Soil Health Indicators Research Articles

Exploring the Role of Jeevamrutha Fermentation and Dosage in Promoting Microbial Health and Sustainable Rice Cultivation

Microbial activity in soil is a key indicator of soil health and fertility, as microbes play a pivotal role in nutrient cycling, organic matter decomposition, and plant growth promotion. The efficacy of Jeevamrutha depends on several factors, including the source of livestock dung, fermentation conditions, and application rates. This study investigates the optimisation of Jeevamrutha fermentation and application rates, combined with Beejamrutha seed treatment, to enhance microbial activity and sustainable rice production in transplanted systems. The present study was conducted during the Kharif season of 2024 at the NSP-6 Farm of Acharya Narendra Deva University of Agriculture and Technology, Kumarganj, Ayodhya (U.P.), India. Jeevamrutha, prepared from various livestock dung sources, was applied at rates of 1000–2500 L ha⁻¹ alongside 20–100% recommended fertiliser doses (RDF) across ten treatments. The experiment assessed microbial populations in Jeevamrutha and its effects on rice growth, yield attributes, and yield. Results revealed significant variations in plant height, tiller numbers, panicle length, grains per panicle, test weight, and yields across treatments. Treatment T3 (90% RDF + 1125 L ha⁻¹ Jeevamrutha) consistently outperformed others, achieving the highest plant height (108.10 cm), tiller numbers (449.65 m⁻²), panicle length (25.00 cm), grains per panicle (190.00), test weight (23.21 g), and grain yield (47.67 q ha⁻¹). Beejamrutha seed treatment supported early vigour, aiding nutrient uptake. Sole Jeevamrutha application (T1) yielded the lowest results, indicating insufficient nutrient supply without RDF. T4 (80% RDF + 1250 L ha⁻¹ Jeevamrutha) also showed promising results, suggesting potential for reduced chemical inputs. These findings advocate Integrated nutrient management (INM) for sustainable rice production, with T3 as an optimal strategy, warranting further research into long-term soil health and scalability.

Quantitative Changes in Selected Soil Health Indices as a Result of Long-Term (23-Year) Cultivation of Winter Wheat in Various Crop Rotations: Case Study for Sandy Soil

Perennial monoculture crops are perceived as detrimental to soil health. This study examines this assumption with regard to winter wheat cultivated in crop rotations with varying cereal shares (50%, 75%, and 100%) and under different irrigation regimes. The experiments were established in light, sandy soil and conducted as static trials over 23 years (1997–2020). This study aims to assess the quantitative changes in parameters indicative of soil fertility and health. The amounts of total organic carbon (TOC), humic substance carbon (HSC), total nitrogen (TN), and available forms of N, P, K, and Mg (AN, AP, AK, AMg) were measured. It was found that, regardless of the research year, higher levels of TOC, TN, AP, AK, and AMg were recorded in the soil following winter wheat cultivated in a rotation with a 100% share of cereals. The amounts of the above-mentioned parameters were higher by 10–30%. The effect of crop rotation on the quantitative changes in HSC and AN was not statistically significant, although a decrease in their amounts was noted (by 10%). The reduction in HSC content was accompanied by a decline in the quality of these compounds, as indicated by Q4/6 values, which were significantly higher in plots with sprinkling irrigation and under winter wheat cultivated in rotations with a 100% cereal share; this was evident in both 1997 and 2020. Sprinkling irrigation resulted in lower amounts of TOC, TN, HSC, AN, and AK, but higher levels of AP and AMg. The results directly indicate that the long-term cultivation of winter wheat in rotations with a 100% cereal share in light soils leads to quantitative changes in soil health indices. These changes are generally positive, favorably affecting the health of light soils, in contrast to the effects observed with irrigation.

