Soil Iron Content Research Articles

Mechanism of Iron Transport in the Triticum aestivum L.–Soil System: Perception from a Pot Experiment

Iron is one of the necessary trace elements for plant growth and the human body. The ‘hidden hunger’ phenomenon in the human body caused by an imbalance of iron in soil is increasingly prominent. Addressing this issue and optimizing soil through regulatory measures to improve the absorption and utilization of iron by crops has become an urgent priority in agricultural development. This study carries out pot experiments to observe the growth process of Triticum aestivum L. under various soil iron environments. Combined with previous research results, the transport mechanism of iron in the soil—Triticum aestivum L. system was systematically explored. The results indicate that during the jointing and maturity stages of Triticum aestivum L., iron was preferentially enriched in the underground parts; at the maturity stage, the iron content in various organs of Triticum aestivum L. shows a trend of increase followed by a decrease with the soil iron content varying in the following sequence: deficient, moderately deficient, medium, moderately adequate, and adequate. The iron-deficient stress environment causes an increase in the effectiveness of rhizosphere iron, resulting in a higher level of iron in the plant stems, leaves, and seeds. Conversely, when the soil iron content is medium or adequate, the effectiveness of rhizosphere iron decreases, leading to a reduction in the iron content in each part of the plant. A concentration gradient of 7.2 mg/kg in the experimental setup is found to be the most favorable to the enrichment of iron in the shoots of Triticum aestivum L. plants. The findings of this experiment provide guidance for the fertilization strategy to mitigate iron deficiency symptoms in plants under similar acidic-alkaline conditions of soil, as well as a systematic mechanism reference and basis for studying the soil-plant-human health relationship.

The macroecology of butyrate-producing bacteria via metagenomic assessment of butyrate production capacity.

Butyrate-producing bacteria are found in many outdoor ecosystems and host organisms, including humans, and are vital to ecosystem functionality and human health. These bacteria ferment organic matter, producing the short-chain fatty acid butyrate. However, the macroecological influences on their biogeographical distribution remain poorly resolved. Here we aimed to characterise their global distribution together with key explanatory climatic, geographical and physicochemical variables. We developed new normalised butyrate production capacity (BPC) indices derived from global metagenomic (n = 13,078) and Australia-wide soil 16S rRNA (n = 1331) data, using Geographic Information System (GIS) and modelling techniques to detail their ecological and biogeographical associations. The highest median BPC scores were found in anoxic and fermentative environments, including the human (BPC = 2.99) and non-human animal gut (BPC = 2.91), and in some plant-soil systems (BPC = 2.33). Within plant-soil systems, roots (BPC = 2.50) and rhizospheres (BPC = 2.34) had the highest median BPC scores. Among soil samples, geographical and climatic variables had the strongest overall effects on BPC scores (variable importance score range = 0.30-0.03), with human population density also making a notable contribution (variable importance score = 0.20). Higher BPC scores were in soils from seasonally productive sandy rangelands, temperate rural residential areas and sites with moderate-to-high soil iron concentrations. Abundances of butyrate-producing bacteria in outdoor soils followed complex ecological patterns influenced by geography, climate, soil chemistry and hydrological fluctuations. These new macroecological insights further our understanding of the ecological patterns of outdoor butyrate-producing bacteria, with implications for emerging microbially focused ecological and human health policies.

Iron status in piglets at three days of age and at weaning and possible seasonal effects on the blood haemoglobin levels in a Swedish outdoor pig-producing farm

