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Govindasamy P, Muthusamy SK, Bagavathiannan M, Mowrer J, Jagannadham PTK, Maity A, Halli HM, G. K. S, Vadivel R, T. K. D, Raj R, Pooniya V, Babu S, Rathore SS, L. M, Tiwari G. Nitrogen use efficiency-a key to enhance crop productivity under a changing climate. FRONTIERS IN PLANT SCIENCE 2023; 14:1121073. [PMID: 37143873 PMCID: PMC10151540 DOI: 10.3389/fpls.2023.1121073] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Accepted: 03/20/2023] [Indexed: 05/06/2023]
Abstract
Nitrogen (N) is an essential element required for the growth and development of all plants. On a global scale, N is agriculture's most widely used fertilizer nutrient. Studies have shown that crops use only 50% of the applied N effectively, while the rest is lost through various pathways to the surrounding environment. Furthermore, lost N negatively impacts the farmer's return on investment and pollutes the water, soil, and air. Therefore, enhancing nitrogen use efficiency (NUE) is critical in crop improvement programs and agronomic management systems. The major processes responsible for low N use are the volatilization, surface runoff, leaching, and denitrification of N. Improving NUE through agronomic management practices and high-throughput technologies would reduce the need for intensive N application and minimize the negative impact of N on the environment. The harmonization of agronomic, genetic, and biotechnological tools will improve the efficiency of N assimilation in crops and align agricultural systems with global needs to protect environmental functions and resources. Therefore, this review summarizes the literature on nitrogen loss, factors affecting NUE, and agronomic and genetic approaches for improving NUE in various crops and proposes a pathway to bring together agronomic and environmental needs.
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Affiliation(s)
- Prabhu Govindasamy
- Division of Agronomy, Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
- *Correspondence: Muthukumar Bagavathiannan, ; Prabhu Govindasamy,
| | - Senthilkumar K. Muthusamy
- Division of Crop Improvement, Indian Council of Agricultural Research (ICAR)-Central Tuber Crops Research Institute, Thiruvananthapuram, India
| | - Muthukumar Bagavathiannan
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, United States
- *Correspondence: Muthukumar Bagavathiannan, ; Prabhu Govindasamy,
| | - Jake Mowrer
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, United States
| | | | - Aniruddha Maity
- Crop, Soil and Environmental Sciences, Auburn University, Auburn, AL, United States
| | - Hanamant M. Halli
- School of Soil Stress Management, Indian Council of Agricultural Research (ICAR)-National Institute of Abiotic Stress Management, Pune, India
| | - Sujayananad G. K.
- Crop Protection, Indian Council of Agricultural Research (ICAR)-Indian Institute of Pulse Research, Kanpur, India
| | - Rajagopal Vadivel
- School of Soil Stress Management, Indian Council of Agricultural Research (ICAR)-National Institute of Abiotic Stress Management, Pune, India
| | - Das T. K.
- Division of Agronomy, Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
| | - Rishi Raj
- Division of Agronomy, Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
| | - Vijay Pooniya
- Division of Agronomy, Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
| | - Subhash Babu
- Division of Agronomy, Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
| | - Sanjay Singh Rathore
- Division of Agronomy, Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
| | - Muralikrishnan L.
- Division of Agricultural Extension, Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
| | - Gopal Tiwari
- Division of Agronomy, Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
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2
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Semagn K, Crossa J, Cuevas J, Iqbal M, Ciechanowska I, Henriquez MA, Randhawa H, Beres BL, Aboukhaddour R, McCallum BD, Brûlé-Babel AL, N'Diaye A, Pozniak C, Spaner D. Comparison of single-trait and multi-trait genomic predictions on agronomic and disease resistance traits in spring wheat. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:2747-2767. [PMID: 35737008 DOI: 10.1007/s00122-022-04147-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 05/28/2022] [Indexed: 06/15/2023]
Abstract
This study performed comprehensive analyses on the predictive abilities of single-trait and two multi-trait models in three populations. Our results demonstrated the superiority of multi-traits over single-trait models across seven agronomic and four to seven disease resistance traits of different genetic architecture. The predictive ability of multi-trait and single-trait prediction models has not been investigated on diverse traits evaluated under organic and conventional management systems. Here, we compared the predictive abilities of 25% of a testing set that has not been evaluated for a single trait (ST), not evaluated for multi-traits (MT1), and evaluated for some traits but not others (MT2) in three spring wheat populations genotyped either with the wheat 90K single nucleotide polymorphisms array or DArTseq. Analyses were performed on seven agronomic traits evaluated under conventional and organic management systems, four to seven disease resistance traits, and all agronomic and disease resistance traits simultaneously. The average prediction accuracies of the ST, MT1, and MT2 models varied from 0.03 to 0.78 (mean 0.41), from 0.05 to 0.82 (mean 0.47), and from 0.05 to 0.92 (mean 0.67), respectively. The predictive ability of the MT2 model was significantly greater than the ST model in all traits and populations except common bunt with the MT1 model being intermediate between them. The MT2 model increased prediction accuracies over the ST and MT1 models in all traits by 9.0-82.4% (mean 37.3%) and 2.9-82.5% (mean 25.7%), respectively, except common bunt that showed up to 7.7% smaller accuracies in two populations. A joint analysis of all agronomic and disease resistance traits further improved accuracies within the MT1 and MT2 models on average by 21.4% and 17.4%, respectively, as compared to either the agronomic or disease resistance traits, demonstrating the high potential of the multi-traits models in improving prediction accuracies.
