International Crops Research Institute for the Semi-Arid Tropics
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Pearl Millet Hybrid Development and Seed Production
No full textPearl millet [ Pennisetum glaucum (L.) R. Br.] is the sixth most important crop grown for food, fodder and forage. It is a potential crop for regions having low soil fertility, high pH, low soil moisture, high temperature, high salinity, and limited rainfall. Being a highly cross-pollinated species, pearl millet was researched comprehensively in 1960s to exploit heterosis. The discovery of cytoplasmic-nuclear male-sterility (CMS) in US proved a milestone in exploiting heterosis commercially in India. With development of fertility restorers of hybrids, a new era of commercially viable hybrid development started. With 25-30% yield advantage in hybrids in comparison to open-pollinating varieties, hybrid development has been a top priority. Hybrids having high yield potential with maturity duration of 75–85 days, and tolerance to different biotic and abiotic stresses remain high priority. Genetic and cytoplasmic diversification is the most important strategy to control downy mildew and other diseases. Genetic diversity in parental lines is critical to enhance genetic potential of hybrids. A large number of genetically diverse hybrids are developed and deployed in different ecological regions. Consequently, productivity has increased from 303 kg/ha during 1950–1954 to 1239 kg/ha during 2020-24 in India due to the widespread use of high-yielding and disease-resistant cultivars with improved production technology. A robust seed production and delivery system is in place to provide high-quality seeds of improved hybrids. During the last three decades, yield levels in seed production plots have doubled, mainly due to the development of high-yielding parental lines and improved crop management skills of farmers. Government policies and protection of hybrids through the Protection of Plant Variety and Farmers' Rights Authority (PPVFRA) have played a pivotal role for increased investment in research and development by the private sector. Good opportunities are coming up to extend advantage of hybrid technology to eastern, central and west African countries
Unravelling the molecular mechanism underlying drought stress tolerance in Dinanath (Pennisetum pedicellatum Trin.) grass via integrated transcriptomic and metabolomic analyses
Get PDFDinanath grass (Pennisetum pedicellatum Trin.) is an extensively grown forage grass known for its significant drought resilience. In order to comprehensively grasp the adaptive mechanism of Dinanath grass in response to water deficient conditions, transcriptomic and metabolomics were applied in the leaves of Dinanath grass exposed to two distinct drought intensities (48-hour and 96-hour). Transcriptomic analysis of Dinanath grass leaves revealed that a total of 218 and 704 genes were differentially expressed under 48- and 96-hour drought conditions, respectively. The genes that were expressed differently (DEGs) and the metabolites that accumulated in response to 48-hour drought stress mainly showed enrichment in the biosynthesis of secondary metabolites, particularly phenolics and flavonoids. Conversely, under 96-hour drought conditions, the enriched pathways predominantly involved lipid metabolism, specifically sterol lipids. In particular, phenylpropanoid pathway and brassinosteroid signaling played a crucial role in drought response to 48- and 96-hour water deficit conditions, respectively. This variation in drought response indicates that the adaptation mechanism in Dinanath grass is highly dependent on the intensity of drought stress. In addition, different genes associated with phenylpropanoid and fatty acid biosynthesis, as well as signal transduction pathways namely phenylalanine ammonia-lyase, putrescine hydroxycinnamoyl transferase, abscisic acid 8’-hydroxylase 2, syntaxin-61, lipoxygenase 5, calcium-dependent protein kinase and phospholipase D alpha one, positively regulated with drought tolerance. Combined transcriptomic and metabolomic analyses highlights the outstanding involvement of regulatory pathways related to secondary cell wall thickening and lignin biosynthesis in imparting drought tolerance to Dinanath grass leaves. These findings collectively contribute to an enhanced understanding of candidate genes and key metabolites relevant to drought response in Dinanath grass. Furthermore, they establish a groundwork for the creation of a transcriptome database aimed at developing abiotic stress-tolerant grasses and major crop varieties through both transgenic and genome editing approaches
Sorghum [Sorghum bicolor (L.) Moench] breeding enrichment potential through genetic comparison of Hungarian and East African lines
