Soybean Iron Deficiency Chlorosis Research Articles

Measuring soybean iron deficiency chlorosis progression and yield prediction with unmanned aerial vehicle

Iron deficiency chlorosis (IDC), a symptom of reduction in chlorophyll and stunted growth, causes a great yield loss in soybean every year in the Midwest, USA and the most efficient method to manage IDC is to plant tolerant cultivars. The assessment of cultivars' tolerance is traditionally performed by visually rating the IDC symptoms based on leaves discoloration twice during the growing season. However, the visual rating method is time-consuming, subjective, not suitable at large scales, labor-intensive, and unaffordable for frequent observation. Therefore, in this study, we used an unmanned aerial vehicle (UAV) as a tool to monitor the soybean cultivars more frequently and more efficiently through image processing approach of the whole field. Images were taken with a DJI Phantom 4 and orthomosaicked in Agisoft Photoscan. A 40-cultivar soybean experimental plots (3000 m2; ▪) at 5 locations in North Dakota, USA (Amenia, Colfax, Leonard (2), and Hunter) for 2 years (2016 and 2017) were used in the study. The orthomosaicked images were processed in MATLAB to calculate the dark green color index (DGCI), which is a good indicator of chlorophyll in soybean leaves. The grayscale DGCI images were then processed in ArcGIS to extract the average DGCI and canopy size (CS) for each plot for each flight. The area under the curve (AUC) was calculated for DGCI, CS, and CS × DGCI product (CDP) to aggregate the values of all flights within each year. The correlation of AUC of CDP and the yield was more consistent among both years and was the better predictor of yield (R2=0.74 and R2=0.79). The latest growth stage (more representative of yield) values of both years were combined to build models for yield prediction and the CDP produced the lowest error (11.72%). Future studies should look into IDC progress measurement involving more cultivars, geographical locations, frequent imaging, as well as methods applied to regular soybean production sites to evaluate various image-based parameters and their interaction for yield predictions.

Effect of Different Doses of Iron Chelate on Plant Growth and in Preventing Iron Deficiency Chlorosis in Soybean

The objective of this research work is to solve the problem due to iron deficiency in soybean crop. Iron deficiency chlorosis is a widespread agricultural problem in soybean crop, especially in alkaline and calcareous soils resulting in complete crop failure. To eradicate this problem, the methodology adopted in this work was conducted with a factorial experimental design of different doses (0, 2.5, 5 and 7.5 mgkg-1) of FeDTPA in 15 different types of soil categorized into low (L1-L5), medium (M1-M5) and high (H1-H5) iron content with soybean crop during Kharif season 2020 at Department of Soil Science and Agricultural Chemistry, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi, India. The experiment ended after 60 days of seed sowing. Plant height and total chlorophyll content of soybean crop were determined at 30 and 60 days after sowing. The highest plant height and total chlorophyll content were observed in 7.5 mgkg-1 dose of Fe in low iron content soil (L1-L5) is 102.36cm and 45.63 SPAD value respectively. Increasing the application of iron doses from 0 to 7.5 mgkg-1 increases the plant height and total chlorophyll content in low iron content soil while in high iron content soil (H1-H5) it decreases the plant height and total chlorophyll content. In conclusion, the use of FeDTPA in iron-deficient soils of the Indo-Gangetic plain will be beneficial for the growth of soybean crops as well as for the eradication of iron deficiency chlorosis.

Applying Spatial Statistical Analysis to Ordinal Data for Soybean Iron Deficiency Chlorosis