BackgroundPiglets are born with limited stores of iron, and with an increasing number of live-born piglets, there may be a risk that the sows cannot provide enough iron to their offspring. The iron content in soil may not meet the demands of today’s piglet, born and reared in an outdoor setting. The study aimed to describe the blood haemoglobin (Hb) levels in pigs reared outdoors and to determine whether piglets have higher Hb levels at weaning when an iron supplement is administered intramuscularly at three days of age, as compared to pigs not given an iron supplement. The seasonal variation in Hb-levels was also to be investigated. The Hb concentration was analysed with a HemoCue 201 + Hb photometer.ResultsIn total 56 litters (399 piglets) were included in the study and sampled at three days of age, while 378 piglets were sampled at weaning. The mean Hb level at three days of age was 91 g/L (48–154 g/L). In total 47% of the piglets had Hb levels < 90 g/L at three days of age. The mean Hb level at weaning was 127 g/L (76–176 g/L), with a lower level (122 g/L) in the group given the iron supplement than in the group not given an iron supplement (132 g/L). Only 1% of the piglets had Hb levels lower than 90 g/L at weaning. Results indicative of a seasonal effect on Hb levels at three days of age was demonstrated. Piglets born in spring had significantly lower Hb levels, and piglets born in autumn had significantly higher Hb levels. No seasonal effect could be demonstrated for Hb levels at day 33.ConclusionsThe results indicate that the natural uptake from the environment was sufficient, but that there was a seasonal effect on the Hb levels at three days of age. This indicates that there might be a need for different routines regarding iron supplementation in outdoor reared piglets depending on the climate and season.

Effect of Different Rice Establishment Methods on Soil Nutrient Status and Carbon Stock in Paddy Growing Soils of Jagtial, Telangana, India

The study was conducted to know the nutrient status, yield, straw and harvest index in 4 different cropping systems viz., (i) manual transplanting; (ii) dry direct seeding; (iii) drum seeding and (iv) machine transplanting 120 soil samples were analysed, 30 from each establishment method. The soil fertility status from different establishment methods was studied by analysing the samples for pH, EC, bulk density, organic carbon, available nitrogen, phosphorous, potassium, iron, zinc, total organic carbon and carbon stock. The study revealed that there is a significant difference in bulk density ranged high in machine transplanting (1.48 Mg m-3) and low in dry direct seeding (1.45Mg m-3). pH of soils was neutral to slightly alkaline in nature and iron content of soils ranged from 5.75 to 8.97 mg kg-1.

Dynamic Studies of Nodulation and Morphological Characterization of Dhaincha (Sesbania aculeate L.) Accessions

Sesbania aculeate is a quick growing, succulent, easily decomposable and produces maximum amount of organic matter and nitrogen in the soil. Sesbania green manure after decomposition, increases humus, available nitrogen and lower down the C:N ratio of soil, aeration, permeability, enhance the boron and iron content in soil and also protect the soil from leaching of nutrients. Besides this significance, current study was carried out during rabi 2022 at ADAC&amp;RI, Trichy to characterize the explored Daincha germplasms for their morpho biometric triats, green biomass yield and nodulation. Daincha germplasm Sivagangai local recorded more green biomass yield (19.8t / ha) and superior contributing biometrical traits. The number of Rhizobium colonies was higher in Sivagangai local as compared to Villupuram local, Vellore local &amp; Pant variety. This could further be utilized for their symbiotic effectiveness determination under field condition and can be effectively utilized in hybridization programs for improving more green biomass yield.

Research on Hyperspectral Modeling of Total Iron Content in Soil Applying LSSVR and CNN Based on Shannon Entropy Wavelet Packet Transform

The influence of some seemingly anomalous samples on modeling is often ignored in the quantitative prediction of soil composition modeling with hyperspectral data. Soil spectral transformation based on wavelet packet technology only performs pruning and threshold filtering based on experience. The feature bands selected by the Pearson correlation coefficient method often have high redundancy. To solve these problems, this paper carried out a study of the prediction of soil total iron composition based on a new method. First, regarding the problem of abnormal samples, the Monte Carlo method based on particle swarm optimization (PSO) is used to screen abnormal samples. Second, feature representation based on Shannon entropy is adopted for wavelet packet processing. The amount of information held by the wavelet packet node is used to decide whether to cut the node. Third, the feature bands selected based on the correlation coefficient and the competitive adaptive reweighted sampling (CARS) algorithm using the least squares support vector regression (LSSVR) are applied to the soil spectra before and after wavelet packet processing. Finally, the Fe content was calculated based on a 1D convolutional neural network (1D-CNN). The results show that: (1) The Monte Carlo method based on particle swarm optimization and modeling multiple times was able to handle the abnormal samples. (2) Based on the Shannon entropy wavelet packet transformation, simple operations could simultaneously preserve the spectral information while removing high-frequency noise from the spectrum, effectively improving the correlation between soil spectra and content. (3) The 1D-CNN with added residual blocks could also achieve better results in soil hyperspectral modeling with few samples.