Understanding the influence of precipitation and nitrogen depositions on the soil health and growth indices of invasive (Solidago canadensis L.) and native (Wedelia chinesis) association

Understanding the influence of precipitation and nitrogen depositions on the soil health and growth indices of invasive (Solidago canadensis L.) and native (Wedelia chinesis) association

Carbon dioxide flush as a soil health indicator in contrasting edaphoclimatic conditions

Carbon dioxide flush as a soil health indicator in contrasting edaphoclimatic conditions

Five-year impacts of biomass crop monoculture on soil enzyme activity, nitrogen pools, and other soil health indicators

Five-year impacts of biomass crop monoculture on soil enzyme activity, nitrogen pools, and other soil health indicators

Evaluating locally available organic amendments to enhance soil health indicators for highbush blueberry production east of the Cascades in the U.S. Pacific Northwest

While Oregon and Washington contribute approximately 60% of the total production of blueberries in the United States, there is still potential for growth on the east side of the Cascade Mountain Range in both states. The region is well suited to organic production of blueberries, but the soils tend to be sandy and low in organic matter. Currently, organic inputs used in blueberry systems are expensive and difficult to source in the region. Therefore, easily available alternatives that can foster productivity at a lesser cost are needed. An organic blueberry field trial was established in 2021 at Oregon State University’s Hermiston Agricultural Research and Extension Center to evaluate four organic amendments, including biochar, grape pomace compost, grape pomace co-composted with biochar, and woodchips (grower standard), each of which were either incorporated to a depth of 0.2 m or, with the exception of woodchips, banded on the soil surface. Soil samples were collected in September 2021 and 2022 and analyzed for a suite of soil health indicators and soil microbial community structure, including Soil pH, electrical conductivity, Total N, NH4-N, NO3-N, available P and K, soil organic matter, extractable organic carbon, active carbon, microbial biomass carbon, β-glucosidase enzyme activity, microbial respiration, and bacterial and fungal community structure and abundance. Results showed that incorporated compost resulted in higher soil organic matter, increased nutrient availability, and greater microbial activity than the other treatments. However, none of the treatments had any effect on the structure of the bacterial community, which by the end of the second year of the study was largely dominated by Acidobacteria, or on the structure of the fungal community, which in both years was dominated by Ascomycota and Mortierellomycota. Data collected from this study will help us understand the suitability of organic inputs to enhance soil health indices while improving resource use efficiency.

An Optimized Multi-Stage Framework for Soil Organic Carbon Estimation in Citrus Orchards Based on FTIR Spectroscopy and Hybrid Machine Learning Integration

Soil organic carbon (SOC) is a critical indicator of soil health and carbon sequestration potential. Accurate, efficient, and scalable SOC estimation is essential for sustainable orchard management and climate-resilient agriculture. However, traditional visible–near-infrared (Vis–NIR) spectroscopy often suffers from limited chemical specificity and weak adaptability in heterogeneous soil environments. To overcome these limitations, this study develops a five-stage modeling framework that systematically integrates Fourier Transform Infrared (FTIR) spectroscopy with hybrid machine learning techniques for non-destructive SOC prediction in citrus orchard soils. The proposed framework includes (1) FTIR spectral acquisition; (2) a comparative evaluation of nine spectral preprocessing techniques; (3) dimensionality reduction via three representative feature selection algorithms, namely the Successive Projections Algorithm (SPA), Competitive Adaptive Reweighted Sampling (CARS), and Principal Component Analysis (PCA); (4) regression modeling using six machine learning algorithms, namely the Random Forest (RF), Support Vector Regression (SVR), Gray Wolf Optimized SVR (SVR-GWO), Partial Least Squares Regression (PLSR), Principal Component Regression (PCR), and the Back-propagation Neural Network (BPNN); and (5) comprehensive performance assessments and the identification of the optimal modeling pathway. The results showed that second-derivative (SD) preprocessing significantly enhanced the spectral signal-to-noise ratio. Among feature selection methods, the SPA reduced over 300 spectral bands to 10 informative wavelengths, enabling efficient modeling with minimal information loss. The SD + SPA + RF pipeline achieved the highest prediction performance (R2 = 0.84, RMSE = 4.67 g/kg, and RPD = 2.51), outperforming the PLSR and BPNN models. This study presents a reproducible and scalable FTIR-based modeling strategy for SOC estimation in orchard soils. Its adaptive preprocessing, effective variable selection, and ensemble learning integration offer a robust solution for real-time, cost-effective, and transferable carbon monitoring, advancing precision soil sensing in orchard ecosystems.