No full textSorghum [Sorghum bicolor (L.) Moench] plays a crucial role as a primary cereal in arid tropical regions, holding global importance for food security and sustainable agriculture. Its cultivation has been steadily increasing in Hungary and various European countries over the past decade. The objective of this investigation was to enhance a Hungarian breeding program at Cereal Research Company, Szeged, by comparing breeding lines from the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT-Nairobi) gene bank with those from Hungary. The outcomes of the analysis, which utilized SSR markers to assess 31 genotypes, including 15 from ICRISAT-Nairobi, could potentially contribute to the enhancement of agronomic traits and the development of genetic resistance against various biotic and abiotic stresses. ICRISAT’s extensive collection of accessions, when correlated with Hungarian breeding lines, could guide the selection of diverse parental combinations for segregating progenies. The study classified the genotypes at a 16% similarity threshold into two main clusters, with both Hungarian and East African genotypes present in two clusters. The overall observed heterozygosity (Ho) in all loci were low when compared with expected heterozygosity (He), suggesting that the alleles were highly homogeneous and even, mainly due to the hybrid nature of the materials under trial. The relatively low (0.421) average polymorphic information content (PIC) of the markers led to the genotypes under trial not being completely distinguished. The low similarity value may imply a substantial level of genetic diversity among the genotypes from the two gene pools. This study established, based on the similarity value that the germplasm under trial from the two regions had substantial level of diversity
Decoding plant defense: accelerating insect pest resistance with omics and high-throughput phenotyping
No full textGenotype screening techniques in crop protection are being revolutionized by integrating multi-omics into high-throughput phenotyping (HTP). This comprehensively explains the biochemical and molecular resistance mechanisms underlying plant–insect interactions. Metabolomics offers insights into the metabolic changes and pathways activated in plants in response to insect damage, while proteomics reveals the dynamic protein expressions and modifications involved in plant defense. Quantitative measurements of unstructured/image-based and semi-structured data require sophisticated storage, processing, and advanced analysis methods. Machine learning (ML) and artificial intelligence (AI) are crucial in this integrated approach, enabling the automated, accurate, and efficient analysis of large datasets. Robust ML models can predict plant resistance levels by analyzing metabolic and proteomic profiles, while deep learning techniques can identify patterns and correlations within complex datasets. Innovations in ML models are needed to account for multiple stress factors simultaneously, reflecting real-field conditions more accurately. Utilizing advanced imaging platforms, sensor technologies, and AI-driven data analysis promises significant advancements in understanding and enhancing plant resistance to insect pests, ultimately contributing to sustainable agriculture and food security. This review provides the significance of interdisciplinary approaches in discovering specific biomarkers and pathways relevant to plant resistance against insect pests
Household Food Security Status and Diet Diversity Predictors of Mother Child-Dyads from Rural Smallholders in Three Agroecological Zones of Malawi
Get PDFData from mother-child dyads (n = 375) living in rural smallholder farming households in Malawi was utilized. Households with an average income of >$4.2 US dollars per member had 60% lower odds (OR: 0.40, 95%CI: 0.19–0.82) of food insecurity. Household food insecurity was a predictor of Minimum Dietary Diversity for Women (MDD-W) (OR: 0.43, 95%CI: 0.21–0.89). Children whose female caregivers met MDD-W had 37 times higher odds (OR: 37.6, 95%CI: 13.9–117) of meeting the recommended dietary diversity score. To address food and nutrition security in this population, an approach that encompasses women’s empowerment and income diversification is required
Designing future peanut: the power of genomics-assisted breeding
Get PDFCultivated peanut (Arachis hypogaea L.), a legume crop greatly valued for its nourishing food, cooking oil, and fodder, is extensively grown worldwide. Despite decades of classical breeding efforts, the actual on-farm yield of peanut remains below its potential productivity due to the complicated interplay of genotype, environment, and management factors, as well as their intricate interactions. Integrating modern genomics tools into crop breeding is necessary to fast-track breeding efficiency and rapid progress. When combined with speed breeding methods, this integration can substantially accelerate the breeding process, leading to faster access of improved varieties to farmers. Availability of high-quality reference genomes for wild diploid progenitors and cultivated peanuts has accelerated the process of gene/quantitative locus discovery, developing markers and genotyping assays as well as a few molecular breeding products with improved resistance and oil quality. The use of new breeding tools, e.g., genomic selection, haplotype-based breeding, speed breeding, high-throughput phenotyping, and genome editing, is probable to boost genetic gains in peanut. Moreover, renewed attention to efficient selection and exploitation of targeted genetic resources is also needed to design high-quality and high-yielding peanut cultivars with main adaptation attributes. In this context, the combination of genomics-assisted breeding (GAB), genome editing, and speed breeding hold great potential in designing future improved peanut cultivars to meet market and food supply demands
Genome-Wide Association-Based Identification of Alleles, Genes and Haplotypes Influencing Yield in Rice (Oryza sativa L.) Under Low-Phosphorus Acidic Lowland Soils
