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Management and Inherent Soil Properties Shape Soil Health Indicators and Soil Organic Carbon Stocks
Management and Inherent Soil Properties Shape Soil Health Indicators and Soil Organic Carb...
Management and Inherent Soil Properties Shape Soil Health Indicators and Soil Organic Carbon Stocks

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자료유형  
 학위논문 서양
최종처리일시  
20250211152132
ISBN  
9798384050186
DDC  
631.4
저자명  
Amsili, Joseph Pierre.
서명/저자  
Management and Inherent Soil Properties Shape Soil Health Indicators and Soil Organic Carbon Stocks
발행사항  
[Sl] : Cornell University, 2024
발행사항  
Ann Arbor : ProQuest Dissertations & Theses, 2024
형태사항  
300 p
주기사항  
Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
주기사항  
Advisor: Van Es, Harold.
학위논문주기  
Thesis (Ph.D.)--Cornell University, 2024.
초록/해제  
요약Pedoclimatic context often defines a soil's basic functions, but human management can have superimposing impacts on soil health and carbon (C) stocks. Therefore, it can be challenging to interpret soil health measurements, define benchmarks, and predict the effects of management on soil organic carbon (SOC) stocks in the context of a region's climates, soils, and cropping systems. In chapter 2, I examined the effects of soil texture, a dominant inherent soil property, and cropping system on soil health indicators in New York State (NYS), USA, soils. Available water capacity measured on disturbed samples was mostly affected by texture, while soil respiration, protein, and wet aggregate stability were mostly impacted by cropping system. Pasture and Mixed Vegetable systems tended to have the highest biological and physical soil health and Annual Grain and Processing Vegetable cropping systems had the lowest. In chapter 3, I developed production environment soil health (PESH) benchmarks for eight physical and biological indicators in the context of region, soil texture, and cropping system. Long Island had lower PESH benchmarks for soil organic matter (SOM) than the rest of NYS, implying that regional PESH benchmarks within a state or region are warranted if the pedoclimatic context varies greatly.In chapter 4, I assessed the effects of tillage system on SOC stocks in long-term continuous corn silage and corn grain experiments. No-till did not lead to consistent benefits in SOC stocks relative to plow-till, across the experiments. The differences in SOC stocks between tillage treatments can be explained by pedoclimatic variables and the amount of C inputs.Prediction of soil health indicators that are expensive to measure can improve the cost-effectiveness of comprehensive assessment of soil health. In chapter 5, I developed pedotransfer functions for available water capacity, field capacity (FC), and permanent wilting point (PWP) using random forest (RF) and traditional multiple linear regression modeling. In chapter 6, pedotransfer functions for soil protein were developed using RF and traditional multiple linear regression modeling. Soil protein was sensitive to management at 36 of 57 long-term experiments and the full RF model was able to predict 92% of those significant effects.
일반주제명  
Soil sciences
일반주제명  
Plant sciences
키워드  
Soil health
키워드  
Soil organic carbon
키워드  
Soil texture
키워드  
Sustainable agriculture
기타저자  
Cornell University Soil and Crop Sciences
기본자료저록  
Dissertations Abstracts International. 86-03B.
전자적 위치 및 접속  
로그인 후 원문을 볼 수 있습니다.

MARC

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■1001  ▼aAmsili,  Joseph  Pierre.▼0(orcid)0000-0002-8293-5824
■24510▼aManagement  and  Inherent  Soil  Properties  Shape  Soil  Health  Indicators  and  Soil  Organic  Carbon  Stocks
■260    ▼a[Sl]▼bCornell  University▼c2024
■260  1▼aAnn  Arbor▼bProQuest  Dissertations  &  Theses▼c2024
■300    ▼a300  p
■500    ▼aSource:  Dissertations  Abstracts  International,  Volume:  86-03,  Section:  B.
■500    ▼aAdvisor:  Van  Es,  Harold.
■5021  ▼aThesis  (Ph.D.)--Cornell  University,  2024.
■520    ▼aPedoclimatic  context  often  defines  a  soil's  basic  functions,  but  human  management  can  have  superimposing  impacts  on  soil  health  and  carbon  (C)  stocks.  Therefore,  it  can  be  challenging  to  interpret  soil  health  measurements,  define  benchmarks,  and  predict  the  effects  of  management  on  soil  organic  carbon  (SOC)  stocks  in  the  context  of  a  region's  climates,  soils,  and  cropping  systems.  In  chapter  2,  I  examined  the  effects  of  soil  texture,  a  dominant  inherent  soil  property,  and  cropping  system  on  soil  health  indicators  in  New  York  State  (NYS),  USA,  soils.  Available  water  capacity  measured  on  disturbed  samples  was  mostly  affected  by  texture,  while  soil  respiration,  protein,  and  wet  aggregate  stability  were  mostly  impacted  by  cropping  system.  Pasture  and  Mixed  Vegetable  systems  tended  to  have  the  highest  biological  and  physical  soil  health  and  Annual  Grain  and  Processing  Vegetable  cropping  systems  had  the  lowest.  In  chapter  3,  I  developed  production  environment  soil  health  (PESH)  benchmarks  for  eight  physical  and  biological  indicators  in  the  context  of  region,  soil  texture,  and  cropping  system.  Long  Island  had  lower  PESH  benchmarks  for  soil  organic  matter  (SOM)  than  the  rest  of  NYS,  implying  that  regional  PESH  benchmarks  within  a  state  or  region  are  warranted  if  the  pedoclimatic  context  varies  greatly.In  chapter  4,  I  assessed  the  effects  of  tillage  system  on  SOC  stocks  in  long-term  continuous  corn  silage  and  corn  grain  experiments.  No-till  did  not  lead  to  consistent  benefits  in  SOC  stocks  relative  to  plow-till,  across  the  experiments.  The  differences  in  SOC  stocks  between  tillage  treatments  can  be  explained  by  pedoclimatic  variables  and  the  amount  of  C  inputs.Prediction  of  soil  health  indicators  that  are  expensive  to  measure  can  improve  the  cost-effectiveness  of  comprehensive  assessment  of  soil  health.  In  chapter  5,  I  developed  pedotransfer  functions  for  available  water  capacity,  field  capacity  (FC),  and  permanent  wilting  point  (PWP)  using  random  forest  (RF)  and  traditional  multiple  linear  regression  modeling.  In  chapter  6,  pedotransfer  functions  for  soil  protein  were  developed  using  RF  and  traditional  multiple  linear  regression  modeling.  Soil  protein  was  sensitive  to  management  at  36  of  57  long-term  experiments  and  the  full  RF  model  was  able  to  predict  92%  of  those  significant  effects.
■590    ▼aSchool  code:  0058.
■650  4▼aSoil  sciences
■650  4▼aPlant  sciences
■653    ▼aSoil  health
■653    ▼aSoil  organic  carbon
■653    ▼aSoil  texture
■653    ▼aSustainable  agriculture
■690    ▼a0481
■690    ▼a0479
■690    ▼a0474
■71020▼aCornell  University▼bSoil  and  Crop  Sciences.
■7730  ▼tDissertations  Abstracts  International▼g86-03B.
■790    ▼a0058
■791    ▼aPh.D.
■792    ▼a2024
■793    ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17163079▼nKERIS▼z이  자료의  원문은  한국교육학술정보원에서  제공합니다.

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