A Symbiotic Fungus Sistotrema Benefits Blueberry Rejuvenation and Abiotic Stress Tolerance.

Blueberry (Vaccinium spp.) rhizosphere microorganisms can significantly increase the absorption area and improve the efficiency of rhizospheric nutrient uptake. However, there has been little research on blueberry rhizosphere microorganisms, especially those that can complement root function deficiency. In this study, we analyzed the rhizosphere fungi of 'O'Neal,' 'Sharpblue,' and 'Premier' blueberry cultivars and found that 'Premier' blueberries showed strong growth potential and relatively high root regulation ability. The dominant symbiotic fungus Sistotrema was correlated with the strong growth of 'Premier' and was directionally screened and isolated based on conserved gene structures and COG function analysis. This fungus was reinoculated onto the roots of 'Gulfcoast' and 'Star' blueberry cultivars. Sistotrema promoted the growth of blueberries and improved their ability to resist stress and grow under adverse conditions, as indicated by maintained or increased chlorophyll content under such conditions. Further analyses showed that Sistotrema has certain functional characteristics such as the ability to dissolve iron in its insoluble form and then release it, to fix nitrogen, and to inhibit nitrification in soil. Thus, it effectively doubled the soil nitrogen content and increased the soluble iron content in soil by 50%. This investigation indicates sistotrema inoculation as an approach to increase blueberry stress tolerance and complete their root nutrition deficiency.

Glomalin mediation of plant functional traits unveiled in an extreme ecosystem

Edaphic filters associated with symbiotic relationships are determinants of plant phenotypes. Here we investigate whether glomalin, a by-product of mycorrhizal fungi that improves soil structure and quality, is involved in the filtering of traits that determine plant ecological strategies. This study was developed in two substrate types (ferruginous and quartzitic) of the harsh and ancient Brazilian grasslands known as campo rupestre. We carried out a vegetation functional survey along with soil sample analyses for glomalin content. We compared glomalin content (and its proportion in relation to soil iron content) between habitats and applied the fourth corner analysis to evaluate its relationship with various plant functional traits. Glomalin content in the ferruginous habitat was significantly higher than in the quartzitic habitat. In ferruginous plots, the persistent form of glomalin (total glomalin, TG) was associated with enhanced plant ruderality, increased specific leaf area and reduced leaf dry matter content. In contrast, in quartzitic plots the freshly produced and more volatile form of glomalin (easily extractable glomalin, EEG) was associated with enhanced plant competitiveness evident as larger leaf area, and reduced ruderality evident as increased leaf density and reduced specific leaf area. The pattern of trait filtering in relation to glomalin content was scale dependent, and differed markedly between the ferruginous and quartzitic habitats, underlining the idiosyncratic nature of plant-soil relationship in these two adjacent plant communities. Our study unveils the association between plant life history and a metabolite produced by a soil symbiont, showing how close interspecific interactions are to ecosystem functionality.

