Oilseed Crop Research Articles

Critical radicle length window governing loss of dehydration tolerance in germinated Perilla seeds: insights from physiological and transcriptomic analyses.

Perilla (Perilla frutescens L. Britt.) is an important oilseed and medicinal crop that frequently faces seasonal drought stress during seed germination, leading to a loss of dehydration tolerance (DT), which affects seed emergence and significantly reduces yield. DT has been successfully re-established for many species seeds. However, the physiological mechanisms and gene networks that regulate Perilla's response to DT loss remain unclear. Phenotypic analysis determined that the window for DT in Perilla seeds occurs at radicle lengths of 0-4mm. Integrating physiological and transcriptomic analyses revealed that the loss of DT promotes the production of reactive oxygen species (ROS) and regulates oxidase activity and gene expression. This implies that DT may influence seed germination by modulating ROS activity. Four radicle length (i.e., 0, 1, 3, and 4mm) stages were analyzed, and 262 differentially expressed genes (DEGs) were identified that responded to DT. The majority of these genes were associated with epigenetics, cell function, and transport mechanisms. Analysis of expression data shows that desiccation inhibits the signaling network of genes encoding small secreted peptides (SSPs) and receptor-like protein kinases (RLKs). Finally, a relevant network diagram of DT response was proposed. Based on this information, we have revealed the metabolism regulation maps of the four main pathways involving these DEGs (i.e., metabolic pathways, cell cycle, plant hormone signal transduction, and motor proteins). In conclusion, these findings deepen our understanding of gene network responses to DT during Perilla seed germination and provide potential target genes for the genetic improvement of drought resistance in this crop.

Correlation and Regression Analyses of Disease and Agronomic Traits of Ethiopian Mustard (Brassica Carinata A. Braun.) Genotypes

Ethiopian mustard (<i>Brassica carinata A. Braun</i>) is an important oilseed crop with significant potential for food and energy production. The study evaluated 36 genotypes using a 6 x 6 lattice design to analyze correlations and regression among traits, aiming to understand their relationships and identify key traits for developing high-performing varieties. The analysis of variance revealed significant variation (p < 0.001) for traits including seed yield, flowering time, maturity date, disease resistance, thousand seed weight, oil content and oil yield; indicating the potential for genetic improvement. However, traits such as downy mildew resistance, leaf spot and branching showed non-significant variation, suggesting these traits may be more influenced by environmental factors than by genetic differences among the genotypes. Pearson correlation coefficients highlighted significant relationships among traits. Days to flowering (r = 0.687) and maturity (r = 0.029) positively correlated with yield, while disease traits negatively impacted seed yield. Notably, Thousand Seed Weight (r = 0.985) strongly correlated with yield, underscoring the importance of seed size. A multiple regression model explained 99.7% of the variation in seed yield, with a highly significant intercept (1863.35, p < 0.001). Key associations were found with secondary branches (12.32), oil content (-46.79) and oil yield (2.19). This study confirms the potential for improving Ethiopian mustard yield through genetic selection of key traits. It is recommended that breeding programs focus on enhancing seed size and disease resistance while considering environmental factors to maximize yield potential.

Detection of Seed Mycoflora Associated with Indian Mustard (Brassica juncea L.) by Various Seed Health Testing Methods

India is one of the major oilseed crops grower. Mustard (Brassica juncea L.) is an important oilseed crop second after groundnut. Mustard is majorly grown in India which carry many seedborne diseases which causes not only seed rot, seedling blight but also reduction in quality of seed parameters which results qualitative and quantitative yield losses. In standard agar plate seed germination per centage ranged from 78.50 to 85.00% and in Modified PDA method ranged from 70.00 to 78.00%. In standard agar plate and Modified PDA method highest per cent of infected seed was observed in Pusa bold 74.25% and 64.50%, respectively. In two seed health testing methods (SHT) were evaluated standard agar plate show higher seed mycoflora than modified PDA method. All two methods were found effective and reliable for the detection of seedborne fungi such as Alternaria brassicae, Fusarium oxysporum, Aspergillus niger, Aspergillus flavus, Rhizoctonia solani, Penicillium sp. and Rhizopus stolonifer isolated from mustard seeds of varieties Krishna, Kranti, Local and Pusa bold.

