Soil Health Assessment Research Articles

Sustainable Wheat Cultivation in Sandy Soils: Impact of Organic and Biofertilizer Use on Soil Health and Crop Yield

Sandy soils are widespread globally and are increasingly utilized to meet the demands of a growing population and urbanization for food, fiber, energy, and other essential services. However, their poor water and nutrient retention makes crop cultivation challenging. This study evaluated the effects of integrating compost and plant growth-promoting rhizobacteria (PGPR; Azospirillum brasilense SWERI 111 and Azotobacter chroococcum OR512393) on wheat (Triticum aestivum L. var. Misr 1) grown in sandy soil under varying levels of recommended NPK (50%, 75%, and 100%) fertilization. Conducted over two growing seasons, the experiment aimed to assess soil health, nutrient uptake, microbial activity, and plant productivity in response to compost and PGPR treatments. The results demonstrated that combining compost and PGPR significantly improved soil chemical properties, such as reducing soil pH, electrical conductivity (ECe), and sodium adsorption ratio (SAR), while enhancing soil organic matter (SOM). Additionally, compost and PGPR improved soil nutrient content (N, P, K) and boosted the total bacterial and fungal counts. The combined treatment also increased urease and phosphatase enzyme activities, contributing to enhanced nutrient availability. Notably, plant productivity was enhanced with compost and PGPR, reflected by increased chlorophyll and reduced proline content, along with improved grain and straw yields. Overall, the results underscore the potential of compost and PGPR as effective, sustainable soil amendments to support wheat growth under varying NPK levels.

Applicability of soil health assessment dominated by biological indicators in facility agriculture

The minimum data set (MDS) is widely used for soil health index (SHI) assessment, with the selection of indicators being crucial. However, biological indicators are less frequently used compared to physical and chemical indicators. This study aimed to establish a biologically-centered MDS for assessing soil health in tomato facility agriculture in Ningbo. Using biochemical, microbiological, and nematode data from 60 continuous cropping soils, we identified key indicators for the MDS. Principal component analysis revealed a strong correlation between the MDS and the total data set (TDS), with the final MDS including available phosphorus, electrical conductivity, β-glucosidase, urease, bacterial Chao1 index, bacterial Shannon index, Ralstonia solanacearum, nematode Shannon index, structure index, and channel index. Random forest modeling showed that the predicted SHI had a high fit with the MDS-SHI, with bacterial and nematode Shannon indices being the most influential predictors, emphasizing the critical role of biological communities in maintaining soil health. Furthermore, the study identified three distinct phases in soil health during long-term continuous cropping: healthy (0–10 years), sub-healthy (11–15 years), and recovery (16–20 years), demonstrating the soil's potential for self-recovery. By incorporating biodiversity as a core component, this biologically-focused MDS provides a more comprehensive and precise method for evaluating soil health in facility agriculture. The findings suggest that this MDS could inform future research and applications in similar agricultural systems. Moreover, the results highlight the vital role of biodiversity in agroecosystem stability, suggesting that future soil health assessments should place greater emphasis on biological indicators to better capture the complexity of soil functions.

Effects of microbial communities during the cultivation of three salt-tolerant plants in saline-alkali land improvement.

Planting vegetation on saline-alkaline land enhances soil fertility and sustainability by improving salt-alkali tolerance. Different salt-tolerant plant species interact with soil microorganisms, enriching bacterial communities and promoting nutrient availability. In this study, mechanisms affecting microbial communities in severely saline-alkaline soils planted with salt-tolerant plants are investigated. Over 4 years, the potential to cultivate three salt-tolerant plant species (tall wheatgrass Agropyron elongatum, chicory Chicorium intybus, and alfalfa Medicago sativa) in severely saline-alkaline soils is compared with a non-cultivated control. Bacterial and fungal communities were characterized through high-throughput sequencing of the 16S rRNA gene V3-V4 region and the V4 region, respectively. Cultivating these three plant species significantly reduces soil electrical conductivity values. Chicory cultivation notably increased soil nutrients, bacterial alpha richness, and fungal alpha diversity and richness. Microbial community structures vary considerably between the control and treatments, significantly correlating with the soil quality index. This index enables an assessment of soil health and fertility by integrating variables such as nutrient content, microbial diversity, and salinity levels. In each plant treatment, particularly alfalfa, the relative abundances of fungal pathogens like Neocosmospora and Gibellulopsis increase, which may pose risks to subsequent crops such as tomatoes, requiring careful consideration in future planting decisions. Conversely, in alfalfa and tall wheatgrass treatments, there was an increase in the relative abundances of fungal genera (e.g., Alternaria and Podospora) that antagonize fungal pathogens, while Paraphoma increased in the chicory treatment. The strong relationship between microorganisms and the rise in pathogen-resistant fungi across different plant treatments highlights robust and beneficial structural characteristics. According to soil quality index scores, each treatment, but especially that of chicory, improved the severely saline-alkaline soil environment.

