Metagenomic analysis of rhizosphere microbiome provides insights into occurrence of iron deficiency chlorosis in field of Asian pears

Background Fe-deficiency chlorosis (FDC) of Asian pear plants is widespread, but little is known about the association between the microbial communities in the rhizosphere soil and leaf chlorosis. The leaf mineral concentration, leaf subcellular structure, soil physiochemical properties, and bacterial species community and distribution had been analysed to gain insights into the FDC in Asian pear plant. Results The total Fe in leaves with Fe-deficiency was positively correlated with total K, Mg, S, Cu, Zn, Mo and Cl contents, but no differences of available Fe (AFe) were detected between the rhizosphere soil of chlorotic and normal plants. Degraded ribosomes and degraded thylakloid stacks in chloroplast were observed in chlorotic leaves. The annotated microbiome indicated that there were 5 kingdoms, 52 phyla, 94 classes, 206 orders, 404 families, 1,161 genera, and 3,043 species in the rhizosphere soil of chlorotic plants; it was one phylum less and one order, 11 families, 59 genera, and 313 species more than in that of normal plant. Bacterial community and distribution patterns in the rhizosphere soil of chlorotic plants were distinct from those of normal plants and the relative abundance and microbiome diversity were more stable in the rhizosphere soils of normal than in chlorotic plants. Three (Nitrospira defluvii, Gemmatirosa kalamazoonesis, and Sulfuricella denitrificans) of the top five species (N. defluvii, G. kalamazoonesis, S. denitrificans, Candidatus Nitrosoarchaeum koreensis, and Candidatus Koribacter versatilis). were the identical and aerobic in both rhizosphere soils, but their relative abundance decreased by 48, 37, and 22%, respectively, and two of them (G. aurantiaca and Ca. S. usitatus) were substituted by an ammonia-oxidizing soil archaeon, Ca. N. koreensis and a nitrite and nitrate reduction related species, Ca. K. versatilis in that of chlorotic plants, which indicated the adverse soil aeration in the rhizosphere soil of chlorotic plants. A water-impermeable tables was found to reduce the soil aeration, inhibit root growth, and cause some absorption root death from infection by Fusarium solani. Conclusions It was waterlogging or/and poor drainage of the soil may inhibit Fe uptake not the amounts of AFe in the rhizosphere soil of chlorotic plants that caused FDC in this study.

Development of A Controlled-Environment Assay To Induce Iron Deficiency Chlorosis In Soybean By Adjusting Calcium Carbonates, pH, And Nodulation

Background Soybean iron deficiency chlorosis (IDC) is an important nutrient stress frequently found in high pH and/or soils high in calcium carbonates. To advance the understanding of IDC resistance in soybean, a rapid (21-day) controlled-environment assay was developed to investigate the effects of nodulation, pH, and calcium carbonate levels on soybean iron deficiency traits. This system was tested on four genotypes known to exhibit differences in iron efficiency, including two standard IDC check cultivars and a pair of near-isogenic lines exhibiting variation at an IDC resistance quantitative trait locus. Visual score, chlorophyll content, plant height, root dry mass, and shoot dry mass were measured to quantify iron stress. ResultsCalcium carbonate levels and nodulation were found to have the greatest effects on IDC severity. Increasing carbonate levels worsened IDC symptoms, while nodulation reduced symptoms in all genotypes. Higher pH levels increased iron deficiency symptoms in check genotypes ‘Corsoy 79’ and ‘Dawson’, but did not induce iron deficiency symptoms in near-isogenic lines. A significant interaction was observed between genotype, nodulation, and calcium carbonate level, indicating that a specific treatment level could discern IDC symptoms between genotypes differing in resistance to IDC. ConclusionsIDC symptoms were successfully induced in the Check Genotypes Experiment as well as the NIL Experiment, indicating the success of using a liquid CaCO3 source and this assay for inducing IDC in controlled environments. However, our results suggest that treatment levels that best differentiate genotypes for their IDC resistance may need to be determined for each experiment because of the unique way in which different genotypes display symptoms and respond to iron deficiency conditions.

