Commercial suitability and characterization of newly developed Erucastrum canariense (Can) sterile cytoplasm based cytoplasmic male sterile (CMS) lines in Indian cauliflower.

The study presents the first to characterize novel Erucastrum canarianse Webb and Berthel (or Can) sterile cytoplasm-based CMS lines in Indian cauliflower (Brassica oleracea var. botrytis L.) and investigating their commercial suitability. Eleven Can-based CMS lines were examined for 12 agro-morphological and yield traits,18 floral traits, four seed yield traits together with three each of the Ogura (source: wild Japanese Radish) and Tour (Source: Brassica tournefortii) cytoplasms. All of the recorded floral and seed traits showed significant (P > 0.05) differences between the CMS lines of each group. Agro-morphological and yield traits in CMS lines and their maintainers, however, were non-significantly different. All the Can- and Ogura-based CMS lines showed flowering and appropriate seed formation by natural cross-pollination. Only two Tour cytoplasm-based CMS lines, Tour (DC-41-5) and Tour (DC-67), produced the smallest malformed flowers and stigma. The highest seed yield per plant in CMS lines was in Ogu (DC-98-4) and the lowest in Tour (DC-67). P14 and P15, two polymorphic mtDNA markers, were discovered for the Can CMS system for early detection. Five primers (ITS5a-ITS4, atpF-atpH, P16, rbeL and trnL), along with their maintainers, were sequenced and aligned to detect nucleotide changes including as additions and or deletions at different positions. The newly introduced E. canariense sterile cytoplasm-based CMS system in cauliflower is the subject of the first comprehensive report, which emphasises their potential as a further stable and reliable genetic mechanism for hybrid breeding.

Historical shifting in grain mineral density of landmark rice and wheat cultivars released over the past 50 years in India.

The 'Green Revolution (GR)' has been successful in meeting food sufficiency in India, but compromising its nutritional security. In a first, we report altered grain nutrients profile of modern-bred rice and wheat cultivars diminishing their mineral dietary significance to the Indian population. To substantiate, we evaluated grain nutrients profile of historical landmark high-yielding cultivars of rice and wheat released in succeeding decades since the GR and its impacts on mineral diet quality and human health, with a prediction for decades ahead. Analysis of grain nutrients profile shows a downward trend in concentrations of essential and beneficial elements, but an upward in toxic elements in past 50 y in both rice and wheat. For example, zinc (Zn) and iron (Fe) concentration in grains of rice decreased by ~ 33.0 (P < 0.001) and 27.0% (P < 0.0001); while for wheat it decreased by ~ 30.0 (P < 0.0001) and 19.0% (P < 0.0001) in past more than 50 y, respectively. A proposed mineral-diet quality index (M-DQI) significantly (P < 0.0001) decreased ~ 57.0 and 36.0% in the reported time span (1960-2010) in rice and wheat, respectively. The impoverished M-DQI could impose hostile effects on non-communicable diseases (NCDs) like iron-deficiency anemia, respiratory, cardiovascular, and musculoskeletal among the Indian population by 2040. Our research calls for an urgency of grain nutrients profiling before releasing a cultivar of staples like rice and wheat in the future.

Optimizing zinc fertilization technology in wheat for its sustainable production and improved human nutrition.

