Plant Breeding Research Articles

Commercial Quality of Potato Tubers of Different Varieties from Organic and Conventional Production System

Research conducted at the Plant Breeding and Acclimatization Institute—NIR in Poland in 2020–2022 assessed the commercial quality of potato tubers depending on the production system (organic, conventional), variety, and weather conditions prevailing in the years of research. The tuber size distribution and the share of tuber defects was assessed. It was found that all tested factors influenced the commercial quality of the yield but to different extents. The production system had the greatest influence on most of the examined features, followed by weather conditions and the least influential factor being the variety. The production system had the greatest impact on such features as: tuber size distribution (all fractions), pest damage, scab infection, tuber deformations, and share of green tubers. Weather conditions had the greatest impact on scab infection, pest damage, deformation, cracks and share of large tubers. The variety factor had the greatest influence on such features as: deformations, cracks, green tubers, black scarf, and share of the smallest (non-commercial) tubers. In the conventional system, a significantly better tuber yield distribution was found. The share of tuber defects varied depending on the production system. In the organic system, a greater share of defects such as scab (68.3 and 41.3) and pest damage was found (6.8 and 0.2), while in the conventional system, a greater share of deformations (9.2 and 4.9), cracks (5.4 and 2.4), and rust spots (0.61 and 0.06) occurred.

Screening of Lentil Genotypes Against Wilt of Lentil (Lens culinaris Medik L.) Caused by Fusarium oxysporum F. SP. Lentis in Glasshouse Condition

Wilt of lentil is most damaging disease of lentil caused by the fungus Fusarium oxysporum f. sp. lentis. It causes severe crop damage from vegetative to reproducing stage which results in yield reduction. Fol isolates shows high variability and aggressiveness according to the climatic condition. So to overcome from this disease there are many ways of management like by cultural, mechanical, biological and chemical practices. Resistant variety selection is one of the best ecofriendly cultural management of the disease. So for this purpose one hundred genotypes were taken and screened through pot screening technique under glasshouse was carried out at the Student Instructional Farm (SIF), in the department of Plant Pathology, Acharya Narendra Deva University of Agriculture and Technology, Kumarganj, Ayodhya, which is situated in the IndoGangatic plains of eastern Uttar Pradesh at a latitude of 26.470 and an altitude of 113 meters above sea level during Rabi 2020-21. One hundred genotypes of lentil were obtained from the Department of Genetics and Plant breeding, N.D. University of Agriculture and Technology, Kumarganj, Ayodhya and IIPR, Kalyanpur, Kanpur (U.P.).Out of 100 genotypes, 41 genotypes were found highly resistant, 30 genotypes resistant, 8 genotypes moderately resistant, 10 genotypes susceptible and 11 genotypes highly susceptible to Fusarium wilt.

Identification of Plant and Soil Characteristics Affecting Stability of Winter Wheat Cultivar in Temperate Climates

This study examines the significant variability in grain yield, thousand-grain weight, protein content, sedimentation value, and falling number among winter wheat cultivars across diverse trial locations, elucidating the profound influence of environmental factors on these traits. Employing Shukla’s stability variance and a multi-trait stability index (MTSI), cultivar stability is comprehensively assessed across multiple traits. Cultivars are ranked based on stability variance and cumulative ranking across all traits, with Bataja emerging as the most stable cultivar according to Shukla variance, while Apostel exhibits the lowest stability. Contrarily, MTSI rankings reveal distinct top performers, such as Medalistika and KWS Spencer. Canonical correspondence analysis (CCA) is utilized to discern relationships between stability and genotype characteristics, as well as trait values and soil properties/weather conditions. These findings contribute to the recommendation of stable cultivars for breeding programs and the optimization of crop management practices. Furthermore, this study underscores the need to explore causal relationships between accompanying variables, facilitating informed recommendations for plant breeders and advancing breeding progress amidst a changing climate. The use of multivariate statistical methods, including CCA, enhances our understanding of cultivar traits and stability, offering valuable insights for sustainable agriculture.

Genome-wide association study of plant color in Sorghum bicolor.