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Affiliation(s)
- Kassa Semagn
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB, T6G 2P5, Canada.
| | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600, Mexico, DF, Mexico
| | | | - Muhammad Iqbal
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB, T6G 2P5, Canada
| | - Izabela Ciechanowska
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB, T6G 2P5, Canada
| | - Maria Antonia Henriquez
- Morden Research and Development Centre, Agriculture and Agri-Food Canada, Morden, MB, R6M 1Y5, Canada
| | - Harpinder Randhawa
- Lethbridge Research and Development Centre, Agriculture and Agri-Food Canada, 5403-1st Avenue South, Lethbridge, AB, T1J 4B1, Canada
| | - Brian L Beres
- Lethbridge Research and Development Centre, Agriculture and Agri-Food Canada, 5403-1st Avenue South, Lethbridge, AB, T1J 4B1, Canada
| | - Reem Aboukhaddour
- Lethbridge Research and Development Centre, Agriculture and Agri-Food Canada, 5403-1st Avenue South, Lethbridge, AB, T1J 4B1, Canada
| | - Brent D McCallum
- Morden Research and Development Centre, Agriculture and Agri-Food Canada, Morden, MB, R6M 1Y5, Canada
| | - Anita L Brûlé-Babel
- Department of Plant Science, University of Manitoba, 66 Dafoe Road, Winnipeg, MB, R3T 2N2, Canada
| | - Amidou N'Diaye
- Crop Development Centre and Department of Plant Sciences, University of Saskatchewan, 51 Campus Drive, Saskatoon, SK, S7N 5A8, Canada
| | - Curtis Pozniak
- Crop Development Centre and Department of Plant Sciences, University of Saskatchewan, 51 Campus Drive, Saskatoon, SK, S7N 5A8, Canada
| | - Dean Spaner
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB, T6G 2P5, Canada.
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3
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Shrestha V, Chhetri HB, Kainer D, Xu Y, Hamilton L, Piasecki C, Wolfe B, Wang X, Saha M, Jacobson D, Millwood RJ, Mazarei M, Stewart CN. The Genetic Architecture of Nitrogen Use Efficiency in Switchgrass ( Panicum virgatum L.). FRONTIERS IN PLANT SCIENCE 2022; 13:893610. [PMID: 35586220 PMCID: PMC9108870 DOI: 10.3389/fpls.2022.893610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 04/01/2022] [Indexed: 06/15/2023]
Abstract
Switchgrass (Panicum virgatum L.) has immense potential as a bioenergy crop with the aim of producing biofuel as an end goal. Nitrogen (N)-related sustainability traits, such as nitrogen use efficiency (NUE) and nitrogen remobilization efficiency (NRE), are important factors affecting switchgrass quality and productivity. Hence, it is imperative to develop nitrogen use-efficient switchgrass accessions by exploring the genetic basis of NUE in switchgrass. For that, we used 331 diverse field-grown switchgrass accessions planted under low and moderate N fertility treatments. We performed a genome wide association study (GWAS) in a holistic manner where we not only considered NUE as a single trait but also used its related phenotypic traits, such as total dry biomass at low N and moderate N, and nitrogen use index, such as NRE. We have evaluated the phenotypic characterization of the NUE and the related traits, highlighted their relationship using correlation analysis, and identified the top ten nitrogen use-efficient switchgrass accessions. Our GWAS analysis identified 19 unique single nucleotide polymorphisms (SNPs) and 32 candidate genes. Two promising GWAS candidate genes, caffeoyl-CoA O-methyltransferase (CCoAOMT) and alfin-like 6 (AL6), were further supported by linkage disequilibrium (LD) analysis. Finally, we discussed the potential role of nitrogen in modulating the expression of these two genes. Our findings have opened avenues for the development of improved nitrogen use-efficient switchgrass lines.
