hevea brasiliensis), and cocoa (Theobroma cacao), alongside diverse smallholder fruit tree and agroforestry systems across Malaysia, Indonesia, Thailand, and the Philippines [1,2,3,4]. These crops are cultivated mainly on highly weathered Oxisols and Ultisols under strongly seasonal monsoonal climates, where repeated wet–dry cycles drive nutrient transport, organic matter turnover, and biological activity [5,6,7,8]. Production systems are also institutionally diverse, ranging from large plantation estates to resource-constrained smallholders with limited access to analytical services.

Sustainability governance in Southeast Asian perennial agriculture is increasingly shaped by certification, ESG reporting, and regenerative agriculture programs that demand demonstrable soil health outcomes [9,10,11]. Yet the diagnostic infrastructure needed to generate, interpret, and verify such outcomes remains limited. As a result, sustainability claims are often anchored in practice adoption rather than functional evidence, while verification systems assess compliance without evaluating outcomes. The focus of this review on critiquing the applicability of temperate-derived soil health frameworks to Southeast Asian perennial systems requires explicit justification. Such frameworks are evidently not designed for tropical conditions, yet they are nevertheless applied in practice, not because practitioners are unaware of this mismatch, but because certification schemes, ESG reporting requirements, and regenerative agriculture standards originating primarily in European and North American markets impose assessment frameworks on SE Asian producers through market access conditions and supply chain requirements [9,10,11,12]. The diagnostic infrastructure gap is therefore not a failure of awareness but a failure of governance, i.e., the absence of context-appropriate assessment tools capable of meeting verification requirements that were designed for fundamentally different agroecological contexts. This distinction between knowing a framework is inappropriate and having a viable alternative is the gap this review addresses (Figure 1).

This gap has structural origins. Prevailing soil health frameworks were developed largely for temperate annual systems and are often applied uncritically to tropical perennial contexts that differ in soil weathering history, biological dynamics, management timescales, and producer diversity [26,27,28,29]. Under these conditions, generalized frameworks risk functioning less as diagnostic tools than as compliance burdens.

This review argues that resolving this gap requires soil health surveillance, understood here as longitudinal, function-oriented, and crop-calibrated observation that links soil assessment to certification, MRV, and ESG reporting through a common diagnostic logic [27,30].

1.1. The Limits of Practice-Led Regeneration Without Diagnosis

Several explanations for inconsistent regenerative agriculture outcomes are well established. Climatic variability, soil degradation legacies, and differences in management intensity are frequently cited as drivers of uncertainty in soil function and crop response [31,32,33]. While supported by substantial evidence, these explanations often assume that regenerative practices are broadly appropriate across sites, with underperformance attributed to external variability or imperfect implementation [29]. This framing treats regeneration as a set of practices rather than as an intervention contingent on soil system state [34].

A key limitation of many regenerative initiatives is the absence of a formal diagnostic step prior to practice deployment [29]. Management decisions are commonly guided by generalized principles or isolated indicators, without integrated diagnosis of soil system state or constraint. As a result, identical practices may be applied to soils with fundamentally different buffering capacities, leading to variable or counterproductive outcomes [26,35].

In annual systems, regeneration is typically associated with practices such as reduced disturbance, cover cropping, and rotational diversity, which address structural features such as bare fallow periods and frequent soil disturbance [36,37]. Southeast Asian perennial systems differ fundamentally. Oil palm, rubber, cocoa, and agroforestry systems maintain continuous canopy cover and root presence, while rotation and livestock integration are constrained by long crop cycles and system structure [3,13,38]. Under these conditions, regenerative management is necessarily oriented toward improving biological function, organic matter dynamics, and spatial heterogeneity within fixed systems [24,39,40,41]. The resulting diagnostic challenge is therefore not whether regenerative principles are applicable, but whether the soil system state is sufficiently characterized to determine which practices are appropriate and in what sequence.

1.2. Scope and Objectives of This Review

This paper is a critical narrative review examining why regenerative agriculture outcomes remain inconsistent in Southeast Asian perennial systems by addressing the diagnostic limitations of existing soil health assessment approaches. The review identifies the requirements for a governance-ready diagnostic logic and evaluates the extent to which current paradigms support effective and equitable regenerative management.

The analysis synthesizes literature on conventional soil testing, soil health frameworks, monitoring systems, and biological diagnostics. It focuses on soil health as the diagnostic foundation of regenerative agriculture, the dimension that most directly determines intervention relevance and constrains governance credibility. Other outcomes, including climate resilience and biodiversity recovery, are understood as contingent on the soil system conditions that this framework addresses [26,37,42]. The contribution is conceptual, developing a diagnostic logic that integrates established foundations into a crop-calibrated, decision-oriented framework for tropical perennial systems [27,43,44]. Specifically, the review (i) synthesizes evidence that soil health is a relational, crop-dependent property; (ii) examines the limitations of episodic, indicator-based assessment in dynamic tropical soils; and (iii) proposes a surveillance-based diagnostic approach linking chemical, physical, and biological functions to system state and intervention risk. Empirical calibration, threshold development, and longitudinal validation are identified as priorities for future research. The aim is not to introduce a new assessment methodology but to integrate existing diagnostic concepts into a coherent framework that supports governance-relevant soil health assessment.

2. Methods for Literature Review

This study adopts a critical narrative review approach to synthesize literature on soil health assessment, monitoring, and biological diagnostics relevant to regenerative agriculture in tropical perennial systems. Literature was identified through targeted searches of Web of Science, Scopus, and Google Scholar, supplemented by backward and forward citation tracking from key review and conceptual papers, covering literature published from 2000 onwards with inclusion of older seminal papers where these remained foundational. Over 100 publications were examined, from which the most relevant studies were selected based on their contribution to assessment purpose, diagnostic interpretation, monitoring logic, biological diagnostics, and relevance to tropical perennial systems. Because this review employed an iterative thematic synthesis rather than a systematic screening protocol, exact record counts were not retained. Search terms covered five domains: (i) conventional soil testing and fertility assessment; (ii) soil health and soil quality frameworks; (iii) soil and land monitoring or surveillance; (iv) regenerative agriculture evaluation; and (v) biological diagnostics, including microbial and enzyme-mediated processes. Inclusion criteria required studies to be peer-reviewed primary research, systematic reviews, or authoritative conceptual papers. Gray literature, conference proceedings, and opinion pieces were excluded unless they provided foundational conceptual framing extensively cited within the peer-reviewed literature; certification standards and governance documents were included where they constituted primary sources for the governance analysis in Section 9. Selection prioritized studies addressing assessment purpose and decision context, with particular emphasis on tropical or highly weathered soils, perennial cropping systems, and temporal or spatial variability. Geographic bias toward temperate systems is an acknowledged limitation; where Southeast Asian primary studies were available, they were prioritized, and their relative scarcity motivates the research priorities identified in Section 10. Evidence was synthesized thematically across five paradigms: (i) fertility testing; (ii) indicator-based frameworks; (iii) soil and land monitoring programs; (iv) soil health surveillance as a diagnostic concept distinct from population-level monitoring; and (v) biological diagnostics. This paper does not follow systematic review reporting guidelines; the synthesis is thematic and critical rather than exhaustive or quantitative, and no PRISMA checklist or flow diagram is included.