Accounting for field variation patterns plays a crucial role in interpreting phenotype data and, thus, in plant breeding. Several spatial models have been developed to account for field variation. Spatial analyses show that spatial models can successfully increase the quality of phenotype measurements and subsequent selection accuracy for continuous data types such as grain yield and plant height. The phenotypic data for stress traits are usually recorded in ordinal data scores but are traditionally treated as numerical values with normal distribution, such as iron deficiency chlorosis (IDC). The effectiveness of spatial adjustment for ordinal data has not been systematically compared. The research objective described here is to evaluate methods for spatial adjustment of ordinal data, using soybean IDC as an example. Comparisons of adjustment effectiveness for spatial autocorrelation were conducted among eight different models. The models were divided into three groups: Group I, moving average grid adjustment; group II, geospatial autoregressive regression (SAR) models; and Group III, tensor product penalized P-splines. Results from the model comparison show that the effectiveness of the models depends on the severity of field variation, the irregularity of the variation pattern, and the model used. The geospatial SAR models outperform the other models for ordinal IDC data. Prediction accuracy for the lines planted in the IDC high-pressure area is 11.9% higher than those planted in low-IDC-pressure regions. The relative efficiency of the mixed SAR model is 175%, relative to the baseline ordinary least squares model. Even though the geospatial SAR model is the best among all the compared models, the efficiency is not as good for ordinal data types as for numeric data.

Development of a controlled-environment assay to induce iron deficiency chlorosis in soybean by adjusting calcium carbonates, pH, and nodulation

BackgroundSoybean 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 calcium 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 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.

Foliar application of 3-hydroxy-4-pyridinone Fe-chelate [Fe(mpp)3 ] induces responses at the root level amending iron deficiency chlorosis in soybean.

Iron (Fe) deficiency chlorosis (IDC) affects the growth of several crops, especially when growing in alkaline soils. The application of synthetic Fe-chelates is one of the most commonly used strategies in IDC amendment, despite their associated negative environmental impacts. In a previous work, the Fe-chelate tris(3-hydroxy-1-(H)-2-methyl-4-pyridinonate) iron(III) [Fe(mpp)3 ] has shown great potential for alleviating IDC in soybean (Glycine max) in the early stages of plant development under hydroponic conditions. Herein, its efficacy was verified under soil conditions in soybean grown from seed to full maturity. Chlorophyll levels, plant growth, root and shoot mineral accumulation (K, Mg, Ca, Na, P, Mn, Zn, Ni, and Co) and FERRITIN expression were accessed at V5 phenological stage. Compared to a commonly used Fe chelate, FeEDDHA, supplementation with [Fe(mpp)3 ] led to a 29% higher relative chlorophyll content, 32% higher root biomass, 36% higher trifoliate Fe concentration, and a twofold increase in leaf FERRITIN gene expression. [Fe(mpp)3 ] supplementation also resulted in increased accumulation of P, K, Zn, and Co. At full maturity, the remaining plants were harvested and [Fe(mpp)3 ] application led to a 32% seed yield increase when compared to FeEDDHA. This is the first report on the use of [Fe(mpp)3 ] under alkaline soil conditions for IDC correction, and we show that its foliar application has a longer-lasting effect than FeEDDHA, induces efficient root responses, and promotes the uptake of other nutrients.

How Raised Beds and Fe-Chelate Affect Soybean Iron Deficiency Chlorosis and Yield

Water-logging and the inability to take up sufficient iron (Fe), causing iron deficiency chlorosis (IDC) in soybean (Glycine max, L. Merr.), can be major yield reducing factors in certain soils in the northern USA and Manitoba, Canada, soybean growing regions. The objective of this research was to evaluate soybean IDC, biomass production, and yield with seeding on raised beds and seed application of the Fe-chelate compound ortho-ortho-Fe-EDDHA. In six environments, soybean were seeded on raised beds and conventionally prepared seedbeds (flat) and with a factorial arrangement of five cultivars (within adapted maturity group 0.1 to 0.9 and variable IDC tolerance) and seed applied Fe-EDDHA using rates of 0 kg·ha−1 and 3.36 kg·ha−1. There were no significant interactions between the factors tested. The plant population was 27% higher on the raised beds compared with flat, and yield was 6.3% higher (2893 kg·ha−1 vs. 2722 kg·ha−1). Total dry plant biomass on raised beds was 9.8% greater compared with flat. The plant population with seed applied Fe-EDDHA was 10.6% lower compared with no application. However, the IDC score was significantly lower 2.2 vs 2.4 (1 = green, 5 = dead) for Fe-EDDHA seed application. Yield and plant biomass were not significantly different between Fe treatments. Raised beds offer an opportunity for soybean growers to reduce the negative influence of excessive water. Further research is needed to determine the long-term effect of raised beds on plant development, IDC expression, and yield. The application of Fe-EDDHA remains a partial solution and should therefore be combined with other methods to reduce IDC. Further research should study other Fe-EDDHA application rates and methods.