A Chemometric Analysis of Soil Health Indicators Derived from Mid-Infrared Spectra

Significant models predicting Soil Organic Carbon (SOC) and other chemical and biological indicators of soil health in an experimental farm with semi-arid Mediterranean Calcisol have been obtained by partial least squares (PLS) regression, with mid-infrared (MIR) spectra of whole soil samples used as independent variables (IVs). The dependent variables (DVs) included SOC, pH, electric conductivity, N, P2O5, K, Ca2+, Mg2+, Na+, Fe, Mn, Cu and Zn. The DVs also included free-living nematodes and microbivores, such as Rhabditids and Cephalobids, and phytoparasitics, such as Xiphinema spp. and other Dorylaimids. More importantly, an attempt was made to determine which spectral patterns allowed each dependent variable (DV) to be predicted. For this purpose, a number of statistical indices were plotted between 4000 and 450 cm−1, e.g., variable importance for prediction (VIP) and beta coefficients from PLS, loading factors from principal component analysis (PCA) and correlation and determination indices. The most effective plots, however, were the “scaled subtraction spectra” (SSS) obtained by subtracting the averages of groups of spectra in order to reproduce the spectral patterns typical in soils where the values of each DV are higher, or vice versa. For instance, distinct SSS resembled the spectra of carbonate, clay, oxides and SOC, whose varying concentrations enabled the prediction of the different DVs.

SOIL HEALTH AND BUSINESS MODELS: A REVIEW AND ANALYSIS CARRIED OUT IN THE NOVASOIL PROJECT

Soil health is critical for sustainable agriculture, healthy ecosystems, and environmental resilience. Soil degradation caused by unsustainable practices must be addressed through innovative economic and environmental solutions. This review explores how innovative environmental monitoring technologies, such as remote sensing, drones, and soil sensors, and innovative business models that influence soil management contribute significantly to the improvement of soil health. This study first highlights the key indicators of soil health, including soil organic carbon, nutrient levels, erosion rates and their potential use in ecosystem service markets, such as carbon credits, to incentivise improved soil management. Additionally, this study considers the legal and policy frameworks necessary to support these business models, with a particular focus on the European Union’s Soil Monitoring Law and its implications for the agricultural and environmental sectors. Together, these innovative components offer a comprehensive analysis of the challenges and opportunities for transforming soil health management into a profitable and sustainable enterprise, contributing to global goals, such as climate mitigation and biodiversity preservation.

Effect of different Rice Based Cropping System on Performance of Soil Health and other Indices in the Western Plain Zone of Meerut, India

Cropping systems can influence various soil properties, with the extent and nature of these effects depending on factors such as crop rotation, nutrient management, and tillage practices. The rice–wheat cropping system is predominant in the Meerut region, the inclusion of legume and alternative cereal crops within this system may significantly influence various indicators of soil health, thereby affecting key soil functional processes. A comprehensive evaluation was conducted to assess the impact of various rice-based cropping systems on soil health and productivity indices. Soil health cards were developed for each system by analyzing key physical (bulk density and aggregate stability) and chemical (pH, available Fe, P, K, Zn, and organic carbon) parameters at two soil depths: 0–15 cm and 15–30 cm. Across all systems, soil health ratings ranged from low to medium. Surface soils generally showed better physical and organic matter conditions, while subsoils exhibited pronounced chemical limitations, particularly with respect to pH and available iron. Among the systems evaluated, rice–wheat–mung bean exhibited the highest surface soil health score (58.62), supported by favorable physical traits and high organic carbon. However, subsoil health declined sharply due to extremely low pH, Fe, and organic matter content. Conversely, the rice–white chickpea–mung bean system showed the highest subsoil score (59.00), with improved nutrient availability at depth despite persistent Fe and pH deficiencies. In terms of productivity indices, the rice–lentil–mung bean system outperformed all others, registering the highest system land use efficiency (87.43%), rice equivalent yield (112.71 q/ha), and system productivity (0.31 kg/ha/day). This was followed closely by the rice–white chickpea–mung bean system. The rice–mustard–mung bean system, despite its strong physical soil characteristics, recorded the lowest land use efficiency (76.75%) and relatively poor productivity indicators. In conclusion, while soil health constraints, particularly related to pH and micronutrients, persist across systems, diversified legume-inclusive rotations like rice–lentil–mung bean and rice–white chickpea–mung bean offer both improved soil resilience and higher productivity, making them promising models for sustainable intensification of rice-based cropping systems.

