Methods, Pedometrics, and Modern Soil Health Concepts

Last updated: July 26, 2026 Русский Español

1. How Soil Science Has Evolved: From Description to Modeling and Digital Technologies

Soil science is one of the youngest natural sciences, and its history is a fascinating journey from empirical description to system-based modeling. Understanding this evolution is critical to grasping why we now study soil not just as a "substrate for plants" but as a highly complex, dynamic, data-driven system.

From the "Realm of Minerals" to an "Independent Body"

For a long time, soil was considered merely a product of rock weathering—a geological formation. However, in the late 19th century, the great Russian scientist Vasily Dokuchaev sparked a scientific revolution by proving that soil is an independent natural body formed through the interaction of five factors. This concept, later formalized in Hans Jenny's famous equation (Jenny, 1941), became the cornerstone of all modern soil science:

Soil = f (Climate, Organisms, Relief, Parent Material, Time)

Dokuchaev and his followers laid the foundations of genetic soil science, where the main object of study is the soil profile—a sequence of genetic horizons reflecting the history of its formation (Scheffer et al., 2018). This was a descriptive and classification stage: soils were studied, described, named (e.g., Chernozems, Podzols), and mapped.

From the Soil Profile to the Soil Process

The 20th century marked a shift from static description to understanding dynamic processes. The focus was no longer solely on the horizons themselves, but on the processes that create them: humification, gleyzation, podzolization, lessivage, and others (Huang et al., 2012).

Here, Roy Simonson's conceptual model (Simonson, 1959) emerges, describing soil formation through four fundamental processes:

1. Additions (input of organic matter, atmospheric precipitation, dust).

2. Losses (removal of substances via groundwater flow, erosion).

3. Transformations (conversion of primary minerals into secondary ones, decomposition of organics).

4. Translocations (movement of substances within the profile: downward with water flow or upward via capillary rise).

This transition was closely linked to the development of related sciences—chemistry, physics, and biology. Soil science ceased to be a purely descriptive discipline and became a natural science that draws on fundamental laws of physics and chemistry to explain phenomena.

The Birth of the Quantitative Approach: Pedometrics and Digital Mapping

By the end of the 20th century, it became clear that traditional, qualitative methods of soil description had a significant limitation—they could not accurately predict soil behavior across space and time. This led to the emergence of pedometrics—the science of applying mathematical and statistical methods in soil science (McBratney et al., 2003).

Pedometrics proposed a fundamentally new approach to mapping. Instead of drawing boundaries between soil 'types' on a map (which is always subjective), scientists began using an equation expanding Jenny's model:

Soil (class or property) = f (soil properties, climate, organisms, relief, parent material, time, space)

This approach, known as Scorpan (McBratney et al., 2003), enabled the creation of quantitative, 'digital' soil maps. Today, we don't just ask "what soil is here?" but rather "what is the probability that the soil organic carbon content at this point exceeds 2%?". Such maps are not just drawings but databases that can be used in Geographic Information Systems (GIS) (Huang et al., 2012). Using spatial modeling techniques (e.g., regression kriging or random forests), we link limited field observation data with continuous layers of data on relief, climate, and vegetation to generate detailed predictions of soil properties for any point in the landscape (Huang et al., 2012).

The Anthropocene: Soil as a Historical-Cultural System

The current stage of soil science development is marked by an awareness of humanity's decisive role. We live in the Anthropocene epoch, where human activity has become the main driver of global changes on the planet (Richter & Tugel, 2012). In this context, soil is no longer seen as 'pristine nature' but as a cultural-historical system shaped by millennia of agriculture, land reclamation, and urbanization.

This led to the concept of anthropedogenesis—the process of soil formation under human influence. Soils become objects not only of natural but also of anthropogenic heritage. Their study now requires integration with history, archaeology, and sociology to understand how centuries of human activity have altered soil properties (Richter & Tugel, 2012).

Thus, 21st-century soil science is a synthesis of:

The classical genetic approach (understanding origin);
Quantitative pedometrics (digital mapping and modeling);
The systemic and ecological perspective (soil as part of Earth's critical zone).

This synthesis allows us to move from simple description to predicting and managing soil processes, which is key to sustainable development and solving global environmental challenges.