3. Existing Paradigms in Soil Health Assessment and Monitoring

3.1. From Fertility Testing to Indicator Frameworks

Soil assessment in agricultural systems has historically been anchored in chemical fertility testing, focused on parameters such as soil pH, extractable phosphorus, exchangeable potassium, and mineral nitrogen [45,46]. This paradigm was developed to support fertilizer recommendations and yield optimization in input-responsive annual systems. In response to the limitations of purely chemical assessment, indicator-based soil health frameworks expanded measurement to include physical and biological attributes. Examples include the Cornell Comprehensive Assessment of Soil Health, the Soil Management Assessment Framework, and indicator suites developed by the Soil Health Institute, which combine chemical, physical, and biological metrics into composite scores intended to reflect overall soil condition [27,44]. These frameworks were primarily designed for benchmarking and comparison across management systems.

At broader scales, soil and land health surveillance approaches have emphasized repeated measurement, spatial coverage, and temporal consistency [30]. These approaches have been used mainly for regional and national condition assessment and long-term reporting rather than crop-specific diagnosis at an operational scale [47,48].

The distinction between soil fertility and soil health is central to the argument developed here. Soil fertility is a narrow chemical concept concerned with the capacity of soil to supply mineral nutrients at rates sufficient to meet crop demand [43,46]. Soil health, by contrast, refers to the continued capacity of soil to function as a living system, encompassing biological activity, physical structure, water relations, and carbon dynamics alongside nutrient availability [26,43]. Throughout this review, ‘soil health’ is used consistently in preference to ‘soil quality’ to reflect the living-systems framing that underpins the diagnostic argument, i.e., soil health explicitly encompasses the biological and dynamic dimensions of soil function that ‘soil quality’ in its original agronomic usage did not always foreground [26,43]. ‘Soil quality’ appears only where citing papers that use this terminology specifically, in which case the original authors’ terminology is preserved. The limitation identified in this review is therefore not simply the continued use of fertility testing, nor the absence of biological and physical indicators. It is that even when such indicators are measured, assessment often remains snapshot-oriented and disconnected from crop-specific functional requirements and temporal trajectories. The problem lies in diagnostic architecture rather than in the chemical emphasis of fertility testing alone. Several compound terms derived from this functional framing recur throughout this review and are defined here for consistency:

Key terms used throughout this review:

•

Functional state—the current capacity of the soil system to perform its chemical, physical, and biological roles in supporting crop production and ecosystem function, assessed through the integrated status of all three dimensions rather than through any single indicator.

•

Functional capacity—the potential of the soil system to perform these roles under given management and environmental conditions, which may differ from realized performance where constraints are present.

•

Functional constraint—a specific limitation in one or more dimensions that restricts the soil system from performing its roles at the level required by the crop, i.e., the primary diagnostic target of surveillance.

•

Functional transition—a sustained shift in functional state over time, distinguished from transient fluctuation by its persistence across sampling events and its inferability from trajectory rather than point-in-time values.

•

Functional trajectory—the direction and rate of change in functional state over time, i.e., the primary interpretive output of longitudinal surveillance and the basis for intervention risk assessment and decision gating.

Table 1. Dominant paradigms in soil assessment and observation identified in the reviewed literature and their stated diagnostic scope and limitations.

Paradigm	Primary Distinguishing Feature	Primary Purpose (as Stated in the Literature)	Temporal Logic	Typical Outputs	Diagnostic Claims	Recurrent Limitations Highlighted in Reviewed Studies	References
Conventional soil testing/fertility assessment	Chemical measurement at a point in time to guide input decisions; does not incorporate biological or physical dimensions or temporal context	Input recommendation and deficiency correction	Single time point or periodic	Chemical properties (e.g., pH, extractable nutrients)	Short-term nutrient sufficiency	Limited ability to resolve temporal dynamics, biological mediation, or physical constraint; weak inference on system trajectory	[6,26,27,29,43,46]
Indicator-based soil health frameworks	Multi-dimensional indicator aggregation to benchmark or score overall soil condition; does not prescribe diagnostic logic or intervention sequence	Benchmarking, comparison across management systems, composite scoring	Predominantly snapshot	Multi-indicator dashboards or indices	Overall soil condition or “health status”	Indicator accumulation without explicit diagnostic logic; limited transferability across crops, soils, and climates	[11,26,27,44,49,50]
Soil and land monitoring program	Repeated measurement at population or landscape scale to detect trends and report condition; designed for policy reporting not operational management decisions	Trend detection and reporting at regional or national scale	Repeated observation	Time-series datasets, spatial maps	Population-level change	Often not crop-calibrated; limited resolution for field-level decision-making. Primary interpretive question is population-level condition change, not individual system readiness or management decision support.	[11,30,42,47,48]
Biological diagnostics (process proxies)	Process-level measurement of biological activity to infer functional capacity; addresses biological dimension only and requires contextual integration with chemical and physical data	Inference of biological mediation and process capacity	Responsive; can be repeated	Activity-based or stoichiometric indicators	Functional constraint beyond chemical stocks	Method sensitivity and environmental contingency; interpretation requires contextual integration	[26,51,52,53,54,55]
Soil health surveillance (organizing concept derived from review)	Crop-calibrated, trajectory-based interpretation of repeated multi-dimensional observations to support individual management decisions; the interpretive logic, not the measurement itself is the defining contribution	Diagnosis of system state, trajectory, and intervention risk	Longitudinal; trajectory-based	Interpretable temporal patterns	Crop-specific readiness to respond; direction, rate, and management implication of functional change at individual system scale—individual decision-support rather than population reporting.	Requires empirical calibration (thresholds, frequency) and supporting data infrastructure	[27,28,30,56,57]

Note: The paradigm categories in this table represent primary diagnostic logic and intended application rather than mutually exclusive practices. In implementation, these approaches frequently co-occur, i.e., conventional soil testing is routinely incorporated into monitoring programs, and biological diagnostics may be integrated into both indicator frameworks and surveillance systems. The boundaries shown reflect differences in primary purpose, interpretive logic, and decision relevance rather than differences in measurement type alone. Soil health surveillance (Row 5) is distinguished from soil and land monitoring programs (Row 3) not by the use of repeated observation, which both employ, but by the crop-calibrated interpretive architecture applied to those observations and the individual management decision-support function it serves.

summarizes the dominant paradigms through this lens.

A recent continental-scale framing by Campbell and team [58] offers a complementary taxonomy of soil quality assessment and similarly distinguishes soil health as a more holistic concept from narrower function-specific assessment. This supports the diagnostic distinction advanced here, although Campbell et al. address continental monitoring rather than crop-specific operational decision support.

3.2. Limits of Indicator Accumulation and Yield-Based Assessment

Before examining these diagnostic limitations, it is important to establish that routine soil testing and soil health surveillance are complementary approaches serving different and legitimate purposes. Routine soil testing remains an appropriate and valuable tool for its intended function, i.e., guiding short-term fertilizer decisions, correcting nutrient deficiencies, and supporting agronomic management at the field scale. The limitations examined in this section arise not from any inherent inadequacy of chemical testing as a method, but from applying snapshot chemical assessment to diagnostic questions, i.e., system readiness, functional trajectory, and intervention risk that require temporal and functional resolution it was not designed to provide. A common response to diagnostic limitations has been to add more indicators. Yet evidence suggests that increasing the number of measured variables does not necessarily improve interpretability or management relevance [11,26,27]. Practitioners are often presented with multi-dimensional dashboards without a clear logic for interpreting system state, trajectory, or intervention risk.