Breeding and biotechnology approaches to enhance the nutritional quality of rapeseed byproducts for sustainable alternative protein sources- a critical review

Global protein consumption is increasing exponentially, which requires efficient identification of potential, healthy, and simple protein sources to fulfil the demands. The existing sources of animal proteins are high in fat and low in fiber composition, which might cause serious health risks when consumed regularly. Moreover, protein production from animal sources can negatively affect the environment, as it often requires more energy and natural resources and contributes to greenhouse gas emissions. Thus, finding alternative plant-based protein sources becomes indispensable. Rapeseed is an important oilseed crop and the world’s third leading oil source. Rapeseed byproducts, such as seed cakes or meals, are considered the best alternative protein source after soybean owing to their promising protein profile (30%–60% crude protein) to supplement dietary requirements. After oil extraction, these rapeseed byproducts can be utilized as food for human consumption and animal feed. However, anti-nutritional factors (ANFs) like glucosinolates, phytic acid, tannins, and sinapines make them unsuitable for direct consumption. Techniques like microbial fermentation, advanced breeding, and genome editing can improve protein quality, reduce ANFs in rapeseed byproducts, and facilitate their usage in the food and feed industry. This review summarizes these approaches and offers the best bio-nutrition breakthroughs to develop nutrient-rich rapeseed byproducts as plant-based protein sources.

Seed priming - induced drought tolerance in Castor: Unravelling the physiological and molecular mechanisms

Castor is an essential nonedible oilseed crop with significant applications in the cosmetic and chemical industries. It is cultivated primarily in rainfed regions, where drought is a common abiotic stress factor. Seed germination, the critical initial stage, is particularly affected by drought. Seed priming with radical scavenging chemicals such as salicylic acid, melatonin, hydrogen peroxide and ascorbic acid has been explored to increase germination under water deficit conditions. This study aimed to evaluate the effects of different concentrations of these chemicals on the physiological and antioxidant enzyme activities of castor under drought stress. Two water regimes were applied in the study. Among the treatments, seed priming with 80 ppm salicylic acid significantly improved key parameters, including the germination % (71 %), speed of germination (4.1 %), vigour index (3141), chlorophyll content (28.3 SPAD units) and relative water content (63 %). It also increased the antioxidant enzyme activities such as CAT, POD, SOD and APX, along with the overexpression of the drought-responsive gene RCECP63 under water deficit conditions. These findings highlight that seed priming, particularly with salicylic acid, offers a promising strategy to enhance castor resilience under prolonged drought stress, providing a practical approach for improving crop performance in drought-prone regions.

Analysis of salinity-induced metabolome changes in Indian mustard (Brassica juncea) roots and shoots: hydroponic versus microplot cultivation.

Brassica juncea L. (family Brassicaceae) or Indian mustard is a fast-growing oilseed crop. Climate changes mean that it is very important to evaluate the effects of salinity stress on B. juncea. The aim of this study was therefore to show the metabolic effect of salinity stress on shoots and roots using two cultivation models - hydroponic and microplot - in different cultivars, including RH-725 and RH-761. Salinity levels of 5, 7.5, and 10 dS m⁻¹ were investigated, and compared with a control of 0 dS m⁻¹, using untargeted metabolomics with gas chromatography-mass spectrometry (GC-MS) post-silylation, focusing on metabolic markers such as proline and glycine-betaine. A total of 56 metabolites were identified, with the most prevalent classes belonging to sugars (8), followed by organic acids (13), amino acids (11), and fatty acids/esters (11). Shoots were found to have a higher sugar content than roots. Increases in unsaturated fatty acids were also associated with salinity stress, compared with a decrease in saturated fatty acids. Absolute levels of proline and glycine-betaine correlated with salinity stress, with the largest increases detected in shoots grown under hydroponic conditions, particularly for the RH-761 cultivar. Multivariate data analyses revealed that roots were more affected than shoots, regardless of cultivation model. These findings might explain the different metabolic behavior of B. juncea's roots and shoots under various levels of salinity, associated with higher levels of free sugars in shoots and lipids in roots. © 2024 Society of Chemical Industry.

Expansion of the Host Range of Xanthomonas euroxanthea: First Occurrence in Sunflower in Bulgaria.