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.

Improving model robustness for soybean iron deficiency chlorosis rating by unsupervised pre-training on unmanned aircraft system derived images

Iron deficiency chlorosis (IDC) is a major yield-limiting factor for soybean production in the mid-western USA. The most practical solution in mitigating losses due to IDC is the development and characterization of IDC tolerant varieties. Leveraging the advanced technique of unmanned aircraft system (UAS) and the thriving deep learning methodology, a convolutional neural network (CNN) could be trained to assist breeders with IDC resistance selection. However, a known difficulty in IDC screening is that the symptoms often vary across diverse genetic backgrounds and spatial or temporal soil heterogeneities. A robust CNN model is desired to mitigate such difficulty. While high robustness usually relies on a sufficiently large labeled training data, the available labeled samples in most breeding programs are normally not enough. Under this limitation, it is critical to find an alternative way to train a robust model. The solution proposed in this study was to apply unsupervised pre-training on the unlabeled aerial images that are much easier to obtain by the UAS. Specifically, a convolutional autoencoder (CAE) was pre-trained on unlabeled sub-images clipped from aerial RGB images; then, the pre-trained weights were reused to initialize the CNN model that was trained on labeled plot-wise sub-images clipped from stitched RGB maps. To test the robustness of this CAE initialized model (CAE1-CNN), two baseline models were equally trained: the first was CAE2-CNN, where the CAE2 was pre-trained with three times of unlabeled data as that of CAE1, by adding wniter wheat and sorghum aerial images; the second was Ran-CNN where the CNN was randomly initialized. Three conditions were considered for testing model robustness: different soybean trials, field locations and vegetative growth stages. Results revealed that both the CAE1-CNN and the CAE2-CNN had relatively better robustness than the Ran-CNN model, i.e., higher R2 and lower RMSE values, especially on different soybean trials and growth stages, which proved that the unsupervised pre-training added gains to the model robustness across diverse trials and growth stages. Similar performances were found between the CAE1-CNN andthe CAE2-CNN model, suggesting that augmenting the unlabled data did not bring significant improvement to model robustness. Additionally, during robustness test on different soybean trials, the unsupervised pre-training seemly showed the potential of alleviating the required number of labeled training samples. These promising findings could contribute to the research on crop stresses by providing a potential path towards developing a robust system for classifying or predicting stress severities under more varied conditions.

Deconstructing the genetic architecture of iron deficiency chlorosis in soybean using genome-wide approaches

BackgroundIron (Fe) is an essential micronutrient for plant growth and development. Iron deficiency chlorosis (IDC), caused by calcareous soils or high soil pH, can limit iron availability, negatively affecting soybean (Glycine max) yield. This study leverages genome-wide association study (GWAS) and a genome-wide epistatic study (GWES) with previous gene expression studies to identify regions of the soybean genome important in iron deficiency tolerance.ResultsA GWAS and a GWES were performed using 460 diverse soybean PI lines from 27 countries, in field and hydroponic iron stress conditions, using more than 36,000 single nucleotide polymorphism (SNP) markers. Combining this approach with available RNA-sequencing data identified significant markers, genomic regions, and novel genes associated with or responding to iron deficiency. Sixty-nine genomic regions associated with IDC tolerance were identified across 19 chromosomes via the GWAS, including the major-effect quantitative trait locus (QTL) on chromosome Gm03. Cluster analysis of significant SNPs in this region deconstructed this historically prominent QTL into four distinct linkage blocks, enabling the identification of multiple candidate genes for iron chlorosis tolerance. The complementary GWES identified SNPs in this region interacting with nine other genomic regions, providing the first evidence of epistatic interactions impacting iron deficiency tolerance.ConclusionsThis study demonstrates that integrating cutting edge genome wide association (GWA), genome wide epistasis (GWE), and gene expression studies is a powerful strategy to identify novel iron tolerance QTL and candidate loci from diverse germplasm. Crops, unlike model species, have undergone selection for thousands of years, constraining and/or enhancing stress responses. Leveraging genomics-enabled approaches to study these adaptations is essential for future crop improvement.

