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Kerr SC, Shehnaz S, Paudel L, Manivannan MS, Shaw LM, Johnson A, Velasquez JTJ, Tanurdžić M, Cazzonelli CI, Varkonyi-Gasic E, Prentis PJ. Advancing tree genomics to future proof next generation orchard production. FRONTIERS IN PLANT SCIENCE 2024; 14:1321555. [PMID: 38312357 PMCID: PMC10834703 DOI: 10.3389/fpls.2023.1321555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Accepted: 12/26/2023] [Indexed: 02/06/2024]
Abstract
The challenges facing tree orchard production in the coming years will be largely driven by changes in the climate affecting the sustainability of farming practices in specific geographical regions. Identifying key traits that enable tree crops to modify their growth to varying environmental conditions and taking advantage of new crop improvement opportunities and technologies will ensure the tree crop industry remains viable and profitable into the future. In this review article we 1) outline climate and sustainability challenges relevant to horticultural tree crop industries, 2) describe key tree crop traits targeted for improvement in agroecosystem productivity and resilience to environmental change, and 3) discuss existing and emerging genomic technologies that provide opportunities for industries to future proof the next generation of orchards.
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Affiliation(s)
- Stephanie C Kerr
- School of Biology and Environmental Science, Queensland University of Technology (QUT), Brisbane, QLD, Australia
- Centre for Agriculture and the Bioeconomy, Queensland University of Technology (QUT), Brisbane, QLD, Australia
| | - Saiyara Shehnaz
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, QLD, Australia
| | - Lucky Paudel
- Hawkesbury Institute for the Environment, Western Sydney University, Penrith, NSW, Australia
| | - Mekaladevi S Manivannan
- Hawkesbury Institute for the Environment, Western Sydney University, Penrith, NSW, Australia
| | - Lindsay M Shaw
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, Australia
- School of Agriculture and Food Sustainability, The University of Queensland, Brisbane, QLD, Australia
| | - Amanda Johnson
- School of Biology and Environmental Science, Queensland University of Technology (QUT), Brisbane, QLD, Australia
- Centre for Agriculture and the Bioeconomy, Queensland University of Technology (QUT), Brisbane, QLD, Australia
| | - Jose Teodoro J Velasquez
- School of Biology and Environmental Science, Queensland University of Technology (QUT), Brisbane, QLD, Australia
- Centre for Agriculture and the Bioeconomy, Queensland University of Technology (QUT), Brisbane, QLD, Australia
| | - Miloš Tanurdžić
- School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, QLD, Australia
| | | | - Erika Varkonyi-Gasic
- The New Zealand Institute for Plant and Food Research Limited, Auckland, New Zealand
| | - Peter J Prentis
- School of Biology and Environmental Science, Queensland University of Technology (QUT), Brisbane, QLD, Australia
- Centre for Agriculture and the Bioeconomy, Queensland University of Technology (QUT), Brisbane, QLD, Australia
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Li Y, Yang X, Tong L, Wang L, Xue L, Luan Q, Jiang J. Phenomic selection in slash pine multi-temporally using UAV-multispectral imagery. FRONTIERS IN PLANT SCIENCE 2023; 14:1156430. [PMID: 37670863 PMCID: PMC10475579 DOI: 10.3389/fpls.2023.1156430] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 08/02/2023] [Indexed: 09/07/2023]
Abstract
Genomic selection (GS) is an option for plant domestication that offers high efficiency in improving genetics. However, GS is often not feasible for long-lived tree species with large and complex genomes. In this paper, we investigated UAV multispectral imagery in time series to evaluate genetic variation in tree growth and developed a new predictive approach that is independent of sequencing or pedigrees based on multispectral imagery plus vegetation indices (VIs) for slash pine. Results show that temporal factors have a strong influence on the h2 of tree growth traits. High genetic correlations were found in most months, and genetic gain also showed a slight influence on the time series. Using a consistent ranking of family breeding values, optimal slash pine families were selected, obtaining a promising and reliable predictive ability based on multispectral+VIs (MV) alone or on the combination of pedigree and MV. The highest predictive value, ranging from 0.52 to 0.56, was found in July. The methods described in this paper provide new approaches for phenotypic selection (PS) using high-throughput multispectral unmanned aerial vehicle (UAV) technology, which could potentially be used to reduce the generation time for conifer species and increase the genetic granularity independent of sequencing or pedigrees.