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Affiliation(s)
- Vivek Shrestha
- Department of Plant Sciences, The University of Tennessee, Knoxville, Knoxville, TN, United States
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, United States
| | - Hari B. Chhetri
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, United States
| | - David Kainer
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, United States
| | - Yaping Xu
- Department of Plant Sciences, The University of Tennessee, Knoxville, Knoxville, TN, United States
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, United States
| | - Lance Hamilton
- Department of Plant Sciences, The University of Tennessee, Knoxville, Knoxville, TN, United States
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, United States
| | | | - Ben Wolfe
- Department of Plant Sciences, The University of Tennessee, Knoxville, Knoxville, TN, United States
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, United States
| | - Xueyan Wang
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, United States
- Noble Research Institute, Ardmore, OK, United States
| | - Malay Saha
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, United States
- Noble Research Institute, Ardmore, OK, United States
| | - Daniel Jacobson
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, United States
| | - Reginald J. Millwood
- Department of Plant Sciences, The University of Tennessee, Knoxville, Knoxville, TN, United States
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, United States
| | - Mitra Mazarei
- Department of Plant Sciences, The University of Tennessee, Knoxville, Knoxville, TN, United States
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, United States
| | - C. Neal Stewart
- Department of Plant Sciences, The University of Tennessee, Knoxville, Knoxville, TN, United States
- Center for Bioenergy Innovation, Oak Ridge National Laboratory, Oak Ridge, TN, United States
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Javed T, I I, Singhal RK, Shabbir R, Shah AN, Kumar P, Jinger D, Dharmappa PM, Shad MA, Saha D, Anuragi H, Adamski R, Siuta D. Recent Advances in Agronomic and Physio-Molecular Approaches for Improving Nitrogen Use Efficiency in Crop Plants. FRONTIERS IN PLANT SCIENCE 2022; 13:877544. [PMID: 35574130 PMCID: PMC9106419 DOI: 10.3389/fpls.2022.877544] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 03/11/2022] [Indexed: 05/05/2023]
Abstract
The efficiency with which plants use nutrients to create biomass and/or grain is determined by the interaction of environmental and plant intrinsic factors. The major macronutrients, especially nitrogen (N), limit plant growth and development (1.5-2% of dry biomass) and have a direct impact on global food supply, fertilizer demand, and concern with environmental health. In the present time, the global consumption of N fertilizer is nearly 120 MT (million tons), and the N efficiency ranges from 25 to 50% of applied N. The dynamic range of ideal internal N concentrations is extremely large, necessitating stringent management to ensure that its requirements are met across various categories of developmental and environmental situations. Furthermore, approximately 60 percent of arable land is mineral deficient and/or mineral toxic around the world. The use of chemical fertilizers adds to the cost of production for the farmers and also increases environmental pollution. Therefore, the present study focused on the advancement in fertilizer approaches, comprising the use of biochar, zeolite, and customized nano and bio-fertilizers which had shown to be effective in improving nitrogen use efficiency (NUE) with lower soil degradation. Consequently, adopting precision farming, crop modeling, and the use of remote sensing technologies such as chlorophyll meters, leaf color charts, etc. assist in reducing the application of N fertilizer. This study also discussed the role of crucial plant attributes such as root structure architecture in improving the uptake and transport of N efficiency. The crosstalk of N with other soil nutrients plays a crucial role in nutrient homeostasis, which is also discussed thoroughly in this analysis. At the end, this review highlights the more efficient and accurate molecular strategies and techniques such as N transporters, transgenes, and omics, which are opening up intriguing possibilities for the detailed investigation of the molecular components that contribute to nitrogen utilization efficiency, thus expanding our knowledge of plant nutrition for future global food security.