Sorghum plant color is the leaf sheath/leaf color and is associated with seed color, tannin and phenol content, head blight disease incidence, and phytoalexin production. In this study, we evaluated plant color of the sorghum mini core collection by scoring leaf sheath/leaf color at maturity as tan, red, or purple across three testing environments and performed genome-wide association mapping (GWAS) with 6,094,317 SNPs markers. Eight loci, one each on chromosomes 1, 2, 4, and 6 and two on chromosomes 5 and 9, were mapped. All loci contained one to three candidate genes. In qPC5-1, Sobic.005G165632 and Sobic.005G165700 were located in the same linkage disequilibrium (LD) block. In qPC6, Sobic.006G149650 and Sobic.006G149700 were located in the different LD block. The single peak in qPC6 covered one gene, Sobic.006G149700, which was a senescence regulator. We found a loose correlation between the degree of linkage and tissue/organ expression of the underlying genes possibly related to the plant color phenotype. Allele analysis indicated that none of the linked SNPs can differentiate between red and purple accessions whereas all linked SNPs can differentiate tan from red/purple accessions. The candidate genes and SNP markers may facilitate the elucidation of plant color development as well as molecular plant breeding.

Estimation of Correlation Coefficient Analysis for Yield and Component Traits in Field Pea (Pisum sativum L.)

The present investigation entitled “Estimation of Correlation Coefficient Analysis for Yield and Component Traits in field pea (Pisum sativum L.)” for 10 characters. The experiment comprising of 23 genotypes of pea were grown in a Randomized Block Design (RBD), with three replications at Research Farm, Department of Genetics & Plant Breeding, Post Graduate College, Ghazipur, during rabi season of 2017-2018, plant to plant and row to row distance was kept 10 cm and 45 cm, respectively. The maximum positive and significant phenotypic correlation coefficient (0.834) was found between seed yield per plant, biological yield per plant. Positive and significant correlations were observed between seed yield per plant and number of pods per plant, plant height. Positive and significant correlations were also recorded between biological yield per plant and plant height, number of pods per plant. Similarly number of pods per plant had positive and significant correlation with plant height.

Deciphering proteomic mechanisms explaining the role of glutathione as an aid in improving plant fitness and tolerance against cadmium-toxicity in Brassica napus L.

Cadmium (Cd) hazard is a serious limitation to plants, soils and environments. Cd-toxicity causes stunted growth, chlorosis, necrosis, and plant yield loss. Thus, ecofriendly strategies with understanding of molecular mechanisms of Cd-tolerance in plants is highly demandable. The Cd-toxicity caused plant growth retardation, leaf chlorosis and cellular damages, where the glutathione (GSH) enhanced plant fitness and Cd-toxicity in Brassica through Cd accumulation and antioxidant defense. A high-throughput proteome approach screened 4947 proteins, wherein 370 were differently abundant, 164 were upregulated and 206 were downregulated. These proteins involved in energy and carbohydrate metabolism, CO2 assimilation and photosynthesis, signal transduction and protein metabolism, antioxidant defense response, heavy metal detoxification, cytoskeleton and cell wall structure, and plant development in Brassica. Interestingly, several key proteins including glutathione S-transferase F9 (A0A078GBY1), ATP sulfurylase 2 (A0A078GW82), cystine lyase CORI3 (A0A078FC13), ferredoxin-dependent glutamate synthase 1 (A0A078HXC0), glutaredoxin-C5 (A0A078ILU9), glutaredoxin-C2 (A0A078HHH4) actively involved in antioxidant defense and sulfur assimilation-mediated Cd detoxification process confirmed by their interactome analyses. These candidate proteins shared common gene networks associated with plant fitness, Cd-detoxification and tolerance in Brassica. The proteome insights may encourage breeders for enhancing multi-omics assisted Cd-tolerance in Brassica, and GSH-mediated hazard free oil seed crop production for global food security.

Heterosis of endophytic microbiomes in hybrid rice varieties improves seed germination.