Soybean iron deficiency chlorosis high-throughput phenotyping using an unmanned aircraft system

BackgroundIron deficiency chlorosis (IDC) is an abiotic stress in soybean [Glycine max (L.) Merr.] that causes significant yield reductions. Symptoms of IDC include interveinal chlorosis and stunting of the plant. While there are management practices that can overcome these drastic yield losses, the preferred way to manage IDC is growing tolerant soybean varieties. To develop varieties tolerant to IDC, breeders may easily phenotype up to thousands of candidate soybean lines every year for severity of symptoms related to IDC, a task traditionally done with a 1–5 visual rating scale. The visual rating scale is subjective and, because it is time consuming and laborious, can typically only be accomplished once or twice during a growing season.ResultsThe goal of this study was to use an unmanned aircraft system (UAS) to improve field screening for tolerance to soybean IDC. During the summer of 2017, 3386 plots were visually scored for IDC stress on two different dates. In addition, images were captured with a DJI Inspire 1 platform equipped with a modified dual camera system which simultaneously captures digital red, green, blue images as well as red, green, near infrared (NIR) images. A pipeline was created for image capture, orthomosaic generation, processing, and analysis. Plant and soil classification was achieved using unsupervised classification resulting in 95% overall classification accuracy. Within the plant classified canopy, the green, yellow, and brown plant pixels were classified and used as features for random forest and neural network models. Overall, the random forest and neural network models achieved similar misclassification rates and classification accuracy, which ranged from 68 to 77% across rating dates. All 36 trials in the field were analyzed using a linear model for both visual score and UAS image-based scores on both dates. In 32 of the 36 tests on date 1 and 33 of 36 trials on date 2, the LSD associated with UAS image-based scores was lower than the LSD associated with visual scores, indicating the image-based scores provided more precise measurements of IDC severity.ConclusionsOverall, the UAS was able to capture differences in IDC stress and may be used for evaluations of candidate breeding lines in a soybean breeding program. This system was both more efficient and precise than traditional scoring methods.

Field-Based Scoring of Soybean Iron Deficiency Chlorosis Using RGB Imaging and Statistical Learning.

Iron deficiency chlorosis (IDC) is an abiotic stress in soybean that can cause significant biomass and yield reduction. IDC is characterized by stunted growth and yellowing and interveinal chlorosis of early trifoliate leaves. Scoring IDC severity in the field is conventionally done by visual assessment. The goal of this study was to investigate the usefulness of Red Green Blue (RGB) images of soybean plots captured under the field condition for IDC scoring. A total of 64 soybean lines with four replicates were planted in 6 fields over 2 years. Visual scoring (referred to as Field Score, or FS) was conducted at V3–V4 growth stage; and concurrently RGB images of the field plots were recorded with a high-throughput field phenotyping platform. A second set of IDC scores was done on the plot images (displayed on a computer screen) consistently by one person in the office (referred to as Office Score, or OS). Plot images were then processed to remove weeds and extract six color features, which were used to train computer-based IDC scoring models (referred to as Computer Score, or CS) using linear discriminant analysis (LDA) and support vector machine (SVM). The results showed that, in the fields where severe IDC symptoms were present, FS and OS were strongly positively correlated with each other, and both of them were strongly negatively correlated with yield. CS could satisfactorily predict IDC scores when evaluated using FS and OS as the reference (overall classification accuracy > 81%). SVM models appeared to outperform LDA models; and the SVM model trained to predict IDC OS gave the highest prediction accuracy. It was anticipated that coupling RGB imaging from the high-throughput field phenotyping platform with real-time image processing and IDC CS models would lead to a more rapid, cost-effective, and objective scoring pipeline for soybean IDC field screening and breeding.