Prediction of material composition in lateritic soil using spectroradiometer

Laterite soil products are vital in coastal region construction. Conventional identification methods are slow and require significant labour. Remote sensing expedites large-scale identification, making it essential in the process. Soil samples were randomly collected from the study area for laboratory analysis to determine soil iron content. Additionally, spectral signatures were obtained from a spectroradiometer to assess iron content variability. The considerable variability in material composition underscores its significance as an indicator of soil characteristics. The iron content serves as a crucial indicator of soil development and can be utilized as an index for assessing material composition in soil. The iron content in soil samples is analysed using Analytical Spectral Device (ASD) Field Spec 4 Spectroradiometer, which operates within the spectral wavelength range of 350–2500 nm. The Vis-NIR reflectance spectroscopy is an innovative and efficient method for predicting soil iron content, requiring less effort compared to conventional techniques. The study uses Partial Least Squares Regression (PLSR), a statistical regression method employed to predict iron content in soil from spectral reflectance data. The application of PLSR resulted in moderate prediction accuracy, with coefficient of determination R2 value for calibration and validation are 0.74 and 0.73, respectively. The analysis revealed that the spectroradiometer in combination with PLSR can accurately predict the material composition in lateritic soil with an efficiency of 70%.

Iron-copper interaction in soil spectra and its effect on the estimation of copper content

It is important to clarify the iron-copper interaction pattern to effectively extract the characteristic bands and improve the inversion accuracy of copper content in soil. In this study, based on experimental samples, spectral feature analysis and analysis of variance (ANOVA) were used to deeply uncover the iron-copper interaction pattern. And used natural samples to build a random forest model to analyze the effect of interaction patterns on inversion accuracy. The results of the study showed that the effect of iron content in soil on spectral reflectance varied with copper content in soil, and similarly, the effect of copper content in soil on spectral reflectance varied with iron content in soil. The effect of iron, copper and their interaction on the spectral reflectance of soil varied with the wavelength. In the wavelength from 400 to 2,500 nm, the effect of iron on the spectral features was more than copper, and in the characteristic wavelength of iron (600–700 nm), even more than 5 times that of copper, the effect of iron on the spectral reflectance played a major role, and the iron content in soil must be considered in the inversion of copper content in soil. The Pearson correlation coefficient method was used as the selected characteristic wavelength, the selected wavelength was used as the independent variable, and the copper content in the soil was the dependent variable. Inversion model was built by the random forest algorithm, and the determination coefficient was 0.73. Under the condition of considering the interaction, the coefficient of determination was 0.87. It was shown that the characteristic wavelength was selected by considering the iron-copper interaction, which can better characterize the response signal of copper in soil. This paper provided a new idea for the accurate inversion of copper content in soil, which can provide technical support for the rapid detection of copper content in soil.

Effect of Continuous Iron Pollution on Morphology and DNA Profile of Pleurotus tuber-regium

Aims: Iron, the fourth most abundant micronutrient in the soil has been classified as a metal of environmental concern, the study was to assess the effect of continuous iron pollution on growth and the molecular profile of Pleurotus tuber-regium.&#x0D; Study design: Experimental research design.&#x0D; Place and Duration of Study: The Bells University of Technology, between December 2019 and October 2020.&#x0D; Methodology: P. tuber-regium sclerotia procured locally were seeded into 1 kg soil polluted with 20, 40, 60, and 80 mg kg-1 FeSO4 respectively. Watering was done with the respective Fe solutions for 59 days at the rate of 100ml/48hrs. The Stipe length, pileus diameter, fruitbody weight, and days for fruitbody emergence were recorded for each treatment. The concentration of iron in soil and mushroom was determined by Atomic Absorption Spectrophotometer (AAS). DNA extracted from the fruitbodies was subjected to Random Amplified Polymorphic DNA (RAPD) analysis using five RAPD primers and a cluster analysis was performed.&#x0D; Results: Fruiting was achieved in all soils (0, 20, 40, 60, 80 mg kg-1) after 23±0.5, 17±1.0, 50±2.5, 58±1.0, and 59±2.0 days respectively. Iron had no significant effect on stipe length while pileus diameter and fruitbody weight increased only at 20mg kg-1. Iron concentration in the soil and mushroom increased as Fe pollution was increased. The bioaccumulation factor was higher in contaminated soil and was between 0.78 and 1.08. The RAPD primers amplified 53 band sizes ranging from 100 - 700 bp and the DNA was clustered in a major group. The percentage polymorphism was 16.9% indicating a low level of genetic variation.&#x0D; Conclusion: The result indicated that continuous Fe pollution at the stated concentrations did not have any significant effect on the DNA profile of Pleurotus tuber-regium but the morphological growth of the mushroom was enhanced at low iron contamination levels.