Seed endophytic microbiomes are shaped by host and environmental factors and play a crucial role in their host growth and health. Studies have demonstrated that host genotype, including hybridization, affects seed microbiomes. Heterosis features are also observed in root-associated microbiomes. It remains unclear, however, whether heterosis exists in seed endophytic microbiomes and whether hybrid microbiota provide noticeable advantages to host plant growth, especially to seed germination. Here, we investigated the structure of seed endophytic bacterial and fungal communities from three hybrid rice varieties and their respective parents using amplicon sequencing targeting 16S rRNA and ITS2 genes. Heterosis was found in diversity and composition of seed endophytic microbiomes in hybrids, which hosted more diverse communities and significantly higher abundances of plant growth-promoting taxa, such as Pseudomonas and Rhizobium genera compared with their parental lines. Co-occurrence network analysis revealed that there are potentially tighter microbial interactions in the hybrid seeds compared with their parent seeds. Finally, inoculation of seed-cultivable endophytes, isolated from hybrids, resulted in a greater promotion of seed germination compared with those isolated from parent lines. These findings suggest that heterosis exists not only in plant traits but also in seed endophytic microbiota, the latter in turn promotes seed germination, which offers valuable guidance for microbiome-assisted rice breeding.IMPORTANCEGenetic and physiological changes associated with plant hybridization have been studied for many crop species. Still, little is known about the impact of hybridization on the seed microbiota. In this study, we indicate that hybridization has a significant impact on the endophytic bacterial and fungal communities in rice seeds. The seed endophytic microbiomes of hybrids displayed distinct characteristics from those of their parental lines and exhibited potential heterosis features. Furthermore, the inoculation of seed-cultivable endophytes isolated from hybrids exhibited a greater promotion effect on seed germination compared with those isolated from the parents. Our findings make a valuable contribution to the emerging field of microbiome-assisted plant breeding, highlighting the potential for a targeted approach that aims to achieve not only desired plant traits but also plant-beneficial microbial communities on the seeds.

Study on the Effect of Trichoderma viride on Phosphate Solubilizing Microorganisms in Paddy Cultivation with the Combination of Chemical Fertilizer on soil Fertility Parameters

Trichoderma viride, biological control with Trichoderma viride is an eco-friendly and has potential approach for managing plant diseases. It also aids in phosphate solubilization. Phosphate Solubilizing Microorganisms are ubiquitous, whose number varies from soil to soil. The present work on, ‘Effect of Trichoderma viride on Phosphate Solubilizing Microorganism in Paddy cultivation” was conducted under lower gangetic alluvial region from 2016-2017(December to May) at Agricultural Experimental Station of Calcutta University, Baruipur, 24 Parganas (South), West Bengal. The Rice variety “Satabdi (IET-4786)” from the department of Genetics and Plant Breeding was sown and recommended fertilizer dose of 80:40:40 kg per hectare. The experiment was carried out with six treatments replicated four times. Out of the six treatments, T3 which received T.V and P.S showed highest Carbon, Phosphorus and Potassium content and T6 showed highest Nitrogen content in the soil

PSTNet: Transformer for aggregating neighborhood features in 3D point cloud semantic segmentation of eggplant plants

Improving the quality of plant point cloud data and achieving precise segmentation is essential for effective plant phenotyping analysis and plant breeding. To ensure data quality and accuracy of segmentation, we employed a self-developed three-dimensional scanning device based on binocular vision to acquire eggplant plant point clouds with high spatial resolution. And we improved down-sampling algorithm Octree Farthest Point Sampling (OFPS) to process the original point cloud data. Furthermore, we proposed a neural network called Plant Segmentation Transformer Network (PSTNet) to achieve semantic segmentation of high spatial resolution eggplant plant datasets. PSTNet comprises the following components: (i) a Neighborhood Feature Aggregator (NPA) for storing and aggregating local neighborhood features of input points, and (ii) a cascaded Point Self-Attention module (PSA) for capturing contextual information. Experimental results demonstrate the remarkable performance of PSTNet in semantic segmentation tasks, with IoU, Precision, Recall, F1-score, and Accuracy values of 92.20 %, 95.30 %, 95.57 %, 95.43 %, and 95.15 %, respectively. Additionally, compared to the Point Cloud Transformer (PCT), which exhibited the second-best performance, PSTNet achieved improvements of 4.37, 1.47, 2.87, 2.17, and 4.22 percentage points in the abovementioned metrics. This method achieves high-precision segmentation of plant point clouds, expands the semantic segmentation method of plants, and lays a solid foundation for plant phenotypic analysis.

