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Advancing the Modeling of Climate Impacts on Annual and Perennial Crops to Inform Adaptation Pathways: Lessons From California Almonds and U.S. Maize
Advancing the Modeling of Climate Impacts on Annual and Perennial Crops to Inform Adaptation Pathways: Lessons From California Almonds and U.S. Maize
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202105147
- ISBN
- 9798297645271
- DDC
- 551.5
- 저자명
- Wu, Shuaiqi.
- 서명/저자
- Advancing the Modeling of Climate Impacts on Annual and Perennial Crops to Inform Adaptation Pathways: Lessons From California Almonds and U.S. Maize
- 발행사항
- [Sl] : University of California, Davis, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 122 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-04, Section: B.
- 주기사항
- Advisor: Monier, Erwan.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Davis, 2025.
- 초록/해제
- 요약Agriculture faces intensifying challenges from climate change, threatening both perennial and annual crops that are crucial to global food and nutrient security. This dissertation advances the modeling of climate impacts on agriculture by developing and applying a novel statistical framework for perennial crops, and by scaling and calibrating a process-based model for annual crops, thereby improving yield projections under climate change and informing adaptation planning.The first study develops a novel modeling framework that integrates climate modeling, horticultural science, and statistical yield modeling, and applies it to California almonds. Results show that increasing minimum temperatures and humidity during the bloom and pollination stage, along with heat stress during the growing stage, are primary drivers of yield losses. Climate change is projected to reduce almond yields by up to 49% by 2100 under the high warming scenario (SSP585). However, sustained innovation gains could more than offset climate damages, highlighting the joint role of technological progress and climate adaptation in perennial systems.The second study expands AquaCrop, a process-based crop model developed by the Food and Agriculture Organization, for regional-scale applications. A flexible Python-based gridded implementation is developed and calibrated using two decades of county-level U.S. maize yield data, which substantially improves model performance compared to the default field-scale calibrated AquaCrop. This county-level calibration approach enables AquaCrop to capture both spatial and temporal dynamics of observation data more effectively than existing approaches, demonstrating the value of regional calibration for large-scale applications and establishing a scalable framework for future continental and global assessments.The third study applies the county-level calibrated AquaCrop to examine U.S. maize production under climate change. Driven by CMIP6 climate projections, simulations indicate that U.S. maize production could decline by 17% by the end of the century under the high warming scenario (SSP585), with particularly severe impacts in the Corn Belt region. Analyses of adaptation strategies show that irrigation, fertilization, and spatial relocation of maize systems can potentially offset climate damages, but feasibility is constrained by water availability, land competition, and resource efficiency. County-level projections provide actionable insights for agricultural policy, crop insurance, and farm-level planning.
- 일반주제명
- Atmospheric sciences
- 일반주제명
- Agriculture
- 일반주제명
- Climate change
- 키워드
- Perennial crops
- 키워드
- Fertilization
- 키워드
- Climate modeling
- 기타저자
- University of California, Davis Atmospheric Science
- 기본자료저록
- Dissertations Abstracts International. 87-04B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520260202105147
■006m o d
■007cr#unu||||||||
■020 ▼a9798297645271
■035 ▼a(MiAaPQ)AAI32241406
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a551.5
■1001 ▼aWu, Shuaiqi.
■24510▼aAdvancing the Modeling of Climate Impacts on Annual and Perennial Crops to Inform Adaptation Pathways: Lessons From California Almonds and U.S. Maize
■260 ▼a[Sl]▼bUniversity of California, Davis▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a122 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-04, Section: B.
■500 ▼aAdvisor: Monier, Erwan.
■5021 ▼aThesis (Ph.D.)--University of California, Davis, 2025.
■520 ▼aAgriculture faces intensifying challenges from climate change, threatening both perennial and annual crops that are crucial to global food and nutrient security. This dissertation advances the modeling of climate impacts on agriculture by developing and applying a novel statistical framework for perennial crops, and by scaling and calibrating a process-based model for annual crops, thereby improving yield projections under climate change and informing adaptation planning.The first study develops a novel modeling framework that integrates climate modeling, horticultural science, and statistical yield modeling, and applies it to California almonds. Results show that increasing minimum temperatures and humidity during the bloom and pollination stage, along with heat stress during the growing stage, are primary drivers of yield losses. Climate change is projected to reduce almond yields by up to 49% by 2100 under the high warming scenario (SSP585). However, sustained innovation gains could more than offset climate damages, highlighting the joint role of technological progress and climate adaptation in perennial systems.The second study expands AquaCrop, a process-based crop model developed by the Food and Agriculture Organization, for regional-scale applications. A flexible Python-based gridded implementation is developed and calibrated using two decades of county-level U.S. maize yield data, which substantially improves model performance compared to the default field-scale calibrated AquaCrop. This county-level calibration approach enables AquaCrop to capture both spatial and temporal dynamics of observation data more effectively than existing approaches, demonstrating the value of regional calibration for large-scale applications and establishing a scalable framework for future continental and global assessments.The third study applies the county-level calibrated AquaCrop to examine U.S. maize production under climate change. Driven by CMIP6 climate projections, simulations indicate that U.S. maize production could decline by 17% by the end of the century under the high warming scenario (SSP585), with particularly severe impacts in the Corn Belt region. Analyses of adaptation strategies show that irrigation, fertilization, and spatial relocation of maize systems can potentially offset climate damages, but feasibility is constrained by water availability, land competition, and resource efficiency. County-level projections provide actionable insights for agricultural policy, crop insurance, and farm-level planning.
■590 ▼aSchool code: 0029.
■650 4▼aAtmospheric sciences
■650 4▼aAgriculture
■650 4▼aClimate change
■653 ▼aAgriculture faces
■653 ▼aPerennial crops
■653 ▼aFarm-level planning
■653 ▼aFertilization
■653 ▼aClimate modeling
■690 ▼a0725
■690 ▼a0404
■690 ▼a0473
■71020▼aUniversity of California, Davis▼bAtmospheric Science.
■7730 ▼tDissertations Abstracts International▼g87-04B.
■790 ▼a0029
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17359622▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