Zinc (Zn) deficiency in soil is a serious constraint affecting the yield and nutritional quality of wheat and, in turn, human health. Zn fertilization for enhancing its density in grains is a prominent technological solution for the problem. Accordingly, the present study (pot experiment) was undertaken to (i) assess the impacts of different Zn fertilization technologies on yield, concentrations of Zn, phytic acid (PA), iron (Fe) and also the bioavailability of Zn in grains and (ii) determine the optimised Zn fertilization technology that balances all the above attributes. To achieve this, six Zn fertilization technologies, namely, soil fertilization alone, combined soil and foliar fertilization at maximum tillering, jointing, flowering, dough stages and also foliar fertilization alone were tested and compared with control (no Zn) in forty different soil series representing two distinct soil orders, Inceptisols and Alfisols. Results showed that relative effectiveness of different Zn fertilization technologies varied for the crop attributes studied. Soil + foliar fertilization was superior in increasing grain yield (10-13% over the control). Moreover, for an optimum balance among all the tested attributes including bioavailability of Zn to human, foliar Zn fertilization at later crop growth stage (i.e. dough) combined with soil fertilization was the best. It was found that biofortified wheat grains obtained through Zn fertilization, on an average, could supply about 1.5 times more bioavailable Zn than the normal grains. Therefore, the outcomes of this study can provide a guideline for sustainable and quality wheat production, which will help address the malnutrition challenge.

Protein and gene integration analysis through proteome and transcriptome brings new insight into salt stress tolerance in pigeonpea (Cajanus cajan L.).

Salt stress is a major constrain to the productivity of nutritionally rich pigeonpea, an important legume of SE Asia and other parts of the world. The present study provides a comprehensive insight on integrated proteomic and transcriptomic analysis of root and shoot tissues of contrasting pigeonpea varieties (ICP1071- salt-sensitive; ICP7- salt-tolerant) to unravel salt stress induced pathways. Proteome analysis revealed 82 differentially expressed proteins (DEPs) with ≥±1.5 fold expression on 2-Dimensional (2D) gel. Of these, 25 DEPs identified through MALDI-TOF/TOF were classified using Uniprot software into functional categories. Pathways analyses using KAAS server showed the highest abundance of functional genes regulating metabolisms of carbohydrate followed by protein folding/degradation, amino acids and lipids. Expression studies on six genes (triosephosphate isomerase, oxygen evolving enhancer protein 1, phosphoribulokinase, cysteine synthase, oxygen evolving enhancer protein 2 and early nodulin like protein 2) with ≥±3 fold change were performed, and five of these showed consistency in transcript and protein expressions. Transcript analysis of root and shoot led to positive identification of 25 differentially expressed salt-responsive genes, with seven genes having ≥±5 fold change have diverse biological functions. Our combinatorial analysis suggests important role of these genes/proteins in providing salt tolerance in pigeonpea.

SpikeSegNet-a deep learning approach utilizing encoder-decoder network with hourglass for spike segmentation and counting in wheat plant from visual imaging.

BACKGROUND: High throughput non-destructive phenotyping is emerging as a significant approach for phenotyping germplasm and breeding populations for the identification of superior donors, elite lines, and QTLs. Detection and counting of spikes, the grain bearing organs of wheat, is critical for phenomics of a large set of germplasm and breeding lines in controlled and field conditions. It is also required for precision agriculture where the application of nitrogen, water, and other inputs at this critical stage is necessary. Further, counting of spikes is an important measure to determine yield. Digital image analysis and machine learning techniques play an essential role in non-destructive plant phenotyping analysis. RESULTS: In this study, an approach based on computer vision, particularly object detection, to recognize and count the number of spikes of the wheat plant from the digital images is proposed. For spike identification, a novel deep-learning network, SpikeSegNet, has been developed by combining two proposed feature networks: Local Patch extraction Network (LPNet) and Global Mask refinement Network (GMRNet). In LPNet, the contextual and spatial features are learned at the local patch level. The output of LPNet is a segmented mask image, which is further refined at the global level using GMRNet. Visual (RGB) images of 200 wheat plants were captured using LemnaTec imaging system installed at Nanaji Deshmukh Plant Phenomics Centre, ICAR-IARI, New Delhi. The precision, accuracy, and robustness (F1 score) of the proposed approach for spike segmentation are found to be 99.93%, 99.91%, and 99.91%, respectively. For counting the number of spikes, "analyse particles"-function of imageJ was applied on the output image of the proposed SpikeSegNet model. For spike counting, the average precision, accuracy, and robustness are 99%, 95%, and 97%, respectively. SpikeSegNet approach is tested for robustness with illuminated image dataset, and no significant difference is observed in the segmentation performance. CONCLUSION: In this study, a new approach called as SpikeSegNet has been proposed based on combined digital image analysis and deep learning techniques. A dedicated deep learning approach has been developed to identify and count spikes in the wheat plants. The performance of the approach demonstrates that SpikeSegNet is an effective and robust approach for spike detection and counting. As detection and counting of wheat spikes are closely related to the crop yield, and the proposed approach is also non-destructive, it is a significant step forward in the area of non-destructive and high-throughput phenotyping of wheat.