Sunflower is a short-season crop of the Asteraceae family and the Helianthus genus and is the fourth most important oilseed crop in the world. During a field campaign, unusual symptoms (necrosis and longitudinal cracking of the petiole) were observed in a sunflower crop grown in the region of Kavarna (Dobrich district, Bulgaria) and strains of the genus Xanthomonas were isolated. Results based on phylogenetic and phylogenomic analyses showed that they clustered with Xanthomonas euroxanthea CPBF 424T species, a pathogenic strain isolated from walnut buds in Portugal and responsible for causing walnut bacterial blight (WBB). The sunflower strain showed five out of eight X. euroxanthea-specific markers (XEA4-XEA8), a pattern also observed in some strains isolated from Solanum lycopersicum, Phaseolus vulgaris and rainwater sources, reinforcing the emergence of a recent lineage-driven by evolutionary adaptations to new plant hosts. This is the first report of X. euroxanthea in sunflower crops in Bulgaria, which represents a potential threat to production and its distribution should be monitored.

Soybean cultivation in Russia under modern conditions

The article presents an analysis of the current state of soybean production in the Russian Federation. It is shown that the production of this valuable high-protein oilseed crop over the previous years has made a huge breakthrough in conquering the market and increasing its share in the country’s total agricultural production. In Russia, in 2023, a record was set for the gross soybean production in history, with a total grain of 6.8 million tons. At the same time, the production leader also changed: from the traditional Far East, where soybeans have been cultivated for more than 100 years, to the Central Federal District, which has achieved the largest gross soybean yields in the country – about 3.0 million tons. Production growth is realized not only through extensive development paths, but also through increasing crop productivity. In 2023, the average yield in the country was 1.92 t/ha, which is also a historical maximum. It should be noted that in increasing productivity, the direction has been taken, according to the Food Security Doctrine, to increase to 75% the share of seeds of domestic varieties in the total volume by 2030, and for this there are all the necessary resources.

Using a Two-Stage Hybrid Dimensionality Reduction Method on Hyperspectral Data to Predict Chlorophyll Content of Camellia oleifera

Camellia oleifera is an oilseed crop that holds significant economic, ecological, and social value. In the realm of Camellia oleifera cultivation, utilizing hyperspectral analysis techniques to estimate chlorophyll content can enhance our understanding of its physiological parameters and response characteristics. However, hyperspectral datasets contain information from many wavelengths, resulting in high-dimensional data. Therefore, selecting effective wavelengths is crucial for processing hyperspectral data and modeling in retrieval studies. In this study, by using hyperspectral data and chlorophyll content from Camellia oleifera samples, three different dimensionality reduction methods (Taylor-CC, NCC, and PCC) are used in the first round of dimensionality reduction. Combined with these methods, various thresholds and dimensionality reduction methods (with/without further dimensionality reduction) are used in the second round of dimensionality reduction; different sets of core wavelengths with equal size are identified respectively. Using hyperspectral reflectance data at different sets of core wavelengths, multiple machine learning models (Lasso, ANN, and RF) are constructed to predict the chlorophyll content of Camellia oleifera. The purpose of this study is to compare the performance of various dimensionality reduction methods in conjunction with machine learning models for predicting the chlorophyll content of Camellia oleifera. Results show that (1) the Taylor-CC method can effectively select core wavelengths with high sensitivity to chlorophyll variation; (2) the two-stage hybrid dimensionality reduction methods demonstrate superiority in three models; (3) the Taylor-CC + NCC method combined with an ANN achieves the best predictive performance of chlorophyll content. The new two-stage dimensionality reduction method proposed in this study not only improves both the efficiency of hyperspectral data processing and the predictive accuracy of models, but can serve as a complement to the study of Camellia oleifera properties using the Taylor-CC method.

Hybrid modeling approaches for agricultural commodity prices using CEEMDAN and time delay neural networks