Genomics-assisted speed breeding for crop improvement: present and future

Global agricultural productivity and food security are threatened by climate change, the growing world population, and the difficulties posed by the pandemic era. To overcome these challenges and meet food requirements, breeders have applied and implemented different advanced techniques that accelerate plant development and increase crop selection effectiveness. However, only two or three generations could be advanced annually using these approaches. Speed breeding (SB) is an innovative and promising technology to develop new varieties in a shorter time, utilizing the manipulation of controlled environmental conditions. This strategy can reduce the generation length from 2.5 to 5 times compared to traditional methods and accelerate generation advancement and crop improvement, accommodating multiple generations of crops per year. Beside long breeding cycles, SB can address other challenges related to traditional breeding, such as response to environmental conditions, disease and pest management, genetic uniformity, and improving resource efficiency. Combining genomic approaches such as marker-assisted selection, genomic selection, and genome editing with SB offers the capacity to further enhance breeding efficiency by reducing breeding cycle time, enabling early phenotypic assessment, efficient resource utilization, and increasing selection accuracy and genetic gain per year. Genomics-assisted SB holds the potential to revolutionize plant breeding by significantly accelerating the identification and selection of desirable genetic traits, expediting the development of improved crop varieties crucial for addressing global agricultural challenges.

Large‐scale breeding applications of unoccupied aircraft systems enabled genomic prediction

AbstractBreeding for improved, reliable cultivars despite growing environmental irregularity can be challenging. Unoccupied aircraft systems (UAS) are a popular high‐throughput phenotyping technology that has been shown to help interpret the mechanisms associated with crop productivity and environmental response, creating potential for improved breeding strategies. Spectral reflectance indices (SRIs), encompassing both vegetation and water indices like normalized difference vegetation index (NDVI), normalized difference red‐edge index, and normalized water index, were employed to assess 4094 winter wheat genotypes across 11,593 breeding plots at Washington State University from 2019 through 2022. SRIs were then used with genomic data in univariate models as covariates and multivariate models as secondary response variables for predictions of grain yield. The prediction accuracy of models was evaluated using a leave‐one‐year‐out validation strategy against a base genomic prediction method. Including SRI data as fixed effects in univariate genomic prediction models can improve prediction accuracy over the control but is unreliable across years. When used in multivariate models, SRIs improve prediction performance across years but require high‐performance computational resources that could limit feasibility. In univariate models, when test year NDVI data were available and used to calculate breeding values, prediction performance was at least 16% better than the control, ranging in prediction accuracy from 0.54 in 2019 to 0.93 in 2020. This study highlights the limited reliability of SRI use in genomic prediction of untested environments and locations. However, a significant application for the technology can be found in early‐season UAS data collection to aid accurate predictions in late season, a helpful tool in tight turnaround times commonly experienced in winter crop breeding programs.

Description of the linear features of the exterior of the first –calfs of different origin

In our country, a linear assessment of the exterior of animals is becoming increasingly widespread, which allows us to objectively assess the influence of the producer bull on the change in the most important exterior features that ensure the se-verity of the dairy type while maintaining high productivity and intensity of use in the herd. This ensures the ranking of animals by body type and helps to accelerate the genetic progress of productivity. In this regard, the introduction of a linear assessment of the daughters of breeding bulls in the herd of a breeding plant is relevant. We conducted research aimed at studying and analyzing the signs of a linear description of the exterior of the first-calf cows of the daughters of Holstein bulls and identifying the most common exterior flaws in one of the breeding plants of the Kirov region. The analysis of the data showed that the evaluated first-calf cows of different origins are characterized by exterior features: average height, body of medium depth, strong physique, pronounced dairy type. The most common disadvantages of the exterior of the examined heifer cows are: interception of the shoulder blades (25.6%), asymmetry of the udder lobes (23.0%), weak headstock (15.4%).