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Affiliation(s)
- Talha Javed
- College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, China
- Department of Agronomy, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | - Indu I
- Indian Council of Agricultural Research (ICAR)-Indian Grassland and Fodder Research Institute, Jhansi, India
| | - Rajesh Kumar Singhal
- Indian Council of Agricultural Research (ICAR)-Indian Grassland and Fodder Research Institute, Jhansi, India
| | - Rubab Shabbir
- College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, China
- Department of Plant Breeding and Genetics, Seed Science and Technology, University of Agriculture Faisalabad, Faisalabad, Pakistan
| | - Adnan Noor Shah
- Department of Agricultural Engineering, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan
| | - Pawan Kumar
- Indian Council of Agricultural Research (ICAR)-Central Institute for Arid Horticulture, Bikaner, India
| | - Dinesh Jinger
- Research Centre, Indian Council of Agricultural Research (ICAR)-Indian Institute of Soil and Water Conservation, Anand, India
| | - Prathibha M. Dharmappa
- Indian Council of Agricultural Research (ICAR)-Indian Institute of Horticultural Research, Bengaluru, India
| | - Munsif Ali Shad
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene, Hubei Hongshan Laboratory, Wuhan, China
| | - Debanjana Saha
- Centurion University of Technology and Management, Jatni, India
| | - Hirdayesh Anuragi
- Indian Council of Agricultural Research (ICAR)- Central Agroforestry Research Institute, Jhansi, India
| | - Robert Adamski
- Faculty of Process and Environmental Engineering, Łódź University of Technology, Łódź, Poland
| | - Dorota Siuta
- Faculty of Process and Environmental Engineering, Łódź University of Technology, Łódź, Poland
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5
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Neuweiler JE, Trini J, Maurer HP, Würschum T. Do lower nitrogen fertilization levels require breeding of different types of cultivars in triticale? TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:993-1009. [PMID: 34958397 PMCID: PMC8942957 DOI: 10.1007/s00122-021-04012-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 11/30/2021] [Indexed: 06/14/2023]
Abstract
The comparably low genotype-by-nitrogen level interaction suggests that selection in early generations can be done under high-input conditions followed by selection under different nitrogen levels to identify genotypes ideally suited for the target environment. Breeding high-yielding, nitrogen-efficient crops is of utmost importance to achieve greater agricultural sustainability. The aim of this study was to evaluate nitrogen use efficiency (NUE) of triticale, investigate long-term genetic trends and the genetic architecture, and develop strategies for NUE improvement by breeding. For this, we evaluated 450 different triticale genotypes under four nitrogen fertilization levels in multi-environment field trials for grain yield, protein content, starch content and derived indices. Analysis of temporal trends revealed that modern cultivars are better in exploiting the available nitrogen. Genome-wide association mapping revealed a complex genetic architecture with many small-effect QTL and a high level of pleiotropy for NUE-related traits, in line with phenotypic correlations. Furthermore, the effect of some QTL was dependent on the nitrogen fertilization level. High correlations of each trait between N levels and the rather low genotype-by-N-level interaction variance showed that generally the same genotypes perform well over different N levels. Nevertheless, the best performing genotype was always a different one. Thus, selection in early generations can be done under high nitrogen fertilizer conditions as these provide a stronger differentiation, but the final selection in later generations should be conducted with a nitrogen fertilization as in the target environment.
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Affiliation(s)
- Jan E Neuweiler
- State Plant Breeding Institute, University of Hohenheim, 70599, Stuttgart, Germany
| | - Johannes Trini
- State Plant Breeding Institute, University of Hohenheim, 70599, Stuttgart, Germany
| | - Hans Peter Maurer
- State Plant Breeding Institute, University of Hohenheim, 70599, Stuttgart, Germany.
| | - Tobias Würschum
- Institute of Plant Breeding, Seed Science and Population Genetics, University of Hohenheim, 70599, Stuttgart, Germany
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6
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Semagn K, Iqbal M, Crossa J, Jarquin D, Howard R, Chen H, Bemister DH, Beres BL, Randhawa H, N'Diaye A, Pozniak C, Spaner D. Genome-based prediction of agronomic traits in spring wheat under conventional and organic management systems. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:537-552. [PMID: 34724078 DOI: 10.1007/s00122-021-03982-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 10/19/2021] [Indexed: 06/13/2023]
Abstract
Using phenotype data of three spring wheat populations evaluated at 6-15 environments under two management systems, we found moderate to very high prediction accuracies across seven traits. The phenotype data collected under an organic management system effectively predicted the performance of lines in the conventional management and vice versa. There is growing interest in developing wheat cultivars specifically for organic agriculture, but we are not aware of the effect of organic management on the predictive ability of genomic selection (GS). Here, we evaluated within populations prediction accuracies of four GS models, four combinations of training and testing sets, three reaction norm models, and three random cross-validations (CV) schemes in three populations phenotyped under organic and conventional management systems. Our study was based on a total of 578 recombinant inbred lines and varieties from three spring wheat populations, which were evaluated for seven traits at 3-9 conventionally and 3-6 organically managed field environments and genotyped either with the wheat 90 K SNP array or DArTseq. We predicted the management systems (CV0M) or environments (CV0), a subset of lines that have been evaluated in either management (CV2M) or some environments (CV2), and the performance of newly developed lines in either management (CV1M) or environments (CV1). The average prediction accuracies of the model that incorporated genotype × environment interactions with CV0 and CV2 schemes varied from 0.69 to 0.97. In the CV1 and CV1M schemes, prediction accuracies ranged from - 0.12 to 0.77 depending on the reaction norm models, the traits, and populations. In most cases, grain protein showed the highest prediction accuracies. The phenotype data collected under the organic management effectively predicted the performance of lines under conventional management and vice versa. This is the first comprehensive GS study that investigated the effect of the organic management system in wheat.