2. New Challenges for Soil Science

For much of its history, soil science was closely linked to agriculture. The key challenge was to assess and enhance soil fertility—its ability to provide plants with nutrients and water for maximum yield. This focus on productivity remains critically important, but in the 21st century, the challenges of soil science have become immeasurably broader. Today, we recognize that soil is a central element ensuring the functioning of our entire planet, and its health is directly linked to global stability.

Let's explore five key areas where soil science extends beyond agronomy.

1. The Climate Agenda: Carbon and Greenhouse Gases

This is perhaps the most prominent and important new challenge. Soil is the largest terrestrial reservoir of organic carbon, storing 2–3 times more carbon than all terrestrial biomass and the atmosphere combined (Baldock, 2012). This concerns soil organic matter (SOM), which, among other things, serves as a vast carbon (C) reservoir. The processes of its accumulation (sequestration) and loss (mineralization with CO₂ release) directly impact the concentration of greenhouse gases in the atmosphere.

The loss of organic carbon due to the plowing of virgin lands, erosion, and unsustainable farming has been a major contributor to the rise in atmospheric CO₂. Conversely, managing soils to increase SOM stocks (e.g., through minimum tillage, use of cover crops, application of organic fertilizers) is considered one of the most effective and cost-efficient ways to mitigate climate change.

Here, soil science addresses the following tasks:

Quantitative assessment of carbon stocks in different soil types and landscapes.
Modeling the dynamics of SOM in response to climate change and land use.
Developing recommendations for maximizing soil carbon sequestration.

In addition to CO₂, soil is a source of other greenhouse gases—methane (CH₄) and nitrous oxide (N₂O). Microbial activity under anaerobic conditions (e.g., in rice paddies) leads to methane formation, while denitrification processes produce the potent greenhouse gas N₂O (Kimura & Asakawa, 2012). Understanding these microbiological processes and their control is another critical task of modern soil science.

2. Water Resources and Their Quality

Soil is not just a medium for plants but also a global regulator of the hydrological cycle. It acts as a giant sponge: receiving, filtering, storing, and slowly releasing water. Soil properties determine how much precipitation recharges groundwater (feeding wells and rivers during dry periods) and how much becomes surface runoff, causing erosion and floods (Wysocki et al., 2012).

Soil science tackles the tasks of:

Assessing soil permeability and water-holding capacity to predict runoff and groundwater recharge.
Spatial modeling of hydrological processes in landscapes (from slopes to large basins).
Monitoring and predicting groundwater quality. Soil is a powerful biofilter. As water percolates through the soil profile, it is cleaned of suspended particles, pathogens, and many organic and inorganic pollutants through sorption onto soil particles and microbial activity (Weil & Brady, 2017).

3. Biodiversity and the 'Hidden World' of Soil

Soil is one of the richest and most diverse habitats on Earth. A single gram of fertile soil can harbor billions of microorganisms belonging to thousands of species. This is not just the 'population' of the soil; it is its engine, driving all key functions (Bonkowski et al., 2012; Asakawa et al., 2012).

Modern soil science aims to:

Assess and conserve soil biodiversity. We know that the diversity of soil organisms (from bacteria and fungi to earthworms and microarthropods) ensures the resilience of ecosystems to stress. The loss of this diversity leads to reduced fertility and the soil's ability to self-cleanse.
Understand functional groups. It's not enough just to know who lives in the soil, but what work they do. Bacteria and fungi are the primary decomposers of organic matter. Mycorrhizal fungi help plants acquire phosphorus. Nitrogen-fixing bacteria supply nitrogen to legumes. Understanding these connections is key to managing soil health.

4. Resilience of Agricultural Landscapes and 'Soil Health'

The traditional concept of 'fertility' often focuses on current yields. The newer concept—'soil health'—is much broader. Soil health is its capacity to function as a living system, providing ecosystem services in the long term (Weil & Brady, 2017). A healthy soil is not just a fertile one. It is resilient to external stresses (drought, waterlogging, compaction), possesses the ability to self-recover, and maintains its structure and biological activity.

The main tasks here are:

Developing soil health indicators. These can be not only agrochemical parameters (NPK content) but also physical (structure, bulk density) and biological ones (microbial biomass, enzyme activity, soil respiration).
Quantifying 'health' for different soils and farming systems.
Developing farming systems aimed not at maximizing yield at any cost, but at maintaining and improving soil health in the long term (Eash et al., 2016).