Yield is similarly limited as a diagnostic indicator. Although observable and relevant to management, it captures only one dimension of soil health, which the literature defines as multifunctional, including carbon sequestration, hydrological regulation, biodiversity support, and biological process capacity [26,27]. A soil may sustain yield while experiencing functional decline, and improvements in soil biological function may not translate immediately into yield response, particularly in perennial systems with long management timescales and compensatory inputs [29,32]. Yield is therefore treated here as a practical agronomic proxy rather than a reliable indicator of soil health.

4. Why Tropical Perennial Systems Expose Diagnostic Limitations

4.1. Soil Function Beyond Fertility Assessment

The characterization of highly weathered tropical soils as “poor” reflects a legacy of temperate-centric soil science in which productive capacity is assessed primarily through chemical fertility proxies, such as extractable nutrients and pH, without integrating the biological and physical processes that regulate nutrient availability [5,6]. Across Southeast Asia, soils are often described as chemically constrained due to low base saturation, acidity, and high phosphorus sorption arising from intense weathering. Within conventional fertility paradigms, these properties are interpreted as indicators of low productive potential. However, many perennial crops, including oil palm, rubber, and cocoa, can sustain production across a wide range of chemical conditions through internal nutrient cycling, deep rooting, and strong plant–microbe interactions [13]. Chemical values interpreted as “poor” under annual-crop frameworks do not necessarily correspond to functional limitation in perennial systems and may obscure substantial within-system heterogeneity [3]. Empirical evidence from oil palm systems illustrates this limitation. Studies show that microbiological properties, including microbial biomass and enzyme activity, are more sensitive indicators of management-induced change than chemical metrics, which often show limited differentiation under comparable conditions [14,17,18,19,59]. In cocoa systems, organic management has been shown to significantly enhance soil microbial community diversity and soil health indicators beyond what chemical metrics capture, demonstrating that biological assessment adds diagnostic value not available from fertility testing alone [59]. In Indonesian cocoa systems specifically, cover crop management produces measurable tradeoffs in microbiome composition and ecosystem service delivery that chemical assessment alone cannot resolve [24]. At a landscape scale, soil organic carbon variability is only partly explained by management, with soil type and land-use history accounting for a large proportion of heterogeneity [15]. These findings indicate that chemical stock measurements alone do not capture the functional determinants of soil response.

4.2. Temporal Dynamics and Structural Heterogeneity

Highly weathered tropical soils exhibit strong temporal dynamics in physical and biological processes. Under humid tropical conditions, soil structure, redox environment, and microbial activity shift rapidly in response to rainfall, temperature, and management, such that snapshot assessments may capture transient states rather than longer-term functional capacity [8,60,61]. Monsoonal rainfall further amplifies this variability through repeated wet–dry cycles that influence nutrient transport, denitrification, and microbial turnover [7,62].

Empirical evidence from Southeast Asian perennial systems reflects these dynamics. Large-scale survey data from oil palm plantations in Peninsular Malaysia show substantial variation in soil organic carbon across sites and survey periods that cannot be predicted from chemical status alone [63]. Multi-site trials in Kalimantan and Sumatra similarly demonstrate divergence in soil health indicators over time under contrasting management regimes, with yield responses remaining variable despite improvements in chemical metrics [15,16,18,19,64]. Chronosequence studies in rubber systems indicate that major shifts in microbial biomass and activity occur during early years following land-use change, highlighting windows in which snapshot assessments capture transient rather than stable system states [20,21,25]. These findings demonstrate that single-point assessments are insufficient to resolve trajectory or intervention outcomes in dynamic perennial systems. The decoupling between chemical indicators and agronomic response reflects a broader diagnostic limitation that snapshot-based approaches cannot address.

5. Limits of Snapshot Assessment and the Case for Surveillance

5.1. Soil Sampling and Surveillance: Definitions in the Reviewed Literature

In agricultural practice, soil assessment is most commonly operationalized through episodic sampling interpreted against thresholds and reference ranges. Existing frameworks, including national soil health assessments and EU soil monitoring initiatives, have contributed substantially to standardization and policy reporting but have been designed primarily for population-level condition assessment rather than crop-specific diagnostic decision-making [11,30,48].

Surveillance, by contrast, denotes structured, longitudinal observation interpreted with explicit temporal logic rather than single-point status [30,57]. In this review, the concept is extended toward operational decision support through the integration of trajectory interpretation, system state diagnosis, and intervention risk assessment within an indicator-flexible, function-constrained architecture. Figure 2 illustrates this contrast in interpretive logic: both panels depict the same type of point measurements, but Panel (a) evaluates them in isolation against generalized benchmarks while Panel (b) integrates them across time to infer functional trajectory and system state. It is important to note that this contrast is not between measurement types, i.e., both episodic assessment and surveillance rely on individual point measurements but between the interpretive frameworks applied to those measurements. Long-term monitoring programs are themselves composed of repeated point measurements; the complementarity between point measurements and longitudinal observation is therefore inherent to surveillance rather than a distinction between them. This operational distinction is the primary contribution of the surveillance concept: it translates repeated observation into crop-calibrated, decision-relevant diagnosis at the management scale. The term ‘longitudinal’ as used throughout this review refers to this decision-support function, i.e., the repeated observation of the same system over time with the explicit purpose of interpreting trajectory and state. It is not synonymous with ‘long-term monitoring’ in the program sense, though it shares the requirement for repeated observation. Longitudinal in this context means temporally integrated and trajectory-focused at the individual system scale. Key distinctions between episodic sampling and surveillance are summarized in

Table 2. Conceptual comparison of episodic soil sampling and soil health surveillance across seven aspects that collectively define the diagnostic purpose, interpretive logic, and decision relevance of each approach. Aspects were selected to reflect the dimensions along which existing soil assessment paradigms differ most consequentially for regenerative governance: what is being measured for, how time is treated, what scale is addressed, how outputs are used, what form outputs take, where cost–benefit reasoning is directed, and how measurement choices are made. These dimensions are derived from the paradigm analysis in Table 1 and the literature reviewed in Section 3, Section 4 and Section 5 [11,26,27,30,42,57]. The table synthesizes existing literature and does not introduce new metrics, thresholds, or validated decision rules.

Aspect	Episodic Soil Sampling	Soil Health Surveillance
Primary objective	Condition assessment [11,26,43]	Trajectory and state diagnosis [27,30,57]
Temporal logic	Single time point [11,27,46]	Longitudinal [30,57,65]
Scale	Plot/field [43,46]	Field to landscape [30,42]
Typical use	Fertility guidance [43,46]	Risk-informed decision support [29,57]
Output	Indicator values [11,26,27]	Interpretable trends [30,57,65]
Cost–benefit focus	Input cost efficiency [43,46]	Asset resilience and risk management [29,42]
Tool flexibility	Fixed indicator sets [11,27,49]	Adaptive tools within invariant diagnostic logic [26,42,57]

Note: Each row represents a dimension along which the two paradigms differ in diagnostic purpose rather than in measurement capability. The rows are not redundant—Primary objective captures intent, Temporal logic captures how time is treated, Scale captures the unit of analysis, Typical use captures the decision context, Output captures the form of results, Cost–benefit focus captures the framing of value, and Tool flexibility captures the relationship between measurement and interpretive logic.