Molecular signatures of parallel adaptive divergence causing reproductive isolation and speciation across two genera

Parallel evolution of reproductive isolation (PERI) provides strong evidence for natural selection playing a fundamental role in the origin of species. However, PERI has been rarely demonstrated for well established species drawn from different genera. In particular, parallel molecular signatures for the same genes in response to similar habitat divergence in such different lineages is lacking. Here, based on whole-genome sequencing data, we first explore the speciation process in two sister species of Carpinus (Betulaceae) in response to divergence for temperature and soil-iron concentration in habitats they occupy in northern and southwestern China, respectively. We then determine whether parallel molecular mutations occur during speciation in this pair of species and also in another sister-species pair of the related genus, Ostryopsis, which occupy similarly divergent habitats in China. We show that gene flow occurred during the origin of both pairs of sister species since approximately 9.8 or approximately 2 million years ago, implying strong natural selection during divergence. Also, in both species pairs we detected concurrent positive selection in a gene (LHY) for flowering time and in two paralogous genes (FRO4 and FRO7) of a gene family known to be important for iron tolerance. These changes were in addition to changes in other major genes related to these two traits. The different alleles of these particular candidate genes possessed by the sister species of Carpinus were functionally tested and indicated likely to alter flowering time and iron tolerance as previously demonstrated in the pair of Ostryopsis sister species. Allelic changes in these genes may have effectively resulted in high levels of prezygotic reproductive isolation to evolve between sister species of each pair. Our results show that PERI can occur in different genera at different timescales and involve similar signatures of molecular evolution at genes or paralogues of the same gene family, causing reproductive isolation as a consequence of adaptation to similarly divergent habitats.

Free iron oxide content in tropical soils predicted by integrative digital mapping

The free iron content is a vital indicator of pedogenic processes in tropical soils and can be used to understand the soil’s weathering history and aid in classification. Despite its importance in agriculture and pedology, laboratory analyses of soil iron content are not widely used because they are costly and time-consuming. Remote sensing data combined with digital soil mapping are effective tools to regionalise soil iron content. They can reduce the number of soil samples needed to characterise soil variability and, consequently, laboratory analysis costs. This study aimed to create a strategy for mapping free iron content using a 35-year time series of Landsat images combined with topographic parameters at two spatial resolutions (5 and 30 m) in a region with high variability in soils and geology in the state of São Paulo, Brazil. The dataset comprised 344 observations of free iron content at 0–20 cm depth over a 2574 km2 area. The dataset was split into calibration and a validation set (85:15%), and the environmental variables were chosen based on the scorpan factors. Spatial prediction functions for free iron were developed using several machine learning algorithms linking soil observations with the environmental variables. We found that the temporal bare soil image improved model performance. Although 5 and 30 m resolution terrain data differed slightly, the best-fit model was obtained at 5 m resolution (root mean square error, 25.09 g kg−1; adjusted R2, 0.84). Among the evaluated machine learning algorithms, Random Forest was the most accurate method for predicting free iron distribution in the study area. The free iron content map can identify soil types in more detail and should be prioritised in future pedological studies.

Phosphorus puts a mortgage on restoration of species‐rich grasslands on former agricultural land