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Affiliation(s)
- Yanjie Li
- State Key Laboratory of Tree Genetics and Breeding, Chinese Academy of Forestry, Beijing, China
- Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Fuyang, Hangzhou, Zhejiang, China
- Key Laboratory of State Forestry and Grassland Administration on Subtropical Forest Cultivation, Fuyang, Hangzhou, Zhejiang, China
- Key Laboratory of Tree Breeding of Zhejiang Province, Fuyang, Hangzhou, Zhejiang, China
| | - Xinyu Yang
- Soybean Research Institute, National Center for Soybean Improvement, Key Laboratory of Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture), State Key Laboratory of Crop Genetics and Germplasm Enhancement, Jiangsu Collaborative Innovation Center for Modern Crop Production, College of Agriculture, Nanjing Agricultural University, Nanjing, China
| | - Long Tong
- Chongqing Academy of Forestry, Chongqing, China
| | - Lingling Wang
- Forestry and Water Conservancy Bureau of Fuyang District in Hangzhou, Hangzhou, China
| | - Liang Xue
- State Key Laboratory of Tree Genetics and Breeding, Chinese Academy of Forestry, Beijing, China
- Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Fuyang, Hangzhou, Zhejiang, China
- Key Laboratory of State Forestry and Grassland Administration on Subtropical Forest Cultivation, Fuyang, Hangzhou, Zhejiang, China
- Key Laboratory of Tree Breeding of Zhejiang Province, Fuyang, Hangzhou, Zhejiang, China
| | - Qifu Luan
- State Key Laboratory of Tree Genetics and Breeding, Chinese Academy of Forestry, Beijing, China
- Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Fuyang, Hangzhou, Zhejiang, China
- Key Laboratory of State Forestry and Grassland Administration on Subtropical Forest Cultivation, Fuyang, Hangzhou, Zhejiang, China
- Key Laboratory of Tree Breeding of Zhejiang Province, Fuyang, Hangzhou, Zhejiang, China
| | - Jingmin Jiang
- Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Fuyang, Hangzhou, Zhejiang, China
- Key Laboratory of State Forestry and Grassland Administration on Subtropical Forest Cultivation, Fuyang, Hangzhou, Zhejiang, China
- Key Laboratory of Tree Breeding of Zhejiang Province, Fuyang, Hangzhou, Zhejiang, China
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Younessi-Hamzekhanlu M, Gailing O. Genome-Wide SNP Markers Accelerate Perennial Forest Tree Breeding Rate for Disease Resistance through Marker-Assisted and Genome-Wide Selection. Int J Mol Sci 2022; 23:ijms232012315. [PMID: 36293169 PMCID: PMC9604372 DOI: 10.3390/ijms232012315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 10/05/2022] [Accepted: 10/07/2022] [Indexed: 11/30/2022] Open
Abstract
The ecological and economic importance of forest trees is evident and their survival is necessary to provide the raw materials needed for wood and paper industries, to preserve the diversity of associated animal and plant species, to protect water and soil, and to regulate climate. Forest trees are threatened by anthropogenic factors and biotic and abiotic stresses. Various diseases, including those caused by fungal pathogens, are one of the main threats to forest trees that lead to their dieback. Genomics and transcriptomics studies using next-generation sequencing (NGS) methods can help reveal the architecture of resistance to various diseases and exploit natural genetic diversity to select elite genotypes with high resistance to diseases. In the last two decades, QTL mapping studies led to the identification of QTLs related to disease resistance traits and gene families and transcription factors involved in them, including NB-LRR, WRKY, bZIP and MYB. On the other hand, due to the limitation of recombination events in traditional QTL mapping in families derived from bi-parental crosses, genome-wide association studies (GWAS) that are based on linkage disequilibrium (LD) in unstructured populations overcame these limitations and were able to narrow down QTLs to single genes through genotyping of many individuals using high-throughput markers. Association and QTL mapping studies, by identifying markers closely linked to the target trait, are the prerequisite for marker-assisted selection (MAS) and reduce the breeding period in perennial forest trees. The genomic selection (GS) method uses the information on all markers across the whole genome, regardless of their significance for development of a predictive model for the performance of individuals in relation to a specific trait. GS studies also increase gain per unit of time and dramatically increase the speed of breeding programs. This review article is focused on the progress achieved in the field of dissecting forest tree disease resistance architecture through GWAS and QTL mapping studies. Finally, the merit of methods such as GS in accelerating forest tree breeding programs is also discussed.