Get PDFRice provides poor yields in acidic soils due to several nutrient deficiencies and metal toxicities. The low availability of phosphorus (P) in acidic soils offers a natural condition for screening genotypes for grain yield and phosphorus utilization efficiency (PUE). The objective of this study was to phenotype a subset of indica rice accessions from 3000 Rice Genome Project (3K-RGP) under acidic soils and find associated genes and alleles. A panel of 234 genotypes, along with checks, were grown under low-input acidic soils for two consecutive seasons, followed by a low-P-based hydroponic screening experiment. The heritability of the agro-morphological traits was high across seasons, and Ward’s clustering method identified 46 genotypes that can be used as low-P-tolerant donors in acidic soil conditions. Genotypes ARC10145, RPA5929, and K1559-4, with a higher grain yield than checks, were identified. Over 29 million SNPs were retrieved from the Rice SNP-Seek database, and after quality control, they were utilized for a genome-wide association study (GWAS) with seventeen traits. Ten quantitative trait nucleotides (QTNs) for three yield traits and five QTNs for PUE were identified. A set of 34 candidate genes for yield-related traits was also identified. An association study using this indica panel for an already reported 1.84 Mbp region on chromosome 2 identified genes Os02g09840 and Os02g08420 for yield and PUE, respectively. A haplotype analysis for the candidate genes identified favorable allelic combinations. Donors carrying the superior haplotypic combinations for the identified genes could be exploited in future breeding programs
Identification and evaluation of BAG (B-cell lymphoma-2 associated athanogene) family gene expression in pigeonpea (Cajanus cajan) under terminal heat stress
Get PDFIntroduction: Heat stress poses a significant environmental challenge, impacting plant growth, diminishing crop production, and reducing overall productivity. Plants employ various mechanisms to confront heat stress, and their ability to survive hinges on their capacity to perceive and activate appropriate physiological and biochemical responses. One such mechanism involves regulating multiple genes and coordinating their expression through different signaling pathways. The BAG (B-cell lymphoma-2 associated athanogene) gene family plays a multifunctional role by interacting with heat shock proteins, serving as co-chaperones, or regulating chaperones during the response to heat stress and development. While numerous studies have explored BAG proteins in model plants, there still remains a knowledge gap concerning crop plants.
Methods: Our study successfully identified nine BAG genes in pigeonpea through genome-wide scanning. A comprehensive in silico analysis was conducted to ascertain their chromosomal location, sub-cellular localization, and the types of regulatory elements present in the putative promoter region. Additionally, an expression analysis was performed on contrasting genotypes exhibiting varying heat stress responses.
Results: The results revealed eight CcBAG genes with higher expression levels in the tolerant genotype, whereas BAG6 (Cc_02358) exhibited lower expression. Upstream sequence analysis identified BAG members potentially involved in multiple stresses.
Discussion: The functional characterization of these BAG genes is essential to unravel their roles in signaling pathways, facilitating the identification of candidate genes for precise breeding interventions to produce heat-resilient pigeonpea
Bioinformatics for Plant Genetics and Breeding Research
Get PDFGlobal food demand is expected to increase between 55 and 70% by 2050. Plant breeders and geneticists are constantly under pressure to develop high-yielding climate-resilient varieties using novel approaches. The quest for simplifying complex traits and efforts for developing high-yielding varieties during the twenty-first century led to a paradigm shift from phenotypic-based selection to genome-based breeding. On one hand, the development and utilization of diverse genetic resources, and advances in genomics on the other hand provided a kick start for the understanding the genetics of economically important complex traits at a faster pace. Further, the next-generation sequencing revolutionized our understanding of the genome architecture. As a result, there has been an increasing demand for statistical and bioinformatics tools to analyse and manage the enormous amount of data generated from sequencing of genomes, transcriptomes, proteome and metabolomes. In this chapter, we review the intervention of bioinformatics and computational tools for deploying the tremendous wealth of data for plant genetics and breeding research
Genomic Selection in Crop Improvement
Get PDFA boost in the crop improvement rate is essential for accomplishing a sustainable food supply and other demands of rapid population growth. Genomic selection (GS), a very promising breeding strategy used effectively in animal breeding, is now used in crop improvement. GS offers a reduced duration of breeding cycles by rapidly selecting better genotypes. Several empirical and simulated research on GS and their implications on agricultural production enhancement have lately been published. We briefly discuss the GS methodology, its present position, the GS advantages over alternative methods of breeding, commonly used prediction models of GS, and factors interfering with the prediction accuracy of GS to provide a comprehensive grasp of the technology. In addition, the integration of speed breeding and other modern techniques for increasing the effectiveness and speed of GS are discussed