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Affiliation(s)
- Kassa Semagn
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB, T6G 2P5, Canada
| | - Muhammad Iqbal
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB, T6G 2P5, Canada
| | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600, Mexico, DF, Mexico
| | - Diego Jarquin
- University of Nebraska - Lincoln, Lincoln, NE, 68583, USA
| | - Reka Howard
- University of Nebraska - Lincoln, Lincoln, NE, 68583, USA
| | - Hua Chen
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB, T6G 2P5, Canada
- Department of Agronomy, School of Life Science and Engineering, Southwest University of Science and Technology, 59 Qinglong Road, Mianyang, 621010, Sichuan, China
| | - Darcy H Bemister
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB, T6G 2P5, Canada
| | - Brian L Beres
- Agriculture and Agri-Food Canada, 5403-1st Avenue South, Lethbridge, AB, T1J 4B1, Canada
| | - Harpinder Randhawa
- Agriculture and Agri-Food Canada, 5403-1st Avenue South, Lethbridge, AB, T1J 4B1, Canada
| | - Amidou N'Diaye
- Crop Development Centre and Department of Plant Sciences, University of Saskatchewan, 51 Campus Drive, Saskatoon, SK, S7N 5A8, Canada
| | - Curtis Pozniak
- Crop Development Centre and Department of Plant Sciences, University of Saskatchewan, 51 Campus Drive, Saskatoon, SK, S7N 5A8, Canada
| | - Dean Spaner
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB, T6G 2P5, Canada.
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7
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Sandhu N, Pruthi G, Prakash Raigar O, Singh MP, Phagna K, Kumar A, Sethi M, Singh J, Ade PA, Saini DK. Meta-QTL Analysis in Rice and Cross-Genome Talk of the Genomic Regions Controlling Nitrogen Use Efficiency in Cereal Crops Revealing Phylogenetic Relationship. Front Genet 2021; 12:807210. [PMID: 34992638 PMCID: PMC8724540 DOI: 10.3389/fgene.2021.807210] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 11/30/2021] [Indexed: 11/13/2022] Open
Abstract
The phenomenal increase in the use of nitrogenous fertilizers coupled with poor nitrogen use efficiency is among the most important threats to the environment, economic, and social health. During the last 2 decades, a number of genomic regions associated with nitrogen use efficiency (NUE) and related traits have been reported by different research groups, but none of the stable and major effect QTL have been utilized in the marker-assisted introgression/pyramiding program. Compiling the data available in the literature could be very useful in identifying stable and major effect genomic regions associated with the root and NUE-related trait improving the rice grain yield. In the present study, we performed meta-QTL analysis on 1,330 QTL from 29 studies published in the past 2 decades. A total of 76 MQTL with a stable effect over different genetic backgrounds and environments were identified. The significant reduction in the confidence interval of the MQTL compared to the initial QTL resulted in the identification of annotated and putative candidate genes related to the traits considered in the present study. A hot spot region associated with correlated traits on chr 1, 4, and 8 and candidate genes associated with nitrate transporters, nitrogen content, and ammonium uptake on chromosomes 2, 4, 6, and 8 have been identified. The identified MQTL, putative candidate genes, and their orthologues were validated on our previous studies conducted on rice and wheat. The research-based interventions such as improving nitrogen use efficiency via identification of major genomic regions and candidate genes can be a plausible, simple, and low-cost solution to address the challenges of the crop improvement program.
