Investigation of eluted characteristics of fulvic acids using differential spectroscopy combined with Gaussian deconvolution and spectral indices

The objective of the present investigation was to evaluate the physical characteristics of the vermicompost and the quality of the purine of the red Californian (Eisenia foetida) using different substrates of feed for these worms. For this purpose, nine treatments were studied: 75% African palm rachis + 25% cattle manure, 50% African palm rachis + 50% cattle manure, 25% African palm rachis + 75% livestock manure, 50% manure of cattle, 50% of manure of cattle, 25% of manure of cattle, 50% of manure of cattle, 50% of manure of cattle, 50% of rach of coconut + 50% of manure of Livestock, 25% coccus rachis + 75% livestock manure. The substrate made up of 50% of rachis of coconut and 50% of livestock manure can be used in nurseries or nurseries for being the one that registered a value of pH 7.3 plus the closest to the neutral compared to the others, besides this (75% of oil palm rachis and 25% of cattle manure) showed a higher content of humic and fulvic acids (0.87 and 0.45 p / p, respectively), compounds that are important for agriculture by stimulating plant growth, in addition to this reflection 0.06% sulfur content, 4.0 ppm boron, 7.0 ppm copper, 47.5 ppm iron, 6.0 ppm manganese, with a presence of microorganisms of the species Trichoderma, Penicillium, Cladosporium sp. in amounts of 1.91x105 UFC / ml, however in this substrate was obtained between 13.3 and 43.5% less liquid slurry in Comparison with other treatments.

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Fulvic Acids and Viral Infections

The Open Conference Proceedings Journal ◽

10.2174/2210289201203010024 ◽

2012 ◽

Vol 3 (1) ◽

pp. 24-29

Author(s):

O. Y. Morales

Keyword(s):

Viral Infections ◽

Fulvic Acids

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Spectral Indices of Centimeter Continuum Sources in Star-forming Regions: Implications on the Nature of the Outflow Exciting Sources

The Astronomical Journal ◽

10.1086/300637 ◽

1998 ◽

Vol 116 (6) ◽

pp. 2953-2964 ◽

Cited By ~ 113

Author(s):

Guillem Anglada ◽

Eva Villuendas ◽

Robert Estalella ◽

Maria T. Beltrán ◽

Luis F. Rodríguez ◽

...

Keyword(s):

Spectral Indices ◽

Star Forming ◽

Star Forming Regions

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The impact of humic and fulvic acids on the dynamic properties of liposome membranes: the ESR method

Journal of Liposome Research ◽

10.3109/08982104.2013.839998 ◽

2013 ◽

Vol 24 (2) ◽

pp. 106-112 ◽

Cited By ~ 4

Author(s):

Dariusz Man ◽

Izabella Pisarek ◽

Michał Braczkowski ◽

Barbara Pytel ◽

Ryszard Olchawa

Keyword(s):

Dynamic Properties ◽

Fulvic Acids ◽

Humic And Fulvic Acids ◽

The Impact

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Assessing Wildfire Burn Severity and Its Relationship with Environmental Factors: A Case Study in Interior Alaska Boreal Forest

Remote Sensing ◽

10.3390/rs13101966 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1966

Author(s):

Christopher W Smith ◽

Santosh K Panda ◽

Uma S Bhatt ◽

Franz J Meyer ◽

Anushree Badola ◽

...

Keyword(s):

Boreal Forest ◽

Ground Truth ◽

Burn Severity ◽

Classification Methods ◽

Spectral Indices ◽

Ground Truth Data ◽

Burn Scar ◽

Interior Alaska ◽

Remote Sensing Methods ◽

The Relationship

In recent years, there have been rapid improvements in both remote sensing methods and satellite image availability that have the potential to massively improve burn severity assessments of the Alaskan boreal forest. In this study, we utilized recent pre- and post-fire Sentinel-2 satellite imagery of the 2019 Nugget Creek and Shovel Creek burn scars located in Interior Alaska to both assess burn severity across the burn scars and test the effectiveness of several remote sensing methods for generating accurate map products: Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), and Random Forest (RF) and Support Vector Machine (SVM) supervised classification. We used 52 Composite Burn Index (CBI) plots from the Shovel Creek burn scar and 28 from the Nugget Creek burn scar for training classifiers and product validation. For the Shovel Creek burn scar, the RF and SVM machine learning (ML) classification methods outperformed the traditional spectral indices that use linear regression to separate burn severity classes (RF and SVM accuracy, 83.33%, versus NBR accuracy, 73.08%). However, for the Nugget Creek burn scar, the NDVI product (accuracy: 96%) outperformed the other indices and ML classifiers. In this study, we demonstrated that when sufficient ground truth data is available, the ML classifiers can be very effective for reliable mapping of burn severity in the Alaskan boreal forest. Since the performance of ML classifiers are dependent on the quantity of ground truth data, when sufficient ground truth data is available, the ML classification methods would be better at assessing burn severity, whereas with limited ground truth data the traditional spectral indices would be better suited. We also looked at the relationship between burn severity, fuel type, and topography (aspect and slope) and found that the relationship is site-dependent.