.

5.2. Diagnostic Value of Trajectory-Based Interpretation

The primary diagnostic advantage of surveillance is trajectory interpretation. In dynamic systems, the direction and rate of change in soil properties often provide more diagnostic information than absolute values measured at a single point [27,65]. Longitudinal data enables differentiation between reversible fluctuations and sustained functional transitions, which is essential for determining whether a system is recovering, stabilizing, or degrading under a given management regime [16,19]. Surveillance also supports resilience assessment: the capacity of a soil system to absorb disturbance while retaining function is best inferred from temporal response, not from static indicator values [20,66]. This capability is particularly relevant in monsoonal environments where temporal variance is high and sampling-date effects are strong [7]. A further contribution is early warning, whereby detecting leading indicators of constraint before yield decline becomes apparent and before functional thresholds are crossed beyond the point of cost-effective remediation [30,42,65,67].

The absence of adequate diagnostic framing produces two recurring failure modes in practice. First, practices are deployed without identifying the dominant constraints limiting soil system response, resulting in interventions that are biologically or physically mismatched to system state, for example, organic inputs applied to soils where structural compaction limits root access or microbial inoculants introduced where habitat constraints preclude persistence [29,37]. Second, outcomes are interpreted without reference to system trajectory: short-term yield stability may be taken as evidence of improvement, even where functional decline is underway and temporarily masked by residual nutrient stocks or compensatory inputs [32]. Both failure modes are magnified in perennial systems, where decisions are embedded in long-lived stand structures, management consequences persist across multiple seasons, and opportunities for course correction are limited [1,13]. Surveillance-based diagnosis reduces both risks by resolving system state before intervention and tracking functional response after deployment.

6. Soil Health as a Crop-Context-Dependent Diagnostic Concept

Soil health is commonly framed as an intrinsic property of soil, expressed through indicator values benchmarked against reference thresholds. The degree to which such thresholds are universal or context-specific varies considerably across frameworks: while some approaches apply generalized reference ranges across soil types and cropping systems, others, including the EU soil monitoring framework, define target values specific to combinations of climate zone, soil type, and land cover, acknowledging that health cannot be assessed independently of ecological and agronomic context [48,58]. This review does not argue that all existing frameworks rely on universal thresholds, but that even context-sensitive frameworks were developed for conditions that differ fundamentally from highly weathered, monsoonal, perennial cropping systems in Southeast Asia and have not been calibrated to the crop-specific functional requirements that determine diagnostic relevance in these systems [26,27]. A relational interpretation in which soil health is evaluated against the specific functional requirements of the crop and management context has long been implicit in agronomy and soil ecology [43,44], but has not been translated into governance frameworks in ways that close the gap [11,29]. Figure 3 illustrates this distinction between descriptive measurement and diagnostic decision value in the context of Southeast Asian perennial systems. As established in Section 4.1, chemical properties interpreted as limiting under temperate benchmarks do not necessarily correspond to functional constraint in perennial systems [13,38]. Conversely, soils that score adequately on chemical indices may be functionally constrained by biological limitation, subsoil compaction, or spatial heterogeneity that standard assessments do not resolve [26,29]. Soil health cannot, therefore, be defined as a universal condition independent of crop, context, and intended use. It is more coherently understood as a relational, crop-dependent diagnostic property, i.e., a characterization that has direct implications for what governance frameworks should require and how certification standards should be constructed.

This relational framing leads to a diagnostic principle that is indicator-agnostic but function-constrained. Rather than prescribing a fixed set of measurements, the diagnostic logic requires three functional dimensions, namely chemical (nutrient availability, buffering, and toxicity control), physical (structure, water relations, and root penetration capacity), and biological (organic matter transformation, nutrient flux mediation, and community function) to be addressed in principle, while leaving indicator choice flexible across monitoring capacities and crop contexts [42,43,57]. Indicators are treated as proxies for these functions rather than as definitions of soil health, and different indicators may validly represent the same functional dimension depending on context [27,68], available methods, and crop sensitivity. Within this logic, the concept does not propose a new index or scoring algorithm; it provides a diagnostic reasoning structure for interpreting existing measurements as evidence of functional constraint, resilience, or readiness to respond. The absence of universal thresholds is deliberate because soil health diagnosis is inherently context-dependent, reflecting crop requirements, soil properties, climatic conditions, and management history. Accordingly, crop-calibrated interpretation of functional state, rather than ranking against a universal health continuum, is the diagnostic objective [26].

7. Biological Mediation and Nutrient Flux: Insights from the Reviewed Literature

7.1. Divergence Between Chemical Sufficiency and Crop Performance

Crop underperformance despite apparently adequate chemical soil status is widely reported and is particularly common in tropical perennial systems [27,38]. The distinction between nutrient stocks and nutrient flux provides a critical explanatory lens: stocks represent the quantity of elements held in soil pools, whereas flux reflects the rate at which these elements become available to plant roots. A soil may contain sufficient total nutrients but release them at rates that do not match crop demand, a discrepancy not captured by conventional chemical testing but resolvable through biological diagnostics. Microbial processes regulate organic matter decomposition, nutrient mineralization, and transformation, mediating the conversion of chemical stocks into plant-available forms [53]. Where biological activity is constrained, due to compaction, acidity, or substrate limitation, nutrient flux may remain limited even under chemically sufficient conditions.

Evidence from oil palm systems illustrates this pattern. Microbial biomass can vary substantially between plantation blocks with similar chemical status, reflecting differences in organic matter availability and soil structure rather than nutrient supply [69]. This indicates that biological capacity cannot be reliably inferred from chemical fertility indicators alone. Such divergence is particularly relevant in perennial systems, where soil–plant–microbe interactions develop over long timescales, and functional constraints may not be evident from single-time-point assessments.

7.2. Biological and Activity-Based Diagnostics Discussed in Prior Studies

Enzyme-based diagnostics provide a practical way to operationalize biological function within a soil health surveillance framework without prescribing a fixed indicator set. Soil enzymes mediate the transformation of organic substrates and link microbial activity to nutrient availability, reflecting functional process capacity rather than static nutrient pools [52]. Their sensitivity to management and compatibility with standard laboratory methods make them useful indicators of the biological dimension of soil function [25,52,70,71]. Ecoenzymatic stoichiometry extends this approach by examining ratios among enzyme activities to infer relative microbial limitation by carbon, nitrogen, or phosphorus [51]. While global analyses suggest a conserved baseline ratio of approximately 1:1:1 for carbon-, nitrogen-, and phosphorus-acquiring enzymes, this balance may shift substantially in highly weathered tropical soils, where strong phosphorus fixation constrains nutrient availability [5,6]. Deviations from this baseline can therefore indicate functional imbalances, such as phosphorus limitation or carbon constraint, that are not captured by conventional extractable nutrient testing.

The diagnostic value of this approach lies in its ability to link observed patterns to underlying constraints and guide intervention. For example, declining enzyme activity alongside stable chemical fertility may indicate biological limitation, suggesting the need to restore substrate availability or soil structure rather than increase nutrient inputs [25].