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Affiliation(s)
- Mehdi Younessi-Hamzekhanlu
- Department of Forestry and Medicinal Plants, Ahar Faculty of Agriculture and Natural Resources, University of Tabriz, 29 Bahman Blvd., Tabriz P.O. Box 5166616471, Iran
- Correspondence: (M.Y.-H.); (O.G.)
| | - Oliver Gailing
- Department of Forest Genetics and Forest Tree Breeding, University of Göttingen, Büsgenweg 2, D-37077 Göttingen, Germany
- Correspondence: (M.Y.-H.); (O.G.)
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Hardner CM, Fikere M, Gasic K, da Silva Linge C, Worthington M, Byrne D, Rawandoozi Z, Peace C. Multi-environment genomic prediction for soluble solids content in peach ( Prunus persica). FRONTIERS IN PLANT SCIENCE 2022; 13:960449. [PMID: 36275520 PMCID: PMC9583944 DOI: 10.3389/fpls.2022.960449] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 08/01/2022] [Indexed: 06/16/2023]
Abstract
Genotype-by-environment interaction (G × E) is a common phenomenon influencing genetic improvement in plants, and a good understanding of this phenomenon is important for breeding and cultivar deployment strategies. However, there is little information on G × E in horticultural tree crops, mostly due to evaluation costs, leading to a focus on the development and deployment of locally adapted germplasm. Using sweetness (measured as soluble solids content, SSC) in peach/nectarine assessed at four trials from three US peach-breeding programs as a case study, we evaluated the hypotheses that (i) complex data from multiple breeding programs can be connected using GBLUP models to improve the knowledge of G × E for breeding and deployment and (ii) accounting for a known large-effect quantitative trait locus (QTL) improves the prediction accuracy. Following a structured strategy using univariate and multivariate models containing additive and dominance genomic effects on SSC, a model that included a previously detected QTL and background genomic effects was a significantly better fit than a genome-wide model with completely anonymous markers. Estimates of an individual's narrow-sense and broad-sense heritability for SSC were high (0.57-0.73 and 0.66-0.80, respectively), with 19-32% of total genomic variance explained by the QTL. Genome-wide dominance effects and QTL effects were stable across environments. Significant G × E was detected for background genome effects, mostly due to the low correlation of these effects across seasons within a particular trial. The expected prediction accuracy, estimated from the linear model, was higher than the realised prediction accuracy estimated by cross-validation, suggesting that these two parameters measure different qualities of the prediction models. While prediction accuracy was improved in some cases by combining data across trials, particularly when phenotypic data for untested individuals were available from other trials, this improvement was not consistent. This study confirms that complex data can be combined into a single analysis using GBLUP methods to improve understanding of G × E and also incorporate known QTL effects. In addition, the study generated baseline information to account for population structure in genomic prediction models in horticultural crop improvement.
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Affiliation(s)
- Craig M. Hardner
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, Australia
| | - Mulusew Fikere
- Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD, Australia
| | - Ksenija Gasic
- Department of Plant and Environmental Sciences, Clemson University, Clemson, SC, United States
| | - Cassia da Silva Linge
- Department of Plant and Environmental Sciences, Clemson University, Clemson, SC, United States
| | - Margaret Worthington
- Faculty Horticulture, University of Arkansas System Division of Agriculture, Fayetteville, AR, United States
| | - David Byrne
- College of Agriculture and Life Sciences, Texas A&M University, College Station, TX, United States
| | - Zena Rawandoozi
- College of Agriculture and Life Sciences, Texas A&M University, College Station, TX, United States
| | - Cameron Peace
- Department of Horticulture, Washington State University, Pullman, WA, United States
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Tapia R, Abd-Elrahman A, Osorio L, Whitaker VM, Lee S. Combining canopy reflectance spectrometry and genome-wide prediction to increase response to selection for powdery mildew resistance in cultivated strawberry. JOURNAL OF EXPERIMENTAL BOTANY 2022; 73:5322-5335. [PMID: 35383379 DOI: 10.1093/jxb/erac136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 04/01/2022] [Indexed: 06/14/2023]
Abstract