5. Data Management and the Digital Revolution

As we discussed in the first chapter, soil science is becoming a data science (Huang et al., 2012). This is due both to the emergence of new remote sensing methods and sensors and to the capability to process vast amounts of data.

New tasks include:

Digital Soil Mapping—creating predictive maps of soil properties worldwide.
Uncertainty management. Soil is incredibly variable even within a single field. A key task is not just to provide an estimate but to specify how accurate that estimate is, i.e., to assess the probability and uncertainty of predictions.
Decision support systems. Using digital maps and models for precision agriculture, land use planning, and assessing risks of erosion and pollution.

So, we see that today's soil science is a comprehensive discipline, addressing challenges from the local to the global scale. In the next chapter, we will move to the key concept of this module—soil as a data source, and how these data allow us to quantitatively assess soil processes and functions.

3. Soil as a Data Source

We have established that soil science has evolved from describing nature to quantitative prediction and solving global problems. However, the foundation of all these achievements is data. Without high-quality, representative, and spatially referenced data, it is impossible to build a digital map, assess carbon stocks, or calculate how climate change will affect fertility. Therefore, a key task of the modern soil scientist is to turn soil into a source of measurable information.

In the past, the primary data sources were a limited set of field descriptions and laboratory analyses. Today, the arsenal of methods has expanded dramatically, and several levels of data acquisition can be identified, each with its capabilities and limitations.

3.1. Traditional Data: In Situ Description and Analysis

This is the foundation of classical soil science. Data are obtained directly in the field and laboratory.

Morphological description of the soil profile. This is the primary and most important source of qualitative information. The researcher describes the genetic horizons, their thickness, boundaries, structure, consistency, and presence of inclusions. This step allows for soil identification and the delineation of its diagnostic horizons, which are the basis for classification systems such as 'Soil Taxonomy' (Ahrens & Arnold, 2012) and the 'World Reference Base'. This is an expert-based, subjective stage, but it sets the direction for all subsequent research.

Sample collection and laboratory analysis. It is the laboratory that turns qualitative characteristics into quantitative data—the very numbers that models use. The standard set of analyses includes:

Particle-size distribution—the proportion of sand, silt, and clay particles. This is a fundamental, 'fingerprint' property of soil, determining its water-holding capacity, cation exchange capacity, and susceptibility to erosion (Weil & Brady, 2017).
Soil organic carbon (C_org) content—the main indicator of organic matter stock and soil health.
pH and cation exchange capacity (CEC)—indicators of chemical activity and the soil's ability to retain cations (Ca²⁺, Mg²⁺, K⁺, NH₄⁺).
Content of available nutrients (nitrogen, phosphorus, potassium)—an agrochemical indicator that, however, provides only a snapshot, as its dynamics change throughout the season (Weil & Brady, 2017).

Key limitation of the classical method: it is point-based and labor-intensive. We can obtain accurate chemical data at a few points, but how do we extrapolate that to an entire field or region?

3.2. Proximal Sensing: On-Site Data

To overcome the limitations of point analyses, scientists developed methods to obtain dense data grids directly in the field without sample collection. These methods are based on measuring physical soil properties that correlate with the chemical and physical characteristics of interest.

Electrical conductivity (EC) and electromagnetic induction (EMI). Soil electrical conductivity is an integrative indicator depending on moisture content, salt concentration, texture (clay content), and mineralogical composition. Modern instruments (e.g., EM38 or ground-penetrating radar) can rapidly scan fields, producing maps of apparent electrical conductivity. Analyzing these maps helps delineate zones with different soil types or salinity levels without digging hundreds of pits (McBratney et al., 2003).
Gamma-ray spectrometry. This method measures the natural gamma radiation flux from radioactive isotopes K⁴⁰, U²³⁸, and Th²³² contained in soil minerals. Data indirectly indicate particle-size distribution, the genesis of parent materials, and even the degree of weathering. This is particularly useful for airborne mapping of large areas (Huang et al., 2012).
Near- and mid-infrared spectroscopy (NIR/MIR). This is perhaps the most rapidly developing method in laboratory soil science. The soil's reflectance spectrum in the near- and mid-infrared range contains information about the molecular composition of organic matter, clay mineral type, and moisture content. Calibration models are built based on spectra, allowing dozens of soil properties—from carbon content to cation exchange capacity—to be assessed rapidly (within minutes) and non-destructively (without chemical reagents). This dramatically increases the throughput of laboratory analysis (Baldock, 2012).