Empirical evidence supports this interpretation. Studies in Southeast Asian systems show that enzyme activities are strongly suppressed under intensive management compared with organic or less disturbed systems, even where chemical properties differ only moderately [17,19,22,54,55,59,61,70]. Recovery of specific enzyme activities may also lag following management change, indicating that biological responses occur over longer timescales than chemical adjustment. Enzymatic indicators are method-sensitive and context-dependent and do not directly quantify realized nutrient flux. Their value lies in providing accessible, process-level evidence that complements chemical assessment and supports functional diagnosis within a broader surveillance framework.

8. Soil Health Surveillance: Diagnostic Logic Synthesized from Literature

Drawing together the preceding synthesis, four diagnostic principles emerge for soil health surveillance. First, diagnosis must be function-oriented: assessment evaluates whether the chemical, physical, and biological functions required by a given crop are sufficient and stable. Interpretation is therefore crop-calibrated and context-specific rather than based on universal benchmarks [26,42]. Second, diagnosis must be integrative across chemical, physical, and biological dimensions, which are complementary and non-substitutable. Adequacy in one cannot compensate for constraint in another, and biological mediation cannot be inferred from chemical status alone [26,27]. Third, diagnosis must be temporal, incorporating trajectories rather than static conditions. Functional state is best inferred from the direction and rate of change rather than point-in-time values [30,65]. Fourth, diagnosis must be decision-relevant, resolving whether intervention is appropriate, which constraint should be addressed first, and whether the system is moving toward or away from functional readiness [29,57]. These principles distinguish surveillance from indicator-based benchmarking and define a diagnostic logic not tied to specific metrics but to how measurements are interpreted. The framework has conceptual parallels with soil health approaches developed for temperate systems [57], but differs in scope. The surveillance concept developed here is calibrated to highly weathered soils, long-cycle perennial crops, monsoonal dynamics, and heterogeneous producer contexts, where baseline system state is often uncharacterized and diagnosis itself is the primary constraint on effective management.

8.1. Indicator Flexibility and Crop-Specific Interpretation

While diagnostic principles are invariant, their implementation requires flexibility in indicator selection. Crops differ in sensitivity to functional constraints: oil palm is strongly affected by subsoil compaction and phosphorus dynamics, rubber by drainage and aeration, and cocoa by carbon dynamics and mycorrhizal function [13,14,20,21,24,25,59,72]. Surveillance, therefore, does not prescribe fixed indicators but requires adaptive interpretation of measurements against crop-specific functional requirements. Diagnostic emphasis shifts across chemical, physical, and biological dimensions depending on dominant constraints, ensuring decision relevance rather than indicator completeness. The mechanism by which crop-specific sensitivity influences diagnostic emphasis warrants explicit clarification. Crop sensitivity does not translate into fixed indicator weighting in the mathematical sense, i.e., the framework does not assign numerical weights to indicators based on crop type. Rather, crop sensitivity determines diagnostic priority: where a crop is not sensitive to a particular constraint, that constraint requires less investigative effort at a given tier and may not warrant escalation to higher-tier diagnosis unless other evidence suggests it is contributing to performance limitation. For example, if rubber is not sensitive to subsoil compaction under a given set of conditions, penetrometer resistance remains a valid physical indicator but would not be prioritized as a primary diagnostic target unless field observations suggest structural constraint is contributing to underperformance. This is a qualitative prioritization of investigative effort rather than a quantitative weighting of indicator scores. It reflects the principle that diagnostic relevance is determined by the functional requirements of the specific crop in the specific context, not by a fixed algorithm applicable across systems. One further distinction warrants clarification. Crop-specific sensitivity influences diagnostic priority and management urgency; it determines which constraints most immediately require intervention in a given production context. It does not, however, reduce the intrinsic importance of the underlying soil function itself. A soil with impaired biological activity, for instance, remains functionally compromised regardless of whether the current crop expresses visible sensitivity to that impairment. Surveillance addresses both the immediate management question, i.e., which constraint to prioritize now, and the longer-term soil health question, i.e., whether the system as a living entity is recovering, stable, or declining. The distinction between crop-specific functional suitability and general soil health is therefore maintained throughout this framework, and both dimensions inform the diagnostic logic. These diagnostic principles, i.e., function-orientation, integrative assessment, temporal logic, and decision-relevance, can be translated into a practical heuristic for interpreting indicator combinations and prioritizing intervention. To illustrate how these diagnostic principles operate in practice,

Table 3. Heuristic decision matrix for surveillance-informed intervention prioritization in Southeast Asian perennial systems. Entries are illustrative and derived from the diagnostic logic synthesized in this review. Empirical validation of threshold combinations and intervention sequences across crop, soil, and climate contexts is required before operational deployment.

Chemical Status	Physical Status	Biological Status	Diagnosis	Priority Intervention
Adequate	Adequate	Low	Biological limitation—nutrient flux constrained despite adequate stocks and permissive physical conditions	Restore organic substrate and microbial habitat; do not increase nutrient inputs; escalate to Tier 2 biological diagnosis
Adequate	Constrained	Adequate	Structural constraint—root access and water relations limiting despite adequate chemistry and biology	Address compaction or drainage before any other intervention; structural remediation is the first priority
Deficient	Adequate	Adequate	Classical nutrient deficiency—chemical correction appropriate	Standard fertility correction; monitor biological response to confirm uptake improvement
Adequate	Constrained	Low	Compound physical-biological constraint—structure limiting biological habitat	Prioritize structural remediation; biological recovery expected to follow improved physical conditions
Deficient	Constrained	Low	Multi-dimensional limitation—system requires preparatory remediation	Full Tier 2 diagnostic workup before any intervention; regenerative practices not yet appropriate
Adequate	Adequate	Adequate— yield declining	Constraint not resolved by standard three-dimension assessment	Investigate subsoil, spatial heterogeneity, or pest and disease factors; escalate to Tier 2 spatial diagnosis
All dimensions improving	—	—	System in functional recovery—trajectory positive	Continue current management; Tier 3 longitudinal monitoring to confirm sustained trajectory
All dimensions declining	—	—	Functional decline underway—intervention urgent	Identify dominant declining dimension and prioritize remediation; surveillance frequency should increase

Note: “Adequate” denotes that indicators for the given dimension fall within crop-calibrated acceptable ranges for the system under assessment—it does not imply universal sufficiency. Assessment of adequacy requires crop-specific reference ranges derived from regional calibration studies, examples of which are provided for Southeast Asian perennial systems in the references are provided in Section 8.4.

presents a heuristic decision matrix linking functional dimension status combinations to indicative intervention priorities. This matrix is illustrative rather than prescriptive, i.e., it is derived from the diagnostic logic synthesized in this review rather than from empirically validated decision rules, and thresholds and weightings require local calibration before operational deployment. Its purpose is to demonstrate that the framework provides actionable diagnostic reasoning rather than remaining purely descriptive, while explicitly acknowledging that empirical validation of decision pathways across crop–soil–climate combinations is a stated research priority (Section 10).

8.2. Tiered Observation Approaches

Operationalizing soil health surveillance requires approaches that accommodate variation in monitoring capacity and decision scale. A tiered structure enables this by linking diagnostic depth to both system needs and resource context [27,30] (

Table 4. Tiered surveillance design: diagnostic workflow and resource-context implementation. Tiers describe both the diagnostic logic progression (screening → targeted diagnosis → longitudinal monitoring) and the resource contexts within which each tier is primarily, though not exclusively, deployed. Any producer type may engage across tiers as diagnostic need dictates.