Effectiveness of FeEDDHA, FeEDDHMA, and FeHBED in Preventing Iron-Deficiency Chlorosis in Soybean

The performance of FeHBED in preventing Fe deficiency chlorosis in soybean (Glycine max (L.) Merr.) in comparison to FeEDDHA and FeEDDHMA was studied, as well as the importance of the ortho-ortho and ortho-para/rest isomers in defining the performance. To this end, chlorophyll production (SPAD), plant dry matter yield, and the mass fractions of important mineral elements in the plant were quantified in a greenhouse pot experiment. All three Fe chelates increased SPAD index and dry matter yield compared to the control. The effect of FeHBED on chlorophyll production was visible over a longer time span than that of FeEDDHA and FeEDDHMA. Additionally, FeHBED did not suppress Mn uptake as much as the other Fe chelates. Compared to the other Fe chelates, total Fe content in the young leaves was lower in the FeHBED treatment; however, total Fe content was not directly related to chlorophyll production and biomass yield. For each chelate, the ortho-ortho isomer was found to be more effective than the other isomers evaluated.

Comparison of Field Management Strategies for Preventing Iron Deficiency Chlorosis in Soybean

Comparison of Field Management Strategies for Preventing Iron Deficiency Chlorosis in Soybean

Comparison of Field Management Strategies for Preventing Iron Deficiency Chlorosis in Soybean

Iron deficiency chlorosis (IDC) is a serious management issue for soybean [Glycine max (L.) Merr.] grown on calcareous soils. Strip trials were established on calcareous Mollisols to study the effects of Fe‐ethylene diamine‐N,N′‐bis (hydroxy phenyl) acetic acid (EDDHA) in‐furrow (IF‐Fe) and of an oat (Avena sativa L.) companion crop on two soybean varieties either tolerant or susceptible to IDC. The severity of IDC varied from low to severe within sites. The susceptible variety produced the highest yield in the absence of IDC. In‐furrow Fe increased the yield of a variety susceptible to IDC under moderate to severe IDC. The oat companion crop increased yield consistently for the susceptible variety under severe IDC and sometimes reduced yield when oat grew beyond 25 cm in height. The tolerant variety without IDC management produced yields similar to those of the susceptible variety with IF‐Fe or an oat companion crop. Oat reduced trifoliate nitrate N and Fe concentration regardless of IDC severity. Trifoliate Fe concentration lowered with IF‐Fe, but only when oat was not planted. Grain protein and oil concentration were affected by variety, but were not affected by IDC management. Soil test factors such as soil organic matter (SOM), pH, electrical conductivity (EC), or diethylene triamine phentaacetic acid Fe (Fe‐DTPA) were poor predictors of the severity of IDC. Variety selection is the most important strategy for lessening the severity of IDC. In‐furrow application of Fe‐EDDHA provides a solution for mitigating moderate to severe IDC and provides less risk than an oat companion crop.

Marker-assisted versus phenotypic selection for iron-deficiency chlorosis in soybean

Iron-deficiency chlorosis (IDC) is an important agronomic trait when soybean (Glycine max (L.) Merr.) is planted on calcareous soil. Breeders need confirmation that marker-QTL (quantitative trait loci) associations developed using association mapping, will be effective in a different set of genotypes than the ones used to identify those markers. Marker-QTL associations that had previously been identified in separate mapping experiments were evaluated. Marker-assisted selection (MAS) was compared to phenotypic selection (PS) to evaluate the association mapping approach to identifying markers. The PS or MAS criteria were used to select the 20 most IDC-tolerant and IDC-susceptible lines, based on three 2009 selection sites. Three sites in 2010 were used as validation to compare the effectiveness of the different selection criteria. A random sample (RS) was generated as a control. When validation was conducted averaged across three 2010 sites, PS was the best method to select for IDC with a mean of 1.78, compared to MAS with a mean of 2.05. Since PS was so effective the process of genotyping, which is required for MAS, was unnecessary. The additive effects of QTL that had been estimated from the original mapping experiments were biased upward when compared to QTL effects estimated from the 2009 data. Also, many markers in the target population of experimental lines did not have a gene frequency of 0.5, which greatly reduced the effectiveness of MAS.