Improving the forecasting accuracy of agricultural commodity prices is critical for many stakeholders namely, farmers, traders, exporters, governments, and all other partners in the price channel, to evade risks and enable appropriate policy interventions. However, the traditional mono-scale smoothing techniques often fail to capture the non-stationary and non-linear features due to their multifarious structure. This study has proposed a CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise)-TDNN (Time Delay Neural Network) model for forecasting non-linear, non-stationary agricultural price series. This study has evaluated its suitability in comparison with the other three major EMD (Empirical Mode Decomposition) variants (EMD, Ensemble EMD and Complementary Ensemble EMD) and the benchmark (Autoregressive Integrated Moving Average, Non-linear Support Vector Regression, Gradient Boosting Machine, Random Forest and TDNN) models using monthly wholesale prices of major oilseed crops in India. Outcomes from this investigation reflect that the CEEMDAN-TDNN hybrid models have outperformed all other forecasting models on the basis of evaluation metrics under consideration. For the proposed model, an average improvement of RMSE (Root Mean Square Error), Relative RMSE and MAPE (Mean Absolute Percentage Error) values has been observed to be 20.04%, 19.94% and 27.80%, respectively over the other EMD variant-based counterparts and 57.66%, 48.37% and 62.37%, respectively over the other benchmark stochastic and machine learning models. The CEEMD-TDNN and CEEMDAN-TDNN models have demonstrated superior performance in predicting the directional changes of monthly price series compared to other models. Additionally, the accuracy of forecasts generated by all models has been assessed using the Diebold-Mariano test, the Friedman test, and the Taylor diagram. The results confirm that the proposed hybrid model has outperformed the alternative models, providing a distinct advantage.

Exploring the biodiversity of plant proteins for sustainable foods: Composition and emulsifying properties of the proteins recovered by aqueous extraction from camelina (Camelina sativa L.) seeds

The food transition towards an increased consumption of plant proteins aimed at limiting environmental impacts requires a diversification of plant protein sources. In this study, we explored the potentialities of the sustainable oilseed crop camelina to provide dietary proteins and to prepare oil-in-water emulsions. An innovative green refinery process, including the removal by ultrasound of the mucilage attached at the surface of the seeds and extraction by grinding in water at pH 8, was used to recover aqueous extracts containing camelina seed proteins. These proteins, mainly composed of the 11S globulin cruciferins and the 2S albumin napins, contained the nine essential amino acids of nutritional interest. Camelina seed proteins exhibited low solubility in the range of pH H 5.5-2, attributed to the cruciferins. Oil-in-water emulsions stabilized by camelina seed proteins revealed the preferential adsorption of napin at the oil/water interface and exhibited physical instability below pH 7. Homogenization of the whole aqueous extracts containing oil bodies (OBs) induced the adsorption of cruciferins and napins together with oleosins at the surface of homogenized OBs, and their sedimentation below pH 6.5. Camelina seed proteins adsorbed at the oil/water interface governed the surface properties of the oil droplets and homogenized OBs and the physical stability of the emulsions as a function of pH. This work brings new insights into the potentialities of camelina seed proteins recovered by aqueous extraction that may serve as natural plant-based functional ingredients in the food industry and that will contribute to the development of innovative and sustainable plant-based food emulsions.

The constructed high-density genetic map helps to explore the genetic regulation of erucic acid, oleic acid, and linolenic acid contents in Brassica juncea

Rapeseed mustard (Brassica juncea L.) is the third most important oilseed crop in the world but the genetic mechanism underlying its massive phenotypic variation remains largely unexplored. In this study, specific length amplified fragment sequencing (SLAF-Seq) was used to resequence a population comprising 197 F8 recombinant inbred lines (RILs), derived from a cross between vegetable-type Qichi881 and oilseed-type YufengZC in B. juncea. In total, 438,895 high-quality SLAFs were discovered, of which 47,644 were polymorphic, and 3,887 of the polymorphic markers met the requirements for genetic map construction. The final map included 3,887 markers on 18 linkage groups and was 1,830.23 cM in length, with an average distance of 0.47 cM between adjacent markers. Using the newly constructed high-density genetic map, a total of 53 QTLs for erucic acid (EA), oleic acid (OA), and linolenic acid (LNA) were detected and integrated into 8 consensus QTLs with two for each of these traits. For each of these three traits, two candidate genes were cloned and sequence analyzed, indicating colocalization with their respective consensus QTLs. The co-dominant allele-specific markers for Bju.FAD3.A03 and Bju.FAD3.B07 were developed and showed co-localization with their consensus QTL and co-segregation with LNA content, further supporting the results of QTL mapping and bioinformatic analysis. The expression level for the cloned homologous genes was also identified, which was tightly correlated with the EA, OA and LNA contents of different lines. The results would facilitate the improvement of fatty acid traits and molecular breeding of B. juncea. More use of the high-density genetic map created in this study is also discussed.