Early-stage impacts of conservation agriculture on soil health in a French Mediterranean irrigated system

ABSTRACT In Mediterranean regions, climate change has intensified droughts and extreme weather events, accelerating soil degradation through organic matter loss, erosion, and nutrient depletion. Conservation agriculture (CA), involving practices like zero or reduced tillage, crop diversification, and soil cover maintenance, offers a resilient strategy for mitigating these challenges. This study examines the short-term effects of irrigated CA on soil health in Mediterranean conditions over 3 years (2021–2023). Using the Biofunctool® field kit (assessing carbon transformation, nutrient cycling, and structure maintenance) alongside functional indicators from the laboratory, the study compares CA with conventional tillage (CT) under different water supply methods: sprinkler, subsurface drip, and rainfed conditions. Results reveal that CA enhances soil health indicators from the first year, improving soil aggregation and carbon transformation compared to CT. By 18 months, Soil Health Index (SHI) scores were 38% higher in CA than in CT. Minimal soil disturbance in CA addressed compaction and maintained SHI 23% higher than CT. Neither the water supply mode nor temporality post-CA adoption significantly affected soil functions in the short term. The complementary use of field-based and laboratory indicators provided a holistic view of soil functionality, confirming the potential of CA to improve soil health under irrigated Mediterranean conditions.

A Bibliometric Review of Machine Learning Applications in Soil Science Using Digital Spectroscopy (2008–2024)

Machine learning (ML) has rapidly advanced soil science, particularly in the last decade. This bibliometric study examines the scholarly production on machine learning in soil science from 2008 to 2024, analysing research impact, leading groups, and trends. The study reveals a significant increase in research output, with China emerging as the most prolific country after 2020. Collaboration networks highlight significant partnerships, with Zhejiang University and the Czech University of Life Sciences acting as central hubs. The analysis underscores the transition of ML in soil science from a nascent field to a well-established area characterized by increased research activity and interdisciplinary collaboration. Soil property prediction using ML techniques has been a major focus, with approximately 60% of studies dedicated to predicting organic carbon, nitrogen, moisture, texture, pH, and heavy metals. Digital soil mapping, soil profile analysis, and soil classification using ML account for about 20% of the research. Soil health assessment, including predicting soil health indicators, optimizing nutrient management, and developing remediation strategies, accounts for around 15% of the studies. Emerging trends include the rise of deep learning, the increased focus on soil health assessment, and the growing integration of ML with remote sensing techniques. While Chinese institutions lead in publication volume, organizations from Greece, Brazil, and Australia demonstrate higher research impact. The study underscores the importance of international collaboration in advancing this field and suggests a need to strengthen international networks and explore emerging interdisciplinary connections.