3.3. Remote Sensing (RS) and Environmental Data

Data obtained from sensors on satellites or unmanned aerial vehicles (UAVs) do not measure soil directly (especially under vegetation), but they provide critically important information on the soil-forming factors discussed in the first chapter.

Satellite imagery (multispectral, radar). These provide data on relief, vegetation cover, surface moisture, and temperature. This data is used as indirect predictors of soil properties. For example, a specific vegetation type may indicate the groundwater level, while snow retention patterns may reflect micro-relief.

Digital elevation models (DEMs). This is the most important data source for pedometrics. DEMs allow the calculation of terrain derivatives that are closely linked to the distribution of moisture, heat, and erosion processes. Key parameters include:

Slope gradient, influencing erosion and accumulation.
Slope aspect, determining insolation and temperature regime.
Topographic Wetness Index (TWI)—an indicator of potential wetness, calculated as a function of the upslope contributing area and slope gradient (Wysocki et al., 2012; McBratney et al., 2003).
Profile and plan curvature, influencing the divergence or convergence of runoff.

3.4. Categorical and Continuous Data

All this information can be divided into two types, which is fundamental for statistical analysis and modeling (McBratney et al., 2003):

Continuous data—numerical values of properties that can take any value within a range: carbon content (g/kg), pH, elevation.
Categorical data—data that belong to classes or categories. For example, soil type name (Chernozem, Sod-Podzolic), geological parent material, vegetation type, drainage class. Categorical data are equally important; they carry qualitative information about processes not easily expressed by a single number.

Data Integration and the Value of Uncertainty

The main advantage of the modern approach is the ability to integrate all these diverse data (analyses, spectra, DEMs, imagery) into a unified framework—a Geographic Information System (GIS). Integration allows not just creating a map, but making it predictive.

Here, a new and very important aspect of data comes to the forefront—uncertainty. Every measurement contains error: laboratory analysis error, interpolation error, sensor error. The task of pedometrics is not only to provide the best estimate of a property at a point but also to assess how much we can trust that estimate (Heuvelink, 1998). It is the quantitative assessment of uncertainty that distinguishes a modern digital map from a traditional one, where boundaries between soils are drawn with solid lines, creating an illusion of precision.

So, we have examined the vast array of data available to the modern soil scientist: from spade and trowel to satellite sensors and infrared spectrometers. All this data is raw material that, using mathematical methods and models, is transformed into knowledge about how soil functions. In the next chapter, we will take the decisive step and talk about soil functions and how this data helps assess them.

4. Soil as a Provider of Functions (Ecosystem Services)

So, we have amassed mountains of data: morphological descriptions, spectra, chemical analyses, digital elevation models. But what is all this effort for? To move from answering "What is this?" to "What does this do and how can it be measured?".

In modern soil science, we view soil not just as a body with a set of properties, but as a functional system actively involved in sustaining life on Earth. The set of processes that soil enables is termed ecosystem services (Eash et al., 2016; Weil & Brady, 2017). The Millennium Ecosystem Assessment framework identifies four categories, all inextricably linked to soil.

Let's examine in detail the key soil functions that underpin these services.

4.1. Biological Productivity: Beyond Just Yield

This is the most 'classical' function, where soil science began. Soil is the medium for plant growth, providing physical support, water, air, and mineral nutrients (Weil & Brady, 2017). However, from a systems perspective, this is not just 'fertility' (the ability to produce a high yield). It is the more fundamental capacity to support primary production—the creation of organic matter.

Physical support and oxygen access for root respiration are provided by soil structure and porosity.
Water supply—is not just the presence of water, but the soil's ability to retain moisture in forms available to plants and to deliver it to roots (soil water available in capillary pores).
Nutrition—is a complex interaction between the soil's solid, liquid, and biological phases, where elements (N, P, K, etc.) stored in organic matter and minerals are transferred to the soil solution, from which roots absorb them.

It is important to understand: This function extends far beyond agroecosystems. It underlies the functioning of forests, steppes, wetlands, and determines how much biomass can be produced per unit area. Therefore, we will discuss assessing the productive capacity of soils in diverse landscapes.

4.2. Regulation of the Hydrological Cycle and Filtration

Soil is a natural regulator of water flows (Wysocki et al., 2012; Weil & Brady, 2017). It acts as a biofilter and buffer.