Impact of Cluster Front Line Demonstration (CFLD) on Yield & Economics of Toria in Rain-fed North Eastern Coastal Plain Zone of Odisha

Toria is one of the most important oilseed crop after harvest of Kharif rice in rainfed areas of Balasore district of Odisha. Krishi Vigyan Kendra, Balasore conducted Cluster Front Line Demonstration programme on Toria during Rabi 2019-20 to assess its impact on yield and income gap through scientific cultivation practices. The study was conducted in four villages of the district covering 180nos. farmer to find out the extension gap, technology gap, technology index and economic indexes of the crop before and after the intervention. The study revealed that, average yield of 8.5q/ha was obtained in the CFLD plot which was 44.06 per cent higher than the farmer’s practice. Similarly, the technology gap, extension gap and technology index were found to be 1.5q/ha, 2.6q/ha and 15 per cent, respectively. The average gross income in demonstrated plot was found to be Rs.34840/ ha as compared to Rs.23420/ha in farmers practice. Likewise, average net income of Rs.17040/ha was found in demonstration plot while it was Rs.8420/ha in farmers plot. The Benefit-cost ratio was found to be 1.96 in the demonstration plot which is higher as compared to 1.56 at farmer’s practices. The Study was conducted by Krishi Vigyan Kendra, Balasore to analyze the yield gap of Toria (Brassica campestris var. toria) through Cluster frontline demonstration in North Eastern Coastal Plain Zone of Odisha.

Domestication and engineering of pennycress (Thlaspi arvense L.): challenges and opportunities for sustainable bio-based feedstocks.

Pennycress, as an emerging oilseed crop with high oil content, faces challenges but offers potential for sustainable bioproducts; ongoing research aims to enhance its traits and quality. Pennycress (Thlaspi arvense L.) is an emerging oilseed crop with many advantages, such as high seed oil (27-39%) and monounsaturated fatty acid (55.6%) content, making it an attractive candidate to produce sustainable bioproducts. However, several challenges are associated with domesticating pennycress, including high silicle shatter, which reduces seed yield during harvest, non-uniformed germination rate and high contents of erucic acid and glucosinolates, which have adverse health effects on humans and animals. Pennycress, which can be easily and rapidly transformed using the floral dip method under vacuum, can achieve trait improvements. Ongoing research for pennycress domestication using mutation breeding, including ethylmethylsulfonate treatment and genome editing, aims to improve its quality. Pennycress can be used as an excellent platform for producing industrially important fatty acids such as hydroxy and epoxy fatty acids and docosahexaenoic acid. In conclusion, pennycress is a promising oilseed crop with multiple advantages and potential applications. Continuous improvements in quality and engineering for producing high-value bio-based feedstocks in pennycress will establish it as a sustainable and economically valuable crop.

Early monitoring of drought stress in safflower (Carthamus tinctorius L.) using hyperspectral imaging: A comparison of machine learning tools and feature selection approaches

Early detection of drought stress is essential for preventing permanent plant damage and minimizing yield loss. This study utilized hyperspectral imaging at the leaf level to visualize drought stress in safflower plants (Carthamus tinctorius L.). Three safflower genotypes, Palenus, A82, and IL-111, were cultivated under three irrigation levels. Stress conditions were simulated by depleting 50%, 70%, and 90% of soil water content, representing unstressed (US), mild stress (MS), and severe stress (SS) conditions, respectively. Hyperspectral images of leaf samples were captured before any visible signs of water scarcity emerged. Classification analysis was performed using the full mean spectral data with partial least squares discriminant analysis, soft independent modeling of class analogy (SIMCA), support vector machines, and artificial neural network (ANN) classifiers. Feature selection methods were applied to extract the most informative wavebands, and ANN was used to build predictive models. Spatial analysis involved pixel-wise classification using both unsupervised (k-means clustering) and supervised (best classifiers) approaches. ANN outperformed other classifiers using the entire spectral data, effectively distinguishing US, MS, and SS classes in the Palenus, A82, and IL-111 genotypes, achieving F1-scores of 92.22%, 96.01%, and 96.47%, respectively. Among the feature selection methods, SIMCA-based features excelled in monitoring stress conditions in the Palenus and A82 genotypes. In supervised spatial analysis, ANN models clearly depicted the progression of stress in leaves across different genotypes. This study demonstrates the potential of hyperspectral imaging to differentiate various levels of drought stress in safflower, an important oilseed crop.