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Affiliation(s)
| | | | | | | | - Kanika Phagna
- Indian Institute of Science Education and Research, Berhampur, India
| | - Aman Kumar
- Punjab Agricultural University, Ludhiana, India
| | - Mehak Sethi
- Punjab Agricultural University, Ludhiana, India
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8
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Semagn K, Iqbal M, Chen H, Perez-Lara E, Bemister DH, Xiang R, Zou J, Asif M, Kamran A, N'Diaye A, Randhawa H, Beres BL, Pozniak C, Spaner D. Physical mapping of QTL associated with agronomic and end-use quality traits in spring wheat under conventional and organic management systems. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:3699-3719. [PMID: 34333664 DOI: 10.1007/s00122-021-03923-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 07/20/2021] [Indexed: 06/13/2023]
Abstract
Using phenotypic data of four biparental spring wheat populations evaluated at multiple environments under two management systems, we discovered 152 QTL and 22 QTL hotspots, of which two QTL accounted for up to 37% and 58% of the phenotypic variance, consistently detected in all environments, and fell within genomic regions harboring known genes. Identification of the physical positions of quantitative trait loci (QTL) would be highly useful for developing functional markers and comparing QTL results across multiple independent studies. The objectives of the present study were to map and characterize QTL associated with nine agronomic and end-use quality traits (tillering ability, plant height, lodging, grain yield, grain protein content, thousand kernel weight, test weight, sedimentation volume, and falling number) in hard red spring wheat recombinant inbred lines (RILs) using the International Wheat Genome Sequencing Consortium (IWGSC) RefSeq v2.0 physical map. We evaluated a total of 698 RILs from four populations derived from crosses involving seven parents at 3-8 conventionally (high N) and organically (low N) managed field environments. Using the phenotypic data combined across all environments per management, and the physical map between 1058 and 6526 markers per population, we identified 152 QTL associated with the nine traits, of which 29 had moderate and 2 with major effects. Forty-nine of the 152 QTL mapped across 22 QTL hotspot regions with each region coincident to 2-6 traits. Some of the QTL hotspots were physically located close to known genes. QSv.dms-1A and QPht.dms-4B.1 individually explained up to 37% and 58% of the variation in sedimentation volume and plant height, respectively, and had very large LOD scores that varied from 19.0 to 35.7 and from 16.7 to 55.9, respectively. We consistently detected both QTL in the combined and all individual environments, laying solid ground for further characterization and possibly for cloning.
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Affiliation(s)
- Kassa Semagn
- Department of Agricultural, Food and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB, T6G 2P5, Canada
| | - Muhammad Iqbal
- Department of Agricultural, Food and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB, T6G 2P5, Canada
| | - Hua Chen
- Department of Agricultural, Food and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB, T6G 2P5, Canada
- Department of Agronomy, School of Life Science and Engineering, Southwest University of Science and Technology, 59 Qinglong Road, Mianyang, 621010, Sichuan, China
| | - Enid Perez-Lara
- Department of Agricultural, Food and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB, T6G 2P5, Canada
| | - Darcy H Bemister
- Department of Agricultural, Food and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB, T6G 2P5, Canada
| | - Rongrong Xiang
- Department of Agricultural, Food and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB, T6G 2P5, Canada
| | - Jun Zou
- Department of Agricultural, Food and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB, T6G 2P5, Canada
| | - Muhammad Asif
- Department of Agricultural, Food and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB, T6G 2P5, Canada
- Department of Agronomy, 2004 Throckmorton Plant Science Center, Kansas State University, Manhattan, KS, 66506, USA
- Heartland Plant Innovations, Kansas Wheat Innovation Center, 1990 Kimball Avenue, Manhattan, KS, 66502, USA
| | - Atif Kamran
- Department of Agricultural, Food and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB, T6G 2P5, Canada
- Department of Botany, Seed Centre, The University of Punjab, New Campus, Lahore, 54590, Pakistan
| | - Amidou N'Diaye
- Crop Development Centre and Department of Plant Sciences, University of Saskatchewan, 51 Campus Drive, Saskatoon, SK, S7N 5A8, Canada
| | - Harpinder Randhawa
- Agriculture, and Agri-Food Canada, 5403-1st Avenue South, Lethbridge, AB, T1J 4B1, Canada
| | - Brian L Beres
- Agriculture, and Agri-Food Canada, 5403-1st Avenue South, Lethbridge, AB, T1J 4B1, Canada
| | - Curtis Pozniak
- Crop Development Centre and Department of Plant Sciences, University of Saskatchewan, 51 Campus Drive, Saskatoon, SK, S7N 5A8, Canada
| | - Dean Spaner
- Department of Agricultural, Food and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB, T6G 2P5, Canada.