Marker-Assisted Breeding Techniques for the Development of Gluten-Free Wheat Varieties: A Comprehensive Review

Ingestion of gluten proteins from wheat, barley and rye poses a significant health risk. The sole treatment for various disease is a lifelong adherence to a strict gluten-free diet. Conventional breeding methods have limitations in producing wheat varieties that maintain baking quality due to the complexity of the wheat genome and the multitude of gluten genes. This review explores the use of RNA interference (RNAi) silencing and CRISPR/Cas9 gene editing techniques. Efficient screening and selection processes for identifying lines with reduced celiac disease epitopes at both the DNA and protein levels, as well as maintaining baking quality, are discussed. The integration of gene editing for the production of wheat products holds significant potential in meeting the growing demand for safer dietary options while ensuring compliance with regulatory standards and addressing the complex challenges associated with disease management. Marker-assisted selection (MAS) offers several advantages over conventional selection methods in plant breeding, including timesaving, cost-effectiveness and goal-oriented outcomes. This review aims to delineate various molecular markers, encompassing sequence-tagged sites (STS), simple sequence repeats (SSR), genotyping by sequencing (GBS), single nucleotide polymorphism (SNP) arrays, exome capture, Competitive Allele Specific PCR (KASP), cleaved amplified polymorphic sequences (CAPS), semi-thermal asymmetric reverse PCR (STARP), and genotyping by target sequencing (GBTS). Additionally, we compile quantitative trait loci (QTL)/genes and their associated markers, which hold potential utility in MAS applications. The rapid advancement of wheat genomics is poised to expedite marker development, QTL mapping, gene cloning, and wheat breeding processes.

Pollinator-assisted plant phenotyping, selection, and breeding for crop resilience to abiotic stresses.

Food security is threatened by climate change, with heat and drought being the main stresses affecting crop physiology and ecosystem services, such as plant-pollinator interactions. We hypothesize that tracking and ranking pollinators' preferences for flowers under environmental pressure could be used as a marker of plant quality for agricultural breeding to increase crop stress tolerance. Despite increasing relevance of flowers as the most stress sensitive organs, phenotyping platforms aim at identifying traits of resilience by assessing the plant physiological status through remote sensing-assisted vegetative indexes, but find strong bottlenecks in quantifying flower traits and in accurate genotype-to-phenotype prediction. However, as the transport of photoassimilates from leaves (sources) to flowers (sinks) is reduced in low-resilient plants, flowers are better indicators than leaves of plant well-being. Indeed, the chemical composition and amount of pollen and nectar that flowers produce, which ultimately serve as food resources for pollinators, change in response to environmental cues. Therefore, pollinators' preferences could be used as a measure of functional source-to-sink relationships for breeding decisions. To achieve this challenging goal, we propose to develop a pollinator-assisted phenotyping and selection platform for automated quantification of Genotype × Environment × Pollinator interactions through an insect geo-positioning system. Pollinator-assisted selection can be validated by metabolic, transcriptomic, and ionomic traits, and mapping of candidate genes, linking floral and leaf traits, pollinator preferences, plant resilience, and crop productivity. This radical new approach can change the current paradigm of plant phenotyping and find new paths for crop redomestication and breeding assisted by ecological decisions.

Estimating maize plant height using a crop surface model constructed from UAV RGB images

Plant height (PH) is an essential agronomic trait that can be used to assist in crop breeding pipelines, assess crop productivity and make crop management decisions. Improving the accuracy of the digital terrain model (DTM) and optimising the PH features of the crop surface model obtained from unmanned aerial vehicle (UAV) images contribute to PH estimation. The influence of the fractional vegetation cover (FVC) on DTM reconstruction accuracy was investigated for the first time, and the influence of the view angle (oblique and nadir) and spatial resolution on the accuracy of maize PH estimation was explored. The results show that the accuracy of the DTM constructed using the inverse distance weighted algorithm was significantly influenced by the FVC conditions. Compared with the DTM constructed using UAV images over bare soil, FVC less than 0.4 was necessary for the accurate construction of the DTM, with average estimation errors of 0.15 m in 2018 and 0.09 m in 2019. Compared with the nadir view, the oblique view resulted in a more accurate 3D reconstruction. When the original spatial resolution of 15 mm was upscaled to 20, 30, 60 and 120 mm, a decreasing trend of PH estimation accuracy was observed, with root mean square error increasing from 0.35 to 0.40 m and mean absolute error increasing from 0.30 to 0.36 m. Overall, this study investigated the optimal FVC conditions for accurate DTM construction and the influence of the view angle and spatial resolution on PH estimation based on UAV RGB images.