Tier	Diagnostic Function	Gate Logic	Indicative Resource Context	Core Measurements	Decision Output
Tier 1	Constraint screening	Flag ambiguity; escalate if performance diverges from chemical status	Smallholder/low resource	Yield trend, visual soil structure, basic chemistry	Constrained or unconstrained; escalate to Tier 2 if ambiguous
Tier 2	Constraint resolution	Identify dominant limiting dimension; sequence intervention accordingly	Managed estates	Tier 1 + expanded chemistry, physical structure proxies, biological activity indicators	Dominant constraint identified; intervention sequence determined
Tier 3	Trajectory monitoring	Interpret direction and rate of functional change across seasons	Industrial/MRV-obligated	Tier 1–2 + Longitudinal chemical–physical–biological (CPB) datasets	Functional trajectory; adaptive management; MRV and ESG reporting

):

Tier 1 provides constraint screening, using a minimal set of interpretable proxies to detect constraint or diagnostic ambiguity. Where crop performance diverges from chemical status, diagnosis escalates.

Tier 2 supports constraint resolution, using targeted measurements to identify dominant limiting processes and guide intervention sequencing across chemical, physical, or biological dimensions.

Tier 3 enables trajectory monitoring, integrating repeated measurements to track functional change over time and support adaptive management, MRV, and ESG reporting [9,10].

Progression across tiers is driven by diagnostic need rather than by routine prescription. The same diagnostic logic applies across scales, while measurement resolution varies with capacity. This structure reconciles accessibility with diagnostic depth. Field-level observations can support screening, while higher-resolution data enable detailed diagnosis and longitudinal monitoring. By separating invariant diagnostic logic from flexible measurement approaches, surveillance supports consistent interpretation across heterogeneous production contexts without requiring uniform analytical capacity.

8.3. Tier 1 Implementation in Resource-Constrained Smallholder Contexts

Smallholders constitute the majority of rubber and cocoa production and a significant proportion of oil palm in Southeast Asia, yet represent the segment least served by existing diagnostic infrastructure [1,2,18,73]. A diagnostic framework that requires laboratory infrastructure for its first tier systematically excludes smallholders from evidence-based regenerative governance. Tier 1 is therefore not a simplified version of the full framework but its intended entry point for the majority of producers in the region, and its design must reflect the resource realities of smallholder operation.

In practice, Tier 1 surveillance in smallholder contexts is conducted through field-observable proxies during routine farm visits, requiring no laboratory access, specialist equipment, or data management systems. These include: visual assessment of soil surface structure and aggregate stability, which can be performed by hand using the slake test or simple drop-shatter method; earthworm count and surface cast observation as a rapid biological activity proxy, implementable in a 30 min field visit; litter decomposition rate estimated by visual inspection of residue breakdown at the soil surface relative to the time since last harvest or pruning; penetration resistance assessed by hand-held rod penetrometer or simple improvised probe, flagging compaction relevant to root access; and basic pH strip testing or digital pH meter where affordable, providing the minimum chemical context needed to interpret biological and physical observations.

These observations, recorded on a standardized field card or mobile application, several of which are now available for low-connectivity environments across SE Asia, generate the minimum dataset for Tier 1 diagnostic screening: specifically, whether crop performance diverges from what chemical status and field biology together would predict. Where divergence is detected, escalation to Tier 2 through cooperative laboratory services, extension officer support, or certification scheme technical assistance becomes the appropriate next step. Recent evidence from Indonesian oil palm smallholdings demonstrates that multi-dimensional soil health assessment at this scale is both feasible and necessary to distinguish management-driven biological change from chemical status alone [18,19].

The equity argument embedded in the tiered design is explicit: Tier 1 provides a diagnostic entry point that is accessible to smallholders without requiring capital investment in laboratory infrastructure, while the escalation logic ensures that more resource-intensive diagnosis is deployed only where field-level screening identifies genuine ambiguity. This structure distributes diagnostic effort according to need rather than capacity and provides a basis for equity-adjusted certification and extension support that the existing snapshot-and-threshold paradigm cannot offer. Figure 4 illustrates how this escalation logic operates under conditions of spatial yield heterogeneity within a perennial management block, demonstrating how Tier 1 field observations trigger Tier 2 biological and physical diagnosis in an underperforming zone where chemical assessment shows no differentiation.

8.4. Indicative Metrics for Crop-Calibrated Surveillance in Southeast Asian Perennial Systems

To illustrate how the tiered surveillance logic can be operationalized, Table 5 presents indicative metrics for three dominant perennial crop systems in Southeast Asia: oil palm, rubber, and cocoa, organized by functional dimension and surveillance tier. The table is intended as a heuristic rather than a standardized protocol, demonstrating how surveillance can be expressed in crop-specific terms using indicators drawn from the reviewed literature. Indicator selection and thresholds remain subject to local calibration and validation prior to operational deployment. Consequently, Table 5 should be interpreted as an illustrative indicator menu rather than a prescriptive assessment protocol. The objective is to demonstrate how crop-calibrated surveillance may be operationalized across contrasting perennial systems while preserving flexibility in local implementation.

Table 5. Indicative soil health surveillance metrics by functional dimension, crop type, and surveillance tier for Southeast Asian perennial systems.

Functional Dimension	Tier	Oil Palm	Rubber	Cocoa	Rationale and SE Asian Source
Chemical	1	Soil pH, extractable P, exchangeable K, Ca, Mg	Soil pH, exchangeable Al, extractable P	Soil pH, exchangeable Ca and Mg, total N	Routine fertility indicators; low cost; interpretable across resource contexts [16,46]
Chemical	2	Soil organic carbon, total N, P sorption capacity, subsoil pH profile	Soil organic carbon, total N, Al saturation (%)	Soil organic carbon, available P, exchangeable cation balance	Resolves buffering capacity and toxicity constraints beyond simple deficiency [5,6,16]
Chemical	3	Longitudinal SOC trajectories, N mineralization potential, P fractionation	SOC stocks by depth, Al toxicity trends across seasons	SOC and N trajectories, Ca:Mg ratios over time	Trajectory interpretation across seasons; supports MRV carbon reporting [10,63]
Physical	1	Visual soil structure assessment, waterlogging observation, compaction by penetration resistance (field rod)	Visual rooting depth assessment, drainage observation	Shade canopy assessment, surface litter depth, visual aggregate stability	Field-observable proxies accessible without laboratory infrastructure [30]
Physical	2	Bulk density, penetrometer resistance at 0–30 cm and 30–60 cm, aggregate stability, saturated hydraulic conductivity	Bulk density, penetrometer resistance, texture and drainage class	Bulk density, aggregate stability, surface organic matter depth, soil moisture retention	Resolves compaction and structural constraints limiting root access and aeration [8,60,74]
Physical	3	Longitudinal bulk density and penetrometer profiles, seasonal hydraulic conductivity, subsoil compaction trends	Longitudinal bulk density and aeration status across wet and dry seasons	Longitudinal aggregate stability and surface organic matter accumulation	Detects management-induced structural recovery or decline across seasons [16]
Biological	1	Earthworm count and surface cast observation, litter decomposition rate (visual), frond decomposition presence	Surface macrofauna observation, root hair density at profile face, litter turnover assessment	Surface macrofauna, fungal hyphal presence in litter layer, visual mycorrhizal root tips	Rapid, low-cost biological proxies reflecting habitat quality and biological activity [39,42]
Biological	2	Microbial biomass carbon, β-glucosidase activity, dehydrogenase activity, acid phosphomonoesterase activity	Microbial biomass carbon, β-glucosaminidase activity, basal soil respiration	Microbial biomass carbon, β-glucosidase activity, mycorrhizal colonization rate	Sensitive to management change; documented in SE Asian perennial contexts [14,24,56,59,69]
Biological	3	Longitudinal enzyme activity profiles (C, N, P cycling enzymes), ecoenzymatic stoichiometry ratios, microbial biomass C:N	Longitudinal microbial biomass carbon, soil respiration trajectories, fungal:bacterial ratios	Longitudinal mycorrhizal colonization, enzyme activity profiles, microbial community composition by amplicon sequencing	Trajectory interpretation of biological function; detects recovery lags documented in SE Asian systems [20,23,51,52]

