東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Determining the Effects of Elevated ...

Ferdush, Jannatul,

FindBook

Google Book

Amazon

博客來

Determining the Effects of Elevated Carbon Dioxide on Soil Acidification, Cation Depletion, and Soil Inorganic Carbon and Mapping Soil Carbons Using Artificial Intelligence /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Determining the Effects of Elevated Carbon Dioxide on Soil Acidification, Cation Depletion, and Soil Inorganic Carbon and Mapping Soil Carbons Using Artificial Intelligence // Jannatul Ferdush.
作者:	Ferdush, Jannatul,
面頁冊數:	1 electronic resource (218 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-02, Section: B.
Contained By:	Dissertations Abstracts International84-02B.
標題:	Soil sciences. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29260040
ISBN:	9798841762560

Determining the Effects of Elevated Carbon Dioxide on Soil Acidification, Cation Depletion, and Soil Inorganic Carbon and Mapping Soil Carbons Using Artificial Intelligence /
Ferdush, Jannatul,

Determining the Effects of Elevated Carbon Dioxide on Soil Acidification, Cation Depletion, and Soil Inorganic Carbon and Mapping Soil Carbons Using Artificial Intelligence /Jannatul Ferdush. - 1 electronic resource (218 pages)

Source: Dissertations Abstracts International, Volume: 84-02, Section: B.

Soil carbon is the largest sink and source of the global carbon cycle and is disturbed by several natural, anthropogenic, and environmental factors. The global increase of atmospheric CO2 affects soil carbon cycling through varied biogeochemical processes. The first chapter is a compilation of current information on potential factors triggering soil acidification and weathering mechanisms under elevated CO2 and their consequences on soil inorganic carbon (SIC) pool and quality. Soil water content and precipitation were critical factors influencing elevated CO2 effects on the SIC pool. The second chapter examines a detailed column experiment in which six soils from the state of Mississippi, USA, representing acidic, neutral, and alkaline pH, were exposed to different CO2 enrichments (100%, 10%, and 1%) for 30 days. The leachates' pH tended to attain an equilibrium state (neutral) with time under CO2 saturation. SIC increased under CO2 saturation, whereas cation exchange capacity (CEC) showed a decreasing pattern in all soils. In the third chapter, an eXplainable artificial intelligence (XAI) was performed to visualize the different forms of soil carbon variability across the Mississippi River Basin area. This model explains key insights and local discrepancies, suggesting a solution to the "Black-Box" issue. The best performing model, stack ensemble, showed improved RMSE (3 to 8%) and spatial variability for soil carbons than other ML models, especially after adding the residuals from regression analyses. Land cover type > soil pH > total nitrogen, > NDVI were identified as the top four crucial factors for predicting SOC when bulk density > precipitation, soil pH > mean annual temperature described SIC. The proposed automatic machine learning (AutoML) model with model agnostic interpolations might be a hallmark to mitigate the C loss under adverse climate change conditions and allow diverse knowledge groups to adopt a new interpretable ML algorithm more confidently. Findings from this study help predict the impact of elevated atmospheric CO2 on soil pH, acidification, and nutrient availability and develop strategies for sustainable land management practices under a changing climate.

English

ISBN: 9798841762560Subjects--Topical Terms:

2122699
Soil sciences.
Subjects--Index Terms:

Digital soil mapping

Determining the Effects of Elevated Carbon Dioxide on Soil Acidification, Cation Depletion, and Soil Inorganic Carbon and Mapping Soil Carbons Using Artificial Intelligence /
LDR:03814nmm a22004453i 4500 001 2429627
005 20250430100836.5
006 m o d
007 cr|nu||||||||
008 261011s2022 miu||||||m |||||||eng d
020 $a 9798841762560
035 $a (MiAaPQD)AAI29260040
035 $a AAI29260040
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Ferdush, Jannatul, $e author. $0 (orcid)0000-0002-0917-4854 $3 3817560
245 1 0 $a Determining the Effects of Elevated Carbon Dioxide on Soil Acidification, Cation Depletion, and Soil Inorganic Carbon and Mapping Soil Carbons Using Artificial Intelligence / $c Jannatul Ferdush.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2022
300 $a 1 electronic resource (218 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 84-02, Section: B.
500 $a Advisors: Paul, Varun Committee members: Ambinakudige, Shrinidhi; Dash, Padmanava; Ahmed, Zia Uddin.
502 $b Ph.D. $c Mississippi State University $d 2022.
520 $a Soil carbon is the largest sink and source of the global carbon cycle and is disturbed by several natural, anthropogenic, and environmental factors. The global increase of atmospheric CO2 affects soil carbon cycling through varied biogeochemical processes. The first chapter is a compilation of current information on potential factors triggering soil acidification and weathering mechanisms under elevated CO2 and their consequences on soil inorganic carbon (SIC) pool and quality. Soil water content and precipitation were critical factors influencing elevated CO2 effects on the SIC pool. The second chapter examines a detailed column experiment in which six soils from the state of Mississippi, USA, representing acidic, neutral, and alkaline pH, were exposed to different CO2 enrichments (100%, 10%, and 1%) for 30 days. The leachates' pH tended to attain an equilibrium state (neutral) with time under CO2 saturation. SIC increased under CO2 saturation, whereas cation exchange capacity (CEC) showed a decreasing pattern in all soils. In the third chapter, an eXplainable artificial intelligence (XAI) was performed to visualize the different forms of soil carbon variability across the Mississippi River Basin area. This model explains key insights and local discrepancies, suggesting a solution to the "Black-Box" issue. The best performing model, stack ensemble, showed improved RMSE (3 to 8%) and spatial variability for soil carbons than other ML models, especially after adding the residuals from regression analyses. Land cover type > soil pH > total nitrogen, > NDVI were identified as the top four crucial factors for predicting SOC when bulk density > precipitation, soil pH > mean annual temperature described SIC. The proposed automatic machine learning (AutoML) model with model agnostic interpolations might be a hallmark to mitigate the C loss under adverse climate change conditions and allow diverse knowledge groups to adopt a new interpretable ML algorithm more confidently. Findings from this study help predict the impact of elevated atmospheric CO2 on soil pH, acidification, and nutrient availability and develop strategies for sustainable land management practices under a changing climate.
546 $a English
590 $a School code: 0132
650 4 $a Soil sciences. $3 2122699
650 4 $a Environmental science. $3 677245
650 4 $a Geochemistry. $3 539092
653 $a Digital soil mapping
653 $a Elevated CO2
653 $a Machine learning
653 $a Soil acidification
653 $a Soil inorganic carbon
653 $a Soil organic carbon
690 $a 0481
690 $a 0768
690 $a 0996
710 2 $a Mississippi State University. $b Geosciences. $e degree granting institution. $3 3817561
720 1 $a Paul, Varun $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 84-02B.
790 $a 0132
791 $a Ph.D.
792 $a 2022
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29260040