EVALUATION OF SOYBEAN CULTIVARS FOR RESISTANCE TO IRON DEFICIENCY CHLOROSIS IN ROWS VERSUS HILLS

Selection of a resistant cultivar is the most practical control measure for iron deficiency chlorosis in soybean (Glycine max L. Merr.). Plant breeders routinely evaluate cultivars for chlorosis resistance in hill plots, but this procedure may overestimate the chlorosis resistance of a cultivar. The objective of this research was to compare the chlorosis scores of soybean cultivars differing in chlorosis resistance, planted in conventional 76-cm rows, or with two, four, or eight plants per hill. In both 2001 and 2002, it was estimated that three plants per hill would give average chlorosis scores most similar to that observed in 76-cm rows. The highest overall precision was given with row plots, and the lowest with two plants per hill. Hill plots are more space-efficient than row plantings, but are much more easily lost due to animal predation.

Microarray analysis of iron deficiency chlorosis in near-isogenic soybean lines.

BackgroundIron is one of fourteen mineral elements required for proper plant growth and development of soybean (Glycine max L. Merr.). Soybeans grown on calcareous soils, which are prevalent in the upper Midwest of the United States, often exhibit symptoms indicative of iron deficiency chlorosis (IDC). Yield loss has a positive linear correlation with increasing severity of chlorotic symptoms. As soybean is an important agronomic crop, it is essential to understand the genetics and physiology of traits affecting plant yield. Soybean cultivars vary greatly in their ability to respond successfully to iron deficiency stress. Microarray analyses permit the identification of genes and physiological processes involved in soybean's response to iron stress.ResultsRNA isolated from the roots of two near isogenic lines, which differ in iron efficiency, PI 548533 (Clark; iron efficient) and PI 547430 (IsoClark; iron inefficient), were compared on a spotted microarray slide containing 9,728 cDNAs from root specific EST libraries. A comparison of RNA transcripts isolated from plants grown under iron limiting hydroponic conditions for two weeks revealed 43 genes as differentially expressed. A single linkage clustering analysis of these 43 genes showed 57% of them possessed high sequence similarity to known stress induced genes. A control experiment comparing plants grown under adequate iron hydroponic conditions showed no differences in gene expression between the two near isogenic lines. Expression levels of a subset of the differentially expressed genes were also compared by real time reverse transcriptase PCR (RT-PCR). The RT-PCR experiments confirmed differential expression between the iron efficient and iron inefficient plants for 9 of 10 randomly chosen genes examined. To gain further insight into the iron physiological status of the plants, the root iron reductase activity was measured in both iron efficient and inefficient genotypes for plants grown under iron sufficient and iron limited conditions. Iron inefficient plants failed to respond to decreased iron availability with increased activity of Fe reductase.ConclusionThese experiments have identified genes involved in the soybean iron deficiency chlorosis response under iron deficient conditions. Single linkage cluster analysis suggests iron limited soybeans mount a general stress response as well as a specialized iron deficiency stress response. Root membrane bound reductase capacity is often correlated with iron efficiency. Under iron-limited conditions, the iron efficient plant had high root bound membrane reductase capacity while the iron inefficient plants reductase levels remained low, further limiting iron uptake through the root. Many of the genes up-regulated in the iron inefficient NIL are involved in known stress induced pathways. The most striking response of the iron inefficient genotype to iron deficiency stress was the induction of a profusion of signaling and regulatory genes, presumably in an attempt to establish and maintain cellular homeostasis. Genes were up-regulated that point toward an increased transport of molecules through membranes. Genes associated with reactive oxidative species and an ROS-defensive enzyme were also induced. The up-regulation of genes involved in DNA repair and RNA stability reflect the inhospitable cellular environment resulting from iron deficiency stress. Other genes were induced that are involved in protein and lipid catabolism; perhaps as an effort to maintain carbon flow and scavenge energy. The under-expression of a key glycolitic gene may result in the iron-inefficient genotype being energetically challenged to maintain a stable cellular environment. These experiments have identified candidate genes and processes for further experimentation to increase our understanding of soybeans' response to iron deficiency stress.