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Monitoring the Foliar Nutrients Status of Mango Using Spectroscopy-Based Spectral Indices and PLSR-Combined Machine Learning Models

Remote Sensing ◽

10.3390/rs13040641 ◽

2021 ◽

Vol 13 (4) ◽

pp. 641

Author(s):

Gopal Ramdas Mahajan ◽

Bappa Das ◽

Dayesh Murgaokar ◽

Ittai Herrmann ◽

Katja Berger ◽

...

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Partial Least Square ◽

Least Square ◽

Partial Least Square Regression ◽

Support Vector ◽

Spectral Indices ◽

Learning Models ◽

Leaf Nutrients ◽

Machine Learning Models

Conventional methods of plant nutrient estimation for nutrient management need a huge number of leaf or tissue samples and extensive chemical analysis, which is time-consuming and expensive. Remote sensing is a viable tool to estimate the plant’s nutritional status to determine the appropriate amounts of fertilizer inputs. The aim of the study was to use remote sensing to characterize the foliar nutrient status of mango through the development of spectral indices, multivariate analysis, chemometrics, and machine learning modeling of the spectral data. A spectral database within the 350–1050 nm wavelength range of the leaf samples and leaf nutrients were analyzed for the development of spectral indices and multivariate model development. The normalized difference and ratio spectral indices and multivariate models–partial least square regression (PLSR), principal component regression, and support vector regression (SVR) were ineffective in predicting any of the leaf nutrients. An approach of using PLSR-combined machine learning models was found to be the best to predict most of the nutrients. Based on the independent validation performance and summed ranks, the best performing models were cubist (R2 ≥ 0.91, the ratio of performance to deviation (RPD) ≥ 3.3, and the ratio of performance to interquartile distance (RPIQ) ≥ 3.71) for nitrogen, phosphorus, potassium, and zinc, SVR (R2 ≥ 0.88, RPD ≥ 2.73, RPIQ ≥ 3.31) for calcium, iron, copper, boron, and elastic net (R2 ≥ 0.95, RPD ≥ 4.47, RPIQ ≥ 6.11) for magnesium and sulfur. The results of the study revealed the potential of using hyperspectral remote sensing data for non-destructive estimation of mango leaf macro- and micro-nutrients. The developed approach is suggested to be employed within operational retrieval workflows for precision management of mango orchard nutrients.

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Characterization of Soil Organic Matter Individual Fractions (Fulvic Acids, Humic Acids, and Humins) by Spectroscopic and Electrochemical Techniques in Agricultural Soils

Agronomy ◽

10.3390/agronomy11061067 ◽

2021 ◽

Vol 11 (6) ◽

pp. 1067

Author(s):

Aleksandra Ukalska-Jaruga ◽

Romualda Bejger ◽

Guillaume Debaene ◽

Bożena Smreczak

Keyword(s):

Molecular Weight ◽

Organic Matter ◽

Soil Organic Matter ◽

Humic Substances ◽

Humic Acids ◽

Agricultural Soils ◽

Fulvic Acids ◽

Sorption Properties ◽

Aliphatic Chains

The objective of this paper was to investigate the molecular characterization of soil organic matter fractions (humic substances (HS): fulvic acids-FAs, humic acids-HAs, and humins-HNs), which are the most reactive soil components. A wide spectrum of spectroscopic (UV–VIS and VIS–nearIR), as well as electrochemical (zeta potential, particle size diameter, and polydispersity index), methods were applied to find the relevant differences in the behavior, formation, composition, and sorption properties of HS fractions derived from various soils. Soil material (n = 30) used for the study were sampled from the surface layer (0–30 cm) of agricultural soils. FAs and HAs were isolated by sequential extraction in alkaline and acidic solutions, according to the International Humic Substances Society method, while HNs was determined in the soil residue (after FAs and HAs extraction) by mineral fraction digestion using a 0.1M HCL/0.3M HF mixture and DMSO. Our study showed that significant differences in the molecular structures of FAs, Has, and HNs occurred. Optical analysis confirmed the lower molecular weight of FAs with high amount of lignin-like compounds and the higher weighted aliphatic–aromatic structure of HAs. The HNs were characterized by a very pronounced and strong condensed structure associated with the highest molecular weight. HAs and HNs molecules exhibited an abundance of acidic, phenolic, and amine functional groups at the aromatic ring and aliphatic chains, while FAs mainly showed the presence of methyl, methylene, ethenyl, and carboxyl reactive groups. HS was characterized by high polydispersity related with their structure. FAs were characterized by ellipsoidal shape as being associated to the long aliphatic chains, while HAs and HNs revealed a smaller particle diameter and a more spherical shape caused by the higher intermolecular forcing between the particles. The observed trends directly indicate that individual HS fractions differ in behavior, formation, composition, and sorption properties, which reflects their binding potential to other molecules depending on soil properties resulting from their type. The determined properties of individual HS fractions are presented as averaged characteristics over the examined soils with different physico-chemical properties.