Role of Organic Agriculture in Enhancing Soil Health: Implications for Physico-Chemical and Biological Properties

Soil health is fundamental to sustainable agriculture and food security. Organic agricultural practices have gained increasing recognition for its capacity to improving soil health. This study evaluates the effects of organic i.e. farmyard manure (FYM) and vermicompost (VC) versus traditional i.e. urea (U) and diammonium phosphate (DAP) farming practices on soil health indicators and wheat growth. Results revealed significant improvements (P<0.05) in both soil physico-chemical and biological properties under organic farming practices compared to traditional methods. Overall, the soil's physico-chemical properties, including organic matter (OM), porosity, and moisture percentage, significantly improved due to organic practices. Organic matter increased by 6.33-fold and 5.70-fold, porosity rose by 2.60-fold and 1.78-fold, and moisture percentage improved by 1.93-fold and 1.81-fold. . Conversely, a reduction in bulk density (BD) and pH was observed under organic practices, specifically vermicompost (VC) and farmyard manure (FYM). The bulk density decreased by 1.19-fold and 1.14-fold, while the pH declined by 1.19-fold and 1.12-fold, respectively, under VC and FYM treatments. While microbial biomass carbon (MBC), nitrogen (MBN), and phosphorus (MBP) were enhanced by 2.18-fold, 3.64-fold and 4.1-fold owing to VC and 2.01-fold, 2.93-fold and 3.08-fold owing to FYM respectively. Similarly, the microbial community, including bacteria (3.45-fold and 6.36-fold), fungi (4.0-fold and 3.45-fold), and actinomycetes (6.36-fold and 4.0-fold), demonstrated a significant increase in population under VC and FYM. Correlation analysis showed that physico-chemical and biological properties are closely interlinked with each other and play a significant role in maintaining the overall soil health. The study highlights that organic farming significantly improves soil physico-chemical properties, boosts microbial biomass and diversity, and enhances overall soil health compared to conventional practices.

Microbial Activity of Rhizospheric Soil and Leaf Litter on Livestock Farms Sown with Bothriochloa Pertusa (L) A. Camus and Dichantium Aristatum

Soil use and management can significantly affect the labile and humified fractions of soil organic matter, to the detriment of biological activity. The aim of the present study was to evaluate the microbial activity of rhizospheric soil and rhizospheric soil plus litter on cattle farms cultivated with Bothriochloa pertusa (L) A. Camus and Dichantium aristatum pastures. Samples of soil, litter and the combination of soil + litter were collected from cattle farms in the municipality of Corozal, department of Sucre, Colombia. The respiratory activity of the soil was determined using the incubation method. The results of the present study indicate that the treatments with the lowest respiration rate (mg g-1 h-1 of C-CO2) was in soils, followed by leaf litter and was higher in the treatments where soil and leaf litter were combined. The highest respiration rate was related to the combined treatments of soil and Bothriochloa pertusa litter. In conclusion, the rate of microbial respiration in soil is an important indicator of microbial activity and soil health. Measurement of microbial respiration rate can be used to assess soil health and monitor soil quality

Assessment of Organic Farming Practices and Their Impact on Soil Health and Ecosystem Services: A Review

Organic farming is increasingly recognized as a sustainable agricultural approach that enhances soil health, biodiversity, and ecosystem services while mitigating environmental impacts. The principles of organic agriculture are based on four key pillars: health, ecology, fairness, and care. This review examines the effects of organic farming practices on soil health indicators, including physical, chemical, and biological properties, and evaluates their contributions to various ecosystem services. Studies show that organic systems promote higher soil organic matter (SOM) content, improved aggregate stability, and greater microbial diversity, resulting in enhanced nutrient cycling, carbon sequestration, and soil fertility. Compared to conventional systems, organic farming demonstrates a 20–40% increase in microbial biomass and a 15–25% increase in SOM, contributing to improved soil structure and resilience. Organic farming also supports biodiversity conservation, with 30–50% more species of plants, insects, and soil microorganisms observed in organic systems. Despite these benefits, yield gaps of approximately 20–25% persist for specific crops, particularly cereals. Organic farming helps achieve sustainability goals by contributing to climate mitigation through enhanced carbon sequestration, reduced greenhouse gas emissions, and improved soil health. Again, biodiversity conservation is a critical component of organic farming's contribution to sustainability. Economic sustainability is another essential aspect of organic farming's contribution to sustainability goals. Economic profitability remains promising, with organic systems achieving 20–30% higher profitability due to premium prices and reduced input costs. Challenges related to productivity, market access, and scalability continue to limit the broader adoption of organic practices. Future research should focus on improving organic inputs, enhancing nutrient cycling, and developing ecosystem service assessments. Improving the economic viability of organic farming is essential for promoting its widespread adoption. Effective policies are essential for encouraging the adoption of organic farming practices. Government policies that provide subsidies, technical assistance, and research funding can enhance the scalability of organic systems. Addressing economic and policy barriers is essential for promoting widespread adoption. The findings suggest that organic farming can significantly contribute to global sustainability goals related to food security, climate change mitigation, and environmental conservation.