Note: Tier 1 metrics are designed to be implementable by field technicians without specialist laboratory access. Tier 2 metrics require standard soil laboratory capacity. Tier 3 metrics require repeated sampling infrastructure and, for molecular methods, specialist laboratory access or external analytical services. All thresholds and interpretive ranges require local calibration against crop-specific functional requirements and regional soil classes before operational use; indicative reference ranges for chemical Tier 1 and Tier 2 metrics can be derived from regional soil fertility databases and crop-specific agronomy manuals, but no universal thresholds are prescribed here. Abbreviations used in this table: SOC = soil organic carbon; MRV = monitoring, reporting, and verification; P = phosphorus; K = potassium; Ca = calcium; Mg = magnesium; Al = aluminum; N = nitrogen. Units are not standardized across indicators, as these vary with laboratory method and regional convention; users should refer to the cited sources for method-specific unit conventions.

Table 5. Indicative soil health surveillance metrics by functional dimension, crop type, and surveillance tier for Southeast Asian perennial systems.

Functional Dimension	Tier	Oil Palm	Rubber	Cocoa	Rationale and SE Asian Source
Chemical	1	Soil pH, extractable P, exchangeable K, Ca, Mg	Soil pH, exchangeable Al, extractable P	Soil pH, exchangeable Ca and Mg, total N	Routine fertility indicators; low cost; interpretable across resource contexts [16,46]
Chemical	2	Soil organic carbon, total N, P sorption capacity, subsoil pH profile	Soil organic carbon, total N, Al saturation (%)	Soil organic carbon, available P, exchangeable cation balance	Resolves buffering capacity and toxicity constraints beyond simple deficiency [5,6,16]
Chemical	3	Longitudinal SOC trajectories, N mineralization potential, P fractionation	SOC stocks by depth, Al toxicity trends across seasons	SOC and N trajectories, Ca:Mg ratios over time	Trajectory interpretation across seasons; supports MRV carbon reporting [10,63]
Physical	1	Visual soil structure assessment, waterlogging observation, compaction by penetration resistance (field rod)	Visual rooting depth assessment, drainage observation	Shade canopy assessment, surface litter depth, visual aggregate stability	Field-observable proxies accessible without laboratory infrastructure [30]
Physical	2	Bulk density, penetrometer resistance at 0–30 cm and 30–60 cm, aggregate stability, saturated hydraulic conductivity	Bulk density, penetrometer resistance, texture and drainage class	Bulk density, aggregate stability, surface organic matter depth, soil moisture retention	Resolves compaction and structural constraints limiting root access and aeration [8,60,74]
Physical	3	Longitudinal bulk density and penetrometer profiles, seasonal hydraulic conductivity, subsoil compaction trends	Longitudinal bulk density and aeration status across wet and dry seasons	Longitudinal aggregate stability and surface organic matter accumulation	Detects management-induced structural recovery or decline across seasons [16]
Biological	1	Earthworm count and surface cast observation, litter decomposition rate (visual), frond decomposition presence	Surface macrofauna observation, root hair density at profile face, litter turnover assessment	Surface macrofauna, fungal hyphal presence in litter layer, visual mycorrhizal root tips	Rapid, low-cost biological proxies reflecting habitat quality and biological activity [39,42]
Biological	2	Microbial biomass carbon, β-glucosidase activity, dehydrogenase activity, acid phosphomonoesterase activity	Microbial biomass carbon, β-glucosaminidase activity, basal soil respiration	Microbial biomass carbon, β-glucosidase activity, mycorrhizal colonization rate	Sensitive to management change; documented in SE Asian perennial contexts [14,24,56,59,69]
Biological	3	Longitudinal enzyme activity profiles (C, N, P cycling enzymes), ecoenzymatic stoichiometry ratios, microbial biomass C:N	Longitudinal microbial biomass carbon, soil respiration trajectories, fungal:bacterial ratios	Longitudinal mycorrhizal colonization, enzyme activity profiles, microbial community composition by amplicon sequencing	Trajectory interpretation of biological function; detects recovery lags documented in SE Asian systems [20,23,51,52]

Note: Tier 1 metrics are designed to be implementable by field technicians without specialist laboratory access. Tier 2 metrics require standard soil laboratory capacity. Tier 3 metrics require repeated sampling infrastructure and, for molecular methods, specialist laboratory access or external analytical services. All thresholds and interpretive ranges require local calibration against crop-specific functional requirements and regional soil classes before operational use; indicative reference ranges for chemical Tier 1 and Tier 2 metrics can be derived from regional soil fertility databases and crop-specific agronomy manuals, but no universal thresholds are prescribed here. Abbreviations used in this table: SOC = soil organic carbon; MRV = monitoring, reporting, and verification; P = phosphorus; K = potassium; Ca = calcium; Mg = magnesium; Al = aluminum; N = nitrogen. Units are not standardized across indicators, as these vary with laboratory method and regional convention; users should refer to the cited sources for method-specific unit conventions.

Several patterns are evident. At Tier 1, biological proxies such as earthworm counts, litter decomposition, and surface macrofauna require no laboratory infrastructure and are readily implementable at the field level. At Tier 2, enzyme-based indicators are consistently supported by Southeast Asian field evidence across oil palm, rubber, and mixed systems, confirming their sensitivity to management change under regional conditions [17]. At Tier 3, emphasis shifts from indicator values to trajectories, including soil organic carbon dynamics, divergence under contrasting management regimes, and biological recovery lags, illustrating the longitudinal evidence required for surveillance-based interpretation.

The crop-specific structure also highlights differentiated diagnostic emphasis. Subsoil compaction and phosphorus dynamics dominate oil palm systems, drainage and aeration constrain rubber, and carbon dynamics and mycorrhizal function are central in cocoa. These differences reinforce the need for crop-calibrated interpretation within a common diagnostic framework.

9. Implications for Regenerative Management and Verification

9.1. Illustrative Demonstration Using Documented Field Patterns

The tiered surveillance framework proposed in this review has not yet been formally implemented as an integrated system across the industry, i.e., its adoption is precisely what this review seeks to encourage and promote. To demonstrate that the diagnostic logic is operationally grounded rather than purely conceptual, the following scenario illustrates how the framework would function if applied to recurring patterns documented in the Southeast Asian empirical literature.

Consider a situation representative of patterns widely reported in oil palm systems across Kalimantan, Sumatra, and Peninsular Malaysia [14,15,16,17,18,19]. A plantation block exhibits divergent fresh fruit bunch yield between two management zones with comparable fertilizer histories. A conventional chemical soil assessment returns similar pH, extractable phosphorus, and exchangeable cation values across zones—consistent with documented observations that chemical metrics show limited differentiation between blocks with contrasting performance [14,18]. Under the existing paradigm, this result provides no diagnostic resolution: the system appears adequate by chemical criteria, yet underperformance persists.