AI in Soil Health Monitoring: A Data-Driven Approach

Abstract: The sustainability of ecosystems and agricultural productivity depend on healthy soil. However, conventional techniques for evaluating soil health are sometimes time-consuming, labor-intensive, and restricted in their geographic reach. New opportunities for scalable, real-time soil health monitoring have been made possible by recent developments in artificial intelligence (AI) and data-driven methodologies. This study examines a thorough AI-powered system for tracking soil health, emphasizing methods for data collection, processing, and predictive modeling. AI models can provide precise forecasts of soil characteristics, health indicators, and possible crop yields by combining data from multiple sources, such as remote sensing, soil sensors, and historical data. This study offers a comprehensive analysis of recent AI applications in soil health monitoring and suggests a reliable, scalable approach intended to incorporate diverse data sources, guaranteeing precise and effective soil health assessment.

Understanding the effect of cropping system on soil health at the Northwestern Ontario Agricultural Research Station in Canada

Anthropogenic activities impact soil in varying degrees, from preserving natural landscapes to intensive agriculture which among the farm practices that impact the soil are the cropping systems. Information on cropping systems and soil impacts in northern territories is still missing. This study assesses the effect of different cropping systems on soil health -physical, chemical and biological soil properties and indicators of soil health - at the Lakehead Agricultural Research Station [LUARS] in northern Ontario, Canada. The study compares three cropping systems (perennial crops-pasture, grass, and annual crops -wheat, barley, corn, soybeans) and two forest areas (conifer plantation and naturally regenerating mixed wood forest) at LUARS. Soil samples were collected at different depths and analyzed for various indicators using the Cornell Soil Health Assessment framework. The results showed the soil health scores varied among cropping systems, with natural forest and perennial crops-pasture having higher scores compared to annual crops -wheat, barley, corn, soybeans. Soil organic matter was found to be lowest in annual crops -wheat, barley, corn, soybeans, while aggregate stability was highest in natural forests. The study also identifies the soil health gap, which represents the difference between the health of a particular cropping system and a benchmark. The soil health gap analysis can help farmers implement practices to improve soil health and increase the resilience and sustainability of agroecosystems. Overall, this study emphasizes the importance of understanding the effect of cropping systems on soil health and provides insights into potential strategies for improving farm practices.

When are you measuring soil β‐glucosidase activities in cropping systems?

AbstractIn situ soil respiration is driven by annual patterns of temperature and soil moisture, but what about extracellular enzyme activities responsible for depolymerizing soil organic matter? We conducted biweekly measurements of potential soil β‐glucosidase activities during a 4‐month period from March soil thawing through July in annually cropped field plots in eastern South Dakota. Our objective was to determine the best sampling time to resolve the effects of crop rotational diversity on soil microbial activities. Potential β‐glucosidase activities were elevated immediately following soil thaw, peaked in May, and declined to their lowest value in mid‐summer. Temperature and precipitation had no value in predicting enzyme activities; however, enzyme activities were affected by crop rotational diversity and responded to current crop and previous crop. These findings are pertinent to the use of soil extracellular enzymes in soil health assessments and as indicators of microbial substrate preference with implications for soil carbon stabilization.

Impact of Agroforestry Practices on Soil Microbial Diversity and Nutrient Cycling in Atlantic Rainforest Cocoa Systems.