MYB transcription factors, their regulation and interactions with non-coding RNAs during drought stress in Brassica juncea

BackgroundBrassica juncea(L.) Czern is an important oilseed crop affected by various abiotic stresses like drought, heat, and salt. These stresses have detrimental effects on the crop’s overall growth, development and yield. Various Transcription factors (TFs) are involved in regulation of plant stress response by modulating expression of stress-responsive genes. The myeloblastosis (MYB) TFs is one of the largest families of TFs associated with various developmental and biological processes such as plant growth, secondary metabolism, stress response etc. However, MYB TFs and their regulation by non-coding RNAs (ncRNAs) in response to stress have not been studied in B. juncea. Thus, we performed a detailed study on the MYB TF family and their interactions with miRNAs and Long non coding RNAs.ResultsComputational investigation of genome and proteome data presented a comprehensive picture of the MYB genes and their protein architecture, including intron-exon organisation, conserved motif analysis, R2R3 MYB DNA-binding domains analysis, sub-cellular localization, protein-protein interaction and chromosomal locations. Phylogenetically, BjuMYBs were further classified into different subclades on the basis of topology and classification in Arabidopsis. A total of 751 MYBs were identified in B. juncea corresponding to 297 1R-BjuMYBs, 440 R2R3-BjuMYBs, 12 3R-BjuMYBs, and 2 4R-BjuMYBs types. We validated the transcriptional profiles of nine selected BjuMYBs under drought stress through RT-qPCR. Promoter analysis indicated the presence of drought-responsive cis-regulatory components. Additionally, the miRNA-MYB TF interactions was also studied, and most of the microRNAs (miRNAs) that target BjuMYBs were involved in abiotic stress response and developmental processes. Regulatory network analysis and expression patterns of lncRNA-miRNA-MYB indicated that selected long non-coding RNAs (lncRNAs) acted as strong endogenous target mimics (eTMs) of the miRNAs regulated expression of BjuMYBs under drought stress.ConclusionsThe present study has established preliminary groundwork of MYB TFs and their interaction with ncRNAs in B. juncea and it will help in developing drought- tolerant Brassica crops.

Instability Analysis and Decomposition of Output of Oilseeds Crops in Rajasthan and India

Aim: The study deals with analysis of instability of area production and productivity of major oilseeds in Rajasthan and India. It also includes calculation of sources of growth of production over the period of study. Area effect, yield effect and interaction effect for crop production were calculated. Place and Duration: The time series data of thirty years, 1990-91 to 2019-20, was analyzed. The data were further divided into Three decades, period I(1990-91 to 1999-2000), period II (2000-01 to 2009-10) and period III (2010-11 to 2019-20). Area production and productivity of major oilseed crops (Rapeseed-mustard, groundnut and soybean) for all three decade in Rajasthan and India were analyzed. Methodology: This paper deals with the analysis of instability in area production and productivity of major oilseed crops in Rajasthan and India. The Cuddy Della Vella Index has been used for instability analysis. It also includes the decomposition analysis to determine factors that were responsible for the change in production of the crops. The additive decomposition method has been used to determine area, yield and interaction effect. Results: Calculations revealed that the magnitude of instability in Rajasthan during period II (2000-01 to 2009-10) remained high for all the selected crops. There was instability of 32.49 percent and 34.40 percent for area and production, respectively, while 13.34 percent of variation was found in productivity. In Rajasthan, the lowest instability was observed during period III (2010-11 to 2019-20) with variation of 7.44 percent, 9.47 percent and 6.62 percent for area production and productivity of groundnut. In India, the highest instability was during period II. The area of Rapeseed-mustard has shown the highest (17.72%) instability compared to the instability of the area of other crops. Variation of production and productivity of groundnut was recorded at 24.79 and 21.88 percent, respectively, which is highest among the selected crops. The decomposition analysis revealed that the area effect was a major source for the growth of production of oilseed in Rajasthan as compared to the yield effect and interaction effect. During period I (1990-91 to 1999-2000), the area effect for rapeseed mustard, groundnut and soybean was 68.08, 87.97 and 91.35 percent. In India, both area and yield effect have a major contribution to the growth of production. During period I, the yield effect (57.50 %) was responsible for growth of rapeseed-mustard production, while groundnut and soybean had a high contribution of area effect with magnitudes of 70.86 percent and 84 percent. Area effects for groundnut and soybean were 118.44 percent and 58.06 percent during period II, respectively. Area effect (42.90%) and yield effect (45.76%) were equally responsible for the growth of rapeseed-mustard. During period III, the yield effect for rapeseed-mustard and soybean was 101.34 percent and 257.7 percent, respectively, while the area effect (52.29 %) was responsible for growth in groundnut production. Conclusion: It has been found that groundnut was the most stable crop in term of area production and productivity. The major source for the growth of production was area effect and Yield effect while interaction effect has least impact on production of crops.