Infiltration—the ability to absorb water, preventing surface runoff, erosion, and flooding. This property depends on the structure of the topsoil.
Storage and retention of moisture. Soil can hold vast volumes of water, releasing it to plants during dry periods and recharging groundwater. This is a key function in regulating river flow.
Water purification. As water passes through the soil profile, it is freed from suspended particles, pathogens, excess nutrients (e.g., nitrates), and many organic pollutants. This occurs through three mechanisms:
Physical filtration in pores.
Chemical sorption onto clay particles and organic matter surfaces.
Biological degradation by microorganisms.

Thus, soil protects the quality of our water resources. This function is particularly important when assessing the resilience of agricultural landscapes and pollution risks.

4.3. Participation in the Global Carbon Cycle and Climate Regulation

As mentioned, soil is the largest terrestrial reservoir of organic carbon (Baldock, 2012). This 'carbon' function has two aspects:

1. Storage (sequestration)—the process of accumulating carbon in stable forms of organic matter. This is the 'capture' of atmospheric CO₂ by plants and its stabilization in the soil. Increasing soil carbon stocks is one of the key ways to mitigate climate change.

2. Emission—the reverse process. During the decomposition (mineralization) of organic matter, carbon returns to the atmosphere as CO₂. Under anaerobic conditions, methane (CH₄) is released. Microbial activity related to nitrogen transformations produces nitrous oxide (N₂O)—a greenhouse gas approximately 300 times more potent than CO₂.

Therefore, managing the organic carbon pool and controlling greenhouse gas fluxes from soil is a major 'new' challenge for soil science, requiring the development of quantitative models.

4.4. Habitat and Maintenance of Biodiversity

Soil is a 'black box' of biodiversity. One square meter of healthy soil contains billions of organisms (Bacteria, Archaea, Fungi, Protista) and thousands of species of invertebrates (nematodes, collembola, mites, earthworms) (Bonkowski et al., 2012; Asakawa et al., 2012). This community performs the work without which the soil would be inert mass:

Decomposition—breaking down dead organic matter (leaves, roots, animal remains). Primary decomposers are bacteria and fungi.
Transformation—converting complex organic molecules into simpler forms available to plants.
Bioturbation—mixing and aerating the soil by animals (earthworms, ants). They create pores that improve aeration and water infiltration.
Symbiosis—interaction with plant roots (mycorrhiza, nitrogen fixation).

Biological diversity is the key to soil resilience. The more diverse the community, the faster and more efficiently it recovers from stress (drought, tillage, pollution) and the higher its capacity to adapt to changes. Therefore, assessing biological indicators (microbial biomass, enzyme activity, soil respiration) is not an academic interest but a practical tool for diagnosing soil 'health'.

4.5. Engineering and Cultural-Historical Potential

These are functions often overlooked in agronomy-focused courses, but they are critically important in the context of modern landscape planning (Weil & Brady, 2017).

Engineering function: Soil serves as the foundation for buildings, roads, and infrastructure. Its bearing capacity, compressibility, and shrink-swell potential (especially in clay soils) determine construction costs and the longevity of structures. Assessing soil engineering properties is a geotechnical task, but the soil scientist provides the primary information on its composition and structure.
Cultural and archival function: Soil is a record of the landscape. Soil horizons, especially buried ones (paleosols), preserve information about past climates, vegetation composition, and human activity (Richter & Tugel, 2012). Archaeological artifacts (pottery, tools, structures) and geochemical anomalies (e.g., traces of ancient fires) are preserved in the soil profile for centuries and millennia. Furthermore, the soil itself is a crucial aesthetic resource shaping the landscape.

4.6. The Functional Approach and the Problem of Uncertainty

The main idea of this module is that we must learn not just to describe these functions in words, but to quantify them using the data we collect.

For example, how do we measure the 'water filtration' function? We cannot run an experiment across an entire basin. Instead, we use data on particle-size distribution, structure, bulk density, and soil moisture to calculate the filtration coefficient using hydrological models and predict how much water will reach the groundwater.

How do we assess the 'carbon sequestration' function? We measure organic carbon content in different horizons and then, using organic matter dynamic models, predict how this stock will change with climate or land-use change.

A crucial element of this process is understanding uncertainty. We always operate with a limited number of measurements. Our model does not give a precise answer but a probabilistic estimate. Therefore, at the end of any functional analysis, we must answer the question: "With what degree of probability can we state that this function will be performed?".