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Advances in Circular Bioeconomy Technologies: From Agricultural Wastewater to Value-Added Resources

Environments ◽

10.3390/environments8030020 ◽

2021 ◽

Vol 8 (3) ◽

pp. 20

Author(s):

Nidhi Mehta ◽

Kinjal J Shah ◽

Yu-I Lin ◽

Yongjun Sun ◽

Shu-Yuan Pan

Keyword(s):

Membrane Separation ◽

Value Added ◽

Fulvic Acids ◽

Chemical Precipitation ◽

Environmental Benefits ◽

Nitrogen And Phosphorus ◽

Piggery Wastewater ◽

Agricultural Wastewater ◽

Treatment Processes ◽

Electrokinetic Separation

This review systematically outlines the recent advances in the application of circular bioeconomy technologies for converting agricultural wastewater to value-added resources. The properties and applications of the value-added products from agricultural wastewater are first summarized. Various types of agricultural wastewater, such as piggery wastewater and digestate from anaerobic digestion, are focused on. Next, different types of circular technologies for recovery of humic substances (e.g., humin, humic acids and fulvic acids) and nutrients (e.g., nitrogen and phosphorus) from agricultural wastewater are reviewed and discussed. Advanced technologies, such as chemical precipitation, membrane separation and electrokinetic separation, are evaluated. The environmental benefits of the circular technologies compared to conventional wastewater treatment processes are also addressed. Lastly, the perspectives and prospects of the circular technologies for agricultural wastewater are provided.

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Organic Materials and Their Chemically Extracted Humic and Fulvic Acids as Potential Soil Amendments for Faba Bean Cultivation in Soils with Varying CaCO3 Contents

Horticulturae ◽

10.3390/horticulturae7080205 ◽

2021 ◽

Vol 7 (8) ◽

pp. 205

Author(s):

Ihab M. Farid ◽

Mohamed A. El-Ghozoli ◽

Mohamed H. H. Abbas ◽

Dalia S. El-Atrony ◽

Hassan H. Abbas ◽

...

Keyword(s):

Humic Substances ◽

Faba Bean ◽

Plant Biomass ◽

Nitrogenase Activity ◽

Organic Amendments ◽

Fulvic Acids ◽

Mineral N ◽

Randomized Design ◽

Long Time ◽

Factor C

Organic amendments are important sources of nutrients that release upon organic matter degradation, yet the stability of these organics in arid and semi-arid regions is relatively low. In contrast, humic substances (HS) are resistant to biodegradation and can keep nutrients in the soil available for the plant over a long time. Combinations between humic substances (HS) and mineral-N fertilizers are assumed to retain higher available nutrients in soils than those recorded for the sole application of either mineral or organic applications. We anticipate, however, that humic substances might not be as efficient as the organics from which they were extracted in increasing NP uptake by plants. To test these assumptions, faba bean was planted in a pot experiment under greenhouse conditions following a complete randomized design while considering three factors: two soils (calcareous and non-calcareous, Factor A), two organics (biogas and compost, Factor B) and combinations of the organics and their extracts (HA or FA) together with complementary doses of mineral-N ((NH4)2SO4) to attain a total rate of 50 kg N ha−1 (the recommended dose for faba bean plants) (Factor C). Results indicated that nitrogenase activity increased significantly due to the application of the used organics. In this respect, compost manure caused higher nitrogenase activity than biogas manure did. Humic substances raised NP-availability and the uptake by plants significantly; however, the values of increase were lower than those that occurred due to the compost or biogas manure. Moreover, the sole application of the used organics recorded the highest increases in plant biomass. Significant correlations were also detected between NP-availability, uptake and plant biomass. This means that HS could probably retain nutrients in available forms for long time periods, yet nutrients released continuously but slowly upon decomposition of organics seemed more important for plant nutrition.

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