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Integrating Genomic and Phenomic Methods to Investigate Maize Response to Nitrogen Fertilizer.
Integrating Genomic and Phenomic Methods to Investigate Maize Response to Nitrogen Fertilizer.
상세정보
- 자료유형
- 학위논문(국외)
- 기본표목-개인명
- 표제와 책임표시사항
- Integrating Genomic and Phenomic Methods to Investigate Maize Response to Nitrogen Fertilizer.
- 발행, 배포, 간사 사항
- 발행, 배포, 간사 사항
- 형태사항
- 109 p.
- 일반주기
- Source: Dissertations Abstracts International, Volume: 87-03, Section: B.
- 일반주기
- Advisor: Thompson, Addie.
- 학위논문주기
- Thesis (Ph.D.)--Michigan State University, 2025.
- 요약 등 주기
- 요약Nitrogen (N) fertilizer is essential for maximizing maize grain yield, yet optimizing N application remains challenging due to complex interactions between genotype, environment, and management practices. Yield response to nitrogen (YieldResp2N) exhibits substantial variation across genotypes and environments, limiting the development of predictable N management strategies and breeding progress. Understanding the physiological mechanisms and the genetic variation underlying N response is critical for developing more efficient maize production systems that balance yield optimization with environmental sustainability. This dissertation employs an integrated multi-scale approach combining transcriptomics, remote sensing, and quantitative genetics to characterize maize N response across different biological and temporal scales.Chapter 1 introduces key research leading up to this work. In Chapter 2, transcriptomic profiling using BRB-seq was conducted on five maize hybrids grown under contrasting N treatments (25 vs 150 lbs N/acre) across three developmental stages (V7, V15, R2). While some genes showed conserved responses, many transcriptomic changes were hybrid-specific, with different genotypes employing distinct molecular strategies for N response. Year-to-year validation using RT-qPCR confirmed that N responses vary substantially across seasons in a hybrid-specific manner, though a core set of genes maintained consistent responses across environments. This chapter has been published (Sheng et al. 2023).Chapter 3 expands the investigation to 21 maize hybrids grown across three years (2020- 2022) under the same N treatments, integrating high-throughput remote sensing from unoccupied aerial systems (UAS) with traditional phenotyping approaches. Genotype rankings for N response were inconsistent across years, with negative correlations between some year pairs. Analysis of variance partitioning revealed that genotype and genotype x environment effects explained a higher proportion of variance for YieldResp2N compared to raw yield, indicating N response represents a distinct trait worthy of targeted breeding efforts but the interaction term remains an obstacle towards application. Novel remote sensing metrics are developed by integrating multispectral imagery with environmental data. The most successful metric, GDDAccum (growing degree days accumulated while productive), quantifies the duration plants remain metabolically active based on reflectance properties and scaled to individual plot characteristics. Chapter 4 further expands the study to 105 maize inbred lines across two years (2022- 2023), focusing on functional staygreen traits measured just before senescence under the same contrasting N regimes. Gas exchange measurements were integrated with vegetation indices (VIs) derived from UAS imagery. CO₂ assimilation rate (A) had a tighter relationship with yield in high N while H2O transpiration rate (E) was more tightly linked in low N. VIs were most tightly linked with A across all years and environments. Genome-wide association study mapping identified 693 unique quantitative trait nucleotides (QTN) across 24 traits, with most QTN being specific to the year and N treatment. This pattern indicates that genetic architecture underlying these traits is strongly influenced by N availability, with important implications for breeding program design. A pleiotropic QTN (chr8_134344930) affecting both A and remotely sensed VIs was identified, with the nearby gene Zm00001eb354870 showing overlap with previous mapping studies.Finally, Chapter 5 provides conclusions. The substantial genotype x environment interactions observed across all scales of investigation highlight the complexity of N response and the limitations of fertilizer recommendations based on average performance. The overall instability of genotype rankings across environments indicates that breeding programs must evaluate candidates across diverse N and environmental conditions if they wish to develop robust varieties. This dissertation establishes a comprehensive framework for investigating complex agricultural traits through integrated multi-scale approaches. Ultimately, this work contributes to the development of more sustainable and efficient maize production systems that optimize yield while minimizing environmental impacts through improved understanding and management of plant-nutrient interactions.
- 주제명부출표목-일반주제명
- 주제명부출표목-일반주제명
- 주제명부출표목-일반주제명
- 주제명부출표목-일반주제명
- 비통제 색인어
- 비통제 색인어
- 비통제 색인어
- 비통제 색인어
- 부출표목-단체명
- 기본자료저록
- Dissertations Abstracts International. 87-03B.