Microorganisms are critical indicators of soil quality due to their essential role in maintaining ecosystem services. However, anthropogenic activities can disrupt the vital metabolic functions of these microorganisms. Considering that soil biology is often underestimated and traditional assessment methods do not capture its complexity, molecular methods can be used to assess soil health more effectively. This study aimed to identify the changes in soil microbial diversity and activity under different cocoa agroforestry systems, specially focusing on taxa and functions associated to carbon and nitrogen cycling. Soils from three different cocoa agroforestry systems, including a newly established agroforestry with green fertilization (GF), rubber (Hevea brasiliensis)-cocoa intercropping (RC), and cocoa plantations under Cabruca (cultivated under the shave of native forest) (CAB) were analyzed and compared using metagenomic and metaproteomic approaches. Samples from surrounding native forest and pasture were used in the comparison, representing natural and anthropomorphic ecosystems. Metagenomic analysis revealed a significant increase in Proteobacteria and Basidiomycota and the genes associated with dissimilatory nitrate reduction in the RC and CAB areas. The green fertilization area showed increased nitrogen cycling activity, demonstrating the success of the practice. In addition, metaproteomic analyses detected enzymes such as dehydrogenases in RC and native forest soils, indicating higher metabolic activity in these soils. These findings underscore the importance of soil management strategies to enhance soil productivity, diversity, and overall soil health. Molecular tools are useful to demonstrate how changes in agricultural practices directly influence the microbial community, affecting soil health.

Different cropping systems impact soil health by improving soil biological activities and total organic carbon content

ABSTRACT This study assesses the impact of different cropping systems on changes in total organic carbon (TOC), its fractions and biological activities to evaluate the changes in soil health. The rice-wheat cropping system (RWCS) had significantly higher total organic carbon (TOC) (38.8%) and soil organic carbon (SOC, 43.6%) compared to the pearl millet-mustard cropping system (PMCS). Active C pools were significantly higher in soils under cotton-wheat (CW) and pearl millet-wheat cropping systems (PWCS) than RWCS, whereas passive C pools (63% of TOC), the microbial biomass carbon (MBC) and dehydrogenase activity (DHA) were highest under RWCS than other systems. Soils under these cropping systems exhibited a significant polynomial relationship of TOC with SOC and dissolved organic carbon (DOC). Two significant canonical discriminant functions involving TOC, SOC, DOC, very labile carbon (CVL), labile carbon (CL), less labile carbon (CLL), recalcitrant carbon (CR), MBC and DHA could distinguish the different cropping systems and contributed 94.7%. The principal component analysis identified that TOC, SOC, pH and EC were the most reliable and contributing variables for assessing soil health under different cropping systems. Three PCs explained the 79.2% variability of the total variance of the original data set.

Phytostabilisation of arsenic contaminated gold mine waste using the native species Juncus usitatus, Poa labillardieri and Themeda triandra

Arsenic (As) contaminated gold mine waste represents a significant issue for ecosystem and human health, as it is a known carcinogen and environmental toxin. Bendigo in Victoria, Australia, has reported As concentrations up to 22,000 mg/kg, exceeding the national soil guidance values (100 mg/kg) by 220-fold. To reduce exposure and potential human health risk, remediation or risk management strategies are required. Traditional remediation, such as excavation and landfill, often fail to improve soil conditions and are expensive. Biological remediation using plants (phytoremediation) represents a cost-effective strategy that results in the restoration of soil function and ecosystem services. There is a need to identify native plants for phytoremediation as current research focuses on fern species, which are not suitable for Bendigo. This study aimed to identify a native phytostabilising plant by conducting a mesocosm experiment involving Juncus usitatus, Poa labillardieri, and Themeda triandra. Plants grew in mine waste for 100 days; all survived and bioaccumulated As in the roots (phytostabilisation). Poa labillardieri grew significantly more biomass (roots 0.24 ± 0.01 g; shoots 0.22 ± 0.03 g (d/w)) and bioaccumulated the most As (~ 100 mg/kg plant biomass). The bacterial community was investigated to assess soil health and As association. The rhizosphere microbiota stabilised after root colonisation for Poa labillardieri and Themeda triandra, as indicated by richness and evenness changes. The two dominant phyla were Actinobacteriota and Proteobacteria, which displayed As tolerance. A keystone taxon, Latescibacterota, exhibited a positive relative association with As remediation. Poa labillardieri is a good candidate for future phytostabilisation trials.