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9
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Saini DK, Chopra Y, Pal N, Chahal A, Srivastava P, Gupta PK. Meta-QTLs, ortho-MQTLs and candidate genes for nitrogen use efficiency and root system architecture in bread wheat ( Triticum aestivum L.). PHYSIOLOGY AND MOLECULAR BIOLOGY OF PLANTS : AN INTERNATIONAL JOURNAL OF FUNCTIONAL PLANT BIOLOGY 2021; 27:2245-2267. [PMID: 34744364 PMCID: PMC8526679 DOI: 10.1007/s12298-021-01085-0] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 09/26/2021] [Accepted: 09/27/2021] [Indexed: 05/04/2023]
Abstract
In wheat, meta-QTLs (MQTLs), ortho-MQTLs, and candidate genes (CGs) were identified for nitrogen use efficiency and root system architecture. For this purpose, 1788 QTLs were available from 24 studies published during 2006-2020. Of these, 1098 QTLs were projected onto the consensus map resulting in 118 MQTLs. The average confidence interval (CI) of MQTLs was reduced up to 8.56 folds in comparison to the average CI of QTLs. Of the 118 MQTLs, 112 were anchored to the physical map of the wheat reference genome. The physical interval of MQTLs ranged from 0.02 to 666.18 Mb with a mean of 94.36 Mb. Eighty-eight of these 112 MQTLs were verified by marker-trait associations (MTAs) identified in published genome-wide association studies (GWAS); the MQTLs that were verified using GWAS also included 9 most robust MQTLs, which are particularly useful for breeders; we call them 'Breeder's QTLs'. Some selected wheat MQTLs were further utilized for the identification of ortho-MQTLs for wheat and maize; 9 such ortho-MQTLs were available. As many as 1991 candidate genes (CGs) were also detected, which included 930 CGs with an expression level of > 2 transcripts per million in relevant organs/tissues. Among the CGs, 97 CGs with functions previously reported as important for the traits under study were selected. Based on homology analysis and expression patterns, 49 orthologues of 35 rice genes were also identified in MQTL regions. The results of the present study may prove useful for the improvement of selection strategy for yield potential, stability, and performance under N-limiting conditions. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s12298-021-01085-0.
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Affiliation(s)
- Dinesh Kumar Saini
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, 141004 India
| | - Yuvraj Chopra
- College of Agriculture, Punjab Agricultural University, Ludhiana, 141004 India
- Present Address: Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583 USA
| | - Neeraj Pal
- Department of Molecular Biology and Genetic Engineering, G. B. Pant, University of Agriculture and Technology, Pantnagar, Uttarakhand 263145 India
| | - Amneek Chahal
- College of Agriculture, Punjab Agricultural University, Ludhiana, 141004 India
| | - Puja Srivastava
- Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, 141004 India
| | - Pushpendra Kumar Gupta
- Department of Genetics and Plant Breeding, Ch. Charan Singh University, Meerut, 250004 India
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10
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Semagn K, Iqbal M, Chen H, Perez-Lara E, Bemister DH, Xiang R, Zou J, Asif M, Kamran A, N'Diaye A, Randhawa H, Pozniak C, Spaner D. Physical Mapping of QTL in Four Spring Wheat Populations under Conventional and Organic Management Systems. I. Earliness. PLANTS 2021; 10:plants10050853. [PMID: 33922551 PMCID: PMC8144964 DOI: 10.3390/plants10050853] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Revised: 04/08/2021] [Accepted: 04/19/2021] [Indexed: 02/07/2023]
Abstract
In previous studies, we reported quantitative trait loci (QTL) associated with the heading, flowering, and maturity time in four hard red spring wheat recombinant inbred line (RIL) populations but the results are scattered in population-specific genetic maps, which is challenging to exploit efficiently in breeding. Here, we mapped and characterized QTL associated with these three earliness traits using the International Wheat Genome Sequencing Consortium (IWGSC) RefSeq v2.0 physical map. Our data consisted of (i) 6526 single nucleotide polymorphisms (SNPs) and two traits evaluated at five conventionally managed environments in the 'Cutler' × 'AC Barrie' population; (ii) 3158 SNPs and two traits evaluated across three organic and seven conventional managements in the 'Attila' × 'CDC Go' population; (iii) 5731 SilicoDArT and SNP markers and the three traits evaluated at four conventional and organic management systems in the 'Peace' × 'Carberry' population; and (iv) 1058 SNPs and two traits evaluated across two conventionally and organically managed environments in the 'Peace' × 'CDC Stanley' population. Using composite interval mapping, the phenotypic data across all environments, and the IWGSC RefSeq v2.0 physical maps, we identified a total of 44 QTL associated with days to heading (11), flowering (10), and maturity (23). Fifteen of the 44 QTL were common to both conventional and organic management systems, and the remaining QTL were specific to either the conventional (21) or organic (8) management systems. Some QTL harbor known genes, including the Vrn-A1, Vrn-B1, Rht-A1, and Rht-B1 that regulate photoperiodism, flowering time, and plant height in wheat, which lays a solid basis for cloning and further characterization.