Assessment of soil health parameters and application of the sustainability index to fields under conservation agriculture for 3, 6, and 9 years in India.

The effect of duration of conservation agriculture adoption on soil carbon dynamics and system sustainability was evaluated on farms of 30 villages in the Nilokheri block of Karnal district, Haryana, India. Sustainability was evaluated, in which a number of soil physical, chemical, and biological parameters were measured and a Sustainability Index (SI) was applied. Soil samples were collected from existing conservation agriculture (CA) and conventional tillage (CT) farms. Villages under CA practices were subdivided as CA3, CA6, and CA9 based on the number of years of CA practice adoption. Results showed that bulk density (BD) of 0-15 cm soil depth was 7% greater in CA3 plots, whereas in CA6 and CA9 plots BD values were only 2% and 3% higher than CT. Soil organic carbon (SOC) in 0-15 cm soil depth was found to be greater by 16.32% in CA3 than CT plots, whereas SOC was higher by 38.77% and 61.22% in CA6 and CA9. In CA, for the 0-15 and 15-30 cm soil depths, labile pools were 36% and 22% greater than CT, respectively. For both the soil depths in CA, the recalcitrant pool was 12% and 9% more than CT, respectively. Microbial biomass carbon (MBC) values of the 0-15 cm soil depth were increased over CT by 18.57%, 47.08%, and 71.5% for CA3, CA6, and CA9 respectively. In CA plots, the SI of 0-15 cm soil depth ranged between cumulative ratings (CR) of 18-21, which indicates that CA practice is "sustainable" for both soil depths. For CT, CR ranged from 25 to 30 for both soil depths resulting in a SI of "sustainability with high input". Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) scores showed that SOC had the maximum weight (0.96) towards sustainability, giving it a rank of 1. Effective rooting depth (ERD), BD, texture, and wilting point (WP) ranked 2, 3, 4 and 5, respectively, indicating their corresponding weight of contribution towards the SI. Farmers in the Karnal district should be encouraged to adopt CA practices as they can increase SOC and move the systems from "sustainable with high input" to "sustainable".

Pullulanase activity: A novel indicator of inherent resistant starch in rice (Oryza sativa. L).

Starch quality studies over the decades highlighted the immense health benefits of resistant starch (RS), but still much is needed to elucidate the science behind its inherent formation. Till date, there is no report to establish the relationship between inherent RS content and pullulanase (PUL) activity in any of the crops. In this study, we emphasize the novel role of debranching enzyme, PUL towards inherent RS formation, using rice as a model crop. Biochemical analysis of 51 rice genotypes for amylose content (AC) revealed a good amount of variation ranging from 7.03 to 37.33%. Further, hierarchical clustering which resulted in 11 genotypes of varying RS (0.33-2.7%), highlighted medium dependency towards amylose and low dependency towards amylopectin content. The discrete differences in microstructure, unimodal distribution and tight packing of starch granules observed in higher RS genotype indicated the higher possibility of compact cluster structure of amylopectin, modulated by PUL. Qualitative and quantitative assays performed validated the relevant role of PUL towards inherent RS content with very high dependency score (R2 = 0.98). This is the first report regarding the fact that higher PUL activity contribute to inherent RS using novel hypothetical 'Pullulanase-Amylopectin Trimming Model'.