Cluster Analysis to Support Soil Health Assessment

Cluster Analysis to Support Soil Health Assessment

REVEALING THE FERTILITY STATUS OF KHANEWAL DISTRICT’S LANDS THROUGH GIS-BASED STUDY

A rapid increase in industrialization and urbanization and population growth requires expansion of agricultural area for food security and raise the importance of soil health assessment to ensure protection and sustainable use of agricultural lands according to their potential. For this purpose, use of digital soil mapping for the analysis of key physicochemical characteristics has been widely used. The GIS enabled the mapping of extensive areas. The purpose of this study is the spatial analysis of soil fertility indicators i.e., soil reaction (pH), Electrical Conductivity (EC), organic matter (OM), micro-macro {Zinc (Zn), Copper (Cu), Iron (Fe), Manganese (Mn), Boron (B)} nutrients in Khanewal districts using GIS-approaches. The result of study shows that soil pH and EC are normal for crop production while almost 95% of samples show low OM, 100% have low available phosphorous content, 33.3% have low K content, 71% have low Fe and 83% have low B content. Therefore, it was recommended that, to continuously incorporate farmyard manure, green manure, and crop residues into the soil over an extended period to address the organic matter deficiency. P, K, Fe and B deficient area should be fertilized with variable rate of respective fertilizer and soil should be periodically tested. It was recommended that the central parts of Khanewal district lands are cultivable as compared to boarder areas.

Integrating IoT for Soil Monitoring and Hybrid Machine Learning in Predicting Tomato Crop Disease in a Typical South India Station.

This study develops a hybrid machine learning (ML) algorithm integrated with IoT technology to improve the accuracy and efficiency of soil monitoring and tomato crop disease prediction in Anakapalle, a south Indian station. An IoT device collected one-minute and critical soil parameters-humidity, temperature, pH values, nitrogen (N), phosphorus (P), and potassium (K), during the vegetative growth stage, which are essential for assessing soil health and optimizing crop growth. Kendall's correlations were computed to rank these parameters for utilization in hybrid ML techniques. Various ML algorithms including K-nearest neighbors (KNN), support vector machines (SVM), decision tree (DT), random forest (RF), and logistic regression (LR) were evaluated. A novel hybrid algorithm, 'Bayesian optimization with KNN', was introduced to combine multiple ML techniques and enhance predictive performance. The hybrid algorithm demonstrated superior results with 95% accuracy, precision, and recall, and an F1 score of 94%, while individual ML algorithms achieved varying results: KNN (80% accuracy), SVM (82%), DT (77%), RF (80%), and LR (81%) with differing precision, recall, and F1 scores. This hybrid ML approach proved highly effective in predicting tomato crop diseases in natural environments, underscoring the synergistic benefits of IoT and advanced ML techniques in optimizing agricultural practices.

Reducing the Sodium Adsorption Ratio Improves the Soil Aggregates and Organic Matter in Brackish-Water-Irrigated Cotton Fields

The assessment of soil health relies on key parameters such as soil aggregates and organic matter content. Therefore, examining the impact of irrigation water ion composition and variations in salinity on soil aggregates and organic matter is imperative, which is key to developing a theoretical basis for the sustainable utilization of saline water resources, particularly in extremely arid regions. This experiment was conducted to investigate the impact of different irrigation water salinity treatments (T3: 3 g/L, T5: 5 g/L, and T7: 7 g/L) on the root zone soil of cotton fields. Each salinity treatment included three variations of the sodium adsorption ratio (SAR) at S10: 10 (mmol/L)1/2, S15: 15 (mmol/L)1/2, and S20: 20 (mmol/L)1/2. Local freshwater irrigation served as the control, resulting in a total of 10 treatments. Our findings show that the soil Ca2+ and Mg2+ content increased with higher irrigation water salinity but decreased with increasing irrigation water SAR. The relative macroaggregate stability and the content of water-stable macroaggregates and soil organic matter (SOM) decreased as the irrigation water salinity and SAR increased. In comparison to T3S20, T5S10 did not improve the soil Na+ content but significantly increased the soil Ca2+ content by 147.76%, while the water-stable aggregate and SOM saw a notable increase of 7.66% and 9.86%, respectively. Reducing the SAR in brackish water lessens its negative impact on soil aggregates in cotton fields. This is primarily because Ca2+ counteracts the dispersive effect of high Na+ concentrations and promotes aggregate formation. Irrigation water with a salinity of 3 g/L and an SAR of 10 (mmol/L)1/2 positively affected the stabilization of soil aggregates and organic matter.