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Affiliation(s)
- Kassa Semagn
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB T6G 2P5, Canada
| | - Muhammad Iqbal
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB T6G 2P5, Canada
| | - Hua Chen
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB T6G 2P5, Canada
- Department of Agronomy, School of Life Science and Engineering, Southwest University of Science and Technology, 59 Qinglong Road, Mianyang 621010, China
| | - Enid Perez-Lara
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB T6G 2P5, Canada
| | - Darcy H Bemister
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB T6G 2P5, Canada
| | - Rongrong Xiang
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB T6G 2P5, Canada
| | - Jun Zou
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB T6G 2P5, Canada
| | - Muhammad Asif
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB T6G 2P5, Canada
- Department of Agronomy, 2004 Throckmorton Plant Science Center, Kansas State University, Manhattan, KS 66506, USA
- Heartland Plant Innovations, Kansas Wheat Innovation Center, 1990 Kimball Avenue, Manhattan, KS 66502, USA
| | - Atif Kamran
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB T6G 2P5, Canada
- Seed Centre, Department of Botany, The University of Punjab, New Campus, Lahore 54590, Pakistan
| | - Amidou N'Diaye
- Crop Development Centre, Department of Plant Sciences, University of Saskatchewan, 51 Campus Drive, Saskatoon, SK S7N 5A8, Canada
| | - Harpinder Randhawa
- Agriculture and Agri-Food Canada, 5403-1st Avenue South, Lethbridge, AB T1J 4B1, Canada
| | - Curtis Pozniak
- Crop Development Centre, Department of Plant Sciences, University of Saskatchewan, 51 Campus Drive, Saskatoon, SK S7N 5A8, Canada
| | - Dean Spaner
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB T6G 2P5, Canada
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11
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Islam S, Zhang J, Zhao Y, She M, Ma W. Genetic regulation of the traits contributing to wheat nitrogen use efficiency. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2021; 303:110759. [PMID: 33487345 DOI: 10.1016/j.plantsci.2020.110759] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 10/14/2020] [Accepted: 11/11/2020] [Indexed: 05/25/2023]
Abstract
High nitrogen application aimed at increasing crop yield is offset by higher production costs and negative environmental consequences. For wheat, only one third of the applied nitrogen is utilized, which indicates there is scope for increasing Nitrogen Use Efficiency (NUE). However, achieving greater NUE is challenged by the complexity of the trait, which comprises processes associated with nitrogen uptake, transport, reduction, assimilation, translocation and remobilization. Thus, knowledge of the genetic regulation of these processes is critical in increasing NUE. Although primary nitrogen uptake and metabolism-related genes have been well studied, the relative influence of each towards NUE is not fully understood. Recent attention has focused on engineering transcription factors and identification of miRNAs acting on expression of specific genes related to NUE. Knowledge obtained from model species needs to be translated into wheat using recently-released whole genome sequences, and by exploring genetic variations of NUE-related traits in wild relatives and ancient germplasm. Recent findings indicate the genetic basis of NUE is complex. Pyramiding various genes will be the most effective approach to achieve a satisfactory level of NUE in the field.
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Affiliation(s)
- Shahidul Islam
- State Agricultural Biotechnology Center, Murdoch University, Perth, WA, 6150, Australia
| | - Jingjuan Zhang
- State Agricultural Biotechnology Center, Murdoch University, Perth, WA, 6150, Australia
| | - Yun Zhao
- State Agricultural Biotechnology Center, Murdoch University, Perth, WA, 6150, Australia
| | - Maoyun She
- State Agricultural Biotechnology Center, Murdoch University, Perth, WA, 6150, Australia
| | - Wujun Ma
- State Agricultural Biotechnology Center, Murdoch University, Perth, WA, 6150, Australia.
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12
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Sandhu N, Sethi M, Kumar A, Dang D, Singh J, Chhuneja P. Biochemical and Genetic Approaches Improving Nitrogen Use Efficiency in Cereal Crops: A Review. FRONTIERS IN PLANT SCIENCE 2021; 12:657629. [PMID: 34149755 PMCID: PMC8213353 DOI: 10.3389/fpls.2021.657629] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Accepted: 04/06/2021] [Indexed: 05/22/2023]
Abstract
Nitrogen is an essential nutrient required in large quantities for the proper growth and development of plants. Nitrogen is the most limiting macronutrient for crop production in most of the world's agricultural areas. The dynamic nature of nitrogen and its tendency to lose soil and environment systems create a unique and challenging environment for its proper management. Exploiting genetic diversity, developing nutrient efficient novel varieties with better agronomy and crop management practices combined with improved crop genetics have been significant factors behind increased crop production. In this review, we highlight the various biochemical, genetic factors and the regulatory mechanisms controlling the plant nitrogen economy necessary for reducing fertilizer cost and improving nitrogen use efficiency while maintaining an acceptable grain yield.
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