Large‐scale biodiversity loss is one of the most urgent global issues. The Convention on Biological Diversity created a vision to ecologically restore ecosystems by 2050. The European Union follows this ambition, and member states are required to select Special Areas of Conservation (SACs) to develop and restore; one example is species‐rich semi‐natural grassland. Species‐rich grassland restoration requires time for restoring both abiotic conditions, e.g. low soil phosphorus concentrations, and biotic conditions, e.g. introduction of missing species. For 507 grasslands (in northern Belgium) situated in SACs, we calculated the time needed for restoring necessary phosphorus‐poor conditions. Only 11% of the grasslands already met the strictest phosphorus‐target. We found that less than a fourth of the other 452 grasslands will reach this phosphorus‐target by 2050 through mowing. P‐mining, a more intensive technique involving fertilization of nitrogen and potassium, could help achieve this phosphorus‐target on about slightly more than a third of these grasslands by 2050. The soil iron concentration strongly affected the restoration times. A total of 65% of the grasslands will require alternatives like topsoil removal or selection of a different, less ambitious grassland target. These calculations do not include the time needed for biotic restoration. Because grassland restoration is a long‐term process, we advocate better protection of still existing species‐rich grasslands. Restoration is a last resort in safe‐guarding biodiversity.

Remotely sensed estimation of total iron content in soil with harmonic analysis and BP neural network

BackgroundThe estimation of total iron content at the regional scale is of much significance as iron deficiency has become a routine problem for many crops.MethodsIn this study, a novel method for estimating total iron content in soil (TICS) was proposed using harmonic analysis (HA) and back propagation (BP) neural network model. Several data preprocessing methods of first derivative (FD), wavelet packet transform (WPT), and HA were conducted to improve the correlation between the soil spectra and TICS. The principal component analysis (PCA) was exploited to obtained three kinds of characteristic variables (FD, WPT-FD, and WPT-FD-HA) for TICS estimation. Furthermore, the estimated accuracy of three BP models based on these variables was compared.ResultsThe results showed that the BP models of different soil types based on WPT-FD-HA had better estimation accuracy, with the highest R2 value of 0.95, and the RMSE of 0.68 for the loessial soil. It was proved that the characteristic variable obtained by harmonic decomposition improved the validity of the input variables and the estimation accuracy of the TICS models. Meanwhile, it was identified that the WPT-FD-HA-BP model can not only estimate the total iron content of a single soil type with high accuracy but also demonstrate a good effect on the estimation of TICS of mixed soil.ConclusionThe HA method and BP neural network combined with WPT and FD have great potential in TICS estimation under the conditions of single soil and mixed soil. This method can be expected to be applied to the prediction of crop biochemical parameters.

OXIDATION-REDUCTION PROCESSES IN THE SOILS OF RICE AGROCENOSES OF THE KUBAN REGION (RUSSIA)

Background: The studies were carried out on the Maryano-Cheburgolsky irrigation array in the Krasnoarmeisky district of the Krasnodar Territory. Aim: This work aims to study the oxidation-reduction processes and activity of iron compounds in meadow-chernozem and meadow-boggy soils of rice agrocenoses of the Kuban region. Methods: Soil samples were taken from paddy fields occupied by rice and perennial grasses, in which the pH value, redox potential (Eh, mV), magnetic susceptibility (χ×10-3 S.I. units), and the content of active iron compounds (FeO and Fe2O3, mg/100 g) were determined. Results and Discussion: The reductive processes created in the soils of rice fields after flooding led to the transformation of poorly soluble Fe2O3 compounds into more active reaction forms of FeO. The maximum content of FeO and the minimum amount of Fe2O3 were recorded during the period of negative Eh values. The maximum reduction of iron in meadow-boggy and meadow-chernozem soils was achieved by the phase of flowering of rice plants at Eh = -127, pH = 7.36 units, rH2 = 10 and Eh = -152...-167 mV, pH = 6.89-7.10 units, rH2 = 9, respectively. Conclusions: In the soil of permanent rice cultivation, the dynamics of Eh and iron compounds are similar to the fields of rice crop rotation. In non-flooded soils of rice fields under perennial grasses, the oxidative regime prevails, and the transformation of iron compounds is not expressed. The minimum values of χ coincide with the maximum content of ferrous iron in soils at negative Eh values, which corresponds to the rice flowering phase. Correlation coefficients of χ with Eh, FeO, and Fe2O3 equal from +0.66 to +0.75, from -0.69 to -0.84, and from +0.74 to +0.77, respectively. The χ value increases in the soil under crops of perennial grasses with a predominance of oxidative processes. The correlation coefficient between χ and Fe2O3 equals from +0.83 to +0.90.