The AGAMOUS-LIKE 16-GENERAL REGULATORY FACTOR 1 module regulates axillary bud outgrowth via catabolism of abscisic acid in cucumber.

Lateral branches are important components of shoot architecture and directly affect crop yield and production cost. Although sporadic studies have implicated abscisic acid (ABA) biosynthesis in axillary bud outgrowth, the function of ABA catabolism and its upstream regulators in shoot branching remain elusive. Here, we showed that the MADS-box transcription factor AGAMOUS-LIKE 16 (CsAGL16) is a positive regulator of axillary bud outgrowth in cucumber (Cucumis sativus). Functional disruption of CsAGL16 led to reduced bud outgrowth, whereas overexpression of CsAGL16 resulted in enhanced branching. CsAGL16 directly binds to the promoter of the ABA 8'-hydroxylase gene CsCYP707A4 and promotes its expression. Loss of CsCYP707A4 function inhibited axillary bud outgrowth and increased ABA levels. Elevated expression of CsCYP707A4 or treatment with an ABA biosynthesis inhibitor largely rescued the Csagl16 mutant phenotype. Moreover, cucumber General Regulatory Factor 1 (CsGRF1) interacts with CsAGL16 and antagonizes CsAGL16-mediated CsCYP707A4 activation. Disruption of CsGRF1 resulted in elongated branches and decreased ABA levels in the axillary buds. The Csagl16 Csgrf1 double mutant exhibited a branching phenotype resembling that of the Csagl16 single mutant. Therefore, our data suggest that the CsAGL16-CsGRF1 module regulates axillary bud outgrowth via CsCYP707A4-mediated ABA catabolism in cucumber. Our findings provide a strategy to manipulate ABA levels in axillary buds during crop breeding to produce desirable branching phenotypes.

Expanding the targeting scope of CRISPR/Cas9-mediated genome editing by Cas9 variants in Brassica

AbstractCRISPR/Cas9, presently the most widely used genome editing technology, has provided great potential for functional studies and plant breeding. However, the strict requirement for a protospacer adjacent motif (PAM) has hindered the application of the CRISPR/Cas9 system because the number of targetable genomic sites is limited. Recently, the engineered variants Cas9-NG, SpG, and SpRY, which recognize non-canonical PAMs, have been successfully tested in plants (mainly in rice, a monocot). In this study, we evaluated the targeted mutagenesis capabilities of these Cas9 variants in two important Brassica vegetables, Chinese cabbage (Brassica rapa spp. pekinensis) and cabbage (Brassica oleracea var. capitata). Both Cas9-NG and SpG induced efficient mutagenesis at NGN PAMs, while SpG outperformed Cas9-NG at NGC and NGT PAMs. SpRY achieved efficient editing at almost all PAMs (NRN > NYN), albeit with some self-targeting activity at transfer (T)-DNA sequences. And SpRY-induced mutants were detected in cabbage plants in a PAM-less fashion. Moreover, an adenine base editor was developed using SpRY and TadA8e deaminase that induced A-to-G conversions within target sites using non-canonical PAMs. Together, the toolboxes developed here induced successful genome editing in Chinese cabbage and cabbage. Our work further expands the targeting scope of genome editing and paves the way for future basic research and genetic improvement in Brassica.

Synergistic approach of PCR-based fragment length analysis and amplicon deep sequencing reveals rich diversity of S-alleles in sweet cherries from the Caucasian region of origin.