Do soil health indicators predict carbon and nitrogen functional stability under drought and heat?

Healthier soils are often assumed to retain function better under climate stress. However, links between common soil health indicators and soil functional resilience to stress are elusive. Our goal was to link soil health status with stress response by quantifying the multifunctional carbon (C) and nitrogen (N) cycling response of soils under different management (forest, conventional, or organic) to drought or combined drought and heat stress. We monitored several C and N cycling functions during a 28-d stress and 28-d recovery period and calculated resistance and resilience indices for each function to create multifunctional C and N cycling indices. We related these indices to baseline soil health properties and microbial community characteristics. Traditional soil health indicators (e.g., total organic C, and microbial biomass and activity) were closely associated with N cycle resilience to drought stress. Indicators that distinguished forest from arable soils (e.g., low pH and chemical fertility, and high porosity, relative abundance of Basidiomycetes, and high C to N ratio) were generally positively related to drought resistance but negatively related to resilience. Bacterial and fungal community diversity were unrelated to either resistance or resilience for either cycle. Adding heat to drought created a strong N cycle stress which affected all sites similarly, and soil baseline properties were not related to either resistance or resilience. For both C and N cycles, stress type was the major determinant of resistance while management was the major determinant of resilience. Our results show that response to drought stress differs depending on the temperature at which it occurs, and that pH, chemical fertility, and SOM are all important components in stress response but affect C and N cycles differently.

Do soil health indicators predict carbon and nitrogen functional stability under drought and heat?

Healthier soils are often assumed to retain function better under climate stress. However, links between common soil health indicators and soil functional resilience to stress are elusive. Our goal was to link soil health status with stress response by quantifying the multifunctional carbon (C) and nitrogen (N) cycling response of soils under different management (forest, conventional, or organic) to drought or combined drought and heat stress. We monitored several C and N cycling functions during a 28-d stress and 28-d recovery period and calculated resistance and resilience indices for each function to create multifunctional C and N cycling indices. We related these indices to baseline soil health properties and microbial community characteristics. Traditional soil health indicators (e.g., total organic C, and microbial biomass and activity) were closely associated with N cycle resilience to drought stress. Indicators that distinguished forest from arable soils (e.g., low pH and chemical fertility, and high porosity, relative abundance of Basidiomycetes, and high C to N ratio) were generally positively related to drought resistance but negatively related to resilience. Bacterial and fungal community diversity were unrelated to either resistance or resilience for either cycle. Adding heat to drought created a strong N cycle stress which affected all sites similarly, and soil baseline properties were not related to either resistance or resilience. For both C and N cycles, stress type was the major determinant of resistance while management was the major determinant of resilience. Our results show that response to drought stress differs depending on the temperature at which it occurs, and that pH, chemical fertility, and SOM are all important components in stress response but affect C and N cycles differently.

Prediction of Metal Nanoparticle Interactions with Soil Properties: Machine Learning Insights into Soil Health Dynamics.