High-throughput phenotyping is an emerging approach in plant science, but thus far only a few applications have been made in horticultural crop breeding. Remote sensing of leaf or canopy spectral reflectance can help breeders rapidly measure traits, increase selection accuracy, and thereby improve response to selection. In the present study, we evaluated the integration of spectral analysis of canopy reflectance and genomic information for the prediction of strawberry (Fragaria × ananassa) powdery mildew disease. Two multi-parental breeding populations of strawberry comprising a total of 340 and 464 pedigree-connected seedlings were evaluated in two separate seasons. A single-trait Bayesian prediction method using 1001 spectral wavebands in the ultraviolet-visible-near infrared region (350-1350 nm wavelength) combined with 8552 single nucleotide polymorphism markers showed up to 2-fold increase in predictive ability over models using markers alone. The integration of high-throughput phenotyping was further validated independently across years/trials with improved response to selection of up to 90%. We also conducted Bayesian multi-trait analysis using the estimated vegetative indices as secondary traits. Three vegetative indices (Datt3, REP_Li, and Vogelmann2) had high genetic correlations (rA) with powdery mildew visual ratings with average rA values of 0.76, 0.71, and 0.71, respectively. Increasing training population sizes by incorporating individuals with only vegetative index information yielded substantial increases in predictive ability. These results strongly indicate the use of vegetative indices as secondary traits for indirect selection. Overall, combining spectrometry and genome-wide prediction improved selection accuracy and response to selection for powdery mildew resistance, demonstrating the power of an integrated phenomics-genomics approach in strawberry breeding.
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Affiliation(s)
- Ronald Tapia
- Gulf Coast Research and Education Center, Institute of Food and Agricultural Science, University of Florida, 14625 County Road 672, Wimauma, FL 33598, USA
- Department of Horticultural Sciences, University of Florida, Gainesville, FL 32611, USA
| | - Amr Abd-Elrahman
- Gulf Coast Research and Education Center, Institute of Food and Agricultural Science, University of Florida, 14625 County Road 672, Wimauma, FL 33598, USA
- School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, FL 32603, USA
| | - Luis Osorio
- Gulf Coast Research and Education Center, Institute of Food and Agricultural Science, University of Florida, 14625 County Road 672, Wimauma, FL 33598, USA
- Department of Horticultural Sciences, University of Florida, Gainesville, FL 32611, USA
| | - Vance M Whitaker
- Gulf Coast Research and Education Center, Institute of Food and Agricultural Science, University of Florida, 14625 County Road 672, Wimauma, FL 33598, USA
- Department of Horticultural Sciences, University of Florida, Gainesville, FL 32611, USA
| | - Seonghee Lee
- Gulf Coast Research and Education Center, Institute of Food and Agricultural Science, University of Florida, 14625 County Road 672, Wimauma, FL 33598, USA
- Department of Horticultural Sciences, University of Florida, Gainesville, FL 32611, USA
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Zahid G, Aka Kaçar Y, Dönmez D, Küden A, Giordani T. Perspectives and recent progress of genome-wide association studies (GWAS) in fruits. Mol Biol Rep 2022; 49:5341-5352. [PMID: 35064403 DOI: 10.1007/s11033-021-07055-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 12/06/2021] [Indexed: 11/25/2022]
Abstract
BACKGROUND Earlier next-generation sequencing technologies are being vastly used to explore, administer, and investigate the gene space with accurate profiling of nucleotide variations in the germplasm. OVERVIEW AND PROGRESS: Recently, novel advancements in high-throughput sequencing technologies allow a genotyping-by-sequencing approach that has opened up new horizons for extensive genotyping exploiting single-nucleotide-polymorphisms (SNPs). This method acts as a bridge to support and minimize a genotype to phenotype gap allowing genetic selection at the genome-wide level, named genomic selection that could facilitate the selection of traits also in the pomology sector. In addition to this, genome-wide genotyping is a prerequisite for genome-wide association studies that have been used successfully to discover the genes, which control polygenic traits including the genetic loci, associated with the trait of interest in fruit crops. AIMS AND PROSPECTS This review article emphasizes the role of genome-wide approaches to unlock and explore the genetic potential along with the detection of SNPs affecting the phenotype of fruit crops and highlights the prospects of genome-wide association studies in fruits.