Hyperlocal Variation in Soil Iron and the Rhizosphere Bacterial Community Determines Dollar Spot Development in Amenity Turfgrass.

Dollar spot, caused by the fungal pathogen Clarireedia spp., is an economically important foliar disease of amenity turfgrass in temperate climates worldwide. This disease often occurs in a highly variable manner, even on a local scale with relatively uniform environmental conditions. The objective of this study was to investigate mechanisms behind this local variation, focusing on contributions of the soil and rhizosphere microbiome. Turfgrass, rhizosphere, and bulk soil samples were collected from within a 256-m2 area of healthy turfgrass, transported to a controlled environment chamber, and inoculated with Clarireedia jacksonii Bacterial communities were profiled by targeting the 16S rRNA gene, and 16 different soil chemical properties were assessed. Despite their initial uniform appearance, the samples differentiated into highly susceptible and moderately susceptible groups following inoculation in the controlled environment chamber. The highly susceptible samples harbored a unique rhizosphere microbiome with suggestively lower relative abundance of putative antibiotic-producing bacterial taxa and higher predicted abundance of genes associated with xenobiotic biodegradation pathways. In addition, stepwise regression revealed that bulk soil iron content was the only significant soil characteristic that positively regressed with decreased dollar spot susceptibility during the peak disease development stage. These findings suggest that localized variation in soil iron induces the plant to select for a particular rhizosphere microbiome that alters the disease outcome. More broadly, further research in this area may indicate how plot-scale variability in soil properties can drive variable plant disease development through alterations in the rhizosphere microbiome.IMPORTANCE Dollar spot is the most economically important disease of amenity turfgrass, and more fungicides are applied targeting dollar spot than any other turfgrass disease. Dollar spot symptoms are small (3 to 5 cm), circular patches that develop in a highly variable manner within plot scale even under seemingly uniform conditions. The mechanism behind this variable development is unknown. This study observed that differences in dollar spot development over a 256-m2 area were associated with differences in bulk soil iron concentration and correlated with a particular rhizosphere microbiome. These findings provide interesting avenues for future research to further characterize the mechanisms behind the highly variable development of dollar spot, which may inform innovative control strategies. Additionally, these results suggest that small changes in soil properties can alter plant activity and hence the plant-associated microbial community, which has important implications for a broad array of agricultural and horticultural plant pathosystems.

Effect of Bacillus megaterium var. phosphaticum Bacteria and L-Alpha Proline Amino Acid on Iron Content in Soil and Triticum aestivum L. Plants in Sustainable Agriculture System

Research was conducted in Poland in 2017–2019 at Siedlce University of Natural Sciences and Humanities in Siedlce. It aimed at determining the effect of the bacteria Bacillus megaterium var. phosphaticum, the amino acid L-alpha proline, and the mineral nitrogen fertiliser regime on iron content in soil during the period of rapid growth of spring wheat plants, as well as in spring wheat grain and straw. The following two factors were examined: (I) biological products: untreated control, Bacillus megaterium var. phosphaticum, L-alpha proline, Bacillus megaterium var. phosphaticum + L-alpha proline; (II) mineral nitrogen fertiliser regime: nonfertilised control, 60 kg N·ha−1, 90 kg N·ha−1, 90 kg N·ha−1 + foliar fertilisation. The study demonstrated that, during the period of rapid spring wheat plant growth, Fe content was the highest in the soil following an application of Bacillus megaterium var. phosphaticum + L-alpha proline + mineral nitrogen fertiliser applied at the rate of 90 kg N·ha−1. This combination resulted in the highest concentration and uptake of iron by spring wheat grain, whereas for straw, the same result was also achieved following mineral nitrogen fertiliser at a rate of 90 kg N·ha−1 + foliar fertilisation.