Metal nanoparticles (MNPs) offer great potential to enable precision and sustainable agriculture. However, a comprehensive understanding of the interactions between multiple MNPs and soil properties, including impacts on overall soil health, remains elusive. Here, 4 different interpretable machine learning models were employed to systematically analyze the interactive effects of 7 soil physicochemical properties, 3 MNP properties, and 3 external factors on soil health indicators including pH, soil microbial biomass, Shannon index, and enzyme activities. We identified soil cation exchange capacity (SCEC), soil organic matter (SOM), soil clay (SC), and exposure duration (ED) as pivotal factors influencing the effects of MNPs on soil health. Notably, the adsorption and fixation of metal ions by soil significantly modulate MNP toxicity over time, underscoring the importance of long-term exposure in soil health research. This study predicts the impact of MNPs on soil health indicators across 12 United States Department of Agriculture (USDA)-classified soil orders from a global perspective. The impact of MNPs on soil health is highly dependent on soil properties, with effects varying across different soil types globally. Entisols, the most abundant and widespread soil, characterized by low water holding capacity and SOM content, showed heightened sensitivity in pH to MNP exposure, with pH changes ranging from 2.6% to 11.5%. In contrast, changes in pH for other soil types were between -2% and 4%. Mollisols and Inceptisols, which represent important cultivated lands in Europe, the United States, Canada, and China, exhibited significant sensitivity in microbial biomass and diversity to long-term MNPs exposure. Over a 365 day exposure period, the microbial biomass changes of Mollisols and Inceptisols ranged from -268% to -60%, while for a 30 day exposure period, changes in microbial biomass were between -20% and -4%. By conducting global predictions across diverse soil types, this research enabled an assessment of potential soil health risks at a global scale, highlighting high-risk regions and countries for targeted analysis to support more science-based, data-driven environmental management and sustainable agricultural practices.

Urban Market Gardening Improves Soil Health: A Case Study in Burkina Faso

In sub-Saharan Africa, urban market gardening is characterized by the intensive use of chemical inputs, which could have adverse effects on soil health. This study therefore aimed to assess the impact of urban market gardening on soil health. Topsoil samples were collected from 69 plots at a market gardening site in Bobo-Dioulasso, Burkina Faso, with cultivation histories ranging from 0 to over 50 years. Twenty-six chemical, biological, and physical soil properties were analyzed. Principal component analysis was used to identify minimum data sets for the assessment of soil health. The selected variables were standardized and aggregated into two soil health indices on a scale from 0 to 100: an overall index based on all properties combined and an average index based on the mean of the biological, physical, and chemical components of soil health. Both indices revealed a clear improvement in soil health over time, with the overall index rising from an initial value of 0.35 to 0.64 after 60 years and the average index rising from 0.30 to 0.62. The average index, which enables the separate assessment of its three components, accounted for a greater share of the temporal variability (R2 = 0.59) than the overall index (R2 = 0.47). These findings highlight the positive impact of urban market gardening practices on soil health at the study site, which was attributed to the large additions of organic amendments.

Collaborative Soil Health Assessment: Integrating Citizen and Scientist Perspectives

Over 60% of European soils are considered unhealthy, which clearly calls for a transformation towards sustainable soil management. Taking care of our soils is imperative, since they are key components of our ecosystems and provide up to 99% of our food. Agricultural long-term field experiments (LTEs) are key to understanding how different agroecological practices affect the soil and have existed since the 1850s in Europe and beyond. LTEs can be seen as core sites that allow researchers extended measurements and monitoring of the trends and change and enable policy makers to gain a deeper understanding into soils, especially in times of climate change. However, they alone will not be able to transform the public perception of soil health and give reasons to protect and enhance soil health. Participatory citizen science is a research method that actively involves and engages the public in hands-on scientific enquiry to generate new knowledge or understanding. Most of the soil citizen science projects so far have been focusing on biodiversity, a weak point in European soil monitoring. Thus, actively engaged citizens could help to evolve our knowledge about the most appropriate field soil health indicators to test the effectiveness of different potentially sustainable soil management practices. This presentation will highlight a transdisciplinary effort from citizens and scientists in assessing soil health at a case study area in the Marchfeld in eastern Austria. We will highlight how soil biological, chemical and physical properties - key aspects of soil health - can be measured by both citizens and researchers, to assess soil health for maintaining fertile soils for future generations and ensuring ongoing food production. Promoting co-creation, fostering knowledge-sharing networks and enabling long-term communication and commitment with citizens will be highlighted as success factors for continuing transdisciplinary cooperation among diverse soil health stakeholders.