Iron Deficiency Chlorosis in Soybean: Soybean Seeding Rate and Companion Crop Effects

Iron deficiency chlorosis (IDC) is an important production problem in soybean [Glycine max (L.) Merr.] grown in the upper Midwest. Although IDC‐tolerant varieties are available to producers, even tolerant cultivars suffer from IDC‐related stresses. Several cultural methods have been utilized in an attempt to reduce IDC losses. Interplant competition from increased soybean seeding rates or companion crops appears to reduce IDC symptoms. Two related studies were established to examine increased seeding rates and the planting of companion crops to reduce IDC of soybean in eight IDC‐prone environments. In one study, soybean was planted at five seeding rates from 432 000 to 926 000 seeds ha−1. In a second study, a glyphosate‐resistant soybean variety was planted as a sole crop or with companion crops of glyphosate‐susceptible soybean or oat (Avena sativa L.). Seeding rate treatments affected IDC scores and yield, but these effects were confounded by environmental interactions. Overall, yield increases from higher seeding rates tended to be small and inconsistent, but among seven environments exhibiting mild to moderate IDC symptoms, increasing seeding rates from 432 000 to 926 000 seeds ha−1 increased yields by 281 kg ha−1 (16%). Blending of a glyphosate‐susceptible with a glyphosate‐resistant variety seemed to have little positive effect on yield of soybean (yield gains varied from −333 to + 566 kg ha−1). Likewise, using oat interseeded with soybean to reduce IDC symptoms and increase soybean yields is unlikely to be a profitable practice for soybean producers.

Comparison of Genotype and Cultural Practices to Control Iron Deficiency Chlorosis in Soybean

Iron deficiency chlorosis is a common problem for soybean [Glycine max (L.) Merr.] grown on calcareous soils. Iron is essential for chlorophyll biosynthesis and any deficiencies may hinder photosynthetic capabilities of the plant that may result in yield loss. Proper management and/or cultivar selection may reduce this problem. The objective of this study was to determine the most effective treatment efficacy of several common fertilizer treatments and several different cultivars for controlling iron deficiency chlorosis in soybean in Kansas. Field evaluations at three locations were conducted for two seasons. In the first season, treatments consisted of: four rates (11.2, 22.4, 44.8, and 89.6 kg ha−1) of 28% granular iron sulfate fertilizer placed with the seed, or 5 cm away from the seed, at planting; two applications of an iron foliar spray; an iron chelate seed treatment; and an untreated control. The following season, the 44.8 kg ha−1 rate was placed with the seed, the iron chelate seed treatment, and the untreated control were re‐tested, along with 44.8 kg ha−1 of 20% granular iron sulfate fertilizer, increased seeding rates of 40 and 52 seeds m−1, two plant residue treatments of 3.6 and 8.4 kg ha−1 with 44.8 kg ha−1 iron fertilizer incorporated into the soil before planting; a resistant genotype; a moderately resistant genotype; and a moderately resistant genotype with 44.8 kg ha−1 iron fertilizer added. Entries were analyzed in a repeated measures design for chlorosis visual scores and a randomized complete block design for agronomic characteristics. The plants were visually evaluated for chlorosis every two weeks until the plants outgrew the chlorosis or reached maturity. In the second year, plants were also evaluated for maturity date, lodging, height, stand, seed weight, and yield. In 1999, no yield data was taken due to severe stand reduction and overall poor growth in all of the plots. In 1999, no treatment was consistently better at reducing iron chlorosis symptoms than the control; however, placing 44.8 kg ha−1 iron sulfate with the seed significantly reduced chlorosis scores compared to the control at one location. In 2000, the resistant genotype had significantly lower chlorosis scores than all other treatments for the first few weeks of growth. In two of the three locations, the resistant genotype had the lowest yield. The iron fertilizer, seeding rate, plant residue, and iron chelate treatments did not significantly reduce chlorosis scores compared to the control. Increasing the seeding rate did significantly increase seed yield at some sites. Iron deficiency chlorosis was not adequately controlled by any of the evaluated commercial fertilizer products. Using resistant genotypes may be the most effective treatment if yield potential for these cultivars can be improved. Increasing seeding rate may represent a viable option to minimize moderate iron deficiency symptoms.