- 전자적 위치 및 접속
- 원문정보보기
MARC
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■040 ▼aMiAaPQ▼cMiAaPQ
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■1001 ▼aWebster, Brandon.
■24510▼aIntegrating Genomic and Phenomic Methods to Investigate Maize Response to Nitrogen Fertilizer.
■260 ▼a[S.l.]▼bMichigan State University. ▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a109 p.
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-03, Section: B.
■500 ▼aAdvisor: Thompson, Addie.
■5021 ▼aThesis (Ph.D.)--Michigan State University, 2025.
■520 ▼aNitrogen (N) fertilizer is essential for maximizing maize grain yield, yet optimizing N application remains challenging due to complex interactions between genotype, environment, and management practices. Yield response to nitrogen (YieldResp2N) exhibits substantial variation across genotypes and environments, limiting the development of predictable N management strategies and breeding progress. Understanding the physiological mechanisms and the genetic variation underlying N response is critical for developing more efficient maize production systems that balance yield optimization with environmental sustainability. This dissertation employs an integrated multi-scale approach combining transcriptomics, remote sensing, and quantitative genetics to characterize maize N response across different biological and temporal scales.Chapter 1 introduces key research leading up to this work. In Chapter 2, transcriptomic profiling using BRB-seq was conducted on five maize hybrids grown under contrasting N treatments (25 vs 150 lbs N/acre) across three developmental stages (V7, V15, R2). While some genes showed conserved responses, many transcriptomic changes were hybrid-specific, with different genotypes employing distinct molecular strategies for N response. Year-to-year validation using RT-qPCR confirmed that N responses vary substantially across seasons in a hybrid-specific manner, though a core set of genes maintained consistent responses across environments. This chapter has been published (Sheng et al. 2023).Chapter 3 expands the investigation to 21 maize hybrids grown across three years (2020- 2022) under the same N treatments, integrating high-throughput remote sensing from unoccupied aerial systems (UAS) with traditional phenotyping approaches. Genotype rankings for N response were inconsistent across years, with negative correlations between some year pairs. Analysis of variance partitioning revealed that genotype and genotype x environment effects explained a higher proportion of variance for YieldResp2N compared to raw yield, indicating N response represents a distinct trait worthy of targeted breeding efforts but the interaction term remains an obstacle towards application. Novel remote sensing metrics are developed by integrating multispectral imagery with environmental data. The most successful metric, GDDAccum (growing degree days accumulated while productive), quantifies the duration plants remain metabolically active based on reflectance properties and scaled to individual plot characteristics. Chapter 4 further expands the study to 105 maize inbred lines across two years (2022- 2023), focusing on functional staygreen traits measured just before senescence under the same contrasting N regimes. Gas exchange measurements were integrated with vegetation indices (VIs) derived from UAS imagery. CO₂ assimilation rate (A) had a tighter relationship with yield in high N while H2O transpiration rate (E) was more tightly linked in low N. VIs were most tightly linked with A across all years and environments. Genome-wide association study mapping identified 693 unique quantitative trait nucleotides (QTN) across 24 traits, with most QTN being specific to the year and N treatment. This pattern indicates that genetic architecture underlying these traits is strongly influenced by N availability, with important implications for breeding program design. A pleiotropic QTN (chr8_134344930) affecting both A and remotely sensed VIs was identified, with the nearby gene Zm00001eb354870 showing overlap with previous mapping studies.Finally, Chapter 5 provides conclusions. The substantial genotype x environment interactions observed across all scales of investigation highlight the complexity of N response and the limitations of fertilizer recommendations based on average performance. The overall instability of genotype rankings across environments indicates that breeding programs must evaluate candidates across diverse N and environmental conditions if they wish to develop robust varieties. This dissertation establishes a comprehensive framework for investigating complex agricultural traits through integrated multi-scale approaches. Ultimately, this work contributes to the development of more sustainable and efficient maize production systems that optimize yield while minimizing environmental impacts through improved understanding and management of plant-nutrient interactions.
■590 ▼aSchool code: 0128.
■650 4▼aPlant sciences.
■650 4▼aHorticulture.
■650 4▼aBioengineering.
■650 4▼aGenetics.
■653 ▼aPhenomics
■653 ▼aNitrogen fertilizer
■653 ▼aEnvironmental data
■653 ▼aVegetation indices
■690 ▼a0479
■690 ▼a0202
■690 ▼a0369
■690 ▼a0471
■71020▼aMichigan State University▼bPlant Breeding, Genetics and Biotechnology - Plant Biology - Doctor of Philosophy.
■7730 ▼tDissertations Abstracts International▼g87-03B.
■790 ▼a0128
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17359362▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
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