Dynamic Analysis of Fermentation Quality, Microbial Community, and Metabolome in the Whole Plant Soybean Silage

Soybean (Glycine max (L.) Merr.) is an important oilseed crop, known for its rich nutritional content and high-quality protein. To address the shortage of feed protein resources and better utilize soybeans as a raw material, this study investigated the feasibility of using whole-plant soybean (WPS) as silage. As the ensiling period is a critical fermentation parameter, identifying the optimal fermentation duration was a key objective. The research involves fermenting WPS for silage production, conducted over five fermentation durations: 7, 15, 30, 60, and 90 days. The fermentation quality, microbial community, and metabolome of WPS silage were analyzed across these different time points. WPS silage fermented for 30 days exhibited optimal fermentation characteristics, with the highest lactic acid (LA) content observed at 30 days (p < 0.05), while butyric acid (BA) was detected only at 60 and 90 days. At 30 days, Enterococcus genera reached its peak relative abundance and was identified as the dominant genus. Random forest analysis highlighted Pantoea genera as the most influential biomarker. Metabolomic analysis revealed that the metabolic pathways involved in the biosynthesis of essential amino acids valine, leucine, and isoleucine were significantly enhanced during the later stages of fermentation compared to the earlier stages. Under natural fermentation conditions, the optimal fermentation period for WPS silage is approximately 30 days. These findings provide a theoretical basis for the utilization of WPS and the subsequent optimization of fermentation quality.

SesamumGDB: a comprehensive platform for Sesamum genetics and genomics analysis.

Sesame (Sesamum indicum L., 2n = 26) is a crucial oilseed crop cultivated worldwide. The ancient evolutionary position of the Sesamum genus highlights its value for genomics and molecular genetics research among the angiosperms of other genera. However, Sesamum is considered a small orphan genus with only a few genomic databases for cultivated sesame to date. The urgent need to construct comprehensive, curated genome databases that include genus-specific gene resources for both cultivated and wild Sesamum species is being recognized. In response, we developed Sesamum Genomics Database (SesamumGDB), a user-friendly genomic database that integrates extensive genomic resources from two cultivated sesame varieties (S. indicum) and seven wild Sesamum species, covering all three chromosome groups (2n = 26, 32, and 64). This database showcases a total of 352 471 genes, including 6026 related to lipid metabolism and 17 625 transcription factors within Sesamum. Equipped with an array of bioinformatics tools such as BLAST (basic local alignment search tool) and JBrowse (the Javascript browser), SesamumGDB facilitates data downloading, screening, visualization, and analysis. As the first centralized Sesamum genome database, SesamumGDB offers extensive insights into the genomics and genetics of sesame, potentially enhancing the molecular breeding of sesame and other oilseed crops in the future. Database URL: http://www.sgbdb.com/sgdb/.

HaVTE1 confers ABA insensitivity by blocking the ABA signaling pathway in sunflowers (Helianthus annuus L.)

Sunflower (Helianthus annuus) is the fourth major oilseed crop in the world, with remarkable tolerance in saline-alkali soils. The VTE1 gene encodes tocopherol cyclase (TC), an enzyme pivotal in the biosynthesis of both vitamin E and vitamin K1. Despite its integral role in the synthesis of these crucial vitamins, the functional analysis of VTE1 under abiotic stress in sunflowers remains scant. In the present investigation, a structural analysis of the VTE1 protein across 155 diverse species revealed a highly conserved evolutionary trace. The expression profiling of HaVTE1 depicted that the HaVTE1 was responsive to the ABA pathway. Transgenic results confirmed that overexpression of HaVTE1 in Arabidopsis and sunflower showed decreased sensitivity to ABA while knocking-down in sunflower exhibited the opposite phenotype. Furthermore, biochemical experiments displayed that HaVTE1 decreases ABA sensitivity by scavenging superoxide contents. Concurrently, the transcriptome analysis revealed that HaVTE1 blocked the upstream of the ABA signaling cascade, which was further confirmed by luciferase assay, resulting in reduced sensitivity to ABA of HaVTE1 overexpression plants. The findings shed light on a theoretical basis for the sunflower responses to ABA signaling and abiotic stresses.