Under a Tier 1 surveillance protocol, field-observable proxies would be applied to detect ambiguity and determine whether escalation is warranted. Penetrometer resistance, visual litter decomposition rate, and surface earthworm counts, all implementable without laboratory access, would flag meaningful divergence between zones. The documented pattern of biological suppression under intensive management despite similar chemical conditions [17,19] suggests that such proxies would detect divergence that chemical testing alone would not resolve. This divergence triggers escalation to Tier 2.

Tier 2 diagnosis, using microbial biomass carbon and enzyme activity measurements of the kind reported by Boafo et al. [14], Thoumazeau et al. [18], and Hidayat et al. [19], would then identify whether biological process capacity, rather than nutrient stock deficiency, is the dominant constraint in the underperforming zone. If subsoil compaction is co-occurring, documented in high-traffic plantation blocks [74], the intervention sequence would prioritize structural remediation before organic amendment, a sequencing that chemical-only assessment cannot support.

At Tier 3, longitudinal tracking of enzyme activity profiles and soil organic carbon dynamics across seasons, of the type reported by Golicz et al. [63] and Hidayat et al. [19], would provide the trajectory evidence needed to distinguish recovery from continued decline under the applied management regime and to generate the time-series data required for MRV and ESG reporting.

This demonstration illustrates that the diagnostic logic is grounded in patterns that Southeast Asian empirical studies have already documented. What is absent is not the evidence base but the interpretive architecture that connects it to operational decisions at the field scale. Establishing that architecture through empirical validation and institutional adoption is the research and governance priority that this review identifies and advocates for.

9.2. Surveillance-Informed Decision Gating

Soil health surveillance enables decision gating by assessing whether soil–crop systems are functionally ready to respond, identifying dominant constraints, and evaluating intervention risk before practices are deployed. This approach shifts regenerative management from prescriptive implementation to conditional deployment based on system state [29]. The need for governance-ready soil assessment in Southeast Asian regenerative systems is increasingly recognized in the literature, with recent proposals for scoring systems linking SOC dynamics and GHG emissions to regenerative management verification [75]. Soil health surveillance extends this logic by incorporating biological and physical functional dimensions alongside carbon metrics, providing a more comprehensive diagnostic foundation for governance accountability. In perennial systems, where management interventions interact with long-lived soil structures, surveillance distinguishes soils that are buffered and responsive from those requiring preparatory remediation. Sequencing interventions according to diagnosed constraints reduces misapplication and improves attribution of outcomes. These outcomes extend beyond yield to include biological activity, carbon dynamics, structural integrity, and hydrological function, which are central to regenerative objectives but poorly resolved by existing assessment approaches [26,27].

9.3. Certification Credibility and Verification

Soil health surveillance strengthens the evidentiary basis of sustainability certification. Many certification frameworks reference soil health but rely primarily on practice adoption or static indicators, creating a disconnect between compliance and demonstrated functional outcomes [11,23,75,76]. The RSPO Principles and Criteria [77] require evidence of soil management practices and reference soil health in the context of good agricultural practice, but verification relies on practice documentation rather than functional outcome measurement. The Rainforest Alliance 2020 Sustainable Agriculture Standard [78] similarly requires soil health management plans and soil cover practices but does not specify diagnostic methods, indicator thresholds, or trajectory requirements for verification. In both cases, the policy intent is consistent with the surveillance logic proposed here, i.e., context-specific management, continuous improvement, and baseline documentation, but the diagnostic infrastructure required to operationalize that intent is absent. In Indonesia specifically, harmonization between national (ISPO) and international (RSPO) certification standards creates additional governance complexity, with traceability and compliance requirements that currently lack the soil health diagnostic infrastructure needed to verify functional outcomes [76]. This pattern reflects a broader documented failure of corporate sustainability initiatives to translate commitments into verifiable on-the-ground outcomes, attributed to insufficient evidence infrastructure linking governance requirements to measurable functional change [12,23]. By documenting functional change and trajectory, surveillance enables differentiation between systems that are improving and those that meet only minimum compliance thresholds. The primary limitation is not policy intent but diagnostic infrastructure. While corporate frameworks often require context-specific management, their effectiveness depends on the availability of soil assessment systems capable of interpreting local conditions and tracking functional change. Surveillance addresses this upstream gap by providing the evidence needed to support verification and decision-making.

ESG and MRV systems increasingly require measurable and auditable evidence of environmental performance, including soil health [9,10]. Surveillance aligns with these requirements by generating time-series data that capture system trajectories and management responses [9,10,75]. Where regenerative practices do not yield immediate outcomes, surveillance supports attribution to specific functional constraints, enabling adaptive management rather than abandonment. By identifying transitional or fragile soil states, organizations can demonstrate risk awareness and respond proactively in systems characterized by long investment horizons. Collectively, surveillance provides enabling infrastructure linking regenerative management, certification credibility, and ESG accountability.

10. Knowledge Gaps and Research Priorities

Several gaps must be addressed before soil health surveillance can be fully operationalized. These include empirical validation, calibration of diagnostic thresholds, and the availability of long-term, context-specific datasets. Evidence demonstrating that surveillance-informed diagnosis improves outcomes remains limited, particularly in Southeast Asian perennial systems. Comparative studies linking diagnosed soil states to management responses are required to establish causal relationships and practical value. Diagnostic thresholds require contextual calibration across crop, soil, and climatic conditions. Many existing interpretations rely on reference values derived from temperate systems or short-term studies, limiting relevance in tropical environments. Long-term datasets remain scarce. Surveillance depends on trajectories, yet most available data are cross-sectional or short-term, constraining interpretation in systems with high temporal variability. Implementation is further limited by costs, logistical complexity, and uneven data infrastructure, particularly in smallholder systems. Future priorities include improving understanding of biological mediation under tropical conditions, integrating subsoil constraints, and developing adaptive systems that iteratively refine diagnosis and intervention strategies. Institutional conditions, including training, data governance, and incentives, are also critical for long-term implementation.

11. Conclusions

Regenerative agriculture offers significant potential to restore soil function in Southeast Asian perennial systems, yet outcomes remain variable. A central constraint is the absence of diagnostic frameworks capable of resolving system state, trajectory, and intervention risk prior to management decisions. Conventional soil testing and indicator-based approaches describe condition but do not provide the functional diagnosis required for decision-making or governance verification. This review reframes soil health as a crop-context-dependent diagnostic property and conceptualizes soil health surveillance as a structured, function-oriented approach to interpreting existing measurements. In governance contexts, this framework provides a basis for aligning regenerative management, certification, and ESG reporting through a shared diagnostic logic. Operational implementation will require empirical calibration across different crop, soil, climatic, and management systems. The contribution of this review is to articulate the diagnostic logic necessary to make such efforts operationally and institutionally tractable.

Declaration of generative AI use: During the preparation of this work the authors used AI-based image generation tools to produce conceptual illustration figures (Figure 1, Figure 2, Figure 3 and Figure 4) and AI-assisted tools to support language editing and manuscript preparation. The authors reviewed and edited all content and take full responsibility for the published article.