Soil eDNA biomonitoring reveals changes in multitrophic biodiversity and ecological health of agroecosystems

Soil health is integral to sustainable agroecosystem management. Current monitoring and assessment practices primarily focus on soil physicochemical properties, yet the perspective of multitrophic biodiversity remains underexplored. Here we used environmental DNA (eDNA) technology to monitor multitrophic biodiversity in four typical agroecosystems, and analyzed the species composition and diversity changes in fungi, bacteria and metazoan, and combined with the traditional physicochemical variables to establish a soil health assessment framework centered on biodiversity data. First, eDNA technology detected rich multitrophic biodiversity in four agroecosystems, including 100 phyla, 273 classes, 611 orders, 1026 families, 1668 genera and 1146 species with annotated classification, and the relative sequence abundance of dominant taxa fluctuates tens of times across agroecosystems. Second, significant differences in soil physicochemical variables such as organic matter (OM), total nitrogen (TN) and available phosphorus (AP) were observed among different agroecosystems, nutrients were higher in cropland and rice paddies, while heavy metals were higher in fish ponds and lotus ponds. Third, biodiversity metrics, including α and β diversity, also showed significant changes across agroecosystems, the soil biota was generally more sensitive to nutrients (e.g., OM, TN or AP), while the fungal communities were mainly affected by heavy metals in October (e.g., Cu and Cr). Finally, we screened 48 sensitive organismal indicators and found significant positive consistency between the developed eDNA indices and the traditional soil quality index (SQI, reaching up to R2 = 0.58). In general, this study demonstrated the potential of eDNA technology in soil health assessment and underscored the importance of a multitrophic perspective for efficient monitoring and managing agroecosystems.

Benchmarking soil organic carbon (SOC) concentration provides more robust soil health assessment than the SOC/clay ratio at European scale

Increasing soil organic carbon (SOC) confers benefits to soil health, biodiversity, underpins carbon sequestration and ameliorates land degradation. One recommendation is to increase SOC such that the SOC to clay ratio (SOC/clay) exceeds 1/13, yet normalising SOC levels based on clay alone gives misleading indications of soil structure and the potential to store additional carbon. Building on work by Poeplau & Don (2023) to benchmark observed against predicted SOC, we advance an alternative indicator: the ratio between observed and “typical” SOC (O/T SOC) for pan-European application. Here, “typical” SOC is the average concentration in different pedo-climate zones, PCZs (which, unlike existing SOC indicators, incorporate land cover and climate, alongside soil texture) across Europe, determined from mineral (<20 % organic matter) topsoils (0–20 cm) sampled during 2009–2018 in LUCAS, Europe's largest soil monitoring scheme (n = 19,855). Regression tree modelling derived 12 PCZs, with typical SOC values ranging 5.99–39.65 g kg−1. New index classes for comparison with SOC/clay grades were established from the quartiles of each PCZ's O/T SOC distribution; these were termed: “Low” (below the 25th percentile), “Intermediate” (between the 25th and 50th percentiles), “High” (between the 50th and 75th percentiles), and “Very high” (above the 75th percentile). Compared with SOC/clay, O/T SOC was less sensitive to clay content, land cover, and climate, less geographically skewed, and better reflected differences in soil porosity and SOC stock, supporting 2 EU Soil Health Mission objectives (consolidating SOC stocks; improving soil structure for crops and biota). These patterns held for 2 independent datasets, and O/T SOC grades were sensitive enough to reflect land management differences across several long-term field experiments. O/T SOC used in conjunction with several other physical, chemical and biological soil health indicators can help support the EU Soil Monitoring Law and achieve several United Nations Sustainable Development Goals.

Comparative assessment of soil health attributes between topsoil and subsoil influenced by long-term wastewater irrigation