Predictions from algorithmic modeling result in better decisions than from data modeling for soybean iron deficiency chlorosis

In soybean variety development and genetic improvement projects, iron deficiency chlorosis (IDC) is visually assessed as an ordinal response variable. Linear Mixed Models for Genomic Prediction (GP) have been developed, compared, and used to select continuous plant traits such as yield, height, and maturity, but can be inappropriate for ordinal traits. Generalized Linear Mixed Models have been developed for GP of ordinal response variables. However, neither approach addresses the most important questions for cultivar development and genetic improvement: How frequently are the ‘wrong’ genotypes retained, and how often are the ‘correct’ genotypes discarded? The research objective reported herein was to compare outcomes from four data modeling and six algorithmic modeling GP methods applied to IDC using decision metrics appropriate for variety development and genetic improvement projects. Appropriate metrics for decision making consist of specificity, sensitivity, precision, decision accuracy, and area under the receiver operating characteristic curve. Data modeling methods for GP included ridge regression, logistic regression, penalized logistic regression, and Bayesian generalized linear regression. Algorithmic modeling methods include Random Forest, Gradient Boosting Machine, Support Vector Machine, K-Nearest Neighbors, Naïve Bayes, and Artificial Neural Network. We found that a Support Vector Machine model provided the most specific decisions of correctly discarding IDC susceptible genotypes, while a Random Forest model resulted in the best decisions of retaining IDC tolerant genotypes, as well as the best outcomes when considering all decision metrics. Overall, the predictions from algorithmic modeling result in better decisions than from data modeling methods applied to soybean IDC.

Differential response of groundnut genotypes for iron (Fe) deficiency chlorosis tolerance and productivity traits under Fe-supplemented and Fe-non-supplemented conditions

Groundnut is sensitive to Fe deficiency under alkaline and calcareous soils and exhibits iron deficiency chlorosis (IDC) causing significant reduction in growth and yield. Genotypes were assessed for IDC related traits such as visual chlorosis rating, SPAD chlorophyll meter reading, chlorophyll and active iron (Fe2+) content across five growth stages and also for productivity traits viz., plant height, number of primary branches, number of pods per plant, pod yield, shelling per cent, 100 seed weight and haulm yield. Comparison between Fe-supplemented and Fe-nonsupplemented condition for IDC related traits showed not much difference among IDC tolerant genotypes across all five growth stages, while significant differences among IDC susceptible genotypes were observed. Maximum reduction in pod yield was observed among IDC susceptible genotypes compared to IDC tolerant and moderately tolerant genotypes. However, recently released variety G 2- 52 with moderate tolerance to IDC and higher yield potential recorded higher pod yield both under Fe applied (1754 kgha–1) and non-applied conditions (1544 kgha–1).

Rating Iron Deficiency in Soybean Using Image Processing and Decision-Tree Based Models

The most efficient way of soybean (Glycine max (L.) Merrill) iron deficiency chlorosis (IDC) management is to select a tolerant cultivar suitable for the specific growing condition. These cultivars are selected by field experts based on IDC visual ratings. However, this visual rating method is laborious, expensive, time-consuming, subjective, and impractical on larger scales. Therefore, a modern digital image-based method using tree-based machine learning classifier models for rating soybean IDC at plot-scale was developed. Data were collected from soybean IDC cultivar trial plots. Images were processed with MATLAB and corrected for light intensity by using a standard color board in the image. The three machine learning models used in this study were decision tree (DT), random forest (RF), and adaptive boosting (AdaBoost). Calculated indices from images, such as dark green color index (DGCI), canopy size, and pixel counts into DGCI ranges and IDC visual scoring were used as input and target variables to train these models. Metrics such as precision, recall, and f1-score were used to assess the performance of the classifier models. Among all three models, AdaBoost had the best performance (average f1-score = 0.75) followed by RF and DT the least. Therefore, a ready-to-use methodology of image processing with AdaBoost model for soybean IDC rating was recommended. The developed method can be easily adapted to smartphone applications or scaled-up using images from aerial platforms.

Metagenomic Analysis of Rhizosphere Microbiome Provides Insights Into Occurrence of Iron Deficiency Chlorosis in Field of Asian Pears

Fe-deficiency chlorosis (FDC) of Asian pear plants is widespread, but little is known about the association between the bacterial biogeography in the rhizosphere soil and leaf chlorosis. The leaf mineral concentration, leaf subcellular structure, soil physiochemical properties, and bacterial species community and distribution have been analyzed. The total Fe in leaves with Fe-deficiency was positively correlated with total K, Mg, S, Cu, Zn, Mo and Cl contents, but no differences of available Fe (AFe) were detected between the rhizosphere soil of chlorotic and normal plants. Degraded ribosomes and degraded thylakloid stacks in chloroplast were observed in chlorotic leaves. Bacterial community and distribution patterns in the rhizosphere soil of chlorotic plants were distinct from those of normal plants and the relative abundance and microbiome diversity were more stable in the rhizosphere soils of normal than in chlorotic plants. Water-impermeable tables reduce the soil aeration, inhibit root growth, and cause some absorption root death from infection by Fusarium solani. The rhizosphere soils of FDC plants have distinct composition, lower relative abundance, and unstable diversity of microbiome. Higher amounts of AFe in the rhizosphere soil of chlorotic plants demonstrated it was the Fe uptake that caused FDC in this study.