So, we have moved from properties to functions. Soil is not just a set of parameters, but a complex system performing critically important work. To manage this system, we need to be able to measure and model it. And that is exactly what—methods of measurement, data processing, and modeling—this module will be dedicated to. In the concluding chapter, we will briefly outline its structure.

5. Module Structure

We have come a long way: from the history of soil science as a descriptive discipline to understanding that in the 21st century, it is primarily a science of data, functions, and the health of complex biogeochemical systems. We have seen that soil is not just a substrate for roots but a critical element of planetary cycles of carbon, water, and biodiversity.

A logical question arises: how exactly do we, as soil scientists, move from a general understanding of these processes to their precise quantitative assessment and management? The answer to this question constitutes the content of our module "Methods, Pedometrics, and Modern Concepts of Soil Health".

This module is structured as a logical sequence that will guide you through all stages of working with soil as a data source and an object of management.

Block 1. The Modern Soil Scientist's Toolkit: Data Acquisition Methods

This first and most fundamental block of the module is dedicated to how we gather primary information about soil. No model is possible without high-quality input data.

You will learn about modern protocols for field soil description and sampling that minimize errors and ensure data representativeness (Weil & Brady, 2017).
We will cover both classical laboratory methods (particle-size analysis, pH determination, carbon content, and cation exchange capacity) and modern high-throughput approaches, such as infrared spectroscopy (NIR/MIR), which can provide information on dozens of soil properties within minutes (Baldock, 2012).
Special attention will be given to proximal methods (electrical conductivity, electromagnetic induction, gamma-ray spectrometry), which allow rapid scanning of large areas to obtain dense data grids for subsequent modeling (McBratney et al., 2003).

Block 2. Pedometrics: From Data to Digital Maps and Predictions

The second block is the 'heart' of the module, its quantitative core. You will learn how a chaotic set of point measurements is transformed into a coherent and predictive digital model of the soil cover.

We will study the main statistical methods for analyzing the spatial variability of soils. You will be introduced to key concepts such as autocorrelation and the variogram, which describe the distance over which soil properties are correlated (Wendroth et al., 2012).
You will master the fundamental method of spatial interpolation—kriging—which provides not only the best estimate of a property at any location but also assesses the error (uncertainty) of that estimate.
We will explore modern methods of Digital Soil Mapping, based on the Scorpan equation (McBratney et al., 2003). You will learn to create predictive maps of carbon content, acidity, or particle-size distribution across a study region, using terrain, climate, and vegetation data as predictors.

Block 3. Soil Functions and Soil Health: How to Assess the 'Work' of Soil?

Based on maps of soil properties, we move to the most important part—the quantitative assessment of soil functions. This block is entirely dedicated to how to mathematically measure the 'benefits' provided by soil.

We will learn how to calculate soil organic carbon stocks and model their dynamics in response to climate and land-use change (Baldock, 2012).
You will learn about methods for assessing hydrological functions—water-holding capacity, filtration, infiltration—based on soil structure and texture data (Wysocki et al., 2012).
Special emphasis will be placed on biological indicators of soil health: how to assess microbial biomass, soil enzyme activity, soil respiration, and how these indicators relate to ecosystem resilience (Weil & Brady, 2017).

Block 4. Integration and Management: From Analysis to Decisions

In the final block, we will bring all acquired knowledge together. We will explore how to integrate data from different sources (laboratory analyses, spectra, remote sensing data, digital elevation models) within Geographic Information Systems (GIS) and how to use them for decision-making.

We will discuss how decision support systems can aid in planning precision agriculture, land reclamation, and soil protection against erosion and pollution.
We will learn to interpret modeling results and present them in a format understandable to agronomists, ecologists, and decision-makers.

The Module's Place in Your Education

It is important to emphasize that this module is instrumental and integrative. Unlike courses like 'Agrochemistry' or 'Crop Science', which delve into specific agronomic practices, here we equip you with universal research methods. You will learn to collect, analyze, and interpret data to independently assess soil condition, its functions, and its health. The skills acquired will form the foundation upon which you can build knowledge from related disciplines. You will not just follow ready-made recommendations, but understand why and how they work, and develop your own solutions for specific soil and climatic conditions.

We will start from the very beginning—with how to correctly pose a question and plan a study to obtain the most useful and reliable information about the soil.

References

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