The self-incompatibility system in sweet cherry (Prunus avium L.) prevents fertilization with own or genetically related pollen, and is genetically determined by the multi-allelic S-locus. Therefore, determining S-alleles is crucial for plant breeding and fruit production, as it enables the selection of compatible combinations of S-genotypes for successful pollination. In this study, S-alleles were identified in a total of 260 genotypes from the Caucasian region, the species' center of origin. S-allele genotyping was conducted using PCR fragment length analysis with the standard marker PaConsI-F/R2 and reference genotypes, complemented by sequence analysis through amplicon deep sequencing. The genotypes collected from Azerbaijan and Turkey exhibit a high allelic richness at the S-locus, particularly compared to modern sweet cherry cultivars worldwide. Nine previously undescribed S-alleles were identified and designated as S45, S46, S47, S48, S49, S50, S51, S52 and S53. Given the expected high diversity for other traits, this plant material represents a valuable resource for further breeding research and introgression of new traits in future breeding programs. Furthermore, our results underscore that fragment length alone may not be sufficient for unambiguous assignment of S-alleles due to minimal length differences between different alleles. To address this issue, an S-allele reference ladder was developed using the rich diversity for precise assignment of the S-alleles. This tool can be applied in future experiments as a robust and cost-effective method for accurate S-genotyping across different runs and laboratories. Additionally, several selected S-genotypes were planted in a trial field and will be maintained as an S-allele reference collection.

BLUP Estimation and Genotype Stability in Arachis hypogaea L. Variety Testing using Mixed Model Equations

Background: One of the major goals of plant breeding is the selection of high yielding superior cultivars having wide or specific adaptation. However, there is a fluctuation in the annual production due to the sensitive behaviour of the genotypes under different environmental conditions referred to as Genotype by Environment Interaction (GEI). The current study aimed to study the contribution of GEI for the adaptation of groundnut lines for spring and/or kharif season. Methods: To assess the contribution of GEI, Multi-Environment Trials (METs) were conducted for 40 confectionery purpose groundnut genotypes at F9 generation along with checks, across three locations for two seasons (spring and kharif). The contribution of environmental effects, genotypic values and genotype × environment interaction values were obtained from genotypic variance-covariance matrix Gi = Σg⨂A using mixed models (MM) in Best linear unbiased predictions (BLUPs).The pooled data was first partitioned into fixed effects of sites across the seasons and BLUP genotypic values (Ggge). The BLUP genotypic values are further partitioned into genetic value (Gg) and their interaction with the environment (Gge) for the adaptability of genotypes across seasons. Result: The results of MET revealed the presence of significant crossover interaction. The demarcation of advance breeding lines for adaptability across the environment as well as for season specific adaptation was done for variety testing. Genotypes having moderate to high Gge values along with high Gg values in spring than kharif, owing to their better performance during the spring season. CGL-11, CGL-23 and CGL-04 were the highest yielding genotypes, with quite high Gge values. This is due to the more favourable environmental conditions interacting positively with genotypes during the spring. Thus, the high Gg value(s) of genotype(s) alone is not a capable factor for commercialization as Gge value is the deciding factor for the adaptability for the targeted season.

Drone‐based imaging sensors, techniques, and applications in plant phenotyping for crop breeding: A comprehensive review

AbstractOver the last decade, the use of unmanned aerial vehicles (UAVs) for plant phenotyping and field crop monitoring has significantly evolved and expanded. These technologies have been particularly valuable for monitoring crop growth and health and for managing abiotic and biotic stresses such as drought, fertilization deficiencies, disease, and bioaggressors. This paper provides a comprehensive review of the progress in UAV‐based plant phenotyping, with a focus on the current use and application of drone technology to gain information on plant growth, development, adaptation, and yield. We reviewed over 200 research articles and discuss the best tools and methodologies for different research purposes, the challenges that need to be overcome, and the major research gaps that remain. First, the review offers a critical focus on elucidating the distinct characteristics of UAV platforms, highlighting the diverse sensor technologies employed and shedding light on the nuances of UAV data acquisition and processing methodologies. Second, it presents a comprehensive analysis of the multiple applications of UAVs in field phenotyping, underscoring the transformative potential of integrating machine learning techniques for plant analysis. Third, it delves into the realm of machine learning applications for plant phenotyping, emphasizing its role in enhancing data analysis and interpretation. Furthermore, the paper extensively examines the open issues and research challenges within the domain, addressing the complexities and limitations faced during data acquisition, processing, and interpretation. Finally, it outlines the future trends and emerging technologies in the field of UAV‐based plant phenotyping, paving the way for innovative advancements and methodologies.