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Affiliation(s)
- Ghassan Zahid
- Department of Biotechnology, Institute of Natural and Applied Sciences, Çukurova University, 01330, Adana, Turkey.
| | - Yıldız Aka Kaçar
- Department of Horticulture, Faculty of Agriculture, Çukurova University, 01330, Adana, Turkey
| | - Dicle Dönmez
- Biotechnology Research and Application Center, Çukurova University, 01330, Adana, Turkey
| | - Ayzin Küden
- Department of Horticulture, Faculty of Agriculture, Çukurova University, 01330, Adana, Turkey
| | - Tommaso Giordani
- Department of Agriculture, Food and Environment, University of Pisa, 56124, Pisa, Italy
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Abstract
The hollies (Ilex L., Aquifoliaceae) form a large (>669 spp.) genus of forest trees and shrubs, which is almost cosmopolitan in mesic environments but most diverse in subtropical China and montane South America. Throughout the range of the genus, Ilex species have been utilized as beverages, medicines, ornamentals, honey plants, timber, and for various other minor uses. Recent studies on the genomics, evolution, and biogeography of Ilex now make it possible to take a systematic approach to understanding and expanding the economic importance of the genus, but information on existing uses is scattered among numerous published and unpublished sources. We therefore review the existing literature on utilization of Ilex species, supplementing this with information from the grey literature and product websites. We show that, despite the number and diversity of known uses, most Ilex species are not known to be utilized at present, suggesting considerable unrealized potential. We highlight gaps in our knowledge and opportunities for expanded usage. Finally, we discuss how the availability of a new phylogeny and whole genome can assist screening of additional wild species for economic potential and facilitate breeding programs for species already under cultivation.
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Mahadevaiah C, Appunu C, Aitken K, Suresha GS, Vignesh P, Mahadeva Swamy HK, Valarmathi R, Hemaprabha G, Alagarasan G, Ram B. Genomic Selection in Sugarcane: Current Status and Future Prospects. FRONTIERS IN PLANT SCIENCE 2021; 12:708233. [PMID: 34646284 PMCID: PMC8502939 DOI: 10.3389/fpls.2021.708233] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 08/24/2021] [Indexed: 05/18/2023]
Abstract
Sugarcane is a C4 and agro-industry-based crop with a high potential for biomass production. It serves as raw material for the production of sugar, ethanol, and electricity. Modern sugarcane varieties are derived from the interspecific and intergeneric hybridization between Saccharum officinarum, Saccharum spontaneum, and other wild relatives. Sugarcane breeding programmes are broadly categorized into germplasm collection and characterization, pre-breeding and genetic base-broadening, and varietal development programmes. The varietal identification through the classic breeding programme requires a minimum of 12-14 years. The precise phenotyping in sugarcane is extremely tedious due to the high propensity of lodging and suckering owing to the influence of environmental factors and crop management practices. This kind of phenotyping requires data from both plant crop and ratoon experiments conducted over locations and seasons. In this review, we explored the feasibility of genomic selection schemes for various breeding programmes in sugarcane. The genetic diversity analysis using genome-wide markers helps in the formation of core set germplasm representing the total genomic diversity present in the Saccharum gene bank. The genome-wide association studies and genomic prediction in the Saccharum gene bank are helpful to identify the complete genomic resources for cane yield, commercial cane sugar, tolerances to biotic and abiotic stresses, and other agronomic traits. The implementation of genomic selection in pre-breeding, genetic base-broadening programmes assist in precise introgression of specific genes and recurrent selection schemes enhance the higher frequency of favorable alleles in the population with a considerable reduction in breeding cycles and population size. The integration of environmental covariates and genomic prediction in multi-environment trials assists in the prediction of varietal performance for different agro-climatic zones. This review also directed its focus on enhancing the genetic gain over time, cost, and resource allocation at various stages of breeding programmes.
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Affiliation(s)
| | - Chinnaswamy Appunu
- Division of Crop Improvement, ICAR-Sugarcane Breeding Institute, Coimbatore, India
| | - Karen Aitken
- CSIRO (Commonwealth Scientific and Industrial Research Organization), St. Lucia, QLD, Australia
| | | | - Palanisamy Vignesh
- Division of Crop Improvement, ICAR-Sugarcane Breeding Institute, Coimbatore, India
| | | | | | - Govind Hemaprabha
- Division of Crop Improvement, ICAR-Sugarcane Breeding Institute, Coimbatore, India
| | - Ganesh Alagarasan
- Division of Crop Improvement, ICAR-Sugarcane Breeding Institute, Coimbatore, India
| | - Bakshi Ram
- Division of Crop Improvement, ICAR-Sugarcane Breeding Institute, Coimbatore, India
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