FORMS OF FERUM IN SOD-PODZOLIC PSEUDOGLEYED SOILS (STAGNIC RETISOLS) OF THE BESKYDY PRE-CARPATHIAN REGION

Problem Statement and Purpose. A significant number of sceletans within HE gl and Eh gl horizons and sesquans on the facets of the prismatic structural segments It gl and Pi gl horizons in sod-podzolic surface-gley soils of the Beskydsky Pre-Сarpathian region indicate the processes of transformation, migration, and partial accumulation of iron compounds. The accumulation of one and a half iron oxides was diagnosed in the ortstein of the studied soils (Kx = 2.7). The profile of sod-podzolic surface-gley soils is charac­terized by differentiation by gross Ferrum content and moderately low degree of iron content. The purpose of our study is to establish and evaluate the gross forms of iron, its silicate, non-silicate, crystallized, amorphous forms in sod-podzolic surface-gley soils of the Beskydsky Pre-Сarpathian region and ortsteins formed within genetic horizons, in order to diagnose the elementary soil-forming processes and establish the genesis. Data &amp; Methods. In our researches we used own materials from field and labora­tory studies. In the laboratory, the method of atomic adsorption spectrophotometry was used to determine the gross and mobile content of iron in soils and ortsteins; the content of iron silicate was calculated as the difference between its gross con­tent and the amount of non-silicate iron. Non-silicate iron was determined by the Coffin method, amorphous Fe compounds by the Tamm method. Crystallized Fe was calculated as the difference between the amount of non-silicate and amorphous. Results. The composition of gross Ferrum is dominated by Fes, the maximum share of which (84.4%) is characteristic of It gl. The maximum share of Fes (33.3%) is characteristic of HE gl, which indicates the processes of destruction of primary and secondary minerals in the upper part of the profile, and is confirmed by the degree of weathering of the soil mass (Fes / Fens = 2.0-2.6). The largest values of the Schwert­man coefficient (0.6-0.8) are characteristic of the overiluvial part, which confirms the dominant role of surface gleying in the genesis and the predominance of the gle­sish-eluvial process in the upper part of the profile. The content of Fegr in ortsteins is grater by 7.2 in comparison with fine earth, and the degree of their Ferrum content is moderately high (Fegr = 6.05-8.41). The composition of the gross Ferrum of ortsteins is dominated by Fens, whose time within the profile gradually increases from 56.0 in HE gl to 62.3% in the transition to the rock horizon. The content of Fens in ortsteins in comparison with fine earth is greater by 9,7-12,1. The share of Fes in ortsteins ranges from 44.0% in the HE gl to 37.7% in the Pi gl horizon. The Schwertman coefficient in the ortsteins of the overiluvial part of the profile is 0.6-0.7, which confirms the theory of their formation under the predominant action of the glesish-eluvial process. This figure is 0.04 in the ortsteins of the transition horizon, which indicates a slight influ­ence of modern gleying and their relict origin.

Prediction of iron content in soil based on microspectrophotometry analysis

Soil is a very important type of trace evidence. The iron content of soil is of great significance in distinguishing soil types, discriminating among different soils, and tracing soils. However, conventional methods for analyzing the iron content of soil are expensive, laborious, and time-consuming. Previous studies have shown that the color of soil correlates well with its hematite content. This article thus deals with the indirect determination of iron content using soil color as a proxy. Soil color measurements were conducted using microspectrophotometry (MSP), and resulting data were transformed into chromaticity value (L*, a*, and b*). Predictions using the redness index in conjunction with a linear regression model were compared with those using the chromaticity value and a back propagation neural network (BPNN) model. The influences of different modeling conditions on the modeling accuracy were compared, and more accurate predictions were achieved when the iron content was higher than 2.13%. The BPNN model produced predictions with R2 and RMSE values of 0.955 and 0.336%, which were better than the predictions of the linear regression model (R2: 0.859, RMSE, 1.07%). We thus demonstrated that MSP can be used for fast, accurate, and non-destructive measurements of soil color and prediction of its iron content. Although the results may not be as precise as conventional laboratory analysis, they still provide more information with acceptable accuracy, which should have promising applications in forensic applications.