The reuse of wastewater (WW) for crop irrigation is increasingly recognized as an alternative to freshwater irrigation in arid and semi-arid regions. However, there is a significant gap in knowledge regarding the soil health index (SHI) and factors influencing topsoil and subsoil in cropland under long-term WW irrigation. This study aimed to comparatively assess soil health attributes between topsoil and subsoil in smallholder farmlands that have been irrigated with WW for over 50 years. This assessment utilized a combination of soil physico-chemical and fertility attributes, along with heavy metal concentrations. The soil health index (SHI) was developed using linear (SHI - L) and nonlinear (SHI - NL) models, based on the Total Data Set (TDS) and Minimum Data Set (MDS). Statistically significant differences (P ≤ 0.05) were observed between topsoil and subsoil for the soil stability index (SSI), organic matter (SOM), calcium carbonate equivalent (CCE), electrical conductivity (EC), sodium adsorption ratio (SAR), macro- and micronutrients, and heavy metals (Zn, Cu, Cd, Pb, and Ni). In contrast, soil bulk density (BD), pH, and cation exchange capacity (CEC) did not show significant differences. The mean SHI - L and SHI - NL values ranged from 0.68 to 0.77 and 0.46–0.53 for topsoil, and from 0.66 to 0.74 and 0.45–0.51 for subsoil, respectively. The SHI values were higher in the topsoil, with increases ranging from 2.3 % to 7.1 % for SHI - L and 0.65–11.3 % for SHI - NL compared to the subsoil. The regression coefficients between SHIs and corn yield were higher in the topsoil (0.46–0.49) than in the subsoil (0.20–0.22). Furthermore, the SHI - NL model demonstrated greater precision than the SHI - L model in predicting corn yield in both soil depths. These findings highlight the effectiveness of SHI assessments, particularly the SHI - NL model, in analyzing changes in soil health indices with depth and their relationship with crop performance in long-term WW-irrigated smallholder farmlands. This research provides valuable insights into addressing soil health challenges in similar agricultural systems.

Nature versus nurture: Quantifying the effects of management, region, and hillslope position on soil health indicators in an on‐farm survey in Minnesota

AbstractA major challenge to implementing effective soil health assessments is how to distinguish the effects of management from underlying soil variability driven by inherent soil properties. This challenge has important consequences for the use of soil health indicators as tools for monitoring and assessment because soil‐forming factors constrain the range of indicator values and the magnitude of management‐induced changes. Here, we present results from a statewide survey of 15 soil health indicators measured on 30 fields on commercial farms across four major land resource areas in Minnesota. Fields included in the study differed in tillage, cover crop implementation, and crop rotation. Within each field, we collected samples from upper and lower hillslope positions to quantify the effect of topography. We consistently detected differences in soil health indicators between regions (13/15 indicators) and hillslope positions (8/15 indicators). However, only wet aggregate stability was sensitive to management across regions and years, highlighting the importance of physical indicators of soil health. This result was surprising in light of existing literature supporting the sensitivity of many soil health indicators to management, but it is consistent with other studies conducted in high organic matter soils in the Upper US Midwest. Our results highlight the need for regionally representative datasets to guide the development of interpretations and benchmarks for soil health indicators. This is particularly important when soil health indicators are applied outside traditional research contexts (such as in commercial soil health testing), where interpretation must take place without the benefit of historical baseline data.

Labile not stable SOC fractions constitute the manageable drivers of soil health advances in carbon farming

The assessment of soil health and the determination of carbon stability in soils are current challenges that have gained momentum through initiatives at national as well as international scales. However, inferring universally valid soil health parameters that are directly linked to carbon permanence remains challenging. Our aim was to evaluate the potential of simultaneous thermal analysis (STA) for an improved monitoring of soil health advances in the context of carbon farming strategies. For this purpose, an on-farm comparison of soil health in ten conservation agricultural systems with adjacent conventional farms was performed based on STA and thermal measures were related to key biological and physical indicators for monitoring soil functions. Using an autocorrelation-based approach, we identified independent thermal indicators, revealing that carbon farming efforts predominantly increased labile soil organic matter fractions in the range of 300–400 °C, while thermally more recalcitrant fractions did not respond to farming system transformation. Similarly, most soil health indicators revealed highest correlation with temperature ranges of mass loss of labile organic matter fractions. The relation of STA with commonly used soil health indicators was highest for soil organic carbon (SOC, r=0.971) and total nitrogen (TN, r=0.981). However, also inference on microbial activity parameters such as dimethylsulfoxid reduction, microbial biomass carbon and substrate-induced respiration could be demonstrated with r>0.663. The results thus highlight key temperature ranges of organic matter stability for future soil monitoring tasks of climate change mitigation potentials of carbon farming and related advances in soil health.

Smartphone offers on-site soil microbiome profiling

A low-cost, portable platform based on autofluorescence detection and machine learning can quickly identify bacterial species to assess soil health.

Application of soil quality evaluation indices and multivariate statistics to assess soil health: a case study

Application of soil quality evaluation indices and multivariate statistics to assess soil health: a case study